From 4ba6dbf8d5fa7711491ea9e2a2ebff295c9316c6 Mon Sep 17 00:00:00 2001
From: bloebp <bloebp@users.noreply.github.com>
Date: Fri, 25 Oct 2024 15:32:01 +0000
Subject: [PATCH] deploy: e79d01280b1f8c7e54c0bda93ceddc932f7eedea

---
 main/.doctrees/environment.pickle             |  Bin 19031186 -> 19113406 bytes
 ...Behind Hotel Booking Cancellations.doctree |  Bin 1019284 -> 1019194 bytes
 .../counterfactual_fairness_dowhy.doctree     |  Bin 104994 -> 104998 bytes
 .../example_notebooks/do_sampler_demo.doctree |  Bin 32041 -> 32043 bytes
 ...owhy-conditional-treatment-effects.doctree |  Bin 137068 -> 221037 bytes
 .../dowhy-simple-iv-example.doctree           |  Bin 47210 -> 47210 bytes
 .../dowhy_causal_api.doctree                  |  Bin 56526 -> 56460 bytes
 .../dowhy_causal_discovery_example.doctree    |  Bin 96366 -> 96354 bytes
 .../dowhy_confounder_example.doctree          |  Bin 37580 -> 37502 bytes
 .../dowhy_demo_dummy_outcome_refuter.doctree  |  Bin 48250 -> 48080 bytes
 .../dowhy_efficient_backdoor_example.doctree  |  Bin 101695 -> 101695 bytes
 .../dowhy_estimation_methods.doctree          |  Bin 85827 -> 85927 bytes
 .../dowhy_functional_api.doctree              |  Bin 56786 -> 56786 bytes
 .../dowhy_ihdp_data_example.doctree           |  Bin 57568 -> 57502 bytes
 .../dowhy_interpreter.doctree                 |  Bin 67082 -> 67066 bytes
 .../dowhy_lalonde_example.doctree             |  Bin 22680 -> 22680 bytes
 .../dowhy_mediation_analysis.doctree          |  Bin 45869 -> 45851 bytes
 .../dowhy_multiple_treatments.doctree         |  Bin 43413 -> 43439 bytes
 .../dowhy_optimize_backdoor_example.doctree   |  Bin 13270 -> 13246 bytes
 .../dowhy_refutation_testing.doctree          |  Bin 86330 -> 86330 bytes
 .../dowhy_simple_example.doctree              |  Bin 183710 -> 183924 bytes
 .../gcm_401k_analysis.doctree                 |  Bin 69085 -> 69091 bytes
 .../gcm_basic_example.doctree                 |  Bin 84973 -> 85057 bytes
 ...cm_counterfactual_medical_dry_eyes.doctree |  Bin 78178 -> 78178 bytes
 .../gcm_cps2015_dist_change_robust.doctree    |  Bin 66770 -> 66770 bytes
 .../gcm_draw_samples.doctree                  |  Bin 38042 -> 38062 bytes
 .../example_notebooks/gcm_falsify_dag.doctree |  Bin 81112 -> 81112 bytes
 .../example_notebooks/gcm_icc.doctree         |  Bin 66547 -> 66559 bytes
 .../example_notebooks/gcm_online_shop.doctree |  Bin 194822 -> 194952 bytes
 .../gcm_rca_microservice_architecture.doctree |  Bin 156324 -> 156450 bytes
 .../gcm_supply_chain_dist_change.doctree      |  Bin 90949 -> 88973 bytes
 .../lalonde_pandas_api.doctree                |  Bin 90428 -> 90484 bytes
 .../load_graph_example.doctree                |  Bin 22830 -> 22830 bytes
 .../dowhy_causal_prediction_demo.doctree      |  Bin 2031493 -> 2031491 bytes
 ..._analysis_nonparametric_estimators.doctree |  Bin 125518 -> 125506 bytes
 .../sensitivity_analysis_testing.doctree      |  Bin 154732 -> 154780 bytes
 .../effect_inference_timeseries_data.doctree  |  Bin 921882 -> 921882 bytes
 ...machinelearning-using-dowhy-econml.doctree |  Bin 136463 -> 136415 bytes
 ...y Behind Hotel Booking Cancellations.ipynb |  194 +-
 .../counterfactual_fairness_dowhy.ipynb       |  286 +-
 .../example_notebooks/do_sampler_demo.ipynb   |   84 +-
 .../dowhy-conditional-treatment-effects.ipynb | 1439 ++++-
 .../dowhy-simple-iv-example.ipynb             |  138 +-
 .../example_notebooks/dowhy_causal_api.ipynb  |  458 +-
 .../dowhy_causal_discovery_example.ipynb      |  428 +-
 .../dowhy_confounder_example.ipynb            |  126 +-
 .../dowhy_demo_dummy_outcome_refuter.ipynb    |  144 +-
 .../dowhy_efficient_backdoor_example.ipynb    |  148 +-
 .../dowhy_estimation_methods.ipynb            |  392 +-
 .../dowhy_functional_api.ipynb                |  106 +-
 .../dowhy_ihdp_data_example.ipynb             |  124 +-
 .../example_notebooks/dowhy_interpreter.ipynb |  414 +-
 .../dowhy_lalonde_example.ipynb               |   72 +-
 .../dowhy_mediation_analysis.ipynb            |   80 +-
 .../dowhy_multiple_treatments.ipynb           |  232 +-
 .../dowhy_optimize_backdoor_example.ipynb     |   30 +-
 .../dowhy_refutation_testing.ipynb            |  166 +-
 .../dowhy_simple_example.ipynb                | 1206 ++---
 .../example_notebooks/gcm_401k_analysis.ipynb |  330 +-
 .../example_notebooks/gcm_basic_example.ipynb |  188 +-
 .../gcm_counterfactual_medical_dry_eyes.ipynb |   86 +-
 .../gcm_cps2015_dist_change_robust.ipynb      |   76 +-
 .../example_notebooks/gcm_draw_samples.ipynb  |  122 +-
 .../example_notebooks/gcm_falsify_dag.ipynb   |  402 +-
 .../nbsphinx/example_notebooks/gcm_icc.ipynb  |  136 +-
 .../example_notebooks/gcm_online_shop.ipynb   |  706 +--
 .../gcm_rca_microservice_architecture.ipynb   |  332 +-
 .../gcm_supply_chain_dist_change.ipynb        |  157 +-
 ...aph_conditional_independence_refuter.ipynb |  136 +-
 ...entifying_effects_using_id_algorithm.ipynb |   56 +-
 .../lalonde_pandas_api.ipynb                  |  370 +-
 .../load_graph_example.ipynb                  |   84 +-
 .../dowhy_causal_prediction_demo.ipynb        | 4794 ++++++++---------
 ...ty_analysis_nonparametric_estimators.ipynb |  178 +-
 .../sensitivity_analysis_testing.ipynb        |  288 +-
 .../effect_inference_timeseries_data.ipynb    |  213 +-
 ...e-machinelearning-using-dowhy-econml.ipynb |  212 +-
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 ...ehind_Hotel_Booking_Cancellations_17_0.png |  Bin 1364 -> 1079 bytes
 ...ehind_Hotel_Booking_Cancellations_19_0.png |  Bin 1461 -> 1365 bytes
 ...oks_counterfactual_fairness_dowhy_12_1.png |  Bin 2105 -> 2175 bytes
 ...oks_counterfactual_fairness_dowhy_17_0.png |  Bin 29006 -> 26995 bytes
 ...oks_counterfactual_fairness_dowhy_19_1.png |  Bin 2277 -> 2151 bytes
 ...oks_counterfactual_fairness_dowhy_20_0.png |  Bin 32299 -> 32504 bytes
 ...oks_counterfactual_fairness_dowhy_23_1.png |  Bin 2232 -> 2143 bytes
 ...oks_counterfactual_fairness_dowhy_24_0.png |  Bin 27362 -> 25674 bytes
 ...example_notebooks_do_sampler_demo_13_0.png |  Bin 1766 -> 2248 bytes
 .../example_notebooks_do_sampler_demo_4_0.png |  Bin 1606 -> 2149 bytes
 ...xample_notebooks_dowhy_causal_api_10_0.png |  Bin 2211 -> 2201 bytes
 ...example_notebooks_dowhy_causal_api_3_1.png |  Bin 7676 -> 6603 bytes
 ...example_notebooks_dowhy_causal_api_4_1.png |  Bin 6387 -> 8390 bytes
 ...example_notebooks_dowhy_causal_api_9_0.png |  Bin 1967 -> 2081 bytes
 ...otebooks_dowhy_confounder_example_12_1.png |  Bin 51421 -> 57590 bytes
 ...notebooks_dowhy_confounder_example_4_0.png |  Bin 51970 -> 52506 bytes
 ...ample_notebooks_dowhy_interpreter_18_0.png |  Bin 39188 -> 37114 bytes
 ...ample_notebooks_dowhy_interpreter_27_0.png |  Bin 30035 -> 28167 bytes
 ...le_notebooks_dowhy_simple_example_39_0.png |  Bin 36120 -> 37024 bytes
 ...le_notebooks_dowhy_simple_example_41_0.png |  Bin 28844 -> 28819 bytes
 ...le_notebooks_dowhy_simple_example_43_1.png |  Bin 44334 -> 46707 bytes
 ...ample_notebooks_gcm_401k_analysis_20_1.png |  Bin 24239 -> 24296 bytes
 ...ample_notebooks_gcm_basic_example_18_1.png |  Bin 31904 -> 31953 bytes
 .../example_notebooks_gcm_icc_10_0.png        |  Bin 40982 -> 41205 bytes
 .../example_notebooks_gcm_icc_16_0.png        |  Bin 16654 -> 17163 bytes
 .../example_notebooks_gcm_icc_27_1.png        |  Bin 18961 -> 18921 bytes
 ...example_notebooks_gcm_online_shop_22_1.png |  Bin 34161 -> 33686 bytes
 ...example_notebooks_gcm_online_shop_34_0.png |  Bin 144013 -> 144666 bytes
 ...example_notebooks_gcm_online_shop_37_0.png |  Bin 22567 -> 22117 bytes
 ...example_notebooks_gcm_online_shop_47_1.png |  Bin 21401 -> 21411 bytes
 ...example_notebooks_gcm_online_shop_50_0.png |  Bin 21685 -> 21906 bytes
 ...example_notebooks_gcm_online_shop_58_0.png |  Bin 25191 -> 25167 bytes
 ...gcm_rca_microservice_architecture_15_1.png |  Bin 33469 -> 34713 bytes
 ...gcm_rca_microservice_architecture_27_0.png |  Bin 25548 -> 25552 bytes
 ...gcm_rca_microservice_architecture_35_0.png |  Bin 27960 -> 27980 bytes
 ...gcm_rca_microservice_architecture_40_0.png |  Bin 8200 -> 8206 bytes
 ...ooks_gcm_supply_chain_dist_change_29_0.png |  Bin 14817 -> 14744 bytes
 ...mple_notebooks_lalonde_pandas_api_16_0.png |  Bin 1691 -> 2301 bytes
 ...mple_notebooks_lalonde_pandas_api_18_0.png |  Bin 2004 -> 1546 bytes
 ...mple_notebooks_lalonde_pandas_api_25_1.png |  Bin 12320 -> 12339 bytes
 ...mple_notebooks_lalonde_pandas_api_26_1.png |  Bin 12302 -> 12337 bytes
 ...analysis_nonparametric_estimators_16_0.png |  Bin 60353 -> 60237 bytes
 ...analysis_nonparametric_estimators_22_0.png |  Bin 69621 -> 69567 bytes
 ...analysis_nonparametric_estimators_25_0.png |  Bin 70902 -> 70405 bytes
 ...analysis_nonparametric_estimators_25_1.png |  Bin 63947 -> 63903 bytes
 ...analysis_nonparametric_estimators_31_0.png |  Bin 77518 -> 75529 bytes
 ...achinelearning-using-dowhy-econml_22_1.png |  Bin 61873 -> 61222 bytes
 ...achinelearning-using-dowhy-econml_29_1.png |  Bin 59600 -> 59547 bytes
 ...oks_counterfactual_fairness_dowhy_17_0.png |  Bin 29006 -> 26995 bytes
 ...oks_counterfactual_fairness_dowhy_20_0.png |  Bin 32299 -> 32504 bytes
 ...oks_counterfactual_fairness_dowhy_24_0.png |  Bin 27362 -> 25674 bytes
 ...example_notebooks_dowhy_causal_api_3_1.png |  Bin 7676 -> 6603 bytes
 ...example_notebooks_dowhy_causal_api_4_1.png |  Bin 6387 -> 8390 bytes
 ...otebooks_dowhy_confounder_example_12_1.png |  Bin 51421 -> 57590 bytes
 ...notebooks_dowhy_confounder_example_4_0.png |  Bin 51970 -> 52506 bytes
 ...ample_notebooks_dowhy_interpreter_18_0.png |  Bin 39188 -> 37114 bytes
 ...ample_notebooks_dowhy_interpreter_27_0.png |  Bin 30035 -> 28167 bytes
 ...le_notebooks_dowhy_simple_example_39_0.png |  Bin 36120 -> 37024 bytes
 ...le_notebooks_dowhy_simple_example_41_0.png |  Bin 28844 -> 28819 bytes
 ...le_notebooks_dowhy_simple_example_43_1.png |  Bin 44334 -> 46707 bytes
 ...ample_notebooks_gcm_401k_analysis_20_1.png |  Bin 24239 -> 24296 bytes
 ...ample_notebooks_gcm_basic_example_18_1.png |  Bin 31904 -> 31953 bytes
 .../example_notebooks_gcm_icc_10_0.png        |  Bin 40982 -> 41205 bytes
 .../example_notebooks_gcm_icc_16_0.png        |  Bin 16654 -> 17163 bytes
 .../example_notebooks_gcm_icc_27_1.png        |  Bin 18961 -> 18921 bytes
 ...example_notebooks_gcm_online_shop_22_1.png |  Bin 34161 -> 33686 bytes
 ...example_notebooks_gcm_online_shop_34_0.png |  Bin 144013 -> 144666 bytes
 ...example_notebooks_gcm_online_shop_37_0.png |  Bin 22567 -> 22117 bytes
 ...example_notebooks_gcm_online_shop_47_1.png |  Bin 21401 -> 21411 bytes
 ...example_notebooks_gcm_online_shop_50_0.png |  Bin 21685 -> 21906 bytes
 ...example_notebooks_gcm_online_shop_58_0.png |  Bin 25191 -> 25167 bytes
 ...gcm_rca_microservice_architecture_15_1.png |  Bin 33469 -> 34713 bytes
 ...gcm_rca_microservice_architecture_27_0.png |  Bin 25548 -> 25552 bytes
 ...gcm_rca_microservice_architecture_35_0.png |  Bin 27960 -> 27980 bytes
 ...gcm_rca_microservice_architecture_40_0.png |  Bin 8200 -> 8206 bytes
 ...ooks_gcm_supply_chain_dist_change_29_0.png |  Bin 14817 -> 14744 bytes
 ...mple_notebooks_lalonde_pandas_api_25_1.png |  Bin 12320 -> 12339 bytes
 ...mple_notebooks_lalonde_pandas_api_26_1.png |  Bin 12302 -> 12337 bytes
 ...analysis_nonparametric_estimators_16_0.png |  Bin 60353 -> 60237 bytes
 ...analysis_nonparametric_estimators_22_0.png |  Bin 69621 -> 69567 bytes
 ...analysis_nonparametric_estimators_25_0.png |  Bin 70902 -> 70405 bytes
 ...analysis_nonparametric_estimators_25_1.png |  Bin 63947 -> 63903 bytes
 ...analysis_nonparametric_estimators_31_0.png |  Bin 77518 -> 75529 bytes
 ...achinelearning-using-dowhy-econml_22_1.png |  Bin 61873 -> 61222 bytes
 ...achinelearning-using-dowhy-econml_29_1.png |  Bin 59600 -> 59547 bytes
 ...ry Behind Hotel Booking Cancellations.html |   30 +-
 ...y Behind Hotel Booking Cancellations.ipynb |  194 +-
 .../counterfactual_fairness_dowhy.html        |  134 +-
 .../counterfactual_fairness_dowhy.ipynb       |  286 +-
 main/example_notebooks/do_sampler_demo.html   |    4 +-
 main/example_notebooks/do_sampler_demo.ipynb  |   84 +-
 .../dowhy-conditional-treatment-effects.html  | 1257 ++++-
 .../dowhy-conditional-treatment-effects.ipynb | 1439 ++++-
 .../dowhy-simple-iv-example.html              |   30 +-
 .../dowhy-simple-iv-example.ipynb             |  138 +-
 main/example_notebooks/dowhy_causal_api.html  |  240 +-
 main/example_notebooks/dowhy_causal_api.ipynb |  458 +-
 .../dowhy_causal_discovery_example.html       |    8 +-
 .../dowhy_causal_discovery_example.ipynb      |  428 +-
 .../dowhy_confounder_example.html             |   34 +-
 .../dowhy_confounder_example.ipynb            |  126 +-
 .../dowhy_demo_dummy_outcome_refuter.html     |   60 +-
 .../dowhy_demo_dummy_outcome_refuter.ipynb    |  144 +-
 .../dowhy_efficient_backdoor_example.html     |   20 +-
 .../dowhy_efficient_backdoor_example.ipynb    |  148 +-
 .../dowhy_estimation_methods.html             |  236 +-
 .../dowhy_estimation_methods.ipynb            |  392 +-
 .../dowhy_functional_api.html                 |   26 +-
 .../dowhy_functional_api.ipynb                |  106 +-
 .../dowhy_ihdp_data_example.html              |   36 +-
 .../dowhy_ihdp_data_example.ipynb             |  124 +-
 main/example_notebooks/dowhy_interpreter.html |  192 +-
 .../example_notebooks/dowhy_interpreter.ipynb |  414 +-
 .../dowhy_lalonde_example.html                |   16 +-
 .../dowhy_lalonde_example.ipynb               |   72 +-
 .../dowhy_mediation_analysis.html             |   16 +-
 .../dowhy_mediation_analysis.ipynb            |   80 +-
 .../dowhy_multiple_treatments.html            |  154 +-
 .../dowhy_multiple_treatments.ipynb           |  232 +-
 .../dowhy_optimize_backdoor_example.html      |    6 +-
 .../dowhy_optimize_backdoor_example.ipynb     |   30 +-
 .../dowhy_refutation_testing.html             |   46 +-
 .../dowhy_refutation_testing.ipynb            |  166 +-
 .../dowhy_simple_example.html                 |  196 +-
 .../dowhy_simple_example.ipynb                | 1206 ++---
 main/example_notebooks/gcm_401k_analysis.html |   10 +-
 .../example_notebooks/gcm_401k_analysis.ipynb |  330 +-
 main/example_notebooks/gcm_basic_example.html |   90 +-
 .../example_notebooks/gcm_basic_example.ipynb |  188 +-
 .../gcm_counterfactual_medical_dry_eyes.html  |   18 +-
 .../gcm_counterfactual_medical_dry_eyes.ipynb |   86 +-
 .../gcm_cps2015_dist_change_robust.html       |    4 +-
 .../gcm_cps2015_dist_change_robust.ipynb      |   76 +-
 main/example_notebooks/gcm_draw_samples.html  |   66 +-
 main/example_notebooks/gcm_draw_samples.ipynb |  122 +-
 main/example_notebooks/gcm_falsify_dag.html   |    8 +-
 main/example_notebooks/gcm_falsify_dag.ipynb  |  402 +-
 main/example_notebooks/gcm_icc.html           |   48 +-
 main/example_notebooks/gcm_icc.ipynb          |  136 +-
 main/example_notebooks/gcm_online_shop.html   |  244 +-
 main/example_notebooks/gcm_online_shop.ipynb  |  706 +--
 .../gcm_rca_microservice_architecture.html    |   86 +-
 .../gcm_rca_microservice_architecture.ipynb   |  332 +-
 .../gcm_supply_chain_dist_change.html         |   11 +-
 .../gcm_supply_chain_dist_change.ipynb        |  157 +-
 ...aph_conditional_independence_refuter.ipynb |  136 +-
 ...entifying_effects_using_id_algorithm.ipynb |   56 +-
 .../example_notebooks/lalonde_pandas_api.html |  174 +-
 .../lalonde_pandas_api.ipynb                  |  370 +-
 .../example_notebooks/load_graph_example.html |   14 +-
 .../load_graph_example.ipynb                  |   84 +-
 .../dowhy_causal_prediction_demo.html         |   50 +-
 .../dowhy_causal_prediction_demo.ipynb        | 4794 ++++++++---------
 ...ity_analysis_nonparametric_estimators.html |   44 +-
 ...ty_analysis_nonparametric_estimators.ipynb |  178 +-
 .../sensitivity_analysis_testing.html         |   88 +-
 .../sensitivity_analysis_testing.ipynb        |  288 +-
 .../effect_inference_timeseries_data.html     |   28 +-
 .../effect_inference_timeseries_data.ipynb    |  213 +-
 ...ce-machinelearning-using-dowhy-econml.html |   80 +-
 ...e-machinelearning-using-dowhy-econml.ipynb |  212 +-
 main/searchindex.js                           |    2 +-
 240 files changed, 18410 insertions(+), 15662 deletions(-)

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diff --git a/main/.doctrees/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.doctree b/main/.doctrees/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.doctree
index 8f9e167d3f436f82e741268097b7f6077ef2f303..4736b20920be5bb482c7bbef66c9962e82e3f964 100644
GIT binary patch
delta 4283
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delta 4432
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diff --git a/main/.doctrees/example_notebooks/counterfactual_fairness_dowhy.doctree b/main/.doctrees/example_notebooks/counterfactual_fairness_dowhy.doctree
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diff --git a/main/.doctrees/example_notebooks/do_sampler_demo.doctree b/main/.doctrees/example_notebooks/do_sampler_demo.doctree
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GIT binary patch
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diff --git a/main/.doctrees/example_notebooks/dowhy-simple-iv-example.doctree b/main/.doctrees/example_notebooks/dowhy-simple-iv-example.doctree
index b148289316644c2b81b83014a4f241165b4517d6..874f60d4afbf91bbea7d08309b88dbc3266da03a 100644
GIT binary patch
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diff --git a/main/.doctrees/example_notebooks/dowhy_causal_api.doctree b/main/.doctrees/example_notebooks/dowhy_causal_api.doctree
index b3e896ffbbf51e4767333268306076cddebce9a7..9a50f5930b848862090555d92f5455e316d3b92d 100644
GIT binary patch
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diff --git a/main/.doctrees/example_notebooks/dowhy_causal_discovery_example.doctree b/main/.doctrees/example_notebooks/dowhy_causal_discovery_example.doctree
index fc5f346303c441a251e603a3d4e73077ae048ed2..a9597ba9565eba9c1c92d6e0234c84a858a2b0fb 100644
GIT binary patch
delta 3894
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MuE7DV!Ogq>1&)<r_5c6?

delta 3964
zcmeHIO>3Q15blx0S~Y?;Ru`uCZD<80q;t-h^D&@7L6Jg(5gSYjA)HUD6zPX6p;X+c
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zZap%z8tE?m`0CK^+ji{eHqAu$yEjgEk6()2&Xqscqo2<`G(7j;;m&-gd*tc@`?kAs
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gfi(`SabS%DYaCePExdVPjsHVyRLfUiVsDQ81FV^g-~a#s

diff --git a/main/.doctrees/example_notebooks/dowhy_confounder_example.doctree b/main/.doctrees/example_notebooks/dowhy_confounder_example.doctree
index 7dad0f67970711897f18aa9654f10c1825bfc193..fcd314256eac5e30b13a76d9e6c32dbda0942653 100644
GIT binary patch
delta 2893
zcmdT`OHUI~6wV+59it#`pPhve4KkhkoI8YvL^Q^QfhI;x7zr^`I|Y=`Rs<uq5OyRc
zoVszN3tbwcHF0Ocij4_7w_;+9F-GGbFy2mWf}pjfY&xr%d%y2H-#zEtd*|)bil0v^
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SqIAJfq5;-}f%MA4p4wl+vC78)

delta 3013
zcmdUw&r1|x7{?j$2M$4`Yh|=Lkyt@HzQ1PXoiIfTp$O3}(4bKy#%9+{+g-IRb2o`D
zLdLURg6L8wFUpdaI<%)QfqAK;b_qK5A4Knrt(tAD?(Su8!~1^U&+|Or=Y8h=^}6iS
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q-kcxi(eR(yE|?R59|=nuh5m$kIiHIDGAh$u57$vJF4a8Scknk{xbtcN

diff --git a/main/.doctrees/example_notebooks/dowhy_demo_dummy_outcome_refuter.doctree b/main/.doctrees/example_notebooks/dowhy_demo_dummy_outcome_refuter.doctree
index e512a8d0ccf58b9d09f92c131a5e23b69ced402f..0881d727407ccb8f270055b3dfa71bc19e2bd4e7 100644
GIT binary patch
delta 3486
zcmeHJ&r4KM6!wh{)<j5+X%y*A5p$e*-uvEtKdw%a1Z`SmnAoTT7Aaa1Dk_x16m1Gz
zq-U~Kgaj?@2gF;7pbOhX(L!()=^qeUn<65*_dTD%Jafj)Fi~&uxO3lkzI)D@^PPLX
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delta 3733
zcmeHK&rcIU7<Eb;bfZCp5H%P!5Q0EE%*^i0?i9oy2M;D1Fag9+5ks*G0#PI&khTY7
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diff --git a/main/.doctrees/example_notebooks/dowhy_efficient_backdoor_example.doctree b/main/.doctrees/example_notebooks/dowhy_efficient_backdoor_example.doctree
index e7914a5f0520f1b98c923b070c8ad9b000db6725..9ecc0c8c909ce3a0aa4e94ab18c1f4aca7dd6787 100644
GIT binary patch
delta 1982
zcmdl#i*5fbwhfb0Chu7)z?+<(SCW`qqEK9rUsN)AVTbJGg=LANDXB#|d8wsE`MIUV
z$qH$SC7J1^sgoNgrA-c4hN(wka$=_NWXD!9?6N}Gj9M~n@|Uzv1L%a!8#85?$gtmh
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Z5kB|nGEYy9Vr<2yM2&fSNgU&8JphSi4iEqU

delta 1932
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RL5~0x{bSl*632L24*(UI^8)|?

diff --git a/main/.doctrees/example_notebooks/dowhy_estimation_methods.doctree b/main/.doctrees/example_notebooks/dowhy_estimation_methods.doctree
index c324bf9ac935fe84ecb2eeb967d0e1d2d0e82819..454f5fad69e556810af5ee4cdd9990f484696b0a 100644
GIT binary patch
literal 85927
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diff --git a/main/.doctrees/example_notebooks/dowhy_functional_api.doctree b/main/.doctrees/example_notebooks/dowhy_functional_api.doctree
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diff --git a/main/.doctrees/example_notebooks/dowhy_ihdp_data_example.doctree b/main/.doctrees/example_notebooks/dowhy_ihdp_data_example.doctree
index b99f6380dccafb556b4f157f7bf09986b616d5ff..91b5c3e883b6c508f6e6c69f92697a51096c1515 100644
GIT binary patch
delta 5153
zcmeHJUr19?80V_XT;M~936&d^RP3hrT<^cBY+3ZsLzWPOghi`1)(Ese<^dBO;a)Np
zeT%S%DDqJqa>(>S%F#n=%X}#4DPMw~dh02uzI$ajg^jLp2f~GW`2O7U`<?CkalY@a
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fY!&W;&+mg=3_dQjspbyP31MJO{$u9(@N)NGn8?BY

delta 5191
zcmbPtkomzuW|julsaH3$oDVTEG}WmvG}NguG6K>TKx_`gmO#FVPKB{f1yI%qh=CGj
zK$Zbe4ybN2V@9GXNIi%T5(nxBDhHB=KrKK6K>C3!LzBr5N(&>&v4S|$12bi5XZq%|
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zF}ZgM=VsRRD(oQs`fbw8W`<^yJ-4MGbI)%3ip-t0Lm6x|&yGA);nT?6)}3KU+|9pt
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bnOMehkh@$c@F6U`zy?v`!ObtvZWRIms&mBU

diff --git a/main/.doctrees/example_notebooks/dowhy_interpreter.doctree b/main/.doctrees/example_notebooks/dowhy_interpreter.doctree
index 2d6a1f514d702ac3ae7451408d9a40082f279b18..55bdc93a91358e599c3dec7745a62bed511feb2c 100644
GIT binary patch
delta 10513
zcmeHNU2IfE6lP&}TQ-&gyZy1-?%o!x1-9Fnd*|M{H`Iy-H5wqOXe)xINn7h*sT46n
z31E~E1&N&c6N08b7+*92H~c6d))y7QLiC9=O<QAl@DGhInrJ*Tv#spjZllsa<L*N{
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zZ3^LqW-g6$ZrMz^Af=Ul-NZ2+T?ESvVjDVlqcRz13_J@%e#bFw3vDZcMGj$xqo+?M
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Xt)peb#>YFlUhenJke?$lPrvU^m0}X2

delta 10420
zcmeHNU2GIZ9JhGAwwJUBy+SQ_#}-Lx)9ZF-c4v1dl!8PF5(*X26SPQ8OAA;^+CqXt
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diff --git a/main/.doctrees/example_notebooks/dowhy_lalonde_example.doctree b/main/.doctrees/example_notebooks/dowhy_lalonde_example.doctree
index ccaee93c82a14db7f760d9c720ef293ad2f98c0f..43e43409bf4f78b0a8b96681ee4c861168b5161b 100644
GIT binary patch
delta 735
zcmbQSk#WXG#tmLVycR~LCKjefre-F_mS&T~h4j!wCJF6B6Nwd0LK8V7JOxc8S40t2
zWb<s1bQVUV$rB{RCre2xv6>oK8Jcf)lzhP?YG`hwUy@>{U;`o)ObpEQ%neK@+sT+P
znHf$lk#QFXI?)taiJ7sXo(a&gj;h9D=0?a;Miv$ZdX`3JlWn9eCI`zZGMSl9E|Z<g
zY;I%%vU#(roDcILaD)xx<_qfoKrT72tql!||Jt*egbd9U6x0;J02U68W~QKUs4%k@
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kGN4GV_kJ`rXo(D{=8UlX<l{bNXzI`snz_a13SR|o0BK6KtN;K2

delta 731
zcmbQSk#WXG#tmLVyyoV{rUvHbmX^jA7ABL!h4j!wCJF6B6Nwd0LK8V7JOxc8S40t2
zWb<s1be75e5)zZ8B$ZfAEUgSJH#<tcU=p>ow9zj~u~V=C5eg;-W_soZhLi1NOqk5g
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zX4WF0P=L$Bg5bEBi6}4#KzblL;UOSk9uD$-rg=5U_nV)aCopc_WVn%$-qzX)BP9b%
oLnE{V1`^5j-jAjRHGzTDoDr6veB7rDO&w|?1FG9x;j6$607(M2AOHXW

diff --git a/main/.doctrees/example_notebooks/dowhy_mediation_analysis.doctree b/main/.doctrees/example_notebooks/dowhy_mediation_analysis.doctree
index 57929aeea2a2958d7f7ff3509544ccc6a7f36ae8..f04bdc1264398ad02659d1518725ffc4b6c18652 100644
GIT binary patch
delta 2167
zcmds&&1(}u7{<HX){jM&8nHzMGYUcv&2+v$FsXX*CKUuB2UCc(x`;@%D1;i?_;Jgj
zbXEw62!ePLEb|BS<i%=_q6hJoi~bKfyQyf{9MXdbfjulUzh~a}c^RI0)ApaW{gKtt
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zxs#CP&VII<Ej`#CU70Q>X&PkIz^!oVVwfo+gp#V;ZFASUCvhXL$5H;~sXaqOOINE=
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z_oN|&G1FLL_fg8^LczUi-6Jb^=v<aI$6BlY`Sf<!t=12JB8#L4n!oNxe>gCu7Zq>Q
kRBLngtoY`c?d2opYkWC*g?qL9`_5z)J=(S>UL6R30j1;bsQ>@~

delta 2177
zcmds&%WG3X6vmTjV(}u;M2sLlhDs5kcbqvhcOEoW1h*oHU_c|bn377+8o`Z^)~dTK
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diff --git a/main/.doctrees/example_notebooks/dowhy_multiple_treatments.doctree b/main/.doctrees/example_notebooks/dowhy_multiple_treatments.doctree
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diff --git a/main/.doctrees/example_notebooks/dowhy_optimize_backdoor_example.doctree b/main/.doctrees/example_notebooks/dowhy_optimize_backdoor_example.doctree
index 7d8cde52e3508ad7b3e70fd1c4a27a157a97d1e2..8c75e5cb3337429299a9c88936c4be02853e58f2 100644
GIT binary patch
delta 453
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delta 513
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PC7C8-@%QFeYFBswPq};&

diff --git a/main/.doctrees/example_notebooks/dowhy_refutation_testing.doctree b/main/.doctrees/example_notebooks/dowhy_refutation_testing.doctree
index 2ecf3bcc2a9c25e82d6e284e8d72a3b6501a3aa7..322abc46105dc2f009d5652c3c2e812a35fcac1b 100644
GIT binary patch
delta 4673
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diff --git a/main/.doctrees/example_notebooks/dowhy_simple_example.doctree b/main/.doctrees/example_notebooks/dowhy_simple_example.doctree
index 52384df649f256a1872d6c9fc0fdf5f336fea753..0da9ee541f0594729382073efc0d3a2cde6e17a1 100644
GIT binary patch
delta 18807
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diff --git a/main/.doctrees/example_notebooks/gcm_401k_analysis.doctree b/main/.doctrees/example_notebooks/gcm_401k_analysis.doctree
index df214867bb7d566936087842a3b5ed3d5a464f69..fdb14175fd59e5898f394fbbd019db7c1ee5d724 100644
GIT binary patch
delta 2789
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delta 2786
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diff --git a/main/.doctrees/example_notebooks/gcm_basic_example.doctree b/main/.doctrees/example_notebooks/gcm_basic_example.doctree
index ee33d6023a290741c4de65cf35292ca2e4b478c9..dd2777aef74672d6fb70c51247bd2287234cbb4f 100644
GIT binary patch
delta 5491
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delta 5536
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diff --git a/main/.doctrees/example_notebooks/gcm_counterfactual_medical_dry_eyes.doctree b/main/.doctrees/example_notebooks/gcm_counterfactual_medical_dry_eyes.doctree
index 01262151771dc2e4465bc0f7915c4e7869477678..8b8ce00c03f6cb8534d3829edada776884c71e4b 100644
GIT binary patch
delta 926
zcmaF#h~?2EmJQR~Sq$|o4JTg+6q&rvJrB&;tl%NY2ocr|QJfs+IT^xv>-h}Axb9U1
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delta 1022
zcmaF#h~?2EmJQR~SuFL8OebFm6q&rvJrB&;tl%NY2ocr|QJfs+IT^xv>-h}Axb9U1
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diff --git a/main/.doctrees/example_notebooks/gcm_cps2015_dist_change_robust.doctree b/main/.doctrees/example_notebooks/gcm_cps2015_dist_change_robust.doctree
index 840c6974722e2c30eb37c65196b99debacaafd2e..1a57d637292d9b205f040cbe19771aac3dc4bae9 100644
GIT binary patch
delta 223
zcmccA$#SWaWka2~f{CSpfn}O`T5^(sv5Ap!nng;gsfk6hk$IwpahkDt(&TyKiwG*(
zoFMU#gRrVZMI{v`Q<KT9uBAX~^B-3ZMs`z^%o6?L*vYZJUXz!*#lrdAe)5xVxNiX|
X@AF6nDgWy6YX?C)w(~PF9%cdnpyEVR

delta 223
zcmccA$#SWaWka2~g1M=IWm;;orI|@ms)=!`X_AFyN^+uEN|HgUQJQ(0<>YzdiwG*(
zoFMU#gRrVZMI{v`3xmn6uBAX~^B-3ZMs^E>%o6?L*vYZJUXz!*#lrdAe)5xVxNiX|
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diff --git a/main/.doctrees/example_notebooks/gcm_draw_samples.doctree b/main/.doctrees/example_notebooks/gcm_draw_samples.doctree
index 72b91e556f26c31d165bd1c4883e4c0d463f3a56..424b273a07b49937defa0b24474a7e78653c82a6 100644
GIT binary patch
delta 3614
zcmeH|&r6g+7{__n)z!V2tefO!JE4*7va|E!op)xoZ86X5F9He7Q@>1u$bupd8lIw?
zQ<p9Vo}>umJrr~(I%GkR9SZ^vUHT6y2%34{efQv`gcmn(-^<K1&ph*G=CjZD(Q543
zYAn9c{BUJ0ex=>n^j~@Fj=+#0A|xsoZ(gV*LGUZ<A%)1rl2~y8Ip%^8HI{Zy7BEB%
zAv>!;A)yR=cx%~$fS7aUn*F_MGu?3%>4OAg#LCnQo<PD=cV()XxlFohjkzTgX8N=8
zG1TXAZWkg@x`?|XI5J=AC~Mi$6Aam@6uJ~+D(vD2dX##eP}vmOzq?)EU0yAh+I-%1
zD>ff;2S7xcMlXUZi9sDLP-h8%Iz34OKm-mbM5O2n2$BGmBn=V*QcSH74P7CEAqiX)
z4>>Srgb%M{^_L#%E;qi&{>;b57kMpraA_c+{KMn_gVwBC?^$eoq2G@b8(yUUJbS&h
znMsJS8m)G!H^(o9GyiFSt5aK;&SjPcTGW>l_v?YLwNW)cUCV!&&m{b@)H}yRkaDh@
z^wu7YAeSC-Ez2?gcp{b4)u}ebF-D}I(%vg2Lw20%y@Fylaw~0@OrLI>426_pWXC1c
ziiq|eZCz6LDb9tRWcm;llv}b293q50rbg3!N;{W8P-st<mOo*AJjb3@fN8gu9_4;E
z^=tp;c5`#u@ALk0=|*fmq|Vq%$Qb}3Sp;jlGHw)*Wkw-O#@Rx8j9W$Em}rO!01+HD
zvkKS<Oeuf?M6XYiuCCf$dwNqUd~5(}&#RuCDmSvch04ZW%gbzkdt3$fbgscAW>xz1
z;$Caq2|_$rY+!l*&%rZoaotaKGp*hZ2L6X?!U;n)3;Hp1FPitM)!`T6<l@LwnEz?y
zZQSAh=ZRkDfW$=mh-Z4f%ZcPG#km&sexiNH_2l_Gv8xI7y8`{(Ot)RcFV207`L8dR
nn*J$`HmS$MZEALQ(#iQ7vps(;=(My>B_<zCRnBgoGjr$<hmZ-2

delta 3568
zcmeHKJ!n%=6y~LAk_KuUD=oCfUaUiH?alqa@5MCY4~Wp(L5B`zs$wlFwic9DsM<wb
zRBl}yRB)<;Q0}78O|XK{ItU^Nbr2UPM<>1S-MkDhsfg1Pn(5@+@80v}oR9O77t4u9
z%ZcR8=7;xIk{8<aH}ckz7-E7sBc;OZ*>V;HpOOhN6kJk)qIn7h7M!TI;y!ghmvb@X
zh*HU-I1CBL9EoV2LGDT}rD`wssb9IAH-sRQlnNY0nwL4H%#G$5M1muXgBLFD>=3Rn
zl^R@UrVg$n5c~ko^UQT21;dnh(Fz1gggsi{6Voi@34+y);-G#lHn!XMukB;{c-OVW
zg4G@Xq1uf6ssf`~?5C}`{K(45073<238-l&fCg#_09GJb3IKvu9X`P7AuM4ekOj;~
zSP=jaj@@8ugRXR!;!o1;*@!>Mg@I1By1Vi}5baeT^NaB(`g2bq_9XRM?{ub_P)Lay
z?w_-fUhU5~)#T>rUiE$d-P*SvwVrQLj|W~=GiL^;s+Zmk_N1N9`f}Tv;~LL!F(VC@
zzKbNO-{=AoDmW8?XBgWmB8V{JZr$oP8%1!6Tp9I*Ap!}Z+(V&p^%^K8(sk=>0zoWY
zrU2iSBBqGsRj0}wG*<{^nAN$Ze;^6sjrb5EIr8m_r7`r(8YF7k>&P?0IIe4e2R+Fo
zR`bOJnaOlzX|g=N-Lk)GnVv5`Ni0}d`EFr_1VAVq-;YAo_{L*p0&2Rjc4QUdBYh1}
z&yTDKj2qR)A4I{tgD4nq)_4)_wgV=<4cY+%5ROz2m*PzCPWgSD>HRo#HQw~Pg2k6V
zyclPi-jPC#>1pTasg|S*F;n9s*MmTR7)d)ewK@7c46MnloP22$m&PV7_|w>%q{H>o
z(|yhk^OwZzc27zoTuh#|&ZgS4rNWh&bLFM#V%-v`Iw$6wDg9vLMD!WmI{P)D*UuN5
g{(MfG0yW&8sW|=m%S^t(JDJp$M@~u~yEWDE8)GN^y8r+H

diff --git a/main/.doctrees/example_notebooks/gcm_falsify_dag.doctree b/main/.doctrees/example_notebooks/gcm_falsify_dag.doctree
index 0b1c7845afb7ddd11aeb99bb4071feb4b10e8905..971f525ce432b07d68bbf658fd812c76141acbd6 100644
GIT binary patch
delta 468
zcmccdljX)wmJL?Qg63*DCDsN8Rt5$-3JQjLhUSwm=I3G&^)C>cd|&wmpScZ+;+Vok
z!l*)-CHlp&lLIB?C$CgpkFNY(Q39IK<|}HyJtp^ko;EqP;Ok_`uh%9U6z!S3`dj_x
z?BdyhLWai3?ladjHl8eTG!KjL;u%L7g$<42t^+A8*3T@NoKPq?`Q))i5iD{LXVxCy
z$d4vD+4e*ty4dEmCoXD<85ttG$579}6l{sVat1Cbp)bFLjSS)Lf@?`AoGXGw9O9<;
UU)N*RwDDUsHu25!-*>tK0J(dMnE(I)

delta 368
zcmccdljX)wmJL?Qf@W$tCDsN8Rt5$-3JQjLhL)2r=I3G&^)C>cd|&wmpP3Dc;+Vok
z!l*)-CHlp&lLIB?C$CgpkFNY(Q39IK<|}HyJ(vs)f!5Xlsm=Zc+>9V@OyN2Z#Z&YE
zM7=A@0ZGObuL=}0Fh+K=xt@{9<O!ceChHuH!Y0M{MQrlAqpySwjNuLkX(`svESa28
zC^z}!u|^Rrau6TX9^c51COO&mL?XJ_=Cvm-YE9mszihI~mlu=o7l}+xEIdE?{nzzC
LcF$(5@9SIvASHGZ

diff --git a/main/.doctrees/example_notebooks/gcm_icc.doctree b/main/.doctrees/example_notebooks/gcm_icc.doctree
index deba08c43d4954321fa5623f316f370a6261d71b..11497f78a3f570a1d3b03fe7a857b492f7e45570 100644
GIT binary patch
delta 2979
zcmeHJOKekD5Y00tE(mN(fq=lo#s#`5&i%~&AqZ^{YWfjWVH297VC?6ZfPvtIj}ie<
z+LA?@fX0B-U6*l?5PfP{RRytOK~W=B-2p;MTZQieiEX$qe1rhu6<PUh=DssWGjryR
z?wiLagVQI2!4q@Ogf0d<Az%y^;Fxa)#_Dm7c;X*N%1K-yNBm>c_$r)`zx&5iYM(W~
z=znXS9{jH<9pg+u%JAJijl~`F*Eb$G=r)z|p&g}s)9?(T_89?Bj4i?28}rUMI%S2&
zKiT%e@niYJH^B|#tMS68-#S@b(A>88)f_K^&v-)5J>EafjREH~udQ{mHbPRA3I&81
z!4#Kxadc6bDKLys4NL+vxTP~5ra}WZ3Yb<}Xmonx5k-Mh4jd#%tprXCwjdc?98rd;
zlrU6SE(5@o?TaGJaBl320?w4wxV1AD(S{hJrIyNP0uFqW2-}FnVw6&;n80*b62I>K
z2zMS@7F8(<j50%&)?5=|E3J##T+&t#wt8hz@B>G+5K2<T!KOD_aO`gP1u)QlZfU6k
zPv?_(xm_(}glQwW<k~Oc&R2C&K^Y|=nA8FkvGPX`H-rJS6x7P4AeoiFwZY2d#ELXh
zf)chRKYxEE!VL&6ffFJ?bMAHj@I?SyS|inF&sMX4>NpzcTZj{PQ@FMq^8PCPT^;fF
zKjQs`71gfK`o+>;sK=#!IcJ@Byzf?Z%-{2vw+5<>o|Rkt%Yf&ct*vteelT{#5#GSq
z0tYAVCGkRe#M&5cAHQOq%g^Vn^Skqt@q}Ge4AnWAwZCH!8KSAmq_c))vcyQcxZu$B
z(aI$C=e3F?FMeZBBtaBp-JHvWn=_DhbGw8sz;*o{it;p}SiX6-GHK_n-;$Lg^(XL;
zo1bImM!k3L_O5wzl}Q<eqxTyyHnl0JQi@?_YVd8aznkXnPQP?zpRq^EEAjEYbFahx
z&bs`;)t9gzUxwEcvW}3C6AWfDZGKpMC&7z9`ahaDGdZVb<{28UD;ClRd%An~_qfH5
jz56riy>7Ae>l$Z`(^Bb^mAYb~nwsH?UA$CsoQ}X#PBJHU

delta 3292
zcmeHJOGuPa6!y2tDXW>OOsIT}_Arpf`+k5_8c9KkJr+j!7{}kn@{x{ZMO08x3wzKR
z+Z9GDO&9gkYtg1QL0Pm<+ZbVG2CZ7v9Y-ZZn(Zv+=jT87p7Y)Fo%0{gz4y-**RLxs
z*M)_>D;~OzuyjG7m7O!fnP5w;*z`u(TF4f?@!@*-AYR0VH)Wd<kJRT3cck=qr@bW;
zeC1V8!<G~<q611cca=nHGD}NhwrJR06Af?anS;!TBjL@~bJhUl*HhRw;DWW~%b}vI
ze8y<#7fmn!59WduGW^w9|8UQcP8p`XIUmbQ(ya=oZr@l25&@+UA&6_OkYs_(&He*n
z+h4h7{|<k1#E$|LbA&Wzk`cubgM!`p9z<eOA&ACEVz_rOe;Fr4Q;C=q1T*k<uY;cl
zONrJ@2LvX{5X8RoxTjoGj?7vj710FB!nt0>i55f<$`!{-8-hHqqFNCms1%qBLcrHu
zXef$q9a=+mzyyM+CJb>T=zK{iq)qf#5T+4km_x^<JP)Q+DlWAWG@eGTk08S=OxPGJ
zDGYVN$wC;uyc(`o=6ST{)Wj$iRt!nNm$N*SX~wluf>Xx02|mXsDW}xrDls=|L`?L#
zUZN;sMvcvDGMjeW=jBo%#uet$j5vey)pwox(?b?i?enBcl<XyP!$4h!b;il5&vd6c
zDH%KIr8T{8R;Q|zy!+jzbW1z$ua2gUqhvgeqC>Myl&VtFHc`~cvM6NrpR%ZPt$(Qn
z*UNI?@j%qb4bb-BnK2d(hK=#&;8?z&GZGA89a5AvG7_?tz#$<;BMWM~L4+hk-`+?J
z+CKa`A;>9ycg*W=z(IytQzLF*HX}Hwny`>f1oI%ao476E$;j=*pq9}mg^4_!^~0Ny
zUC{n6+qwU#Vevwt160A}!~v)sU*<CRSq|;v7w1tshrh0}X7zdS{Ch<l9=rSh{&r`6
z(|RbHcr|OPjj!1IBQi(j6i<Hf`LFh@jI;(jyE;y^ciEA;miADv#g0UKhZm$xJ&%dC
Z(b}1)HqoOw?zFU?gum-)pJmmh{{<O`b!Gqn

diff --git a/main/.doctrees/example_notebooks/gcm_online_shop.doctree b/main/.doctrees/example_notebooks/gcm_online_shop.doctree
index 101b3f39ffb6b0bfe004f0f815b2e022385e6742..f59986edc99b550937b3d0f08fe951d365790ab5 100644
GIT binary patch
delta 15892
zcmeHOYiwM_70$Vd9cP`yyIwmc565e=1Q46+dEA*h16CR!JOlw8un-WZIDi9oT1*p1
zr66ja6|Y1@*I_DJkS(YHiAN(>T9sCEkP4(fsx(#k1tN)Pc?1H2234hv)HC<4ch|d)
zV-l%qU^h~H_uez-%sDe>zVpr8{n?kb5C5aK>fWlI|E}Lv^~w<RjIMa)iW>9hTBr$*
zj;#!?dit@T<N2rRspq*;N=>9BcuhPJ9Q-Vq2ygpba*z0)AU+S`e_1|H36B%*d7jUR
z7=7oN@56janR0z>&8R0lvm*u#LHwCV6Owwwm5lj9P_7kI!P>D@LJ`lSE~iv8yx^#0
z__cc=t_4>v_cUr?L`fQ4_53g5S`y;96iuRLqNUkY2g~A8dP=#HN`<Pa&qsGW`zXv8
z0^9q9pk~Ru;Pu}=9rt`CU5*9`s@1*>I>t^<aL?D=)d~$t&*P}HB@Lay;7C<4_QJw2
zI-^I&HbR}}`qU>3-Sh;C1@n$9ihD|9YvC#nEm4mLBcHb>7-|%#jtlMjf-`2;E{Ba)
z<LKB6^)T0$*eA3nG~w2*1lO)$l5<8ih7mnLHK)(#LTZY!6fO~jnvt{M>RhjG4f_B2
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zy?*GnmDg3K!gqWs+z;^8Xkt%axEx$ymjW|97tRm5_g|hk@#Wz1Pp+M8IanR4o$hjQ
z@8PXbSF#)g2j1N_&D9_abKw3-mV;zK_TN3tB_WGT!XRL;FFT1<Ad9QO){^W$Xx%t|
z@@FymZ-BClH&-U$V(itLZ;<xE_Ba0&j2*b4GTg^TfN$S$Ut?0w!JHud^?Df2elH1p
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z2<kOZKeY43Sun$Xa`0Xm^T|s+o9N$;F*n0|A(lOI4ty}DigWHVvtPg?)!`*Gtrx<A
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zIWOu~s@w1<xT$r7o$K-CKNvWVkaG#ir0^Yg?r&-99Wbv3&blR2Ntt`rI$tfCps2lA
zi&=RM!g0q==cfyc)s)G+F_n(yPG{{D&$ADAIuA6=@p#WIH*H`&%rx{mYhwAY#I!7P
z8qHv@^M~5P&6Q@y)lRe7)aR@#YPq-1dAaE8Lz^63_?mrdlk?V@rhBb3rvU?195K#Z
zJZZMSg)n1*bJ-L!_GcNUeaP7#$~0O%<G~1(jQ>jG=>?NM%KaRo_DN@CDg~{b&b5g$
L1zBpGw>SJ3@eVuy

delta 15429
zcmeHOYm8OZ6`pm5d0k-c9flbO<Z+o9e8kI{{oecNgo0?R;)5w@s0_hDA&i1zr7_yb
zs10qksHF$lZ53sVwWdiGdRwohNgbN{Xp>e+YnrApW~`6aS`@8qO?`BobM9;IJh)1K
zD82LRaL?Lnuf5LN>s#O2o3Fn;`KxbDuDPk^u8!vYH4pD<*gUhUs(Z^9*Dp$w;Vn*j
z&%Wns{Jn$PCwuPqlY5(+xN$u}j8W7hL}|(VLwoLt2fs@ES!&8OA@;}unBOEAcQqB1
z8^$H2l=}<+c5(Wi$Lb+2J)>PIC~BaJay!2Rn*E{sZ*8KAXzdb@3C4^OTKbnBXirea
ziK~cbBvGhMrRNXc2nj+w?n+NnPB;q4&=SH8F|P3#Q-W}9mz)gk3C1X=u14LSGK49+
zb3Ux{UpuhPu5E`OHyTg7oNLcBp3=l$viIJE40J2Tg`kRgMEi%n*WQG43E@&t2*Q}h
zh_pvKV5NW0V{`4=PN+}6@%RRaE7$WFR}{U0c4_~Ow`V1o;6xMY3h7CFBSNd<Xp5&A
z)95CNL()SBZ-l7=-6y3|S}{X$MRx}tM@I<GiHAR;L!&iOO{J9Ra+eC_ab>SL89tA;
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diff --git a/main/.doctrees/example_notebooks/gcm_rca_microservice_architecture.doctree b/main/.doctrees/example_notebooks/gcm_rca_microservice_architecture.doctree
index 6c1b06dda316a6d17bce8b4b389e04ecd1365bd2..57266135c64d627bb5b05a9066774e209e0b9331 100644
GIT binary patch
delta 5469
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delta 5407
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diff --git a/main/.doctrees/example_notebooks/gcm_supply_chain_dist_change.doctree b/main/.doctrees/example_notebooks/gcm_supply_chain_dist_change.doctree
index 6204e84fc7a2d0b293300412892b09843db5af58..6b45ac7cb871cef1d50b5ea80fd699f21f9654f8 100644
GIT binary patch
delta 816
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delta 2507
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diff --git a/main/.doctrees/example_notebooks/lalonde_pandas_api.doctree b/main/.doctrees/example_notebooks/lalonde_pandas_api.doctree
index 93c0fd588e754d04a5874006ad3a95488b852931..1e7d8392dafbb1d6a5d05063f334891aa1b9dfe5 100644
GIT binary patch
delta 6490
zcmeI0U2GIp6vy3#((M>wx7#ij((RN27Kyj_`_A-Bcma(;(rBng8oHri6DX7dA%R5U
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delta 6859
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diff --git a/main/.doctrees/example_notebooks/load_graph_example.doctree b/main/.doctrees/example_notebooks/load_graph_example.doctree
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GIT binary patch
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diff --git a/main/.doctrees/example_notebooks/prediction/dowhy_causal_prediction_demo.doctree b/main/.doctrees/example_notebooks/prediction/dowhy_causal_prediction_demo.doctree
index 575d8a15cf03c0ec95fe546d397a445108c878ea..fbe371da1c3875b24eccb57566d073c4c21f6510 100644
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diff --git a/main/.doctrees/example_notebooks/sensitivity_analysis_nonparametric_estimators.doctree b/main/.doctrees/example_notebooks/sensitivity_analysis_nonparametric_estimators.doctree
index baa7d5c84e41b08b7c19c6714b6390cd17ce1795..06e43fb58d0070ffb92ee994ed877fe00fc1cc74 100644
GIT binary patch
delta 3596
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delta 3563
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diff --git a/main/.doctrees/example_notebooks/sensitivity_analysis_testing.doctree b/main/.doctrees/example_notebooks/sensitivity_analysis_testing.doctree
index 20ae411b394f99865eb7d956820d1af952b68b0c..a325a170a58158668e347c9ae77850ddee640881 100644
GIT binary patch
delta 4858
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delta 4849
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diff --git a/main/.doctrees/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.doctree b/main/.doctrees/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.doctree
index 6760fe41917b8da03695130d19c564a72ab45dc0..acd96fe4f33af81da9c993d1d008ae26134bf8d8 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb b/main/.doctrees/nbsphinx/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb
index bc24d1bd8..d64c6ce45 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb	
+++ b/main/.doctrees/nbsphinx/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb	
@@ -33,10 +33,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:02.230079Z",
-     "iopub.status.busy": "2024-10-25T14:27:02.229894Z",
-     "iopub.status.idle": "2024-10-25T14:27:04.402470Z",
-     "shell.execute_reply": "2024-10-25T14:27:04.401788Z"
+     "iopub.execute_input": "2024-10-25T14:27:53.724769Z",
+     "iopub.status.busy": "2024-10-25T14:27:53.724366Z",
+     "iopub.status.idle": "2024-10-25T14:27:55.916639Z",
+     "shell.execute_reply": "2024-10-25T14:27:55.915964Z"
     }
    },
    "outputs": [],
@@ -62,10 +62,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:04.404988Z",
-     "iopub.status.busy": "2024-10-25T14:27:04.404638Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.250015Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.249372Z"
+     "iopub.execute_input": "2024-10-25T14:27:55.919296Z",
+     "iopub.status.busy": "2024-10-25T14:27:55.918760Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.190465Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.189803Z"
     }
    },
    "outputs": [
@@ -300,10 +300,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.280116Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.279501Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.284147Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.283572Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.220678Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.220223Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.224807Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.224191Z"
     }
    },
    "outputs": [
@@ -350,10 +350,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.286109Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.285755Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.307632Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.306998Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.226723Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.226394Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.248688Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.248099Z"
     }
    },
    "outputs": [
@@ -404,10 +404,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.309647Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.309298Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.441718Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.441005Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.250826Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.250445Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.383867Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.383165Z"
     }
    },
    "outputs": [],
@@ -423,10 +423,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.444102Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.443907Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.467593Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.466896Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.386351Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.385935Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.411099Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.410466Z"
     }
    },
    "outputs": [],
@@ -441,10 +441,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.469952Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.469596Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.620438Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.619916Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.413428Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.413166Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.569158Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.568528Z"
     }
    },
    "outputs": [
@@ -781,10 +781,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.622468Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.622119Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.710762Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.710107Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.571185Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.570888Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.660158Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.659526Z"
     }
    },
    "outputs": [
@@ -966,10 +966,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.712763Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.712407Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.719476Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.718818Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.662169Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.661835Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.669639Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.668965Z"
     }
    },
    "outputs": [],
@@ -991,21 +991,21 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.721832Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.721379Z",
-     "iopub.status.idle": "2024-10-25T14:27:28.574024Z",
-     "shell.execute_reply": "2024-10-25T14:27:28.573385Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.671964Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.671447Z",
+     "iopub.status.idle": "2024-10-25T14:28:19.311987Z",
+     "shell.execute_reply": "2024-10-25T14:28:19.311389Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 588.5505$"
+       "$\\displaystyle 588.8275$"
       ],
       "text/plain": [
-       "588.5505"
+       "588.8275"
       ]
      },
      "execution_count": 10,
@@ -1035,21 +1035,21 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:28.576288Z",
-     "iopub.status.busy": "2024-10-25T14:27:28.575850Z",
-     "iopub.status.idle": "2024-10-25T14:28:45.128033Z",
-     "shell.execute_reply": "2024-10-25T14:28:45.127467Z"
+     "iopub.execute_input": "2024-10-25T14:28:19.314140Z",
+     "iopub.status.busy": "2024-10-25T14:28:19.313711Z",
+     "iopub.status.idle": "2024-10-25T14:29:36.043385Z",
+     "shell.execute_reply": "2024-10-25T14:29:36.042777Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 572.8318$"
+       "$\\displaystyle 572.7207$"
       ],
       "text/plain": [
-       "572.8318"
+       "572.7207"
       ]
      },
      "execution_count": 11,
@@ -1080,21 +1080,21 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:28:45.129925Z",
-     "iopub.status.busy": "2024-10-25T14:28:45.129711Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.613277Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.612611Z"
+     "iopub.execute_input": "2024-10-25T14:29:36.045426Z",
+     "iopub.status.busy": "2024-10-25T14:29:36.045173Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.421339Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.420713Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAQCAYAAABwQqNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAABJ0AAASdAHeZh94AAAEwklEQVR4nO3Yb8zWVRkH8A+YQ8dYuIyx1QJ70sTyTysJJVHTkNT5L9GtqdWW5KLFEiG3Xlxebul0jYFp05ZDZ75SF/kPpNCJleZWuGyaMAEFVhQkTGUME3pxfj/48eO+vZ/nfjZ5w/fN9dzXuc55vuf7O+c65zoj9uzZ4xAODj7SyZmZ5+AHOA1HYStexqKIeHI48Zm5HhO68NkcEeN7kc7Mb2Nxj7DdEXFYq98ncTNm4GP4J5YgI+KtLv/rAszBCY0+f8GCiHh+OLwOED8zb8c8bMSj2IKP44s4C20xhxRfYTsWdvC/04N4jZeQXdrOwFextMVzAH/COPwW/8BkRdgZmTk1Ira2+tyG+cpiWqLM7TO4GN/IzGsi4tf98tpP/My8VhHyfsyKiF2t9sOHE9/Atoi4qUtbT0TES8pED0Bm1qvxl62mXyjC/zAift6IX4Af4ae4ruEfjxuwGSdFxL8bbWfjaWUX7RV/qLxGNhpHVQTe1EHIavD3+o3/MJCZJ2IKNuGJhn8A07Eed7W6Bd7F1Zk5uuGfoOjz56bwEBHP4G1lh/fNq7nyv1YNthC7q1z3eezEi+381kd8E6My8yp8Spn437AyIt4fzGQ+ALMqe29rrLMruzwidjc7RMTbmflH5eNMwYqqaQ12YXJmHh0RW+o+mTkNY5RU1DevpvinVnYnVilC7kVmrsTlEfGfPuObGI8HWr51mfmdiHh2cPPZH5l5JK7C+/hVq/mzlV3dpfsaRfzjVOJHxH8z88dYgFcyc4mS+wdwEX6H7w2H18jG3+MqOw97lANiDE7CckzDQ8OIr7EY5ygfYDROxD2YiKWZeXKvCXXBFRiLZRGxodX20cpu79K39o9tOiNiIS5TFum1uBEzsQH3tdPRUHk1V379If6HiyJiffX75cy8FK/hzMw8rUopQ42vJ9S+Dfwd12XmO5iLm3DpICbVRr217+mjb0dk5nzcgjtwJ/6F43ErHszMUyJifr+8mit/W2VXNYQEEbEDT1U/J/cZ3wt3V3baIOP3IjM/h9OV6263qy37dkAbtX9bY8yzcBsejYjrI2JtROyIiL8qi2MT5mbmp/vl1RT/tTaBFuoi5Mg+43uhPhtGf2BUZ3Q7aGvUXI/r0v/YyjbPhAsr+0w7uFpcLyr6faFfXk3xVyi5+4TMHNkOtO9AXddnfC9MqezaQcaDzDwCVysH2r1dwmoBp7e5ZuYYTMUOvNBoGlXZbtfJ2n/AFXuwvPYSiYg38Jhy/ZvTGmg6zlNW+bJ+4iv/pNZduvZPVHIqjaKl0T6Qmcd3KdpmKk8aSzsctPXcXlcuARMxuz28stseiIh3G/7nKjsrMz/R4vN15YPtVKrmTujJq/28MFvZRguqe/sqHINLlC/43YjYPoz4K5U8uRJvKIXKAC7AEUpe/FkHniuUoucYpVBqot7a7Yq2je8rQt1RvUW9ii8rNcBq/KQV/zB+j3Pxamb+RjlwJykpaQRubD9JDIXXflswIjYqbzJ3KnlwjvI+8ximRsQjw4lXtv/jiuDfxPU4E3/At3Bhp0q5GzJzEr6i+0Hb5Po6voT7FNHnVjwWYUpbxKoYO195enhFOWTnKunxSZwXEYuGw2vEoSflg4f/AwCgH7LXaj9HAAAAAElFTkSuQmCC",
       "text/latex": [
-       "$\\displaystyle 665.8939$"
+       "$\\displaystyle 665.7087$"
       ],
       "text/plain": [
-       "665.8939"
+       "665.7087"
       ]
      },
      "execution_count": 12,
@@ -1152,10 +1152,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.615677Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.615201Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.621512Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.621053Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.423578Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.423122Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.429351Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.428817Z"
     }
    },
    "outputs": [],
@@ -1214,10 +1214,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.623490Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.623060Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.938842Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.938178Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.431270Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.430903Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.747821Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.747158Z"
     }
    },
    "outputs": [
@@ -1275,10 +1275,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.941138Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.940909Z",
-     "iopub.status.idle": "2024-10-25T14:29:13.065607Z",
-     "shell.execute_reply": "2024-10-25T14:29:13.065061Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.750542Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.750104Z",
+     "iopub.status.idle": "2024-10-25T14:30:03.877966Z",
+     "shell.execute_reply": "2024-10-25T14:30:03.877325Z"
     }
    },
    "outputs": [
@@ -1292,17 +1292,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "            d                                                                  ↪\n",
-      "──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪\n",
+      "──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪\n",
       "d[different_room_assigned]                                                     ↪\n",
       "\n",
       "↪                                                                              ↪\n",
-      "↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪\n",
+      "↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪\n",
       "↪                                                                              ↪\n",
       "\n",
       "↪                                \n",
-      "↪ arking_spaces,booking_changes])\n",
+      "↪ _list,is_repeated_guest,hotel])\n",
       "↪                                \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -1333,10 +1333,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:13.067762Z",
-     "iopub.status.busy": "2024-10-25T14:29:13.067301Z",
-     "iopub.status.idle": "2024-10-25T14:29:13.662343Z",
-     "shell.execute_reply": "2024-10-25T14:29:13.661462Z"
+     "iopub.execute_input": "2024-10-25T14:30:03.879921Z",
+     "iopub.status.busy": "2024-10-25T14:30:03.879726Z",
+     "iopub.status.idle": "2024-10-25T14:30:04.429652Z",
+     "shell.execute_reply": "2024-10-25T14:30:04.428779Z"
     }
    },
    "outputs": [
@@ -1353,20 +1353,20 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "            d                                                                  ↪\n",
-      "──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪\n",
+      "──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪\n",
       "d[different_room_assigned]                                                     ↪\n",
       "\n",
       "↪                                                                              ↪\n",
-      "↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪\n",
+      "↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪\n",
       "↪                                                                              ↪\n",
       "\n",
       "↪                                \n",
-      "↪ arking_spaces,booking_changes])\n",
+      "↪ _list,is_repeated_guest,hotel])\n",
       "↪                                \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)\n",
       "\n",
       "## Realized estimand\n",
-      "b: is_canceled~different_room_assigned+hotel+lead_time+days_in_waiting_list+total_of_special_requests+is_repeated_guest+guests+total_stay+required_car_parking_spaces+booking_changes\n",
+      "b: is_canceled~different_room_assigned+total_of_special_requests+lead_time+booking_changes+guests+required_car_parking_spaces+total_stay+days_in_waiting_list+is_repeated_guest+hotel\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -1435,10 +1435,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:13.664814Z",
-     "iopub.status.busy": "2024-10-25T14:29:13.664603Z",
-     "iopub.status.idle": "2024-10-25T14:29:23.806800Z",
-     "shell.execute_reply": "2024-10-25T14:29:23.806156Z"
+     "iopub.execute_input": "2024-10-25T14:30:04.432276Z",
+     "iopub.status.busy": "2024-10-25T14:30:04.432062Z",
+     "iopub.status.idle": "2024-10-25T14:30:14.546038Z",
+     "shell.execute_reply": "2024-10-25T14:30:14.545398Z"
     }
    },
    "outputs": [
@@ -1473,10 +1473,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:23.808758Z",
-     "iopub.status.busy": "2024-10-25T14:29:23.808549Z",
-     "iopub.status.idle": "2024-10-25T14:29:34.025025Z",
-     "shell.execute_reply": "2024-10-25T14:29:34.024457Z"
+     "iopub.execute_input": "2024-10-25T14:30:14.548407Z",
+     "iopub.status.busy": "2024-10-25T14:30:14.547975Z",
+     "iopub.status.idle": "2024-10-25T14:30:25.173143Z",
+     "shell.execute_reply": "2024-10-25T14:30:25.172558Z"
     }
    },
    "outputs": [
@@ -1486,7 +1486,7 @@
      "text": [
       "Refute: Use a Placebo Treatment\n",
       "Estimated effect:-0.2622285649999514\n",
-      "New effect:0.05476307825276116\n",
+      "New effect:0.05477212242057929\n",
       "p value:0.0\n",
       "\n"
      ]
@@ -1511,10 +1511,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:34.027214Z",
-     "iopub.status.busy": "2024-10-25T14:29:34.026763Z",
-     "iopub.status.idle": "2024-10-25T14:29:43.366837Z",
-     "shell.execute_reply": "2024-10-25T14:29:43.366281Z"
+     "iopub.execute_input": "2024-10-25T14:30:25.175030Z",
+     "iopub.status.busy": "2024-10-25T14:30:25.174848Z",
+     "iopub.status.idle": "2024-10-25T14:30:34.558139Z",
+     "shell.execute_reply": "2024-10-25T14:30:34.557490Z"
     }
    },
    "outputs": [
@@ -1524,8 +1524,8 @@
      "text": [
       "Refute: Use a subset of data\n",
       "Estimated effect:-0.2622285649999514\n",
-      "New effect:-0.26207674572027684\n",
-      "p value:0.8400000000000001\n",
+      "New effect:-0.2622173443627082\n",
+      "p value:0.92\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb b/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb
index 1e1c7e2f7..cf1029f1b 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb
@@ -6,10 +6,10 @@
    "id": "ef2e40dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:46.944617Z",
-     "iopub.status.busy": "2024-10-25T14:29:46.944409Z",
-     "iopub.status.idle": "2024-10-25T14:29:46.947584Z",
-     "shell.execute_reply": "2024-10-25T14:29:46.947116Z"
+     "iopub.execute_input": "2024-10-25T14:30:38.147108Z",
+     "iopub.status.busy": "2024-10-25T14:30:38.146926Z",
+     "iopub.status.idle": "2024-10-25T14:30:38.150156Z",
+     "shell.execute_reply": "2024-10-25T14:30:38.149691Z"
     }
    },
    "outputs": [],
@@ -63,10 +63,10 @@
    "id": "46026b9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:46.949450Z",
-     "iopub.status.busy": "2024-10-25T14:29:46.949136Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.493396Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.492798Z"
+     "iopub.execute_input": "2024-10-25T14:30:38.152010Z",
+     "iopub.status.busy": "2024-10-25T14:30:38.151838Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.714423Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.713788Z"
     }
    },
    "outputs": [],
@@ -296,10 +296,10 @@
    "id": "37212ae1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.495655Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.495355Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.544883Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.544247Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.717189Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.716548Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.768199Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.767583Z"
     }
    },
    "outputs": [
@@ -336,41 +336,41 @@
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>0.30</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>0.40</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>-0.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1.83</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>-0.03</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1.48</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>-0.38</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -378,11 +378,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade\n",
-       "0   0.0     1.0  2.8  37.0       0.30\n",
-       "1   0.0     0.0  3.3  32.5       0.40\n",
-       "2   0.0     1.0  3.7  33.0       1.83\n",
-       "3   0.0     0.0  2.7  35.0      -0.03\n",
-       "4   0.0     1.0  3.0  44.0       1.48"
+       "0   0.0     1.0  3.5  34.0       0.19\n",
+       "1   0.0     0.0  2.7  31.0      -0.10\n",
+       "2   0.0     0.0  4.0  47.0       1.12\n",
+       "3   0.0     1.0  3.4  48.0       0.27\n",
+       "4   0.0     1.0  3.3  41.0      -0.38"
       ]
      },
      "execution_count": 3,
@@ -466,10 +466,10 @@
    "id": "2280c2df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.546794Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.546609Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.549844Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.549368Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.770157Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.769963Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.773096Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.772631Z"
     }
    },
    "outputs": [],
@@ -506,10 +506,10 @@
    "id": "d70d0187",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.551520Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.551344Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.553938Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.553482Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.774953Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.774604Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.777377Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.776800Z"
     }
    },
    "outputs": [],
@@ -536,10 +536,10 @@
    "id": "6fdaa7be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.555548Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.555374Z",
-     "iopub.status.idle": "2024-10-25T14:30:45.998017Z",
-     "shell.execute_reply": "2024-10-25T14:30:45.997472Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.779130Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.778851Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.652975Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.652290Z"
     }
    },
    "outputs": [
@@ -596,7 +596,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 125.98it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 216.76it/s]"
      ]
     },
     {
@@ -608,12 +608,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -0.479681464045815$"
+       "$\\displaystyle -0.484024452360675$"
       ],
       "text/plain": [
-       "-0.47968146404581513"
+       "-0.4840244523606755"
       ]
      },
      "execution_count": 6,
@@ -651,10 +651,10 @@
    "id": "79c2a233",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.000962Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.000472Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.012865Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.012322Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.655697Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.655463Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.667905Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.667352Z"
     }
    },
    "outputs": [
@@ -692,46 +692,46 @@
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.019617</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.076392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>-0.020426</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>-0.10</td>\n",
+       "      <td>-0.266839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1.83</td>\n",
-       "      <td>0.110852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>0.746310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>-0.03</td>\n",
-       "      <td>-0.087888</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.621925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1.48</td>\n",
-       "      <td>0.354123</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>-0.38</td>\n",
+       "      <td>0.307828</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -739,11 +739,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade     preds\n",
-       "0   0.0     1.0  2.8  37.0       0.30  0.019617\n",
-       "1   0.0     0.0  3.3  32.5       0.40 -0.020426\n",
-       "2   0.0     1.0  3.7  33.0       1.83  0.110852\n",
-       "3   0.0     0.0  2.7  35.0      -0.03 -0.087888\n",
-       "4   0.0     1.0  3.0  44.0       1.48  0.354123"
+       "0   0.0     1.0  3.5  34.0       0.19  0.076392\n",
+       "1   0.0     0.0  2.7  31.0      -0.10 -0.266839\n",
+       "2   0.0     0.0  4.0  47.0       1.12  0.746310\n",
+       "3   0.0     1.0  3.4  48.0       0.27  0.621925\n",
+       "4   0.0     1.0  3.3  41.0      -0.38  0.307828"
       ]
      },
      "execution_count": 7,
@@ -769,10 +769,10 @@
    "id": "56d88825",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.015893Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.014874Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.025958Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.025448Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.670921Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.670015Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.681645Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.681052Z"
     }
    },
    "outputs": [
@@ -810,46 +810,46 @@
        "      <th>0</th>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.390805</td>\n",
-       "      <td>28.614502</td>\n",
-       "      <td>-0.501187</td>\n",
-       "      <td>0.096867</td>\n",
+       "      <td>3.132895</td>\n",
+       "      <td>26.030219</td>\n",
+       "      <td>-0.791247</td>\n",
+       "      <td>0.112971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.890805</td>\n",
-       "      <td>25.046875</td>\n",
-       "      <td>-0.554893</td>\n",
-       "      <td>0.099256</td>\n",
+       "      <td>2.332895</td>\n",
+       "      <td>23.030219</td>\n",
+       "      <td>-0.859527</td>\n",
+       "      <td>0.024230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.290805</td>\n",
-       "      <td>24.614502</td>\n",
-       "      <td>0.762635</td>\n",
-       "      <td>0.121437</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.632895</td>\n",
+       "      <td>39.030219</td>\n",
+       "      <td>-0.410656</td>\n",
+       "      <td>0.255002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.290805</td>\n",
-       "      <td>27.546875</td>\n",
-       "      <td>-0.788582</td>\n",
-       "      <td>0.081481</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.032895</td>\n",
+       "      <td>40.030219</td>\n",
+       "      <td>-1.120428</td>\n",
+       "      <td>0.213736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.590805</td>\n",
-       "      <td>35.614502</td>\n",
-       "      <td>0.447159</td>\n",
-       "      <td>0.168353</td>\n",
+       "      <td>2.932895</td>\n",
+       "      <td>33.030219</td>\n",
+       "      <td>-1.533649</td>\n",
+       "      <td>0.151092</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -857,11 +857,11 @@
       ],
       "text/plain": [
        "   Race  Gender       GPA       LSAT  avg_grade  preds_cf\n",
-       "0   1.0     1.0  2.390805  28.614502  -0.501187  0.096867\n",
-       "1   1.0     0.0  2.890805  25.046875  -0.554893  0.099256\n",
-       "2   1.0     1.0  3.290805  24.614502   0.762635  0.121437\n",
-       "3   1.0     0.0  2.290805  27.546875  -0.788582  0.081481\n",
-       "4   1.0     1.0  2.590805  35.614502   0.447159  0.168353"
+       "0   1.0     1.0  3.132895  26.030219  -0.791247  0.112971\n",
+       "1   1.0     0.0  2.332895  23.030219  -0.859527  0.024230\n",
+       "2   1.0     0.0  3.632895  39.030219  -0.410656  0.255002\n",
+       "3   1.0     1.0  3.032895  40.030219  -1.120428  0.213736\n",
+       "4   1.0     1.0  2.932895  33.030219  -1.533649  0.151092"
       ]
      },
      "execution_count": 8,
@@ -879,16 +879,16 @@
    "id": "a1176177",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.027938Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.027500Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.375239Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.374574Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.683558Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.683368Z",
+     "iopub.status.idle": "2024-10-25T14:31:35.007955Z",
+     "shell.execute_reply": "2024-10-25T14:31:35.007259Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -928,10 +928,10 @@
    "id": "725bf447",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.377196Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.376904Z",
-     "iopub.status.idle": "2024-10-25T14:31:38.758403Z",
-     "shell.execute_reply": "2024-10-25T14:31:38.757313Z"
+     "iopub.execute_input": "2024-10-25T14:31:35.010094Z",
+     "iopub.status.busy": "2024-10-25T14:31:35.009713Z",
+     "iopub.status.idle": "2024-10-25T14:32:27.107893Z",
+     "shell.execute_reply": "2024-10-25T14:32:27.106827Z"
     }
    },
    "outputs": [
@@ -988,7 +988,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 378.38it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 300.00it/s]"
      ]
     },
     {
@@ -1000,12 +1000,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -0.489383465264695$"
+       "$\\displaystyle -0.469321951712711$"
       ],
       "text/plain": [
-       "-0.48938346526469506"
+       "-0.46932195171271096"
       ]
      },
      "execution_count": 10,
@@ -1037,16 +1037,16 @@
    "id": "ad7671e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:31:38.761035Z",
-     "iopub.status.busy": "2024-10-25T14:31:38.760474Z",
-     "iopub.status.idle": "2024-10-25T14:31:39.177782Z",
-     "shell.execute_reply": "2024-10-25T14:31:39.177212Z"
+     "iopub.execute_input": "2024-10-25T14:32:27.110200Z",
+     "iopub.status.busy": "2024-10-25T14:32:27.109984Z",
+     "iopub.status.idle": "2024-10-25T14:32:27.473342Z",
+     "shell.execute_reply": "2024-10-25T14:32:27.472700Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1114,10 +1114,10 @@
    "id": "c6baef0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:31:39.179712Z",
-     "iopub.status.busy": "2024-10-25T14:31:39.179403Z",
-     "iopub.status.idle": "2024-10-25T14:32:31.417906Z",
-     "shell.execute_reply": "2024-10-25T14:32:31.417327Z"
+     "iopub.execute_input": "2024-10-25T14:32:27.475630Z",
+     "iopub.status.busy": "2024-10-25T14:32:27.475191Z",
+     "iopub.status.idle": "2024-10-25T14:33:22.001405Z",
+     "shell.execute_reply": "2024-10-25T14:33:22.000747Z"
     }
    },
    "outputs": [
@@ -1174,7 +1174,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 357.97it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 334.07it/s]"
      ]
     },
     {
@@ -1186,12 +1186,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -1.11022302462516 \\cdot 10^{-16}$"
+       "$\\displaystyle 1.11022302462516 \\cdot 10^{-15}$"
       ],
       "text/plain": [
-       "-1.1102230246251565e-16"
+       "1.1102230246251565e-15"
       ]
      },
      "execution_count": 12,
@@ -1223,16 +1223,16 @@
    "id": "84b1c218",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:31.420521Z",
-     "iopub.status.busy": "2024-10-25T14:32:31.420061Z",
-     "iopub.status.idle": "2024-10-25T14:32:31.758128Z",
-     "shell.execute_reply": "2024-10-25T14:32:31.757513Z"
+     "iopub.execute_input": "2024-10-25T14:33:22.003960Z",
+     "iopub.status.busy": "2024-10-25T14:33:22.003716Z",
+     "iopub.status.idle": "2024-10-25T14:33:22.416143Z",
+     "shell.execute_reply": "2024-10-25T14:33:22.415532Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
diff --git a/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb b/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb
index 3bbb8561a..87dec46fe 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb
@@ -64,10 +64,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:36.434267Z",
-     "iopub.status.busy": "2024-10-25T14:32:36.434085Z",
-     "iopub.status.idle": "2024-10-25T14:32:36.439833Z",
-     "shell.execute_reply": "2024-10-25T14:32:36.439376Z"
+     "iopub.execute_input": "2024-10-25T14:33:27.109007Z",
+     "iopub.status.busy": "2024-10-25T14:33:27.108503Z",
+     "iopub.status.idle": "2024-10-25T14:33:27.114326Z",
+     "shell.execute_reply": "2024-10-25T14:33:27.113860Z"
     }
    },
    "outputs": [],
@@ -81,10 +81,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:36.441481Z",
-     "iopub.status.busy": "2024-10-25T14:32:36.441309Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.880757Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.880128Z"
+     "iopub.execute_input": "2024-10-25T14:33:27.116019Z",
+     "iopub.status.busy": "2024-10-25T14:33:27.115721Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.587055Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.586335Z"
     },
     "scrolled": true
    },
@@ -100,10 +100,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.883067Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.882758Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.887826Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.887344Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.589909Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.589283Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.594954Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.594328Z"
     }
    },
    "outputs": [],
@@ -122,21 +122,21 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.889654Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.889309Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.944011Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.943403Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.597136Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.596688Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.654604Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.653965Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 1.63621412453131$"
+       "$\\displaystyle 1.62365401783177$"
       ],
       "text/plain": [
-       "1.6362141245313135"
+       "1.623654017831768"
       ]
      },
      "execution_count": 4,
@@ -160,10 +160,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.946125Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.945756Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.949164Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.948693Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.656823Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.656430Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.660018Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.659521Z"
     }
    },
    "outputs": [],
@@ -193,10 +193,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.950745Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.950575Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.955534Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.954967Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.661934Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.661597Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.666782Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.666273Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.957464Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.957130Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.964356Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.963904Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.668754Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.668404Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.676084Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.675591Z"
     }
    },
    "outputs": [],
@@ -251,10 +251,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.965959Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.965793Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.977352Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.976885Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.678139Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.677776Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.689639Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.689128Z"
     }
    },
    "outputs": [],
@@ -267,21 +267,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.979201Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.978787Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.996081Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.995479Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.691537Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.691213Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.709379Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.708862Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 1.1216896229639$"
+       "$\\displaystyle 1.02180320673952$"
       ],
       "text/plain": [
-       "1.1216896229638995"
+       "1.0218032067395173"
       ]
      },
      "execution_count": 9,
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb
index c6522ee9a..5c17fcba5 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:40.102787Z",
-     "iopub.status.busy": "2024-10-25T14:32:40.102299Z",
-     "iopub.status.idle": "2024-10-25T14:32:40.120054Z",
-     "shell.execute_reply": "2024-10-25T14:32:40.119592Z"
+     "iopub.execute_input": "2024-10-25T14:33:30.785394Z",
+     "iopub.status.busy": "2024-10-25T14:33:30.785170Z",
+     "iopub.status.idle": "2024-10-25T14:33:30.803802Z",
+     "shell.execute_reply": "2024-10-25T14:33:30.803173Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:40.122004Z",
-     "iopub.status.busy": "2024-10-25T14:32:40.121577Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.593233Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.592547Z"
+     "iopub.execute_input": "2024-10-25T14:33:30.805886Z",
+     "iopub.status.busy": "2024-10-25T14:33:30.805462Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.324248Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.323511Z"
     }
    },
    "outputs": [],
@@ -64,10 +64,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.595508Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.595216Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.651970Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.651120Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.326932Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.326590Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.383169Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.382591Z"
     }
    },
    "outputs": [
@@ -76,19 +76,19 @@
      "output_type": "stream",
      "text": [
       "         X0        X1   Z0        Z1        W0        W1 W2 W3         v0  \\\n",
-      "0 -0.080632  1.506814  0.0  0.225366 -0.233580  0.169233  2  2  15.317527   \n",
-      "1  0.000844  2.339207  0.0  0.572259 -0.782164  0.032473  2  2  16.253707   \n",
-      "2 -0.965817  0.816462  1.0  0.476658 -1.627418  0.414192  2  3  29.035199   \n",
-      "3  1.846773  1.236049  0.0  0.708715 -0.767937 -0.039566  3  2  24.549901   \n",
-      "4  2.093956  0.654808  0.0  0.882181 -1.590903 -0.137460  2  0  21.239132   \n",
+      "0  0.381310  0.546194  1.0  0.333875 -1.084854 -1.511882  3  2  29.116836   \n",
+      "1 -0.575241 -1.335250  1.0  0.390085  0.235700 -2.045413  0  3  15.339584   \n",
+      "2 -1.503456 -0.155814  1.0  0.021253  1.379312  0.517977  1  0  18.797177   \n",
+      "3  1.233182  0.004915  1.0  0.896209 -0.033976 -0.089706  3  1  36.356471   \n",
+      "4 -1.100251 -0.057382  1.0  0.346384 -0.423885  0.690338  2  2  30.524211   \n",
       "\n",
       "            y  \n",
-      "0  257.916920  \n",
-      "1  336.641725  \n",
-      "2  273.191908  \n",
-      "3  601.159182  \n",
-      "4  486.369924  \n",
-      "True causal estimate is 15.528281358714274\n"
+      "0  379.475032  \n",
+      "1   64.896549  \n",
+      "2  114.841747  \n",
+      "3  478.142873  \n",
+      "4  229.026784  \n",
+      "True causal estimate is 11.247611011059492\n"
      ]
     }
    ],
@@ -110,10 +110,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.654640Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.654106Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.696339Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.695798Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.385824Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.385535Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.429722Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.429107Z"
     }
    },
    "outputs": [],
@@ -128,10 +128,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.699091Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.698637Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.887684Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.887046Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.432404Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.432152Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.624456Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.623809Z"
     }
    },
    "outputs": [
@@ -167,10 +167,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.890028Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.889611Z",
-     "iopub.status.idle": "2024-10-25T14:32:42.235167Z",
-     "shell.execute_reply": "2024-10-25T14:32:42.234495Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.626876Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.626447Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.990517Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.989827Z"
     },
     "scrolled": true
    },
@@ -185,9 +185,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -226,10 +226,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:42.237197Z",
-     "iopub.status.busy": "2024-10-25T14:32:42.236904Z",
-     "iopub.status.idle": "2024-10-25T14:32:42.583511Z",
-     "shell.execute_reply": "2024-10-25T14:32:42.582863Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.992731Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.992350Z",
+     "iopub.status.idle": "2024-10-25T14:33:33.339932Z",
+     "shell.execute_reply": "2024-10-25T14:33:33.339388Z"
     }
    },
    "outputs": [
@@ -246,43 +246,43 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+v0*X1+v0*X0\n",
+      "b: y~v0+W0+W2+W1+W3+v0*X0+v0*X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.528159699566729\n",
+      "Mean value: 11.24771198163937\n",
       "### Conditional Estimates\n",
-      "__categorical__X1             __categorical__X0\n",
-      "(-2.9099999999999997, 0.129]  (-3.393, -0.533]      3.088191\n",
-      "                              (-0.533, 0.0432]      7.058095\n",
-      "                              (0.0432, 0.525]       9.181673\n",
-      "                              (0.525, 1.103]       11.941213\n",
-      "                              (1.103, 3.796]       15.592924\n",
-      "(0.129, 0.714]                (-3.393, -0.533]      6.872997\n",
-      "                              (-0.533, 0.0432]     10.755943\n",
-      "                              (0.0432, 0.525]      13.311670\n",
-      "                              (0.525, 1.103]       15.585864\n",
-      "                              (1.103, 3.796]       19.772407\n",
-      "(0.714, 1.232]                (-3.393, -0.533]      9.134048\n",
-      "                              (-0.533, 0.0432]     13.011229\n",
-      "                              (0.0432, 0.525]      15.566041\n",
-      "                              (0.525, 1.103]       17.909869\n",
-      "                              (1.103, 3.796]       21.807051\n",
-      "(1.232, 1.838]                (-3.393, -0.533]     11.329517\n",
-      "                              (-0.533, 0.0432]     15.572366\n",
-      "                              (0.0432, 0.525]      17.904951\n",
-      "                              (0.525, 1.103]       20.317267\n",
-      "                              (1.103, 3.796]       24.246892\n",
-      "(1.838, 5.002]                (-3.393, -0.533]     15.332247\n",
-      "                              (-0.533, 0.0432]     19.324308\n",
-      "                              (0.0432, 0.525]      21.504073\n",
-      "                              (0.525, 1.103]       23.944776\n",
-      "                              (1.103, 3.796]       28.138810\n",
+      "__categorical__X0              __categorical__X1           \n",
+      "(-4.4190000000000005, -1.184]  (-3.4819999999999998, -0.27]     2.860144\n",
+      "                               (-0.27, 0.307]                   5.956420\n",
+      "                               (0.307, 0.814]                   7.813714\n",
+      "                               (0.814, 1.419]                   9.770860\n",
+      "                               (1.419, 4.311]                  12.949686\n",
+      "(-1.184, -0.582]               (-3.4819999999999998, -0.27]     4.998142\n",
+      "                               (-0.27, 0.307]                   8.056506\n",
+      "                               (0.307, 0.814]                   9.879637\n",
+      "                               (0.814, 1.419]                  11.824167\n",
+      "                               (1.419, 4.311]                  15.024859\n",
+      "(-0.582, -0.071]               (-3.4819999999999998, -0.27]     6.351348\n",
+      "                               (-0.27, 0.307]                   9.377655\n",
+      "                               (0.307, 0.814]                  11.165485\n",
+      "                               (0.814, 1.419]                  13.175461\n",
+      "                               (1.419, 4.311]                  16.254339\n",
+      "(-0.071, 0.519]                (-3.4819999999999998, -0.27]     7.444772\n",
+      "                               (-0.27, 0.307]                  10.609427\n",
+      "                               (0.307, 0.814]                  12.529936\n",
+      "                               (0.814, 1.419]                  14.460766\n",
+      "                               (1.419, 4.311]                  17.563421\n",
+      "(0.519, 3.619]                 (-3.4819999999999998, -0.27]     9.709384\n",
+      "                               (-0.27, 0.307]                  12.750406\n",
+      "                               (0.307, 0.814]                  14.564136\n",
+      "                               (0.814, 1.419]                  16.526277\n",
+      "                               (1.419, 4.311]                  19.575332\n",
       "dtype: float64\n"
      ]
     }
@@ -314,10 +314,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:42.585730Z",
-     "iopub.status.busy": "2024-10-25T14:32:42.585292Z",
-     "iopub.status.idle": "2024-10-25T14:32:47.317461Z",
-     "shell.execute_reply": "2024-10-25T14:32:47.316598Z"
+     "iopub.execute_input": "2024-10-25T14:33:33.341903Z",
+     "iopub.status.busy": "2024-10-25T14:33:33.341694Z",
+     "iopub.status.idle": "2024-10-25T14:33:38.265423Z",
+     "shell.execute_reply": "2024-10-25T14:33:38.264343Z"
     }
    },
    "outputs": [
@@ -334,23 +334,936 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: Data subset defined by a function\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 21.474097597869555\n",
-      "Effect estimates: [[23.92786788]\n",
-      " [22.51303763]\n",
-      " [17.01246052]\n",
-      " ...\n",
-      " [22.90897968]\n",
-      " [15.56349972]\n",
-      " [25.6520251 ]]\n",
+      "Mean value: 15.267669524379059\n",
+      "Effect estimates: [[12.90664534]\n",
+      " [14.38138836]\n",
+      " [10.2357385 ]\n",
+      " [10.23987427]\n",
+      " [13.47109986]\n",
+      " [11.19468597]\n",
+      " [ 8.71140809]\n",
+      " [14.27102838]\n",
+      " [14.77774453]\n",
+      " [ 9.61839297]\n",
+      " [15.59684456]\n",
+      " [19.68728306]\n",
+      " [13.76608678]\n",
+      " [22.10228465]\n",
+      " [21.00718484]\n",
+      " [10.83986598]\n",
+      " [18.26539699]\n",
+      " [21.54713752]\n",
+      " [20.58758719]\n",
+      " [17.66321804]\n",
+      " [15.54830641]\n",
+      " [13.17608218]\n",
+      " [21.30665667]\n",
+      " [17.25260255]\n",
+      " [16.76473542]\n",
+      " [12.14064485]\n",
+      " [19.97405448]\n",
+      " [19.00251288]\n",
+      " [11.35059086]\n",
+      " [20.22038945]\n",
+      " [20.04743649]\n",
+      " [10.11288552]\n",
+      " [18.16878219]\n",
+      " [14.11827826]\n",
+      " [12.3183592 ]\n",
+      " [20.50438781]\n",
+      " [17.42956696]\n",
+      " [15.68772439]\n",
+      " [20.37568615]\n",
+      " [12.86751839]\n",
+      " [16.60542198]\n",
+      " [15.13267292]\n",
+      " [11.1276136 ]\n",
+      " [19.66905016]\n",
+      " [ 8.65818075]\n",
+      " [14.12847087]\n",
+      " [13.1443254 ]\n",
+      " [14.21545231]\n",
+      " [11.34122461]\n",
+      " [17.70535722]\n",
+      " [14.32058541]\n",
+      " [12.74878696]\n",
+      " [11.44668147]\n",
+      " [19.16437979]\n",
+      " [16.54197279]\n",
+      " [18.10513861]\n",
+      " [14.51118135]\n",
+      " [21.35868267]\n",
+      " [ 8.77959824]\n",
+      " [17.84519068]\n",
+      " [17.21129456]\n",
+      " [16.67419526]\n",
+      " [17.39785843]\n",
+      " [15.76837168]\n",
+      " [10.86722358]\n",
+      " [14.6715169 ]\n",
+      " [14.89835161]\n",
+      " [17.04686222]\n",
+      " [14.48667435]\n",
+      " [16.66594093]\n",
+      " [14.56128981]\n",
+      " [20.14249386]\n",
+      " [10.36170885]\n",
+      " [18.39657393]\n",
+      " [ 7.47143542]\n",
+      " [15.18909267]\n",
+      " [17.94803876]\n",
+      " [10.2032646 ]\n",
+      " [13.43462399]\n",
+      " [ 8.17497236]\n",
+      " [10.07499358]\n",
+      " [12.48877858]\n",
+      " [11.82906593]\n",
+      " [15.36362058]\n",
+      " [13.6090933 ]\n",
+      " [17.06079318]\n",
+      " [18.26409511]\n",
+      " [13.33224459]\n",
+      " [11.755378  ]\n",
+      " [15.17132044]\n",
+      " [13.33230185]\n",
+      " [10.45490005]\n",
+      " [21.18709346]\n",
+      " [15.01311924]\n",
+      " [11.72365471]\n",
+      " [21.26991562]\n",
+      " [13.29190176]\n",
+      " [19.92643768]\n",
+      " [16.90790474]\n",
+      " [17.51032902]\n",
+      " [18.88750317]\n",
+      " [12.80985805]\n",
+      " [24.75916998]\n",
+      " [14.33463832]\n",
+      " [14.12386723]\n",
+      " [15.37102754]\n",
+      " [10.29608484]\n",
+      " [13.39710456]\n",
+      " [17.31736753]\n",
+      " [11.22318368]\n",
+      " [15.48416813]\n",
+      " [16.23934648]\n",
+      " [13.82774575]\n",
+      " [14.53030707]\n",
+      " [14.55146397]\n",
+      " [12.6354816 ]\n",
+      " [19.28797727]\n",
+      " [17.35094533]\n",
+      " [10.9784859 ]\n",
+      " [13.4492492 ]\n",
+      " [15.40772192]\n",
+      " [15.57198414]\n",
+      " [15.92431296]\n",
+      " [11.87757454]\n",
+      " [16.8856623 ]\n",
+      " [23.47537601]\n",
+      " [18.12208163]\n",
+      " [15.14986711]\n",
+      " [10.56906557]\n",
+      " [14.48131411]\n",
+      " [16.26447362]\n",
+      " [12.91546473]\n",
+      " [11.37852567]\n",
+      " [14.24560624]\n",
+      " [16.51904611]\n",
+      " [14.79927997]\n",
+      " [11.35679738]\n",
+      " [18.60165901]\n",
+      " [15.01743548]\n",
+      " [14.04279446]\n",
+      " [18.10881439]\n",
+      " [16.70144315]\n",
+      " [13.23799299]\n",
+      " [16.72905592]\n",
+      " [17.12408819]\n",
+      " [ 9.3793803 ]\n",
+      " [20.7650646 ]\n",
+      " [15.05016365]\n",
+      " [16.01509607]\n",
+      " [13.69188919]\n",
+      " [15.3320399 ]\n",
+      " [18.71384061]\n",
+      " [19.67131172]\n",
+      " [17.44365811]\n",
+      " [11.9362698 ]\n",
+      " [13.59657777]\n",
+      " [14.63298498]\n",
+      " [12.83723052]\n",
+      " [15.66583142]\n",
+      " [19.85320136]\n",
+      " [12.00137471]\n",
+      " [18.1832713 ]\n",
+      " [15.56393982]\n",
+      " [22.62845798]\n",
+      " [12.98821458]\n",
+      " [11.32235399]\n",
+      " [20.34115035]\n",
+      " [19.67424878]\n",
+      " [11.00334334]\n",
+      " [11.16535237]\n",
+      " [ 8.20358919]\n",
+      " [17.31478377]\n",
+      " [13.67732429]\n",
+      " [17.90738032]\n",
+      " [14.48755595]\n",
+      " [13.0916963 ]\n",
+      " [13.14483303]\n",
+      " [21.08400961]\n",
+      " [11.77325363]\n",
+      " [13.68274944]\n",
+      " [19.83498302]\n",
+      " [ 6.87666018]\n",
+      " [14.02289941]\n",
+      " [15.28890978]\n",
+      " [20.50102975]\n",
+      " [15.62421512]\n",
+      " [ 3.46593099]\n",
+      " [21.61730522]\n",
+      " [17.39909959]\n",
+      " [10.92009638]\n",
+      " [14.19099052]\n",
+      " [12.3322625 ]\n",
+      " [17.90886919]\n",
+      " [18.14528107]\n",
+      " [ 9.39007138]\n",
+      " [17.21813489]\n",
+      " [11.27105476]\n",
+      " [15.09029809]\n",
+      " [15.59403066]\n",
+      " [10.09060825]\n",
+      " [21.06459406]\n",
+      " [19.70316126]\n",
+      " [10.19314462]\n",
+      " [16.03243019]\n",
+      " [11.14764834]\n",
+      " [19.570197  ]\n",
+      " [16.58103343]\n",
+      " [19.38935846]\n",
+      " [15.7329264 ]\n",
+      " [18.60551816]\n",
+      " [18.04755999]\n",
+      " [16.55639234]\n",
+      " [16.42245249]\n",
+      " [13.86106717]\n",
+      " [18.08414384]\n",
+      " [11.89376442]\n",
+      " [13.66221955]\n",
+      " [19.26562877]\n",
+      " [17.70670915]\n",
+      " [14.39568196]\n",
+      " [16.04062192]\n",
+      " [20.50209343]\n",
+      " [12.11284361]\n",
+      " [ 9.92862696]\n",
+      " [18.71329691]\n",
+      " [15.72159952]\n",
+      " [11.78303019]\n",
+      " [13.04017919]\n",
+      " [21.94420655]\n",
+      " [14.28309336]\n",
+      " [17.72979409]\n",
+      " [20.60842717]\n",
+      " [10.562978  ]\n",
+      " [12.41484783]\n",
+      " [19.27292806]\n",
+      " [10.91275329]\n",
+      " [12.68964806]\n",
+      " [11.52485149]\n",
+      " [14.64524774]\n",
+      " [12.98702141]\n",
+      " [18.58621104]\n",
+      " [11.97915435]\n",
+      " [16.34670631]\n",
+      " [15.36286377]\n",
+      " [13.48201044]\n",
+      " [17.69455693]\n",
+      " [23.95681748]\n",
+      " [10.50291634]\n",
+      " [11.71394102]\n",
+      " [19.2597351 ]\n",
+      " [17.72919983]\n",
+      " [15.57843398]\n",
+      " [14.1253797 ]\n",
+      " [14.85635364]\n",
+      " [17.10613209]\n",
+      " [17.23369518]\n",
+      " [16.57732513]\n",
+      " [15.38769389]\n",
+      " [19.50998612]\n",
+      " [19.67430637]\n",
+      " [15.15762711]\n",
+      " [10.21216344]\n",
+      " [10.80723859]\n",
+      " [15.57361084]\n",
+      " [23.41244325]\n",
+      " [15.64968538]\n",
+      " [16.33855256]\n",
+      " [13.90866931]\n",
+      " [17.62647247]\n",
+      " [13.16153957]\n",
+      " [23.63088074]\n",
+      " [19.15358844]\n",
+      " [ 7.95633966]\n",
+      " [23.57578361]\n",
+      " [14.96444551]\n",
+      " [18.4494613 ]\n",
+      " [18.12274179]\n",
+      " [14.27897796]\n",
+      " [14.64370282]\n",
+      " [10.04035689]\n",
+      " [ 8.39191134]\n",
+      " [16.5332055 ]\n",
+      " [17.39201016]\n",
+      " [15.54725288]\n",
+      " [10.64633795]\n",
+      " [18.60816961]\n",
+      " [12.79857671]\n",
+      " [18.51204188]\n",
+      " [ 7.59202366]\n",
+      " [16.03793707]\n",
+      " [13.73442364]\n",
+      " [20.01871619]\n",
+      " [17.53284781]\n",
+      " [15.14911203]\n",
+      " [15.41085646]\n",
+      " [14.82947824]\n",
+      " [18.9260236 ]\n",
+      " [15.13304999]\n",
+      " [13.22028714]\n",
+      " [11.05681636]\n",
+      " [13.68045045]\n",
+      " [23.93409502]\n",
+      " [15.84133004]\n",
+      " [19.85846642]\n",
+      " [15.29980831]\n",
+      " [11.80093892]\n",
+      " [10.02116584]\n",
+      " [10.47854537]\n",
+      " [23.77081591]\n",
+      " [16.02600876]\n",
+      " [11.13885351]\n",
+      " [13.10188261]\n",
+      " [22.57287333]\n",
+      " [12.00848329]\n",
+      " [11.06904278]\n",
+      " [17.86309052]\n",
+      " [15.36887032]\n",
+      " [12.49435281]\n",
+      " [12.56892844]\n",
+      " [16.62475601]\n",
+      " [16.4097723 ]\n",
+      " [14.94946833]\n",
+      " [10.15495342]\n",
+      " [12.5798636 ]\n",
+      " [12.193208  ]\n",
+      " [14.25206611]\n",
+      " [13.73369225]\n",
+      " [21.09937452]\n",
+      " [12.45129895]\n",
+      " [13.55392984]\n",
+      " [ 9.23541319]\n",
+      " [23.55056547]\n",
+      " [16.56157929]\n",
+      " [15.49158581]\n",
+      " [10.9692139 ]\n",
+      " [11.41457418]\n",
+      " [10.65759651]\n",
+      " [12.48467753]\n",
+      " [14.11350647]\n",
+      " [15.21063653]\n",
+      " [15.08448287]\n",
+      " [15.51540541]\n",
+      " [20.55287075]\n",
+      " [14.37157697]\n",
+      " [ 7.60202077]\n",
+      " [13.69363318]\n",
+      " [16.17269706]\n",
+      " [22.63040935]\n",
+      " [10.16618253]\n",
+      " [14.79396272]\n",
+      " [16.02133259]\n",
+      " [12.6170273 ]\n",
+      " [13.85630913]\n",
+      " [11.15916418]\n",
+      " [14.30906246]\n",
+      " [14.66288501]\n",
+      " [ 6.64945822]\n",
+      " [17.25310644]\n",
+      " [14.9569415 ]\n",
+      " [15.29113202]\n",
+      " [13.40263718]\n",
+      " [16.99625962]\n",
+      " [18.5676548 ]\n",
+      " [11.75588981]\n",
+      " [20.70218436]\n",
+      " [12.31108149]\n",
+      " [15.57970344]\n",
+      " [16.17039482]\n",
+      " [13.39083961]\n",
+      " [19.84639395]\n",
+      " [16.00747199]\n",
+      " [17.21256167]\n",
+      " [15.4321025 ]\n",
+      " [16.51894958]\n",
+      " [18.4251407 ]\n",
+      " [16.13142056]\n",
+      " [14.77283395]\n",
+      " [10.18265218]\n",
+      " [13.85665255]\n",
+      " [10.64003832]\n",
+      " [18.51569451]\n",
+      " [23.45629922]\n",
+      " [14.55871641]\n",
+      " [14.63751592]\n",
+      " [18.7038773 ]\n",
+      " [11.72776316]\n",
+      " [15.20117379]\n",
+      " [12.86950055]\n",
+      " [22.79897311]\n",
+      " [18.06368107]\n",
+      " [19.49170358]\n",
+      " [16.39335424]\n",
+      " [18.53414767]\n",
+      " [14.16119695]\n",
+      " [16.91572938]\n",
+      " [16.55502657]\n",
+      " [18.33488977]\n",
+      " [13.81012512]\n",
+      " [13.02156749]\n",
+      " [17.88523899]\n",
+      " [16.9981438 ]\n",
+      " [15.30424096]\n",
+      " [16.24948139]\n",
+      " [15.83473636]\n",
+      " [16.41948984]\n",
+      " [12.88773228]\n",
+      " [11.87755277]\n",
+      " [19.07885003]\n",
+      " [10.95174188]\n",
+      " [16.23218235]\n",
+      " [ 7.97084721]\n",
+      " [18.30730604]\n",
+      " [12.19161956]\n",
+      " [ 9.71453652]\n",
+      " [23.75973645]\n",
+      " [16.10751006]\n",
+      " [15.71275792]\n",
+      " [18.3084786 ]\n",
+      " [13.61660579]\n",
+      " [22.09299679]\n",
+      " [19.17060307]\n",
+      " [13.344013  ]\n",
+      " [14.66177673]\n",
+      " [15.51299494]\n",
+      " [13.46145837]\n",
+      " [17.07992661]\n",
+      " [17.37102391]\n",
+      " [ 7.16615571]\n",
+      " [19.83210784]\n",
+      " [17.13395781]\n",
+      " [15.43182341]\n",
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+      " [16.4777697 ]\n",
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+      " [15.4483342 ]\n",
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+      " [15.45704791]\n",
+      " [12.2904376 ]\n",
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+      " [ 6.8548771 ]\n",
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+      " [21.3025275 ]\n",
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+      " [19.74950101]\n",
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+      " [10.77472051]\n",
+      " [ 9.56828675]\n",
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+      " [10.4788618 ]\n",
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+      " [12.77703072]\n",
+      " [17.76607885]\n",
+      " [12.88695571]\n",
+      " [18.3418768 ]\n",
+      " [ 5.89679332]\n",
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+      " [14.2099386 ]\n",
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+      " [19.3156884 ]\n",
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+      " [19.09943079]\n",
+      " [ 9.34594549]\n",
+      " [ 9.91253099]\n",
+      " [14.89562912]\n",
+      " [ 3.07201464]\n",
+      " [20.12334328]\n",
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+      " [15.04540936]\n",
+      " [ 5.33392645]\n",
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+      " [ 9.66663477]\n",
+      " [20.00666912]\n",
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+      " [ 7.81450778]\n",
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+      " [14.2554375 ]\n",
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+      " [13.36996935]\n",
+      " [ 7.37739711]\n",
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+      " [15.98075942]\n",
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+      " [ 7.75479875]\n",
+      " [14.92148554]\n",
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+      " [14.09396003]\n",
+      " [11.43573504]\n",
+      " [ 8.05222199]\n",
+      " [ 9.7097168 ]\n",
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+      " [15.10181643]\n",
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+      " [19.12996796]\n",
+      " [16.48484147]\n",
+      " [11.69848801]\n",
+      " [12.28642099]\n",
+      " [17.99929908]\n",
+      " [16.34600366]\n",
+      " [10.18131767]\n",
+      " [12.841883  ]\n",
+      " [14.32772172]\n",
+      " [20.91998241]\n",
+      " [23.33023066]\n",
+      " [17.42409745]\n",
+      " [18.22417335]\n",
+      " [18.62045872]\n",
+      " [16.66820177]\n",
+      " [10.97529334]\n",
+      " [12.2524657 ]\n",
+      " [13.97271331]\n",
+      " [14.8257459 ]\n",
+      " [13.89350847]\n",
+      " [14.23319451]\n",
+      " [19.03293852]\n",
+      " [17.46231246]\n",
+      " [13.81299592]\n",
+      " [24.67534577]\n",
+      " [18.09309886]\n",
+      " [17.69602172]\n",
+      " [17.02443336]\n",
+      " [16.4685286 ]\n",
+      " [15.19852788]\n",
+      " [17.48037572]\n",
+      " [14.60174273]\n",
+      " [14.85787483]\n",
+      " [15.01620843]\n",
+      " [17.29467765]\n",
+      " [19.85128443]\n",
+      " [18.00018613]\n",
+      " [10.72947937]\n",
+      " [19.65478552]\n",
+      " [10.48931283]\n",
+      " [11.98932786]\n",
+      " [19.89704706]\n",
+      " [11.17264618]\n",
+      " [20.13961574]\n",
+      " [19.92136557]\n",
+      " [13.91154672]\n",
+      " [22.19960389]\n",
+      " [15.28329025]\n",
+      " [ 8.20572948]\n",
+      " [15.03259966]\n",
+      " [11.54066153]\n",
+      " [17.47475084]\n",
+      " [12.09493529]\n",
+      " [12.89414985]\n",
+      " [27.4703543 ]\n",
+      " [15.6414925 ]\n",
+      " [13.05638996]\n",
+      " [18.38303655]\n",
+      " [21.27298202]\n",
+      " [13.51419934]\n",
+      " [15.60881231]\n",
+      " [14.22350239]\n",
+      " [15.2084249 ]\n",
+      " [17.33164613]\n",
+      " [10.77311022]\n",
+      " [14.60803611]\n",
+      " [12.61505335]\n",
+      " [ 7.8426579 ]\n",
+      " [15.6586185 ]\n",
+      " [12.25674442]\n",
+      " [14.15349524]\n",
+      " [14.48888026]\n",
+      " [16.03195259]\n",
+      " [16.4059316 ]\n",
+      " [ 9.62081148]\n",
+      " [13.81136501]\n",
+      " [ 9.78588639]\n",
+      " [22.2621919 ]\n",
+      " [17.71046534]\n",
+      " [13.65214743]\n",
+      " [17.15702414]\n",
+      " [18.73547983]\n",
+      " [18.47265599]\n",
+      " [15.69172039]\n",
+      " [11.24702021]\n",
+      " [17.16723421]\n",
+      " [13.01433774]\n",
+      " [10.6410377 ]\n",
+      " [16.16613755]\n",
+      " [22.1500304 ]\n",
+      " [11.5248423 ]\n",
+      " [ 8.94300795]\n",
+      " [14.65291482]\n",
+      " [20.86251932]\n",
+      " [20.06906932]\n",
+      " [13.67132003]\n",
+      " [15.79306143]\n",
+      " [14.86626984]\n",
+      " [12.88594758]\n",
+      " [15.44917132]\n",
+      " [13.88340672]\n",
+      " [14.58176868]\n",
+      " [16.82253049]\n",
+      " [15.5551518 ]\n",
+      " [14.96311331]\n",
+      " [ 9.01451518]\n",
+      " [20.36977421]\n",
+      " [12.97486582]\n",
+      " [14.09306063]\n",
+      " [ 8.08414129]\n",
+      " [13.63466685]\n",
+      " [17.01066197]\n",
+      " [21.06640024]\n",
+      " [13.23935049]\n",
+      " [17.6809231 ]\n",
+      " [17.6758432 ]\n",
+      " [ 9.82627011]\n",
+      " [18.98174477]\n",
+      " [11.63461713]\n",
+      " [14.87028157]\n",
+      " [15.42029064]\n",
+      " [ 7.45766328]\n",
+      " [17.15644357]\n",
+      " [18.42078586]\n",
+      " [17.19992649]\n",
+      " [19.90136524]\n",
+      " [18.12825871]\n",
+      " [17.3767844 ]\n",
+      " [11.9633955 ]\n",
+      " [ 6.85485707]\n",
+      " [19.06364978]\n",
+      " [ 8.44097892]\n",
+      " [12.17154843]\n",
+      " [16.6864876 ]\n",
+      " [ 8.33408186]\n",
+      " [14.00261847]\n",
+      " [14.12077598]\n",
+      " [15.01337669]\n",
+      " [12.16479932]\n",
+      " [14.55316329]\n",
+      " [13.31724044]\n",
+      " [11.5729382 ]\n",
+      " [11.4190672 ]\n",
+      " [14.56249742]\n",
+      " [ 6.33786666]\n",
+      " [13.52862815]\n",
+      " [18.17130753]\n",
+      " [18.50135751]\n",
+      " [13.28209306]\n",
+      " [16.76670271]\n",
+      " [12.98667281]\n",
+      " [15.18229719]\n",
+      " [16.51132785]\n",
+      " [23.23687763]\n",
+      " [18.3374187 ]\n",
+      " [16.02888301]\n",
+      " [23.5549559 ]\n",
+      " [18.16764372]\n",
+      " [15.05194278]\n",
+      " [18.37433976]\n",
+      " [14.35191557]\n",
+      " [18.28278914]\n",
+      " [12.63762715]\n",
+      " [11.53327183]\n",
+      " [15.97709567]\n",
+      " [12.11587264]\n",
+      " [13.67959057]\n",
+      " [10.39692145]\n",
+      " [11.89972404]\n",
+      " [12.81424374]\n",
+      " [19.43859557]\n",
+      " [11.26525835]\n",
+      " [17.08835748]\n",
+      " [17.8959596 ]\n",
+      " [14.12365959]\n",
+      " [20.59560679]\n",
+      " [17.06096365]\n",
+      " [18.07247797]\n",
+      " [24.39201819]\n",
+      " [15.01755365]\n",
+      " [15.62720641]\n",
+      " [10.56369581]\n",
+      " [13.96581874]\n",
+      " [17.00095255]\n",
+      " [18.62267886]\n",
+      " [19.11328605]\n",
+      " [18.48449261]\n",
+      " [18.8122607 ]\n",
+      " [ 9.36405072]\n",
+      " [15.36521508]\n",
+      " [11.79840485]\n",
+      " [13.61227422]\n",
+      " [20.2746336 ]\n",
+      " [10.42621519]\n",
+      " [14.66847901]\n",
+      " [16.70452605]\n",
+      " [22.17665051]\n",
+      " [14.23592962]\n",
+      " [ 8.29622405]\n",
+      " [11.62441771]\n",
+      " [14.73706899]\n",
+      " [16.91326237]\n",
+      " [16.97793647]\n",
+      " [16.46893716]\n",
+      " [15.64543299]\n",
+      " [11.29510719]\n",
+      " [11.10635205]\n",
+      " [17.76345427]\n",
+      " [13.64205605]\n",
+      " [19.07462592]]\n",
       "\n"
      ]
     }
@@ -377,10 +1290,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:47.321461Z",
-     "iopub.status.busy": "2024-10-25T14:32:47.321217Z",
-     "iopub.status.idle": "2024-10-25T14:32:47.399122Z",
-     "shell.execute_reply": "2024-10-25T14:32:47.398507Z"
+     "iopub.execute_input": "2024-10-25T14:33:38.271067Z",
+     "iopub.status.busy": "2024-10-25T14:33:38.270821Z",
+     "iopub.status.idle": "2024-10-25T14:33:38.338128Z",
+     "shell.execute_reply": "2024-10-25T14:33:38.337509Z"
     }
    },
    "outputs": [
@@ -388,7 +1301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "True causal estimate is 15.528281358714274\n"
+      "True causal estimate is 11.247611011059492\n"
      ]
     }
    ],
@@ -401,10 +1314,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:47.401075Z",
-     "iopub.status.busy": "2024-10-25T14:32:47.400739Z",
-     "iopub.status.idle": "2024-10-25T14:32:51.025348Z",
-     "shell.execute_reply": "2024-10-25T14:32:51.024291Z"
+     "iopub.execute_input": "2024-10-25T14:33:38.340024Z",
+     "iopub.status.busy": "2024-10-25T14:33:38.339812Z",
+     "iopub.status.idle": "2024-10-25T14:33:41.999243Z",
+     "shell.execute_reply": "2024-10-25T14:33:41.998216Z"
     }
    },
    "outputs": [
@@ -421,23 +1334,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.471429603219727\n",
-      "Effect estimates: [[16.07009773]\n",
-      " [20.03571549]\n",
-      " [ 8.96384864]\n",
+      "Mean value: 11.182367944351238\n",
+      "Effect estimates: [[12.79993158]\n",
+      " [ 3.7112351 ]\n",
+      " [ 5.76948667]\n",
       " ...\n",
-      " [ 7.14006316]\n",
-      " [ 6.11592992]\n",
-      " [17.9611212 ]]\n",
+      " [12.36146871]\n",
+      " [16.2363937 ]\n",
+      " [19.84331551]]\n",
       "\n"
      ]
     }
@@ -469,10 +1382,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:51.028686Z",
-     "iopub.status.busy": "2024-10-25T14:32:51.028450Z",
-     "iopub.status.idle": "2024-10-25T14:35:04.352159Z",
-     "shell.execute_reply": "2024-10-25T14:35:04.351570Z"
+     "iopub.execute_input": "2024-10-25T14:33:42.001875Z",
+     "iopub.status.busy": "2024-10-25T14:33:42.001655Z",
+     "iopub.status.idle": "2024-10-25T14:35:55.150017Z",
+     "shell.execute_reply": "2024-10-25T14:35:55.149406Z"
     }
    },
    "outputs": [
@@ -489,28 +1402,28 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.504044662998552\n",
-      "Effect estimates: [[16.19325628]\n",
-      " [20.24036528]\n",
-      " [ 9.09597254]\n",
+      "Mean value: 11.238487459603933\n",
+      "Effect estimates: [[12.89507289]\n",
+      " [ 3.68601742]\n",
+      " [ 5.73421756]\n",
       " ...\n",
-      " [ 6.97654031]\n",
-      " [ 6.38853545]\n",
-      " [17.95558472]]\n",
-      "95.0% confidence interval: [[[16.32550901 20.45287404  9.01676031 ...  6.48860366  6.24417781\n",
-      "   18.00102197]]\n",
+      " [12.45379182]\n",
+      " [16.37185436]\n",
+      " [20.01769281]]\n",
+      "95.0% confidence interval: [[[13.01604156  3.28553672  5.52193098 ... 12.55393218 16.58389431\n",
+      "   20.28215107]]\n",
       "\n",
-      " [[16.65259291 20.99199809  9.42151539 ...  7.02160604  6.88463651\n",
-      "   18.34895763]]]\n",
+      " [[13.26912412  3.79973322  5.80719612 ... 12.80864789 16.96236057\n",
+      "   20.89301761]]]\n",
       "\n"
      ]
     }
@@ -547,10 +1460,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:04.355501Z",
-     "iopub.status.busy": "2024-10-25T14:35:04.354468Z",
-     "iopub.status.idle": "2024-10-25T14:35:08.003677Z",
-     "shell.execute_reply": "2024-10-25T14:35:08.002354Z"
+     "iopub.execute_input": "2024-10-25T14:35:55.153511Z",
+     "iopub.status.busy": "2024-10-25T14:35:55.152703Z",
+     "iopub.status.idle": "2024-10-25T14:35:58.816092Z",
+     "shell.execute_reply": "2024-10-25T14:35:58.814692Z"
     }
    },
    "outputs": [
@@ -558,16 +1471,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[15.94311345]\n",
-      " [11.71072198]\n",
-      " [16.1674401 ]\n",
-      " [14.5076398 ]\n",
-      " [13.21364746]\n",
-      " [14.86479933]\n",
-      " [16.99195916]\n",
-      " [18.03051279]\n",
-      " [14.49054386]\n",
-      " [14.20683584]]\n"
+      "[[13.05623832]\n",
+      " [12.98682823]\n",
+      " [14.53693928]\n",
+      " [12.44537756]\n",
+      " [10.66232785]\n",
+      " [13.12070985]\n",
+      " [12.92463772]\n",
+      " [12.90844965]\n",
+      " [13.94570379]\n",
+      " [15.15154461]]\n"
      ]
     }
    ],
@@ -600,10 +1513,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:08.006967Z",
-     "iopub.status.busy": "2024-10-25T14:35:08.006733Z",
-     "iopub.status.idle": "2024-10-25T14:35:08.105745Z",
-     "shell.execute_reply": "2024-10-25T14:35:08.105237Z"
+     "iopub.execute_input": "2024-10-25T14:35:58.819860Z",
+     "iopub.status.busy": "2024-10-25T14:35:58.819053Z",
+     "iopub.status.idle": "2024-10-25T14:35:58.918933Z",
+     "shell.execute_reply": "2024-10-25T14:35:58.918293Z"
     }
    },
    "outputs": [
@@ -611,7 +1524,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<econml.dml.dml.DML object at 0x7f21861582e0>\n"
+      "<econml.dml.dml.DML object at 0x7f24fcb02250>\n"
      ]
     }
    ],
@@ -639,10 +1552,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:08.107677Z",
-     "iopub.status.busy": "2024-10-25T14:35:08.107380Z",
-     "iopub.status.idle": "2024-10-25T14:35:09.081183Z",
-     "shell.execute_reply": "2024-10-25T14:35:09.080648Z"
+     "iopub.execute_input": "2024-10-25T14:35:58.920922Z",
+     "iopub.status.busy": "2024-10-25T14:35:58.920613Z",
+     "iopub.status.idle": "2024-10-25T14:35:59.899494Z",
+     "shell.execute_reply": "2024-10-25T14:35:59.898801Z"
     }
    },
    "outputs": [
@@ -651,17 +1564,17 @@
      "output_type": "stream",
      "text": [
       "            X0        X1   Z0        W0        W1        W2        W3  v0  y\n",
-      "0     1.848937 -0.903341  1.0 -1.357018  0.359030 -0.158734  0.547312   1  1\n",
-      "1    -0.166756 -1.358425  1.0 -0.545606 -2.206641 -0.613952 -0.342323   0  0\n",
-      "2    -1.009845 -0.463540  1.0  1.071495 -1.730563 -1.159340  1.685405   1  1\n",
-      "3     1.721945 -0.219205  0.0 -0.401258 -1.359670  0.215030 -0.403456   0  0\n",
-      "4    -0.227010 -0.505793  1.0 -0.853896 -0.787990 -1.158951  0.733975   1  1\n",
+      "0     0.382932  2.345769  0.0 -1.428223  0.526978 -1.831798 -0.937137   0  0\n",
+      "1     0.323482  0.191478  0.0  1.653139 -0.727859 -0.366949 -1.571791   0  1\n",
+      "2     0.263324 -0.088392  0.0  1.087412  1.253476  1.720935  0.482786   1  1\n",
+      "3     0.779199  1.292685  1.0  0.481355  2.038458 -0.642406  1.758478   1  1\n",
+      "4     0.134317  0.842260  0.0  0.295010  0.232721 -0.160742  0.205964   1  1\n",
       "...        ...       ...  ...       ...       ...       ...       ...  .. ..\n",
-      "9995  1.216954 -0.117298  1.0  1.190832 -1.800529 -1.404404  1.478945   1  1\n",
-      "9996  1.670534  0.426617  1.0  1.868842  0.514159 -0.522367 -1.006508   1  1\n",
-      "9997  0.634803 -0.151029  1.0  0.108213 -1.416494 -0.408316  0.783284   1  1\n",
-      "9998  1.089458 -0.117143  1.0 -1.723675 -2.177703 -0.913882  0.198161   0  0\n",
-      "9999  1.615717 -1.471271  1.0  0.224152 -0.409710 -1.051757  0.457010   1  1\n",
+      "9995  1.866050  0.530130  0.0  1.023069  0.168696 -1.391754 -0.191480   0  0\n",
+      "9996  0.912797  1.169468  0.0  0.965489 -0.489325 -1.148618  0.095484   0  1\n",
+      "9997 -0.642280  1.079737  0.0  2.789080 -0.074786 -0.972265  0.125235   1  1\n",
+      "9998  1.951512  2.407281  0.0  0.464253  0.681882  0.421487 -0.183010   1  1\n",
+      "9999 -0.505607  3.069471  1.0  2.014816 -0.287527 -1.342139  0.203378   1  1\n",
       "\n",
       "[10000 rows x 9 columns]\n"
      ]
@@ -694,10 +1607,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:09.083263Z",
-     "iopub.status.busy": "2024-10-25T14:35:09.082877Z",
-     "iopub.status.idle": "2024-10-25T14:35:19.067139Z",
-     "shell.execute_reply": "2024-10-25T14:35:19.066537Z"
+     "iopub.execute_input": "2024-10-25T14:35:59.901688Z",
+     "iopub.status.busy": "2024-10-25T14:35:59.901213Z",
+     "iopub.status.idle": "2024-10-25T14:36:10.763795Z",
+     "shell.execute_reply": "2024-10-25T14:36:10.763202Z"
     }
    },
    "outputs": [
@@ -714,25 +1627,25 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 0.5517154689937798\n",
-      "Effect estimates: [[0.56087692]\n",
-      " [0.53034022]\n",
-      " [0.52440591]\n",
+      "Mean value: 0.3968213931846329\n",
+      "Effect estimates: [[0.46341228]\n",
+      " [0.38036417]\n",
+      " [0.36709047]\n",
       " ...\n",
-      " [0.54895095]\n",
-      " [0.55540547]\n",
-      " [0.55409699]]\n",
+      " [0.3676809 ]\n",
+      " [0.54003283]\n",
+      " [0.44825981]]\n",
       "\n",
-      "True causal estimate is 0.5364\n"
+      "True causal estimate is 0.3365\n"
      ]
     }
    ],
@@ -763,10 +1676,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:19.069709Z",
-     "iopub.status.busy": "2024-10-25T14:35:19.069475Z",
-     "iopub.status.idle": "2024-10-25T14:35:52.756931Z",
-     "shell.execute_reply": "2024-10-25T14:35:52.756365Z"
+     "iopub.execute_input": "2024-10-25T14:36:10.766201Z",
+     "iopub.status.busy": "2024-10-25T14:36:10.765992Z",
+     "iopub.status.idle": "2024-10-25T14:36:37.524658Z",
+     "shell.execute_reply": "2024-10-25T14:36:37.523187Z"
     },
     "scrolled": true
    },
@@ -791,18 +1704,18 @@
       "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: Data subset defined by a function\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 16.396141098363664\n",
-      "Effect estimates: [[16.20952269]\n",
-      " [20.31761618]\n",
-      " [ 8.99574685]\n",
+      "Mean value: 12.312450374293359\n",
+      "Effect estimates: [[12.87600707]\n",
+      " [ 4.04658904]\n",
+      " [13.00104868]\n",
       " ...\n",
-      " [15.89593262]\n",
-      " [ 6.86027739]\n",
-      " [18.00872617]]\n",
+      " [12.45626241]\n",
+      " [16.20365244]\n",
+      " [19.68928866]]\n",
       "\n"
      ]
     }
@@ -832,10 +1745,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:52.759447Z",
-     "iopub.status.busy": "2024-10-25T14:35:52.759229Z",
-     "iopub.status.idle": "2024-10-25T14:35:53.312342Z",
-     "shell.execute_reply": "2024-10-25T14:35:53.311783Z"
+     "iopub.execute_input": "2024-10-25T14:36:37.529354Z",
+     "iopub.status.busy": "2024-10-25T14:36:37.529123Z",
+     "iopub.status.idle": "2024-10-25T14:36:38.088908Z",
+     "shell.execute_reply": "2024-10-25T14:36:38.088380Z"
     }
    },
    "outputs": [
@@ -844,30 +1757,30 @@
      "output_type": "stream",
      "text": [
       "            X0        X1        X2        X3        X4   Z0        Z1  \\\n",
-      "0    -1.928622 -1.460600 -1.060879 -0.428721 -0.585863  0.0  0.833845   \n",
-      "1    -0.354271 -0.970412 -2.236240 -1.434037 -0.052678  1.0  0.014492   \n",
-      "2    -0.074867 -1.492009  0.798875  0.294336 -1.352786  1.0  0.453896   \n",
-      "3    -1.130675  0.104157  0.189918  1.204923 -0.750851  1.0  0.005845   \n",
-      "4    -1.319822  1.815768  0.287191 -0.016356  0.631682  1.0  0.509215   \n",
+      "0    -1.908063  1.816612 -1.782549  0.702879  0.428015  1.0  0.853746   \n",
+      "1    -0.841354  1.831363  0.622043 -2.117324  2.878573  0.0  0.547690   \n",
+      "2     0.148732 -0.151823  0.801329 -0.784210  0.265481  1.0  0.257635   \n",
+      "3    -0.461454  1.294300 -1.239469 -1.050843  2.835228  1.0  0.763303   \n",
+      "4    -0.015284  0.043538 -0.944665 -2.759310  1.061391  0.0  0.181512   \n",
       "...        ...       ...       ...       ...       ...  ...       ...   \n",
-      "9995 -0.548747 -1.033007 -1.326244 -1.708462  1.071464  1.0  0.214273   \n",
-      "9996 -1.567399 -1.900533  0.286898 -1.991567  0.838530  1.0  0.057444   \n",
-      "9997 -0.748685 -0.461194 -1.679336  0.511443 -2.390655  1.0  0.503427   \n",
-      "9998 -0.875187 -0.830123 -1.068104 -1.842930 -0.387148  1.0  0.207693   \n",
-      "9999 -0.880693  0.763965  0.942783 -1.150989 -2.767426  1.0  0.271196   \n",
+      "9995 -0.622211  0.630081 -0.504322  0.090627  1.190173  1.0  0.491685   \n",
+      "9996  0.438390  0.571294 -0.981292  1.184738  1.261173  0.0  0.934166   \n",
+      "9997 -0.149968  1.402410  0.989695  0.092680  3.186254  0.0  0.296926   \n",
+      "9998  1.221613 -0.114503 -1.586336 -0.079467  1.314031  1.0  0.029542   \n",
+      "9999 -2.146341  1.379550 -0.623245 -0.703477  1.266934  1.0  0.801060   \n",
       "\n",
       "            W0        W1        W2        W3        W4  v0          y  \n",
-      "0     1.538836  0.356235  0.562544 -1.229352 -1.412987   1 -12.553256  \n",
-      "1     0.629364  1.026922 -0.971291 -0.524729  0.799605   1  -2.751671  \n",
-      "2    -0.536827  0.753210  0.798975  0.652538 -0.751729   1   9.283271  \n",
-      "3     1.518184  1.360522  0.413248 -0.392738 -1.794745   1   7.341700  \n",
-      "4     0.143308  0.676154  0.564906 -0.280576 -1.100754   1   8.983174  \n",
+      "0    -0.268623 -0.019217  1.039168  1.864035  1.528477   1  31.875318  \n",
+      "1    -0.640923  1.041966  1.959528  0.571702 -1.069802   1  22.447270  \n",
+      "2    -1.254305  1.170127 -0.166980 -0.295455  1.066763   1  10.654918  \n",
+      "3     0.610376  0.068494  1.902022  2.266399  0.332300   1  35.074787  \n",
+      "4    -2.346280 -0.076409  0.007971 -1.487368 -1.287827   0 -21.300166  \n",
       "...        ...       ...       ...       ...       ...  ..        ...  \n",
-      "9995  0.727406 -0.769579  0.287496  0.919897 -0.996558   1   3.489504  \n",
-      "9996  2.535569  1.591533 -0.929597 -0.160212 -0.719416   1  10.491038  \n",
-      "9997 -0.196327 -0.294150 -0.834032  0.684718  0.132787   1  -7.312851  \n",
-      "9998  1.698052 -0.275638 -1.178926 -0.288767 -1.333009   1  -5.539157  \n",
-      "9999  1.114213 -0.004539 -0.946504 -2.163850 -1.660811   1 -12.611732  \n",
+      "9995 -0.161351 -1.566292  1.007896  0.678905 -0.321988   1  10.933773  \n",
+      "9996 -0.962916  0.585787  2.459705 -0.293871  0.312810   1  31.844450  \n",
+      "9997 -0.298513  1.390892  1.936229  0.492862  0.157574   1  41.957955  \n",
+      "9998 -1.055555  0.676774 -0.010229  0.528897  1.068823   1  16.869197  \n",
+      "9999  0.400382 -0.040731  0.135707  0.114662 -0.318792   1  10.570814  \n",
       "\n",
       "[10000 rows x 14 columns]\n"
      ]
@@ -891,10 +1804,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:53.314386Z",
-     "iopub.status.busy": "2024-10-25T14:35:53.313991Z",
-     "iopub.status.idle": "2024-10-25T14:36:01.874288Z",
-     "shell.execute_reply": "2024-10-25T14:36:01.873644Z"
+     "iopub.execute_input": "2024-10-25T14:36:38.090829Z",
+     "iopub.status.busy": "2024-10-25T14:36:38.090621Z",
+     "iopub.status.idle": "2024-10-25T14:36:46.681131Z",
+     "shell.execute_reply": "2024-10-25T14:36:46.680528Z"
     }
    },
    "outputs": [
@@ -911,25 +1824,25 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W4,W0,W2,W1,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4\n",
+      "b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 6.437717655807301\n",
-      "Effect estimates: [[-4.16181067]\n",
-      " [ 0.774689  ]\n",
-      " [11.02032889]\n",
+      "Mean value: 18.87771266237887\n",
+      "Effect estimates: [[27.78513921]\n",
+      " [23.45800939]\n",
+      " [10.86480525]\n",
       " ...\n",
-      " [-5.22952473]\n",
-      " [-1.44104664]\n",
-      " [ 1.56876933]]\n",
+      " [32.21640236]\n",
+      " [17.6237358 ]\n",
+      " [15.38137178]]\n",
       "\n",
-      "True causal estimate is 1.87902954705715\n"
+      "True causal estimate is 10.035889599894295\n"
      ]
     }
    ],
@@ -966,10 +1879,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:36:01.876340Z",
-     "iopub.status.busy": "2024-10-25T14:36:01.875964Z",
-     "iopub.status.idle": "2024-10-25T14:36:01.929723Z",
-     "shell.execute_reply": "2024-10-25T14:36:01.929090Z"
+     "iopub.execute_input": "2024-10-25T14:36:46.683234Z",
+     "iopub.status.busy": "2024-10-25T14:36:46.682868Z",
+     "iopub.status.idle": "2024-10-25T14:36:46.734130Z",
+     "shell.execute_reply": "2024-10-25T14:36:46.733551Z"
     }
    },
    "outputs": [
@@ -986,23 +1899,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W4,W0,W2,W1,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4\n",
+      "b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3\n",
       "Target units: Data subset provided as a data frame\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 2.043488559407042\n",
-      "Effect estimates: [[ 4.54315475]\n",
-      " [10.77609008]\n",
-      " [-5.22952473]\n",
-      " [-1.44104664]\n",
-      " [ 1.56876933]]\n",
+      "Mean value: 22.84531697294303\n",
+      "Effect estimates: [[21.97693322]\n",
+      " [27.02814169]\n",
+      " [32.21640236]\n",
+      " [17.6237358 ]\n",
+      " [15.38137178]]\n",
       "\n",
-      "True causal estimate is 1.87902954705715\n"
+      "True causal estimate is 10.035889599894295\n"
      ]
     }
    ],
@@ -1037,10 +1950,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:36:01.931771Z",
-     "iopub.status.busy": "2024-10-25T14:36:01.931466Z",
-     "iopub.status.idle": "2024-10-25T14:42:08.323291Z",
-     "shell.execute_reply": "2024-10-25T14:42:08.321792Z"
+     "iopub.execute_input": "2024-10-25T14:36:46.736219Z",
+     "iopub.status.busy": "2024-10-25T14:36:46.735838Z",
+     "iopub.status.idle": "2024-10-25T14:42:54.994281Z",
+     "shell.execute_reply": "2024-10-25T14:42:54.993146Z"
     }
    },
    "outputs": [
@@ -1049,9 +1962,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.061049698929667\n",
-      "p value:0.19999999999999996\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.122022363488286\n",
+      "p value:0.2\n",
       "\n"
      ]
     }
@@ -1073,10 +1986,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:08.329668Z",
-     "iopub.status.busy": "2024-10-25T14:42:08.328215Z",
-     "iopub.status.idle": "2024-10-25T14:42:12.021669Z",
-     "shell.execute_reply": "2024-10-25T14:42:12.020587Z"
+     "iopub.execute_input": "2024-10-25T14:42:54.998347Z",
+     "iopub.status.busy": "2024-10-25T14:42:54.998083Z",
+     "iopub.status.idle": "2024-10-25T14:42:58.695637Z",
+     "shell.execute_reply": "2024-10-25T14:42:58.694552Z"
     }
    },
    "outputs": [
@@ -1085,8 +1998,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.04397103511404\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.117896772844379\n",
       "\n"
      ]
     }
@@ -1110,10 +2023,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:12.025077Z",
-     "iopub.status.busy": "2024-10-25T14:42:12.024837Z",
-     "iopub.status.idle": "2024-10-25T14:42:48.792071Z",
-     "shell.execute_reply": "2024-10-25T14:42:48.790906Z"
+     "iopub.execute_input": "2024-10-25T14:42:58.700132Z",
+     "iopub.status.busy": "2024-10-25T14:42:58.699125Z",
+     "iopub.status.idle": "2024-10-25T14:43:35.533982Z",
+     "shell.execute_reply": "2024-10-25T14:43:35.533454Z"
     }
    },
    "outputs": [
@@ -1122,9 +2035,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:-0.039633288288616876\n",
-      "p value:0.3019778331621793\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:-0.011817586304644403\n",
+      "p value:0.4059910355354093\n",
       "\n"
      ]
     }
@@ -1149,10 +2062,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:48.796726Z",
-     "iopub.status.busy": "2024-10-25T14:42:48.795358Z",
-     "iopub.status.idle": "2024-10-25T14:43:18.092151Z",
-     "shell.execute_reply": "2024-10-25T14:43:18.090388Z"
+     "iopub.execute_input": "2024-10-25T14:43:35.537328Z",
+     "iopub.status.busy": "2024-10-25T14:43:35.536426Z",
+     "iopub.status.idle": "2024-10-25T14:44:04.666511Z",
+     "shell.execute_reply": "2024-10-25T14:44:04.665911Z"
     }
    },
    "outputs": [
@@ -1161,9 +2074,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.02989599562622\n",
-      "p value:0.3803137657532839\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.145734147056782\n",
+      "p value:0.2631763306816288\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb
index 7c77d19bd..9966878e7 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:21.254404Z",
-     "iopub.status.busy": "2024-10-25T14:43:21.254225Z",
-     "iopub.status.idle": "2024-10-25T14:43:21.269730Z",
-     "shell.execute_reply": "2024-10-25T14:43:21.269238Z"
+     "iopub.execute_input": "2024-10-25T14:44:08.071035Z",
+     "iopub.status.busy": "2024-10-25T14:44:08.070614Z",
+     "iopub.status.idle": "2024-10-25T14:44:08.087961Z",
+     "shell.execute_reply": "2024-10-25T14:44:08.087444Z"
     }
    },
    "outputs": [],
@@ -29,10 +29,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:21.271373Z",
-     "iopub.status.busy": "2024-10-25T14:43:21.271200Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.722132Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.721479Z"
+     "iopub.execute_input": "2024-10-25T14:44:08.090072Z",
+     "iopub.status.busy": "2024-10-25T14:44:08.089711Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.710219Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.709558Z"
     }
    },
    "outputs": [],
@@ -60,10 +60,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.724525Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.724091Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.757499Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.756885Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.713069Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.712479Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.748957Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.748334Z"
     }
    },
    "outputs": [],
@@ -115,10 +115,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.759646Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.759301Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.928671Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.928110Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.751614Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.751198Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.928111Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.927475Z"
     }
    },
    "outputs": [
@@ -170,10 +170,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.930905Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.930492Z",
-     "iopub.status.idle": "2024-10-25T14:43:23.187851Z",
-     "shell.execute_reply": "2024-10-25T14:43:23.187235Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.930622Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.930129Z",
+     "iopub.status.idle": "2024-10-25T14:44:10.237344Z",
+     "shell.execute_reply": "2024-10-25T14:44:10.236669Z"
     }
    },
    "outputs": [
@@ -215,10 +215,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:23.189952Z",
-     "iopub.status.busy": "2024-10-25T14:43:23.189536Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.115355Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.114774Z"
+     "iopub.execute_input": "2024-10-25T14:44:10.239523Z",
+     "iopub.status.busy": "2024-10-25T14:44:10.239123Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.199909Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.199359Z"
     }
    },
    "outputs": [
@@ -260,7 +260,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 3.987497536617294\n",
+      "Mean value: 4.122408234201723\n",
       "p-value: [0, 0.001]\n",
       "\n"
      ]
@@ -289,10 +289,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:26.117422Z",
-     "iopub.status.busy": "2024-10-25T14:43:26.117065Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.557525Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.556949Z"
+     "iopub.execute_input": "2024-10-25T14:44:13.201881Z",
+     "iopub.status.busy": "2024-10-25T14:44:13.201532Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.643838Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.643149Z"
     }
    },
    "outputs": [
@@ -301,9 +301,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:3.987497536617294\n",
-      "New effect:-0.025021095946670787\n",
-      "p value:0.86\n",
+      "Estimated effect:4.122408234201723\n",
+      "New effect:-0.022474970252860365\n",
+      "p value:0.92\n",
       "\n"
      ]
     }
@@ -335,10 +335,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:26.559590Z",
-     "iopub.status.busy": "2024-10-25T14:43:26.559200Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.609942Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.609331Z"
+     "iopub.execute_input": "2024-10-25T14:44:13.646000Z",
+     "iopub.status.busy": "2024-10-25T14:44:13.645623Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.696430Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.695781Z"
     }
    },
    "outputs": [
@@ -348,22 +348,22 @@
        "<table class=\"simpletable\">\n",
        "<caption>IV2SLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.889</td> \n",
+       "  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.904</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.889</td> \n",
+       "  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.904</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1361.</td> \n",
+       "  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1876.</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>1.30e-188</td>\n",
+       "  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>2.04e-231</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:43:26</td>     <th>                     </th>     <td> </td>    \n",
+       "  <th>Time:</th>                 <td>14:44:13</td>     <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>  1000</td>      <th>                     </th>     <td> </td>    \n",
@@ -380,24 +380,24 @@
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th> <td>    9.6387</td> <td>    0.977</td> <td>    9.865</td> <td> 0.000</td> <td>    7.721</td> <td>   11.556</td>\n",
+       "  <th>Intercept</th> <td>    8.9729</td> <td>    0.877</td> <td>   10.231</td> <td> 0.000</td> <td>    7.252</td> <td>   10.694</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>education</th> <td>    3.9875</td> <td>    0.108</td> <td>   36.889</td> <td> 0.000</td> <td>    3.775</td> <td>    4.200</td>\n",
+       "  <th>education</th> <td>    4.1224</td> <td>    0.095</td> <td>   43.308</td> <td> 0.000</td> <td>    3.936</td> <td>    4.309</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td> 2.539</td> <th>  Durbin-Watson:     </th> <td>   1.959</td>\n",
+       "  <th>Omnibus:</th>       <td> 1.392</td> <th>  Durbin-Watson:     </th> <td>   1.913</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.281</td> <th>  Jarque-Bera (JB):  </th> <td>   2.587</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.499</td> <th>  Jarque-Bera (JB):  </th> <td>   1.460</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td> 0.121</td> <th>  Prob(JB):          </th> <td>   0.274</td>\n",
+       "  <th>Skew:</th>          <td>-0.067</td> <th>  Prob(JB):          </th> <td>   0.482</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 2.940</td> <th>  Cond. No.          </th> <td>    24.5</td>\n",
+       "  <th>Kurtosis:</th>      <td> 2.870</td> <th>  Cond. No.          </th> <td>    25.1</td>\n",
        "</tr>\n",
        "</table>"
       ],
@@ -405,12 +405,12 @@
        "\\begin{center}\n",
        "\\begin{tabular}{lclc}\n",
        "\\toprule\n",
-       "\\textbf{Dep. Variable:}    &      income      & \\textbf{  R-squared:         } &     0.889   \\\\\n",
-       "\\textbf{Model:}            &      IV2SLS      & \\textbf{  Adj. R-squared:    } &     0.889   \\\\\n",
-       "\\textbf{Method:}           &    Two Stage     & \\textbf{  F-statistic:       } &     1361.   \\\\\n",
-       "\\textbf{}                  &  Least Squares   & \\textbf{  Prob (F-statistic):} & 1.30e-188   \\\\\n",
+       "\\textbf{Dep. Variable:}    &      income      & \\textbf{  R-squared:         } &     0.904   \\\\\n",
+       "\\textbf{Model:}            &      IV2SLS      & \\textbf{  Adj. R-squared:    } &     0.904   \\\\\n",
+       "\\textbf{Method:}           &    Two Stage     & \\textbf{  F-statistic:       } &     1876.   \\\\\n",
+       "\\textbf{}                  &  Least Squares   & \\textbf{  Prob (F-statistic):} & 2.04e-231   \\\\\n",
        "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{                     } &             \\\\\n",
-       "\\textbf{Time:}             &     14:43:26     & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Time:}             &     14:44:13     & \\textbf{                     } &             \\\\\n",
        "\\textbf{No. Observations:} &        1000      & \\textbf{                     } &             \\\\\n",
        "\\textbf{Df Residuals:}     &         998      & \\textbf{                     } &             \\\\\n",
        "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
@@ -419,15 +419,15 @@
        "\\begin{tabular}{lcccccc}\n",
        "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{Intercept} &       9.6387  &        0.977     &     9.865  &         0.000        &        7.721    &       11.556     \\\\\n",
-       "\\textbf{education} &       3.9875  &        0.108     &    36.889  &         0.000        &        3.775    &        4.200     \\\\\n",
+       "\\textbf{Intercept} &       8.9729  &        0.877     &    10.231  &         0.000        &        7.252    &       10.694     \\\\\n",
+       "\\textbf{education} &       4.1224  &        0.095     &    43.308  &         0.000        &        3.936    &        4.309     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       &  2.539 & \\textbf{  Durbin-Watson:     } &    1.959  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &  0.281 & \\textbf{  Jarque-Bera (JB):  } &    2.587  \\\\\n",
-       "\\textbf{Skew:}          &  0.121 & \\textbf{  Prob(JB):          } &    0.274  \\\\\n",
-       "\\textbf{Kurtosis:}      &  2.940 & \\textbf{  Cond. No.          } &     24.5  \\\\\n",
+       "\\textbf{Omnibus:}       &  1.392 & \\textbf{  Durbin-Watson:     } &    1.913  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.499 & \\textbf{  Jarque-Bera (JB):  } &    1.460  \\\\\n",
+       "\\textbf{Skew:}          & -0.067 & \\textbf{  Prob(JB):          } &    0.482  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.870 & \\textbf{  Cond. No.          } &     25.1  \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "%\\caption{IV2SLS Regression Results}\n",
@@ -438,25 +438,25 @@
        "\"\"\"\n",
        "                          IV2SLS Regression Results                           \n",
        "==============================================================================\n",
-       "Dep. Variable:                 income   R-squared:                       0.889\n",
-       "Model:                         IV2SLS   Adj. R-squared:                  0.889\n",
-       "Method:                     Two Stage   F-statistic:                     1361.\n",
-       "                        Least Squares   Prob (F-statistic):          1.30e-188\n",
+       "Dep. Variable:                 income   R-squared:                       0.904\n",
+       "Model:                         IV2SLS   Adj. R-squared:                  0.904\n",
+       "Method:                     Two Stage   F-statistic:                     1876.\n",
+       "                        Least Squares   Prob (F-statistic):          2.04e-231\n",
        "Date:                Fri, 25 Oct 2024                                         \n",
-       "Time:                        14:43:26                                         \n",
+       "Time:                        14:44:13                                         \n",
        "No. Observations:                1000                                         \n",
        "Df Residuals:                     998                                         \n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "Intercept      9.6387      0.977      9.865      0.000       7.721      11.556\n",
-       "education      3.9875      0.108     36.889      0.000       3.775       4.200\n",
+       "Intercept      8.9729      0.877     10.231      0.000       7.252      10.694\n",
+       "education      4.1224      0.095     43.308      0.000       3.936       4.309\n",
        "==============================================================================\n",
-       "Omnibus:                        2.539   Durbin-Watson:                   1.959\n",
-       "Prob(Omnibus):                  0.281   Jarque-Bera (JB):                2.587\n",
-       "Skew:                           0.121   Prob(JB):                        0.274\n",
-       "Kurtosis:                       2.940   Cond. No.                         24.5\n",
+       "Omnibus:                        1.392   Durbin-Watson:                   1.913\n",
+       "Prob(Omnibus):                  0.499   Jarque-Bera (JB):                1.460\n",
+       "Skew:                          -0.067   Prob(JB):                        0.482\n",
+       "Kurtosis:                       2.870   Cond. No.                         25.1\n",
        "==============================================================================\n",
        "\"\"\""
       ]
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb
index be6e5a976..73dc771c4 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb
@@ -13,10 +13,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:28.420037Z",
-     "iopub.status.busy": "2024-10-25T14:43:28.419847Z",
-     "iopub.status.idle": "2024-10-25T14:43:29.830914Z",
-     "shell.execute_reply": "2024-10-25T14:43:29.830311Z"
+     "iopub.execute_input": "2024-10-25T14:44:15.712292Z",
+     "iopub.status.busy": "2024-10-25T14:44:15.711873Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.236256Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.235633Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:29.833247Z",
-     "iopub.status.busy": "2024-10-25T14:43:29.832855Z",
-     "iopub.status.idle": "2024-10-25T14:43:29.887255Z",
-     "shell.execute_reply": "2024-10-25T14:43:29.886670Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.238913Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.238393Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.277145Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.276625Z"
     }
    },
    "outputs": [
@@ -72,33 +72,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.301186</td>\n",
+       "      <td>-3.122581</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.042313</td>\n",
+       "      <td>-7.031578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.811655</td>\n",
+       "      <td>-0.929257</td>\n",
        "      <td>True</td>\n",
-       "      <td>6.596058</td>\n",
+       "      <td>3.592709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.766516</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7.338039</td>\n",
+       "      <td>-1.290274</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-3.556782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.661593</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6.659962</td>\n",
+       "      <td>-0.461701</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.099132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.343496</td>\n",
-       "      <td>True</td>\n",
-       "      <td>11.734157</td>\n",
+       "      <td>-0.916448</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.665845</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -108,33 +108,33 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.519674</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9.643222</td>\n",
+       "      <td>-1.115158</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1.502734</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>2.292310</td>\n",
+       "      <td>-0.206983</td>\n",
        "      <td>True</td>\n",
-       "      <td>11.775713</td>\n",
+       "      <td>5.886683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>1.160542</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7.793988</td>\n",
+       "      <td>-0.601760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.381091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.425328</td>\n",
+       "      <td>-0.760529</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.713643</td>\n",
+       "      <td>3.084998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>1.438840</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8.366888</td>\n",
+       "      <td>-3.552914</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-6.888076</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -142,18 +142,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0     v0          y\n",
-       "0    0.301186  False   1.042313\n",
-       "1    0.811655   True   6.596058\n",
-       "2    0.766516   True   7.338039\n",
-       "3    0.661593   True   6.659962\n",
-       "4    2.343496   True  11.734157\n",
-       "..        ...    ...        ...\n",
-       "995  1.519674   True   9.643222\n",
-       "996  2.292310   True  11.775713\n",
-       "997  1.160542   True   7.793988\n",
-       "998  1.425328   True   7.713643\n",
-       "999  1.438840   True   8.366888\n",
+       "           W0     v0         y\n",
+       "0   -3.122581  False -7.031578\n",
+       "1   -0.929257   True  3.592709\n",
+       "2   -1.290274  False -3.556782\n",
+       "3   -0.461701  False  0.099132\n",
+       "4   -0.916448  False -0.665845\n",
+       "..        ...    ...       ...\n",
+       "995 -1.115158  False -1.502734\n",
+       "996 -0.206983   True  5.886683\n",
+       "997 -0.601760  False -0.381091\n",
+       "998 -0.760529   True  3.084998\n",
+       "999 -3.552914  False -6.888076\n",
        "\n",
        "[1000 rows x 3 columns]"
       ]
@@ -184,10 +184,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:29.889220Z",
-     "iopub.status.busy": "2024-10-25T14:43:29.888861Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.053081Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.052362Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.279121Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.278752Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.459567Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.458879Z"
     },
     "scrolled": true
    },
@@ -204,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAHFCAYAAADPHZKNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZOElEQVR4nO3de4xU9dnA8WcXYYAK641LCctbkIQG76K2qLHQUg01RqMxEjEFq6QSNRW1Cmm8YLVgahrbVNFqleKl1WrVtlioVfFWtAqFFBUTBOPKAqLIrmBddHfePxq2L0Up8HL2mYHPJzl/zMxhfw8xyJdzfjNTUy6XywEAkKA2ewAAYM8lRACANEIEAEgjRACANEIEAEgjRACANEIEAEizV/YA29LW1haNjY3Ro0ePqKmpyR4HANgO5XI5Pvzww+jXr1/U1m77mkdFh0hjY2PU19dnjwEA7ISGhobo37//Ns+p6BDp0aNHRPzrN9KzZ8/kaQCA7dHc3Bz19fXtf49vS0WHyObbMT179hQiAFBltmdbhc2qAEAaIQIApBEiAECait4jsr1aW1vjk08+yR6jEF26dPmvb30CgGpV1SFSLpdj9erVsX79+uxRClNbWxsDBw6MLl26ZI8CALtcVYfI5gjp3bt3dO/efbf70LPNH+i2atWqGDBgwG73+wOAqg2R1tbW9gjZf//9s8cpTK9evaKxsTE+/fTT6Ny5c/Y4ALBLVe3mg817Qrp37548SbE235JpbW1NngQAdr2qDZHNdvfbFbv77w+APVvVhwgAUL2ECACQpmo3q27LlybP7tD13pp+coeuBwC7C1dEAIA0QgQASLNb3pqpZLNmzYpJkyZFY2NjlEql9udPO+206NGjR9xzzz2J0wEdoaNvH5PL7fttc0Wkg5155pnR2toav//979ufe/fdd2P27Nnxne98J3EyAOh4QqSDdevWLc4+++y4++6725+79957Y8CAATFixIi8wQAggRBJMGHChPjzn/8cK1eujIiImTNnxvjx4314GQB7HHtEEhxxxBFx2GGHxaxZs+LEE0+MV199NWbPds8YgD2PEEly/vnnx8033xwrV66MUaNGRX19ffZIANDh3JpJcvbZZ8c777wTd9xxh02qAOyxdssrItXwVqm6uro444wzYvbs2XHaaadljwMAKVwRSbRy5coYO3bsFp8nAgB7kt3yikil++CDD2LevHkxb968uPXWW7PHAYA0QiTBEUccER988EHceOONMWTIkOxxACCNEEnw1ltvZY8AABXBHhEAIE3Vh0hbW1v2CIUql8vZIwBAYar21kyXLl2itrY2Ghsbo1evXtGlS5fd7iPSy+VyrF27NmpqaqJz587Z4wDALle1IVJbWxsDBw6MVatWRWNjY/Y4hampqYn+/ftHp06dskcBgF2uakMk4l9XRQYMGBCffvpptLa2Zo9TiM6dO4sQAHZbVR0iEdF+28KtCwCoPlW/WRUAqF5CBABII0QAgDRCBABII0QAgDRCBABIU2iIzJgxIw499NDo2bNn9OzZM4YPHx5/+tOfilwSAKgihYZI//79Y/r06bFgwYJ45ZVX4utf/3qceuqp8eqrrxa5LABQJQr9QLNTTjlli8c33HBDzJgxI1588cU46KCDilwaAKgCHfbJqq2trfHb3/42Nm7cGMOHD//Mc1paWqKlpaX9cXNzc0eNBwAkKHyz6j/+8Y/Ye++9o1QqxQUXXBCPPPJIDB069DPPnTZtWtTV1bUf9fX1RY8HACQqPESGDBkSixYtipdeeikmTpwY48aNi9dee+0zz50yZUo0NTW1Hw0NDUWPBwAkKvzWTJcuXWLw4MERETFs2LB4+eWX46c//WncfvvtW51bKpWiVCoVPRIAUCE6/HNE2trattgHAgDsuQq9IjJlypQYPXp0DBgwID788MO4//77Y968eTF37twilwUAqkShIfLuu+/Gt7/97Vi1alXU1dXFoYceGnPnzo1vfvObRS4LAFSJQkPkl7/8ZZE/HgCocr5rBgBII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDSFhsi0adPi6KOPjh49ekTv3r3jtNNOizfeeKPIJQGAKlJoiDzzzDNx4YUXxosvvhhPPPFEfPLJJ3HiiSfGxo0bi1wWAKgSexX5w+fMmbPF45kzZ0bv3r1jwYIFccIJJxS5NABQBQoNkf/U1NQUERH77bffZ77e0tISLS0t7Y+bm5s7ZC4AIEeHbVZta2uLSy65JI477rg4+OCDP/OcadOmRV1dXftRX1/fUeMBAAk6LEQuvPDCWLJkSfzmN7/53HOmTJkSTU1N7UdDQ0NHjQcAJOiQWzMXXXRR/PGPf4xnn302+vfv/7nnlUqlKJVKHTESAFABCg2RcrkcF198cTzyyCMxb968GDhwYJHLAQBVptAQufDCC+P++++Pxx57LHr06BGrV6+OiIi6urro1q1bkUsDAFWg0D0iM2bMiKamphgxYkR88YtfbD8eeOCBIpcFAKpE4bdmAAA+j++aAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAIE2hIfLss8/GKaecEv369Yuampp49NFHi1wOAKgyhYbIxo0b47DDDotbbrmlyGUAgCq1V5E/fPTo0TF69OjtPr+lpSVaWlraHzc3NxcxFgBQISpqj8i0adOirq6u/aivr88eCQAoUEWFyJQpU6Kpqan9aGhoyB4JAChQobdmdlSpVIpSqZQ9BgDQQSrqiggAsGcRIgBAmkJvzWzYsCGWLVvW/njFihWxaNGi2G+//WLAgAFFLg0AVIFCQ+SVV16JkSNHtj++9NJLIyJi3LhxMXPmzCKXBgCqQKEhMmLEiCiXy0UuAQBUMXtEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0HRIit9xyS3zpS1+Krl27xle+8pX429/+1hHLAgAVrvAQeeCBB+LSSy+Na665JhYuXBiHHXZYnHTSSfHuu+8WvTQAUOEKD5Gf/OQnMWHChDj33HNj6NChcdttt0X37t3jrrvuKnppAKDCFRoimzZtigULFsSoUaP+vWBtbYwaNSrmz5+/1fktLS3R3Ny8xQEA7L72KvKHv/fee9Ha2hp9+vTZ4vk+ffrE0qVLtzp/2rRpMXXq1CJHqhpfmjw7ewQ60FvTT84egQ7kvzf8W0W9a2bKlCnR1NTUfjQ0NGSPBAAUqNArIgcccEB06tQp1qxZs8Xza9asib59+251fqlUilKpVORIAEAFKfSKSJcuXWLYsGHx5JNPtj/X1tYWTz75ZAwfPrzIpQGAKlDoFZGIiEsvvTTGjRsXRx11VBxzzDFx8803x8aNG+Pcc88temkAoMIVHiJnnXVWrF27Nq6++upYvXp1HH744TFnzpytNrACAHuewkMkIuKiiy6Kiy66qCOWAgCqSEW9awYA2LMIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgTWEhcsMNN8Sxxx4b3bt3j3322aeoZQCAKlZYiGzatCnOPPPMmDhxYlFLAABVbq+ifvDUqVMjImLmzJlFLQEAVLnCQmRntLS0REtLS/vj5ubmxGkAgKJV1GbVadOmRV1dXftRX1+fPRIAUKAdCpHJkydHTU3NNo+lS5fu9DBTpkyJpqam9qOhoWGnfxYAUPl26NbMZZddFuPHj9/mOYMGDdrpYUqlUpRKpZ3+9QBAddmhEOnVq1f06tWrqFkAgD1MYZtV33777Vi3bl28/fbb0draGosWLYqIiMGDB8fee+9d1LIAQBUpLESuvvrq+NWvftX++IgjjoiIiKeffjpGjBhR1LIAQBUp7F0zM2fOjHK5vNUhQgCAzSrq7bsAwJ5FiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAafbKHoDP9tb0k7NHAIDCuSICAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQpLETeeuutOO+882LgwIHRrVu3OPDAA+Oaa66JTZs2FbUkAFBlCvvSu6VLl0ZbW1vcfvvtMXjw4FiyZElMmDAhNm7cGDfddFNRywIAVaSmXC6XO2qxH//4xzFjxoxYvnz5Z77e0tISLS0t7Y+bm5ujvr4+mpqaomfPnh01JgDw/9Dc3Bx1dXXb9fd3h+4RaWpqiv322+9zX582bVrU1dW1H/X19R04HQDQ0TrsisiyZcti2LBhcdNNN8WECRM+85z/vCLS1NQUAwYMiIaGBldEAKBKbL6jsX79+qirq9v2yeUddOWVV5YjYpvH66+/vsWveeedd8oHHnhg+bzzztuhtRoaGv7rWg6Hw+FwOCrzaGho+K9/1+/wFZG1a9fG+++/v81zBg0aFF26dImIiMbGxhgxYkR89atfjZkzZ0Zt7fbfDWpra4vGxsbo0aNH1NTU7MiYVKHNBe0KGOx+/Pnes5TL5fjwww+jX79+//Xv/UJvzaxcuTJGjhwZw4YNi3vvvTc6depU1FLsBnZkcxNQXfz55vMU9vbdlStXxogRI+J//ud/4qabboq1a9e2v9a3b9+ilgUAqkhhIfLEE0/EsmXLYtmyZdG/f/8tXivwIgwAUEUKe/vu+PHjo1wuf+YBn6VUKsU111wTpVIpexRgF/Pnm8/ToR9oBgDwf/nSOwAgjRABANIIEQAgjRABANIIEQAgjRABoBDPPfdcnHPOOTF8+PBYuXJlRETcc8898fzzzydPRiURIlSMTZs2xRtvvBGffvpp9ijA/9PDDz8cJ510UnTr1i3+/ve/t3+zelNTU/zoRz9Kno5KIkRI99FHH8V5550X3bt3j4MOOijefvvtiIi4+OKLY/r06cnTATvj+uuvj9tuuy3uuOOO6Ny5c/vzxx13XCxcuDBxMiqNECHdlClTYvHixTFv3rzo2rVr+/OjRo2KBx54IHEyYGe98cYbccIJJ2z1fF1dXaxfv77jB6JiCRHSPfroo/Hzn/88jj/++KipqWl//qCDDoo333wzcTJgZ/Xt2zeWLVu21fPPP/98DBo0KGEiKpUQId3atWujd+/eWz2/cePGLcIEqB4TJkyI733ve/HSSy9FTU1NNDY2xn333ReXX355TJw4MXs8Kkhh374L2+uoo46K2bNnx8UXXxwR0R4fd955ZwwfPjxzNGAnTZ48Odra2uIb3/hGfPTRR3HCCSdEqVSKyy+/vP3POkT40jsqwPPPPx+jR4+Oc845J2bOnBnf/e5347XXXou//vWv8cwzz8SwYcOyRwR20qZNm2LZsmWxYcOGGDp0aOy9997ZI1FhhAgV4c0334zp06fH4sWLY8OGDXHkkUfGlVdeGYccckj2aAAUSIgAsMuNHDlym3u8nnrqqQ6chkpmjwjpFi5cGJ07d26/+vHYY4/F3XffHUOHDo1rr702unTpkjwhsKMOP/zwLR5/8sknsWjRoliyZEmMGzcuZygqkisipDv66KNj8uTJccYZZ8Ty5ctj6NChcfrpp8fLL78cJ598ctx8883ZIwK7yLXXXhsbNmyIm266KXsUKoQQIV1dXV0sXLgwDjzwwLjxxhvjqaeeirlz58YLL7wQY8aMiYaGhuwRgV1k2bJlccwxx8S6deuyR6FC+BwR0pXL5Whra4uIiL/85S/xrW99KyIi6uvr47333sscDdjF5s+fv8UnKIM9IqQ76qij4vrrr49Ro0bFM888EzNmzIiIiBUrVkSfPn2SpwN2xumnn77F43K5HKtWrYpXXnklrrrqqqSpqERChHQ333xzjB07Nh599NH4wQ9+EIMHD46IiIceeiiOPfbY5OmAnVFXV7fF49ra2hgyZEhcd911ceKJJyZNRSWyR4SK9fHHH0enTp22+OZOoPK1trbGCy+8EIccckjsu+++2eNQ4YQIALtc165d4/XXX4+BAwdmj0KFc2uGFPvuu+92f6Gd3fVQfQ4++OBYvny5EOG/EiKk8NkgsHu7/vrr4/LLL48f/vCHMWzYsPjCF76wxes9e/ZMmoxK49YMALvMddddF5dddln06NGj/bn/e/WzXC5HTU1NtLa2ZoxHBRIiVJSPP/44Nm3atMVz/uUE1aNTp06xatWqeP3117d53te+9rUOmohKJ0RIt3HjxrjyyivjwQcfjPfff3+r1/3LCapHbW1trF69Onr37p09ClXCJ6uS7oorroinnnoqZsyYEaVSKe68886YOnVq9OvXL2bNmpU9HrCDtncjOkS4IkIFGDBgQMyaNStGjBgRPXv2jIULF8bgwYPjnnvuiV//+tfx+OOPZ48IbKfa2tqoq6v7rzHi3XBs5l0zpFu3bl0MGjQoIv61H2Tz/6COP/74mDhxYuZowE6YOnXqVp+sCp9HiJBu0KBBsWLFihgwYEB8+ctfjgcffDCOOeaY+MMf/hD77LNP9njADhozZow9Imw3e0RIs3z58mhra4tzzz03Fi9eHBERkydPjltuuSW6du0akyZNiu9///vJUwI7wv4QdpQ9IqTZ/Da/zf9yOuuss+JnP/tZfPzxx7FgwYIYPHhwHHrooclTAjvCu2bYUUKENP/5P6wePXrE4sWL2/eLALD7c2sGAEgjREhTU1Oz1f1k95cB9izeNUOacrkc48ePj1KpFBH/+nj3Cy64YKsvx/rd736XMR4AHUCIkGbcuHFbPD7nnHOSJgEgi82qAEAae0QAgDRCBABII0QAgDRCBABII0QAgDRCBEg1b968OPLII6NUKsXgwYNj5syZ2SMBHUiIAGlWrFgRJ598cowcOTIWLVoUl1xySZx//vkxd+7c7NGADuJzRIDC/OIXv4hrr7023nnnnait/fe/e0499dTYf//9o1evXjF79uxYsmRJ+2tjxoyJ9evXx5w5czJGBjqYKyJAYc4888x4//334+mnn25/bt26dTFnzpwYO3ZszJ8/P0aNGrXFrznppJNi/vz5HT0qkESIAIXZd999Y/To0XH//fe3P/fQQw/FAQccECNHjozVq1dHnz59tvg1ffr0iebm5vjnP//Z0eMCCYQIUKixY8fGww8/HC0tLRERcd9998WYMWO2uFUD7Ln8nwAo1CmnnBLlcjlmz54dDQ0N8dxzz8XYsWMjIqJv376xZs2aLc5fs2ZN9OzZM7p165YxLtDBfPsuUKiuXbvG6aefHvfdd18sW7YshgwZEkceeWRERAwfPjwef/zxLc5/4oknYvjw4RmjAglcEQEKN3bs2Jg9e3bcdddd7VdDIiIuuOCCWL58eVxxxRWxdOnSuPXWW+PBBx+MSZMmJU4LdCRv3wUK19bWFv37949Vq1bFm2++GYMGDWp/bd68eTFp0qR47bXXon///nHVVVfF+PHj84YFOpQQAQDSuDUDAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKT5XwyCYX4WLeCLAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -227,10 +227,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.055102Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.054905Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.171620Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.170896Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.461790Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.461277Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.604972Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.604336Z"
     }
    },
    "outputs": [
@@ -246,7 +246,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -269,10 +269,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.173708Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.173497Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.195174Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.194575Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.607110Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.606755Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.628217Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.627598Z"
     }
    },
    "outputs": [],
@@ -295,10 +295,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.197054Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.196869Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.206063Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.205471Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.630190Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.629898Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.638836Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.638319Z"
     },
     "scrolled": true
    },
@@ -334,43 +334,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.334809</td>\n",
+       "      <td>-1.759866</td>\n",
        "      <td>False</td>\n",
-       "      <td>2.523580</td>\n",
-       "      <td>0.070441</td>\n",
-       "      <td>14.196215</td>\n",
+       "      <td>-2.350318</td>\n",
+       "      <td>0.951203</td>\n",
+       "      <td>1.051300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.356694</td>\n",
+       "      <td>-3.705130</td>\n",
        "      <td>False</td>\n",
-       "      <td>-2.405052</td>\n",
-       "      <td>0.945274</td>\n",
-       "      <td>1.057894</td>\n",
+       "      <td>-7.813499</td>\n",
+       "      <td>0.998083</td>\n",
+       "      <td>1.001920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.751573</td>\n",
+       "      <td>-0.973429</td>\n",
        "      <td>False</td>\n",
-       "      <td>2.686315</td>\n",
-       "      <td>0.197265</td>\n",
-       "      <td>5.069321</td>\n",
+       "      <td>-1.255691</td>\n",
+       "      <td>0.837799</td>\n",
+       "      <td>1.193603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.645999</td>\n",
+       "      <td>0.218265</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.176300</td>\n",
-       "      <td>0.233166</td>\n",
-       "      <td>4.288799</td>\n",
+       "      <td>-0.253118</td>\n",
+       "      <td>0.408403</td>\n",
+       "      <td>2.448563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.560783</td>\n",
+       "      <td>-0.332033</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.851092</td>\n",
-       "      <td>0.265294</td>\n",
-       "      <td>3.769405</td>\n",
+       "      <td>-1.476009</td>\n",
+       "      <td>0.636166</td>\n",
+       "      <td>1.571916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -382,43 +382,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.299631</td>\n",
+       "      <td>0.166965</td>\n",
        "      <td>False</td>\n",
-       "      <td>4.398113</td>\n",
-       "      <td>0.075232</td>\n",
-       "      <td>13.292265</td>\n",
+       "      <td>-0.419433</td>\n",
+       "      <td>0.429489</td>\n",
+       "      <td>2.328350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>1.232811</td>\n",
+       "      <td>-0.992005</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.883010</td>\n",
-       "      <td>0.085162</td>\n",
-       "      <td>11.742291</td>\n",
+       "      <td>-3.588534</td>\n",
+       "      <td>0.842017</td>\n",
+       "      <td>1.187624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>1.060851</td>\n",
+       "      <td>-1.459873</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.086283</td>\n",
-       "      <td>0.116364</td>\n",
-       "      <td>8.593744</td>\n",
+       "      <td>-3.009158</td>\n",
+       "      <td>0.921539</td>\n",
+       "      <td>1.085141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.754843</td>\n",
+       "      <td>-1.169292</td>\n",
        "      <td>False</td>\n",
-       "      <td>5.241172</td>\n",
-       "      <td>0.031457</td>\n",
-       "      <td>31.789811</td>\n",
+       "      <td>-2.088625</td>\n",
+       "      <td>0.877902</td>\n",
+       "      <td>1.139079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>1.704140</td>\n",
+       "      <td>-1.273194</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.767435</td>\n",
-       "      <td>0.034726</td>\n",
-       "      <td>28.796507</td>\n",
+       "      <td>-5.106035</td>\n",
+       "      <td>0.895499</td>\n",
+       "      <td>1.116696</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -426,18 +426,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0     v0         y  propensity_score     weight\n",
-       "0    1.334809  False  2.523580          0.070441  14.196215\n",
-       "1   -1.356694  False -2.405052          0.945274   1.057894\n",
-       "2    0.751573  False  2.686315          0.197265   5.069321\n",
-       "3    0.645999  False  0.176300          0.233166   4.288799\n",
-       "4    0.560783  False  1.851092          0.265294   3.769405\n",
-       "..        ...    ...       ...               ...        ...\n",
-       "995  1.299631  False  4.398113          0.075232  13.292265\n",
-       "996  1.232811  False  3.883010          0.085162  11.742291\n",
-       "997  1.060851  False  3.086283          0.116364   8.593744\n",
-       "998  1.754843  False  5.241172          0.031457  31.789811\n",
-       "999  1.704140  False  3.767435          0.034726  28.796507\n",
+       "           W0     v0         y  propensity_score    weight\n",
+       "0   -1.759866  False -2.350318          0.951203  1.051300\n",
+       "1   -3.705130  False -7.813499          0.998083  1.001920\n",
+       "2   -0.973429  False -1.255691          0.837799  1.193603\n",
+       "3    0.218265  False -0.253118          0.408403  2.448563\n",
+       "4   -0.332033  False -1.476009          0.636166  1.571916\n",
+       "..        ...    ...       ...               ...       ...\n",
+       "995  0.166965  False -0.419433          0.429489  2.328350\n",
+       "996 -0.992005  False -3.588534          0.842017  1.187624\n",
+       "997 -1.459873  False -3.009158          0.921539  1.085141\n",
+       "998 -1.169292  False -2.088625          0.877902  1.139079\n",
+       "999 -1.273194  False -5.106035          0.895499  1.116696\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
@@ -456,10 +456,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.207751Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.207545Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.216194Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.215722Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.640650Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.640291Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.648663Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.648184Z"
     }
    },
    "outputs": [
@@ -494,43 +494,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.580815</td>\n",
+       "      <td>0.755504</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.397887</td>\n",
-       "      <td>0.955898</td>\n",
-       "      <td>1.046136</td>\n",
+       "      <td>6.365941</td>\n",
+       "      <td>0.782084</td>\n",
+       "      <td>1.278635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.360499</td>\n",
+       "      <td>0.479045</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.336263</td>\n",
-       "      <td>0.932877</td>\n",
-       "      <td>1.071952</td>\n",
+       "      <td>5.289250</td>\n",
+       "      <td>0.692317</td>\n",
+       "      <td>1.444425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.626459</td>\n",
+       "      <td>-0.380484</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.675073</td>\n",
-       "      <td>0.759713</td>\n",
-       "      <td>1.316286</td>\n",
+       "      <td>4.133301</td>\n",
+       "      <td>0.345114</td>\n",
+       "      <td>2.897595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.343496</td>\n",
+       "      <td>-1.065471</td>\n",
        "      <td>True</td>\n",
-       "      <td>11.734157</td>\n",
-       "      <td>0.990191</td>\n",
-       "      <td>1.009907</td>\n",
+       "      <td>4.594604</td>\n",
+       "      <td>0.142170</td>\n",
+       "      <td>7.033826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.436224</td>\n",
+       "      <td>0.479045</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.542532</td>\n",
-       "      <td>0.270430</td>\n",
-       "      <td>3.697812</td>\n",
+       "      <td>5.289250</td>\n",
+       "      <td>0.692317</td>\n",
+       "      <td>1.444425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -542,43 +542,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.936492</td>\n",
+       "      <td>-1.332082</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.103130</td>\n",
-       "      <td>0.977981</td>\n",
-       "      <td>1.022515</td>\n",
+       "      <td>4.402306</td>\n",
+       "      <td>0.095554</td>\n",
+       "      <td>10.465275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>0.479328</td>\n",
+       "      <td>-0.778026</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.944845</td>\n",
-       "      <td>0.701475</td>\n",
-       "      <td>1.425568</td>\n",
+       "      <td>3.073351</td>\n",
+       "      <td>0.212161</td>\n",
+       "      <td>4.713404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>2.136369</td>\n",
+       "      <td>-2.635462</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.453021</td>\n",
-       "      <td>0.985179</td>\n",
-       "      <td>1.015044</td>\n",
+       "      <td>-0.007357</td>\n",
+       "      <td>0.011558</td>\n",
+       "      <td>86.520595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.868190</td>\n",
+       "      <td>-0.625354</td>\n",
        "      <td>True</td>\n",
-       "      <td>8.101663</td>\n",
-       "      <td>0.974811</td>\n",
-       "      <td>1.025840</td>\n",
+       "      <td>4.370008</td>\n",
+       "      <td>0.258435</td>\n",
+       "      <td>3.869445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>0.537524</td>\n",
+       "      <td>-0.161104</td>\n",
        "      <td>True</td>\n",
-       "      <td>6.396701</td>\n",
-       "      <td>0.725461</td>\n",
-       "      <td>1.378433</td>\n",
+       "      <td>5.344751</td>\n",
+       "      <td>0.432882</td>\n",
+       "      <td>2.310096</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -586,18 +586,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0    v0          y  propensity_score    weight\n",
-       "0    1.580815  True   7.397887          0.955898  1.046136\n",
-       "1    1.360499  True   9.336263          0.932877  1.071952\n",
-       "2    0.626459  True   4.675073          0.759713  1.316286\n",
-       "3    2.343496  True  11.734157          0.990191  1.009907\n",
-       "4   -0.436224  True   4.542532          0.270430  3.697812\n",
-       "..        ...   ...        ...               ...       ...\n",
-       "995  1.936492  True   9.103130          0.977981  1.022515\n",
-       "996  0.479328  True   7.944845          0.701475  1.425568\n",
-       "997  2.136369  True   9.453021          0.985179  1.015044\n",
-       "998  1.868190  True   8.101663          0.974811  1.025840\n",
-       "999  0.537524  True   6.396701          0.725461  1.378433\n",
+       "           W0    v0         y  propensity_score     weight\n",
+       "0    0.755504  True  6.365941          0.782084   1.278635\n",
+       "1    0.479045  True  5.289250          0.692317   1.444425\n",
+       "2   -0.380484  True  4.133301          0.345114   2.897595\n",
+       "3   -1.065471  True  4.594604          0.142170   7.033826\n",
+       "4    0.479045  True  5.289250          0.692317   1.444425\n",
+       "..        ...   ...       ...               ...        ...\n",
+       "995 -1.332082  True  4.402306          0.095554  10.465275\n",
+       "996 -0.778026  True  3.073351          0.212161   4.713404\n",
+       "997 -2.635462  True -0.007357          0.011558  86.520595\n",
+       "998 -0.625354  True  4.370008          0.258435   3.869445\n",
+       "999 -0.161104  True  5.344751          0.432882   2.310096\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
@@ -624,21 +624,21 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.217993Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.217645Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.264148Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.263636Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.650402Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.650102Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.697364Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.696725Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 5.22322409908235$"
+       "$\\displaystyle 5.04346104281236$"
       ],
       "text/plain": [
-       "5.223224099082352"
+       "5.043461042812358"
       ]
      },
      "execution_count": 8,
@@ -655,21 +655,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.266121Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.265760Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.282025Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.281395Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.699397Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.699079Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.715106Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.714610Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 0.24237321967821$"
+       "$\\displaystyle 0.184546201364702$"
       ],
       "text/plain": [
-       "0.2423732196782096"
+       "0.18454620136470185"
       ]
      },
      "execution_count": 9,
@@ -693,10 +693,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.283965Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.283677Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.390023Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.389357Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.717339Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.716772Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.734654Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.734106Z"
     }
    },
    "outputs": [
@@ -706,31 +706,31 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.983</td> \n",
+       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.928</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.983</td> \n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.928</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.872e+04</td>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   6458.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
+       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:43:30</td>     <th>  Log-Likelihood:    </th>          <td> -1423.4</td> \n",
+       "  <th>Time:</th>                 <td>14:44:17</td>     <th>  Log-Likelihood:    </th>          <td> -1393.1</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2851.</td> \n",
+       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2790.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2861.</td> \n",
+       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2800.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -738,24 +738,24 @@
        "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>x1</th> <td>    2.6696</td> <td>    0.038</td> <td>   70.632</td> <td> 0.000</td> <td>    2.595</td> <td>    2.744</td>\n",
+       "  <th>x1</th> <td>    1.7154</td> <td>    0.022</td> <td>   78.441</td> <td> 0.000</td> <td>    1.672</td> <td>    1.758</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>x2</th> <td>    5.0716</td> <td>    0.060</td> <td>   84.656</td> <td> 0.000</td> <td>    4.954</td> <td>    5.189</td>\n",
+       "  <th>x2</th> <td>    4.9826</td> <td>    0.060</td> <td>   82.359</td> <td> 0.000</td> <td>    4.864</td> <td>    5.101</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td> 1.123</td> <th>  Durbin-Watson:     </th> <td>   2.013</td>\n",
+       "  <th>Omnibus:</th>       <td> 0.921</td> <th>  Durbin-Watson:     </th> <td>   2.067</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.570</td> <th>  Jarque-Bera (JB):  </th> <td>   1.034</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.631</td> <th>  Jarque-Bera (JB):  </th> <td>   0.805</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td> 0.075</td> <th>  Prob(JB):          </th> <td>   0.596</td>\n",
+       "  <th>Skew:</th>          <td> 0.058</td> <th>  Prob(JB):          </th> <td>   0.669</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 3.046</td> <th>  Cond. No.          </th> <td>    3.31</td>\n",
+       "  <th>Kurtosis:</th>      <td> 3.075</td> <th>  Cond. No.          </th> <td>    2.77</td>\n",
        "</tr>\n",
        "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
@@ -763,13 +763,13 @@
        "\\begin{center}\n",
        "\\begin{tabular}{lclc}\n",
        "\\toprule\n",
-       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared (uncentered):}      &     0.983   \\\\\n",
-       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.983   \\\\\n",
-       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          & 2.872e+04   \\\\\n",
+       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared (uncentered):}      &     0.928   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.928   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          &     6458.   \\\\\n",
        "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):}          &     0.00    \\\\\n",
-       "\\textbf{Time:}             &     14:43:30     & \\textbf{  Log-Likelihood:    }          &   -1423.4   \\\\\n",
-       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               }          &     2851.   \\\\\n",
-       "\\textbf{Df Residuals:}     &         998      & \\textbf{  BIC:               }          &     2861.   \\\\\n",
+       "\\textbf{Time:}             &     14:44:17     & \\textbf{  Log-Likelihood:    }          &   -1393.1   \\\\\n",
+       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               }          &     2790.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         998      & \\textbf{  BIC:               }          &     2800.   \\\\\n",
        "\\textbf{Df Model:}         &           2      & \\textbf{                     }          &             \\\\\n",
        "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     }          &             \\\\\n",
        "\\bottomrule\n",
@@ -777,15 +777,15 @@
        "\\begin{tabular}{lcccccc}\n",
        "            & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{x1} &       2.6696  &        0.038     &    70.632  &         0.000        &        2.595    &        2.744     \\\\\n",
-       "\\textbf{x2} &       5.0716  &        0.060     &    84.656  &         0.000        &        4.954    &        5.189     \\\\\n",
+       "\\textbf{x1} &       1.7154  &        0.022     &    78.441  &         0.000        &        1.672    &        1.758     \\\\\n",
+       "\\textbf{x2} &       4.9826  &        0.060     &    82.359  &         0.000        &        4.864    &        5.101     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       &  1.123 & \\textbf{  Durbin-Watson:     } &    2.013  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &  0.570 & \\textbf{  Jarque-Bera (JB):  } &    1.034  \\\\\n",
-       "\\textbf{Skew:}          &  0.075 & \\textbf{  Prob(JB):          } &    0.596  \\\\\n",
-       "\\textbf{Kurtosis:}      &  3.046 & \\textbf{  Cond. No.          } &     3.31  \\\\\n",
+       "\\textbf{Omnibus:}       &  0.921 & \\textbf{  Durbin-Watson:     } &    2.067  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.631 & \\textbf{  Jarque-Bera (JB):  } &    0.805  \\\\\n",
+       "\\textbf{Skew:}          &  0.058 & \\textbf{  Prob(JB):          } &    0.669  \\\\\n",
+       "\\textbf{Kurtosis:}      &  3.075 & \\textbf{  Cond. No.          } &     2.77  \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "%\\caption{OLS Regression Results}\n",
@@ -800,25 +800,25 @@
        "\"\"\"\n",
        "                                 OLS Regression Results                                \n",
        "=======================================================================================\n",
-       "Dep. Variable:                      y   R-squared (uncentered):                   0.983\n",
-       "Model:                            OLS   Adj. R-squared (uncentered):              0.983\n",
-       "Method:                 Least Squares   F-statistic:                          2.872e+04\n",
+       "Dep. Variable:                      y   R-squared (uncentered):                   0.928\n",
+       "Model:                            OLS   Adj. R-squared (uncentered):              0.928\n",
+       "Method:                 Least Squares   F-statistic:                              6458.\n",
        "Date:                Fri, 25 Oct 2024   Prob (F-statistic):                        0.00\n",
-       "Time:                        14:43:30   Log-Likelihood:                         -1423.4\n",
-       "No. Observations:                1000   AIC:                                      2851.\n",
-       "Df Residuals:                     998   BIC:                                      2861.\n",
+       "Time:                        14:44:17   Log-Likelihood:                         -1393.1\n",
+       "No. Observations:                1000   AIC:                                      2790.\n",
+       "Df Residuals:                     998   BIC:                                      2800.\n",
        "Df Model:                           2                                                  \n",
        "Covariance Type:            nonrobust                                                  \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "x1             2.6696      0.038     70.632      0.000       2.595       2.744\n",
-       "x2             5.0716      0.060     84.656      0.000       4.954       5.189\n",
+       "x1             1.7154      0.022     78.441      0.000       1.672       1.758\n",
+       "x2             4.9826      0.060     82.359      0.000       4.864       5.101\n",
        "==============================================================================\n",
-       "Omnibus:                        1.123   Durbin-Watson:                   2.013\n",
-       "Prob(Omnibus):                  0.570   Jarque-Bera (JB):                1.034\n",
-       "Skew:                           0.075   Prob(JB):                        0.596\n",
-       "Kurtosis:                       3.046   Cond. No.                         3.31\n",
+       "Omnibus:                        0.921   Durbin-Watson:                   2.067\n",
+       "Prob(Omnibus):                  0.631   Jarque-Bera (JB):                0.805\n",
+       "Skew:                           0.058   Prob(JB):                        0.669\n",
+       "Kurtosis:                       3.075   Cond. No.                         2.77\n",
        "==============================================================================\n",
        "\n",
        "Notes:\n",
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb
index 9968eea06..34e2daee9 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb
@@ -14,10 +14,10 @@
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@@ -1246,7 +1246,7 @@
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-      "Causal Estimate is 0.033971892284519356\n"
+      "Causal Estimate is 0.0339718922845158\n"
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@@ -1980,23 +1962,25 @@
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@@ -2011,15 +1995,15 @@
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-       "value": " 6/6 [00:00&lt;00:00, 602.08it/s]"
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@@ -2036,6 +2020,22 @@
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diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb
index 895f2ef69..ffed51bc6 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
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+     "iopub.execute_input": "2024-10-25T14:49:17.350583Z",
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@@ -61,10 +61,10 @@
    "execution_count": 2,
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-     "shell.execute_reply": "2024-10-25T14:48:23.526730Z"
+     "iopub.execute_input": "2024-10-25T14:49:18.889258Z",
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@@ -73,11 +73,11 @@
      "output_type": "stream",
      "text": [
       "   Treatment    Outcome        w0\n",
-      "0   6.173883  11.125651 -0.338081\n",
-      "1   1.376022   2.994333 -4.672328\n",
-      "2   2.204764   4.649972 -3.743316\n",
-      "3  10.198390  20.007447  3.995013\n",
-      "4   6.082778  12.156471  0.083552\n"
+      "0   3.141553   6.185347 -2.846096\n",
+      "1   2.853058   5.837480 -3.058273\n",
+      "2   8.414148  16.227897  2.123453\n",
+      "3   6.970236  13.863212  0.923817\n",
+      "4   2.492288   5.095843 -3.246356\n"
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@@ -96,16 +96,16 @@
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     {
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",
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3Zo8RQiA7O9v2/MMPP8S6deucmr+6utr2tclkQkVFBSIjIy857tixY/jzn/+M7777DnV1dU7NDaj/odeZYAMAtGrVyqUx9scDUiZCfXv37rV9bTKZMHbsWKfWAkgdOqxKSkqaPd6ZNYeFhTk8b2jNWsSAAxFJhABeegk4cED678iRvEtFRBSIamqA31vZOaVjR2D1aqBDBwYbfNyQxGjERYbiZHlNg3UcdABiI6UWmaSMCCfqnpSXlzsEAXbv3i37fOXl5ZcEHHbs2IHRo0fLCh7Uz35QWkhIiEfGiAbq1ti3Hq2srJRdr6K8vLzZY5qr4dGQhtasRazhQESStDTA2rN3xw7pORERBZ6NG107/vRpoF8/oF66L/meIL0Oc8YnAZCCC/asz+eMT0KQnjcklKLXN/9x7MKFC4qdz7pFwH7uCRMm2IINLVq0wEMPPYQvv/wS+/btQ1lZGWpqaiCk7oYQQmDOnDmKrUfLlPq51/+ZBxpmOBCRlN0we7bj92bPBkaPZpYDEVEgEQL44x9dG9OyJd8r/MjY/nF496FkzFuT41BAMjYyFHPGJ2Fs/7gmRpMa6ndb+Prrr/F///d/isz90Ucf2eowtGjRAhs3bnQoTtgQZ7ou+AP7n3tSUhL279/vvcX4MAYciEjKZsjMdPxeZqb0/TFjvLMmIiLyPKMRcLXyeXw8ICOFmbRrbP84jEqKxfb8Mpw+X4OYcGkbBTMbvCMsLAytW7dG5e+dYE6dOqXY3NZWm4DU9rG5YAMAW3FGf2dfV+H06dNeXIlv45YKokCQng4kJUn/rU8IqVBkQ2bPduzF3tQ8RETk+378EXA1/ffAAW7D80NBeh2u7t4Wt1/eCVd3b8tgg5ddc801tq+dabPorGPHjtm+HjJkSLPHCyHw66+/OjW3/XYRX6k3YM/+Z37mzBnk5uZ6cTXOq79Nx9s/ewYciPxd/WKQ9V90UlOBQ4caHpuZCXTpIgUYhAAmT5bmmTz50nmsGJQgIvJNTQWgm6LTAS+/3Pj7AhG57eabb7Z9vWrVKoeChu6ora116fiUlBQUFRU5dWzr1q1tX9t3y/AVgwcPRtu2bW3PV6xY4cXVOM/+5w54/2fPgAORv6tfDHLJkosBASGa77NeWAjMnAmkpFwMTBw6JAUq6msouMEABBGRb0hNBfLyXB8nhPReYTIpvyYiAgBMnDgR0dFSd5ALFy5g8uTJiszbsWNH29c//fRTk8dWVVXhhRdecHruuLiL9T4ONXZzS8OCg4Md/r5vvPEGsrKyvLgi59j/3AHv/+wZcCDyZ0JId52CgqTnQUHAwoUXAwLV1VLf9OZkZgLjxjl+b/r0S+9m1Q9upKY2nV1BRETaIIT0uu6KAQOA9euBnTul13y2xCRSTXh4OBYtWmR7/tVXX+G+++7D2bNnmx2bmZmJRx55BJ9//vklfzZixAjb1//973+xdu3aBucoLS3Frbfeit9++83pNV9xxRW2r/fu3Su7raQ3TZs2DT179gQgZQqMHj260Z+RvfLycrz33nsYPXq02ku8RFxcnEPQ4fXXX3doq+ppbhWNNBqN2LJlCzZt2oSsrCzk5OSgpKQERqMRkZGRiI+Px9ChQzFhwgSMGjUKOhkVjIuKirBy5UqsXr0aR48exZkzZ9CuXTt07doVt912Gx566CF06tTJnb8Gkf9JT5fSYh9++GIAAADMZqCqSvp6xw7g2Wfln8Oa5TB2rPTcPrhhNkvfe/LJiwENa6tNFqEkItIeOcUi9+2TuhlZg9pEpKpnnnkGu3btwvLlywFIQYd169bh3nvvxXXXXYdOnTrBYDCgvLwcBQUF2L17NzZu3IijR48CcAwuWD399NNYunQpKisrYbFYcPvtt+Phhx/G+PHj0aFDB5w9exY///wzPvzwQ5SWliIiIgK33HILvvjii2bXO2LECHTq1AlFRUUQQmDs2LHo27cvunbtihC7QrPTpk1rcG1aEB4ejlWrVmHYsGE4e/YsSktLMX78eAwePBi33347Bg4ciKioKNTU1KC0tBT79+/Htm3bsGnTJphMJnTp0sUr637kkUewdOlSAMBnn32GDRs2YODAgQgPD7cd079/fyxcuFD9xQgZTp48Ke677z4RHh4uADj16Nevn9i6datL53n33XdFWFhYk/O2bt1avPfee3L+Gjbl5eUCgCgvL3drHiKP2LhRiL59pf829D2LRYjBg4UAhGjVSgi9Xvq6oYdO1/ifOfPo1OnieVNSGj7Gev6gIGldaWmXrp+IiLyrsdfw5h4nT3psib50vVZdXS1ycnJEdXW1t5dCHjR8+HDbZ5QuXbo0epz9Z5kff/zRpXNYLBaxcOFCodfrnf4cZn189NFHDc75zTffiODg4GbHh4WFifXr14s5c+bYvjd8+PAm15uent7s57n663Jlfiv7n/2cOXOcGuPK/4fc3FzRr18/l3/mjf07+PHHHx2Oc5azaz5//ry48sorm1ybsz/bhrjyGidrS0VhYSG+/PLLS3qwxsXFYfDgwRgxYgSSkpIcKmTu378f1157Lb799lunzjF//nw8++yzuHDhgu17PXv2xPDhw9G9e3fb9yorKzFp0iTPRGeIvG3jRmD8eMctCvXrJixZcjGroaqq6Wrj7m5xKCqSzjtzZuOFxqznN5uldU2Zwi0WRERaIoTUlUiObduUXQsRNUmn02HWrFnYt28fHnzwQbRq1arJ46OionD33Xfjm2++wQMPPNDgMRMmTEB6ejr69+/f4J8HBQVh9OjRyMrKcihe6YyRI0ciOzsbM2fOxNVXX4127dqhRYsWLs2hBT169EBWVhbee+899OnTp8ljdTodLr/8cvz1r39FupdqmLVu3RpbtmzBihUrcOutt6JLly5o1aqVrB0H7tL9HilxSWZmJgYPHgwAuOqqq/DYY49hzJgx6Nq1q8NxJ0+exCuvvIK3337b1o4jJCQEe/fuRe/evRudf9WqVbjjjjtsz5OSkvDZZ58hOTnZYQ2PPPIIDhw44DDutttuc/Wvg4qKCkRGRqK8vBwREREujyfyCCGAPn0cO0pYeydbtzUA0h5ao9Gza3OWTucYZEhJ4RYLIiJvMxqBmBigosL1scnJUp0fD1zE+tL1Wk1NDfLz85GYmIjQ0FBvL4f8mMlkwrZt25CXl4czZ86gtrYWrVu3RqdOndCnTx/07dv3kjaJjRFCICsrC5mZmSgtLUV4eDji4uJw7bXXIjY2VuW/iW8pLCzE1q1bcfr0aZw7dw4GgwFRUVHo0aMHBgwYYCvw6a9ceY2TFXDIysrC/Pnz8de//tUhCNCYN998E9Ps7n7edddd+O9//9vgsbW1tUhKSkLe71WS4+PjsXfvXkRFRV1ybFlZGQYOHGhrzdKzZ0/k5OQgONi10hS+9AZGAcZai+GNN4C6OsA+qqzXSxd6ALBr18W6Cb7kyiuB7ds9cqFKRESNEEJqgVxYKG+8h4LHvnS9xoADEfkzV17jZG2pSE5Oxv/+9z+ngg0AMHXqVAwZMsT2fN26daiyFq6r58svv7QFGwCpqmZDwQYAiI6Oxuuvv257npubiy+//NKpNRFpnv1WiYa2LFgs0l2lzEzfDDYA0tq7dGHLTCIibzIa5QcbAGk7BrfIERFRAzzWFvP222+3fV1TU2Orllrf119/bfu6Y8eOuPPOO5ucd8KECQ5tP/7zn/+4t1AirbBvMZmZCeTmenc9aikslAIqQkiBh6QkBiCIiDzphx/cG19YCJhMyqyFiIj8iscCDvX3sVQ0sE+wuroaGzdutD0fO3Zss9sjgoODMdZu/3paWhpqamrcXC2Rl1g/cG/cKL+Aly/KzAQWL3Ysfsm7ZURE6hNCamEsxzvvAOvXS6/hBoOy6yIiIr/gsYBD/YyGmJiYS445cOAAjHbF7oYNG+bU3PbH1dTUOBSSJPIZ9lsopkyRLuACyV//ejGjY8cOKcODiIjUVVMDFBe7Pu7BB4Fnn5VqC8XHK78uIiLyCx4JOAghHIpExsXFITEx8ZLj9u/f7/C8Z8+eTs1f/7icnBwZqyTyMvstFPadKAJF/ToUEydyewURkdrsamG55Lvvmm67TEREBA8FHD7//HMcPnzY9vzBBx9ssAdo/SyIzp07OzV/ly5dHJ7n5+e7vkgibxICePllICjI2yvRjqIibq8gIlKTxQIsWiRvbE0NUFmp7HqIiMjvqB5wOH78OKZPn2573qZNG8ycObPBY+vXdWjTpo1T54iMjHR4fv78+SaPNxqNqKiocHgQeZU1u8FXu02oidsriIjUsW4d0EjXsEZFRgKffQZs3QpovDUlERF5n6oBh6qqKkyYMAGlpaW27y1fvvySApJWlfUi5S1btnTqPPWPay7gsHjxYkRGRtoeCQkJTp2HSFH2BSKZ3dA4vV76+TDLgYhIOUIACxYADWScNqm8HLjrLmDwYHXWRUREfkW1gENdXR3uu+8+7LDuSQcwefJk3HPPPY2Oqa2tdXjeXIeKxo6rP099M2fORHl5ue1R6E7vaSI56heIZHZD4ywWZjkQESnNZAIKCuQFc5u5ziIiIrJSJeBgsVjw8MMPY82aNbbv3XPPPVi2bFmT48LCwhyeO9vesv5x9eepz2AwICIiwuFB5FH1C0S6eocpEE2cCPTtyyKSRERKMBik96FbbnFtXHw8EB6uzpqIiMjvKB5wsFgseOyxx/Dll1/avnfXXXfh3//+N4KaSRlv3bq1w/MqJ/cV1j8unG+EpGVCANOmXfo9alpREXDwIItIEhEppWNHqY6DK4qLAbsW5kRERE1RNOBgsVgwceJEfPbZZ7bv3Xnnnfjyyy+d2h7Rvn17h+fFTvaFrn9cu3btnBpH5BWpqYHZ9lIpO3YAXbow04GIyF0TJ7o+JiyMWXlEROQ0xQIOFosFTz75JD7++GPb9+644w589dVXTtdi6NOnj8PzY8eOOTWu/nF9+/Z1ahyRxwkB2HVtIZkKC4HJk6Wimww8EBG5zmwGPvnE9XFdugAhIcqvh4iI/JIiAQdrsOGjjz6yfe+OO+7A119/jRYtWjg9T79+/RyeZ2VlOTWu/nFJSUlOn5NIddZuFOnpzG5Q0qFDUtHNyZO5xYKIyFULFsgbV1IiFZwkIiJygtsBB6WCDQCQkJCA7t27255v3rzZqXH2x/Xo0QPx8fEunZdINfbdKGbOBKZO9faK/M+hQ8Dixd5eBRGR77BYgL/9zfVxnTsD27dLBSeJiIic4FbAoaFgw5133ikr2GA1YcIE29ebNm1CQUFBk8cXFBQ4BBzsxxN5nX03isxMIC/Pu+vxV7NmST9r+2wSIiJqWGUlUF3t+riCAmD/fuXXQ0REfkt2wEEIgaeeesoh2DBhwgR89dVXsoMNAPD444/bullYLBYsaCblb/78+bBYLACAoKAgPP7447LPTaQoIYCXXwas3Vn0qnShJavHH5eySA4cYCcLIqKmREQATz8tb+zs2Xx9JSIip8n6BCSEwDPPPIMPP/zQ9r27777b7WADIBV8fPTRR23PP/jgA3zwwQcNHrt8+XKsWLHC9vyxxx67pPAkkddYsxvMZun574ExUsmJE1IWCSD93NPSvLseIiKtslgAuxtGLjl+nDUciIjIac61j6jnP//5D95//33bc51Oh7Nnz+LWW291eo4//vGPGDVqVIN/tnTpUmzevBmHDx8GADz11FNYs2YN7rvvPnTs2BFFRUX44osvsHbtWtuYHj16YMmSJXL+OkTK27gRuOMOKauBgQbPCwqSsktGj2b7NiKi+taulRc0iIwEtmxhDQciInKarIBDVVWVw3MhBL7//nuX5rjvvvsa/bN27dphw4YNGDNmDPLz8wEAq1evxurVqxs8PjExERs2bEC7du1cWgORKoQApkwBamq8vZLAZTZfzHIYM8bbqyEi0g4hgEmTXBvTrh3w978DI0YALMxNREQu0Oym8p49e2Lv3r2YNm0aIiIiGjwmMjIS06ZNw969e9GjRw8Pr5CoEWx9qQ3WLAfuNSYiuqimBigudm1MVRVw770MNhARkct0Qmj/arympgabN2/G0aNHUVpairZt26Jr16644YYbYFAgra+iogKRkZEoLy9vNLhB1KT0dGDaNGDZMqlw4c6d3l4RWaWkMMuBiMhq/nxgzhzXxoSGAhcueL34sS9dr9XU1CA/Px+JiYkIDQ319nKIiBTlymucTwQc1OZLb2CkQUIAQ4dKKfy9ejG7QUt0OuDKK4Ft21jLgYjIYgFCQi4WM3aWXg+cPSt1t/AiX7peY8CBiPyZK69xsmo4EJEdazcKQAo26HT+ncY/fz7QrZv0dVgYcPfdrl+8eooQUt94k4lFzoiIXnnF9dfrJ56Qaj5o/AM+ERFpEwMORO4QQqoTEBR08SLOn4MNQUHAmjUXMwaMRiAqCjhzxtsra1xQEPDzz8BNN3l7JURE3mOxAAsWuD5u924pU4yImpSdnY1169Zh48aNKCgowOnTp1FTU4P27dujQ4cOuOqqq3DzzTdj5MiRzHqhgKLZopFEPsGa3aDVO/xKs+/+AEhZA1lZQEYGEB3t3bU15sQJqa6GPweCiIias3YtUFvr+rjCQnktNIkCxN69ezFu3DgMGDAAM2bMwPfff4/c3FyUl5fDaDTi+PHj2LlzJ95++23ceuut6NmzJz766CNYNNA2/eOPP4ZOp4NOp0PXrl29vRzyUww4EMlln93gL958E8jMBJKSGq95oNc7dn9ISACuukoKPGg1Yp+ZeTFIQkQUaIQA/vAHeWOXLuWWNKJGvPPOO0hOTsaGDRscvh8XF4fBgwdj+PDh6N27N1q0aGH7s+PHj+OJJ57A6NGjUV5e7uklE3kcAw5EcvlCdkP79sBnnwErVwLORK4/+QTo1w8oLW08I8BiafiOV1SUtu+CzZoFbNwoBVPS0729GiIizzEagSNH5I195x1miBE1YPbs2Zg8eTLMv18HBgUFYcqUKdi7dy9OnDiB7du3Y9OmTTh48CBOnz6NDz/8EAkJCbbx33//Pa699lqUlJR4669A5BGs4UAkhzW7QesFIs+dk4o66nTO3d0qLJSO3bEDaOoNMCbm0jteERHAr78CeXmO38/LAw4fBg4ckDINvGXnTuD++6VgyuTJwMGD7FxBRIFBp5My0KqrXR97+DAL7xLV8/XXX+OVV16xPW/fvj3Wr1+PKxupd9KmTRs8/vjjuOeee/Doo4/im2++ASDVfXjkkUewfv166HhNQn6KAQciOTZskD7AajnYAABt2kgXmgaD9GF/3z6ptVlDoqOBAQOkYxMSpIerhg6VHvUJAVxxhevzKa20VPrvoUNAaiowdqx310NE5AkhIUB4uLyAQ0KCNJ6IAEhbIp566inb8/DwcPz444/o169fs2PDwsLw5Zdf4vbbb8f69esBACkpKVi2bBmef/55tZZM5FXcUkHkKiGAF16QthZoyfz5UhDE/pGVdfGuVEICMG4c8OCDDT9uvhmIj1dnbWlpwK5d6swt1/Tp2g8YEREpoaYGOH1a3thTp7S9XY7Iw1577TVUVFTYni9dutSpYINVcHAwVqxYgTZt2ti+t2TJEtTU1DR4/Ny5c22FHW+44QanzrFp0ybbmIYyJ2644QbodDo8/vjjtu8dO3bMYYz947HHHmvyfCaTCf/+97/x6KOPom/fvmjbti1atGiByMhIDBw4EA8//DA+/vhjp2tWZGRk4IUXXsDll1+O9u3bIyQkBLGxsRgyZAhmzZqFnJwct34Op06dwpIlSzB06FB06NABISEhiI+PxwMPPIAd1lb39ezbtw/PPvss+vTpg1atWiEsLAx9+/bFH//4R5w8edKp9dirq6vDV199hUceeQR9+vRBdHQ0DAYDOnXqhJEjR+Jvf/sbysrKXJ5XkwSJ8vJyAUCUl5d7eynkCzZsEEL6qKqdR1CQEIMHC2GxePuncymLRVqbXu/9n1P9x+OPC9G3rxAbN3r7p0REpJ6FC+W9RiYmClFQ4O3V2/jS9Vp1dbXIyckR1dXV3l4KKejs2bOidevWAoAAILp37y4sMq+95s+fb5sHgPjXv/7V4HFz5syxHTN8+HCn5v7xxx8d5q5v+PDhDn/e3OPRRx9t9Fyff/65SEhIcGqeli1bihMnTjQ6V0lJibj99tubnScoKEg8/fTTzf5+NfRzWL16tYiOjm50bp1OJ5YvX26bw2KxiJkzZwq9Xt/omMjISPHzzz8383/lopSUFNGzZ89m/55t2rQRK1ascHpeT3LlNY5bKohcIQQwbZq3V3Ep+3aVY8Z4ezWOTCagoEB7GSEA8NFH0n9fegkYOZI1HYjI/1gswKJF8sZWVEg1e4gIAJCWlobKykrb84kTJ8quvfDEE09g3rx5tqKT3333ncNWDTUNGTIEoaGhKCoqQnZ2NgAgNDQUw4cPb/D4AQMGNPj9P//5z3jttdccvhcaGmq7Y3/hwgUcPnwYZ86cAQBUV1fDaDQ2ONfx48cxcuRIHDp0yPY9vV6Pfv36oV27diguLsbBgwcBAGazGf/617+Qk5OD9evXIzw83Km/d3p6Ou68806YzWYEBwdjwIABaNOmDQoLC5H3ew0yIQQmTZqEuLg4jB8/HlOnTsXbb78NQNo+k5SUhNDQUBw8eBCnTp0CAJSXl+OWW27BgQMH0LFjxybX8O6772Lq1Km2/+8AEB0djV69eqFly5Y4fvw4cnNzAQDnzp3DxIkTUVxcjFmzZjn1d9Qk9eMf2udLEXPyMi1mN1gfer12sxwKCoTYuVOIjAwhoqO9/7Nq6JGS4u2fEhGR8uRmNwBCfPiht1fvwJeu1/wmw2HjRmYC2pk8ebLDHehdu3a5Nd/AgQNtc0VERAiz2XzJMWpkOFh99NFHtmO6dOni0tpff/11h3PEx8eLTz75RFy4cOGSY3/77TexYMEC0bFjR5Gfn3/Jn5vNZnHdddc5zPfYY49dkg2Rm5srbr75ZofjHnnkkUbXWP/n0LZtW6HT6cSMGTPE2bNnHY795ZdfRIcOHWzH9uvXT3z++ee2TIMPP/xQmEwm2/EWi0W89957IigoyDbmiSeeaPJntn79eqHT6WzHX3PNNWLz5s2XZMkcOnRI3HrrrbbjdDqdSE1NbXJuT3PlNY4BB+Fbb2DkRRaLED17eufDcOvWQmze3PyH9dhYIWpqvP2Tapo1+GB9ZGYK0aOHd4MNWt6SQkQkl9ksRKtW8l8br7hCU6+LvnS95hcBB+uWSIDvkb8bPHiw7UNgaGioqK2tdWu+iRMnOnwgzsnJueQYLQYcDh06JIKDg21jBwwYIE6ePNnsOKPRKIxG4yXff//99x3W+6c//anROcxms7jrrrscjv/hhx8aPLb+zwGAeO+99xqdOz093eHYkJAQ0bJlS5GVldXomJkzZ9qODwsLazDgIoQQ58+fF+3bt7cd+8ADD4i6urpG57VYLOLBBx90CIBoiSuvcSwaSeSM9HSgQwfg9xQnj+veHbjuOmD37ksLQ9o/duzQfuuyhAQgOfnio39/4Px5767JfksKEZG/qKyUCkbKVVTEgpGBLC1Nem8E+B75u9N2xVfj4+MRHOze7vQuXbo0Or+WLV26FHV1dQCAli1b4r///S86dOjQ7LiQkBCENND1ZtmyZbavk5KSsHjx4kbn0Ov1eP/999G2bVvb9/75z386te4RI0bgmWeeafTPR44ciZ49e9qem0wmzJw5E4MGDWp0zHPPPWf7+sKFC9i9e3eDx33wwQco+b3lfGJiIlasWIGgoKBG59XpdHjnnXdsxUX379+PH374odHjtYwBB6LmCAHMmAH8/iLhFdYq4fU/rNd/qNVlQk0Gg3Qh8+uvQBMvvKoLCgJefln6/01E5A8iIoAtW4B27Vwb17YtkJHhG0FsUocQ0nui9X2Z75EA4NA1wL7LhFz15/CFrgTW7gpWDz30EHr16iV7vtzcXFsdCQCYNm1as4GcqKgoPPHEE7bnGzZsaLTLh72nn3662WOuuuoql8bEx8cj3u76u7EOGh9//LHt6ylTpiA0NLTZtURERODOO++0PU9PT292jBYx4EDUnLQ0KXvAG/7xD9/JXHCHNZASFeW9NTDLgYj80blzwO8F25wSEiK1Mb7qKt8MYpMyrNkN1sJ2fI8EAIcPtQYFrsvqf+isrq52e061ZWVlORTOvPfee92aLyMjw+H5+PHjnRp3++23276ura1ttJ2lvauvvrrZY2JjY21fJyYmOpW5ERcXZ/v67Nmzl/z5uXPnsG/fPtvzUaNGNTun1WWXXWb7OjMz0+lxWsIuFURNEQKwi6B63MqVwPTpgdE9wWAAsrIcM0mEAB55BDh40HNdLmbPBkaPDoyfORH5NyGAmTNdG2MyeTf4S95nn91gV0nfluUQwO+RUVFRtm0P5eXlbs9Xf47o6Gi351Rb/Tv4gwcPdmu+XLvtyu3atWu2y4PVwIEDHZ7n5eXhuuuua3KMfTChMa1atXLp+PpjqqqqLvnzffv2wWJ3Hfv888+jRYsWTs1dVFRk+7rEm9nWbmDAgagpixcDJ0547/w7d2qz1aVaEhKkh1VqKtBIappqsrKADRuAceM8e14iIqWZTMBvv7k+7n//Ax56SPHlkI+wr91gT8stuD0kOjraFnAoLS11e776c/hCwMF+24fBYEBERIRb89lnBLRv397pceHh4TAYDLY2mw1lFtTXUP0IJY8HANHAtqP6/5/l1mJQIsjlDdxSQdQYiwWYM8e7a9DrA3fPpPUOi97DL1MWC/DCC4H5Myci/9KiheMdame98QZfAwNVc++9gXxdAqB79+62r4uLi90u8mhfYFCn06Fbt25uzecJSm8rsQYMANc/4Nuf35kaDt5y4cIFReaxeCrbV2EMOBA1ZtEi4PcKvF5jsQCFhYFZJdxkAgoKPLeVwt6hQ8CSJZ4/LxGRktavl/f+cexYYL7vUPPvvYF8XQJckrJfv/6AKywWi8Oe/L59+6KdqwVeG2GWE2h0UpTdlqvz58+7/SHYvnDmeRe6lgkhHGpJKFHEUy3113b69GkIIVx+HD161Cvrdxe3VBA1xJPZDe+/D7RsCURHS60364uJ8e+CkY2xdq8oKZFauw0f7tnzv/QScMUV0l5VIiJfIwQwd67r43Q64McfA/N9hxzfexsTqNclkNoq2lu5cqVD8UJXbNiwAWfsCrreeOONDR5nf9e/trbWqbmd2V4gl32BRCEEcnNz0bt3b9nzxcTE2L4uLCyEyWRyKtMhPz/fIdhhP4/W1K8FcerUKZe2j/g6BhyIGrJ6tWfurAcFSftknWiNE5Dsazps3Qp8+y3w6queO//jjwPHjwdscSwi8mEmk1Rw11V9+gB9+yq/HvId9espkc3gwYMxaNAg7Nq1CwCwatUq5OfnIzEx0eW5li1b5vD8mWeeafA4+xoJzrbNtO+I0BS93daZhmoPNOSaa65xeP7jjz+6FXC44oorbF/X1tYiMzPzknM05Ndff3V4fuWVV8peg9oGDhyIsLAw29aKjIwM9O/f38ur8hxuqSCqTwjggQc8cy6zGdi82TPn8nVDhkh33aw9wT3hxAkgJcVz5yMiUkpIiLx99mfPBmy6PJEz/vKXv9i+rq2txdNPP+3yHF988QU2btxoez527FgMGDCgwWO7dOli+zovL6/BLgj1fffdd06to3Xr1ravnW3JGRMTg0GDBtmev/fee04HKxoydOhQtGzZ0vb8s88+c2rcJ598Yvu6S5cu6Nq1q+w1qK1Fixa46aabbM8//PBDL67G8xhwIKpv3TrAU32QA7z4kkvq9wT3lHvu4f8fIvI969cDTnwwcdC5M7B9e8CmyxM545577sGoUaNsz9PT0zF16lSnx//yyy8OQYrWrVtfku1gzz4DoK6uDt9++22T869cudLpDAf77RElJSU4d+6cU+NeeOEF29d79uzBa6+95tS4hoSFheEBuxt9K1aswP79+5scs2HDBqSnp9ueN5YdoiV//vOfbV9v3boV7777rhdX41kMOBDZEwK4/37PnS/Aiy85zVsdKwCpfkT79oDdGxsRkaYJATz8sOvjCgqAZi70iQKdXq/HypUrHT6sv/XWW5gwYQKKiooaHWexWPDWW29h7NixDsUO3333XfTq1avRcR07dnTYYvDSSy81ep7169dj0qRJTv9dBg4ciBYtWtie/+1vf3MqW+H+++/H4MGDbc9ffPFFLF26tMlildXV1Xj77bcb7Ozxl7/8xZblUFtbi1tuuQW5ubkNzpORkYH77a7VY2NjXfo7e8uwYcNw33332Z5PnToVS5cuRV0zBepra2uxZs0a3HjjjTh27Jjay1QFazgQ2auqkj5gqqVtW2DtWinV1SqAiy85zZsdKwCgtBR49lmpewXrORCR1m3YIG2NkGP2bGDMGL7WETUhJiYGmzZtwrhx43D48GEA0jaGlJQUjBs3DmPGjEFCQgJatmyJkydPYufOnfjvf/+L/Px82xxBQUF499138dBDDzV7vpkzZ2L8+PEApMKKgwYNwrRp0zB06FC0aNEC+fn5+Pbbb7F27VoAwGOPPYaPP/642XnDwsIwYcIEfPXVVwCARYsW4eOPP0a/fv3QqlUr23EjRozAtGnTbM+Dg4Px9ddfY8iQISgpKYEQAjNmzMCHH36I+++/H1dccQWioqJw4cIF5OXlYcuWLVi7di3Onz+PW2655ZJ19OrVC6+//jqeffZZAMCxY8dw2WWX4fHHH8dNN92Etm3b4uTJk1i7di0+//xzW2AjKCgIH3/8sUPnDC1bsWIFDh06hKysLJjNZsyYMQPvvPMO7rnnHgwdOhTt27eHEALnzp3DoUOHsHPnTqSlpdkyT9zZuuJVgkR5ebkAIMrLy729FPK2m24SQro3pOzjf/8TYudOIQoLvf039F0FBdLP0PrIzBQiKUkInU6d/2cNPRIShNi40ds/CSKixlksQlx2mfzXudhYIWpqvP23aJAvXa9VV1eLnJwcUV1d7e2lkIpOnz4t7rjjDgHApUdiYqJISUlx6VxTpkxxau65c+eKH3/80eF7TSkqKhLdunVrcs5HH320wbGHDx8Wffv2denvnp+f3+ha/vnPfwqdTufUPKGhoeK7775r8u/mys/Bas6cObbjhw8f7tSY4cOH28bMmTOnyWPPnz8v7rrrLpf/zTT3s/M0V17juKWCyColRb20+dBQIDkZiI9XZ/5AkJAg/Qytj/79pcwDT0Z7CwuBmTM9e04iIlekpQF79rg+7u23gZ07pVo5zLojckr79u3x3XffYcuWLbjzzjsdijDWp9PpkJycjGXLluHgwYMYM2aMS+d688038c9//hNt2rRp8M979eqF1atXY46Lbd07duyI3bt345///CdGjRqFTp06ORRxbEq3bt2QlZWF119/HZ07d27y2B49emDu3LmXtIi0N336dOzYsQOjR4926KBhLyQkBPfeey+ys7Nxxx13OLVOLWndujX++9//YsOGDbjhhhsQ1Ewx9K5du+LZZ5/FL7/8ounCmE3RCcEr54qKCkRGRqK8vNyh9QwFgPR0YNo0YNky4N575aegNkWnA668Eti2jSmqSissdOwVXlAA3HWX+lsvUlKklGMiIi0RAhg6VAoauOrcOSAyUvElKcmXrtdqamps7RJD2fo6YJhMJmzbtg0FBQU4ffo0jEYj2rVrhw4dOmDIkCHo0KGD2+cwGo3YvHkzfvvtN1RWVqJDhw7o378/hgwZosDfwD05OTnIyspCSUkJLly4gPDwcHTu3BmXX365y61DS0tLsXnzZpw4cQIVFRWIiopC586dMXz48CYDO76mvLwcW7ZswfHjx1FaWgqdTofIyEh07doVSUlJDl1KtMSV1zgGHOBbb2CkIPsLs7g4oLhYvXPFxgJHj/KukdqMRimL5MwZdc/Tq5fU354BJCLSEqNR6jTRQFG2JoWFSRljGn+P8qXrNQYciMifufIaxy0VFLisbRYBdYMNb77JFFVPMRiAjAxA7cj3oUNAaqq65yAicpXBAPz8s+vj2rZ1LGZMRESkEAYcKDBZ2yw2s2/KbXo98OmnQKdO6p6HLureHXAxbU+WadNYy4GItOfAAdfHHD8uZUcQEREpjAEHCkzW7IYm+gUrwmKR6gyYTOqehy4ymVxPJ5YjN5dZDkSkLUIAzz3n+rjoaG4RIyIiVTDgQIHHE9kNv/4qVftmxW/PMxiAX36RLqDVduutwMaN6p+HiMgZNTXAiRPOH6/TAWvXArt28X2KiIhUEeztBRB5nH3tBjUEBQGDBkmtMMk7evQAdu+WMh0mTJC6V6jBbAZefBG46SbeHSQi73M1u0EIYNgwoJE2e0RERO5ihgMFFiGAWbPUPYfZDGzerO45qHkJCUC/fq7d7ZNj1y4piEVE5E1mM/DJJ66PY7CUiIhUxIADBZYNG6RtDmrS66UtGywo6H2bNwN1deqfZ9Ys/v8mIu+aOFHe69DWrcqvhYiI6HcMOFDgEAKYPl3981gs0pYN3vX2LmutDr0HXuZ27uT/byLyHrNZ6ogkBwOmRESkIgYcKHCkpgJ5eZ45F7McvM9kkmo3WCyeOR/bZBKRt7zyivzXH3ZSIiIiFbFoJAUGIYCpUz13Pvt2mKz87R0Gg5RpUlJy8XtCAOPGqdM289AhKag1dqzycxMRNcZiAZYulTe2a1fgp5/4PkVERKphwIECg5rZDTod0LevlM5qX3wrJoYXcd6WkCA9rGpqgLIy9c43bRrw228swkZEnrN+PVBVJW9sVZX0XkVERKQSBhzI/23cCIwfr978QgA5OcCZM8CYMeqdh9yn00nt386cUWf+/HzAaGRLVCLyDCGAefPkjX3zTeCOOxgYV5ngVjsi8kOuvLaxhgP5r/R0oEsXKYVe7U4FrNngGwwGICtLKvK4di3QqZOy89fVsSUqEXmOySQve69bN2DyZCA+Xvk1EQBA/3vBYoun6ggREXmQ9bVN70RxdgYcyD8JAcycKRUN9ERbRPuaDaRtCQlAcjIQHAwUFSk//+zZDDwRkWeEhDhuG3NWZSXfr1QWFBQEAKitrfXySoiIlGd9bbO+1jWFWyrIP6WlAZmZ6s0fFSXtmw0Jufg91mzwHUJIgQE1ZGZK//64vYaI1GYyAadOuT6OhSJVFxQUhNDQUFy4cAFt2rTx9nKIiBRVWVmJ0NBQpwIOzHAg/6Pmh0kAaNtW6n5w1VXSnXLrg6mpvsPaMlMtzHIgIk8wGIBHH3V93KefKr8WukTr1q1RWVnJOg5E5FeEELhw4QJat27t1PEMOJD/UTO7oU8fYNcuoHt3deYnzzAYpH8j69YBf/iD8vNnZQEbNig/LxGRPYsFePtt18e9/ro0llQVHh4Oi8WCEvv2zEREPq6kpAQWiwXh4eFOHc+AA/kXtbMbysrYQsxfJCQAN98spRYrzWIB5sxhlgMRqUtuS0yjUarjQKoKDQ1FTEwMSktLUVZWxkwHIvJpQgiUlZWhtLQUMTExCHWyKxtrOJB/USu7YdAg4IUXgBtv5L5Xf6Lm1grWciAiNbnTEvNvfwMiIpRdDzUoOjoatbW1OHXqFMrKyhAZGYmWLVsiKCgIOp3O28sjImqSEAJmsxnV1dUoLy9HbW0toqKiEB0d7fQcOsFwKyoqKhAZGYny8nJE8A3YdwkBDBmiTsChZUvpbpATrV/IxxQWAvv2AWfPSs9nzwaOHlVm7p49gd9+A3hRSURKS00Fxo6VN7ZXL+DgQZ97bfLV6zUhBGpqanDu3DlUVFSwVSYR+Ry9Xo+IiAi0adMGoaGhLgVMGXCA776BUT1Go5Qmr9ZeydWrgfHj1ZmbtMFoBDp3Bk6fVm7ODRvkfyggImqIuwH2Fi2AigrAyXRYrfCH6zWLxQKz2Qyz2eztpRAROSUoKAhBQUHQy7zxyoAD/OMNjH6XlwcMHgycO6fsvDodcOWVwLZtPndHiFy0ciXw8MPKzccsByJSmtEIxMbKf69r317K7vKxLYK8XiMi8j3MDyf/0qOHFBRQmhDSxZnJpPzcpB1CAMuWKTtnbi6wZImycxJRYAsJAdq0kTe2d29g506fCzYQEZFvYsCB/M8XXyg3V9u2QEaGdHG2Ywcv0PydWkVH581jCzoiUk5qqvxaM2fPstsSERF5DLtUkH9ZsgSYO9e9Oa68Epg/H+jQQbooi49XZGmkcUIAL78MBAUBSu+tNRqBtWuB225Tdl4iCjxCAFOnyh+/ZAmD50RE5DGs4QDuCfQL6enAlClSDQclPizW1UkfPClwuFPx3RkdOwKRkcAbbwA33aTeeYjIv9XUAGFh8rKmfLweEa/XiIh8D7dUkO8TAnjpJakwn1J3pl95RZl5yDdYsxvUbHt64gRw4ID0b5VxXiKSa9Mm+Vu0WI+IiIg8jAEH8m3p6UCXLlJ9BSUtXco994HEZAIKCjzz/3zHDqlWBBGRq4QApk2TN9Zak4j1iIiIyINYw4F8lxDAzJnS3RqlVVUB69cDt96q/NykPQaDdBFeUnLxeydPSnUX3n1X2XPp9VI2xejRPpnSTERelJoqdb5xVZs20jaK7t0VXxIREVFTWMMB3BPos9Tcc+/j+1xJAUIAQ4dKXSvUeJlMSQHGjFF+XiLyT0JILS3lBBz27weSkpRfk4fxeo2IyPdwSwX5JiGAWbPUnZ/7XAObyQQcO6ZOsCEoSMpyYLyXiJxlNAL5+fLG9u6t7FqIiIicxIAD+aa0NGDnTmXn7NNHupu9c6f04D7XwGYwAFu2ANHRys9tNrOWAxG5ZvNmqYOSHKWlyq6FiIjISdxSAabo+RwhgMGDlQ84tGsHHD/OIAM5KiyU7ioOH67svNy2Q0TOEkIKih865PpYvR64cAEIDVV+XR7G6zUiIt/DDAfyPWpkN7RsCWzdymADXSohAbj+eunfx6uvKjevEEBWFrBhg3JzEpF/Sk2VF2wAgKgoBjWJiMhrGHAg3+JOS7DGfPutdCHH6t3UlKFDpbuESjKbgblzWcuBiBrnzvteUBCD6URE5FUMOJBvMRqBw4eVnXPvXiA+Xtk5yf9YLMpmOFixlgMRNcWdYpEtWkhZWkRERF7CgAP5lk2bpLvCSnr1VenDJFFTKiuBmhp15mbHCiJqjDvFIuvqpKADERGRlzDgQL5DCGD2bOXnraoC1q1Tfl7yLxERQEYGMGOG8nMzy4GIGuLu+15dHXD+vHLrISIichEDDuQ71CgWabVgAe8wU/OGDgUWL5buOOoVfvmcPZv/BonIUVqa1K5Zruhov+hOQUREvosBB/INQkhp50p/yLMqLARMJnXmJv8zdKhU+V1JmZnAkiVAUhKQnq7s3ETke9zNbggNBbZvZ8FIIiLyKp0Q8m+pnT9/Hrt27UJWVhaysrKwc+dO/PbbbzD/vse+S5cuOHr0qEtzHj16FImJiS6vpUOHDjh58qTL4wD2dfYJqanA2LHKzde1q5TV0LYt0KEDEBPDwpHkmrw8YMgQ4OxZ5eZs2RKorgYGDwa2bWMrO6JAZjQCsbHAuXPyxoeESK8nagXqvYDXa0REvidY7sDevXsjNzcXbsQriJxjzW7Q6ZRLOS8vB/7v/3jnh+Tr0QP4+Wegf3/l5qyulv5rrekwZoxycxORbwkJAdq0kR9wMJmk+g2RkUquioiIyCWyAw6HDh1Sch2Nuv7669GyZctmj4uOjvbAasgrTCagoEDZ/e2dOkkXc0Tu6NtXCloZjcrOGxQkBdlGj2aWA1GgMhql9z65WL+BiIg0QHbAwSosLAyXXXYZrrjiCiQnJ+Orr75CSkqKEmsDAHzyySfo2rWrYvORj0lPB6ZNA5YuBf7wB6CsTJl5S0qkQAYzHMgdlZVAba3y85rNzHIgCnSbNslv2fz668ziIyIiTZAdcFi5ciWSk5PRu3dv6O32B27atEmJdRFJGQ0vvQQcOAAsWiSlhbobcPjnP4FevYABA3ghRu6LiAB+/RXYtw946ill52aWA1Hgcrdg5L//DTz/vGLLISIikkt2JaEHH3wQffv2dQg2ECkqLU26ywsAhw4B+fnuz3nuHHDzzSwQScoZOhSYOBFISFB2XvssByIKLCYTcPy4/PHHj7PzEhERaQKjBaRN1kKRQUHKzvvqq/JTVIkaYzQCJ04oP69eL/0esDgvUWAxGKSA43PPuT62Rw9pLLP4iIhIAxhwIG2yZjf83mJVMTU10r57IiXpdEBYmPLzWixAYSHvVBIFok6dgOXLXR9XWSm1eiYiItIAt4tGEinOmt2g1yuTjZCYCMyfL30o7NVL2ndPpKSQEKBrV6mWg1LZCG++CVxzjfTBgXcqiQLP2rWuB91/+QXo0oWvGUREpBmaDzi8+OKLOHDgAAoKClBVVYWoqCh06tQJ11xzDcaPH4/Ro0dDx4Jq/sW+doMSqqtZrZvUZTIBp04pu/Xhk0+AyZNZMJIoEAkBTJ/u+rhTp4Bhw5RfDxERkUya31Lx9ddfY9++fSgvL0dtbS1Onz6NXbt24e2338bYsWMxYMAAZGRkeHuZpBRrdoNSH7L+9jfuZSX1GQzSncXoaOXmzMxkwUiiQJWaChw96toYnQ5YsoQ1X4iISFM0H3Bo27YthgwZgpEjR2Lo0KFo166dw5/v378f119/PVasWOH0nEajERUVFQ4P0giTCSgoUO6C6csvpX2wRGrr0QPYvRvYuRNYt05qZ+mu2bP54YEo0AghZTfJGcfONkREpDGaCzjodDpceeWVePvtt3HkyBGcOXMG27ZtQ3p6OrZu3YqSkhJkZmbi7rvvto2pq6vDM888g9TUVKfOsXjxYkRGRtoeCUq3syP5DAZg+3agd29l5isqYsE98pyEBCA5GRg7Fvj5Z/fnO3iQ/36JAk1qKnDkiLyx7GxDREQao7mAQ5cuXbBjxw4899xzSExMbPCYK664Av/5z3/wxhtv2L5nNpsxZcoU1NbWNnuOmTNnory83PYoLCxUbP2kgH//G/jtN/fmaNUKyMjgdgryjspKqXaIu2prgRYt3J+HiHyD3OwGK3a2ISIijdFcwMEVU6dOxRNPPGF7npeXh9WrVzc7zmAwICIiwuFBGmGxAAsXuj9Pjx7A0KFAfLz7cxG5SqkWmUajtD2DiAKD0eh67QZAeq9bt07a0sVAOxERaYhPBxwAYNasWQ7PN2zY4KWVkNvS06V2XlVV7s+1dy/3sZL3nDmj3Fzz5jE9mihQbNokrx10TQ0wcqS0pYuBdiIi0hCfDzh069YNXbp0sT0/ePCgF1dDsgkBzJwJHD+u3JwsuEfeEhqq3Fw7dzJ4RhQIhJDet+R49VVmNRARkSb5fMABAOLi4mxfn1HyziJ5Tlqa1AZQSWwrSN6ydauy8zF4RuT/TCZg3z55Y996i68RRESkSX4RcKiyS8Fv2bKlF1dCsrhzV6cprNZN3iCE9O8uKEi5ORk8I/J/wcHyiz0eOsRCkUREpEk+H3AwGo3Iy8uzPY+NjfXiakgWNbIbAFbrJu9IS5OKtpnNys2p0zF4RuTv7Ipgu4wdbYiISKOCvb0Ad3377bcOGQ7XXnutF1dDLlM6u6FNG2DDBiAkRHoeE8N9reQ51uwGvV5e4bem5i0okIJn/PdM5H/MZuDTT+WPN5mkdrzsukVERBrj0wGHU6dOYcaMGbbner0eEyZM8OKKyGVKZzf8/DPQv79y8xG5wmSSAgNKBhusXnuNwQYifzVxorxxBoNUMPLqqxlsICIiTdLUloqMjAxMmjQJv/32W7PH7tu3DyNGjEBBQYHte4888gj69u2r5hJJSda7wUrq3FnZ+YhcYTBI2yl27pQemZlAUpIycy9bxi0VRP7IbAY++UTeWKMRePJJYPBgZddERESkENkZDgsXLsTChQsv+X5tba3t62PHjiG0gfZwDz/8MN5///1Lvm80GrF8+XIsX74cl112GUaMGIGBAwciNjYW4eHhqKysRF5eHlJTU7Fu3TpY7O4iDho0CMuWLZP71yFvsN4NVoJOB/TpA4SHKzMfkVwJCdIDAFJTgZwcZebNzeWWCiJ/1MC1lEsqK4FWrZRZCxERkcJkBxzq6upgNBqbPa6hY+yDEo3Zs2cP9uzZ49Raxo8fjw8//BARTCf0LdZU0EcfdX8uIYADB6QtGmPGuD8fkbuUrueg0wGXXw68+SZw003uz0dE3mexAEuXujcHr32IiEjDNLWlomvXrrj33nsRFxfX7LF6vR6jRo3CqlWrsHr1arRr184DKyRFCQG89JJy87ENJmmJ0vUczp0DDh6Ufmc2bpS2aqSnKzM3EXlHZSVQUyN/fLBPl+IiIqIAoBNCm5/OTpw4gZycHBQUFKCsrAzV1dVo2bIl2rRpgx49euDKK69E69atFTlXRUUFIiMjUV5eziwJT0lPBx57DCgqUnbe2Fjg6FGmnZM2FBYCJSWO3/v3v4HXX3dv3l69gEOHpH3b27ZJ2Q9E5Jt+/RUYNkze2PbtpdeZAHnP4/UaEZHv0WxovGPHjujYsaO3l0FqEAKYOVO5YEOfPsDKldKHLrbBJC2xr+cASP/277/f/XkPHZL+u2MHtxER+br33pM/dscOvucREZGmaTbgQH5syRJlW2GeOSO1wuRFF2md0QgcOaLcfEFB0jai0aOZ5UDki8xm4LPP5I9nsUgiItI4TdVwoABgsbhfkdteeDiwdSuDDeQbNm8G6uqUm89svpjlQES+R+77YevWwDffSFsqiIiINIwBB/KsxYuBqipl5oqOBnbtArp3V2Y+IjVZu1YEBSk7rzXLQZvleIioMXI7VLz5ptSVacIE5ddERESkMAYcyHMsFmDRImXm0umArCwGG8h3pKVJ2Qhms7LzMsuByDdVVgLV1a6P++QToFMn5ddDRESkAgYcyHOUzG4QgntXyXdYsxv0Kr3ksiUske/JyJA3rrBQartLRETkAxhwIM9QMrvhxReBNWu4d5V8h8kEFBRIvwdqsFj4IYTIlwgBTJrk+ri775aKLrNuERER+Qh2qSDPWL9eueyGa68Fbr1VmbmIPMFgkLY9lJQ4ft9kAm6+GTh3zr3533wTuOMOfggh8hWpqcDRo66PW7cO+OorxZdDRESkFp0QzMGtqKhAZGQkysvLERER4e3l+B8hgKFDpbsy7v5z0+mAK68Etm1jG0DyD4WFwO7dwG23yZ+jRw/g0CH+ThD5AiGA3r2B3FzXx+r1wNmzQIBeq/B6jYjI93BLBanPmk6uRGxLCKaOk39JSJACaO7Iy5PumBKR9qWmygs2AFKGAz9oExGRD2HAgdRnTSfPzAR69nR/vr/9janj5D8sFuD1192fZ9o0Fo0k0johpN9VuWprlVsLERGRBzDgQJ6RkAD07w+cOOHePDod8MYb/GBF/qOyEqipcX+e/HzAaHR/HiJSjzvZDQAwdy7f/4iIyKcw4ECe06IFUFfn3hzcUkH+Jjxc2s/trro6YPNm9+chInW4m90A8P2PiIh8DgMO5Dnr17t3B3bzZmDnTml7BrdUkL8wmYCyMmXmmjWLdz+JtMpoBA4flj++dWsgI4Pvf0RE5FMYcCDPEAL44x/ljw8NldphJicD8fHKrYvI2wwGYMsWICrK/bl27gTS0tyfh4iUp9NJQQM5EhKAnByge3dl10RERKQyBhzIM1JTpbZ9chmN0l53In/Uo4fUGlOJoqrMciDSppAQoHNneWOLioD27ZVdDxERkQcw4EDqc3ffql4P/PQTW4GRf+vQATh3zv15fvuNe7yJtCgtDcjOlje2TRspQ4KIiMjHMOBA6nO3Knd0NDB4sHLrIdIigwFYutT9eYxGqUArEWmHEFL2kVzLlrF2AxER+SQGHEhd7mY3dO0KZGXxQov8nxDAu++6fxezthZYtEiZNRGRMkwmIC9P/ni2gyYiIh/FgAOpy92q3GVlQEyMcush0iqTCSgoUOZDxYIFgMXi/jxEpIyQEPeCiQUF3CpFREQ+KdjbCyA/9+OP7n3wiY6WLtSI/J3BILV8LSmRfmeGDZP/AcNkAs6fByIjlV0jEclTXS2/RktQEPDLL8z0IyIin8QMB1KPEMDEie7Ncfy4lCVBFAgSEqTWr716uX8389dflVkTEbnvtdfkj42Kkl4biIiIfBADDqSemhqguNi9OViZmwJReLiU3eOOJ57gnm8iLUhLA+bOdX1cu3bAunXArl3MbiAiIp/FgAOpZ+NG98YHBQEZGbzQosBTUyPVL3HHyZNASooy6yEieYSQH/w7dw4YMQKIj1d8WURERJ7CgAOpQwjgqafcm6NVK6aRUmBSqjjclCnMciDylvR0oH17oKhI3vioKGb4ERGRz2PAgdRRUwOcPu3eHN26sWAkBSaDQcrwcdfRo6yBQuQNQgAzZgClpfLnmDuXGX5EROTzGHAgdSxd6v4cp06xDRgFJp1Oql+ixDxE5HlpacDOne7N8fHHzFAiIiKfx4ADKc9iARYskD/+zTelC7UdO3h3hwKTwSAVilu3DvjoI2l7kRxmM7B5s7JrI6KmCQHMmuX+PIWFDLoTEZHPC/b2AsgPrVkjBR3kevJJIDRUufUQ+aKEhIs1TIYMAfr1kzfP7NnA6NHMdiDyFCWyG7ZsATp3ZtCdiIh8HjMcSFlCAM895/4cRHRRUhKwaZO8sXl5vEtK5ClKZTckJbE7BRER+QUGHEhZKSnAiRPuzZGersxaiPxJebm8cQYDi68SeYoS2Q0AoOflGRER+Qe+o5FyhACmTnVvDp1Oqv/ALAeii4QA5s2TN/bUKSkQSETqEkLawuSOoCBg61YgIkKZNREREXkZAw6knNRU4PBh9+YQQioWmZamzJqI/IHJ5N7v1sSJUoo2s4eI1GMyAQUF7s1hNgN9+iizHiIiIg1gwIGUodS+VUBKJX35ZWY5EFmFhABRUfLHFxcDBw4AL73E3ysitRgMQGYmsHYt0KGDvDmiolg0mYiI/AoDDqQMkwnIyVFmLouF7cCI7BmNwPHj7s/D7CEidSUkSNsiTp1ybVxICLBqFbB3LztTEBGRX2FbTFLGpk1ATY28sbGxwIoV0n+tYmJ40UVkpdMBkZFAaal78wQFSdlDbJNJpA4hpNbOrurdGxg/nr+XRETkd5jhQO4TAnj8cfnjjUZg5EggOfnig+3AiC4yGKRCcq1buzeP2cwsByK1pKdLWymKilwfe/Iks/qIiMgvMeBA7ktJkfaIyxUfz7Z9RM3p3h1o3979eaxZDqzlQKQcIYAZM4CSEnnjlyxhVh8REfklBhzIPUoUizx+nHd2iJpjNEq1TdzFLAci5aWlATt3yh+/aBGDgERE5JcYcCD3pKUBu3a5N0dCAjMciJqj0wFt2igzFzvBEClHicD7sWNSUJGIiMjPMOBA8gkBTJvm/jynTjHDgag5BgOQkQFER7s/FzvBECnH3ewGAAgPZ8FIIiLySww4kHypqcChQ+7NMX++1Lece1eJmtejB7B7t/Q707On/A8offsC27fz947IXUIAs2e7P88jj/D3kYiI/BIDDiSPEMD06e7Ps2YN0KmT+/MQBYqEBODMGSA3V/6WiAMHgJwcZddFFIjS0qQAoLuWL5cyj4iIiPwMAw4kj9EIHD7s/jxM6yZyjRBS/QW9my/frOFA5B6lshsA6X2wslKZuYiIiDSEAQeSZ/Nmqdq9XMHBUqX8HTuYRkrkCpMJKChw/27ojh1SKz4iksf6uyhXhw7AZ58BK1cCW7cCERHKrY2IiEgjGHAg1ylxV6euDiguBuLjlVkTUaAwGKRgwc6dUhHJli3lz7VwIdO4ieSy/i7KLeR66hTQrh3w4IPA4MHKro2IiEgjGHAg1y1Zosye1XnzmNJNJEdCApCcDAwZ4l7AoKoKWLxYuXURBZr9+4GyMvnjZ8/m+yAREfk1BhzINRYLsGCBMnMVFbF+A5E71q2T6qm445VXmOVAJMfGjcD48e7N8dtvfB8kIiK/xoADuWb9eqC62v15Nm9m/QYidwghBf/ktsa0qq6Wfq+JyHlCAC++6F4tI0DaXtiihTJrIiIi0iAGHMh5QgDz57v3ASckRCqOdf31rN9A5I60NClop0Q6Nrc3EbkmLQ3Ytcv9eWpqpEwlIiIiP8WAAzlPiQ84tbVA377KrYkoECnVGtMqM1P6/Sai5inZDlOnkzKVGPAjIiI/xYADOUepDzidOgHh4cqsiShQKdUa0x6L1xE5Jy1NmcLJgPQ7V1joU3UczBaBjMOlWLW7CBmHS2G28HWDiIgaF+ztBZCPsGY3uOvkSanIXWio+3MRBSprO76SEqm97MMPA2fPujfn4cPShx7WVSFqnH3w3d2A35w5wG23ATExPvF7Z7YIvPVDHj7ako9z1bW277dp2QKPD0vElBE9EKR3s6YMERH5HWY4UPOsF1juFqcDgKgoZeYhCnTW1phBQe4HG6zzhYS4Pw+RP7MG390NNuh0UrHWQYN8op5RSnYxrli4Ef9IP+QQbACAc9W1+Ef6IVyxcCNSsou9tEIiItIqBhyoeSYTcOyY++nWbdsCGRk+cSeHyCcouZfcx9K6iTxOyeC7D22lSMkuxqSVWThXVdvkceeqajFpZRaDDkRE5IABB2qewQBs2SK/9kKPHtJ+1927ge7dFV0aUUBLSwN27lRmLoOBGQ5ETbHWTnE3+P7mm9LvrQ+0hjZbBOatyXFpzLw1OazrQERENgw4kHO6dweio+WNzcsDzpzxibRRIp+hdKeKkyeBlBRl5iLyRz//LAXeb7tN/hx6PfDppz6zlWJ7fhmKy2tcGlNcXoPt+WUqrYiIiHwNAw7knNRUaVuFXKyAT6QsNTpVPPUUf0+JGiIE8NJLUgB9zRr581gsPrOVAgBOn3ct2ODuOCIi8j8MOFDzhACmTXNvjtxcn7nAIvIJ1k4VO3dKW5b69nV/zqIiZjkQNcS+U5PcoFxiovS76gNbKaxiwuV1lJI7joiI/A/bYlLzUlOlgIE74uO5P5xIaQkJ0sNoBEpLlZnzqaekO7DsJkMksS8W6U4G0IULQP/+PhNsAIAhidFo06pFswUj7bUK0WNIoswtmERE5HeY4UBNUyK7AZBqODDDgUgdBoN053TdOmn7kjvBvaIioIbp0EQ21uwGd4INH3wgZSP5ULDBylRrdun4sf1iEaRnwJKIiCQMOFDTjEbgyBH35ujZ06dSSIl8UkICMG4csGAB8Nxz7s2VlgYkJQHp6cqsjchXKdUK84EHfKJIZH1vfn8IVbWu1YmJa9NSpdUQEZEvYsCB1JebC+S41laLiGSyWIB//Uv++J49gYULgQMHpCJ5LCJJgUyJ7AYAqHV+S4JWpGQX45/f57k8TihYx5aIiHwfAw7UtM2bAbNr6ZSX0OulO0T84KJpZotAxuFSrNpdhIzDpeyj7qsqK93bEpGbK23PAKQPWmlpyqyLyNdYsxvc1aeP1E7Th5gtAjO+3SdrbOkFbskiIqKLWDSSGieEtB/cXfZtwLitQpNSsosxb02OQ7/1uMhQzBmfhLH947y4MnJZRASwZQtwxx3AqVPuzRUUJH3gGj2aRSQp8GzYAGRluT/PyZM+9/639UipS4Ui7f106IzCqyEiIl/GDAdqnMkk9RyXQ6eTsiN27pQerOGgWSnZxXh2ZZZDsAEATpbX4NmVWUjJLvbSyki2yy9XpmuF2cwsBwpMQgBz50q/Ay1auDdXly4+16Up47D814+TFUZmyBERkQ0DDtS4kBCpEJ0cvXsD110HJCdLDx8slhUIzBaBeWty0NClofV789bk8OLR1+h0QGSkMnNZsxy4JYoCibV2A+B+/YU9e3wuaHek5LzssQLA9vwy5RZDREQ+jQEHapzJBBQUyBt79izbYGpEU7UZtueXXZLZYE8AKC6v4cWjrzEYgIwMZbZBMMuBAo21doNeoUsknc6ngnZmi8DmQyVuzXH6POs4EBGRhDUcqHEhIUB0NFBe7vrYX37hFgoNaK42Q3rOSafmceXi0WwR2J5fhtPnaxATHoohidHsye4NnTtLv79KbK2wFn5lLQcKBPbZDUoQwqfqGP2aewYXTO61mogJD1VoNURE5OsYcKDGrV8P5OfLG9upk7JrIZdZazPUv6dWXF6DSSuzkJwQiaxC54JJ7VobkHG4tNkgQnMBDgYjPMhgkLpNJCa6PxcLv1KgsM9usLjxobtlS2DGDGDwYKBDByAmxid+d1KyizH1811uzREbYcCQxGiFVkRERL5OJ4T8HL/z589j165dyMrKQlZWFnbu3InffvsN5t/bKHbp0gVHjx51a4FFRUVYuXIlVq9ejaNHj+LMmTNo164dunbtittuuw0PPfQQOrn54baiogKRkZEoLy9HRESEW3P5DSGAK6+UX6G7vFyqlk8eZ7YIbD1cismfZ+Fctfu933UA2rcOwenKi1tkOoQb8MDQzujaLgwx4aG4oksU3v4xD8u+z210nmeuT8TqPcUOwYjYCAPuH3JxHgYgFFZSIn3QcUffvsBnn0kfmliLhfxdaiowdqz78+h0QFUVEOo7d/pTsosxaaX7XTmu79kOn04cqsCKLsXrNSIi3yM74NC7d2/k5uaiqeHuBhzee+89/OlPf8KFCxcaPaZ169Z47bXX8Mwzz8g+D9/AGuDORVdCAnDsGFOvvSAluxhzV+/HyQqjt5ciC1txquDTT4FHH5U/PiZGquXiA3dnidwiBDB0qJQZpES9hQ0blAleeICpzoKhi9JxVmYrTHvX9WyHzxhwICKi38muiHTo0KEmgw3umj9/Pp599lmHYEPPnj0xfPhwdO/e3fa9yspKTJo0CQsXLlRtLQHHmlIqV3ExYPTND7y+bP3eE5i0Mstngw0AW3Gq4pFHgFWrgNmz5Y3/6ScGGygwmExSsFypa5vp032iUGRKdjGuWqxMsAEAurZtpcg8RETkH2RnOOh+v3sdFhaGyy67DFdccQWSk5Px1VdfISUlBYD8DIdVq1bhjjvusD1PSkrCZ599huTkZNv3MjMz8cgjj+DAgQMO42677TaXz8eIeT3uppS2bQsUFfFDikLs6x5EtwzBwVPnUVBWBSEEWhmCcLrCiCpjHdIOnPb2UhWhAxAbGYpfXhzB7RVKsliAVq1cDwa2aiUFLG66SZ11EWnJZ59JQTolBAcD589reltFY7V+3HFg/li0DAlScMaLeL1GROR7ZBeNXLlyJZKTk9G7d2/o7VpHbdq0ya0F1dbW4k9/+pPteXx8PH755RdERUU5HHfllVfil19+wcCBA1FUVAQA+NOf/oRx48YhOJi1MGVzN7sBADZvZrBBIQ0VYfR31lacK346ggpjLQAdru7eFld1a8sAhDvWrZOXeVRVJRW/27GD26TIf6WnA1OnSgECpURFafp3xmwRmPHtPkWDDVd2aaNasIGIiHyT7C0VDz74IPr27esQbFDCl19+iby8PNvz119//ZJgg1V0dDRef/112/Pc3Fx8+eWXiq4n4CjRDiwhQZm1BLjVWccxaWVWQAUb7C1KOYi3fjyMt37Mw4MfbMMVCzdyq4VcQgDz58sfv3On9NpA5I+EAF56CTh4UMrOU0JSkvR7o+Hg+1s/5OKcQtsorB6+uqui8xERke9TNlqggK+//tr2dceOHXHnnXc2efyECRMQF3exwNx//vMf1dbm99zNbvj734Ht29mdwk1mi8Bd72zBtK/3eHspmnKuqhaTWN9BHpMJsAvkyjJ7tk/sRydymRKB9vrKytzvEKMis0Xgoy1HFZ83Jly720eIiMg7NBVwqK6uxsaNG23Px44d2+z2iODgYIy1qzeQlpaGmprAvCPsNpMJkNtVpHVrYPJkqec4yWK2CPxj42/oOWs9dhac8/ZyNGvemhyYLfzg65KQEKBzZ/fSuzMzmeVA/scaaA9SaBvAO+9ImQ07dmg6u2F7fpkibZPtxUVKrY2JiIjsaSrgcODAARjt9hgPGzbMqXH2x9XU1DgUkiQXGAzAokXyxnbpIn2ooWaZLQJb8s7gtdTf8FrqQWzJPYNVu4rQ5+UNWPZ9HvhZumnF5TXYnl/m7WX4FpMJOHXK/QyFWbOY5UD+xZrdYDYrM19pKZCcDMTHKzOfSk6fV/7GzJzxSayzQ0REl9BUdcX9+/c7PO/Zs6dT4+ofl5OTg0GDBim2roAhBPDqq/LG7t8vXbiNGaPsmvxMSnYxZny7z2Hf7Fs/HvbiinzTJxn5GJIYzYtbZxkM0oeqkhLp+YcfAm+/7fo81loO/D0nf2Cf3aBUwGHRIqkehML1rZR2pORC8we5ICxEj1FJsYrOSURE/kFT74j1W2h27tzZqXFdunRxeJ6fn6/UkgJLaiqQmyt/PPd4NykluxiTVmYpXqQrEKVkn8LAualYlp6LVbuLkHG4lNssmpOQIN15HTQI2LBB/jzMciB/oXR2AwDU1ACVlcrNpwKzReDTjKOKznnBZGHmGRERNUhTGQ4VFRUOz9u0aePUuMjISIfn55tpa2U0Gh22btQ/b0ASApg+3b05Cgqk1G0N71v1NLNFYOuRUvx8qAQf/MJAmJIumMz4R/oh2/OoVi3wyNVd0K19a8SEhzIDojFGI3DsmPzxv/3G33PyfdbsBr0esFiUm7d3byA8XLn5VPDWD7k4q0LgW41tGkRE5Ps0FXCorHdXoGXLlk6Nq39ccwGHxYsXY968ea4tzt+lpgKHDjV/XGPatAF+/ZUfQuw0tH2C1HO2qhbLvr/YiSE2IhRzb0vC2P5xTYwKQDqd9PtaWipvfE0NcPnlwJtvAjfdpOTKiDzHZJKC5EoGGwDg7FlNB+RSsovxj3Q3MhmbwA4VRETUEE1tqaitdfxg1lyHisaOqz9PfTNnzkR5ebntUVhY6NpC/Y0Q0nYIOeLjgXXrgH37gO7dlV2XD+P2Ce87WVHDNpoNMRiAXbuA5cvlja+rAw4elLrScGsF+SprXZPMTKmDizvCw4GPPwbWr5fm02iwwWwRmLt6f/MHyhAbYWCHCiIiapCmAg5hYWEOz51tb1n/uPrz1GcwGBAREeHwCGhpaVIxODmMRmDkSM1X5PYEs0Ug43Apvss6jj/9Z6+3l0O/++PXe1jfob6EBGD8ePfmOHRIyowi8lUJCVIh1YIC9+Z55x3g0UeBm2/W9Hvh9vwynKwwNn+gDHNv68ctbERE1CBNbalo3bq1w/Oqqiq0atWq2XFVVVUOz8M1vn9SU9zdx7ppk2bv5niK2SLw1g+5+GjLUcX7mpP7LpjMePP7XDw/qpe3l6ItShTKmz5dynbQ8YMG+SAhgCefdH+eZcuABx/U/O/ByfJqxeeMbBmMpXcN5NY1IiJqlKYyHNq3b+/wvLjYuVTo+se1a9dOsTX5PXf2scbHA337Kr8mH5KSXYwrFm7EP9JzGWzQsDe+z8XPh0qY6WAvPh744APgqqvkz8EsB/JlNTVAUZH781gLJmvcmUpl1/j8yJ7Ienk0gw1ERNQkTQUc+vTp4/D8mJOV1Osf1zfAPwS7xLqP9bnnXB9bVCRtqQhQ6/eyToOvsAB4+MPt6DcnBXNWZWPFz0fwXdZxttN84gkppdwd06ezlgP5po0b3Z8jMtJnCiafvaBcwOH5kT3x/Khe3EZBRETN0tSWin79+jk8z8rKwq233trsuKysLIfnSUlJiq7L73XqBHz0kevj9JqKV3nU+r0nMPnzXd5eBrmoptaCTzIcA5RxkaGYMz5Au1m42yITuJjlMHasMmsi8gQhgBdecG+OefOkoJ2G6zbYyzh8WpF5DMF6TB3ZU5G5iIjI/2nqE2NCQgK623U62Lx5s1Pj7I/r0aMH4n3kzV8zKiuBahl7O6OjNb9nVQ3r9xbjuc93gfd0/UNxeQ2eDdRuFjodoETR3GnTmOVAviU1FThyxL05/vc/KWDvA15Zl4Ndx5tuGe6sV+8eyMwGIiJymqYCDgAwYcIE29ebNm1CQTPVowsKChwCDvbjyUm//ur6mKQkqbOFD6SRKknKbMhq/kDyKQLAvDU5MNVZkHG4FKt2FwXGdguDAdi2zf2gw5EjAb29inyMEFKQzF0nTvhE7YbF63Pw/s/5isw1MD4Ct1/uG0EWIiLSBs0FHB5//HEEBQUBACwWCxYsWNDk8fPnz4fl94KHQUFBePzxx1Vfo18RAnjgAdfH5eRIjwCSks3MBn9WXF6DqxZ/j/vf34rpX+7G/e9vxbVLf/D/zIcePeQFHYl8VWoqkJvr3hz9+gGZmZoPupvqLPjXT8oEG0b2aYfVU65TZC4iIgocmgs49O3bF48++qjt+QcffIAPPvigwWOXL1+OFStW2J4/9thjlxSepGZs2ACcPStv7Msv+2UatdkikHG4FN/tKsL7Px3BqykHsGTDATz/JWs2+LuyekXVTgbKdovu3d2ryWI2Sy1yibROCGDWLPfn2b8fiI11fx6VfbwlX5Egef+41ljx2FAFZiIiokCjE0LeJ8aFCxdi4cKFl3y/trbWlnEAAIYGov8PP/ww3n///UbnPnPmDK666iocPnzY9r3bbrsN9913Hzp27IiioiJ88cUXWLt2re3Pe/TogYyMDFktMSsqKhAZGYny8nJEKLGf2VcIAbRvD5SWyhsfGwscPar5OzzOMlsE3vohFx9tOcoWl2SjAxAbGYpfXhzhv/uWjUagY0egrEz+HL16AQcPBmRdF/IhRiPQoQNQXu7+XF9+Cdx7r/vzqOjud7cg89g5t+eZNa4vnrq+m/sLclPAXq8REfkw2V0q6urqYHRiz25Dx9TWNv1hrl27dtiwYQPGjBmD/HwpFXD16tVYvXp1g8cnJiZiw4YNsoINAW3RIvnBhtdfB/7v//wm2JCSXYwZ3+5ji0u6hIC03WJ7fhmu7t7W28tRh8EApKcDycny52C3CvIFISFAaKgyAYe//x245x5NB9mKztYoMs+j13RVZB4iIgo8mttSYdWzZ0/s3bsX06ZNazSKHRkZiWnTpmHv3r3o0aOHh1fo4ywWYM4c+ePfe89nqnM3JyW7GM+uzGKwgZr0ya/52JJ7xn8LSl5+OZCY6N4c06f75TYr8iM1NcCpU8rMVVio6aKRZovAyQr3Aw63DohDSLBmLxeJiEjjZG+p8KSamhps3rwZR48eRWlpKdq2bYuuXbvihhtuaHDLhqsCMkVvwQLgr3+VPz44GDh/XrpT5MPMFoFrl/6A4nJl7gJpkV4HyP1srAMQHRaC2bf0RUx4KP74nz04VVHDwpkA4iJDMWd8Esb2j/P2UpRhNErdKtz5ABUUJLXZ9fHXBfJD6elSZ4p77wXmznVvrrZtgbVrgfh46aFRW/LO4MEPtrk9z7L7LtdMZ4qAvF4jIvJxPhFwUFvAvYFZLFJaqdksf4727aW7Oz6+pSLjcCnuf3+rt5ehCmvtgc1/vhE7j51Fes5JrNhy1KXxAPDuQ8m2D9XWbBAAAR90aOjn49PMZimQ6K61a4FbbnF/HiKlCAEMHQrs2CEFxdx570tKAlJSgIQE5dangpTsYjy3MguW5g9t1r8nDsWwntrYshpw12tERH6AOXKBaM0a+RdcM2cCO3cCWVk+H2wAgNPn/TOzwfpheM74JIQE63F197Z4eXw/vPdQMuIiHe8+x0WG4pnrEy/5fmxk6CUfpsf2j8O7DyUjtt6xUa1aoE2rFqr8XbTKGnCZtybHP7ZXyK3nUt+ddwIbNyozF5ES0tKkYAPgXrABAE6cAGJi3F+TilKyizFJoWADgItvKERERDIwwwEBFjF3tzPFoEFSwEHDRbKaY7YIbM8vw+nzNThdYcQr6w94e0my3TowDjHhBvxv9wmHlo5Npfvb//1jwkMxJDEaQXpdo99vSEPHAsD2/DKk55zEd7uLUHbhYk2MuMhQ3HZZHJYr1A9ea7546irfLyhpNEqdZ86dc3+unj2B337z6dcJ8hNCAEOGAJmZysw3YACwZ49m/22bLQLXLP4ep843X9TbWdxSQURE7lAgf5Z8yoYN7t3JLC6W9nj7aHZDSnYx5q7OUaSQlje1NgTh1bsGYtzAjgCAWbckOR0sCNLrGvxw3Nj3XZnj6u5tcXX3tnipkfUM6hyFeWtyHGpmhIUE4YLJzbuOXuYXmTIGA7B3L3DkCHDDDe7NlZvLjhWkDWlpygUbAKCkRNPvgW/9kKdosAEAYsJZk4WIiORjwCGQCCFVkZdj6VLgppukVFKNXmg1x5pm6otmjeuL8upaAAJXd2uHq7q3dQgouBIs8ITG1jO2fxxGJcVeEoxIzS7G7FXZDlkRvqRdmG/+TlwiIQHIyVFmrmnTmOVA3iUEMGuW+/MsWQKMGiV9reH3wJTsYvwj/ZCic8ZFXsxgIyIikoNbKhBAKXo1NUB4OFBX5/rYyy+X6jb46IcHs0Vg4NxUn7yTHhcZil9eHNFoxoK/sG7TOHG2CnPX5uB8jYx/p17SKiQIr99zme8XjxQCGDxY2jalhA0bmOVA3qNUlk1cHFBUpOn3PzU6LumgvaK4AXO9RkTkR1g0MpDodEDLlvLGWrdS+Khf885oPthQ/1JW9/tjzvgkvw82ABezIu66MgF/u3ugT9UpqzKZMWllFlKyi729FPekpSkXbACkLAfGtMkbhACmTlVmruJiKWCvYdvzyxQNNrRp1UJzwQYiIvJNDDgEkpAQ+QGH117TbBppU8wWgYzDpZi7er+3l9KkicO6XtL5oaEuEYHC2g2jfucMrZu7er/vdqwQApg9W9k5rbUciDzNaATy8pSbr1bb273Sc04qOt/b9wfmew8RESmPNRwCSUoKcPq0vLHLlgEPPqjplNL61u4+gZn/2+cTqfk3JcU2WmgxUDVU7+HsBSNe+l82zlVp8+L/ZIURy9IP4Q+je3t7Ka4zmYCCAuXnnT0bGDPGp147yA/8+KMy2TU6HZCeDmg4fT8luxgrthxVZC4dpGD3VRqqCURERL6NAYdAIQTw1FPyxxcWaroyd31PfrId6QdKvL0Mp1iLcmmt8KMWNPQzGdM/DlsPlyLjyBkAOuh1wIdbjqDSqFjXebe88UMeDpyswPuPDPb2UlxjMEjV/Pftk+4MFxQAH3/sXlcbQNr77kOvHeQHhJC287hr4UJg9GiprolGmS0Cc1ZlKzpnoGzjIyIiz2DRSARIEaLqaqBVK3lje/YEvv9eqmDvA578ZAfSD8jM5PAwLRbl8kWmOgsGLUjDBaN26nQ8dV0iZt2S5O1lyGc0Al26AKdOuTfPp58CixcDb7whdbohUltKCnDzze7NodMBVVVAqLa3dS1Lz1WsM0V0WAssunOApt+PAuJ6jYjIz7CGQ6BIT5c/9vx5qRWYD1i7+4Qmgw2RLYMRZghy+F5cANdoUFpIsB5PX9fN28tw8MHP+TDVSVkX1loiq3YXIeNwqW/UeTAYgF9+ASIj3Ztn2jTgwAHgpZdYQJLUp1R2g177l0dKt8F8+dZ+fD8iIiLFcUtFIBACmDdP3tgtW4DOnX0iHdpUZ8Gfv9nj7WU46N8pArPGJdn6mLNGg3qmjOiJf/10RDPdSASAmd/sxah+HTBvTY5DBfm4yFDMGZ+k/Yv7Hj2kvfDJyfLnOHdO+u+OHVIXjDFjFFkaUYNSU6Vipe6KjtZ03RGzRWDemhxF54yN0HY2BxER+SZuqUAApOgZjUCHDkB5uetjz51z/w6nB6RkF+Ol77JRdkEbrTtbtgjCq3cPxPjLOnp7KQFFyfRiNVk/xvhMhsvq1cCrr0oBSLmCgqTAxbZtmv4gRz5MCKB3b2UCDp99Bjz0kPvzqCTjcCnuf3+rYvPFRYbilxdHaD4I7vfXa0REfkj7OYPkvpAQKUvBVXq9T2Q2rN9bjEkrszQTbJh8Y3dkzxvDYIMXTBnRA21atfD2MppljfLOW5PjG9srbr1VKijpDrP5YpYDkRqMRuDIEWXm+uc/Nb0FKHV/saLzsVAkERGphQGHQJCWJlWed5XG70KaLQL/2PgbJn+e5e2lOLi2R3teuHlJkF6HJRMGeHsZThEAistrsD2/zNtLad7589KHOSXMnq3pD3Lko9LTpQLHZoW2VO3cqdng2Pq9xfgk45gic+l0wDsP+EimFRER+SQGHPydENIFvhxhYZoNOqRkF+OKhRux7Ps8aOmjS3RYC1u9BvKOsf3j8N5DyYiLdNyPrNUY0OnzUm0HTReWNBiUK6KXmanZD3LkozZulLJwjh9Xbk6dDnj5Zc0Fx1Kyi/Hc51mKLevt+wdh3EAGG4iISD0sGunvTCYgL0/e2MREaTuGxqRkF+PZlVmaCjRYLby9P7MbNGBs/ziMSop1KNJ59oIRkz/fpbl/NzHhoUjJLtZ2YUmdDoiKAkpLlZlv9mxg9GjNBjTJhwgBTJmiXAaO/bwFBdJ7qEa2FpotAjO+lZGt2Ihl912OcQO59Y+IiNTFDAd/J7d+AwCcPCldbGmItTK31j40AsAz1yfy4k1DgvQ6XN29LW6/vBOu7t4W4wZ2xLsPJWuqEntcpBQIeXZllkOwAQBOltfg2ZVZSMlWdq+2LAaDlGKu1AevAweApCT32vUSAVJXikMqFIpt21YqkqqRYAMAvPl9Ls5V1SoyV4RBh9sv76TIXERERE1hwMHfpaUBe/e6Pq5NG81dbAFSW8n6H8y8rW1YCN55IBkzxyV5eynUjLH947Blxgi8cFNPby8FAFBebcJL/8tuMICmucKSe/cqdxf5wgXg4EHgpZc0l7JOPkQIYPp05eaLjAQyMqTg2u7dQPfuys3tppTsYvzzewW6b/yuwihgqrMoNh8REVFjuKXCnwkh7UGVY8sWTV1sWR0vu+DV84/rH4tRSR1QdsGE6NYGxEaEYkhiNLdR+JAgvQ7Tb+qF3rHhmPHtPsXuGMpRZbKgytT4Rb99Ycmru7f13MIuWYgAFixQfl5r14oxY5Sfm/yfktkNOp1UX6RHD2XmU5DSWymsPss4ionXdVN8XiIiInsMOPgzk0nag+qqPn2Avn2VX4+bUrKL8dfV+726hoev7urdD36kGGudh62HS5Fx5AwAHYL1OkXvIirFWljSa0wm4JgyVfEdWAvzsZ4DuUrp7Aa9HoiPV24+Bb31g3JbKewdK6tSfE4iIqL6GHDwZwYD8OuvrmcqnD2rqUJZgHYKRXr9gx8pKkivw7Ce7TCsZzvb9/rEhWPqv7NQ6+1/bHZiwr1cd8JgkLKehg4FyhRs4ykEsxxIHqVrN0RFaTLoZbYIvL3psCpzd4lupcq8RERE9ljDwd89+aRrx8fGSh8ANBRsMFsE5q7e7/VgA6CBD36kurH943DwlXHoEt3S20uBDlJhSU20Wu3RQ9rXvn27svPq9ZpsP0ga5k6754a0bQtkZWnqfc9q+hdZqtRa0OukjD0iIiK1MeDgz+rqgB9/dG1MSQnQvr0665FpWfohnKxQuOWZizT1wY9UF6TXYcldl3l1DdZ7rXPGJ2mnRkhCAtC1q7JzWiwXsxyInGEyAYWFys1XWgq0a9f8cR62dvcJrN13UpW5n7ouESHBvAQkIiL18d3GX6WnS9kKrtJIWqnZIpBxuBS3vvkz3vghz6tr0eQHP1LdkMRor7bQjI0MxbsPJWNs/zivraFB7dsDq1YBc+YALVooN++0acxyIOcYDMDf/67snBpr0ZqSXYwpX+5SfF69TmrhzK5KRETkKToheIVXUVGByMhIlJeXIyIiwtvLcZ8QwJAhUsVtVyxYADz2mNcLZ6VkF2PemhzNtL+MiwzFnPFJ2vvgR6pLyS7GpJVZHj/v4M5t8O+nr9b2HUghpKDm6dPKzKfXS+0yQ7ltiepJT5cCUm+8Adx0k/Rvb/BgqX2lUq68UtoupJGA+7VLf1D0PfCGXu1wXc/2ePjqrtp+XWmG312vEREFAAYc4IdvYKmpwNixro/TwAWXt4tD6gB0iDDg7/dcjjOVRsSEs+1loEvJLsYfv96DCyazR88brNfhrQcGaTfQVV0NtFK46NyGDfJeu8h/CSEVK92xQwoybNsmbb9R+t9Jhw5SJxYN1HHIOFyK+9/fqth8E4d1xcvj+yk2nzf53fUaEVEA8N0wNzVMCOlOkBx5edLeWC8wWwS25J7Bi9/s82qwAQDm3tYPw3q0w+2Xd8LV3dsy2BDgxvaPw965YzCufwePnrfOIjBpZRZSsos9el6n1Srfpg/Tp3NbBTlKS5OCDYD038WLgVmzlD1H9+6aKpZ84ly1ovPdlCRjeyUREZFCGHDwN+60CouPB0JClF2PE1Kyi3Ht0h/w4IptKK9W4UOMkzS7Z568LkivwzsPXYmnruvq8XPPXb0fZosGP4S3bq38B7RDh6QPlElJmttTT14ghNTBRG93qTJ/vrJbKQBpK09MjLJzymCtXbR8s3J1i2IjDCx2TEREXhXs7QWQgoSQ7hDKVVIiZTh48C6Pt7dQAMATw7piVFIst05Qs2bd0g8D46Mw9Qvli7k15mSFEdvzy3B197YeO6dT1q0DjCp0j5k/X5r3pZeAkSM1saeevMQ+u8FKqX9zPXoAX34p/fuKifF6doNatYvm3taP72tERORVDDj4E7nZDQYD8PXXQHKyRy+6zBaBeWtyvBZsaNOqBZZMGMCMBnLJ+Ms6okWQzqOFTdP2F2sr4CCEVGRWp1N+C4T1A6W1VeaYMcrOT77BPrvBYlF+/vPngf79vR5oANQLvN+dHM/3NyIi8jpuqfAX7mQ3REYC48d7vDvF9vwyr3WimDWuD3bOHsWLMZJlbP84/PLiCPx74lC0CglS/Xwf/XoM6/eeUP08TjOZgIICdest6PXSB07WdAhM1uwGpYMNISHA2rVSFycNBBvUDLwP69lOhVmJiIhcw4CDvzAagSNH5I09fRqo8fwH/405Jz1+TquYiFCmmZJbgvQ6DOvZDq/fcxk88S9p8ue7sH6vRgpIGgzSh8HMTKBvX3XOYbFI51iyRJ35Sbus2Q1qbKeprQWuu87r7Z+t1Ay8x0awxSwREXkfAw7+YvNmoK5O/viNG5VbixPMFoH/7fbeHduYcF6IkTLG9o/Duw8lo02rFqqeRwB47nMNda1ISJBS0otVXs/Cheqk1JN2qZlB07s3EB6u/LwynT6vTrAhOqwFi0USEZEmMODgDxqq5O2qefM8mrq8Pb8MZRc834JTByAuMpQXYqSosf3jsHP2KFzvgRTmuav3Y0vuGazaXYSMw6Xe7WAREgJ07qxuYceqKqlzBQUOgwHYtk2drklnz3qt/XND1Ap+33l5J2bxERGRJrBopD+w3g1y5y7g8eMe7VCh1l2dplgvveaMT+KFGCkuSK/Dncnx+Cn3jKrnOVlhxIMrttmex0WGYs74JO/UIzGZgFOn1A9WLloEzJzpXlCVfMvevcoHBqKigC1bNFG7weqKLlGqzHtTUqwq8xIREbmKV2/+wHo3SO7FeEQE8OuvHr0I88aWhtjIULz7UDILRZJqvLFn+mR5DZ5d6aWtFtZaDhkZQLSKWUNVVVIbTgoMQgCTJik/79//DnTvrvy8MpgtAhmHSzH9S+Vb7DKLj4iItIQZDv5izx55GQ4hIUBWlscvwjyZ4TDlxu4Y1qM9hiRGM7OBVDUkMRpxkaEe7b4iIGXvzFuTg1FJsZ7/N56QID127wZKSqQCttdco/x5FiwAbr1V3e0bpA2LFwMnVKjx8847wGOPef3fUEp2Meau3o+TFUZV5mcWHxERaQkzHPyBEMAf/yhv7HvveTzY8Mq6/Zj+5W6Pna9nh3Bc3b0tL8BIdUF6HeaMT/JI1wp7AkBxeQ2255d5+Mx2EhKA5GTg8svVmX/HDqlVIvk3iwWYP1+duQsLvV6/ISW7GJNWZqkWbHjhpl7M4iMiIk1hwMEfpKYCeXnyxr7zjkeLRb6ybj/e//mox84HsCMFeZa1a4U3tld4ozbKJb7/Xr25X37Zo69X5AWLF0tZMkr79lupjasX6zeYLQIzvt2n2vxRrYIxZUQP1eYnIiKSgwEHXycEMGuW/PGHD3vsjs+aPSc8GmxgRwrylrH947Blxgi8cFNPj5736Jkqj57vEkJId6fVSlnPygI2bFBnbvI+i0XqmKSGa68F4uPVmdtJv+aewbmqWtXmf+WOAczkIyIizWHAwdeZTFLQQK7OndVpPVZPSnYxpn6hfHGsxrAjBXlbkF6H6Tf18mjQ4R/ph7AsPdd7rTKtHXPUykIwm4G5c5nl4K/WrgVqFf5AHhoKfPMN0L69svO6KCW7GM+szFRt/meuT8S4gR1Vm5+IiEguBhx8XUiItHdarlOnVM9wMFsE5q7OUfUc9bEjBWnFlBE9ER2mflDP6h/phzBsyffe7Vqxc6f0+Ne/lM92YC0H/5CeDiQlSf8FpCDSc88pew6dDujdG7jzTmXndZG1bkNVrRutqxsR2kKPdx5IxsxxSYrPTUREpASdELxVVFFRgcjISJSXlyMiIsLby3GN0Si1o6uSkUq9eTPQrZvqaaZT/p2JtftOqXoOq4nDuuKmpFh2pCBNWb+3GM99nuXRc+oA7wfdamqA1q2lzASl6HRSYGP1amDUKOXmJc8RAhg6VAoeDR4stXVOSQHGjVP+XLGxwNGjXqvdYLYIXLFwoypbKeIiDPhlxsiAeq/z6es1IqIAxQwHX9eihXRR76o+fYDrrlM92PDkJ9s9EmyIiwzFew8l4+Xx/diRgjRn3MA4PHN9osfPO29Njve2VwBScCAyUtk5hZBe8x5/3PEOOfmOtDQp2ABI/01NBSZOVP48SUnA9u1eLRS59XCpKsGG0GB9wAUbiIjINwV7ewHkpjVrpEJbrjp7VtpKoeKF2IK1OUg/UKLa/H1jwzHphu6ICQ9lRgNp3sxxSbgsvg1e+HoPjHXKp1bXZ98q8+rubVU/X4MMBunu9ZVXAuXlys5dVCQ9XnoJGDlSvUKVpCwhpG4jQUFS5oteD9x+uzpb+8rKgJgY5ed1QcaRM6rM+/o9l/E9j4iIfAIzHHyZO3tet21TNdiwfu8JrPglX7X5dQBWTbkWt1/eiRkN5DPGDeyInPljMX1kTxiCPPNv1uutMnv0APbulbZ+qYE1HXyLNbvBus3GYlE+2NCxo9QCc8cOr2Y3SJT/Pb91YBwLRBIRkc9gwMGXpaQAJ07IG9uqlbJrsWO2CPz5m72qzQ8AT1+fiJBg/vMl3xOk1+GFUb2Qs+BmPD9S/Q4WMeGhqp+jWe3bS3eb1XLHHcDGjerNT8qwz25Q0+nTQL9+Xm+DCQBKx8KDdMCy+wYpOykREZGK+InNVwkBPPmkvLH/+Y+qLcJ+zTuDC0YFi8TVM6JPe1bkJp8XpNfh+VG9MLafeinfUS2DMSRRpcwCV7z+urrz19QAU6awXabW1c9uUEtdnVQU2cvW7j6BN37IU3TOOwZ1ZEYfERH5FAYcfJU72Q16Zf63my0CGYdLsWp3ETIOl8JsEVi/9wQe+3i7IvM35qnruqs6P5En9YhRr9L62eo6vJpyQLX5nWKxAIsWqX+eQ4ek4oOkTdbsBoXef5qk00nn8mIA6pV1OZjy5S7F51084TLF5yQiIlITi0b6IiGAqVPljdXpgCVLpL7kbhRZS8kuxrw1OSguv7g/vFVIEKpM6t65iosM1cYdWyKFXN29Ld76Udm7oPaW/yTVUvFaVtD69fLa9soxfTpw8CALSGqRyQQUFMgrcuwqIYDCQtULIzfmlXX78f7PRxWf98lh3EpIRES+h+9cvsholPqKy2F/ISZTSnYxnl2Z5RBsAKB6sAEA5oxPYjop+ZWrurVFm1YtVD3H+z/nw+SBzhiXEAKYP99zAQBmOWiXwSC1qOzcWb1z/PWvwM6d0sNLBSPX7j6hSrChW/tWmD2eWwmJiMj3MODgi3Q6ICpK3ti//92tCzGzRWDemhx4OlG1TasWeO+hZIztH+fhMxOpK0ivw5IJA1Q9h0UAn2UcVfUcDbLe1fZkavvEiazloFU5OdK/B7WsWQMMGgQkJ3ulYOT6vcWqbKMwBOuw8YUbFJ+XiIjIE7ilwhcZDNIdnGuvlbIVXPHvfwMvvCD71Nvzyy7JbFBTyxZ6TBreA1NG9GBmA/mtsf3j8N5DyZi7OgcnK9T5/Xr7920b0WEhiI1siSGJ0er/ThkMUoCzpMTx+xaL9PplNCp/zhMnpBo3N9+s/NwknxDAtGnqnuPIEa9to0jJLsZzn2epMvc/7hnE9z8iIvJZDDj4qpUrXQ82AEBRkVsXZKfPey7YAADP39QLzwxnkUjyf2P7x2FUUize+iEX/0jPVXz+sqpaLFh3sYBkXGQo5oxPUj9rKCFBethbu1adYIPVU09Jr4+s5aAdRiNw+LC65+jaFQgJUfccDTBbBGZ8s0+VuSde2xXjBjKzj4iIfBe3VPgii0XaF+2qbt3c3tcaEx4qe6wcFTW1Hj0fkTcF6XWYflMvPDGsq+rnKi6vwbMrs5CSXaz6uRx4oq5DUZHUKpO0Y9Mm9QtGnjzpVn0iuaZ+vhPnqpV/r7qpb3u8fGs/xeclIiLyJAYcfNGiRfLuDlZVATExbp367AXPXszpwDuUFHhGJcV65DwCwLw1OTBbPFjzwFN1Hbp0AdLT1T0HOUcI4Mkn1T1HdDSwZYvHt1M89ekOrM8+pfi8E6/pig8eHaL4vERERJ7GgIOvsViAhQtdG2MwABkZbmc3mC0CC9blyB4vx9Xd23r0fERaMCQxGnGRoR4JtxWX12B7fpkHzvQ7a12HnTuBzEwgTqV08ZISYPJkFpD0tvR0IClJyjpRQ+vWwKpVwJ49QHfPbr9bu7sIG3NOKz5vt3at8PJtzGwgIiL/wICDr5GT3SAEMGSI21W7PV0wMqpVC1zVjQEHCjxBeh3m/N4CzxNBB0/XZkFCgtRJoF+/SwtKKsnaJtP6oZcZD54lBPDSS8DBg+rMr9cDu3YBt93m8a4UZovAn77Zq8rc88b3V2VeIiIib2DAwZdYLFLAwVUmE1BZ6fbpN+acdHsOVyyeMICVuSlgje0fh3cfSkZspPp1U/JLLqh+jgbpdEBkpLrneOgh4NZbgQMHpA+/zHjwnCVLpGwWtYSHX1qQ1EPe/D4XNbXK16Roodfhmp7tFJ+XiIjIWxhw8CXr1gHV1a6P69NHujBzg9ki8L/dJ9yaw1lRrVrgvYeS1a+eT6RxY/vH4ZcXR+DlW/qqep5/fp+L9Xs9XDwSkLZX7NoF/O9/QMuW6pyjtPRiVtiOHdKHYGY7qE/O9j9X6PVe60qxfu8JLPte+U4yADAqKYaBdiIi8isMOPgKIYAFC+SNPXvW7crd2/PLUKZywcgwQxA+e2IIMmePYrCB6HdBeh3ahatfCG/y51lYv9czQUUHCQlSSnzPnp4538KFzHZQWkNbViZOlAoVq8Vikeo2pKWpd44GpGQX47nPd0GtfzkPDu2q0sxERETewYCDrzCZgGPHXB/35ZdSYTY3K3ene2A7xd//7zJc16s97+4Q1eOJdrQCwHOf7/J8m0xA+tC4V5398JewfgjescPjH1b9krVOg30Qx2wGPv5Y/XPr9cDLL3sscGS2CMxQqW4DABiC9biKhZKJiMjPMODgKwwG4JdfXO9dv2GD28W0zBaB73arVGEcQKsWem6hIGqCtWuFJ8xdvd+zbTKFAGbP9tz5rIKCPPph1W+lpV2s02AN4tx8s2fObbEAhYVuZ/A5w2wRePG/e3Cuuk61c0wa3o0BdyIi8jsMOPiShATXAw6ffirdbXKDtJ2i1q05mvL+o4MZbCBqgn3XCrWdrDDi4y35ngs6pKVJWVieZjYzy8FdQkhBm6Ag6bleD4wfD2zcqM755s+X2qnaP9xs9+yMlOxiDFvyPf6bpV7gPVivw7SRvVSbn4iIyFsYcPAlOp3rF1ZCAGVlbp32ZIW6LfPOVLrY5pMoAI3tH4d3HkiGJ26ALlh3ANcu/UH97RXWD6x6L70V6fXAHXeo9wHZnzRUp8Ga3WANalssQK16wWmsXAkMGiS1VLU+VG6HmZJdjGdXZuFkhbrvU3dc3onZDURE5JcYcPAlLVpcrLburG7dgHbutdg6c17dCy1P7E8n8gfjBsbhrfsHeeRcJ8tr8OzKLHWDDiYTUFAgfVD1BosFqKkBpkzh1oqmNFSnwRvBoiNHXH8PdIPZIjBvTY5qBSKtdDpg0YQBKp+FiIjIOxhw8CVr1rh+YX7hgtv7W3ccLXVrfGN0AOIiQzEkMVqV+Yn80biBHfHeQ8lo07KFquexfsiatyZHve0VBoN0h9w+RX7dOqngYESEOudsyKFDQGqq4/cauqMfqBqq0/Dkk9LXngwWRUW5vq3QDdvzy1Bcrm6GHwA8fV0iQoJ5OUZERP4p2NsLICcJATz1lGtjXnxRunPnxv7W9XuLsTHntOzxjbFeMs4Zn8Q0UiIXje0fh3BDCzy4Ypuq5xEAistrsD2/DFerVT0/IUF61HfjjcDYsdJddU+YOlUKPOh0l97RHznSox90NcW+ToPZLP131iwgK8uz62jbFsjIUL1eg73T59UPNjx1XSJmjvNMfRYiIiJv0GRI/ejRo9DpdC4/YmNjvb109aSkACUlro3573+BTp3knzK7GM99nqVKOmlsZCjeZWcKItmu6t4WcZGh8MTH4I0eaIt7iQ4d3K4/45K8vItZDg3d0Q9US5Y41mkwm6VMFE9sQeneXSoounMnsHu39NyD2oWpG9x4677LMesWBhuIiMi/aTLgQPUIATz3nOvj8vNl73c1WwRmfLtP1tjmvHxLX/zy4ggGG4jc4MnOFR9uOap+Acn6rNst3nzTc+e87TbpA/Ydd1ysTRDI7TMtFmDhQu+d//Bh4MwZjxSHrK/aZMbSFHWya1r+3gr61svl3xAgIiLyFT6xpeL6669Hy5Ytmz0uOtpPawEYjcDRo66Pc2O/69YjpThXpU618XbhBm6jIFLA2P5xePehZLz4zV6UV9epeq55a3IwKinWs7+78fFSPQdPqa0FZs50/J59+8wxYzy3Fi1YvBioqvLe+fV6KdgzerRHt7RM/HgHvj+o/FZCqw8eHYxhPdwr5kxEROQrfCLg8Mknn6Br167eXob3yLmzFh8P/Pqr7P2uL3y1S9Y4Z7ArBZFyxvaPQ3hoCzz4gbr1HFSv5dAQkwk4ftxz52uMNcvBwx98vcpiARYt8v4aPBzsGf63H3CstFq1+eMiQ3FVNw/+DhEREXkZt1T4AjlV0o8fB3JyZJ1uwdr9OH3evc4WDWFXCiJ1XNVNquegtuJz6n0Qa5DBAPztb549Z0OsWQ5dugRO14p167yb3WBlzXLwwJaWeWv2qRpsAFgomYiIAg8DDlonBPCHP8gbK+MizVRnwYpfjso7XxPYlYJIPdZ6DjpA1SKSL36zB8vSc7FqdxEyDpeq1y7TSghg2TJ1z+GKwkJpy4W/13MQAvjTn7y9ConFIv3c3Wzv3JxqkxkfbSlQ9RzvPDCItYuIiCjg+MSWioBmNEqFs+QoKJAu0lzYVvHJr0flnasZsZGhmDM+iRdbRCqx1nOYtyYHxeXqtPOrtQD/SD9kex6n9u+1VrZU2MvMlApL1q/14E9SU6UWoZ6WmAjMny+1wOzQ4eL3Y2JUbYe5fm8xnv96t2rzA8A7DyRj3EC+/xERUeBhwEHrfvhB3t20oCDgl19cvkhbs6fI9XM1YvIN3dErNhwx4dI2CmY2EKlrbP84jEqKxfb8MmzJK8FbP8oMVjqpuLwGz67MUq/FrbVThbUl8Jw5wNq1yp+nOcHBQJ1dUc6FC4EXX7zYycJfpKcD06YBp9UrmNik/HygfXuP1WswWwSmfZGFdfvUa/tqCNJh2f3MbCAiosDlZ1dLfkYIYOpUeWOjo4GEBJeGPPXpDuwtqpB3vgZc27M9br+8E67u3pbBBiIPCdLrcHX3tujZIdwj5xOQOliotr0iIUFqi3jZZVJdAW+oq9cBpKpK6uBQX3o6kJSk3ToP9ddn/3zjRmD8eODAAaC01Dvr82AL0pTsYvSfk6JqsAEA/m9wAoMNREQU0Hwi4PDiiy9i4MCBaNOmDUJCQtChQwckJydjypQpSE1NhfDX/bSpqcCRI66N0emk7hRZWS5lN7yyLgcbc5S7qxUd1oLFIYm8yJPdYKwdLFS1apW2aicsWiTVF7ASApg8WfrAPnmyttYKXLo+iwV46SXp+cyZ0vdq1NmK4zT7FqQqSskuxqSVWaiutTR/sJsS24apfg4iIiIt84mAw9dff419+/ahvLwctbW1OH36NHbt2oW3334bY8eOxYABA5CRkeHtZSpLbnaDEEBFhdQW00mmOgve/znf9XM1YeHt/ZnVQORFQxKjERcZqmoRSXup+4vVm1wIqW6CllpS1s9ysK97cOiQ9FxL6q9v0SLpwz0g1aXIzfXe2uyp3JXCbBGYu3q/KnM35OGru3rsXERERFrkEwGHtm3bYsiQIRg5ciSGDh2Kdu3aOfz5/v37cf3112PFihVOzWc0GlFRUeHw0JzUVCAvT97Y2bNduli7fJ6yF8bPXJ+IcQM7KjonEbnG2rkCULdzhdW3WUXqbaswmaQiuFrLGpg9W6rvsHQpMH26459Nny5tU/DUFoumtnMIIdVmsLdwobYCOFYqd6XYnl+GkxVGVeau76nruiIk2Ccus4iIiFSjyXdCnU6HK6+8Em+//TaOHDmCM2fOYNu2bUhPT8fWrVtRUlKCzMxM3H333bYxdXV1eOaZZ5DqxF2lxYsXIzIy0vZIcLHWgeqEkFJd5XLhYu2bncdRpWBa6fMje2LmuCTF5iMi+aydK2Ij1d9eUVFTp962CmvxyJ07pS1jQUHqnEcOs1l6va7f1eHQoYtbGF56Sd1gifU9o7FzpaZemsFgNHo/gBMaevH/q/1jxw7VulKcPu+ZbSOjkmIw65Z+HjkXERGRlumEjxdAePPNNzHN7s5Njx49kJOTgxYtWjQ6xmg0wmi8eIejoqICCQkJKC8vR0REhKrrdYrRKG2JOHNG3vjcXKBHj0b/2GwR2J5fhuNlF/Dit/ug1E3JyNBgZP11NLdSEGmM9Xf+ZEUN/vL1btSq9Kp/Y+/2+OjxIepMbuXu66O3pKQo230hPR2YOFH6etIkxyC1/bmEAHr31s6WifpWr5aKVXpIxuFS3P/+VlXPMfXG7vjjmD6qniNQVVRUIDIyUjvXa0RE1CxNZji4YurUqXjiiSdsz/Py8rB69eomxxgMBkRERDg8NMVgADIygJAQeeM7dWr0j1Kyi3Ht0h9w//tb8edvlAs2AMAT1yYy2ECkQdbOFXcO6oRrerZrfoBMP/5WAlOdyoX4DAapKG79u+Jbtqh7Xnfo9dKWhi5dpEdzWyya63YhhFTosaBAesyf7/jn9tvqGspu0JIFCzyWaWG2CGTkqRuoigwNwvOjeqt6DiIiIl/i8wEHAJg1a5bD8w0bNnhpJQrq1k3+Htba2ga/nZJdjGdXZqG4XPmU0taGYEwZ0VPxeYlIWdf3bK/q/HNWZatXy8HK2irT/tFPw+nrFou0xcIaIJg50/FDtn2AwX57xGOPSQGWV191nC8tTSr0aFW/u0RmpnRMQ7UbtEbFeg1WpjoL/vT1HvSZvQFv/CizNpKTnri2GwPvREREdvwi4NCtWzd06dLF9vzgwYNeXI1CVq1yfYxOB/z8M9BAxobZIjBvTQ7U+hhwz5XxvMgi8gEPX90Vav6qfrGjEFcs2IiUbBW7VjTElzoVWQMCgFRYcvz4i/UXliy52D2iqEj6MD5vnhS0sAYmnAkiTJkCrFunzeyGv/wFWL9e9XoNZovAlM+z0Hv2Bvw36zhq1Q6EAejajm0wiYiI7AV7ewFKiYuLw7FjxwAAZ3xtb299QgBPPun6uOhoYPDgBv9oe36ZKpkNVqOSYlWbm4iUExKsx1PXJWL5T8q2wrV3rroWk1Zm4b2HkjG2f5xq57ER4tIuEVp3221AbKxUdNKaobBjB7B376XHVlVJbSxXr5YCE87IywOeekq59Srphx8UbXNqrVFy+nwNYsJDMSQxGhtzTuIPX+9BlcmsyDmcFROufoFWIiIiX+I3AYeqqirb1y1btvTiShRQXQ2UuVjtPSwM2Lat0TtFalbmjouULvCIyDfMHJeEPYXl2KpWV4nfvfDVLoxKilU/+yk19dIuEVpnbfVZn7GRlo0LFzb+Z405edL1dXlCXp7091cgsyEluxjz1uQ4BNTbtGqBc1UNby1Uiw5ALN8LiYiILuEXWyqMRiPy8i7uy4yN9fG77X/7m+tjwsKkug+NUPOuy5zxSdxOQeRjPp04FGr/1lbXCoz5xyZsyTujXl0HIaQiif7O1WCDlkVFyS+KbKexukTeCDYAfC8kIiJqiF8EHL799luHDIdrr73Wi6txk8UCLF7s+rjTpy8tHGbnii5Rin+40OuAdx7wUMo0ESkqJFiPp69PVP08eSVVePCDbeg3JwVrd59Q/gQmE3D8uPLzknqOH3c7gKJ2XSJXxEaG4l1PbR8iIiLyMT6/peLUqVOYMWOG7bler8eECRO8uCI3rV0r70JMp5O6UzSynWTH0TLFL8zeun8Qxg3kBRaRr5o5LgkA8P7P+Yq2yG1ITa0FU77chY8y8vH1M9codyfYYJBqH5SUNPznBQXAhAkea71ITqitBTZvBsaMkT2F2nWJnPH4NV0xul8shiRGM7OBiIioEZrLcMjIyMCkSZPw22+/NXvsvn37MGLECBTY7YN95JFH0LdvXzWXqB4hLu2n7gyDAfjppwa7U1hlHC51Y2GO2rRqgfceSsa4gR0Vm5OIvGPmuCQcXHAzbrvMM7/PO4+dw8C5qcp2sWioTab1cfPNUkFd0g69Hnj5ZbeCQGrWJXLGOw8Mwpzb+uHq7m0ZbCAiImqC5jIcjEYjli9fjuXLl+Oyyy7DiBEjMHDgQMTGxiI8PByVlZXIy8tDamoq1q1bB4vFYhs7aNAgLFu2zIurd5Pc1OA+fYBhwxr9Y7NFYP0+ZVKZ70ruiFfvvpwXWER+JCRYj/uHdMbqPSpseWjABZPZc10sDAYgKwsYMQI4fFjdc5FzLBagsNCtwpHe7Abxwk09GXAnIiJykuYCDvb27NmDPXv2OHXs+PHj8eGHHyKiibv8mmcwSL3kmyj+2KBTpxq9cEvJLlasNVhrQzCDDUR+akhiNOIiQz2apj7jm72e6WJx4ACDDd7Stq20VbB+kciYGLe6VHjj36tV13ZhHj8nERGRr9JcwKFr166499578dNPP6G4uOmUW71ej5EjR2LKlCm47bbbPLRClX3wgfPHhoQAW7ZIvdztLtysPcnTc05ixZajii1t8YQBDDYQ+akgvQ5zxidh0sosj53zXHUd7lv+K56/qTeuUis1PVC6WGiJfZAhJgaIj1dkWut72+nzNYgJ///27jw+qvre//h7si9kIUFIwhpILIRFQURwQUSRqICKXEULIiIXRdHberVibXGrVaza6lXx54JVlEXrQ8BaRUFBWtTIIpAgsiSsiUACWYAsZM7vj5gxE7LMcmbN6/l4zOMx35nv95wPmeE7Zz7zXaJ0Vb8UvWbiZ5yjfDm6AgCAQGMxDP9dSevgwYPKy8vT3r17VVJSopMnTyo6OlqJiYnKyMjQ4MGD1a5dO7fPU1ZWpoSEBJWWlvp2hITVWpc4OHXK8TbHjkkJCbbiJ1sL9fCyPBWVmf+rz8LpQzWsV7LpxwXgPz7eXKg7F27w+hqLiTHhenJ8f/OnWFRV1SVljx0z97hoWlSUtH271K2bqYf9ZGuhHlme59OFIi2q25Fi7e9Gknz3Eb+5XgMAOMzvRjg0lJaWprS0NjRP8qOPnEs2xMbWXdz97JOthR79ddLXi3QB8LwrB6TqBevZumvRJq+e99iJGt2+YINeummgufPjIyLqvvyScPCO6mopMdHUQ36ytVB3LNjg0y0w69MLc8ZmkWwAAMAJfrdLRZtlGNK99zrXpl0727zYWquhBz7Y4oHAfsEwUqBtGHN2Z02/qIdPzn3nuxv18WYTd7Corq5b5waeFxIiffllizsmOavWaujhZXk+TTZIdSMbXvbGIqcAAAQZvx7h0KZUVUn5+c61+eknqbJSio7W17uLdexEjWdik5SaEKUh6WwtB7QVv7+qrySLXv3KyX7JTYakme9u0PCcDrr6rDSltY/RkPQk139VjoyUcnKkAwekq66SSkpMjRcNWK3SiROmHvL/Vu3wyBRBR916QQ+Nykpx7z0IAEAbRsLBX3z5pVTrwk4Sn30mjRunBV/vMT2khhhGCrQ9v78qSwO7ttf9/9isiionpnuZYM2OI1qz44ikuoTnnLFZrv+63LVr3W3TJunQIenmm6W8PPOCRZ3QUOkPf5Auv1yyuP958cnWQj33+Q4TAnPNSzcN0pUDGNEAAIA7mFLhD9xZRf2++/TJloP619Yic2P6WWJMuOYxjBRos64ckKrv51yuYT4c4VRYWqk7FmzQJ1vdnGrRtavUr59UXGxOYLBXW1s3mmTFCvcPZTX0m8Wb3I/JRf9zaQbJBgAATEDCwR9UV0v797vW9scf9a9n/m5uPD8bMyBV6x8aRbIBaONCQyxaMH2oEqJ9NyjOkPTAP7bo3zuPqNbqxoz+yEjp22+lPn1Miw0NhITUjXJwcpuTWquhdbuKtXTTAa3bVay/fr5dJ2usHgqyZe1jwjXr0jN9cm4AAIKNX2+L6S1+sc1SQYGUnu50M0PSlk4ZGjflOVOGsEpSZJhFz11/trkrxQMIeJ7eCcdRSbERevzqfq7/Al1VVbdzxaFD5gaGOikpdZ9pkZEOVW9qy0uL5LOFIhnV57/84noNAOAU1nDwF9OmudTMIimlolgRtadUHRbudhhxkaFa/4fLFRHG4BcA9rL7peqeSzP1t5W+m1cvSSXHqzXz3Q26dMMZuu2iXs4v6BcZKX33nbRli3T0aN2v8bNnuz7SrK1ITJT+9a+6hTePHpWSkqROnU6v17GjU8mGpra89EWyITYyVM/811kkGwAAMBEJB39w6pS0apVjdSMjpblzpcxMqVMnbdp3VLd/ut+UZIMkXT+4K8kGAM26+9JM/f0/+Tp20ruLSDZl5Q+HtfKHw64tKlm/kKQkffopyYaWJCdLH30kdelSdzNJrdXQI8t9v+WlJA3PTNb8qeexODIAACYj4eAPpkxxvG5VlXTbbVJMjD7ZWqjfbTqs0vgOpoVyWVaKaccCEHxCQyx68roBfjG1ol7Rz4tKvuzKUHjDqFtzICSkblvHtq59e+k3v5ESEmyJbXXsaGqiod7Xu4vtplH4SohFem3KEJINAAB4AAkHX6utld5917k2Tz+tT677b9Mv+JNiIzTEhyvRAwgM2f1SNWN4ul5Zk+/rUCTVDb+3SHpkeZ5GZaU498Wxulrau7flZENCglRa6m6YgaG8XLrvPikqyqOn+XjzQd33j80ePYejpl+Uzsg+AAA8hISDry1b5nQT46mn9EjY+aaH8vjV/fiFB0Craq2Gln3v5haVJjNUt33mt/klGtYr2fGGkZF1WzkePtx8nQ4dpIwMqabG7Tj9Xvv2pi1A3Fit1dC3+SV6Zc0ufbm9hb+3l4RY6pINs6/M8nUoAAAELRIOvmQY0hNPON3McvKk+mz4SoWZ55kWylX9U9hzHIBDvs0v8Yuh8E35PK/IuYSDZL+eQ1OWLQv+ZIPFUrdV6CefOLzgozOa2onCVyad103pHWI1eVgPRjYAAOBhfNL60ooVdSulO8mQdM9/Fjm9z3lzQiU9f+MgU44FIPgdKvf9l8bmvP7vAn2y1cTRF4YhPfqoecfzV4Yh5eXV3UxWvxOFPyQbpl/UQ49f21/TLupJsgEAAC/g09ZX6hcqc4FFUmr5EUXUmrNK/KxLM5lKAcBhHeM8O7/fXY8sz1Ot9fSEbK3V0LpdxVq66YDW7Spuss5pqqvbzg4WISF1n0smJbMl/9qJokdyjH5/VV9fhwEAQJvClApfqV+ozFndumnVn17Wg/85bMpWmLGRoZp1aabbxwHQdgxJT1JqQpSKSiv94otkY02t5dDUkH6HttNsbY2H//xHmjXLrNB9y2qV9u2r+3wyaVqFP02/+dM1/X0dAgAAbQ4jHHwlMlJ66inn21VV6RNLBxWZtBXmM/91FqMbADglNMSiOWPrFtrz197j/ve/t41iaG5If/12mq1OwejaVRo06PTbwIHSW295bJFFr3rhBWn9+rrkiolrOBSVnjTtWO5IjAnXUGfX9gAAAG6zGIaJYycDVFlZmRISElRaWqr4+HjvnNQwpLPOkrZscbxNp0764u2PNHXlT26fPiosRH+deLbze9YDwM/8aSHA5nSKi9Tx6lpVVDU9Bc0iKSUhSmt/N9L55GtVldStm3TokPuB+pLFIg0eLH3zjcvJk/odKA6VV6pjXJTO7pqohz7cog83HlCtH1xlzJs0iM+7IOCT6zUAgFuYUuEr1dXSnj1ONTEOHdL9awplxsCUjX+8XNERoW4fB0Dbld0vVaOyUvRtfolW5BVp/r8LfB3SaX4qr2rxeZe305TqRgL8+9/SeedJJSWuB+lrhuHWVIpPthbq4WV5Kirzv8RTYnSYnrxuAMkGAAB8hISDr0RE1A3Tzc11uIlhsaj0xCkpPMLt02/ad8z5i2sAaCQ0xKJhvZI1rFeyIkItemVNvq9DconLX5YzMqRNm35Z46GoSDp61L7OQw9JBQXuhOdZycnS2rUuJxtuX7DBA0G5738uzWRRZAAAfIyEg6+sWOFUskGSQqxWDd23RWt6nuP26f15WzsAgWn2lVk6q0uiHlq6VSXHa3wdjlMe+nCLosNDXPslvGvXultTqqqke+91L7jmtGsnVVQ0/7zFIvXpI82dKx079svjSUlSp06/lDt2lLp0afYwjadLDElPUmiIRbVWQ/cu+d79f4cH/N/EszXm7M6+DgMAgDaPhIMvGEbdL15OslosuverBVqTPsjtRcr8fVs7AIHpygFpGt0v1e4L6tHj1brv/e91vLrW1+E163hVrW5fsEHzJg2yTRNp/AXbJfW7XBw4IF11lXtTLywWqUcP6bHHpPbtpQceaHkdIMOoO99ll7m8EGRT63SkxEfpxiHdtOqHIr98TWcMTyfZAACAnyDh4AsrVkjffed0sxDDUGr5EUXUnnJrS8zk2AgNSU9yuT0AtKR+mkVDo/ul6Nevfa2vd/v3Wgf3vve94iJzVVT2y9oPSbERenRsXyXHRbqWhKgfAdFw6kW96mrHExGGIeXnSx06SCNGtL5YpRtTJSTZdvdovOZjUVmlnvv8R5eO6WmRYSG6P7uPr8MAAAA/Y5cKeXnVY8OoW2AsJ8epZrUhoRr366dV3C7J7S0xX7ppkK4cwAJaALyr1mpo8GMrdPRk0ztGBJL6X/l7dIhxfxTEvn2/JCIMQ7r5Zmnbtrr7jYWESOecU7ejxP79pycwGmplqkRLqk9ZNfTPK1VyvNql9r60cPpQ1igKUuxSAQCBhxEO3ubC7hSSFGKtVUH7zjoeHevW6WcMTyfZAMAnQkMs+tO1/TXz3Y2+DsVtjX/lT02I0pyxWe6vAVFVJRUXN51skCSr9ZcdJVpaO8JFtVZDL6zcoXlrdqmyxmrqsb2FNYoAAPAfJBy8LTJSWrNG6t3bqWYVYZGqcWN3isToMD1xbX9dOSDN5WMAgLuuHJCmGfuPBexuFs0pKq3UHQs26OVJg9zbgrF+zYfWRi44OU2iuYUfG/pka6HuXeLfa204gjWKAADwHyQcfGHzZqeqG5L2J6aqOtS1l2toepLemT6UrcEA+IW63Sza/7ybReAN2W9K/XiEBz7YorjIcA3tlex6n2vyyIWWFn6snxJypKJKsxYG9sgTi6SUhCjWKAIAwI+QcPA2w5BuvdWpJhZJySdLXV4s8sbzupFsAOBXrhyQqtH9UvT17mJNf+s7nQjwX9XrHTtRo1+//o17UyxMFIgLP7qi/hNuztgsPu8AAPAjIb4OoM05caLlfdMbsUqacOOTGnfzcy7vTMHwUgD+KDTEogsyOujZ68/ydSimKyyt1O0LNuix5blat6tYtVbPrc9cfcqq17/arT8u3arXv9qt6lN1ay/UWg09sjzvtGRDIIsJD9X/XJqplHj7z7WUhCj3p7MAAADTsUuFvLzq8e9/Lz3xhMPVHx9+s14bdr3Lp4uLCtOmP17OLz4A/FpTw/6DSUp8pB4e11ejslJaXEvBkbUWGvrzx3l69at8NcxnWCzSiDPPUFpilN75Zp8n/1le95vLMnXPZWc6/XdCcGCXCgAIPCQc5MUPMKu1bqGvU45tCWeVtCUlU1ff/GzdFaQL/m/iQI05m4UiAfi/+i+Rj3+Uq9zCcl+H4xGJMeE6dqLGVk6KDde1Z3fWZVkpKi6v0h+Xb1XJ8V+ebxcZqtsu7KlZl2ZKku1LdofYSL3zTYE+3vqT1/8NvtIuMkzfzyGB3paRcACAwEPCQV78ADt2TGrf3qkmh6ITdOHMN12aTtE9KVqr7x/pdDsA8LVpb36jlT8c8XUYfiMiLETR4SEqPelYwjoY3XpBD/1xbF9fhwEfIuEAAIGHNRy8KcS5P7dV0sSb/uzy2g3XDOzsUjsA8LXXbzlP5/VwLkEbzKpPWdt0skGSRmWl+DoEAADgJBIO3hQfL02Y4HD1BQOytbtDN5dPtzhnn0cXKgMAT5o4xPX+D8Elle0uAQAISCQcvMlqld5/36GqhqQJeV/UtXFRUVmVvs0vcbk9APhSSkK0r0OAj1l+vrHdJQAAgYmEgzcdO+ZwVYukmFNVGrn7O7dOeag8OFd8BxD8hqQnKTWBbX3bMra7BAAgsJFw8KZZs5yqbki6598LJTfW9ewYx8U6gMAUGmLRnLFZ4nfttufmYd21cPpQrf3dSJINAAAEMBIO3lJbK737rlNNLJLSyg4rota1hcKY8wog0GX3S9XLkwYx0qGNuaJfqob1SmYaBQAAAS7M1wG0GR9+6HSTo5HtNH7S0y7vUsGcVwDBILtfqkZlpejb/BIdKq9U/uHj+uvKHb4OCx5gUd00CpLlAAAEBxIO3mAY0p/+5FSTtwdcrhcvuElF8R2cPl1iTLieHN+fYagAgkZoiEXDeiXbyr1T4/TI8jwVlrJOTbCoT4+TLAcAIHhYDMONBQKCRFlZmRISElRaWqr4+HjzT1BVJXXqJJWWOlTdkHQyNEJZv31fCnF81ovFIt0zMlOzLs3kYg1A0Ku1Gvo2v0Sf5RXpjX8XyKK6/hOBKTUhSnPGZpEsR7M8fr0GADAdIxy8ISJC6trV4YSDRVJkbY1iqyt1PCrG4dO8eONAXTkgzcUgASCw1I96GNYrWUPSkxjxEIAmD+2mwT2S1DGubhoFyXIAAIILCQdvqK6Wdu92qskjl9zmVLLhin6dSDYAaLMar/PQMS5KWanxuvaltdp95ISvw0MTQizSH8b0VUQY61cDABCs+JT3hvDwuqSDgwxJE3JXObUd5k3ndnchMAAIHvUjHq4+u7OG9UpWQky47rnsTF+HhWZMvyidZAMAAEGOT3pvKCuTTjm+taVFUkpFsVPbYYaEMgwVABrrGMd2mr4QFxWqbY9ma+yAFDX+dAqxSDOGp2v2lVk+iQ0AAHgPUyq8wcGFHw1Jjw+/RTs69tCPZ/RwajvMIxVVLgYHAMFrSHqSUhOiVFRayYKSXnT9OV0VHRGqF246R89cb9Xb6wq0p+SEuifFaPKwHoxsAACgjSDh4A3x8dLcudL997dYrVYWvT14nKrDI5w+Bb/iAcDpQkMsmjM2S3cs2ODrUNqUy7JSbPcjwkI07aKePowGAAD4Cj8xeINhSIsWtV7N4vy0CIvqthIbkp7kQmAAEPyy+6Xq5UmDlJpAYtbT+EwCAAANMcLBG6qrpf37W61WExouOZF0qK85Z2wWW4kBQAsa7mJRVFapkooqrd15WF9sP+Lr0PxWRKhUXet4fT6TAABAYyQcvCEyUsrJkS69VMbOnactoCXVrd9gsVpVHRLq8GFTEqI0Z2yWsvulmhYqAASr+l0s6k27qKc+2nRQsxZtZH2HRl66aaBCQiy2qSgN/z6Wn8uJMeE6dqLG9jifSQAAoDESDt7SqZN05EiTyQap7gIu2lqjkbu/06qMIS0eauoFPXR5VoqGpCfxKxIAuGHM2WkKCbFo5rus8VBvdN+OunJAmiTp5UmD9MjyPBWWVtqer08s1I8YOVReqY5xUXwmAQCA05Bw8JaICFm7dpPl2LFmkw6GpHvWvqtVvc5tcWrF5Vkpdr/SAQBcd+WAVM0LOf2LdVt189B02/2GU1GaSizwWQQAAFpCwsFbqqtVtf+AoluoYpGUWn5EEbWnWtwS81A5F8QAYKamvlgfPV6lx/65rU0lIRJjwjW0URKh8VQUAAAAR5Fw8JbISE2/6yWFbstTQmVFk1WORcXpxzN6tJhskNgCEwA8oakv1qP7perb/BJ9llekN/5d4JvAvOjJ8f2ZFgEAAExDwsFLqk9ZtbY6Vup1rlvHYbsxAPCe+iTEsF7JGpKe1Oy0i8SYukRxw0UU/dWorI7asr9URWVVtsdS4iP18Li+LPgIAABMRcLBS95eV2DKcdhuDAB8o6mtNZNiI5SSEG1LBH+bX6LP84r0wcYDOupnyQeLpP8enq7ZV2ap1mqw4CMAAPA4i2EYbX43sLKyMiUkJKi0tFTx8fEeOceFT67U/mPuzQN+4caBGntWmkkRAQA8pfEXel+vBzF5aDf9YUxfRYSF+OT8gBm8cb0GADAXIxy84GR1rdvJhiv7dSLZAAABoqX1IIpKT+pIRbWOnaiWxSKFhoTo+ZU7JNXtVtRYTESoTlTXNnuuEItkGE23tahuG8uHx/VjBAMAAPA6Eg5e8MTHeW61D7NIL9x0jknRAAB8oaXdHvqkxp22PkRqQpTmjM2SJN2xYIMk+6RCffpg+kXp+n9r8mVp5nmm4gEAAF8h4eAFu48cd6v9rEvP5GIRAIJYU9tyNlxX4eVJg05LSKT8nJDI7peqgd3at/g8AACAL5Bw8IJoN+bMxkSE6q6RGSZGAwDwRy2NgGgtIdHa8wAAAL5AwsELMjrG6fMfDrvUdsbwnlwwAgBaTEg48jwAAIC3sVy1F4SGupYwiA4P1V0jM02OBgAAAAAAzyPh4AWJ0REutZs4pCujGwAAAAAAAYmEgxckxbqWcLg8K8XkSAAAAAAA8A4SDl5w7ES1021S4iM1JD3JA9EAAAAAAOB5JBy8wJURDg+P68t0CgAAAABAwCLh4AUpCdFO1f+/iWezbzoAAAAAIKCRcPCCIelJSomPdKjutAt7aMzZnT0cEQAAAAAAnkXCwQtCQyx6eFzfVuuNyuqoP4xpvR4AAAAAAP6OhIOXZPdL1bxJg5QYE37ac9HhIXrhxoF69eZzfRAZAAAAAADmC/N1AK05cOCAFixYoGXLlqmgoEBHjhxRhw4d1KNHD40bN06TJk1S586BMQUhu1+qRmWl6OtdxVq3+4gki4b1StbQnsksEAkAAAAACCoWwzAMXwfRnHnz5ul///d/dfz48WbrtGvXTn/5y180Y8YMl89TVlamhIQElZaWKj4+3uXjAAAAwDO4XgOAwOO3UyoeffRR3XHHHXbJhszMTF188cXq1auX7bGKigrdfvvtevzxx30RJgAAAAAAaIJfJhyWLl2qOXPm2MpZWVlav369fvzxR3355ZfauXOncnJy1KdPH1udP/zhD1q2bJkvwgUAAAAAAI343ZSKmpoaZWVlaefOnZKkLl26aPPmzWrfvv1pdUtKSjRgwAAdOHBAUt0IiLy8PIWFObc0BUP0AAAA/BvXawAQePxuhMOiRYtsyQZJevbZZ5tMNkhSUlKSnn32WVt5x44dWrRokcdjBAAAAAAALfO7hMOSJUts99PS0nTttde2WH/8+PFKTU21ld977z2PxQYAAAAAABzjVwmHkydP6rPPPrOVs7OzW50eERYWpuzsbFt5xYoVqqys9FiMAAAAAACgdX6VcNi2bZuqqqps5QsuuMChdg3rVVZWatu2babHBgAAAAAAHOdXCYfc3Fy7cmZmpkPtGtfLy8szLSYAAAAAAOA857Zz8LCCggK7crdu3Rxq1717d7tyfn5+i/WrqqrsRlKUlZU5FiAAAAAAAHCIX41waPzFPzEx0aF2CQkJduXy8vIW6//5z39WQkKC7da1a1en4gQAAAAAAC3zq4RDRUWFXTk6Otqhdo3rtZZwmD17tkpLS223ffv2ORcoAAAAAABokV9NqaipqbErt7ZDRXP1Gh+nscjISEVGRjoXHAAAAAAAcJhfjXCIjY21Kzu6vWXjeo2PAwAAAAAAvMuvRji0a9fOrnzixAnFxMS02u7EiRN25bi4OKfOaxiGJBaPBAAA8Ff112n1120AAP/nVwmHM844w65cWFioDh06tNqusLDQruxIm4bq13xg8UgAAAD/Vl5eftqC4QAA/+RXCYfevXvblffs2aP+/fu32m7Pnj125T59+jh13rS0NO3bt09xcXGyWCxOtXVFWVmZunbtqn379ik+Pt7j50Pg4z0DZ/GegbN4z8BZ3n7PGIah8vJypaWlefxcAABz+FXCoW/fvnblDRs2aMyYMa2227Bhg105KyvLqfOGhISoS5cuTrUxQ3x8PBd1cArvGTiL9wycxXsGzvLme4aRDQAQWPxq0ciuXbuqV69etvLq1asdatewXkZGhk+SBwAAAAAA4Bd+lXCQpPHjx9vuf/nll9q7d2+L9ffu3WuXcGjYHgAAAAAA+IbfJRymTp2q0NBQSZLVatVjjz3WYv1HH31UVqtVkhQaGqqpU6d6PEZ3RUZGas6cOYqMjPR1KAgQvGfgLN4zcBbvGTiL9wwAoDUWww/3Fpo2bZreeOMNW/nVV1/Vbbfddlq9V155Rbfffrtdu9dee80rMQIAAAAAgOb5ZcLhyJEjGjp0qHbt2mV7bNy4cZo4caLS0tJ04MABLVy4UB999JHt+YyMDK1bt87pLTEBAAAAAID5/DLhIEk7duzQ6NGjlZ+f32rd9PR0rVixQhkZGV6IDAAAAAAAtMbv1nCol5mZqc2bN+vuu+9udqulhIQE3X333dq8eTPJBgAAAAAA/IjfjnBoqLKyUqtXr1ZBQYGKi4uVnJysHj16aMSIESxUBAAAAACAH/LbEQ4NRUVFafTo0ZoxY4YefPBBzZgxQ6NHj/ZasuHAgQN66qmndMEFF6hz586KjIxU586ddcEFF+ipp57SgQMHPB6DYRj6+OOPNXnyZPXu3Vvx8fGKj49X7969NXnyZH388ccKgNxRUKuqqtKqVav0xz/+UWPGjFHPnj0VFxeniIgInXHGGRo4cKBuv/12rVixwmOv1S233CKLxeL0bd68eR6JB80rKChw6bVKSUnxWEz0M/7LlfdKw1uPHj1Mi4V+xrfKy8u1Zs0a/fWvf9XNN9+svn37KiwszNTX2h+ueyT6JAAICgZa9PLLLxuxsbGGpGZv7dq1M+bNm+exGPbs2WNccsklLcYgyRg5cqSxZ88ej8WBphUVFRkTJ0404uLiWn2N6m99+/Y1vv76a9NjmTJlisMxNLy9/PLLpseCluXn57v0WnXq1Mkj8dDP+DdX3isNb+ecc45psdDP+M6ZZ55pWCyWFv/O3bt3d+sc/nDdYxj0SQAQLMKaSkKgzqOPPqo5c+bYPZaZmam0tDTt37/ftotGRUWFbr/9dh0+fFgPPfSQqTEcPHhQF154ofbt22d7LC4uTllZWTIMQ9u2bVN5ebkkadWqVbrooov09ddfKzU11dQ40Lx9+/Zp0aJFpz2empqqLl26KC4uTkVFRfrhhx9ktVolSbm5ubrwwgu1ePFijR8/3iNxpaWlqX///g7V7datm0digOOGDx+u6OjoVuslJSWZfm76Gf83evRop+oXFBRo+/bttvKkSZPMDkkS/Yy3/fjjjx49vj9c90j0SQAQVHyc8PBbH374oV0GPSsry1i/fr1dnZycHKNPnz529ZYuXWpaDLW1tcbgwYNtx7ZYLMYjjzxiVFRU2OqUl5cbc+bMsfvFY/DgwUZtba1pcaBlOTk5tr/90KFDjXnz5hn5+fmn1SssLDTuuusuu9cqIiLC+OGHH0yLpeEvj1OmTDHtuDBf4xEOTb1nvIF+JjiNGzfOrp85cuSIacemn/Gd+r97bGyscf755xuzZs0y5s+fb2RnZ7s9wsEfrnsMgz4JAIINCYcmVFdXGxkZGbYPsS5duhglJSVN1i0uLjY6d+5sq5uZmWnU1NSYEscbb7xh96E+d+7cZus++eSTdnXffPNNU2JA69avX29cffXVp12YNef555+3e62uu+4602Lhi0Dg8JeEA/1M8CksLDTCwsJsr9P1119v6vHpZ3xnwYIFRl5e3mlfrBu+Jq4kHPzluscw6JMAINiQcGjCW2+9ZfcBtmTJkhbrL1682K7+22+/bUocPXv2tB2zX79+LWbua2trjX79+tnq9+rVy5QY4BlDhgyxvVZRUVHG8ePHTTkuXwQCh78kHOhngs9TTz1l99769NNPTT0+/Yz/cTfh4C/XPYZBnwQAwSYgdqnwtiVLltjup6Wl6dprr22x/vjx4+3mDb733ntux7B+/Xrt3r3bVp45c6ZCQpp/uUJCQnTHHXfYyrt27dLGjRvdjgOecfXVV9vuV1ZWqqCgwHfBoM2inwlOb7zxhu1+t27ddNlll/kwGgQCf7jukeiTACAYkXBo5OTJk/rss89s5ezsbIWFtby2ZlhYmLKzs23lFStWqLKy0q04li1bZlceM2ZMq20a11m6dKlbMcBzGi/8V1ZW5qNI0JbRzwSftWvX2i0WOXXq1Ba/sAH+ct0j0ScBQDDiKqSRbdu2qaqqyla+4IILHGrXsF5lZaW2bdvmVhwNM/Rdu3ZV165dW23TrVs3denSpcljwL80HtHQsWNH3wSCNo1+Jvg0HN1gsVg0depUH0aDQOAv1z0SfRIABCMSDo3k5ubalTMzMx1q17heXl6eaXE4GkPjuu7GAM8wDEPvv/++rZyamqr09HTTz5OTk6MrrrhCnTt3VmRkpOLj45Wenq5x48bpL3/5i3766SfTzwnX/e53v9OAAQOUmJioiIgIderUSYMGDdJdd92lTz/9VIZhmH5O+pngUl5ebjc0/rLLLlP37t09ek76mcDnL9c9jWOhTwKA4EDCoZHGvzw7um9444u6/Px8t+LYs2eP0zE0jsPdGOAZ7777rm0vc0n69a9/LYvFYvp58vLy9Mknn+jgwYOqrq5WeXm5CgoKtHz5ct13333q3r27fvvb39r9sgXfWbJkibZs2aLS0lLV1NTo0KFD2rhxo1588UVlZ2erf//+WrdunannpJ8JLosXL9bx48dt5WnTpnn8nPQzgc9frnsk+iQACEYkHBppPJc+MTHRoXYJCQl25fLycpdjOH78uGpra52OoXEctbW1OnnypMtxwHz79+/XPffcYysnJiZq9uzZHjlXWFiYevfureHDh2vEiBHq27evQkNDbc9XVVXpueee07Bhw3T06FGPxADHJScna8iQIbr00kt13nnnqUOHDnbP5+bmavjw4Xr99ddNOR/9TPBpOJ0iKSlJ11xzjcfPST8T+PzhukeiTwKAYEXCoZGKigq7cnR0tEPtGtdz54PX1RjMjgPmOnHihMaPH6/i4mLbY6+88sppC0i6Iz4+XjNmzNCKFStUUVGhbdu2afXq1friiy+0detWHT16VC+99JI6depka7Nx40Zdc801slqtpsWB1lksFg0ePFgvvviidu/erSNHjuibb77R559/rq+//lqHDx/Wd999pwkTJtjanDp1SjNmzNCnn37q9vnpZ4LLtm3b7EbATJo0SZGRkR45F/1McPGH6x534vBELAAA85BwaKSmpsau3NpKzc3Va3wcb8Rgdhwwz6lTpzRx4kTl5OTYHrvzzjt1/fXXm3qe559/XvPmzdOoUaOa/LIRFxenO+64Q99//72ysrJsj69Zs0ZvvfWWqbGgZd27d1dOTo5mzpzZ7Boe55xzjt577z09//zztsdqa2t11113uf1/m34muDQc3SB5djoF/Uxw8YfrHnfi8EQsAADzkHBoJDY21q7s6DZPjes1Po43YjA7DpjDarVq8uTJWr58ue2x66+/Xn/72998FlOnTp20dOlSRURE2B575plnfBYPWjZr1izdeuuttvLOnTtP2z7OWfQzwaOmpsbui/zgwYM1YMAAH0ZUh34mMPjDdY87cXgiFgCAeUg4NNKuXTu78okTJxxq17heXFyc12MwOw64z2q16pZbbtGiRYtsj1133XV655137OY5+0JGRoZuuOEGW3nr1q3av3+/DyNCS37/+9/blf/1r3+5dTz6meDx0Ucf6dChQ7bybbfd5sNo7NHP+D9/uO5xJw5PxAIAMA8Jh0bOOOMMu3JhYaFD7RrXa7zgmzPCw8PtFkByNIbGdRMTE33+pbYts1qtmjZtmt5++23bY9dee60WLVrk1FBRTxo5cqRd+YcffvBRJGhNz5497VZid/e1op8JHg2nU8TExOjGG2/0YTSno5/xb/5w3SPRJwFAsCLh0Ejv3r3tyg23aGpJ43p9+vQxLQ5HY2hc190Y4Dqr1arbbrtNb775pu2xa665RosXL/abZIMkpaam2pWPHDnio0jgiIavlxmvFf1M4Dt48KDdaJcJEyYoPj7ehxGdjn7Gv/nLdU/jWOiTACA4kHBopG/fvnblDRs2ONSucb2GC2W5G0dubq6qq6tbbVNVVaXc3FzTYoBr6pMN8+fPtz12zTXXaMmSJQoPD/dhZKdrPAzVmVXB4X0NXy8zXiv6mcD397//3W4rQU8uFukq+hn/5i/XPY1joU8CgOBAwqGRrl27qlevXrby6tWrHWrXsF5GRoa6dOniVhwjRoyw3a+srNQ333zTaptvvvlGVVVVtvIll1ziVgxwXiAlG6S6+dQNpaSk+CgStKaqqko7d+60lc14rehnAl/DviYzM1PDhw/3YTRNo5/xb/5y3SPRJwFAMCLh0ITx48fb7n/55Zfau3dvi/X37t1r98HbsL2rxo4da/cF1ZGtxBrWiYiI0JgxY9yOA45rKtlw7bXX+m2yoba2VgsXLrSVY2NjNXDgQB9GhJZ88MEHdr8UX3jhhW4fk34msK1Zs0Y7duywlRvuZOIv6GcCgz9c90j0SQAQjEg4NGHq1Km2BYesVqsee+yxFus/+uijslqtkqTQ0FBNnTrV7RgSExM1YcIEW/mdd96x+3WzsR07dujdd9+1lSdMmGC3+BI8yzAMTZ8+3S7ZMH78eC1evNgvkw2S9PTTT2vbtm228rhx4+y2r4P/+Omnn/TAAw/YyiEhIaZc4NPPBLaGi0WGhoZqypQpPoymafQzgcEfrnsk+iQACEoGmnTrrbcakmy3V199tcl68+bNs6s3bdq0Zo+Zn59vV3fKlCktxrBz504jPDzcVn/QoEHGoUOHTqtXVFRknH322bZ6ERERxq5du5z698J1VqvVmD59ut1rO2HCBKOmpsbtYzvznrnzzjuNt99+26isrGzxmNXV1cbDDz9sWCwW23HDw8ONHTt2uB0vHPOf//zHmDFjhvHDDz+0Wnfz5s1GVlaW3fvglltuabY+/UzbUFpaasTExNhej7Fjx7p8LPqZwDdlyhTb37l79+4uHcMT1z2GQZ8EAG2d/yyX72eeeuoprV69Wrt27ZIkTZ8+XcuXL9fEiROVlpamAwcOaOHChfroo49sbTIyMvTkk0+aFkOvXr00d+5c/eY3v5FUt0DTWWedpZkzZ+rcc8+VYRjKycnRSy+9pKKiIlu7uXPnqmfPnqbFgZa99957evXVV21li8Wio0ePOjWs895779WoUaPcimPr1q168cUXNXPmTGVnZ+ucc87Rr371KyUmJiokJESHDh3St99+q4ULF2r//v128b722mvKyMhw6/xwXFVVlV555RW98sorOuusszRy5EgNGDBAKSkpiouLU0VFhXbu3KlPP/1U//znP22/JErSwIED9be//c20WOhnAtOiRYvspth4a7FI+hnfevzxx/X444+f9nhNTY3t/p49exQVFXVancmTJ9t9VjXmD9c9En0SAAQdHyc8/NqPP/5opKen22Xmm7ulp6e3+suNs1n+eg8++KDdr0TN3SwWi/HQQw+Z8C+HM+bPn+/Qe6Sl2/z585s8tjPvmYsvvtjp8yYmJhqLFi3yzB8Gzfriiy9cep+MHTvWOHz4cIvHpp9pG8477zzba9KpUye3RlTRzwSOOXPmuPw540hfYPZ1j2HQJwFAW8caDi3IzMzU5s2bdffddze7r3lCQoLuvvtubd682WO/3PzpT3/SZ599psGDBzdbZ/Dgwfr8889bnXeJ4DVhwgSdd955Dq0ZkZKSotmzZysvL0833HCDF6JDQz169NANN9yg1NTUVuuGhIRo1KhRWrp0qZYtW6YOHTp4JCb6mcCRm5trt3r/lClTFBbmnQGL9DPBzV+ueyT6JAAIFhbDMAxfBxEIKisrtXr1ahUUFKi4uFjJycnq0aOHRowYocjISK/FsWPHDuXk5KiwsFCSlJqaqnPPPVeZmZleiwH+rbq6Wrm5udq1a5cKCwtVUVEhq9WqhIQEnXHGGRo0aBDvFz9y8OBB5eXlae/evSopKdHJkycVHR2txMREZWRkaPDgwWrXrp1XY6KfQWvoZ4Kfv1z3SPRJABDISDgAAAAAAADTMaUCAAAAAACYjoQDAAAAAAAwHQkHAAAAAABgOhIOAAAAAADAdCQcAAAAAACA6Ug4AAAAAAAA05FwAAAAAAAApiPhAAAAAAAATEfCAQAAAAAAmI6EAwAAAAAAMB0JBwAAAAAAYDoSDgDgJ0aMGCGLxSKLxaKHH37Y1+EAAAAAbiHhAAAOKigosCUEzL4VFBT4+p8HAAAAmIqEAwAAAAAAMF2YrwMAgEARHR2t0aNHt1rv22+/1dGjRyVJUVFRuvjiix06NgAAABBMLIZhGL4OAgCCyYgRI7R69WpJUvfu3ZkuAQAAgDaJKRUAAAAAAMB0JBwAAAAAAIDpSDgAgJ9wdFvMHj162Oq9+eabkqTa2lq9//77uvrqq9WzZ09FRUUpMTFRF110kV599VXV1taedpzy8nI988wzuvDCC9W+fXtFREQoLS1N1113nVauXOnSv2Hjxo2aPXu2hgwZorS0NEVGRio5OVkDBgzQPffco5ycHJeOCwAAgMDDopEAEOAOHjyom266ybZuRL2qqiqtXbtWa9eu1eLFi7V8+XLb4pRr1qzRxIkTVVhYaNemsLBQH3zwgT744AP99re/1TPPPONQDIcOHdKdd96p999//7TnSkpKVFJSoi1btuj555/XjTfeqFdffVWxsbEu/osBAAAQCEg4AEAAq6io0OWXX67c3FxJUnp6urp3764TJ05o06ZNqq6uliStXLlSkydP1vvvv6+vvvpKo0ePVmVlpSwWi/r27auOHTvq0KFD2rp1q+3Yzz77rHr27Kk777yzxRi2b9+u7Oxsu8Uxw8PDlZWVpeTkZJWVlWnLli2qqqqSJC1cuFDbt2/Xl19+qbi4OJP/IgAAAPAXTKkAgAD28MMPKzc3V+eff742bNig3bt364svvtA333yjgwcP6uqrr7bV/cc//qFPP/1UN9xwgyorK3XrrbfqwIED2rJli1auXKktW7Zoy5YtOvPMM21tHnzwQVVUVDR7/vLyco0dO9aWbEhMTNSLL76oo0ePatOmTVq5cqVycnJUUlKiuXPnKiIiQpK0YcMG3XHHHZ75owAAAMAvkHAAgABWXFysiy++WKtWrdLAgQPtnktOTtaSJUvUs2dP22Pjxo1TYWGhHnjgAb3++utKTU21a9OvXz99+OGHCg0NlSSVlZXpgw8+aPb8999/v3bs2CFJSk1N1fr16zVz5szTpkvExMTovvvu09KlSxUSUvfR884777CmAwAAQBAj4QAAASw0NFTz589XZGRkk89HRERo2rRptnJ1dbXOPPNMPfbYY80es0+fPrrkkkts5bVr1zZZr6ioSPPnz7eV33zzTbvkRlOys7N1yy232MovvPBCi/UBAAAQuEg4AEAAGzVqlNLT01usM3ToULvyrbfeqrCwlpfwadgmLy+vyTqLFi2yrcvQv39/XX755Y6ErClTptjuu7obBgAAAPwfCQcACGDDhg1rtU5KSorTbRpOtTh69GiTdRruijFq1KhWj1nvrLPOst0/ePCgDh486HBbAAAABA52qQCAANY4mdCUmJgYt9qcOHGiyTqbN2+23f/nP/9p2ynDWYcPH1ZaWppLbQEAAOC/SDgAQACr3/XBk20Mw2jy8eLiYtv97du3a/v27U7HIkmlpaUutQMAAIB/Y0oFAMAlx48fN+U4VqvVlOMAAADAv5BwAAC4JDEx0XZ/7ty5MgzDpduIESN89m8AAACA55BwAAC4pOFaED/99JMPIwEAAIA/IuEAAHDJ+eefb7u/bt06H0YCAAAAf0TCAQDgkiuuuMJ2f926ddq2bZsPowEAAIC/IeEAAHDJuHHj9Ktf/UpS3U4WM2bMUE1NjY+jAgAAgL8g4QAAcElISIiee+45WSwWSdJXX32l7OxsHThwoNW227Zt01133aWnn37a02ECAADAR8J8HQAAIHBdccUVeuKJJzR79mxJ0qpVq9SzZ09dd911uuSSS9S9e3fFxMSorKxMBw8e1KZNm7Rq1Srb9Is5c+b4MnwAAAB4EAkHAIBbHnjgAXXq1EkzZ85UZWWlqqurtXDhQi1cuNDXoQEAAMCHmFIBAHDb1KlTtX37dt15551KSEhosW67du101VVX6e9//7vuu+8+L0UIAAAAb7MYhmH4OggAQPCora3Vhg0blJeXp+LiYp08eVKxsbFKSUlR79691bdvX4WHh/s6TAAAAHgYCQcAAAAAAGA6plQAAAAAAADTkXAAAAAAAACmI+EAAAAAAABMR8IBAAAAAACYjoQDAAAAAAAwHQkHAAAAAABgOhIOAAAAAADAdCQcAAAAAACA6Ug4AAAAAAAA05FwAAAAAAAApiPhAAAAAAAATEfCAQAAAAAAmI6EAwAAAAAAMB0JBwAAAAAAYLr/D6/nWIzDOh48AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -133,10 +133,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:23.897438Z",
-     "iopub.status.busy": "2024-10-25T14:48:23.897091Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.010767Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.010093Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.282289Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.281756Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.416513Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.415788Z"
     }
    },
    "outputs": [
@@ -173,10 +173,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.012794Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.012597Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.017465Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.016882Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.418822Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.418593Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.424665Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.424081Z"
     }
    },
    "outputs": [
@@ -209,10 +209,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.019249Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.019067Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.025696Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.025087Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.426859Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.426645Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.434827Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.434270Z"
     }
    },
    "outputs": [
@@ -262,10 +262,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.027483Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.027300Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.422163Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.421606Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.436912Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.436501Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.863942Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.863214Z"
     }
    },
    "outputs": [
@@ -273,12 +273,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 0.011132662400108018\n"
+      "Causal Estimate is 1.0019898175815642\n"
      ]
     },
     {
      "data": {
-      "image/png": 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x6NAhzJkzB4sWLcKwYcPMek6V9f333+OVV16pcPJw+vRpnD59GomJiZg0aRK++uors4+5cOFCTJs2zeAJyY0bN3Djxg3s2bMHc+bMwaRJk/Dll18a/TmuikOHDmHQoEG4ceOG3q9rOnadOHECCxYsQLdu3bB8+XI0btzYJvFQ9ZNKpYiLi9Pb7YTKtG7d2qlmu7Ux+QYqzHTXr1+/whiFQoHCwkLx+sMPP2zWsR9++GEx+S4oKIBCoTD7DZfIHFu2bMH9+/fF65oZ1sqYOHEi3nnnHXHB5qpVq8xKvhcuXIgXX3wRQNkiz+joaPj7++PKlSs6pVqLFi1CZmYmVq1aZfDkdfLkyfjxxx91bgsNDUXTpk3h4eGBnJwcXLp0SScxMbbpxM2bN9GvXz8cPXpUvE0qlaJly5Zo0KAB8vPzkZaWJr6GW7duRefOnbFjxw4EBQWZfO5A2ZqOefPmAQA8PDwQFRUFf39/sbMSUFabGBoaiosXLwIAfv75Z7P+FhQXF+uUrI0bN87g2P/+97/46KOPdG5r2LAhwsLCUKtWLWRkZODSpUsAytqiPvHEE1i0aJHRY+bm5qJfv37Ys2ePzu2RkZFo2LAhMjMzcfLkSahUKowYMcJkh4aqmDdvHqZNm6ZzW/369REREYGioiKkp6fj/v37+PHHH5Gfn2/2GpszZ87oJN5169ZFaGgo5HI5iouLoVQqxddNEAQsXLgQSqVS77oImUyGvn37AgAOHDiAu3fvAig7aQoPD9f7+OUngDTJvoa3tzdatGgBf39/SKVS3Lx5E6dPnxZ/T3fu3InY2FgcOXIE9erVM+s5k+PTbAJTvpUglUlLS8MjjzzilAm480VsZWq1WmeBZaNGjRAaGlphXHp6us71Fi1amHX88uNOnjxZiSiJDCv/MXu/fv0qfawmTZrolKvs3r3b5G5qt2/fxtSpUwGUJf7Xrl3D0aNH8c8//+Ds2bM4fvw4OnfuLI5fv349PvnkE73HOn78uE7inZCQgEuXLuHChQvYvn07tm7din379uH69eu4fv06Fi1ahK5duxo82SgpKcHw4cPFxNvLywvvv/8+bt26hfT0dPz999/Yu3cv7ty5g59++kn8mPfixYsYM2aMWTvJHTlyBPPmzYOnpyc+/fRT3L59G0eOHMHff/+NkydP4uLFi/Dx8YFEItFJcn/77bcKs/D6bNq0Sew+U/4Y2r7//nudxHvgwIE4fPgwrl+/Lm44lpGRgcOHD4vfj9LSUjz33HNITU01+PgzZszQSbzj4uJw7tw5nDx5En///TfS0tJw4cIFDBw4EGq1Gk899ZTJ51QZBw4cwCuvvCJeb9iwIVauXInr169jx44d2LdvHzIzM/H555/D09MTy5Ytw8aNG806tkQiQY8ePbBgwQIolUpkZWXh4MGD2LZtG3bs2IGMjAycP38ezz77rHif5ORkvbPrDRo0QHJyMpKTk3XKi5566inx9vL/9JUhtWjRAh9++CFOnDiBnJwcHDt2DCkpKfj777+Rnp6OW7du4dNPP4WPjw+AsnUf3FOi5omMjMRjjz1m7zAckkqlglKptHcYleLyyfevv/4q9uUGgDFjxuh9Iy8/O25unVGzZs10rmtmvYis5cCBA+JlLy8vtG7dukrH69Chg3hZpVKZrHG9f/8+CgoKMG3aNPz4448VPjmKiYnBtm3bdBLw9957T+wEpG3dunXi5YcffhhLliwx+LvWsGFDTJgwATt37kRcXJzeMZ9//rlYH+3r64vt27dj1qxZFWYaa9WqhYkTJ2L79u3w9vYGUHZSo+kAY0xOTg6kUinWrFmD1157DbVr19b5erNmzVCrVi0AZbPWmr8vN2/exObNm00e/+effxYvd+vWTe9mEJcuXdKZEX7jjTewbt06vQvP27Zti5SUFLFWvaCgADNmzND72Glpafj222/F6wMHDsT69evRvHlznXEhISFYs2YNhg0bZrM2lS+++KJ4MlSnTh2kpKRgyJAhOrNeMpkM06dPx/LlyyGRSMyOZfbs2UhJScGzzz6r03pTW1hYGBYsWKBzgvP5559X6KdvDb1798bp06cxc+ZMREdH653Zq1OnDl577TUkJyfDzc0NALB69WqcOXPG6vGQ/ZjT79uVOWtXGJdOvq9cuSLO2AFlrddmzpypd2z5j3y0F6YZU36zElM/KIWFhVCpVDr/6F+CICAjIwOpqanIyMgwa2aypsvMzBQvBwUFVbmdZfkTRu3jG9K8eXN8/PHHBr8uk8mwaNEiMbbCwkKdOnWNy5cvi5eN1TWXp0k+tBUWFuKLL74Qr3/22Wc6nY30efDBB3USUXPrhidNmiSWGhgTFhamU7KmnVjrc/fuXaxfv168npCQoHfcF198IS6O7dKlS4XSk/I8PDx0vh+bNm3SmYTQ+O6778Tfsdq1a2PhwoV6X2ugrJTn+++/t8kGTQcPHsTBgwfF6++++y4iIiIMjh88eDCefPJJs49f/oTJmNdff10sR1IqlRYt+DWXt7e32aVjXbt2xciRIwGUfZK7atUqq8dD1Uv7fe7AgQM6ZYWky1m7wrhs8p2Xl4ehQ4fi9u3b4m0LFiyoMCOmUf6HXyaTmfU45ceZSr4/+ugj+Pn5if8MzcK4IoVCgfnz5yMxMRErV65EYmIi5s+f73IbCZR3584d8bK5J4XGlD+G9vENmTx5Mjw8PIyOadmyJfr06SNe1zerrP37ol2jXRmbNm3CzZs3AZTV8E6YMMGs+2knuHv37jWrK4glC1y1j7927VpkZ2cbHPv777+La028vb0xYsSICmMEQdBJ4l955RWzErfQ0FB069YNQFnSpq9rxsqVK8XLw4cP1+nepE9gYKBFSa+5tOPw8fHBxIkTTd5Hs3+DtUmlUsTGxorXtT95shftT5UcIR6yjHayvX37dsybN098nzPn0zFXxW4nTqakpASjRo3SmUl54YUXEB8fb/A+mm24NcydXSw/rvxxyps5cyamT58uXlepVEzAUZZ461v1rdlGNz4+3uF3tLIV7XaA1ui+UL4bj6ld1AAY7TSibcCAAdi0aROAsnUUubm5Ys0qAJ2kZsuWLZg6dSrefPPNSi0i027z1qNHD7H0w5Tg4GD4+/vj3r17KCkpwbFjx9ClSxeD4+VyOR588EGz44qPj8eUKVOQn5+PgoIC/P777zq1xNq0u5wMGTJE7yxPamqquKgPAB599FGzY2nTpg3++ecfAGUdNrTjUCqVOov+zF1LMGDAAHz33Xdmx2CO/fv3i5e7d+8ulgYZ07FjRwQGBiIrK8uix1Iqlfj7779x4sQJ3Lx5Ezk5ORU6+GjXyF+5csWi41tKpVJh69atOHbsGC5duoScnBwUFBRArVaLY7RLuGwdD1mXQqHggspKiouLc8rFloALJt+CIGDs2LE6taXx8fEmNxTRThCAsoTHnDeA8n2Syx+nPE9PT5u1r3JW5tS8JScnIyIiwml/EauiTp06YmmIsVlUc5U/hqFPgzRq1apltARAW3R0tHi5tLQU58+f11lsNnToUDRv3lwsgfjyyy/x7bffolu3bujVqxc6deqETp06mfVR44kTJ8TLhw4dMlgXro/2762puuHQ0FCLusvI5XIMHjwYv/32G4Cy0hN9yffZs2exb98+8bqhkhPt5+nm5mZ0EqE87U405Z9n+dph7e+dMeaOs4R2LJYcPzo6Wjy5MCUtLQ3Tp0/HX3/9pZPYmnLv3j2zx1ri9u3b+O9//4uff/7Zon77toqHrM/QpBL9y9PTE2q1WucEWC6XIy4uzqkn3Fwq+RYEAePHj9dp2zVs2DAsW7bMYB2jRvmawLy8PLOS7/IfWTtrfZI9KZVKk7MCmlXP+haj1XQBAQFi8q1dRlVZ5Y9hKvn29/c3+fujUbduXZ3r2jO2QNkf2o0bN2LQoEFiwlVSUoJ//vlHTKLc3NzQoUMHxMfHY8KECQZLbbSfh1KprPSqeFMnNJXZCCMhIUFMvnfv3o0LFy4gLCxMZ4x2KUmTJk3wyCOP6D2W9vMsLS2t9MfU5Z9n+e9N+e+dIeaOs4R2LJYc39yxGzZswLBhw3TayZqrMvcx5cKFC+jVq1elfmZtEQ9ZHxdSmkcikej8THt7e+Oxxx5z6sQbcKGab0EQ8PTTT2Pp0qXibUOGDMHy5cvNKiEp/7H39evXzXrc8uMCAwPNuh/9y9zVzM666rmqtDtPXL9+3awFksYcO3ZMvCyRSCokheWZqvXWVv5THX2JwgMPPIATJ07g66+/1tsHu7S0FPv27cP06dMREhJSoSe4Rm5urtlxGWNqUW9lPm159NFHdeqnyy+8VKvV+OWXX8TrTz31lMHHsdXzLF9qYe732Raf3GnHUpWfN32uXr2KkSNH6tTWT548GatWrYJCocC9e/dQWFgItVot/jP0KYQ1CIKA+Ph4MfGWSCR44oknsGTJEhw7dgxZWVnIz8/XiUezlwQ5D3Mmlahi9UBeXh5WrFjh9Gu9XCL5FgQBkyZNwpIlS8TbBg8ejN9//93s2u2WLVvqXNdsuGBK+XHOfrZmD+Z+WuCqnypoFs1p7N27t9LHEgRBp3tDZGSkyRNGS056yr/ZGOqM4enpiRdeeAFHjhzBjRs38Mcff2DatGlo27atTolHdnY2nnnmGSxYsKDCMbRnxP/zn//oJCuW/Bs/frzZz89cbm5uOv2wtScFAIi9pTWMJXvaz9Pb27vSz7P8FuflZ/TN/T7bIqHQjqUqP2/6fPHFF+IJjJ+fHw4ePIjvvvsOgwcPRsuWLeHn51ch4bflif7GjRtx+PBh8fovv/yC1atXIyEhAW3atEHdunUrrMtw1YkHZ8bvWdUkJyc7dbezGp98axJv7ZmBwYMHIykpyewFWAB0Nh4ByjbWMEf5ca1atTL7MalMcHCwyY/2nXnVc1X17t1b57r2jKmlNm3apLNArVevXibvo1KpzOqIApR9nK7NnC3cGzRogOHDh2Pu3Lk4fPgwrly5grffflsnAZkxY0aFhaENGzYUL2u6njgS7YT6woULYj9yQHcmvEOHDkZP2rWfZ15entXakpX/3pi7R0H577G1Y7FkrwRzYtH+6H/q1Klm/Y3WbolpbdrxdO/e3azuMbaMh2zD3L+ZpJ8zb7AD1PDk21qJNwA0bdpU5+N97U4KxmiPCw8PN3u7avqXVCo1uVjOmVc9V1WHDh10yjPWrFlT6c2cyi88NnfHPO2FgcZod62oW7dupWr0GzdujNmzZ+v04L53716FGLQ7lJgbX3WKiopCu3btxOuahDs/P19n111TJQ7abeaAqn3yoS0mJkbn76T2984Yc8dZQvt1Mvf42dnZJjeIAnQ/nTTVBx4oazt7/Phxs2LQ/ptk7iJOS+MBoHPiRo5PoVBU+KSJLOfMnx7U2GxFX+I9ZMiQSiXeGkOHDhUvp6SkmDzrUiqVOsm39v3JMpGRkYiPj68wAy6Xy126zaDG66+/Ll4uLi422LrOmN9++w1bt24Vr8fFxZndWeLXX381OaakpERnZX/37t0tjlHbsGHDdK5rt8UDdFvjXb161SEXN2kn1klJSSgoKMCqVavEcgkPDw+MGjXK6DEaN26MNm3aiNf1bV5UGV5eXjrJn2aBqCnm/CxYqkePHuLlkydP6nR4MeT3339HSUmJyXGm2r+Wt3Tp0gr18IZoL9Q3p2VnZeI5efKk1U64yLYEQcCFCxewevVqe4dSIzhzqWmNTL7VajWeeeYZncR76NCh+P333yudeAPAhAkTxK4OgiDgvffeMzr+3XffFWuS3NzczN7kg/SLjIzE1KlTkZCQgKFDhyIhIQFTp051+cQbKGuXqb2BzV9//YWXXnrJ7Pvv2rVLJ2GvXbu2yfab2n777TeTpVjffPONzgnr008/XWGMJS3eypdXlO/K8uCDD+r0vH755Zet0orRmkaPHi3+TcrOzsaaNWt0Sk4GDBhgVseO1157Tbz8xx9/YMOGDVaJT/tv1tGjR/H7778bHb9mzRrs2bPHKo+tLT4+XqdN6xtvvGF0/P37903+fdZo3LixeHnHjh1Gx968eRNvvfWWWccFoLOo1txt3y2JRxAEvPDCC2bHQ9VPs4FOcnIyPv/8c4tO3sgwZy81rXHJt1qtxnPPPYdFixaJtw0fPrzKiTdQlvxpz1T9+OOPBjstLFiwQGcGavz48RUWbZLlpFIpQkJCEB0djZCQEJctNSlPKpXil19+0Xmz//rrrzF06FCdDTjKEwQBX3/9NeLi4nSS2e+++w4PPPCA2Y8vCAIGDx5sMMFYs2aNToLYrl07vZu2jBo1Cu+++y6uXbtm9PFKSkp0Zvu9vLwqlF8AwJw5c8Ta8NOnT6NHjx44deqUyedz6dIlzJo1C6+88orJsVURGBios0HR3Llz8ddff4nXze2qMXr0aHHbek23jMWLF5s8mcnPz8eyZct0yjq0PfnkkzrdbiZNmoSdO3fqHbt3716bdQGRy+U6J5PJycl45ZVXUFpaWmGsSqXCkCFDzN5sRnvNxDfffGNwu3ilUok+ffpYtGmP9uu6ZcsWnU5C5sRz4MABgxsW5eXl4amnnmL5ggPT3pV5//79Zu2WS+Zx9lJTidqSqSYnkJSUhJEjR4rXJRIJevfubXZXE6Bse2btWURtWVlZ6NSpk7gJCAA8/vjjGDVqFBo3boyrV6/it99+w/r168Wvh4eHY+/evZVqM6hSqeDn54fs7OxK9RPWKCgowMWLFxEaGlphpTzVHGfOnEH//v11fj5lMhn69++Pvn37omnTppDJZLhx4wYOHz6MFStW6NSHu7m54bvvvsMzzzxj9HGWLFkizooGBQUhNDQUO3fuhEwmw9NPP41HH30UderUwZUrV/Dnn3/qbA/u5eWFffv26ZRKaPTs2RPbt2+HRCJBly5d0LVrV7Rp0wb16tWDTCbD3bt3cfz4cSxdulSnnve///0vPvjgA72x/vrrrxg7dqz4KZRUKsXAgQPRp08fhIeHw9fXFzk5Obh58yaOHz+OHTt2iN0mEhISdLokabz99tt45513AJSVRFQlAVq1apXekrTAwEBcu3bN7EmDmzdvIjY2VqdmODIyEsOGDUO7du1Qt25dFBcX4+7du1AoFDh48CD++usvMSEw9Fbw999/47HHHhMTXalUijFjxmDQoEFo0KABMjMzsWHDBixduhSlpaV48skndUpP/vnnH/Ts2dPcl8OggoICtG3bVqfF2EMPPYSnn34akZGRKC4uxsGDB7FgwQJcuXIF9erVQ5s2bcSTGUPfy7S0NDz44IPi8/Py8sKkSZPQp08fsYf+tm3bsGTJEuTl5aFp06aIjo7Gxo0bjR4XKOvBHhwcLL7GEokEbdq0QZMmTXTek95//320bt0aQFn7zRYtWugsohw8eDDi4+MRFBSEnJwcHDhwAIsWLcLly5dRq1YtjBs3TpzsadasmU6nHEdWk9+XuIFO1Xl4eMDT01OnttuRN9ixJF+rccm3dlJQWYsXLzbaXuzs2bPo27evWYvaQkNDsWXLFoSHh1cqFibfZKlbt27h2WeftbiuMDQ0FN999x369u1rcqz271mzZs2we/dudO/e3WR3CS8vL6xevdrgY2iSb0uMHTsWixYtMnqCvXHjRowZM8bi3f+qI/kuKipC48aNK2xuNGXKFItKf4CyBHzEiBEGZ6eNMfZWsGzZMiQkJOidadbWrl07pKSk6NRiWiv5Bspq93v06KFzcqmPj48PVq9ejV9++QWJiYkAjCfJX375JaZOnWry8evVq4dNmzbhq6++Muu4QFmN+NNPP220lrv8a7Rv3z488sgjJmdKa9Wqhe+++06nrJHJt/0JgoB58+Y59YJAW+revTsCAwNx584do3874+PjERERAaVSiZycHPj6+iI4ONhhZ7wtydcc8xk4uBYtWuDEiROYMmWKwRfYz88PU6ZMwYkTJyqdeBNVRr169bBq1Srs3r0bQ4YMqbA7qzaJRIK2bdti/vz5OHXqlFmJtz5NmjTB4cOHMXbsWIMbm/Tu3RtHjhwx+hgzZszA2LFjdcpnDOnQoQP+/PNP/PzzzyY/2erfvz/Onj2LmTNnon79+kbHenp6onfv3vjmm28wd+5ck3FUlYeHB0aPHl3h9nHjxll8rAYNGiAlJQXLly9H+/btTW5737JlS7zyyismyyHGjBmDvXv3okOHDnq/Xrt2bbz88svYvXu30Z+3qtL8nE2ePNngz1m3bt1w4MABnXp/U6ZMmYIVK1YY7L7j4eGBESNG4MSJEwZLdAwZO3Ysjh49iqlTp6Jdu3aoU6eOyZ/XTp06Yd++fTode8rr3Lkzdu3apXftBNnXzp07mXgbEBMTg169eiE6Oho9evRAfHx8hYWT2o0UamqpaY2b+a5uBQUF2L59OzIyMnD79m2xfVrPnj2tstMbZ76pqoqKirB//34olUpkZmaisLAQgYGBaNCgATp27GhWr21L3L17FykpKbhy5Qry8vLQqFEjdO/e3eK2gkqlEunp6bh06RLu3buH0tJS+Pr6olmzZmjbti2aNm1aqfjUajVSU1Nx4sQJZGVl4f79+/Dx8UG9evUQERGB1q1bQyaTVerYjubWrVvYvXs3rl+/jrt378Ld3R3+/v4ICwtD69atdXqEm0uhUGDfvn24efOmuOipV69eOgsiq0N2dja2bduGS5cuobS0FI0bN0ZsbKxOS1hLaXZPPXbsGO7du4c6deqgSZMm6NGjh85mRtVJoVBgz549yMzMhEwmQ6NGjdCxY0eEhobaJR5rqonvSyw3MW7WrFkVTj4FQXCa2W1jXLrspKZh8k1ERDWRM70vmZMgCoKA+fPnc9t4A6KiojB8+HB7h2EzluRr5q9CJCIiInIxCoUCycnJOkm1voV/SqWSibcBHh4e3OtEC5NvIiIiIj0MlZGoVCokJSWhZ8+eqFOnDvLy8iosmqZ/DR482ClLSWyFyTcRERFROYIgmNwZl33WTevZs6dDtga0J56GEBEREZXDMpKq8/X1Rbdu3ewdhsPhzDcRERG5NH0LKtkusOr69evHchM9mHwTERGRyzK0oLJt27Z2jMq5yWQyDBo0iOUmBjD5JiIiIpdkbEFlSkoKZDIZ8vPz7RCZ8+ratSt69erFGW8j+MoQERGRyzFnQWVRUVE1RVNzNG/enIm3CXx1iIiIyOWYs6CytLS0mqKpGTT18mQcy06IiIioxiu/qJKdTKyPCyzNw+SbiIiIajR9iyq9vb3tGFHNwgWWlmHyTURERDWWoUWVeXl5doim5nB3d0enTp0QGhqKkJAQznhbgMk3ERER1UjmLKqkyhk6dChnuiuJyTcRERHVSNylsuokEgnUarV4XS6XIy4ujol3FTD5JiIiohqJu1RWnVqtRt++feHj4yN2M2GJSdUw+SYiIqIaycfHx94h1Ag+Pj6Ijo62dxg1BpNvIiIiqnEUCgU2bdpk7zBqBF9fX3uHUKMw+SYiIqIaxVCHE6rI3d0dJSUlBr8ul8u5cY6VsWiHiIiIagx2ODGfh4cHBg8ebHRMXFwca7ytjDPfREREVGOww4n52rZti6ioKEil0gqbELGrie0w+SYiIqIagx1OzBcREQEAiIyMREREBJRKJXJyctjVxMaYfBMREVGNwcWB5ilfyy2VShESEmK/gFwIT2mIiIioxggODmYCbgbWctsPX3UiIjsaP348JBIJJBIJxo8fb+9wnE5ISIj4+i1ZssTe4ZiN33frEgQBGRkZSE1Nxc6dO1FQUGDvkBxaz549WcttRyw7IarBbty4gS1btuCvv/7CiRMnkJWVhaysLLi5uaFOnTpo2rQp2rdvj+7du2PgwIGQyWT2DpmIyCIKhaLCYkEyzNfXF926dbN3GC6NyTdRDXT58mV88MEHWLRoEYqLi/WOycvLw9WrV7Fv3z58/fXX8PX1xZNPPon//e9/aNKkSTVHTOScQkJCcOnSJQDA4sWLOYtdzdjP23L9+vVjuYmdMfkmqmFWrlyJcePGITc3V+d2T09PNGvWDPXq1YNUKsXNmzdx+fJl5OfnAyjrELBgwQL8/PPP2Lt3L9q0aWOP8ImIzCIIAnewtABbBzoOJt9ENcjnn3+O1157DWq1Wrytf//+eOmll9C9e3d4e3vrjC8qKsL27duxYsUKLFmyBEVFRcjPz8fdu3erO3SiSsnIyLB3CJWyZMkSp6pRtzdBEMQ2eD4+PgCAQ4cOsa2gmby9vfHSSy/B3Z1pnyPgd4GohtiwYYNO4u3n54fff/8dffv2NXgfDw8P9OnTB3369MGsWbMwY8YM/Pbbb9UVMhGRSazprrq8vDxcuXKFrQQdBJNvohrgxo0bGDt2rJh4+/j4YMeOHYiJiTH7GMHBwfj111/RvXt3eHl52SpUIiKzsabbevgpgeNgxT1RDfD555/rlIrMnTvXosRb2+TJk9GpUyeDXz906BA++eQTDB48GBEREfDz80OtWrUQEBCAVq1aYcKECVi1ahUEQTDr8SrTcm3JkiXifcyZyTlw4ACmTp2Kjh07IjAwEB4eHvD09ETdunURExODYcOG4ZNPPoFCoTB6nKKiImzZsgUzZ85Enz590KxZM/j4+MDDwwMNGjRAhw4d8PLLL+PgwYNmPQ9r27lzp/i6SCQSHD582KL7R0dHi/edNGmS3jF5eXlYs2YNpk+fjp49e6JJkyaQyWTw8vJCo0aN8PDDD2PGjBk4ffq02Y+rr11gaWkp/vzzT8THx+OBBx6AXC6HRCLB4MGDTd7XkHv37uH333/Hf/7zHzz88MNo2LAhvLy8IJPJ0KRJE/Tu3RvvvPMOrly5YvQ4KSkp4mNqFlsCwIQJE3Ref+1/5ctjKvNzX1RUhMTERIwYMQLNmzeHr68vvL29ERISgkGDBuG7777D/fv3zTqWocc/ceIEpkyZgqioKPj5+cHHxwctWrTApEmTcPToUbOObS2s6bYu9j53HJz5JnJy2dnZWLBggXg9PDwczzzzjNUf5/Lly+jRowcuXryo9+t3797F3bt3oVAosGTJEkRFReGPP/6w6+Ke3NxcPP300/j999/1fv3OnTu4c+cOUlNTsXLlSsyYMQPp6elo1apVhbHr16/HuHHjDNbDZ2ZmIjMzE4cOHcL8+fMxZMgQLF68GH5+flZ9TsZ07doVYWFhuHDhAgDg559/Rrt27cy675EjR5CWliZeT0hIqDBm4cKFmDZtGvLy8vQe48aNG7hx4wb27NmDOXPmYNKkSfjyyy/h6elp0fPIyMjAmDFjsGfPHovuZ8xbb72FTz75BEVFRXq/fu3aNVy7dg3//PMPPvjgA8ycORNvv/02JBKJ1WKoii1btmDy5Ml6f/8uXbqES5cuYf369Xj33Xcxb948jBw50qLjl5aWYvbs2fjoo48qnDifO3cO586dw6JFizB79mzMnj27Ss/FXDt37uRsrZWU382S7IvJN5GT27p1q84b1HPPPWeThCE7O1vnjd/T0xPh4eEICAhArVq1kJWVhVOnTonJTXp6Ojp16oQDBw4gIiLC6vGYY8iQIdi6dat4XSqVIjw8HI0aNYK7uztUKhUuXLiA27dvi2MMzdhnZGToJN5yuRzh4eHw8/NDaWkprl+/jnPnzomlP6tWrcKFCxewd+/eauufLpFIMG7cOLz99tsAgN9++w2ff/65WYusfv75Z/FyWFgYunbtWmHMmTNndBLvunXrIjQ0FHK5HMXFxVAqleJMsCAIWLhwIZRKJTZu3Gj2z+Tdu3fxyCOPiCcQ9evXR3h4OCQSCc6fP2/WMfQ5efKkTuLdsGFDcSfEgoICXLhwAdevXwcAFBcX491338WNGzd0Tmw1AgICxLUU27dvFzd0ad26tcE2nVX5Gfj111+RkJCAkpIS8TY/Pz+0bNkStWrVwunTp3Hr1i0AZSdAo0ePxtWrVzF9+nSzH+PFF1/E999/DwCoXbs2oqKiIJPJcPHiRfF7qlar8fbbb6NRo0Z49tlnK/18zKFQKJCSkmLTx3Al3M3SsTD5JnJy5d+g+vTpY7PHaty4MSZOnIhBgwahbdu2FZK6vLw8/Prrr5g5cyaysrKgUqnw5JNPWlz+YA1r167VSbxfe+01vPbaa6hXr16FsRkZGdiwYQN++OEHo8d86KGHkJCQgAEDBiA8PLzC12/cuIEvv/wSc+bMQUlJCY4fP45Zs2Zh7ty5VX9CZho3bhzeeecdqNVq3Lp1C5s2bcKgQYOM3qekpERnoe24ceP0JssSiQQ9evTAk08+iX79+qFp06YVxly4cAGffPIJFi5cCABITk7GV199hSlTppgV/9tvvw2VSoVWrVrhyy+/RO/evcVY1Gq1mJRbys3NDQMHDsTIkSPRt29fvT8HaWlpmD17NlauXAmgbKZ/wIABePzxx3XGxcTEIDk5GYBun+9XXnnF6n2+U1NTMXHiRDHx9vPzw9y5c/HUU0/Bw8MDQNmJzpo1a/DCCy/g+vXrUKvVePXVV9GmTRs88sgjJh9jw4YNyMrKQt26dfH5559j9OjR4rEBYNu2bXjyySeRmZkJAHj99dcxZswYseuItQmCIL6+ZB6JRIIHHngA169f11mYyvaCjonJNxn0zDPP6HwMTaa1bt3aZAJnbdr1xd7e3mjdurVNHqdFixbIyMhArVq1DI7x9vbGpEmT0L17d3To0AEqlQpHjhzB1q1bbXpSoM/atWvFy08++SQ+/fRTg2NDQkLwwgsv4IUXXkBpaaneMePHj8eLL75o9DEbNmyIDz/8EDExMRg9ejSAsgTurbfegr+/v+VPohJCQ0PRrVs37NixA0DZjLap5Ds5OVlMrCQSCcaOHat33OzZs1G7dm2jxwoLC8OCBQsQGhqKmTNnAihbk/DCCy/Azc3NZPwqlQpRUVHYtWtXhddMIpGgefPmJo+hz08//WQy9tatW+PPP//E5MmTxRnvTz75pELyXZ2ef/55FBYWAij7/dq6dSs6dOigM0YqlWLIkCGIjo5Gly5dcOvWLajVajzzzDM4e/asydc9KysL/v7+2L17t95PqR555BH8+eef4q6I2dnZWLlypcGfk8rStBO8cOECO5uYQfNzEBAQgPbt28Pd3V2nJaOvry+Cg4M54+2AmHyTQWlpadi3b5+9wyATNEkTUDYzbU6CUxmW1O0+8MADePHFF/Hhhx8CKNv4p7qT78uXL4uXLdlK2dDrZypx0zZq1Ch89dVX2LNnD3Jzc7F582aLa3CrYty4cWLyvW7dOty7d89o8q9dcqKpG9fHktfg9ddfxzfffIMrV65AqVTi0KFDiI2NNeu+P/zwg9VPViyJ/dNPP0ViYiIKCgqwZ88e3Lx5Ew0aNLBqPOY4fPgwdu/eLV6fNWtWhcRbW3h4OD777DOxXv/ixYtYu3YthgwZYvKxPv30U6PlYV27dkXnzp2xd+9eAGX12NZMvtlO0DKaGe3yibVUKmU7QSfA0yEiJ6ddr1xds6vm6Ny5s3j5wIED1f742jW21d2lAbDv8x8xYoT4/AsLCw0uOAXKOoCsW7dOvK5voWVlSKVSnWTb3NcgJiZG57WzB7lcjqioKPG6PX5+AYjlLwDg5eWFF154weR9xowZg4YNG+o9hiG1a9fGuHHjTI7r0aOHeDk9Pd3keHOdP38eSUlJTLwtwBpu58aZbyInp1nsBVg2O13Vx/zrr79w9OhRnD9/HiqVCvn5+To7a965c0e8bKp1my3ExsZizZo1AMpmUps1a4YXX3wRcrm8yse+desWtm7diuPHj+PatWtQqVRiaYDGuXPnxMvV/fzlcjmGDBmCX3/9FQCQmJiI5557Tu/Y33//XfwZkslkGDFihFmPoVQq8ffff+PEiRO4efMmcnJyKnQSSU1NFS+b+xp0797drHFVcfr0aWzfvh1paWm4desWcnJydBYzAtCpLbfHzy8AcZYZKPv0xpzOOZra9h9//BEAzOoY0759e7P+dgQFBYmX7927Z3K8OdRqNXbu3GmVY7kC1nDXDEy+iZxcnTp1xNKT7Oxsmz5Wbm4u3nvvPXz33XcWzVJZ643aEpMmTcJnn32GO3fuQK1WY9asWXjvvffQq1cv9OzZE506dUKHDh0s6kJx6dIlvPbaa1i1alWFZM0Yezz/hIQEMfneu3cvzp07p3eRqHbJyZAhQ0yenKSlpWH69On466+/dE62TDH3NahsTbc5du3ahVdffRX79++36H72+P4BwNmzZ8XLbdq0Mft+2j3+L168CEEQjM6Sas+UG6O9wNJQu0lLlZaWmt2bvKbz9vbGgw8+iLS0NJ2/r97e3oiJiUFERARruGsIJt9kkK0W7tVk9njN6tatKybf2rPN1paVlYVHH30Ux48ft/i+hnor21K9evWwfv16DB06FDdu3ABQNmO/adMmceMODw8PdO3aFaNGjcJTTz1lNBE/ePAgHnvssUolYuVnxavDo48+iiZNmuDq1asAypLsd999V2fM+fPndWZGTZUebNiwAcOGDavU8zH3Ptb4ZEKfhQsXYvLkyRadMGjY4/sHQKe1pb7uLIZoj1Wr1bh37x4CAgIMjq/MJ2aVeR31MXczrprO29sb06ZNg7u7Ox555BEumqzhmHyTQdXdtYMqp3nz5uLOjNeuXUNWVhYCAwOt/jjPPPOMTuLds2dPjB49Gu3atUPTpk3h6+sLLy8vsS1cSkoKevXqZfU4LNG5c2ecPn0a33zzDRITEyvsulhUVIS///4bf//9N9566y0sXLhQb2eQ3NxcDB06VEy8a9WqhZEjR2LgwIGIiopCkyZN4O3trZPEvP3223jnnXds+vyMkUqlGDNmjNjl5ZdffsE777yj00JQe9a7cePGePTRRw0e7+rVqxg5cqRO541x48ahb9++aNmyJRo1agSZTKbTom78+PFITEy0OG5rO3r0KJ5//nkxYQwICMCECRPwyCOPoEWLFmjQoAG8vLx0Ovn07NkT27dvt3osltBO+rVfV1PKJ9PapWmORK1WW/QJUk02cOBAsXUrF03WfEy+iZxcjx49sH79evH6vn37MHDgQKs+RlpaGlavXi1e//DDD8U2cobYcmc6Q+0A9ZHL5Zg5cyZmzpwJpVKJnTt3Yvfu3UhJSdHZTv7GjRsYPHgw1q1bh/79++scY/HixWLdb61atbB161adxWf6OMLOfAkJCWLyffHiRezcuVOsqVar1fjll1/EsU899ZTRTjlffPEFcnNzAZT1mt6zZ4/enUC1OcJrAAAffPCBOMMaEhKC3bt3o3Hjxkbv4wix+/v7IysrC4Bl8ZQvCXOkhdga+fn5yMrKssunYo6mZ8+erOF2Mfwcg8jJ9e7dW+f6smXLrP4Y2htehISEYMaMGSbvo93qzxjtGb3i4mKz7mNoi3dTgoODMWbMGHz77bc4efIkzp49i5dfflmcbRUEAdOmTatwP+3nP3r0aJOJN2D+87elVq1aoX379uJ17ZnuXbt26SwqNNXlRPs1mDp1qsnEG3CM10CtVmPz5s3i9bfeestk4g3Yb5Gltvr164uXLdndU3usj48PvL29rRpXVajVauTk5ODu3btWK11xdg8//LC9Q6BqxuSbyMm1bdsWHTt2FK+vXLnS6kmPZgc/oKwzgjlbhe/atcusY2vX+Jpbs67dRaMqwsPD8cUXX+jM4p85cwYXL17UGaf9/LVfa0PUarVZXSaqg3ZS/ccffyA/Px+AbiLevn17k8m0pa/B/fv3K7U+wNru3Lmjs6DPnNjPnDmj0z/fGO0yGWsnk+3atRMva3c+MUX7Z0/75Mse1Go1CgsLkZeXh5ycHLEzDv3LEU70qHox+SaqAbSTx6KiIowfP77SiYBKpRIXKGqYOyOtcfv2bZ0yFWOaNWsmXj5x4oTJ8ZpFk9Y0bNgwnetVff7JycniQkd7094qXKVSYfXq1SgoKMAff/whjjGnx7Olr8HSpUsdoqTA0riBst0wzaW9eY/mxMZaevbsKV4+c+aMWV1alEol/vnnH73HsDbtxLqwsLDC35z8/HxkZmbi9u3buHfvHnJycrjAUg+ejLgeJt9ENcATTzyBJ554Qrz+999/Y9KkSRYvZkpNTUWHDh1w6tQpndu1P6bfu3evyeNOmzbN7EREe2bu2rVrJmfMP/vsM9y6dcvkcS05+Sjf6qx8Zwjt56/ZOdKQvLw8vaUr9lK3bl2dGvaff/4Zq1evFttS1qpVC6NHjzZ5HEteg5s3b+Ktt96qZMTWFRgYqFPaZCr2kydP4ssvvzT7+I0aNRIvnzlzxvIAjRg5cqROb+/XXnvNZPKqPcbd3R1PP/20VWPSUKvVOon17du3kZmZKf7e5+fn4+7duxatz3BVvr6+9g6BqhmTb6IaQCKRIDExUaeP86JFi/DII4/g0KFDJu9/5coVPP/882jXrp3eBEK7rvzq1at488039R6npKQEr776KpYuXWp27LGxsTqbd7z00ksGe4gvWrTI7A4ivXr1wrx583R2ANUnNzcX//vf/8TrQUFBaNGihc4Y7ee/YsUKnQWu2m7fvo2BAwdW6Kpib9qlJ1u3bsXcuXPF6wMGDDCrO472a/DNN98Y/LlSKpXo06ePuFDQ3tzd3dGtWzfx+rvvvluhrEgjNTUVffv2tag7iHZpyO+//65TnlNVPj4+ePXVV8XrO3fuxKRJk/R+oiAIAmbOnImkpCTxtqeffhpNmza1WjzlH698Yl1aWoq7d+8iPz+fu1WaSS6XIzg42N5hUDVjtxOiGsLPzw8pKSkYPHiwmBjt2LEDHTp0QGxsLB577DFERUUhMDAQUqkUN2/exLlz55CcnIx9+/YZnaHq2rUrOnbsKG6z/cknn2D//v1ISEhAWFgY8vPzcfz4cSxevFicNZ88eTK+//57k3FLpVK88cYbeOmllwAAx44dQ5s2bTBlyhS0adMGarUaZ86cwfLly7Fjxw64u7vjqaee0unUoU9GRgamTZuG1157Db169ULnzp3RunVr1K1bFx4eHsjKysKhQ4eQmJioU3P51ltvVWh39+yzz+KTTz7B/fv3IQgCnnjiCYwdOxaDBg1CgwYNcPfuXezcuROLFi3C7du3IZfLMWDAAPz2228mn3910CTYWVlZKC0txcGDB8Wvmbud/Msvv4wlS5agtLQUubm56NatGyZNmoQ+ffogICAAmZmZ2LZtG5YsWYK8vDw0bdoU0dHR2Lhxo62eltleeeUVbNu2DUBZSVHbtm0xefJkdO/eHbVr18a1a9ewceNG/PrrrygpKcFDDz2EWrVqmbWt/JgxY/DJJ59AEATcuHEDLVq0wEMPPYT69evrdI9ZuHChzgJKc82cORPJycnYvXs3gLLOO7t378akSZPQpk0buLm54dSpU1i0aBGOHDki3i8iIgKff/65xY9nDdnZ2SwvMRO3iXdNTL6JapAmTZpgx44dePXVV/HDDz+I9a779+83q17Uy8sLL774os5snsayZcvQpUsXseQjJSUFKSkpFcZJJBLMnj0bPXr0MCv5BoD//Oc/2Lp1K9auXQugLHGePn16hXHu7u74/vvv4ebmZjL51igpKcHWrVuxdetWk2NnzJiBZ555psLt9evXR2JiIkaOHImSkhIIgoDExES9Pax9fHywfPlyi3dRtCVNaclXX32lc3tgYCAGDBhg1jFat26NuXPnYurUqQDKau+//vprfP311xXG1qtXD6tWrarwePbSr18/TJ8+XZzxv3fvHj7++GN8/PHHFcaGhYVh5cqVGD9+vFnHjoqKwvvvv49Zs2ZBrVajuLhYb9I+b968SsXu5uaGjRs3YvDgwWIt95kzZ/D6668bvM+DDz6ITZs26exIWZ2YeJvGbeJdG0+3iGoYmUyGb775BmfPnsWLL75o1mYN0dHR+Pjjj3HhwgXMmTNHbw1ieHg4Dh06VKEHdvnjbNiwAbNnz7YoZqlUihUrVmDWrFkGd5ls27YtduzYYXYN68cff4zhw4ejbt26Jh+7V69eSElJwUcffWRw3NChQ/HXX38Z3MXUzc0Njz32GI4cOYJ+/fqZFWN10jfDPWrUKJ2NZUyZMmUKVqxYYfBnysPDAyNGjMCJEyf0nsDZ0+eff45vv/3W4Oyzj48Pnn32WRw9etTiDU5mzpyJPXv24JlnnkF0dDT8/PyM9ky3lFwux9atW7FgwQKjsTVo0ACffvop9u3bZ/aW8VS9YmNjkZCQgKlTpzLxdmESNRttOjSVSgU/Pz9kZ2dXadvlgoICXLx4EaGhofDy8rJihOQMzp07h9TUVGRlZeH27dtwc3ODv78/mjVrhvbt2xvdelqfjIwM7NixA9evX4e7uzsaNWqEBx980Kzez6bcv38f//zzDy5cuICCggI0atQIbdu2NZj0muPs2bM4deoUlEqluNBQLpcjLCwM7du3t6gcQK1W48iRIzh06BBu374NX19fNGrUCF27dnWZhKe0tBT79u3DsWPHcO/ePdSpUwdNmjRBjx49HHJDF22FhYXYtWsX0tPTcf/+fdStWxdNmzZFz549HaoftjGpqak4evQoMjMzIQgC6tWrh+joaLRr186sNqBVUVhYaHIdhblKSkpw9epV7N69W9zAqaby9PTEE088wYS7BrMkX2Py7eCYfBMRUXVTq9UoKipCaWkp3Nzc4OHhAYlEInY5MbZGRDPOFFdKvocMGYKYmBh7h0E2ZEm+xppvIiIiEmm6lWgn2G5ubpDL5ZDJZJDL5UZ3meWcXkVVmTyjmofJNxEREQH4tz93eZo2gsXFxQZ7+Lu5uaFWrVoWtWqsCWQymdF9DdhOkMrjgksiIiKCWq022Z/7/v37BktO5HI5CgsLbRGaQ4uNjTX6dbYTpPI4801ERERijXdl3bt3z+VKTuRyObp164b69esjOTlZ5+SF7QTJECbfREREVOWt4F0t8Qb+ndWOjIxEREQElEolcnJy4Ovri+DgYM54k15MvomIiMiqvcldQc+ePXVmtaVSqcU94sk18ZSMiIiI4OHhwQTcTJpyE6LKYPJNREREkEgkbIlnJi6ipKpg2QkREREBAIqLi+0dgkOTyWQYNGgQF1FSlTD5JiIiIuTn5+P+/fv2DsPuWrVqhcuXLyMnJ0e8TSaTITY2Ft26deOMN1UZk28iIiIXo2/7+OzsbHuH5RCuXLmCKVOm4MqVK+xcQjbB5NvFuGIrKCIi+pe+7eOlUikEQajWOBz1/UilUuHKlSvsXEI2w+TbRWjO2Kv7jysRETkOQ9vH2+O9Qa1WQ61WV7m/uC1ol5wQWRs/Q3ER7u5l51lFRUV2joSIiOzBnO3jq5MgCBAEwSEXefr6+to7BKrBmHy7CKlUCh8fH57NExG5qKpuH29tRUVFuHv3rkPFBJT18A4ODrZ3GFSDMfl2Ib6+vsjNzUVJSYm9QyEiomqiVqtRWFiIgoICe4ciUqvVyM3NRVZWlr1DqYA9vMnW+NPlQnx9fSGRSHD9+nXWfhMRuYD8/HxkZmbi9u3byM3NtXc4ory8POTn5+P27dv2DkUkl8sRHx/PHt5kc1xw6ULc3d3RtGlTXL58GVevXkWjRo3EWnAiIqo51Go17t+/73Clhmq1GgUFBbh9+zZOnTrlECcE3bp1Q1hYGNsJUrVh5uVifHx8xAT87Nmz8PHxga+vLzw8PCCVSiGRSOwdIhERVUFBQYFDLazUdDUpLi7G/fv3kZ+fjwsXLuDq1av2Dg0AUK9ePbYVpGrF5NsF+fj4IDw8HDk5OcjJycGNGzfsHRIREVlI06ZPEARIpVK4ubmhpKTEIWaTy9PUnV+7dg03b950qBl5djah6sbk20W5u7ujTp06qFOnDgRBQElJCevAiYicxPnz57Fz506d7eB9fHxQWlrqUAsrNUpKSlBUVORwG+uwswnZA5NvglQqhYeHh73DICIiMygUCqxYsaLC7Y444+3o2NmE7IHJNxERkZMQBAHJycn2DsPpeXt7Y+DAgexsQnbB5JuIiMhJKJVKh1pM6Wi8vLwgkUiQn59vcIy3tzemTZvGbl9kNzX6s5acnBzs2LED8+bNw7hx4xAVFQV3d3dIJBJIJJJKrW7OyMgQ72/Jv4YNG1r/CRIRkUsQBAEZGRk4efKkvUNxaCNGjMCgQYOMjhk4cCATb7KrGvvTFxERgbNnzzrc4g4iIiJLKBQKJCcnc8bbBLlcjpCQEEilUsTHx1d4zeRyOeLi4lhqQnZXY5PvM2fOVMvjdO/eHTKZzOS4gICAaoiGiIhqEoVCgaSkJHuH4RS0F09GRkYiIiICSqUSOTk58PX15SY65DBqbPKt4ePjgzZt2qBdu3Zo27Ytfv/9d6suVklMTGRzfiIisrqSkhKsX7/e3mE4PEMz2lKplO/P5JBqbPL9yy+/oG3btoiIiNA5001JSbFfUERERGZQKBRYv3498vLy7B2Kw5LJZBg+fLhYakLkLGps8j1mzBh7h0BERGQxlpqYZ9CgQQgLC7N3GEQWq7HJNxERkbNhH2/T2KObnB0/pyEiInIQ7ONtWt++fZl4k1Nj8k1EROQgcnJy7B2Cw5PL5fYOgahKmHxX0RtvvIGYmBj4+/vDw8MDDRo0QNu2bfHiiy9i8+bN7DNORERm8/HxsXcIDs3T0xPBwcH2DoOoSljzXUXlF8VkZmYiMzMTR48exTfffIOoqCj88MMP6Ny5s50iJCIiqhkGDhzIzibk9Jh8V1HdunXRvHlz+Pr64v79+zh//jyysrLEr6enp6N79+74/vvv8fTTT5s8XmFhIQoLC8XrrP0jIqp5BEHQ2QAmKCgIV65cwalTp+wdmsNq1KgRateuDUEQmICTU2PybSGJRIL27dtjwoQJ6NevH0JDQyuMOXz4MD7++GOsWLECQNlGCc899xyCgoLQt29fo8f/6KOP8M4779gkdiIisj9928VLJBKWKZpw/fp1JCYmcpt4cnoStYv9to8fPx6JiYkAgGbNmiEjI8Nmj/XVV19hypQp4vXw8HCcPHkStWrVMngffTPfTZs2RXZ2NheZEBE5kfKz28HBwTh9+jR7eJvQvXt3yGQynD9/HufOnTM4Lj4+ngk4OQyVSgU/Pz+z8jXOfNvQSy+9hGPHjmHRokUAgHPnzmHt2rUYNmyYwft4enrC09OzukIkIiIb0De77evri5KSEjtG5RyOHj2KKVOmYO/evUbHJScnV9jFmsgZ2PwnNiUlBW+++Sbi4uLQrl07hIeHo3nz5nrHXr58GUqlEkql0tZhVZtZs2bpXN+0aZOdIiEiouqg2aGy/JqdnJwc5Ofn2ykq55GTk4NDhw6ZXPOkUqlqVL5ArsNmM99//fUXpk+fjvT0dJ3b1Wo1JBKJ3vskJCRg+/btkEgk2LlzZ43oEBIWFoZmzZrh0qVLAMDFNERENRh3qLSOO3fumDWOfdHJGdlk5vujjz5CXFwc0tPToVardf4ZM23aNHHcsmXLbBGaXTRq1Ei8rN0JhYiIahbuUGkdAQEBZo3z9fW1cSRE1mf15Hvx4sWYNWuWmET7+/tjwoQJmDdvHlq3bm30vv369YO/vz+AmlWekZeXJ16WyWR2jISIiGyJM7GmeXt7G/26XC5H+/btTS5ak8vl3HCHnJJVk++7d+/ilVdeEa+PGTMGly5dwk8//YQpU6agSZMmRu/v7u6Oxx57DGq1GhkZGbh8+bI1w7OLwsJCndXaDRs2tGM0RERkS+aWS7gquVyO/v37Gx0TFxcHd3d3xMXFmRzHxZbkjKz6U/vTTz/h3r17kEgkGDhwIJYuXWrxR0Lt2rUTL588edKa4dnFypUrdWa+u3btasdoiIjIVgRBwOHDh+0dhkOLi4tDVFQU4uPjK8xsy+VynfaBkZGRZo0jcjZWXXCpvchk7ty5lTrGAw88IF62ZQ/u6nDz5k3MmDFDvC6VSjF06FA7RkRERFWlr3+3VCoVb6OyEkvtzi7lN8aJjIxERESE3tdRm7njiJyJVZNvTSePFi1aGGwnaIqm5hsAsrOzrRGW1ezduxeJiYmYNm0aIiIijI5NTU3FqFGjdNogjRs3jmfqREROTF//bk1iybZ3/2rXrh18fHzg7e0t1maXT5ilUilCQkJMHsvccUTOwqrJd1ZWFiQSCYKCgip9DO02hFXZfPP999/H+++/X+H24uJi8fKlS5fg5eVVYczYsWPxww8/VLi9sLAQCxYswIIFC9CmTRv07t0bMTExaNiwIXx9fXH//n2cO3cOmzdvxoYNGyAIgnjfhx56CPPnz6/08yEiIvvS9O8uT6VScdfKcnbt2gXg3xMTzlQT/cuqybePjw/u3btXpU0Ebt26JV6uW7dupY9TUlKis027IfrGaCfohhw/fhzHjx83K5ZBgwZh0aJF3B6eiMhJsX935WhOTFijTfQvq56K1q9fH2q1GmfOnKn0Mfbv3y9ersoMui2EhIRg5MiROn27DZFKpejTpw/WrFmDtWvXIjAwsBoiJCIiW2D/7qpJTk7W+TSYyJVZdea7Y8eOOH36NO7cuYPdu3fj4Ycftuj+paWl4kd3bm5uFt9f29tvv42333670vfXJyQkBMuXLwcAXLt2DSdPnoRSqcSdO3eQn58PmUwGf39/hIeHo3379qhdu7ZVH5+IiKqP9sJK7U9lyXKareBZu01k5eR7wIABWLp0KQBg5syZ4lbx5vr888+hVCohkUjQrVs3h965qnHjxmjcuLG9wyAiIhvQt7CSqoadYIjKWLXsZNiwYWjRogUAYPfu3UhISEBRUZFZ912wYAFmzZolXn/jjTesGRoREZFZNAsrmXhblyNPqBFVJ6sm325ubvjyyy/FVc3Lli1Dq1atMH/+fKSnp+ssZLxz5w5OnjyJn376CQ8//DD+85//oLS0FBKJBMOGDcNjjz1mzdCIiIhM4sLKyjH1KTe3gif6l0RdlX5+Bnz33Xd48cUX9X5N83Dlf1E1t7dr1w7bt2+Ht7e3tcNySiqVCn5+fsjOzma3FCIiG8vIyEBiYqK9w3A6Xbp0wZ49ewx+nd1OqKazJF+zSePN559/HuvXr0dgYCDUarX4DyhLuiUSic5tmv9HjRqFHTt2MPEmIiK7UCgU9g7BYcXGxhrc6r1Pnz7cCp7ITDaZ+dbIzc3FDz/8gMTERKSmpuptM+Tj44NHH30Ur732Grp06WKrUJwWZ76JiKpHeno6/vzzzypt8FaTJSQkIDg42OhW79odYrgVPLkSS/I1mybf2rKzs5Gamorbt28jNzcX/v7+aNiwIWJiYuDubtWmKzUKk28iItsztHsllZHL5Zg6dSoTaSIDLMnXqi3r9fPzQ9euXavr4YiIiMzCRZamtW7dmok3kZVwypmIiFxK+dKI4uJithU0IS0tDY888ggTcCIrYPJNREQug5vnVA53qCSyHibfRETkEljXXTXcoZLIOmyafBcUFODYsWM4efIk7t69i7y8PItWkb/11ls2jI6IiFwF67qrjjtUElmHTZLvGzdu4H//+x9+//135ObmVvo4TL6JiMgalEolS02M0Oy/YQh3qCSyHqsn37t378bAgQOhUqmq1CvV1Fa1RERE5mLJhH4dOnRAq1atkJubixUrVhgcFxcXx8WWRFZi1eT75s2bGDhwILKzs3WS56CgIDRq1Ig7VxIRkV2wZEK/Vq1aiYsopVJphcWocrkccXFx3KGSyIqsmnzPmTNHTLylUilef/11/Oc//0GTJk2s+TBEROSCKrN7ouY+KpUK3t7eyMvLq6ZonYP2JwKRkZGIiIjgDpVENmbV5Hvjxo3i5W+//RbPPPOMNQ9PREQuSl+LQFOzsmwraFr5TwSkUinbCRLZmFWTb6VSCYlEggYNGjDxJiIiqzDUIlClUiEpKQmdOnVCRESEuCBQqVRCoVDgwIED1R2qU+EiSiL7sGryrVlg2bJlS2seloiIXJQ5LQL37duHffv2wd3dHVKpFEVFRdUUneOSy+Vo3bo19uzZY3AMF1ES2YdVk+/g4GCcOXMGBQUF1jwsERG5KEtaBJaUlNg4GscWGRmJyMhInVrtoKAgLqIkcjBWTb579eqF06dP4+TJkygtLYWbm5s1D09ERC6GLQLN17Fjxwr12lxESeR4rPrb95///AdSqRQ5OTlYtmyZNQ9NREQuiC0CzePl5WWwfluziDI6OhohISFMvInszKq/ga1bt8brr78OtVqNadOm4cSJE9Y8PBERuZigoCDuEWGG2NhYJtVETsLqv6kffvgh/vOf/+Du3bvo0qULPvzwQ9y6dcvaD0NERDWcQqHAV199xd7cZggICEBGRgZSU1ORkZEBQRDsHRIRGSBRV2UPeCOWL1+O8ePHo7i4GAAQGhqKhg0bwsPDw7zAJBJs27bNFqE5FZVKBT8/P2RnZ0Mul9s7HCKiamGovSDpV34DIS6qJKpeluRrVl1wqbFhwwZ8+OGHKC4uFtsPXrhwARcvXjTr/mq1Wmd7eiIich3mtBckXeU/HdD0QI+Pj2cCTuRgrF528sMPP+CJJ55Aeno6AOgk0Wq12qx/RETkuixpL0jGJScnswSFyMFYdeb70KFDmDx5sk4C3aJFC3Tq1AkNGzbkohkiIjLp9OnT9g6hxlCpVFAqldwynsiBWDX5/uKLL8SSkfr162PZsmXo3bu3NR+CiIhqMEEQ2CnLytgrncixWDX53rFjh3h55cqV6Ny5szUPT0RENZxSqWR3EwMkEkmlSjPZK53IsVg1+c7MzIREIkF4eDgTbyIishhnaf8VGxsLf39/sZPJ5s2bLT6GXC43uPkOEdmHVRdc1qlTBwDQtGlTax6WiIhcBGdp/6VQKNCxY0fExMTAx8enUseIi4vj5jtEDsaqv5EhISFQq9W4d++eNQ9LREQuIjg4mHsa/D/NYknA8pMSuVzONoNEDsqqZSeDBw/GgQMHkJqaCpVKxT+gRESkQxAEsZVgXl4evL29xdIIqVQKqVSKuLg4brDz/zRlOJqTEmMtGL29vdG3b1+d15OIHI9Vk++JEyfis88+w927d/Hpp5/i/ffft+bhiYjIiSkUCiQnJ+tNILV3ZIyMjETnzp2xd+9eO0TpWDQz3uaclAwcOJAz3UROwKqnxfXr18fixYshkUjw8ccf48cff7Tm4YmIyIkIgoCMjAykpqZi+/btSEpKMjhzq9mRUaFQQKFQ1NjEOywszOyx5RdLRkZGIj4+vsKnyiwxIXIuErUNtpTctm0bRo0ahTt37uCRRx7BM888g86dO6Nhw4Zwd7fJjvY1lkqlgp+fH7Kzs1nGQ0ROw9gstzHe3t4oLS1FYWGhjSKzr7Fjx6KwsBDr1q1Dfn6+0bGGEmpN6U5OTg58fX1ZYkLkACzJ16yafLu5uelc12y4UxkSiQQlJSXWCMupMfkmImejUChYs21At27d0Lt3b/FTgUOHDuH8+fMoKioSx2iX4BCRc7AkX7PqNLQm2db8r0m8bTC5TkREDkgQBKxbt87eYTg8qVSKsLAwhIWFcSabyMVYvQZEk2gz4SYicj07d+40WU7hykJCQircJpVK9d5ORDWTVZPvf/75x5qHIyIiJyIIAvbv32/vMByWTCZjkk1E1k2+e/ToYc3DERGRE1EqlZz1NmLQoEEsJyEi65edEBGRa9JsCEO6vL292YObiERMvomIyCos3QLdFXh7e2PatGlss+uA1Gq13n+CIBj8GsdzvKF/lrRH5V8DIiIrcdQ3heoaX1JSgqtXryIvLw9qte7ie33/mzOmMmMd6fgtWrTA9OnTHe575Urj9Y0lsqdqSb6Liopw8OBBnDt3Dnfv3kVBQQH8/f1Rv359tG/fXmcHL9KvpKQExcXFdv8jxvE1e7wjxeJs44n04QJUIirPpsn39u3bMW/ePGzatAnFxcUGxzVr1gyTJ0/Gc889Bz8/P1uG5LTq1q1r7xCIiIiIqIpssr18bm4uXnzxRfz8888A/v0ITt9ul9pfa9iwIRITE/Hoo49aOySnpdkxiYiIiIgcW7VvLw8AhYWF6NOnD3bv3g21+t8dLzU8PT3h6emJ+/fvQxCEfwP5/3Hu7u74888/MWjQIGuG5bSYfBMRERHZnmZ3ds0/qVRa4TZD/wDg3r171b+9PABMnToVu3btEgMJCAjAxIkT8cQTTyAmJga1a9cWx168eBF79uxBYmIi/vrrL0gkEpSUlGDUqFFIS0tDaGiotcMjIiKqNpa+gXO8dcc7Uiwc79jjq8qSyVKrznwrFArExMSIM9pDhw7FDz/8AH9/f5P33bRpE5588kmoVCoAwKhRo7Bs2TJrhea0OPNN2iz5I2PpHyZn+iPJ8dU7Xq1W4/bt28jPz8exY8dQVFQk/jxq/1yWv83Q/5aMdabjJyQkIDQ0VGcMEbkGTb5W7TPfy5YtQ2lpKSQSCfr164c//vjD7Pv269cPGzduRPfu3VFaWoqVK1ciLy8P3t7e1gzRab300kvw8vJyqDdkjq/+8UTVTaFQIDk5WZwYCQsLs3NEjkkulyM0NJQ7WBKRSVad+Y6NjcXBgwchkUhw9uzZSv2RTkhIwNKlSyGRSLB+/Xr069fPWuE5JUvOpIiIrEmhUCApKcneYTiF+Ph47mBJ5MIsydeseoquVCohkUjQsmXLSs+ODBw4UOd4RERU/QRBQHJysr3DcCht2rSBTCbTuU0ulzPxJiKLWLXs5M6dOwCARo0aVfoYDRs2FC/fvXu3yjEREZHllEqlWGpCZUn2448/DqDstcnJyYGvry+Cg4NZakJEFrFq8u3n54fbt28jKyur0se4ffu2eJllFkRE1UsQBCiVSpw8edLeoTiUxx57TEyyQ0JC7BsMETk1qybfTZo0QVZWFtLT03Hz5k00aNDA4mP89ddf4uXGjRtbMzwiIjKi/OJK+pePj4+9QyCiGsKqn5X16tULQNnMyaxZsyy+/7lz57B48eKywKRS9OjRw5rhERFROYIgICMjA8nJyUhKSnK5xNvLy8uscTk5OTaOhIhchVVnvuPj4zFv3jwAwOLFixEYGIgPP/zQrHo4hUKBAQMGID8/HxKJBI8++ijq1KljzfCIiEiLq8909+3bF/Xr18fSpUtNjvX19a2GiIjIFVh15rtTp054/PHHxe3k58yZgzZt2uDHH3/EjRs3KowvKSnBvn378MILL+Chhx7CpUuXAJRtWPDhhx9aMzQiItKiaSPoqom3XC5Hx44dERISYnJ9kVwuR3BwcDVFRkQ1ndW3l//pp5/QpUsXnD17FhKJBOnp6XjuuecAAIGBgahXrx48PDyQk5ODy5cvo7i4GACg3W587ty5eOihh6wdGhERgW0EASAuLk78VDYuLs5oP3PtsUREVWX1vyZ169bF33//ja5du0KtVou78qnVaty6dQsKhQLHjx/H+fPnUVRUpJN0y2QyfPfdd5gyZYq1wyIiov/nym0E9fXljoyMRHx8fIUZcPbwJiJbsPrMN1DW9WT79u346aef8OWXXyItLU38mr4NNb28vPDkk0/i9ddfR4sWLWwREhER/T9XTbwB/e9BQFkCHhERwR7eRGRzVt1e3pBz585h7969OHv2LO7evYvCwkL4+/ujfv36aNeuHWJjY+Ht7W3rMJwSt5cnImtbvXo1jh8/bu8w7Ioz2kRkTZbkazaZ+S4vPDwc4eHh1fFQRERkgCAI2L59e41NvD09PVFYWGjW2OTkZERERHBmm4iqXbUk30REZHua3Sn1lU0oFAqsW7cO+fn5do7S+iQSCYYNG4bIyEgolUpcuHABO3fuNHoflUoFpVLJ3SqJqNox+SYiqgH09eyWy+WIi4uDIAhYsWKFHaOzrWHDhiEqKgpA2dbv5m6Iw41ziMgerJ5837x5U/zYr3HjxnB3N/8hsrKykJeXBwBo2LAhPDw8rB0eEVGNIggCdu7ciZSUlApfU6lURlvo1QQ9e/YUE28NczfE4cY5RGQPVi12U6lUaN68OUJDQ/Hwww9bfP+kpCSEhoYiNDQUn376qTVDIyKqcRQKBebNm6c38XYFvr6+6NatW4Xbg4ODuXEOETksqybfq1atEmeun3/+eYtmvQFg4sSJqF27NtRqtVnb/RIRuSrNDpWuXDoRHR2td8GkVCpFXFyc0fty4xwisher/uXZtm2beDk+Pt7i+3t5eWHgwIEAytoTarabJyJyZYIgICMjA6mpqcjIyEBJSYnL71AJAGlpaRAEQe/XuHEOETkqq9Z8a9pXBQYGVrq14MMPP4zffvsNAHDs2DE0a9bMavERETkbfQspvb29xU8ZXZmpjiXcOIeIHJFVk+9Lly5BIpGgefPmlT6GdtKuVCqtERYRkVPSlJaUx8T7X6bKbqRSKdsJEpFDserpv+YNwcfHp9LH0L6vK9cyEpFrEwSBpSVmYMcSInI2Vk2+NbV1d+7cqfQx7t69K16uShJPROTMlEqlTqmJK2nVqhU6duxochw7lhCRM7Jq2Un9+vVx584dnDp1CgUFBfDy8rL4GIcPHxYv16tXz5rhERE5rPK7U7pq4v3AAw9gxIgRAMpaBhrbHIgdS4jIGVk1+e7YsaOYeP/5558YM2aMRfcXBEFcbAkAbdu2tWZ4REQOSd+iSk9PTztGZD9nzpyBQqFAZGQkoqKiIJVKDe7cyY4lROSMrJp8P/bYY/j5558BAP/973/Rt29fBAYGmn3/zz77DGfOnIFEIkFwcDBatmxZpXhycnJw9OhRHDlyBEeOHMHhw4dx+vRplJaWAgCaNWuGjIyMKj3G1atX8csvv2Dt2rXIyMhAVlYWAgMDERISgscffxxPPfUUmjRpUqXHIKKay9CiSs1Owa4oOTkZERERkEql7FhCRDWORK1Wq611sOLiYoSHh+PKlSsAgJiYGKxYscKs7ieff/453njjDQiCAIlEgrlz52Lq1KmVjiUiIgJnz56FsadX1eT7+++/x6uvvorc3FyDY2rXro3PPvsMzz33XKUeQ6VSwc/PD9nZ2SZ3bCMi5yIIAubPn++yJSbGJCQksEsJETkNS/I1q04d1KpVCx9//LGY8B4/fhxt2rTB888/j3/++Qf379/XGX/27FksXLgQbdu2xeuvvy4m3pGRkXj++eerFMuZM2eMJt5V9e677+L555/XSbxbtGiBHj166Jxs3L9/H5MnT8b7779vs1iIyDm58qJKU9jtiohqKquWnQDA6NGjceTIEXz++eeQSCTIy8vDwoULsXDhQgBldYyenp7IycnRmxzXq1cP69evh4eHh1Xi8fHxQZs2bdCuXTu0bdsWv//+e5Xbd61ZswazZ88Wr7dq1QpLly7VqVE/dOgQxo0bB4VCAQD43//+h5iYGDz++ONVemwicl5cVGk+thAkoprK6sk3AMyZMweNGzfGG2+8gZKSEgAQE+2CggIUFBSIYyUSifi1Dh06ICkpySq7Wv7yyy9o27atWDeokZKSUqXjFhcX49VXXxWvBwUFYdeuXahTp47OuPbt22PXrl2IiYnB1atXAQCvvvoq+vfvD3d3m7zsROTADO1USRWxhSAR1WQ2W7Eybdo0nDx5EhMnTjTar1utVuOhhx7Czz//jD179lhtO/kxY8YgMjLS6otyli9fjnPnzonX586dWyHx1ggICMDcuXPF62fPnsXy5cutGg8ROT7NosryM93cqVI/thAkoprMplOw4eHh+PHHH7FgwQIcOXIEJ0+exJ07d1BQUIA6deqgUaNG6NKli1P189buStC4cWMMGTLE6PihQ4eiUaNGuH79OgDgjz/+wFNPPWXTGInIcZSUlGD9+vX2DsMpsIUgEbmCaql/cHNzQ4cOHdChQ4fqeDibyc/Px9atW8XrcXFxJktI3N3dERcXh8WLFwMAtmzZUukNiIjIuSgUCqxfv54z3CZ06tQJERERbCFIRC6Bf+UsoFAodHrvPvzww2bdT3tcQUGBuAiTiGouTakJE2/D5HI54uPj0bdvX4SEhDDxJiKXYNWZ74kTJwIAoqOjMW3atEod4+uvv8aRI0cgkUjw008/WTO8KktPT9e53qJFC7PuV37cyZMn8dBDD1ktLiJyLIIgVLmrUk3WoUMHtGrVijPdROSSrPpXb8mSJUhMTNQpzbDUtm3bsGTJEixZssR6gVlJ+Q15zF2NX34R6cWLF60VEhE5IPbvNq5Vq1ac6SYil8WedxYo/2bq7+9v1v38/Px0rhvbPKKwsFCntIVv4ETOhxvEGCaTydhGkIhcGqcdLFB+h06ZTGbW/cqPM/bG/NFHH8HPz0/817RpU8sDJSK74gYxRERkiMMl35rFSeYmttWpuLhY57q5m+WUH1f+ONpmzpyJ7Oxs8d/ly5ctD5SIqp0gCMjIyEBqaioEQWACbkB+fj6USqW9wyAishuHKzvRLGoMCAiwcyQVld8sqKCgwKwd6rR39NR3HG2enp7w9PSsXIBEZBf6dq/kTraGsSyHiFyZw7w7qFQqzJkzB9euXYNEIkGrVq3sHVIFtWvX1rmel5dnVvJdvtUYZ8SInJ8gCFAqlTh16hT2799f4eslJSV2iMo58G8gEbmySiffYWFhBr+2fft2o1/XplarkZ+fj1u3buncPmDAgMqGZjPld+K8fv06AgMDTd5Ps7ulhjn3ISLHpW+mm8wjl8u54JKIXFqlk++MjAxIJJIKt2uS6UuXLpl9LLVaDQDi8cLCwsSe4Y6kZcuWOtcvXbqE6Ohok/cr/1pw62Qi55Weno4VK1bYOwynFRcXxxaDROTSqvQXUK1W6/wz9jVj/zTc3d0xcuRI7Nixw2hdtL1ERUXpXD9y5IhZ9ys/zhFLaojItPT0dPz555/2DsMpaXaz5OQDEbm6Ss98L168WOe6Wq3GxIkTIZFI0Lp1a0yfPt2s40ilUtSuXRuNGjVCTEyMWTXU9tK0aVM0b94c58+fB1BWXmMO7XHh4eEICgqySXxEZD5NzXZOTg58fX2N7rYoCAJ27tyJlJSU6g3SicnlcjzxxBPIzc01+foSEbmSSiffCQkJFW7TlIo0adJE79drgqFDh2LOnDkAgJSUFCiVSqP1i0qlUif5Hjp0qM1jJCLj9NVsy+VyxMXFVZiZVSgU2LRpEzt0WCguLs7stT9ERK7EqtMQ3bt3R/fu3RETE2PNwzqUCRMmwM3NDUDZbNh7771ndPy7774LQRAAAG5ubpgwYYLNYyQiwxQKBZKSkiosllSpVEhKSoJCoQBQ9vu9fft2JCUlMfG2AMtLiIiMs2qrQVf4SDYyMhIJCQlYtGgRAODHH39EbGwsJk2aVGHsggUL8NNPP4nXx48fX2HRJhFVH0EQkJycbHRMcnIyBEHA5s2bmXSbgeUlRESWkajLr5SsId5//328//77FW4vLi4WZ6IB6N3QZuzYsfjhhx8MHjsrKwudOnUSa78B4PHHH8eoUaPQuHFjXL16Fb/99hvWr18vfj08PBx79+61uM2gSqWCn58fsrOzIZfLLbovEenKyMhAYmKivcOoUTjLTURkWb7mMJvsWFtJSQkKCwtNjtM3xtj270BZn+5Nmzahb9++uHjxIgBg7dq1WLt2rd7xoaGh2LRpE/t7E9kZZ7ItFxYWBg8PD1y4cAFFRUXi7YZq5ImIyLgam3zbWosWLXDixAnMmjULS5Ys0bvZhp+fHxISEvDBBx9U2B2TiKofd1a03IULFxAfH48RI0aY3R2GiIgMs2rZiWYhojVIJBKn2Z65oKAA27dvR0ZGBm7fvo26desiJCQEPXv21FvWYgmWnRBZjyAImD9/PnemtJBcLsfUqVOZbBMRGWC3shO1Wg2JRFJhw52azsvLC3379rV3GERkglQqRVxcHJKSkuwdilNRqVRQKpUICQmxdyhERE7P6tMYlUm8JRKJ+I+IyJYiIyMRHx/PT5IsxHp5IiLrsOrM9z///GP22NLSUty9exepqalYtWoVUlNTIZFIMGHCBIwbN86aYRER6YiMjERERASUSiVUKhU2b96MvLw8e4dlF25ubigtLTU5jvXyRETW4TCtBn/55Rc8//zzyMvLwzvvvIM333zT3iE5BNZ8E1mf9tbyubm52Lx5s71Dsitvb2+jJx+s+SYiMs4pWw0+9dRTCAgIwMCBAzF79my0bdsW/fv3t3dYRFTDpKenY+PGjS47061PTEwM9u3bZ/DrcXFxTLyJiKzEof6a9u/fHwMHDoRarcarr75q73CIyIkJgoCMjAykpqYiIyMDJSUl+OOPP7BixQom3uVERETorYPnVvFERNbnMDPfGkOHDsX69etx+vRpHDp0CO3bt7d3SETkZBQKBZKTk9lS0AxyuVzs2a2pg2cvbyIi23G45Ds0NFS8nJaWxuSbiADo1mkbSwwVCgVbCVpAu6REKpWynSARkY05XPKtvbX7zZs37RgJETkKfTPZ+rY3FwQBycnJ9gjR6XB7eCIi+3C45Hv//v3iZW7JTkSGZrJVKhWSkpJ0apI1rQPJsNjYWLRs2ZIlJUREduJQyfe1a9fw5ZdfitcjIiLsGA0R2Zs5M9nJycmIiIiAVCrlRjAm9OzZEz169LB3GERELs0hpj0KCgrw66+/okuXLrh16xYAwN/fn28SRC7OnJlszdbnAODj41MdYTklX19fdOvWzd5hEBG5PKvOfPfu3dui8cXFxbhz5w7OnTuHkpISqNVqcYv5t99+G7Vq1bJmeETkZMydyeaMt2n9+vVjmQkRkQOwavKdkpIiJs+W0E661Wo1XnrpJbz00kvWDI2InJC5W5prxp05c8aW4Tit4cOHc2ElEZGDsPo0iFqttvif5n5dunTBunXrMH/+fGuHRUROKDg42OQ2vZo+1QqFQmfBNv2L5ThERI7DqjPfs2fPtmi8h4cH5HI5mjVrhnbt2qFRo0bWDIeInJxUKkVcXJzRvt1xcXEAwBaDRrAsh4jIcdg1+SYiMiUyMhLx8fEV+nzLZDLExsaKuzK6YotBd3d3lJSUmBxnbvkOERHZnkO1GiQi0icyMhIRERHYuXMn9u/fj/z8fOTn5yMlJQVHjhxBq1at7B1itfL19UW7du2QkpJicqy3tzeCg4NtHxQREZmFyTcROYXTp0/rTTZVKhX27dtX/QHZSc+ePdGtWzekp6ebNT46OppdToiIHAiTbyJyOIIgQKlUIicnB76+vggKCnL5mm5fX1/069dP7FpibilJy5YtbRkWERFZyKbJ9+nTp5GZmYk7d+4gPz8fAQEBCAgIQFhYGAICAmz50ETkpBQKRYX6bm9vb+Tl5dkxKvvSzHZrz2BrOsEYq3XXdIIhIiLHYdXku6ioCEuXLsWqVauwe/dug28KEokEkZGR6NWrF5577jlERUVZMwwiclIKhUJvZxNXTbzlcjni4uL09ug2txMMS06IiByLRK1ptF1FX375JT7++GPcvHkTAGDOYTUb6/Tr1w/z5s1DeHi4NUKpUVQqFfz8/JCdnW2y3zGRMxMEAfPnz3fJriXaunfvjsDAQPj6+iI4ONhk8qzvkwJjSTsREVmfJflalZPv27dvY9y4cUhOThYTbu3dKg0+cLkxvr6++P777zF69OiqhFPjMPkmV5GRkYHExER7h2FXHTt2RN++fXXq3c1JwMvXyJtzHyIish5L8rUqlZ2oVCr06tUL6enp4hbxarUaUqkUUVFR6NChA5o1awZ/f394eXkhOzsbt2/fxrFjx3Do0CFkZWUBKEvEc3Jy8NRTTyE/Px8TJ06sSlhE5IS4EUxZbXv52X9zZrGlUilCQkKqIUIiIqqqSiffarUaw4YNQ1pamjiL7efnh+nTp2PixIlo3Lixyftv3LgR8+fPx19//SUm7pMnT0ZwcDAeffTRyoZGRE7I1TeC8fDwMNhKMSkpCfHx8SwjISKqASr9uWRiYiK2bdsmJs39+/eHQqHAm2++aTLxBspmuwcMGIAtW7Zg2bJl8PHxgUQiQUlJCZ5//nmzdm0joppBEAQIggAvLy97h+KwkpOTIQiCvcMgIqIqqlTyXVhYiLfeeku8Pnz4cKxZswYNGjSoVBCjR4/Gxo0b4e3tDYlEggsXLmDBggWVOhYROReFQoH58+dj6dKlKCgosHc4duHh4YGioiKjY1QqFZRKZTVFREREtlKp5Hvbtm24cuUKJBIJmjZtisTERLi5uVUpkK5du+Kjjz4SF2AuWbKkSscjIscmCAK2b9+OpKQkl+9w0rZtW7PGsS6eiMj5VSr53rRpk3j5nXfegUwms0owL7zwAkJDQ6FWq3H06FHcunXLKsclIseiUCgwb948vTXOrkQulyM+Ph4RERFmjXf1ungiopqgUsn3vn37AAC1atXCkCFDrBaMRCLB8OHDAZQtyDxw4IDVjk1EjkGzkY6rz+L26dMHU6dORWRkpLhbpTHcrZKIqGaoVPKt2UgnLCzM6r2n27VrV+FxiKhmKCkpwfr16+0dht3JZDJ06tRJ7MWt2a3SGO5WSURUM1TqL/mtW7cgkUjQsGFDa8ejc0xNH3Aicn7p6en47LPPXHareG2xsbEVEunIyEjEx8dXmNDQlKawzSARUc1QqT7fXl5eKCoqssmbaH5+vnjZ09PT6scnouqj2Xlx9+7dOHfunL3DcRgBAQF6b4+MjERERAR3qyQiqsEqlXzXr18f2dnZuHLlirXjweXLl3Ueh4ick0KhQHJysst3MtHH2MJJ7lZJRFSzVWo6RbPo5/r160hPT7dqQJs3b67wOETkXDSLKpl4V8SFk0RErq1SyXefPn3Eyz/88IPVgrl27Ro2btwIAKhduzZiY2Otdmwiqh6CICA5OdneYTgsLpwkInJtlXoHGDBgAICydoDffvstjh49apVgXnrpJeTn50MikaBPnz5wd69UVQwR2ZFSqeSMtx5cOElEREAla76joqIQFxeH5ORklJSUoF+/fkhJSUHLli0rHcjUqVOxatUq8fqrr75a6WMRkf24ev9ubX379oWPjw8XThIRkajS7wQff/wx3NzcIJFIkJmZiU6dOmHhwoUWH+fSpUvo27cvvv76awBlG+0MHjwYnTp1qmxoRGQngiAgNzfX3mE4DB8fH0RHRyMkJISJNxERAahC8h0TE4P58+dDrVZDIpFApVLh+eefxwMPPIBPPvkEJ06cgCAIeu+blZWFdevWYeTIkYiIiMBff/0lHqd58+aVSuKJqHoJgoCMjAykpqYiIyMD6enpmD9/vs6i6ZpEUw43c+ZMeHt7m3UfbgdPRETlSdRqtboqB3jzzTfx4YcfQiKRQHMoiUQCoKwfeFBQEPz9/eHp6Yns7Gzcvn0b169fF++vSbrVajUaNWqE7du3Izw8vCoh1SgqlQp+fn7Izs62+m6iRJWlUCiwadMmlyoxiY2NFXehTE9Px4oVK4yOl8vlmDp1Kme8iYhcgCX5WpVXNL7//vuIiYnBs88+C5VKJSbearUa+fn5OHv2rM5t2rRv79OnD3755RfUq1evqiERkQ1oNsw5deoU9u/fb+9wqp1MJhMvR0VF4dq1a9izZ4/B8exqQkRE+lR55lvj6tWrmDNnDhYtWoT79+//+wD/n2Br037Ihx56CK+//jri4+P1jnV1nPkmR8ANc/TPZKenp2Pjxo06u/3K5XLExcWxqwkRkQuxJF+zWvKtkZ2djS1btmDHjh04cOAAMjMzcefOHRQUFKBOnToICAhA8+bN0a1bN/Tu3Rvt27e35sPXOEy+yd40G+YQkJCQUGH3Sc0nAtwOnojIdVVr2Ul5fn5+GDFiBEaMGGHtQxNRNeOGObr01bhzO3giIrIEd7EhcnHGZm65YY4uHx8fe4dAREROjsk3kQvTV8utXbPsSt1MiIiIqgOTbyIXZaiWW6VSISkpCT179kRpaakdInNc3ECIiIiqisk3kQsyp5Y7JSWleoJxItw0h4iIqopL8olcEGu5LSeXyxEcHGzvMIiIyMlx5pvIiVirrR1ruS3HTXOIiMgamHwTOQlTiyONKZ+0s2uH+bhpDhERWROTbyInYGpxZHx8vMHkUN8ujL6+vpDJZMjPz7dZzM6uefPm6Nq1KzfNISIiq2LyTeTgzFkcmZycjIiICDFJ1Mx07927F2fOnKkwnmUnpnXt2pWb5xARkdUx+SZycOYsjlSpVFAqlQgJCdFbnmKITCaDu7s7k/FyuLiSiIhshZ+lEjk4cxPjnJwcsTzF3E4m+fn5GDx4MNq1a1eVEGscLq4kIiJb4cw3kYMzt7e0j48P1qxZY/Hx//77b1y9etXi+zk7Dw8PuLm56dS9c3ElERHZGpNvIgcXHBwMuVxudDZbLpcDQKV6d7ti4g0AI0eOREhIiFVaNxIREZmL7zJEDk4qlSIuLs7omLi4OG59bgG5XI6QkBBIpVKEhIQgOjpavE5ERGRLfKchcgKRkZGIj48XZ7g15HK52GaQvbvNx5puIiKyF5adEDmJyMhIREREsEyiCry9vTFw4EDWdBMRkd0w+SZyIpoyCX1Onz5dvcE4GW9vb0ybNg3u7vyzR0RE9sMpM6IaQBAEHD9+3N5hOLSOHTsy8SYiIrtj8k1UA2RkZKCwsNDeYTi0gIAAe4dARETEshMiZ6dQKLBy5Up7h2EXjz76KKRSKbZs2WJyrLn90omIiGyJyTeRE9PsaOmKZDIZOnfuDADYt2+fyT7o3C6eiIgcActOiJyIIAjIyMhAamoqLly4gHXr1tk7JLsZNGgQpFKp2X3Q2RWGiIgcAWe+iZyEQqFAcnJypXaxrGl69uyp0y5Q0we9/OvD7eKJiMjRMPkmcgKuXF5SnpeXF7p161bhdvZBJyIiZ8Dkm8gBCYIgJpE+Pj7YtGmTvUNyGJ06dTKYUBvrg05EROQImHwTORiWlxgXGBho7xCIiIgqjZ/HEjkQTXkJE2/DtmzZAkEQ7B0GERFRpTD5JnIAgiC4fPcSc6lUKiiVSnuHQUREVCksOyGyM5aZWC4nJ8feIRAREVUKk28iO2IXk8rhbpVEROSsWHZCZCclJSVYv369vcNwOB4eHka/zt0qiYjImTH5tlBGRgYkEonF/xo2bGjv0MmBKBQKfPHFF8jLy7N3KA5FJpPh8ccfNzqGu1USEZEzY9kJUTVjqYlhgwYNQmRkJKRSKXerJCKiGonJdxV1794dMpnM5LiAgIBqiIYcnSAI3DBHj/KJNXerJCKimorJdxUlJiZyRz0y286dO9mpA0CtWrUwatQo5ObmGkysuVslERHVREy+iaxIe1v48kmlQqFASkqKfQN0EMXFxZBKpYiOjrZ3KERERNWKyTeRlejr1y2TyRAbG4uHH34YycnJdozO8fATACIickVMvomswNAiyvz8fKSkpGDPnj0oKiqyQ2SOi726iYjIFXH1ElEVCYJgclabibcu9uomIiJXxeSbqIqUSiW3hrdQ69at2bmEiIhcEt/9quiNN95ATEwM/P394eHhgQYNGqBt27Z48cUXsXnzZqjVanuHSDbG2mXLpaWlQRAEe4dBRERU7Zh8V1FSUhJSU1ORnZ2N4uJiZGZm4ujRo/jmm28QFxeH6Oho7N27195hko0IgsDkuxJUKhWUSqW9wyAiIqp2XHBZRXXr1kXz5s3h6+uL+/fv4/z588jKyhK/np6eju7du+P777/H008/bfJ4hYWFKCwsFK+znME+DLUM1NyuUqlw/vx5pKeno7S01N7hOiWetBARkSti8m0hiUSC9u3bY8KECejXrx9CQ0MrjDl8+DA+/vhjrFixAgBQUlKC5557DkFBQejbt6/R43/00Ud45513bBI7mUdfy0C5XI7WrVsjLS2NJ0RWwm4nRETkiiRqFiXbzFdffYUpU6aI18PDw3Hy5EnUqlXL4H30zXw3bdoU2dnZkMvlNo2XDLcMJPPI5XI89thj2Lx5s9GZbblcjqlTp3LRJRER1QgqlQp+fn5m5Wuc+bahl156CceOHcOiRYsAAOfOncPatWsxbNgwg/fx9PSEp6dndYVIWsxpGUj6xcbGomXLlmJ5jlQqNXoSExcXx8SbiIhcEt/9bGzWrFk61zdt2mSnSMgUtgysnO7duyMuLg4hISFiQh0ZGYn4+PgKZ/9yuRzx8fGIjIy0R6hERER2x5lvGwsLC0OzZs1w6dIlAMCpU6fsHBEZwgWAlRMYGKj39sjISEREROhduEpEROSqmHxXg0aNGonJt3YnFHIcgiDg/v379g7DKRlbOCmVShESElJ9wRARETk4Jt/VIC8vT7wsk8nsGAnpo1AosG7dOuTn59s7FKfDbeKJiIgsw+TbxgoLC3Hu3DnxesOGDe0YDWlo+nWfOnUK+/fvt3c4TosLJ4mIiCzD5NvGVq5cqTPz3bVrVztGQ4D+Pt5UkYeHBwYPHgwAevuex8XFceEkERGRhZh829DNmzcxY8YM8bpUKsXQoUPtGBGxj7d53Nzc8Nprr8HdvexPBBdOEhERWQffPS2wd+9eTJ48GadPnzY5NjU1Fb1794ZSqRRvGzduHGcK7Yh9vM0XEhIiJt7Avwsno6OjdVoKEhERkWU4822BwsJCLFiwAAsWLECbNm3Qu3dvxMTEoGHDhvD19cX9+/dx7tw5bN68GRs2bIAgCOJ9H3roIcyfP9+O0RP7eJsvLCzM3iEQERHVSEy+K+n48eM4fvy4WWMHDRqERYsWcXt4O2Pibb769evbOwQiIqIaiZ8dWyAkJAQjR45Eo0aNTI6VSqXo06cP1qxZg7Vr1xrciISqD/t4m49tF4mIiGyDM98WCAkJwfLlywEA165dw8mTJ6FUKnHnzh3k5+dDJpPB398f4eHhaN++PWrXrm3niElDoVBgx44d9g7DaRjbOIeIiIgqj8l3JTVu3BiNGze2dxhkBnY4sYy3tzc3ziEiIrIRlp1QjcYOJ5br378/u5kQERHZCN9hqUZjhxPLdOnSBVFRUfYOg4iIqMZi8k01GhNv83Xq1Al9+vSxdxhEREQ1GpNvqrEUCgU2b95s7zCcxsmTJ3V60xMREZH1MfmmGkmzyDIvL8/eoTgNlUqlsyMrERERWR+Tb6pxuMiy8nJycuwdAhERUY3GVoPkcARBgFKpRE5ODnx9fREcHGy0+0b58YIgsNa7ktjfm4iIyLaYfJNDUSgUSE5O1kme5XI54uLiEBkZadZ4mUxWLbHWNHK5nP29iYiIbIxlJ+QwNHXa5WetVSoVkpKSoFAozBrPrdErJy4ujv29iYiIbIzvtOQQzKnTTk5OFrtxsK7bcr6+vujevXuFTwbkcjni4+P1frJARERE1sWyE3II5myGo+nGERISws1zKqGkpAQ7duwQr8tkMsTGxqJbt26c8SYiIqomfMclh2Bulw3NOHblMEwikehc18x0ly/Hyc/PR0pKCk6fPl1tsREREbk6znyTQzC3y4ZmHLty/Gv48OHw8fERu70EBQXhypUryMnJgY+PD1avXm30/snJyYiIiODsNxERUTVg8k0OITg4GHK53GgpiXY3DnPG1zTe3t46mwYZ6wITEhICAMjIyDD5KYF2OQ8RERHZFpNvcghSqRRxcXFISkoyOEa7G4c542sSb29vTJs2TZzRNqf/OWB5OQ8RERHZFj9nJocRGRmJ+Ph4yOVyndsNdeOIjIxEbGxsdYZoN/3794e7uztCQkIQHR2NkJAQs8pELC3nISIiItvizDc5lMjISERERJi9w2XLli2xf//+ao6yenXp0gVRUVGVuq+l5TxERERkW0y+yeFIpVKj9cfa28n7+PhAJpM5/cY6+jqSeHt7o3///pVOvAHLy3mIiIjItph8k0PQTqiNzXbr207ey8urOkO1uq5du6JXr14AYPaMvyU05TzlXzdjCzaJiIjINph8k93pS6j1JYaa7eTLKygoqJY4bcXHx0dMsm3VccTSch4iIiKyDSbfZFeGEmqVSoWkpCRxoWVN3k7e29u7Wh7HVDkPERER2R6nvciqBEFARkYGUlNTkZGRAUEQjI41lVAnJyeLJSk1tad3+e4uREREVHNx5pusxtzyEQ1zEmrNBjDO2ofa09MThYWFBr/OTiNERESuhTPfZBWa8pHyybSmfEShUFS4jyUbwDhjH2pfX18MGjTI6Bh2GiEiInItfNenKrOkfESbuQm1j48PBEGAm5tbpWO0h379+iEqKsqijYOIiIioZmPZCVWZueUjBw4cQMeOHcWZXnM2gJHJZFi9erXTlZ307NlTTKwr02lEEAQUFRUZrZknIiKiiqRSKTw8PBz2k2Um31Rl5ibGmzdvxt69e8UacHM2gHHWzXMCAgJ0rpvqNKJWq3Hjxg2cPn0a586dg0qlglqttnGURERENZevry/Cw8MRERGBxo0bQyKR2DskAEy+yQosqccu30LQ2AYwxcXFTpt8W/KaZGdnY+XKlbhz5w68vb3RokUL1K9fH15eXk5XakNERGRvpaWlKCwsxK1bt3D27FkcPXoUfn5+GDp0aIXJMXtg8k1VZk75SHnJycmIiIiAVCrVW5YhCAKWLl1qw6htx5IOJtnZ2fjjjz8gkUgwYsQIBAUFOezHZERERM6mV69euHbtGv766y9x8s/eCTjf5anKpFIpWrdubdF9NDXgmn7gQNnujtHR0QgJCUFubq4NIq0e5nYwKS4uFhPv+Ph47jhJRERkZRKJBE2aNMGIESMgk8mQlJRk952xOfNNVSYIAtLS0iy+3+bNm8XL5fuB37lzx2rxVRdjPc31uXjxIrKzs5GQkOCUrRSJiIichbe3N4YOHYoffvgB586ds3jS0Jo4zUZVZo3dJ7X7gQuCgEOHDlkpOtvz8PDA2LFjMXXqVItaB545cwb16tVDYGCgDaMjIiIioGw9VlBQEM6cOWPXOJh8U5VZsw1gcnIyMjIycP/+fasd09YGDx6MsLAwi0pGSkpKcOHCBURERNgwMiIiItLWokULXLp0ya6lJ0y+qcqsWTKhUqlw9OhRqx3PlqqyUY5KpUJxcTEaN25sg8iIiIhInyZNmkAQBNy9e9duMbDmmywiCEKFzWIq0+3EmMrUj1ubRCLR6bMtl8vx2GOPwcfHx+yNcozRnHF7eXlZJV4iIiIyTfO+W1hYaLcYmHyT2RQKhd5+3I899hjatm2LlJQU+wVnZd27d0dwcDByc3OrnGjro9m50t2dv4JERETVRfO+W1paar8Y7PbI5FQUCoXenShVKhVWrFhhh4hsa/v27WL3EmM7UxIRERFZgjXfZJIgCEhOTrb4fh4eHjaIpvpod2Ahx/D2229DIpE4zBbBNcWSJUvE11XTd5+cW1FREVq0aAGJRGL1CRJX+nl54YUXIJFIkJCQYO9QqAZh8k0mVbaVYFFRkQ2iqX7JyclimYg9paSkiG942v/c3d0REBCA0NBQdO/eHdOmTcOff/5p89e/tLQUcrkcEokEbdu2NTpWrVajbt26YsyLFi0yOj4xMVEc+91331kzbCKXMH/+fLGX8bBhw+wdjtN644034OHhgaVLl+Lw4cP2DseqLl26hFdeeQUtW7aEj48PAgIC0KFDB8yZMwd5eXkO8ziZmZlYv3493nrrLfTr1w+BgYHi+8P48eOtFmd1YvJNJlmzlaAzUqlUUCqV9g7DoNLSUty9excZGRnYuXMn5s2bh+HDhyMoKAjvv/8+SkpKbPK4bm5u6NKlCwDg+PHjRk/Q0tPTdTZO2rlzp9Fja3+9e/fuVYzUNbnS7KQ2V33e2nJycvDJJ58AAN58801+UlQFwcHBSEhIgFqtxv/+9z97h2M169atQ0xMDObOnYvTp08jLy8Pd+/exaFDh/D666/joYcewrlz5xzicRo0aIBBgwbhvffeQ3JyMm7fvl3luOyNyTeZxN0XHe8E5Pnnn0dqaqr4b+/evdi4cSM+/vhj9OnTBxKJBLdu3cL//vc/PPzww7h165ZN4tAkxoIgYM+ePQbHaZJpNzc3neumxgcGBqJVq1bWCJWMGD9+PNRqNdRqNdc41ADfffcdbt++jeDgYIwYMcLe4Ti9V155BQCwadOmGjH7ffToUYwcORIqlQq1a9fGBx98gD179mDbtm145plnAJRtAjdgwIAqvffZ4nGCg4Px2GOPVTomR8Hkm0zStBJ0Rtaa8XG0E5D69eujdevW4r9OnTqhX79+eOONN7BlyxakpaXhoYceAgAcOHAAQ4YMsUkZivas9I4dOwyO03xNkwicP38e165d0zs2MzNT3H2sa9eunLUjskBpaSm+/vprAMDo0aOt2qXJVUVERIildV999ZWdo6m6qVOnIj8/H+7u7tiyZQv++9//onPnzujduzcWLlyITz/9FEBZYvz555/b/XHeeustrFu3Djdu3MClS5ewYMGCSsfkKPhbSSZJpVLExcXZO4xK6dq1KxISEjBkyBB4e3tX6hhyuRzBwcFWjsy2WrVqhd27d4sJ+O7du/HNN99Y/XE6dOgg9kw1Nput+drw4cPRvHlzo+NZckJUeVu3bsXly5cBAGPGjLFzNDWH5rX8448/HO6TUEscOHBA/Bv79NNPo3PnzhXGvPLKK+LmcfPnz0dxcbFdH+edd97BwIED0aBBA4vjcFRMvskskZGRiI+Pd8oZ8JCQEMTExGDgwIGVun9cXJxTzh7JZDIsXbpUnDn+7LPPDP5xKyoqwrfffotevXqhXr168PDwQMOGDdG/f3/88ssvBhecenp6omPHjgCAgwcP6t204OLFi7h69SqAspOhrl27ArBO8l1QUIA5c+agbdu28PX1ha+vLzp27Iivv/5ab617cXExGjZsCIlEYtYJZVpamlg/rJmlqYx//vkHCQkJCAsLg7e3N+RyOaKjo/Haa68Z/ARA49q1a5gxYwbatm0LPz8/1KpVCw0aNEB0dDRGjx6NJUuW6NTbaxbmTpgwQbwtNDS0wkJd7b78xuqky3eYUalUePvttxEdHY3atWujfv366N+/f4Wyo8zMTLz55puIioqCj48P6tatiyeeeMLkDrZpaWl4//330bdvXwQFBcHT0xO1a9dGixYtkJCQgH379um9X2Wet7aqfI8qo6SkBF999RU6duyIOnXqwN3dHf7+/ujRowdWr15d6eNqWsK2aNEC0dHRRsda+rNlicr+TQEq/szdu3cPs2fPRlRUFGrXro2AgAD06tULv/32m9nxVPX7q1m0mpeXhzVr1pj9uI5G+2dL+3dFm1Qqxbhx4wCUvfb//POPwz6Os2KfbzJbZGQkIiIidHa4PHPmDPbu3Wvv0Ay6d+8eBEGAVCoVTyD0bRTUunVrpKWlVbg9Li6uUtvHO4qoqCj06dMHW7ZswbVr13Dw4EFxkaRGRkYG+vXrh1OnTuncfvPmTWzatAmbNm3CggULsGbNGgQEBFR4jO7du2PHjh0oLCzE/v37KyTMmpKTFi1aoEGDBujatSsSExMNlqlokm+5XI4HH3zQ4HO7efMm4uLicOzYMZ3bDx48iIMHD2LLli1YvXq1zolTrVq1MG7cOMyZMwdbt27F1atX0aRJE4OPoenK4u7uLr5JWKKgoAATJkzA8uXLK3wtLS0NaWlp+O677/Dbb79h0KBBFcbs3LkTAwcOrJAAZWZmIjMzE2lpaVi+fDkCAwMrfXJpicuXL+PRRx8Vy4IAIDc3F5s2bcKWLVvw22+/YcSIEThx4gT69+8vnnQBZUnL2rVrsXnzZmzatAm9evWqcPyUlBS9txcVFeHcuXM4d+4cfv75Z8yYMQMfffSRVZ5TVb9HlaFQKDB69GgcP35c5/bs7Gzs2LEDO3bswJw5c/Dqq69afGxNAtOpUyej42z5s1XVvynaLl68iD59+uD8+fPibbm5uUhJSUFKSgpWr16NZcuWGdywzFrf32bNmqFhw4a4ceMGNm3ahKeeespo3I5q165dAAAfHx+0a9fO4LgePXqIl3fv3m1xnXV1PY6zYvJNFpFKpeKCLEEQsGrVKvsGZEJqaiouXbokJtH6TiA0u1c+8sgjem93do8++ii2bNkCoOwNVzv5vn//Ph555BFcuHABADB48GBMnDgRjRs3xsWLF/H1119j+/bt2LVrFwYNGoQdO3aIiyY1tJPtnTt3Vki+Ncm0ZsZb839aWhru3r2LOnXqiGNzcnLEhKRLly4VHkvb0KFDcfLkSUyZMgWDBg1CQEAATp8+jffeew8KhQLr1q3DDz/8gOeee07nfpMmTcKcOXMgCAJ+/vlnzJw5U+/xi4uL8csvvwAA+vXrh4YNGxqMRR+1Wo3hw4djw4YNAIBBgwYhPj4eYWFhkEqlOHDgAD7//HMolUoMHz4cu3fvRvv27cX7FxYWYtSoUVCpVPD19cXzzz+PXr16oX79+igqKsLFixexZ8+eCr+DHTp0QGpqKtasWYM333wTALB582Y0btxYZ1xoaKhFzwcoq9m/cuUKZs6cibi4OHh7e2PXrl2YPXs2VCoVnn76abRv3x4DBw5Efn4+PvjgA/To0QO1atVCcnIyPvjgAxQWFmL8+PE4e/Zshb0ASkpK4OPjgwEDBqB3795o2bIl5HI5MjMzkZ6eji+//BKXLl3Cxx9/jAceeEBnRq0yz7uq36PK2L9/P/r27Yvs7GyEhYVh2rRp6NChA4qKirBu3TrMnTsXpaWlmDFjBh5//HE88MADZh/7ypUr4icXHTp0MDiusj9b5rDG3xRtI0eOxMWLFzF58mQMHz4cfn5+OHHiBD755BOcOXMGSUlJaNy4Mb744osK97X297djx45Yu3Yttm/fbvHr4ig0+1aEh4cb3WG5ZcuWFe7jiI/jrJh8U6VZ0v/7wQcfRE5Ojs7sRXXRbJYTHx+PyMhInRMIbYZud3baPbi1ZyyBslo6zZvkm2++iffee0/8Wrt27TBs2DCMHTsWy5Ytw549e7Bw4UI8//zzOsfo3Lkz3N3dUVJSoreURHNbt27dAJT9sQ0MDERWVhZ2796tM6u2Z88ecctfUyUnmtntnj176jzXvn37olWrVrh58ya+/fbbCsn3Aw88gG7dumHnzp1YsmSJweR7/fr1YpeYiRMnGo1Fnx9//BEbNmxArVq1sHbt2gplLp06dcLYsWPRrVs3pKen4+WXXxZni4CyWSDNx+G//vprhdnHTp06YfTo0fjiiy90euX6+PigdevWOHTokM5ztsbP9rFjx7B9+3bExsaKt7Vv3x4tWrTAwIEDkZOTg9jYWKjVahw4cECs7wfKEpfAwEC88MILUCqV2LBhA4YMGaJz/AcffBBXrlyBv79/hcfu27cvXnzxRQwcOBBbt27FO++8g3HjxomJW2Wed1W/R5bKzMzEE088gezsbMTFxeHPP//UWYvSrVs3BAUFYerUqSgtLcWPP/5oUbmTdumPZr2HPpX92TKHNf6maDt48CB+/fVXjB49Wrytffv2GDFiBLp164bjx4/jyy+/xNNPP43WrVvr3Nfa39927dph7dq1uHr1Km7evFnpGmRrLCJfvHixxT2uCwoKkJWVBQAICgoyOrZOnTrw8fFBbm6uuIbA0R7HmTn/tB7ZjbmLTmJjY/HEE0/gqaeeqlDyUJ0cZbOc6la3bl3x8t27d8XLhYWF+PHHHwGUlae8/fbbFe4rkUjw7bffisfQdFHQVrt2bfGNXjt5Bip2LtF4+OGHAVSs+7ak3vull17SSbw1AgICxBnR1NRUZGdnVxgzadIkAGUnI7t379Z7/MWLFwMo6yxj6cfuarVa7LM8ZcoUg/XlderUwZw5cwCUJURnz54Vv3bjxg3xsrHXwt3dvdrWYrz88ss6ibfGgAED0KxZMwDArVu38N577+kk3hoTJkwwukA3MDBQb+Kt4eHhIb5ely5dqlByZAlrfI8sNX36dNy8eROhoaFISkrSuwj82WefhY+PDwAYrG835MqVK+Ll+vXrGxxnq58ta/1N0TZw4ECdxFvD19cXCxcuBFD2Kez333+v83VbfH+1X1PNCYYz0X7Prl27tsnxmp/D+/fvO+TjODMm31Rp5rbf0/5YydRZsDHe3t7o3LkzZDJZpe7v6Jvl2Ir2Hz/tP4qHDx/GvXv3AJT1eTb00a9cLkd8fDwA4OTJk7h+/XqFMZo38JycHJ2ESFPX3aBBA7Ro0UK8XZOIl6/71iRkXl5eRj82B4x3ctDUGKrValy8eLHC10eMGAE/Pz8A/ybZ2jS1qQAwduxYox+b6nPy5EnxU57hw4cbHaud/Givn2jUqJF4WV+M9jBq1CiDX4uJiQFQllyNHDlS7xiZTCb+HPxfe3ceVlWd/wH8fdkVWdxCQEEUTHDL3RLX0vCXppJjthialUtlOmM1WuOSbfZUM2NTyq8Ut0lcWtRqMi0R5zcuuIUsBiiLC6YCIiD7/f7+4Llnzrnclbsi79fz8Dz3XL7nnM+95/Dlc7/3u5iSvFRXV6OgoAAZGRlS/1whhPR77T7T5rDGNTLHhQsXpAGCq1ev1lt/enl5SV1N5AtTmUI+n7+8O5c2W91b1qxTNPQN1gMavk3p1asXAODgwYOK39ni+sr7p8s/wJhLvkZDU3+mTJli9nmrqqqkx9pdvnTx9PQEAFRWVjrleZozdjuhJtPM/22o64l8mj61Wo0ff/zRpGN36NAB48ePh6urKyoqKhR9sB966CHk5eVJfRtDQkKQn59v0tfBzXmKqKaSv2Z5K1ZaWpr0WFdrptzQoUOlZd7T0tIU/7yBhq/LNfO0HjlyREp+tft7y8sDDf+sKysr0apVK9TU1ODEiRPS+YxV2vIPddrk/yR1XfNWrVrhySefxLp167Bz506sXbtW0Qq5detWabaUpnQ5kXd90DXFlj7yf+jR0dHo1q0bLl68iEWLFuGf//wnpk6dipEjR2Lw4MEm/VOzNkP9jzUt1h06dDCY+GnK6ftbrKiowNq1a5GYmIj09HTFNynaNF9tN4U1rpE5NDN8+Pv7G00GNUmru7u7WeeQJ+uGroGt7i1r1ikaxj6EDxkyBOnp6cjKykJNTY0Uuy2ur/w9raioMPmY2rS7x9iL5lsnACat+6CZvcrcBi97nac5Y8s3NZkp83/Lp+kzp494TU0Nunfvjm7duqFPnz7o2rWrdBwXFxd069YNY8eOxdixYxEeHq7zK25dnG2xHHuQJyjypFT+j9rQV9QAFIMNdbXGjRgxQurHKO9OoC/5HjBgAFq3bo3a2lrpq/WUlBSpxcSU+b0NzdsuHyirL3nTdD0pKyvD7t27Fb/TtAYOHTq0SStsXr9+3ex9ACj617q7u2Pfvn3SbDspKSlYtmwZoqOj4e/vj5iYGHz55ZcGk1NrM+U9Nzafvqacrrjz8vLQp08fLFu2DKmpqUZfmyUtZda4RubQNDyMHj1aaunTRzNLjKYrj6nkSY+h98ZW95Y16xRTj6Ppdy2EUHSrs8X1lb+n5n4wcgby/3+mdPHQfMAwpeuII87TnLHlmyxiaPo+7Wn6zGl11nQRMXWQmLmt8C2JfF7le++9V2cZSwcAtWvXDr169UJaWpqUcN++fVvqFqCdfLu7u2PIkCFISkpCcnIyxowZY/fFdQYMGID+/fvjzJkzSEhIkKYSPH78ODIyMgA0rdUbUCaW+/btM/k+1k40oqKicO7cOezbtw/79u1DcnIycnJyUFlZif3792P//v34+OOP8cMPPxhNUpqDmTNnIjc3V5qve8aMGYiMjJTmiVapVFCr1VLLsLwLirmsdY1MUV1dLS1LLh8Arcu1a9ekbhjGymrr2LGj9Li4uNhgY4Ot7y1rrUzb1OPY4vrKPyQYGptgjPwbgqbq3Lmz2TF4eXmhffv2KCoqUowP0KWkpERKirt06eKU52nOmHyTxQxN3ydnbquzOcm6phVes8CELs11sRxLHThwQHosT4LlreC///67wS4F8q9i9c3LO3LkSKSlpeHGjRs4f/48cnNzoVarFQMy5aKjo5GUlCQl3Zr+3+7u7mZ9TWyJ5557Di+++CIOHz6M3NxchIWFSa3erVu3NtjH2RD5IFd/f3+LvmZ2dXXFlClTpD6ehYWF+PHHH/Hpp5/i1KlTOHXqFObOnev0034ac/78eanr2LJly/D222/rLGduP2h9rHmNjElLS5MWuDKWYGjGGgAN04SaQ558l5SUGG05t/a9Ze06RXMcQ+/Z77//DqAhSZd3C7HF9ZW3rFvSkGNs8SNTNGW2E6DhQ9eRI0eQk5ODuro6veNZ5HO0N2WtC3udp7lqeZkI2YRmmj7tLiJymtZpU5mbrOtbhVMzuKcl/WFrpKWl4eeffwbQ8E9fPoet/J/R8ePHDR5H0xdbez85TT9uoKG7iSapHjZsmM6BV5oPAseOHUN1dbU0TdqAAQOk0e+29tRTT6FVq1YQQmDTpk2orKyUFuN47LHHmjyLiPzDhr7ZVJoqMDAQs2fPxtGjR6WW0e+++65RNwNrtTzaS3p6uvRY34BNQNmXVxdTX7ctr5E2+SBkYzMuaWbwiIiIMPtDqDyp055W1BSm3lv6WLtOARq6xBii+X1ERISir7otrq/mPfX09ER4eLhVjmlvmnq3oqJC+jZGF/lc5prZqZzxPM0Vk2+yG1P6iGs0tYtIZGQkXnnlFcTFxSE2NhZxcXF45ZVXWmTiXVlZiWeeeUb6an7JkiWK1oeBAwdKX1tu3rxZb1JQVlYmfaMQFRWld2CUvKuIZpU+oHGXE437779fGlC7adMmaUpAe3Q50fDz85MGv23evBm7d++W4mhqlxOg4QOEZmaf//3f/1WM/rcWd3d3aXW4uro6aZYJDXn/X82AJmemGeAKGB7Mpj2lnDZTX7c9rpGGPPlOTU3VW+7LL7+UxkAsWbLE7A9QgwYNkl6/saTVEGP3lj7WrlM0x9EnJSVF6sKh/S2BLa6v5j3t37+/RX2+hRAW/zSl1RuAYpYUfTPdaBYgAxq+NdC16qyznKe5YvJNdqVpnTY2qtmSLiKmtMLf7TIyMhAdHS319x41alSjhSw8PT2lQYdpaWmKxTA0hBB46aWXpEGbL730kt5zBgUFSQNfDx06JLVQylvE5Xx9faWWOvlCIvZMvoH/DrzMz8/Ha6+9BgDo3r27Ytljc7m4uGDZsmUAGqbUe+aZZwwmgrdv324037HmK1t9ampqpFajNm3aKLocAMrp5ByxuJW55FNRbtq0SWeZdevWYc+ePQaPY+rrtsY1MpU8+d62bZvOwYDJycnSglBDhgyR7ktzeHh4SLOMyFuWtVl6b+lj7ToFAPbu3auzO2F5ebn0frm4uDRaTMva17e6ulr64NScl0AfMmSIVCdv2LBB59SKH330kbTa5CuvvKL3g4ZKpYJKpdLZn96a57kbsc832Z2mj/iRI0dw/PhxxVeaugZqUmPXr19XDNqpqKhASUkJUlNT8fPPP+PAgQNSi/ewYcOwe/dunRXb8uXL8fXXX+PixYtYuXIlzp07h9mzZyMwMFBaCjopKQlAQ0v1Cy+8YDCuESNG4MKFC9JsDW5ubhg2bJje8tHR0Th79qw057OLi4velnJbGTlyJHr06IGsrCypH+qsWbMs7rYxb948HDhwAN988w127dqF06dPY+7cuRgyZAj8/Pxw+/ZtnD9/HklJSdi7dy+8vLwUicjPP/+M1atXY8SIEXjkkUfQt29fdOzYEZWVlcjKysL69etx+vRpAMCcOXMa9ans378/vLy8UFVVhb/85S9wd3dHaGio9GE0ODjYqab26t+/P3r37o20tDTEx8ejpKQEM2fORGBgIC5fvoxt27Zh9+7dGD58uMFuBOa8bkuvkSmEEFLSNnDgQJw6dQrR0dFYvnw5oqKicPPmTXz99dfYsGED6urqEBQUhK+++qrJjQaTJ0/G4cOHceLECWkMjjZL7y1DrF2nDBo0CE8++SQOHz6MadOmwdfXV1pe/rfffgMAvPjii9I883LWvL7JyclSv33tlVmbm7///e8YPnw4KisrMX78eCxbtgxjxoyRut1puj716NEDf/rTnxx+nn//+9+KD4vyGbxycnIafVhv6rcCdiXIqZWWlgoAorS01NGh2ER9fb3Izc0VqampIjc3V9TX1zs6JLu4dOmS+PDDD0VxcbHJ+xw6dEgAMPmnY8eO4p133hG1tbUGj5ubmyt69uxp8FjDhw8XRUVFRmPcuHGjYr/BgwcbLJ+YmKgo369fP4PlV6xYIZU1RP5eHTp0yGjca9askcq7uLiIS5cuGd3HFDU1NWL+/PlCpVIZvV5hYWGKfeWv1dDP5MmTxZ07d3Se/7XXXtO7n/x9SUhIkJ7Pzc3VG4chcXFxAoAIDQ01WG7UqFECgBg1alSj3505c0a0bdtWb8x9+vQRV69elbZXrFhh0esWwrJrZIqcnBxp/2+//VZMmDBB7/H79esn8vPzzT6H3M2bN4Wnp6cAIDZv3qyzjKX3lqH7RQjL6xR5fBcvXhRhYWF6j/PYY48ZrOOsdX1nzZolAIhevXrpLdOc7N27V/j6+up9L3r06CGys7MNHkNT1tDfvDXOo6lbTP0xpqKiQnz44YciJyfHaFlzmJOvtbzv48mpsIuI5VxcXODn54eQkBCMGDECixYtwldffYXLly9j2bJlRlutunbtil9//RX/+Mc/MGrUKLRv3x7u7u4ICAhATEwMtm7diuTkZIMzEmhodxkx1oqt3SXF3l1ONGbOnCk9HjdunEUrscq5u7vjs88+w6+//oqXX34Zffr0gZ+fH1xdXeHn54f77rsPc+bMwe7du6WvXzWWLFmCr776CvPnz8ewYcMQEhICLy8veHl5oWvXrpg+fTq+++47fPvtt3pbsN9//318/vnnGDFiBNq1a6d3xUFncd999+Hs2bOYN28eQkND4e7ujnbt2mHIkCH48MMPceLECYP9gzXMed2WXCNTyLuc9OvXD99++y3effdd9OrVC61bt4a/vz9GjBiB+Ph4nDx50uLpUNu3b4/Y2FgADX3IdbHGvWWINeuUsLAwnDp1CsuWLUNkZCRat24NPz8/jBw5Uvo2xFAdZ43rW1VVha+//hoAsGDBArPfD2c0adIkpKamYvHixejRo4d0Lw4aNAhr1qzBmTNnrDKo1F7naW5UQlgwUSrZ3O3bt+Hn54fS0tImz7xAzufy5cvYsWMHnn32WYMr0ZF9HDhwQOrHuWPHDmnpayJLvfnmm3jnnXfg5+dn8sBFSx0/flyaZejChQtmL9bjaCtXrsSqVasAwKK53K1l27ZtmDlzJtq3b4+8vLwWtRjM3ejOnTtYt24dpkyZYvICfaYwJ19jMyMRtXgbN24E0NBqOHnyZAdHQ3cTTcu3NeZ2NtXQoUMRGxuL+vp6vPfee3Y7791IrVbj3XffBQC8+uqrTLzJKph8E1GLduHCBWl5+dmzZxtd+pvIHJrkW9eAQFt699134ebmhoSEBKOrDJJ+u3btQmZmJkJCQrBw4UJHh0N3Cc52QkQtzpUrV3Dnzh1cvHgRr7/+Ourq6uDl5YXFixc7OjS6i9y8eVOa+cfeyfe9996LjRs34sKFCygoKLDaOIaWpr6+HitWrMDYsWOdanYgat6YfBNRi/PUU08pVlYDgNWrVyMoKMhBEdHdSD7Y0t7JN6AcSExN8+STTzo6BLoLMfkmohardevW6NGjBxYtWoS4uDhHh0N3GU3yrVKp7Nrnm4icG/t8E1GLk5SUBCEEKioqcObMGSbeZBNLliyBEAJqtZoD9cywcuVKaRl1orsRk28iIiIiahGc4UMdk28iB9As+lFXV+fgSIiIiFoOzf9dYwvQ2RKTbyIH8PLyAgBUVlY6OBIiIqKWo6qqCgAcOq0sk28iB/D19YWnpycKCgocHQoREVGLUVBQADc3N7Rr185hMTD5JnIAV1dXhIeHIysryyn6nxEREbUEWVlZ6Nq1Kzw8PBwWA5NvIge59957UVJSgsLCQkeHQkREdNcrKirCtWvXcO+99zo0DibfRA4SEhKCjh07Ys+ePbh586ajwyEiIrpr3bp1C1999RXatm2Lbt26OTQWJt9EDuLq6oo//OEP8Pb2xq5du5Ceno7q6mpHh0VERHTXqKmpwfnz57Fz5064ublh+vTpDu1yAgAqwQ6nTu327dvw8/NDaWkpfH19HR0O2UBlZSW+//575Ofnw9XVFV27dkXHjh3h5eUFFxd+PiYiIjKHWq1GdXU1bty4gby8PNTV1SE4OBgTJ0602YJX5uRrTL6dHJPvlqOsrAzZ2dnIzs7GrVu3UF1dDbVa7eiwiIiImhUXFxd4enrC19cX4eHh6NGjB/z8/Gx6TnPyNcfNMH6XuHLlCrZt24a9e/ciLy8PN2/eRIcOHdC1a1c8+uijePrppxEcHOzoMKkZ8PHxwYABAzBgwABHh0JEREQ2wpZvC6xfvx5LlixBRUWF3jJt2rTBhx9+iLlz5zbpHGz5JiIiInJu5uRr7FDaRG+99Rbmz5+vSLwjIiIwatQodO/eXXquvLwc8+bNw9tvv+2IMImIiIjIiTD5boI9e/ZgxYoV0nZUVBROnTqFrKwsJCUlIScnBykpKYiMjJTK/OUvf8HevXsdES4REREROQl2OzFTbW0toqKikJOTAwDo3LkzUlNT0bZt20Zli4uL0bdvX1y5cgVAQ8t4RkYG3NxM72rPbidEREREzo3dTmwoMTFRSrwB4OOPP9aZeANAu3bt8PHHH0vb2dnZSExMtHmMREREROScmHybaefOndLjoKAgTJ061WD52NhYBAYGStu7du2yWWxERERE5NyYfJuhsrISBw4ckLZjYmKMdiFxc3NDTEyMtP3TTz+hqqrKZjESERERkfNi8m2GzMxMxfLfw4cPN2k/ebmqqipkZmZaPTYiIiIicn5Mvs2Qnp6u2I6IiDBpP+1yGRkZVouJiIiIiJoPJt9myMvLU2yHhISYtF9oaKhiOzc311ohEREREVEzwuXlzXD79m3Ftr+/v0n7+fn5KbbLysr0lq2urlZ0bSktLdV5biIiIiJyDpo8zZQZvJl8m6G8vFyx3apVK5P20y5nKPl+7733sGrVqkbPd+nSxaRzEREREZFjlJWVNWp01cbk2wy1tbWKbVMXy9Eup30cuaVLl+KPf/yjtH3r1i2EhoaioKDA6MWklun27dvo0qULLl26xIWYqBHeH2QM7xEyhveIcUIIlJWVISgoyGhZJt9m8Pb2VmxXVVWhdevWRvfTnlpQ+zhynp6e8PT0bPS8n58fb3gyyNfXl/cI6cX7g4zhPULG8B4xzNRGUg64NEObNm0U23fu3DFpP+1yPj4+VouJiIiIiJoPJt9m6Nixo2K7sLDQpP20y3Xo0MFqMRERERFR88Hk2ww9e/ZUbOfn55u0n3a5yMhIk8/p6emJFStW6OyKQgTwHiHDeH+QMbxHyBjeI9alEqbMiUIAgEuXLinm9l61ahWWL19udL9Vq1Zh5cqViuN07tzZFiESERERkRNjy7cZunTpgu7du0vbhw8fNmk/ebnw8HAm3kREREQtFJNvM8XGxkqPk5KSUFBQYLB8QUGBIvmW709ERERELQuTbzPNnj0brq6uAAC1Wo3Vq1cbLP/WW29BrVYDAFxdXTF79mybx0hEREREzonJt5kiIyMRFxcnbX/xxRf44osvdJaNj4/Hhg0bpO1Zs2Y1GrRJRERERC0Hk+8mWLNmjaLv9/PPP4/Jkydj+/bt2L17N5544gm0a9cO8+bNk8p4eXkhKCgIV65csXl8Qgj88MMPmDlzJnr27ClNit+zZ0/MnDkTP/zwAzjO1r6qq6vxyy+/YPny5Zg4cSK6desGHx8feHh4oGPHjujfvz/mzZuHn376yWbXZtasWVCpVGb/rF+/3ibxUIO8vLwmXZdOnTrZLCbWIc6jKfeG/Kdr165Wi4V1iH2VlZUhOTkZf/vb3/DMM8+gV69ecHNzs+q1vXLlCtasWYPhw4cjODgYnp6eCA4OxvDhw7FmzRq75CxAC6xzBDVJVlaWCAsLEwDM+mnTpo1Yv369zeLKz88XY8aMMRrH2LFjRX5+vs3ioAbXrl0TM2bMED4+PibfI7169RLHjh2zeixxcXFm368AxLp166weC/1Xbm5uk65LQECATeJhHeJcmnJvyH8GDhxotVhYh9hPjx49hEqlMvi+hoaGWnSOdevWCW9vb4fmLEK0zDqHy8s3UUREBFJTU/HGG29g06ZNuH37dqMyLi4uCAwMhIeHB3JzcwEA5eXlmDdvHm7cuIE333zTqjFdvXoV0dHRuHTpkvScj48PoqKiIIRAZmYmysrKAAC//PILRowYgWPHjiEwMNCqcdB/Xbp0CYmJiY2eDwwMROfOneHj44Nr167h/Pnz0tiA9PR0REdHY8eOHTYboBsUFIQ+ffqYVFY+vSbZ3siRI9GqVSuj5dq1a2f1c7MOcT4PP/ywWeXz8vLw22+/SdtPP/20tUMCwDrE1rKysmx6/LfeegsrVqxQPBcREYGgoCBcvnwZFy5cAGDbnAVowXWOg5P/u8LOnTsVn84CAwPFJ598IqqqqqQyKSkpIjIyUlFuz549Vouhvr5eDBo0SDq2SqUSq1atEuXl5VKZsrIysWLFCsWn6UGDBon6+nqrxUFKKSkp0ns9bNgwsX79epGbm9uoXGFhoXjppZcU18bDw0OcP3/earHIW63i4uKsdlyyjHbLt677wx5Yh9wdHn30UUUdcvPmTasdm3WI/WjeZ29vb/HAAw+Il19+WSQkJIiYmBiLW76//fZbRZ0TFRUlTp06pShj65xFiJZd5zD5tlBNTY0IDw+XborOnTuL4uJinWWLiopEcHCwVDYiIkLU1tZaJY6NGzcq/kg++OADvWXff/99RdlNmzZZJQZq7NSpU2Ly5MmNKjZ91q5dq7g2jz32mNVi4T9O5+QsyTfrkOavsLBQuLm5Sddl+vTpVj0+6xD72bZtm8jIyGiUZMqvQVOSb2fJWYRo2XUOk28LbdmyRXFD7Ny502D5HTt2KMpv3brVKnF069ZNOmbv3r0Nfiqsr68XvXv3lsp3797dKjGQdQwZMkS6Nl5eXqKiosIqx+U/TufkLMk365Dmb82aNYp7af/+/VY9PusQx7M0+XaWnEWIll3ncLYTC+3cuVN6HBQUhKlTpxosHxsbq+irtGvXLotjOHXqFC5evChtL1iwAC4u+i+ti4sL5s+fL21fuHABZ86csTgOso7JkydLj6uqqpCXl+e4YKhFYB1yd9i4caP0OCQkBA899JADoyFn5Aw5C8A6h8m3BSorK3HgwAFpOyYmBm5uhsewurm5ISYmRtr+6aefUFVVZVEce/fuVWxPnDjR6D7aZfbs2WNRDGQ92gPpdA3mJbIm1iHN37///W/FQMvZs2cbTGao5XGWnAVgncO/TAtkZmaiurpa2h4+fLhJ+8nLVVVVITMz06I45J/+unTpgi5duhjdJyQkBJ07d9Z5DHIs7Zbue+65xzGBUIvBOqT5k7d6q1QqrqZMjThLzgKwzmHybYH09HTFdkREhEn7aZfLyMiwWhymxqBd1tIYyDqEENi9e7e0HRgYiLCwMKufJyUlBRMmTJAWVfD19UVYWBgeffRRfPjhh/j999+tfk4yzeuvv46+ffvC398fHh4eCAgIwIABA/DSSy9h//79NllognVI81ZWVqboTvDQQw8hNDTUpudkHdL8OEvOoh1LS6xzmHxbQLuF0tS5TLUrRc0c4E2Vn59vdgzacVgaA1nHl19+Kc2vCgBPPfUUVCqV1c+TkZGBH3/8EVevXkVNTQ3KysqQl5eHffv24dVXX0VoaCj++Mc/KlpJyD527tyJc+fOobS0FLW1tbh+/TrOnDmDTz/9FDExMejTpw+OHj1q1XOyDmneduzYgYqKCml7zpw5Nj8n65Dmx1lyFoB1DpNvC2j3xfX39zdpPz8/P8W2ZgL5pqioqEB9fb3ZMWjHUV9fj8rKyibHQZa7fPkyXnnlFWnb398fS5cutcm53Nzc0LNnT4wcORKjR49Gr1694OrqKv2+uroaf/3rX3H//fejpKTEJjGQbu3bt8eQIUPw4IMPYujQoejQoYPi9+np6Rg5ciQ2bNhglfOxDmn+5F1O2rVrhylTptj8nKxDmh9nyFkA1jkAk2+LlJeXK7ZNWZVOVzlLbuSmxmDtOMgyd+7cQWxsLIqKiqTn4uPjrbqKoa+vL+bOnYuffvoJ5eXlyMzMxOHDh3Ho0CGkpaWhpKQEn332GQICAqR9zpw5gylTpkirb5L1qVQqDBo0CJ9++ikuXryImzdv4vjx4zh48CCOHTuGGzdu4OTJk5g2bZq0T11dHebOnYv9+/dbfH7WIc1bZmam4puQp59+Gp6enjY5F+uQ5s0ZchZL4rBFLI7C5NsCtbW1im1jo4b1ldM+jj1isHYc1HR1dXWYMWMGUlJSpOdefPFFTJ8+3arnWbt2LdavX49x48bp/Ofs4+OD+fPn49dff0VUVJT0fHJyMrZs2WLVWOi/QkNDkZKSggULFujt3z9w4EDs2rULa9eulZ6rr6/HSy+9ZPHfLeuQ5k3e6g3YtssJ65DmzRlyFkvisEUsjsLk2wLe3t6KbVOn39Eup30ce8Rg7TioadRqNWbOnIl9+/ZJz02fPh1///vfHRZTQEAA9uzZAw8PD+m5jz76yGHx0H+9/PLLePbZZ6XtnJycRlN2mYt1SPNVW1urSGoHDRqEvn37OjCiBqxDnJMz5CyWxGGLWByFybcF2rRpo9i+c+eOSftpl/Px8bF7DNaOg8ynVqsxa9YsJCYmSs899thj+Oc//6noO+kI4eHhePzxx6XttLQ0XL582YERkcYbb7yh2P7Xv/5l0fFYhzRf3333Ha5fvy5tP/fccw6MRol1iPNxhpzFkjhsEYujMPm2QMeOHRXbhYWFJu2nXU57QJU53N3dFQMQTI1Bu6y/v7/DE76WRK1WY86cOdi6dav03NSpU5GYmGjWV3C2NHbsWMX2+fPnHRQJyXXr1k0x4t/S68I6pPmSdzlp3bo1nnjiCQdG0xjrEOfiDDkLwDoHYPJtkZ49eyq25VPnGKJdLjIy0mpxmBqDdllLYyDTqdVqPPfcc9i0aZP03JQpU7Bjxw6nSbwBKJYUBoCbN286KBLSJr821rgurEOan6tXryq+9Zg2bRp8fX0dGFFjrEOci7PkLNqxtMQ6h8m3BXr16qXYPn36tEn7aZeTD0yxNI709HTU1NQY3ae6uloxyb2lMZBpNIl3QkKC9NyUKVOwc+dOuLu7OzCyxrS/3jNnRDrZlvzaWOO6sA5pfjZv3qyYrs0ec3ubi3WIc3GWnEU7lpZY5zD5tkCXLl3QvXt3afvw4cMm7ScvFx4erlgutSlGjx4tPa6qqsLx48eN7nP8+HHF4gdjxoyxKAYyrjkl3kBDH025Tp06OSgSkquurkZOTo60bY3rwjqk+ZHXIxERERg5cqQDo9GNdYhzcZacBWCdw+TbQrGxsdLjpKQkFBQUGCxfUFCguJHl+zfVpEmTFMmbKVM6yct4eHhg4sSJFsdB+ulKvKdOneq0iXd9fT22b98ubXt7e6N///4OjIg0vv76a0WLYnR0tMXHZB3SvCQnJyM7O1vals+A4yxYhzgnZ8hZANY5EGSRjIwM4erqKgAIAOK5554zWH7OnDlSWVdXV5GZmWmVOJ544gnpuK1atRLZ2dl6y2ZlZYlWrVpJ5Z988kmrxEC6qdVq8eyzz0rvNwARGxsrampqHB2aXu+9954i3ieeeMLRIZEQ4tq1ayIkJES6Li4uLiIjI8Mqx2Yd0nzExcUp/o9cvXrV0SE1wjrENuTXPjQ01Oz9nSVnEaJl1zlMvq1AO7H6/PPPdZZbv369otycOXP0HjM3N1dRNi4uzmAMOTk5wt3dXSo/YMAAcf369Ublrl27Ju677z6pnIeHh7hw4YJZr5dMp1arxfPPP6+4ltOmTRO1tbUWH9uce+TFF18UW7duFVVVVQaPWVNTI1auXClUKpV0XHd3d4OVIjXdf/7zHzF37lxx/vx5o2VTU1NFVFSU4prPmjVLb3nWIXen0tJS0bp1a+n9nzRpUpOPxTqk+bE0+RbCNjmLEKxzzOE8Uys0Y2vWrMHhw4dx4cIFAMDzzz+Pffv2YcaMGQgKCsKVK1ewfft2fPfdd9I+4eHheP/9960WQ/fu3fHBBx9g8eLFABoGSPTr1w8LFizA4MGDIYRASkoKPvvsM1y7dk3a74MPPkC3bt2sFgcp7dq1C59//rm0rVKpUFJSYtbXZX/6058wbtw4i+JIS0vDp59+igULFiAmJgYDBw7EvffeC39/f7i4uOD69es4ceIEtm/frpiLV6VS4YsvvkB4eLhF5yfdqqurER8fj/j4ePTr1w9jx45F37590alTJ/j4+KC8vBw5OTnYv38/vv/+e8US3f3797fqYkysQ5qHxMRERbcjew20ZB1iX2+//TbefvvtRs/LV3TMz8+Hl5dXozIzZ85U/N/R5gw5C9DC6xwHJ/93jaysLBEWFqb41KfvJywszGgrgLmfIDWWLVumaHHQ96NSqcSbb75phVdOhiQkJJh0Txj6SUhI0Hlsc+6RUaNGmX1ef39/kZiYaJs3hoQQQhw6dKhJ98SkSZPEjRs3DB6bdcjdaejQodI1CAgIsOhbNNYhzmvFihVN/p9hyt+6tXMWIVjnmIMDLq0kIiICqampWLhwod65Vv38/LBw4UKkpqbarBXgnXfewYEDBzBo0CC9ZQYNGoSDBw9i9erVNomBnM+0adMwdOhQkwZ3durUCUuXLkVGRoZihTqyvq5du+Lxxx9vNB+yLi4uLhg3bhz27NmDvXv3WrzQhT6sQ5xXenq6YlaIuLg4u60NwDrk7uIsOQvQMusclRBCODqIu01VVRUOHz6MvLw8FBUVoX379ujatStGjx4NT09Pu8WRnZ2NlJQUaUWowMBADB48GBEREXaLgZxLTU0N0tPTceHCBRQWFqK8vBxqtRp+fn7o2LEjBgwYwPvDQa5evYqMjAwUFBSguLgYlZWVaNWqFfz9/REeHo5BgwY1WpbZ1liHkDbWIXcfZ8lZgJZT5zD5JiIiIiKyE3Y7ISIiIiKyEybfRERERER2wuSbiIiIiMhOmHwTEREREdkJk28iIiIiIjth8k1EREREZCdMvomIiIiI7ITJNxERERGRnTD5JiIiIiKyEybfRERERER2wuSbiIiIiMhOmHwTEREREdkJk28iIgB5eXlQqVQ2+cnLy3P0yyMiIifB5JuIiJps1qxZ0oeMWbNmOTqcFiMpKUnxAY+Img83RwdAROQMWrVqhYcffthouRMnTqCkpAQA4OXlhVGjRpl0bCIiIoDJNxERACAgIAA//vij0XKjR4/G4cOHzdqHiIhIg91OiIiIiIjshMk3EREREZGdMPkmIrIB+WC4pKQkAEBlZSW2bNmCSZMmoXv37vD29oZKpcKiRYv0Hic5ORmLFi1C//79ERAQAA8PD9xzzz0YPHgw/vznP+P8+fMmx1RTU4OffvoJS5cuxbhx4xAaGgpvb294eHggICAAgwcPxqJFi5CSkmLy69u8ebP03ObNm/XO+LJp0ybF/itXrpR+N3r0aOn5tLQ0LFq0CL1790bbtm3h5eWFnj17YvHixbhy5YrOWH744QdMnz4dISEh8PDwQNu2bTFs2DB89NFHqK6uNvn90SgpKcE//vEPTJw4Ed26dUObNm3g7e2NsLAwTJs2DVu2bEFdXZ3R4+gbFHnr1i188skniI6ORmBgIDw9PREYGIgJEyYgISEB9fX1eo+pGeA6ZswYxfP63nf5e0tETkIQEZHJRo0aJQAIACI0NFRvOU0ZAOLQoUPi7NmzIjIyUvG85ueVV15ptH92drZ48MEHdZaX/7i6uoqFCxeK2tpag3Hv27dPtG3b1ujxND9Tp04Vt27dMun1mfKTkJCg2H/FihXS70aNGiXUarVYvXq1cHV11XsMPz8/ceLECekYt27dEv/zP/9j8Ly9evUShYWFBt8buY8//lj4+/sbfT0RERHi2LFjBo916NAhxT6a54KDgw0ee8iQIeLGjRs6jxkXF2fW+z5q1CiTXzsR2QcHXBIR2Vhubi4ee+wxFBcXAwA6d+6MsLAw1NTUICcnp1H5o0ePYtKkSSgqKpKe8/LyQlRUFPz9/VFcXIy0tDTU1dWhvr4ea9euRXZ2Nvbu3Qs3N93Vel5enjRLCwD4+voiPDwcfn5+qK+vR2FhIXJyciCEAAB88803uHjxIo4ePapzthbNzDDnzp3D1atXAQBBQUHo06ePzvMHBwcbfI9WrlyJt956S4otKioKnp6eyMzMxPXr1wEApaWlGD9+PM6dO4f27dtj3LhxUit9YGAgwsPDUV9fj19//RUVFRUAgPT0dEyePBlHjx6Fi4v+L3vr6uowZ84cbNmyRfF8aGgoQkJCAADZ2dm4du2a9HjMmDHYu3cvHnroIYOvTePIkSN4+OGHUVNTA5VKhcjISAQEBODWrVtITU2VWrxPnDiBKVOmIDk5uVHMffr0wcMPP4zi4mLFNxT6Zurp27evSbERkR05OvsnImpOmtLy7evrKwCIBx54QNFyK4QQtbW1Ijc3V9q+dOmS6NChg7Rv586dxbZt20R1dbViv+LiYvHaa68JlUollX3jjTf0xvPJJ5+I/v37i7/97W8iOztbZ5nCwkKxdOlS4ebmJh1z8eLFBt8PeUtsXFycwbJy8pbvtm3bCpVKJfz9/UVCQoKoqamRyqnVahEfH69oEZ8/f76YN2+eACCioqLEL7/8ojh2eXm5eOGFFxTXYMuWLQbjef311xXl4+LiRFZWVqNyv/zyi+IbjI4dO4qrV6/qPKZ2y7fmus6dO7fRPoWFheKRRx5RlN+2bZveeHW1qhNR88C/WCIiMzQl+QYgxo4dK6qqqowePyYmRtFlQl/3A434+HipvLu7u7h8+bLOcmVlZUbPrbF9+3bpmN7e3qKkpERvWWsk3wBEq1atxOnTp/WWf/PNNxWvU6VSicjISL2xqdVq8cADD0j7jBkzRu+xjx49qvgQ89lnnxmM/datW4oEfMGCBTrLaSfIAMSaNWv0Hre6ulpx3LFjx+oty+SbqPnigEsiIhtzd3dHQkICPD09DZY7e/asNG+4m5sbduzYgQ4dOhjc54UXXsDYsWMBALW1tYiPj9dZrk2bNibHO2PGDDzwwAMAgIqKCuzfv9/kfZvqz3/+M/r376/39/PmzZMe19bWQgiB+Ph4+Pv76yyvUqmwYMECafvYsWN6BzK+//77UnebJ554AvPnzzcYq5+fn+J93rRpE8rKygzuAwDDhw/Ha6+9pvf3Hh4eisG3R48eNTj4koiaJybfREQ2NmHCBKnfsCHyGUEmTJiAXr16mXT8uLg46fHBgwfNjk+X+++/X3p84sQJqxzTkBdeeMHg74ODg9GlSxdpu2fPnhgxYoTBfYYNGyY9rqysRG5ubqMyxcXF2Ldvn7S9ZMkSk+IdMWIEwsLCAAB37tzB0aNHje4j/zCgj3zF1MrKSly8eNGkeIio+eCASyIiGxs5cqRJ5TQrZwLAuHHjTD5+v379pMenTp2CEEIxtZ22Gzdu4MCBA/j1119x9epV3L59u9GUfPKBoJcvXzY5lqYICwtDp06djJbr1KkTLl26BED54UCfwMBAxbZ8wKnGkSNHoFarAQDt2rXDgAEDTAkZQMP7rknoT548ifHjxxssP3z4cKPH7Ny5s2L71q1bJsdDRM0Dk28iIhvr3r270TJCCKSlpUnbGzduxPfff2/S8SsrK6XHNTU1uH37Nvz8/BqVy8/Px6uvvopvvvnGpHmqNWydAJqSeANA69atzdpHXh5oaKHWlpqaKj2uqalBTEyMSbEADTO9aNy4ccNoeVNi9vb2VmzripmImjcm30RENubr62u0TGlpqSIhPnv2bJPPV1pa2ij5TklJwfjx45uUSDdloRpzeHh42GUfTb9uOfl0juXl5U3u315aWmq0jLE+/7roipmImjf2+SYisjFD80traOaltgZNNwr5sWNjY6XE293dHU8//TQSExNx7tw5FBcXo6qqCqJhBiwIIbBixQqrxePMrPW+a7/nRET6sOWbiMgJaM/asXPnTvzhD3+wyrETEhKkftvu7u44cOCAYmCfLqbM3nE3kL/vUVFRSE9Pd1wwRNQisOWbiMgJeHt7K6YD/P333612bM30hUDDVHrGEm8A0sDGu528H7ZmJU0iIlti8k1E5CQ0c2sDMGnqOlPl5+dLj4cMGWK0vBAC//nPf0w6trxLTXPsnyx/z2/evIns7GwHRmM67a5MzfG9J2qpmHwTETmJCRMmSI/37NmjGAxoidraWrPK//jjj7hy5YpJZeWt9fJZV5qLwYMHo3379tL2hg0bHBiN6bQXTWqO7z1RS8Xkm4jIScyZMwft2rUD0DAQ8MUXX7TKcYOCgqTHycnJBsveuXMHixcvNvnY8rm0s7KyzA/Owdzc3BSvd+3atTh9+rQDIzKN9hzmzfG9J2qpmHwTETkJHx8fvPvuu9L2jh07MGPGDJ2Lw2g7efIknnnmGXz55ZeNfqdZfh4Adu/eje+++07nMYqKijBx4kT89ttvJsc8cOBA6XFqaqpdlqK3toULFyIiIgJAQwvy+PHj9b5HcqWlpVi/fr3RxXVsITAwUJGAf/zxx2bN3U5EjsPZToiInMjcuXNx5swZxMfHA2hIwL///ns8/vjjGDFiBIKDg+Hp6YnS0lIUFBTg7NmzOHDgAPLy8gAoE22NF154AWvWrEF5eTnUajUmT56MmTNnYtKkSQgICEBJSQmOHDmCjRs3oqioCL6+vnjkkUewfft2o/GOHTsWwcHBuHLlCoQQiImJQWRkJLp27aqYi3vhwoU6Y3MGPj4+2LNnD4YPH46SkhIUFRVh0qRJGDx4MCZPnoy+ffuibdu2qKqqQlFREdLT03H8+HEkJSWhpqYGoaGhDon7mWeewZo1awAAW7duxb/+9S/07dsXPj4+UpnevXvj7bffdkh8RKQbk28iIiezbt06dOnSBcuXL4darUZ5eTk2bNjQ5P7I99xzDzZv3ozHH38cdXV1UKvV2Lx5MzZv3tyorLe3NxITE3H8+HGTju3m5obNmzdj8uTJ0pzZmZmZyMzMVJSbMmVKk2K3l8jISJw4cQJTpkyRphtMSUlBSkqKgyPT780338TPP/+MkydPAmgYMPrLL78oynB5eiLnw24nRERORqVS4Y033sC5c+fw1FNPNVomXVvbtm0xbdo0fPXVV3jyySd1lomNjcXBgwfRu3dvnb93dXXF+PHjcfr0acXAT1M8+OCDSEtLw9KlS3H//fejQ4cOcHd3N+sYziA8PBynT5/G+vXr0bNnT4NlVSoV7rvvPixfvhwHDx60U4RKbdq0wf/93/9hw4YNmDhxIkJDQ9G6dWuoVCqHxENEplEJzk9EROTUampqcPz4ceTk5ODmzZuora1FmzZtEBwcjJ49eyIyMtKkVTSBhinpTp8+jZMnT6KoqAg+Pj4IDAxEdHS0Ys5rapjr/NixY7h+/Tpu3boFT09PtG3bFuHh4ejTp480OJaIyBxMvomIiIiI7ITdToiIiIiI7ITJNxERERGRnTD5JiIiIiKyEybfRERERER2wuSbiIiIiMhOmHwTEREREdkJk28iIiIiIjth8k1EREREZCdMvomIiIiI7ITJNxERERGRnTD5JiIiIiKyEybfRERERER2wuSbiIiIiMhOmHwTEREREdnJ/wPYaychNXfsKgAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -308,10 +308,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.424105Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.423808Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.427313Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.426826Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.866201Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.865876Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.869666Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.869147Z"
     }
    },
    "outputs": [
@@ -319,8 +319,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DoWhy estimate is 0.011132662400108018\n",
-      "Actual true causal effect was 0\n"
+      "DoWhy estimate is 1.0019898175815642\n",
+      "Actual true causal effect was 1\n"
      ]
     }
    ],
@@ -350,10 +350,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.428925Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.428747Z",
-     "iopub.status.idle": "2024-10-25T14:48:26.190730Z",
-     "shell.execute_reply": "2024-10-25T14:48:26.190026Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.871540Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.871357Z",
+     "iopub.status.idle": "2024-10-25T14:49:21.772321Z",
+     "shell.execute_reply": "2024-10-25T14:49:21.771054Z"
     }
    },
    "outputs": [
@@ -362,9 +362,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:0.01112672213326368\n",
-      "p value:0.84\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:1.0019914265231784\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
@@ -386,10 +386,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:26.193192Z",
-     "iopub.status.busy": "2024-10-25T14:48:26.192980Z",
-     "iopub.status.idle": "2024-10-25T14:48:28.341409Z",
-     "shell.execute_reply": "2024-10-25T14:48:28.340837Z"
+     "iopub.execute_input": "2024-10-25T14:49:21.774965Z",
+     "iopub.status.busy": "2024-10-25T14:49:21.774747Z",
+     "iopub.status.idle": "2024-10-25T14:49:24.097491Z",
+     "shell.execute_reply": "2024-10-25T14:49:24.096787Z"
     }
    },
    "outputs": [
@@ -398,9 +398,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:-4.757591404255024e-05\n",
-      "p value:0.82\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:-3.169002204966631e-05\n",
+      "p value:0.96\n",
       "\n"
      ]
     }
@@ -423,10 +423,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:28.343876Z",
-     "iopub.status.busy": "2024-10-25T14:48:28.343331Z",
-     "iopub.status.idle": "2024-10-25T14:48:30.349956Z",
-     "shell.execute_reply": "2024-10-25T14:48:30.349370Z"
+     "iopub.execute_input": "2024-10-25T14:49:24.101515Z",
+     "iopub.status.busy": "2024-10-25T14:49:24.100617Z",
+     "iopub.status.idle": "2024-10-25T14:49:26.158253Z",
+     "shell.execute_reply": "2024-10-25T14:49:26.157654Z"
     }
    },
    "outputs": [
@@ -435,9 +435,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:0.011014981045932223\n",
-      "p value:0.98\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:1.0016764361269999\n",
+      "p value:0.84\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
index de9eb78ac..a4a47d5c8 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
@@ -20,10 +20,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:32.827006Z",
-     "iopub.status.busy": "2024-10-25T14:48:32.826598Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.275970Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.275324Z"
+     "iopub.execute_input": "2024-10-25T14:49:28.798605Z",
+     "iopub.status.busy": "2024-10-25T14:49:28.798413Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.322332Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.321572Z"
     }
    },
    "outputs": [],
@@ -70,10 +70,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.278529Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.278026Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.329795Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.329283Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.324892Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.324558Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.377443Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.376750Z"
     }
    },
    "outputs": [
@@ -109,54 +109,54 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.920419</td>\n",
-       "      <td>1.795900</td>\n",
-       "      <td>5.340684</td>\n",
-       "      <td>58.169113</td>\n",
+       "      <td>0.911649</td>\n",
+       "      <td>0.078613</td>\n",
+       "      <td>2.341508</td>\n",
+       "      <td>24.421081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.163070</td>\n",
-       "      <td>0.869024</td>\n",
-       "      <td>16.179971</td>\n",
-       "      <td>165.504448</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.562554</td>\n",
+       "      <td>-0.616058</td>\n",
+       "      <td>-0.305178</td>\n",
+       "      <td>-5.510270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.055829</td>\n",
-       "      <td>0.227073</td>\n",
-       "      <td>15.099906</td>\n",
-       "      <td>154.320058</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.098908</td>\n",
+       "      <td>-0.478822</td>\n",
+       "      <td>-1.843562</td>\n",
+       "      <td>-21.410585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.116559</td>\n",
-       "      <td>-1.527957</td>\n",
-       "      <td>-4.061567</td>\n",
-       "      <td>-43.885508</td>\n",
+       "      <td>1.196146</td>\n",
+       "      <td>-0.922624</td>\n",
+       "      <td>-0.333307</td>\n",
+       "      <td>-6.761015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.380152</td>\n",
-       "      <td>-1.710498</td>\n",
-       "      <td>3.028824</td>\n",
-       "      <td>24.619150</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.147047</td>\n",
+       "      <td>0.834155</td>\n",
+       "      <td>3.801952</td>\n",
+       "      <td>42.695680</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    Z0        W0        W1         v0           y\n",
-       "0  0.0 -0.920419  1.795900   5.340684   58.169113\n",
-       "1  1.0  0.163070  0.869024  16.179971  165.504448\n",
-       "2  1.0  1.055829  0.227073  15.099906  154.320058\n",
-       "3  0.0  1.116559 -1.527957  -4.061567  -43.885508\n",
-       "4  1.0  0.380152 -1.710498   3.028824   24.619150"
+       "    Z0        W0        W1        v0          y\n",
+       "0  0.0  0.911649  0.078613  2.341508  24.421081\n",
+       "1  0.0  0.562554 -0.616058 -0.305178  -5.510270\n",
+       "2  0.0 -1.098908 -0.478822 -1.843562 -21.410585\n",
+       "3  0.0  1.196146 -0.922624 -0.333307  -6.761015\n",
+       "4  0.0  1.147047  0.834155  3.801952  42.695680"
       ]
      },
      "execution_count": 2,
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.332986Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.331978Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.338475Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.338023Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.380258Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.379729Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.386381Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.385443Z"
     }
    },
    "outputs": [],
@@ -217,10 +217,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.341191Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.340745Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.506082Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.505536Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.388609Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.388222Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.551343Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.550862Z"
     }
    },
    "outputs": [
@@ -260,10 +260,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.508215Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.508010Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.649505Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.648956Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.553699Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.553285Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.686690Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.685968Z"
     }
    },
    "outputs": [
@@ -277,9 +277,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d              \n",
-      "─────(E[y|W0,W1])\n",
+      "─────(E[y|W1,W0])\n",
       "d[v₀]            \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,U) = P(y|v0,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,U) = P(y|v0,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -315,10 +315,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.651358Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.651165Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.668176Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.667675Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.688832Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.688605Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.706898Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.706254Z"
     },
     "scrolled": true
    },
@@ -361,7 +361,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.995081558025035\n",
+      "Mean value: 10.001669761926992\n",
       "\n"
      ]
     }
@@ -393,10 +393,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.670033Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.669845Z",
-     "iopub.status.idle": "2024-10-25T14:48:36.314914Z",
-     "shell.execute_reply": "2024-10-25T14:48:36.314274Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.709031Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.708826Z",
+     "iopub.status.idle": "2024-10-25T14:49:32.254456Z",
+     "shell.execute_reply": "2024-10-25T14:49:32.253868Z"
     }
    },
    "outputs": [
@@ -406,8 +406,8 @@
      "text": [
       "Refute: Use a Dummy Outcome\n",
       "Estimated effect:0\n",
-      "New effect:1.6615045299294222e-05\n",
-      "p value:0.8200000000000001\n",
+      "New effect:-0.00019410486144059497\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:36.316824Z",
-     "iopub.status.busy": "2024-10-25T14:48:36.316632Z",
-     "iopub.status.idle": "2024-10-25T14:48:36.319758Z",
-     "shell.execute_reply": "2024-10-25T14:48:36.319276Z"
+     "iopub.execute_input": "2024-10-25T14:49:32.256460Z",
+     "iopub.status.busy": "2024-10-25T14:49:32.256226Z",
+     "iopub.status.idle": "2024-10-25T14:49:32.259576Z",
+     "shell.execute_reply": "2024-10-25T14:49:32.259075Z"
     }
    },
    "outputs": [],
@@ -477,10 +477,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:36.321410Z",
-     "iopub.status.busy": "2024-10-25T14:48:36.321220Z",
-     "iopub.status.idle": "2024-10-25T14:48:37.857928Z",
-     "shell.execute_reply": "2024-10-25T14:48:37.857340Z"
+     "iopub.execute_input": "2024-10-25T14:49:32.261348Z",
+     "iopub.status.busy": "2024-10-25T14:49:32.261142Z",
+     "iopub.status.idle": "2024-10-25T14:49:33.736648Z",
+     "shell.execute_reply": "2024-10-25T14:49:33.736046Z"
     }
    },
    "outputs": [
@@ -490,8 +490,8 @@
      "text": [
       "Refute: Use a Dummy Outcome\n",
       "Estimated effect:0\n",
-      "New effect:-3.1229077590307696e-05\n",
-      "p value:0.98\n",
+      "New effect:0.0001040097892780143\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb
index 398a51496..6cf5c1fa4 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb
@@ -60,10 +60,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:40.354271Z",
-     "iopub.status.busy": "2024-10-25T14:48:40.354088Z",
-     "iopub.status.idle": "2024-10-25T14:48:41.825502Z",
-     "shell.execute_reply": "2024-10-25T14:48:41.824954Z"
+     "iopub.execute_input": "2024-10-25T14:49:36.739242Z",
+     "iopub.status.busy": "2024-10-25T14:49:36.739043Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.311834Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.311060Z"
     }
    },
    "outputs": [],
@@ -96,10 +96,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:41.828036Z",
-     "iopub.status.busy": "2024-10-25T14:48:41.827520Z",
-     "iopub.status.idle": "2024-10-25T14:48:41.834343Z",
-     "shell.execute_reply": "2024-10-25T14:48:41.833865Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.314753Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.314145Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.321034Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.320528Z"
     }
    },
    "outputs": [],
@@ -166,10 +166,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:41.836213Z",
-     "iopub.status.busy": "2024-10-25T14:48:41.835891Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.021934Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.021272Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.322903Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.322715Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.515062Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.513731Z"
     }
    },
    "outputs": [
@@ -200,10 +200,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.024108Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.023894Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.027430Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.026864Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.517592Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.517271Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.520547Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.519988Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.029680Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.029277Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.117677Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.117123Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.522657Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.522451Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.609168Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.608621Z"
     }
    },
    "outputs": [
@@ -233,13 +233,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                                          ↪\n",
-      "──────────(E[injury|neuromusc fatigue,previous injury,team motivation,contact  ↪\n",
+      "──────────(E[injury|previous injury,contact sport,team motivation,tissue disor ↪\n",
       "d[warm-up]                                                                     ↪\n",
       "\n",
       "↪                        \n",
-      "↪ sport,tissue disorder])\n",
+      "↪ der,neuromusc fatigue])\n",
       "↪                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -287,10 +287,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.119502Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.119319Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.190244Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.189744Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.611345Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.610878Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.680986Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.680355Z"
     }
    },
    "outputs": [
@@ -304,13 +304,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                                          ↪\n",
-      "──────────(E[injury|neuromusc fatigue,previous injury,team motivation,tissue d ↪\n",
+      "──────────(E[injury|previous injury,team motivation,tissue disorder,neuromusc  ↪\n",
       "d[warm-up]                                                                     ↪\n",
       "\n",
       "↪          \n",
-      "↪ isorder])\n",
+      "↪ fatigue])\n",
       "↪          \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -351,10 +351,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.191967Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.191782Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.330829Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.330248Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.683106Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.682732Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.819223Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.818628Z"
     }
    },
    "outputs": [
@@ -368,9 +368,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                        \n",
-      "──────────(E[injury|previous injury,fitness,team motivation])\n",
+      "──────────(E[injury|fitness,previous injury,team motivation])\n",
       "d[warm-up]                                                   \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,fitness,team motivation,U) = P(injury|warm-up,previous injury,fitness,team motivation)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,fitness,previous injury,team motivation,U) = P(injury|warm-up,fitness,previous injury,team motivation)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -433,10 +433,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.332821Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.332623Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.336273Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.335823Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.821388Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.820989Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.824706Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.824214Z"
     }
    },
    "outputs": [],
@@ -471,10 +471,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.338005Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.337687Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.341363Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.340893Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.826594Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.826227Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.830039Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.829523Z"
     }
    },
    "outputs": [
@@ -505,10 +505,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.343068Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.342787Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.469277Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.468680Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.831863Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.831511Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.958211Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.957600Z"
     }
    },
    "outputs": [
@@ -563,10 +563,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.471317Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.470953Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.478299Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.477823Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.960406Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.959936Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.967714Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.967094Z"
     }
    },
    "outputs": [
@@ -635,10 +635,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.480258Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.479919Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.483513Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.482932Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.969778Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.969328Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.972904Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.972332Z"
     }
    },
    "outputs": [],
@@ -662,10 +662,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.485307Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.484898Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.488793Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.488301Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.974698Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.974343Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.978052Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.977594Z"
     }
    },
    "outputs": [
@@ -712,10 +712,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.490658Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.490347Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.495220Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.494655Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.979768Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.979590Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.984458Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.983973Z"
     },
     "pycharm": {
      "name": "#%%\n"
@@ -773,10 +773,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.497010Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.496841Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.696737Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.696052Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.986330Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.985872Z",
+     "iopub.status.idle": "2024-10-25T14:49:39.202167Z",
+     "shell.execute_reply": "2024-10-25T14:49:39.201523Z"
     },
     "pycharm": {
      "name": "#%%\n"
@@ -793,9 +793,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       " d            \n",
-      "────(E[Y|L,K])\n",
+      "────(E[Y|K,L])\n",
       "d[X]          \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,L,K,U) = P(Y|X,L,K)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,K,L,U) = P(Y|X,K,L)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -849,10 +849,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.698842Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.698510Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.799244Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.798541Z"
+     "iopub.execute_input": "2024-10-25T14:49:39.204089Z",
+     "iopub.status.busy": "2024-10-25T14:49:39.203898Z",
+     "iopub.status.idle": "2024-10-25T14:49:39.303434Z",
+     "shell.execute_reply": "2024-10-25T14:49:39.302879Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb
index 48d0fa484..e24be3a17 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb
@@ -18,10 +18,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:45.719871Z",
-     "iopub.status.busy": "2024-10-25T14:48:45.719690Z",
-     "iopub.status.idle": "2024-10-25T14:48:45.735581Z",
-     "shell.execute_reply": "2024-10-25T14:48:45.734972Z"
+     "iopub.execute_input": "2024-10-25T14:49:42.435913Z",
+     "iopub.status.busy": "2024-10-25T14:49:42.435733Z",
+     "iopub.status.idle": "2024-10-25T14:49:42.452524Z",
+     "shell.execute_reply": "2024-10-25T14:49:42.451905Z"
     }
    },
    "outputs": [],
@@ -35,10 +35,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:45.737803Z",
-     "iopub.status.busy": "2024-10-25T14:48:45.737356Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.194277Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.193582Z"
+     "iopub.execute_input": "2024-10-25T14:49:42.454383Z",
+     "iopub.status.busy": "2024-10-25T14:49:42.454033Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.052593Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.051894Z"
     }
    },
    "outputs": [],
@@ -66,10 +66,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.196701Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.196365Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.479895Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.479232Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.055162Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.054799Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.342278Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.341401Z"
     }
    },
    "outputs": [
@@ -109,62 +109,62 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.146419</td>\n",
-       "      <td>0.719678</td>\n",
-       "      <td>1.474503</td>\n",
-       "      <td>-0.660766</td>\n",
-       "      <td>0.254804</td>\n",
-       "      <td>0.248627</td>\n",
+       "      <td>0.802889</td>\n",
+       "      <td>0.192251</td>\n",
+       "      <td>0.478185</td>\n",
+       "      <td>1.207485</td>\n",
+       "      <td>1.327381</td>\n",
+       "      <td>-1.431369</td>\n",
        "      <td>True</td>\n",
-       "      <td>17.465511</td>\n",
+       "      <td>15.397068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.798909</td>\n",
-       "      <td>1.288351</td>\n",
-       "      <td>-0.142688</td>\n",
-       "      <td>-0.924025</td>\n",
-       "      <td>-0.057606</td>\n",
-       "      <td>1.141256</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.384253</td>\n",
+       "      <td>0.034604</td>\n",
+       "      <td>0.432514</td>\n",
+       "      <td>-1.598181</td>\n",
+       "      <td>-1.038358</td>\n",
+       "      <td>-0.583790</td>\n",
        "      <td>True</td>\n",
-       "      <td>16.122811</td>\n",
+       "      <td>-1.885605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.754634</td>\n",
-       "      <td>1.463066</td>\n",
-       "      <td>0.969293</td>\n",
-       "      <td>-0.656878</td>\n",
-       "      <td>0.754685</td>\n",
-       "      <td>0.745172</td>\n",
+       "      <td>0.963544</td>\n",
+       "      <td>0.067244</td>\n",
+       "      <td>-1.716632</td>\n",
+       "      <td>-1.006641</td>\n",
+       "      <td>1.038315</td>\n",
+       "      <td>-0.177907</td>\n",
        "      <td>True</td>\n",
-       "      <td>22.201326</td>\n",
+       "      <td>5.922537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.141993</td>\n",
-       "      <td>-0.216277</td>\n",
-       "      <td>1.013538</td>\n",
-       "      <td>0.923354</td>\n",
-       "      <td>-1.233122</td>\n",
-       "      <td>-0.472881</td>\n",
+       "      <td>0.819110</td>\n",
+       "      <td>-0.634177</td>\n",
+       "      <td>0.209765</td>\n",
+       "      <td>-0.045081</td>\n",
+       "      <td>0.311348</td>\n",
+       "      <td>-1.310749</td>\n",
        "      <td>True</td>\n",
-       "      <td>5.330300</td>\n",
+       "      <td>3.810727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.844711</td>\n",
-       "      <td>0.899253</td>\n",
-       "      <td>-1.059884</td>\n",
-       "      <td>-0.306315</td>\n",
-       "      <td>1.385281</td>\n",
-       "      <td>0.708705</td>\n",
+       "      <td>0.999171</td>\n",
+       "      <td>2.072248</td>\n",
+       "      <td>1.195379</td>\n",
+       "      <td>-0.336769</td>\n",
+       "      <td>-0.377211</td>\n",
+       "      <td>1.341917</td>\n",
        "      <td>True</td>\n",
-       "      <td>18.422903</td>\n",
+       "      <td>23.129556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -180,63 +180,63 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9995</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.367345</td>\n",
-       "      <td>0.798867</td>\n",
-       "      <td>1.208066</td>\n",
-       "      <td>-0.793425</td>\n",
-       "      <td>-1.796504</td>\n",
-       "      <td>0.140363</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.771256</td>\n",
+       "      <td>0.464651</td>\n",
+       "      <td>1.593722</td>\n",
+       "      <td>0.695716</td>\n",
+       "      <td>-0.538747</td>\n",
+       "      <td>-2.698634</td>\n",
        "      <td>True</td>\n",
-       "      <td>5.591300</td>\n",
+       "      <td>1.915655</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.223508</td>\n",
-       "      <td>-0.319477</td>\n",
-       "      <td>0.129182</td>\n",
-       "      <td>-1.441393</td>\n",
-       "      <td>0.052671</td>\n",
-       "      <td>1.265595</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.187240</td>\n",
+       "      <td>0.350261</td>\n",
+       "      <td>1.437157</td>\n",
+       "      <td>2.566900</td>\n",
+       "      <td>-0.473194</td>\n",
+       "      <td>-0.907145</td>\n",
        "      <td>True</td>\n",
-       "      <td>15.172138</td>\n",
+       "      <td>16.509397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.306972</td>\n",
-       "      <td>1.745715</td>\n",
-       "      <td>0.505480</td>\n",
-       "      <td>0.092517</td>\n",
-       "      <td>1.253911</td>\n",
-       "      <td>0.763002</td>\n",
+       "      <td>0.801760</td>\n",
+       "      <td>0.990208</td>\n",
+       "      <td>2.271466</td>\n",
+       "      <td>-0.731640</td>\n",
+       "      <td>-0.067994</td>\n",
+       "      <td>-1.118754</td>\n",
        "      <td>True</td>\n",
-       "      <td>24.799447</td>\n",
+       "      <td>11.595858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.879923</td>\n",
-       "      <td>1.333993</td>\n",
-       "      <td>0.135373</td>\n",
-       "      <td>-0.938021</td>\n",
-       "      <td>0.600853</td>\n",
-       "      <td>2.210361</td>\n",
-       "      <td>True</td>\n",
-       "      <td>26.230045</td>\n",
+       "      <td>0.081604</td>\n",
+       "      <td>-0.255929</td>\n",
+       "      <td>0.852812</td>\n",
+       "      <td>-0.913018</td>\n",
+       "      <td>-0.753771</td>\n",
+       "      <td>-1.655911</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-13.153870</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.295673</td>\n",
-       "      <td>1.801730</td>\n",
-       "      <td>1.293909</td>\n",
-       "      <td>-0.421017</td>\n",
-       "      <td>1.161574</td>\n",
-       "      <td>-0.149046</td>\n",
+       "      <td>0.963800</td>\n",
+       "      <td>0.065191</td>\n",
+       "      <td>0.795102</td>\n",
+       "      <td>0.108932</td>\n",
+       "      <td>-1.039872</td>\n",
+       "      <td>-1.137772</td>\n",
        "      <td>True</td>\n",
-       "      <td>21.223555</td>\n",
+       "      <td>1.890986</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -244,31 +244,31 @@
        "</div>"
       ],
       "text/plain": [
-       "       Z0        Z1        W0        W1        W2        W3        W4    v0  \\\n",
-       "0     1.0  0.146419  0.719678  1.474503 -0.660766  0.254804  0.248627  True   \n",
-       "1     0.0  0.798909  1.288351 -0.142688 -0.924025 -0.057606  1.141256  True   \n",
-       "2     0.0  0.754634  1.463066  0.969293 -0.656878  0.754685  0.745172  True   \n",
-       "3     0.0  0.141993 -0.216277  1.013538  0.923354 -1.233122 -0.472881  True   \n",
-       "4     0.0  0.844711  0.899253 -1.059884 -0.306315  1.385281  0.708705  True   \n",
-       "...   ...       ...       ...       ...       ...       ...       ...   ...   \n",
-       "9995  1.0  0.367345  0.798867  1.208066 -0.793425 -1.796504  0.140363  True   \n",
-       "9996  1.0  0.223508 -0.319477  0.129182 -1.441393  0.052671  1.265595  True   \n",
-       "9997  0.0  0.306972  1.745715  0.505480  0.092517  1.253911  0.763002  True   \n",
-       "9998  0.0  0.879923  1.333993  0.135373 -0.938021  0.600853  2.210361  True   \n",
-       "9999  0.0  0.295673  1.801730  1.293909 -0.421017  1.161574 -0.149046  True   \n",
+       "       Z0        Z1        W0        W1        W2        W3        W4     v0  \\\n",
+       "0     1.0  0.802889  0.192251  0.478185  1.207485  1.327381 -1.431369   True   \n",
+       "1     1.0  0.384253  0.034604  0.432514 -1.598181 -1.038358 -0.583790   True   \n",
+       "2     0.0  0.963544  0.067244 -1.716632 -1.006641  1.038315 -0.177907   True   \n",
+       "3     0.0  0.819110 -0.634177  0.209765 -0.045081  0.311348 -1.310749   True   \n",
+       "4     0.0  0.999171  2.072248  1.195379 -0.336769 -0.377211  1.341917   True   \n",
+       "...   ...       ...       ...       ...       ...       ...       ...    ...   \n",
+       "9995  0.0  0.771256  0.464651  1.593722  0.695716 -0.538747 -2.698634   True   \n",
+       "9996  0.0  0.187240  0.350261  1.437157  2.566900 -0.473194 -0.907145   True   \n",
+       "9997  0.0  0.801760  0.990208  2.271466 -0.731640 -0.067994 -1.118754   True   \n",
+       "9998  0.0  0.081604 -0.255929  0.852812 -0.913018 -0.753771 -1.655911  False   \n",
+       "9999  0.0  0.963800  0.065191  0.795102  0.108932 -1.039872 -1.137772   True   \n",
        "\n",
        "              y  \n",
-       "0     17.465511  \n",
-       "1     16.122811  \n",
-       "2     22.201326  \n",
-       "3      5.330300  \n",
-       "4     18.422903  \n",
+       "0     15.397068  \n",
+       "1     -1.885605  \n",
+       "2      5.922537  \n",
+       "3      3.810727  \n",
+       "4     23.129556  \n",
        "...         ...  \n",
-       "9995   5.591300  \n",
-       "9996  15.172138  \n",
-       "9997  24.799447  \n",
-       "9998  26.230045  \n",
-       "9999  21.223555  \n",
+       "9995   1.915655  \n",
+       "9996  16.509397  \n",
+       "9997  11.595858  \n",
+       "9998 -13.153870  \n",
+       "9999   1.890986  \n",
        "\n",
        "[10000 rows x 9 columns]"
       ]
@@ -317,10 +317,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.482529Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.482131Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.525030Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.524450Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.345518Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.344713Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.390702Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.390082Z"
     }
    },
    "outputs": [],
@@ -340,10 +340,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.528466Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.527414Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.706532Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.705955Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.393805Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.393260Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.577814Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.577097Z"
     }
    },
    "outputs": [
@@ -367,10 +367,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.709021Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.708620Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.751585Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.750995Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.580599Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.580113Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.625243Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.624687Z"
     }
    },
    "outputs": [
@@ -402,10 +402,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.753943Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.753529Z",
-     "iopub.status.idle": "2024-10-25T14:48:48.016014Z",
-     "shell.execute_reply": "2024-10-25T14:48:48.015375Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.627637Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.627178Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.929684Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.929050Z"
     }
    },
    "outputs": [
@@ -419,9 +419,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -429,9 +429,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -459,10 +459,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:48.018128Z",
-     "iopub.status.busy": "2024-10-25T14:48:48.017760Z",
-     "iopub.status.idle": "2024-10-25T14:48:48.070273Z",
-     "shell.execute_reply": "2024-10-25T14:48:48.069364Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.931940Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.931548Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.986178Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.985585Z"
     }
    },
    "outputs": [
@@ -479,19 +479,19 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.999489976408874\n",
+      "Mean value: 10.000263653094079\n",
       "p-value: [0.]\n",
       "\n",
-      "Causal Estimate is 9.999489976408874\n"
+      "Causal Estimate is 10.000263653094079\n"
      ]
     }
    ],
@@ -517,10 +517,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:48.073301Z",
-     "iopub.status.busy": "2024-10-25T14:48:48.072740Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.286702Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.286129Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.989155Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.988639Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.198041Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.197483Z"
     }
    },
    "outputs": [
@@ -537,18 +537,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.103626751304049\n",
+      "Mean value: 10.721226135478332\n",
       "\n",
-      "Causal Estimate is 11.103626751304049\n"
+      "Causal Estimate is 10.721226135478332\n"
      ]
     }
    ],
@@ -575,10 +575,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.288762Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.288345Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.782963Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.782304Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.200120Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.199746Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.714955Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.714348Z"
     }
    },
    "outputs": [
@@ -595,18 +595,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.995565463987674\n",
+      "Mean value: 9.968475783307126\n",
       "\n",
-      "Causal Estimate is 9.995565463987674\n"
+      "Causal Estimate is 9.968475783307126\n"
      ]
     }
    ],
@@ -632,10 +632,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.784927Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.784738Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.834176Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.833579Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.717043Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.716661Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.770839Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.770181Z"
     }
    },
    "outputs": [
@@ -652,18 +652,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.725131389529945\n",
+      "Mean value: 9.909765204233272\n",
       "\n",
-      "Causal Estimate is 9.725131389529945\n"
+      "Causal Estimate is 9.909765204233272\n"
      ]
     }
    ],
@@ -692,10 +692,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.836216Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.835796Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.884224Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.883685Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.772957Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.772583Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.820036Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.819536Z"
     }
    },
    "outputs": [
@@ -712,18 +712,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.220427986837883\n",
+      "Mean value: 10.461426422877393\n",
       "\n",
-      "Causal Estimate is 11.220427986837883\n"
+      "Causal Estimate is 10.461426422877393\n"
      ]
     }
    ],
@@ -750,10 +750,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.886237Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.885806Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.926831Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.926172Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.821965Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.821600Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.860360Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.859800Z"
     },
     "scrolled": true
    },
@@ -773,9 +773,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "## Realized estimand\n",
       "Realized estimand: Wald Estimator\n",
@@ -788,17 +788,17 @@
       " ⎡ d     ⎤\n",
       "E⎢───(v₀)⎥\n",
       " ⎣dZ₀    ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.178003799779505\n",
+      "Mean value: 10.897552036840848\n",
       "\n",
-      "Causal Estimate is 10.178003799779505\n"
+      "Causal Estimate is 10.897552036840848\n"
      ]
     }
    ],
@@ -823,10 +823,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.928691Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.928469Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.967977Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.967315Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.862344Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.861860Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.898139Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.897524Z"
     }
    },
    "outputs": [
@@ -845,9 +845,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "## Realized estimand\n",
       "Realized estimand: Wald Estimator\n",
@@ -860,17 +860,17 @@
       " ⎡        d             ⎤\n",
       "E⎢──────────────────(v₀)⎥\n",
       " ⎣dlocal_rd_variable    ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.840139131031695\n",
+      "Mean value: 82.74381691014266\n",
       "\n",
-      "Causal Estimate is 9.840139131031695\n"
+      "Causal Estimate is 82.74381691014266\n"
      ]
     }
    ],
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb
index b8d082977..ace4c1412 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb
@@ -40,10 +40,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:54.509881Z",
-     "iopub.status.busy": "2024-10-25T14:48:54.509444Z",
-     "iopub.status.idle": "2024-10-25T14:48:55.966892Z",
-     "shell.execute_reply": "2024-10-25T14:48:55.966260Z"
+     "iopub.execute_input": "2024-10-25T14:49:51.461325Z",
+     "iopub.status.busy": "2024-10-25T14:49:51.460897Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.001966Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.001350Z"
     }
    },
    "outputs": [],
@@ -108,10 +108,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:55.969320Z",
-     "iopub.status.busy": "2024-10-25T14:48:55.968829Z",
-     "iopub.status.idle": "2024-10-25T14:48:55.999697Z",
-     "shell.execute_reply": "2024-10-25T14:48:55.999058Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.004473Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.003974Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.035012Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.034410Z"
     }
    },
    "outputs": [
@@ -178,10 +178,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.001638Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.001272Z",
-     "iopub.status.idle": "2024-10-25T14:48:56.020609Z",
-     "shell.execute_reply": "2024-10-25T14:48:56.020014Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.037028Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.036643Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.055967Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.055429Z"
     }
    },
    "outputs": [
@@ -195,9 +195,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -244,10 +244,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.022640Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.022198Z",
-     "iopub.status.idle": "2024-10-25T14:48:56.041005Z",
-     "shell.execute_reply": "2024-10-25T14:48:56.040397Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.057882Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.057521Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.077329Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.076796Z"
     }
    },
    "outputs": [
@@ -261,7 +261,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -274,7 +274,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -320,10 +320,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.042869Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.042519Z",
-     "iopub.status.idle": "2024-10-25T14:48:57.414935Z",
-     "shell.execute_reply": "2024-10-25T14:48:57.414385Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.079235Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.078865Z",
+     "iopub.status.idle": "2024-10-25T14:49:54.571285Z",
+     "shell.execute_reply": "2024-10-25T14:49:54.570596Z"
     }
    },
    "outputs": [
@@ -337,7 +337,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -389,10 +389,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:57.417131Z",
-     "iopub.status.busy": "2024-10-25T14:48:57.416661Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.494315Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.493680Z"
+     "iopub.execute_input": "2024-10-25T14:49:54.573599Z",
+     "iopub.status.busy": "2024-10-25T14:49:54.573181Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.719567Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.718932Z"
     }
    },
    "outputs": [
@@ -474,10 +474,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.496401Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.496104Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.500866Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.500260Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.721711Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.721291Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.726399Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.725799Z"
     }
    },
    "outputs": [],
@@ -498,10 +498,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.502776Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.502425Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.637460Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.636870Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.728198Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.727872Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.871557Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.870894Z"
     }
    },
    "outputs": [
@@ -515,9 +515,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -525,9 +525,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
+      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -556,10 +556,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.639515Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.639079Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.648742Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.648196Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.873814Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.873414Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.882826Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.882316Z"
     }
    },
    "outputs": [
@@ -576,12 +576,12 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -610,10 +610,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.650708Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.650356Z",
-     "iopub.status.idle": "2024-10-25T14:49:00.826329Z",
-     "shell.execute_reply": "2024-10-25T14:49:00.825689Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.884769Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.884326Z",
+     "iopub.status.idle": "2024-10-25T14:49:58.107599Z",
+     "shell.execute_reply": "2024-10-25T14:49:58.106908Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb
index d5d30ec87..ad122b59f 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:03.450361Z",
-     "iopub.status.busy": "2024-10-25T14:49:03.450184Z",
-     "iopub.status.idle": "2024-10-25T14:49:04.894868Z",
-     "shell.execute_reply": "2024-10-25T14:49:04.894240Z"
+     "iopub.execute_input": "2024-10-25T14:50:00.713000Z",
+     "iopub.status.busy": "2024-10-25T14:50:00.712815Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.361743Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.360936Z"
     }
    },
    "outputs": [],
@@ -39,10 +39,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:04.897425Z",
-     "iopub.status.busy": "2024-10-25T14:49:04.896943Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.079459Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.078917Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.364477Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.364081Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.402570Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.401795Z"
     }
    },
    "outputs": [
@@ -268,10 +268,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.081410Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.081049Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.309582Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.309012Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.404748Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.404524Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.663390Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.662764Z"
     }
    },
    "outputs": [
@@ -321,10 +321,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.311987Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.311602Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.330223Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.329633Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.665918Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.665687Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.692454Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.691777Z"
     }
    },
    "outputs": [
@@ -338,13 +338,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪\n",
+      "────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])\n",
+      "↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -382,10 +382,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.332117Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.331932Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.357518Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.356901Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.695013Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.694649Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.724867Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.724282Z"
     }
    },
    "outputs": [
@@ -402,23 +402,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪\n",
+      "────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])\n",
+      "↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y_factual~treatment+x15+x11+x22+x18+x17+x19+x24+x3+x5+x25+x8+x6+x20+x12+x2+x10+x16+x21+x4+x1+x13+x7+x9+x23+x14\n",
+      "b: y_factual~treatment+x20+x5+x24+x1+x13+x23+x12+x2+x9+x11+x21+x25+x3+x7+x6+x10+x16+x4+x14+x8+x15+x22+x18+x17+x19\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 3.9286717508727182\n",
+      "Mean value: 3.9286717508727174\n",
       "p-value: [1.58915682e-156]\n",
       "\n",
-      "Causal Estimate is 3.9286717508727182\n",
+      "Causal Estimate is 3.9286717508727174\n",
       "ATE 4.021121012430829\n"
      ]
     }
@@ -450,10 +450,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.360162Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.359774Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.385344Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.384813Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.728257Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.727144Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.754870Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.754224Z"
     }
    },
    "outputs": [
@@ -488,10 +488,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.387995Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.387773Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.437381Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.436791Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.757845Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.757329Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.811581Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.810925Z"
     }
    },
    "outputs": [
@@ -526,10 +526,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.440034Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.439795Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.458740Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.458203Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.814930Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.814336Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.833434Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.832406Z"
     }
    },
    "outputs": [
@@ -537,7 +537,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 4.028748218389614\n",
+      "Causal Estimate is 4.028748218389554\n",
       "ATE 4.021121012430829\n"
      ]
     }
@@ -566,10 +566,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.461338Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.461125Z",
-     "iopub.status.idle": "2024-10-25T14:49:06.373059Z",
-     "shell.execute_reply": "2024-10-25T14:49:06.372451Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.835767Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.835509Z",
+     "iopub.status.idle": "2024-10-25T14:50:03.806040Z",
+     "shell.execute_reply": "2024-10-25T14:50:03.805349Z"
     }
    },
    "outputs": [
@@ -578,8 +578,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:4.028748218389616\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:4.028748218389554\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -603,10 +603,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:06.375152Z",
-     "iopub.status.busy": "2024-10-25T14:49:06.374711Z",
-     "iopub.status.idle": "2024-10-25T14:49:07.305902Z",
-     "shell.execute_reply": "2024-10-25T14:49:07.305180Z"
+     "iopub.execute_input": "2024-10-25T14:50:03.808487Z",
+     "iopub.status.busy": "2024-10-25T14:50:03.807933Z",
+     "iopub.status.idle": "2024-10-25T14:50:04.795778Z",
+     "shell.execute_reply": "2024-10-25T14:50:04.795062Z"
     }
    },
    "outputs": [
@@ -615,9 +615,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:-0.4684785244087347\n",
-      "p value:0.0\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:-0.46487915108125505\n",
+      "p value:0.02\n",
       "\n"
      ]
     }
@@ -640,10 +640,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:07.307874Z",
-     "iopub.status.busy": "2024-10-25T14:49:07.307686Z",
-     "iopub.status.idle": "2024-10-25T14:49:08.165221Z",
-     "shell.execute_reply": "2024-10-25T14:49:08.164542Z"
+     "iopub.execute_input": "2024-10-25T14:50:04.797982Z",
+     "iopub.status.busy": "2024-10-25T14:50:04.797758Z",
+     "iopub.status.idle": "2024-10-25T14:50:05.691224Z",
+     "shell.execute_reply": "2024-10-25T14:50:05.690648Z"
     }
    },
    "outputs": [
@@ -652,9 +652,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:4.033024672491376\n",
-      "p value:0.9199999999999999\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:4.0276366446854865\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb
index 724bc2820..e55d0021f 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "91bf56d5",
+   "id": "4782d6c6",
    "metadata": {},
    "source": [
     "# DoWhy: Interpreters for Causal Estimators\n",
@@ -16,13 +16,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6fd00d50",
+   "id": "656882ee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:10.548698Z",
-     "iopub.status.busy": "2024-10-25T14:49:10.548495Z",
-     "iopub.status.idle": "2024-10-25T14:49:10.563739Z",
-     "shell.execute_reply": "2024-10-25T14:49:10.563275Z"
+     "iopub.execute_input": "2024-10-25T14:50:08.145627Z",
+     "iopub.status.busy": "2024-10-25T14:50:08.145442Z",
+     "iopub.status.idle": "2024-10-25T14:50:08.162554Z",
+     "shell.execute_reply": "2024-10-25T14:50:08.161967Z"
     }
    },
    "outputs": [],
@@ -34,13 +34,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c3dc2921",
+   "id": "8ec4f244",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:10.565507Z",
-     "iopub.status.busy": "2024-10-25T14:49:10.565160Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.018540Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.017936Z"
+     "iopub.execute_input": "2024-10-25T14:50:08.164479Z",
+     "iopub.status.busy": "2024-10-25T14:50:08.164132Z",
+     "iopub.status.idle": "2024-10-25T14:50:09.702731Z",
+     "shell.execute_reply": "2024-10-25T14:50:09.702092Z"
     }
    },
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3442b96",
+   "id": "9d10adce",
    "metadata": {},
    "source": [
     "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome.\n",
@@ -67,13 +67,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cb28add2",
+   "id": "18916cff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.021117Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.020616Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.308024Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.307402Z"
+     "iopub.execute_input": "2024-10-25T14:50:09.705387Z",
+     "iopub.status.busy": "2024-10-25T14:50:09.704926Z",
+     "iopub.status.idle": "2024-10-25T14:50:09.999977Z",
+     "shell.execute_reply": "2024-10-25T14:50:09.997127Z"
     }
    },
    "outputs": [
@@ -81,7 +81,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8002\n"
+      "8979\n"
      ]
     },
     {
@@ -120,62 +120,62 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.993429</td>\n",
-       "      <td>0.769897</td>\n",
-       "      <td>-0.847178</td>\n",
-       "      <td>0.928229</td>\n",
-       "      <td>0.365930</td>\n",
+       "      <td>0.477734</td>\n",
+       "      <td>-1.192526</td>\n",
+       "      <td>1.272471</td>\n",
+       "      <td>1.959071</td>\n",
+       "      <td>0.463038</td>\n",
        "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.352911</td>\n",
+       "      <td>2.389759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.258673</td>\n",
-       "      <td>1.128014</td>\n",
-       "      <td>1.153442</td>\n",
-       "      <td>-0.152917</td>\n",
-       "      <td>-0.825393</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.718861</td>\n",
+       "      <td>-1.115188</td>\n",
+       "      <td>1.201292</td>\n",
+       "      <td>0.061369</td>\n",
+       "      <td>0.767281</td>\n",
+       "      <td>2</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.112612</td>\n",
+       "      <td>1.776849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.628177</td>\n",
-       "      <td>1.448514</td>\n",
-       "      <td>-0.126484</td>\n",
-       "      <td>0.329410</td>\n",
-       "      <td>0.498247</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.257282</td>\n",
+       "      <td>-1.308327</td>\n",
+       "      <td>1.547448</td>\n",
+       "      <td>-0.995869</td>\n",
+       "      <td>-0.904392</td>\n",
+       "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.348269</td>\n",
+       "      <td>0.436070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.038758</td>\n",
-       "      <td>1.613230</td>\n",
-       "      <td>-1.136255</td>\n",
-       "      <td>-0.818521</td>\n",
-       "      <td>0.476712</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.418268</td>\n",
+       "      <td>-2.240148</td>\n",
+       "      <td>0.774776</td>\n",
+       "      <td>-1.044587</td>\n",
+       "      <td>-1.388704</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.148678</td>\n",
+       "      <td>-1.452268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.913642</td>\n",
-       "      <td>0.656800</td>\n",
-       "      <td>-0.932051</td>\n",
-       "      <td>0.890903</td>\n",
-       "      <td>0.285856</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.566311</td>\n",
+       "      <td>-0.552725</td>\n",
+       "      <td>-0.426374</td>\n",
+       "      <td>-0.460174</td>\n",
+       "      <td>-1.011157</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>2.210334</td>\n",
+       "      <td>1.907566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -191,63 +191,63 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9995</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.977562</td>\n",
-       "      <td>0.001779</td>\n",
-       "      <td>0.070824</td>\n",
-       "      <td>1.416120</td>\n",
-       "      <td>0.177606</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.532125</td>\n",
+       "      <td>-1.826497</td>\n",
+       "      <td>2.749183</td>\n",
+       "      <td>-0.531833</td>\n",
+       "      <td>-0.714357</td>\n",
        "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.725697</td>\n",
+       "      <td>2.050131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.560897</td>\n",
-       "      <td>1.904908</td>\n",
-       "      <td>-1.730092</td>\n",
-       "      <td>-0.805939</td>\n",
-       "      <td>0.013775</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0.774825</td>\n",
+       "      <td>-0.537176</td>\n",
+       "      <td>2.724776</td>\n",
+       "      <td>-0.928348</td>\n",
+       "      <td>1.096883</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.110946</td>\n",
+       "      <td>3.010047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.948574</td>\n",
-       "      <td>-0.252089</td>\n",
-       "      <td>0.609017</td>\n",
-       "      <td>1.770531</td>\n",
-       "      <td>1.559547</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.285903</td>\n",
+       "      <td>-1.000688</td>\n",
+       "      <td>-0.396921</td>\n",
+       "      <td>1.056061</td>\n",
+       "      <td>0.455609</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.394935</td>\n",
+       "      <td>1.949979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.472237</td>\n",
-       "      <td>0.603944</td>\n",
-       "      <td>-0.890167</td>\n",
-       "      <td>0.221969</td>\n",
-       "      <td>0.023440</td>\n",
+       "      <td>0.364756</td>\n",
+       "      <td>-0.711436</td>\n",
+       "      <td>0.945276</td>\n",
+       "      <td>-0.430878</td>\n",
+       "      <td>-0.168530</td>\n",
        "      <td>2</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.973352</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.781639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.697132</td>\n",
-       "      <td>2.406748</td>\n",
-       "      <td>0.145082</td>\n",
-       "      <td>0.197808</td>\n",
-       "      <td>-0.390090</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.068676</td>\n",
+       "      <td>0.019125</td>\n",
+       "      <td>0.541005</td>\n",
+       "      <td>1.748337</td>\n",
+       "      <td>1.798937</td>\n",
+       "      <td>1</td>\n",
        "      <td>True</td>\n",
-       "      <td>2.136826</td>\n",
+       "      <td>2.058443</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -256,30 +256,30 @@
       ],
       "text/plain": [
        "       Z0        Z1        W0        W1        W2        W3 W4     v0  \\\n",
-       "0     0.0  0.993429  0.769897 -0.847178  0.928229  0.365930  3   True   \n",
-       "1     0.0  0.258673  1.128014  1.153442 -0.152917 -0.825393  1   True   \n",
-       "2     0.0  0.628177  1.448514 -0.126484  0.329410  0.498247  3   True   \n",
-       "3     0.0  0.038758  1.613230 -1.136255 -0.818521  0.476712  3  False   \n",
-       "4     0.0  0.913642  0.656800 -0.932051  0.890903  0.285856  1   True   \n",
+       "0     0.0  0.477734 -1.192526  1.272471  1.959071  0.463038  3   True   \n",
+       "1     0.0  0.718861 -1.115188  1.201292  0.061369  0.767281  2   True   \n",
+       "2     0.0  0.257282 -1.308327  1.547448 -0.995869 -0.904392  0   True   \n",
+       "3     0.0  0.418268 -2.240148  0.774776 -1.044587 -1.388704  0  False   \n",
+       "4     1.0  0.566311 -0.552725 -0.426374 -0.460174 -1.011157  3   True   \n",
        "...   ...       ...       ...       ...       ...       ... ..    ...   \n",
-       "9995  0.0  0.977562  0.001779  0.070824  1.416120  0.177606  3   True   \n",
-       "9996  0.0  0.560897  1.904908 -1.730092 -0.805939  0.013775  2   True   \n",
-       "9997  0.0  0.948574 -0.252089  0.609017  1.770531  1.559547  1   True   \n",
-       "9998  0.0  0.472237  0.603944 -0.890167  0.221969  0.023440  2  False   \n",
-       "9999  0.0  0.697132  2.406748  0.145082  0.197808 -0.390090  2   True   \n",
+       "9995  1.0  0.532125 -1.826497  2.749183 -0.531833 -0.714357  3   True   \n",
+       "9996  0.0  0.774825 -0.537176  2.724776 -0.928348  1.096883  3   True   \n",
+       "9997  1.0  0.285903 -1.000688 -0.396921  1.056061  0.455609  3   True   \n",
+       "9998  0.0  0.364756 -0.711436  0.945276 -0.430878 -0.168530  2   True   \n",
+       "9999  1.0  0.068676  0.019125  0.541005  1.748337  1.798937  1   True   \n",
        "\n",
        "             y  \n",
-       "0     3.352911  \n",
-       "1     1.112612  \n",
-       "2     3.348269  \n",
-       "3     1.148678  \n",
-       "4     2.210334  \n",
+       "0     2.389759  \n",
+       "1     1.776849  \n",
+       "2     0.436070  \n",
+       "3    -1.452268  \n",
+       "4     1.907566  \n",
        "...        ...  \n",
-       "9995  3.725697  \n",
-       "9996  1.110946  \n",
-       "9997  4.394935  \n",
-       "9998  0.973352  \n",
-       "9999  2.136826  \n",
+       "9995  2.050131  \n",
+       "9996  3.010047  \n",
+       "9997  1.949979  \n",
+       "9998  1.781639  \n",
+       "9999  2.058443  \n",
        "\n",
        "[10000 rows x 9 columns]"
       ]
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be4c7348",
+   "id": "2c885e19",
    "metadata": {},
    "source": [
     "Note that we are using a pandas dataframe to load the data."
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad1a7989",
+   "id": "f9abeb14",
    "metadata": {},
    "source": [
     "## Identifying the causal estimand"
@@ -321,7 +321,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e0769ad",
+   "id": "314290fb",
    "metadata": {},
    "source": [
     "We now input a causal graph in the GML graph format."
@@ -330,13 +330,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f0b7456b",
+   "id": "2453757e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.310902Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.310373Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.354518Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.353904Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.005934Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.005390Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.049282Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.048722Z"
     }
    },
    "outputs": [],
@@ -354,13 +354,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1085e11e",
+   "id": "1b6a5704",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.357497Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.357024Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.541477Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.540854Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.052009Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.051419Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.232227Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.231670Z"
     }
    },
    "outputs": [
@@ -382,13 +382,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7c543ea6",
+   "id": "f6320675",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.543790Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.543545Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.585980Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.585334Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.234601Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.234156Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.276704Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.276114Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4310abb3",
+   "id": "d4b956ed",
    "metadata": {},
    "source": [
     "We get a causal graph. Now identification and estimation is done."
@@ -419,13 +419,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "104b7d83",
+   "id": "63070c4c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.588416Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.588016Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.851524Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.850872Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.279016Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.278596Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.551219Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.550674Z"
     }
    },
    "outputs": [
@@ -439,9 +439,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -449,9 +449,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cd24c65",
+   "id": "ad70a2c2",
    "metadata": {},
    "source": [
     "## Method 1: Propensity Score Stratification\n",
@@ -478,13 +478,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2272009b",
+   "id": "563dcd39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.853504Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.853316Z",
-     "iopub.status.idle": "2024-10-25T14:49:13.422757Z",
-     "shell.execute_reply": "2024-10-25T14:49:13.422162Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.553408Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.552970Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.182266Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.181617Z"
     }
    },
    "outputs": [
@@ -501,18 +501,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 0.9849750790391165\n",
+      "Mean value: 1.0072519271114253\n",
       "\n",
-      "Causal Estimate is 0.9849750790391165\n"
+      "Causal Estimate is 1.0072519271114253\n"
      ]
     }
    ],
@@ -526,7 +526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e9db2d6",
+   "id": "c56716f3",
    "metadata": {},
    "source": [
     "### Textual Interpreter\n",
@@ -537,13 +537,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "d99297ee",
+   "id": "55a78134",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:13.424859Z",
-     "iopub.status.busy": "2024-10-25T14:49:13.424415Z",
-     "iopub.status.idle": "2024-10-25T14:49:13.458624Z",
-     "shell.execute_reply": "2024-10-25T14:49:13.457942Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.184256Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.184056Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.219007Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.218328Z"
     }
    },
    "outputs": [
@@ -551,7 +551,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9849750790391165 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0072519271114253 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f384957",
+   "id": "e66205d8",
    "metadata": {},
    "source": [
     "### Visual Interpreter\n",
@@ -578,19 +578,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "05cc5f4f",
+   "id": "0407ba00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:13.460582Z",
-     "iopub.status.busy": "2024-10-25T14:49:13.460362Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.183525Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.182991Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.221240Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.220850Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.741755Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.740992Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29bb9d50",
+   "id": "4cf3c662",
    "metadata": {},
    "source": [
     "This plot shows how the SMD decreases from the unadjusted to the stratified units. "
@@ -614,7 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d5a9b2c",
+   "id": "0db5e1a0",
    "metadata": {},
    "source": [
     "## Method 2: Propensity Score Matching\n",
@@ -625,13 +625,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d664abac",
+   "id": "c03f0b1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.185706Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.185301Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.243200Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.242636Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.744010Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.743789Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.803046Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.802441Z"
     }
    },
    "outputs": [
@@ -648,18 +648,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.0059102988172535\n",
+      "Mean value: 0.987197165766296\n",
       "\n",
-      "Causal Estimate is 1.0059102988172535\n"
+      "Causal Estimate is 0.987197165766296\n"
      ]
     }
    ],
@@ -674,13 +674,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "dfb41720",
+   "id": "f3f01e14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.245101Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.244762Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.277002Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.276465Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.805066Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.804865Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.838984Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.838456Z"
     }
    },
    "outputs": [
@@ -688,7 +688,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0059102988172535 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.987197165766296 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -699,7 +699,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5e69bd0",
+   "id": "5c4e0141",
    "metadata": {},
    "source": [
     "Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator."
@@ -707,7 +707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f398aa07",
+   "id": "980676bd",
    "metadata": {},
    "source": [
     "## Method 3: Weighting\n",
@@ -721,13 +721,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "322eabb5",
+   "id": "486ab88d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.279043Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.278686Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.329134Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.328450Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.840868Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.840671Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.893559Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.892900Z"
     }
    },
    "outputs": [
@@ -744,18 +744,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.0267251598760518\n",
+      "Mean value: 1.170035426800626\n",
       "\n",
-      "Causal Estimate is 1.0267251598760518\n"
+      "Causal Estimate is 1.170035426800626\n"
      ]
     }
    ],
@@ -771,13 +771,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "79310206",
+   "id": "8195b82c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.331113Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.330816Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.364771Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.364155Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.895780Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.895397Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.931539Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.930866Z"
     }
    },
    "outputs": [
@@ -785,7 +785,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0267251598760518 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.170035426800626 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -797,19 +797,19 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "7b0ceca9",
+   "id": "2e854085",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.366431Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.366250Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.719079Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.718480Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.933721Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.933359Z",
+     "iopub.status.idle": "2024-10-25T14:50:12.276396Z",
+     "shell.execute_reply": "2024-10-25T14:50:12.275777Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -825,7 +825,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "833ce544",
+   "id": "1ef4bb9f",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb
index 666878c52..34be1cef5 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb
@@ -21,10 +21,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:17.212952Z",
-     "iopub.status.busy": "2024-10-25T14:49:17.212510Z",
-     "iopub.status.idle": "2024-10-25T14:49:19.947589Z",
-     "shell.execute_reply": "2024-10-25T14:49:19.946909Z"
+     "iopub.execute_input": "2024-10-25T14:50:15.194386Z",
+     "iopub.status.busy": "2024-10-25T14:50:15.194192Z",
+     "iopub.status.idle": "2024-10-25T14:50:17.935105Z",
+     "shell.execute_reply": "2024-10-25T14:50:17.934424Z"
     }
    },
    "outputs": [],
@@ -46,10 +46,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:19.949917Z",
-     "iopub.status.busy": "2024-10-25T14:49:19.949720Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.004109Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.003471Z"
+     "iopub.execute_input": "2024-10-25T14:50:17.937992Z",
+     "iopub.status.busy": "2024-10-25T14:50:17.937688Z",
+     "iopub.status.idle": "2024-10-25T14:50:17.998148Z",
+     "shell.execute_reply": "2024-10-25T14:50:17.997435Z"
     }
    },
    "outputs": [
@@ -57,7 +57,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 1639.7735077993884\n"
+      "Causal Estimate is 1639.8254852564396\n"
      ]
     },
     {
@@ -75,10 +75,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.743</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00973</td>\n",
+       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00972</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:49:19</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>\n",
+       "  <th>Time:</th>                 <td>14:50:17</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   445</td>      <th>  AIC:               </th> <td>   9093.</td>\n",
@@ -98,18 +98,18 @@
        "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th>     <td> 4555.0799</td> <td>  406.701</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.776</td> <td> 5354.383</td>\n",
+       "  <th>Intercept</th>     <td> 4555.0717</td> <td>  406.705</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.761</td> <td> 5354.383</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>treat[T.True]</th> <td> 1639.7735</td> <td>  631.493</td> <td>    2.597</td> <td> 0.010</td> <td>  398.678</td> <td> 2880.869</td>\n",
+       "  <th>treat[T.True]</th> <td> 1639.8255</td> <td>  631.496</td> <td>    2.597</td> <td> 0.010</td> <td>  398.725</td> <td> 2880.926</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td>303.258</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>\n",
+       "  <th>Omnibus:</th>       <td>303.265</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.328</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.724</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td> 2.709</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
@@ -126,8 +126,8 @@
        "\\textbf{Dep. Variable:}    &       re78       & \\textbf{  R-squared:         } &     0.015   \\\\\n",
        "\\textbf{Model:}            &       WLS        & \\textbf{  Adj. R-squared:    } &     0.013   \\\\\n",
        "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     6.743   \\\\\n",
-       "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.00973    \\\\\n",
-       "\\textbf{Time:}             &     14:49:19     & \\textbf{  Log-Likelihood:    } &   -4544.7   \\\\\n",
+       "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.00972    \\\\\n",
+       "\\textbf{Time:}             &     14:50:17     & \\textbf{  Log-Likelihood:    } &   -4544.7   \\\\\n",
        "\\textbf{No. Observations:} &         445      & \\textbf{  AIC:               } &     9093.   \\\\\n",
        "\\textbf{Df Residuals:}     &         443      & \\textbf{  BIC:               } &     9102.   \\\\\n",
        "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
@@ -137,13 +137,13 @@
        "\\begin{tabular}{lcccccc}\n",
        "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{Intercept}     &    4555.0799  &      406.701     &    11.200  &         0.000        &     3755.776    &     5354.383     \\\\\n",
-       "\\textbf{treat[T.True]} &    1639.7735  &      631.493     &     2.597  &         0.010        &      398.678    &     2880.869     \\\\\n",
+       "\\textbf{Intercept}     &    4555.0717  &      406.705     &    11.200  &         0.000        &     3755.761    &     5354.383     \\\\\n",
+       "\\textbf{treat[T.True]} &    1639.8255  &      631.496     &     2.597  &         0.010        &      398.725    &     2880.926     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       & 303.258 & \\textbf{  Durbin-Watson:     } &    2.085  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4770.328  \\\\\n",
+       "\\textbf{Omnibus:}       & 303.265 & \\textbf{  Durbin-Watson:     } &    2.085  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4770.724  \\\\\n",
        "\\textbf{Skew:}          &   2.709 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
        "\\textbf{Kurtosis:}      &  18.097 & \\textbf{  Cond. No.          } &     2.47  \\\\\n",
        "\\bottomrule\n",
@@ -162,8 +162,8 @@
        "Dep. Variable:                   re78   R-squared:                       0.015\n",
        "Model:                            WLS   Adj. R-squared:                  0.013\n",
        "Method:                 Least Squares   F-statistic:                     6.743\n",
-       "Date:                Fri, 25 Oct 2024   Prob (F-statistic):            0.00973\n",
-       "Time:                        14:49:19   Log-Likelihood:                -4544.7\n",
+       "Date:                Fri, 25 Oct 2024   Prob (F-statistic):            0.00972\n",
+       "Time:                        14:50:17   Log-Likelihood:                -4544.7\n",
        "No. Observations:                 445   AIC:                             9093.\n",
        "Df Residuals:                     443   BIC:                             9102.\n",
        "Df Model:                           1                                         \n",
@@ -171,11 +171,11 @@
        "=================================================================================\n",
        "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
        "---------------------------------------------------------------------------------\n",
-       "Intercept      4555.0799    406.701     11.200      0.000    3755.776    5354.383\n",
-       "treat[T.True]  1639.7735    631.493      2.597      0.010     398.678    2880.869\n",
+       "Intercept      4555.0717    406.705     11.200      0.000    3755.761    5354.383\n",
+       "treat[T.True]  1639.8255    631.496      2.597      0.010     398.725    2880.926\n",
        "==============================================================================\n",
-       "Omnibus:                      303.258   Durbin-Watson:                   2.085\n",
-       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4770.328\n",
+       "Omnibus:                      303.265   Durbin-Watson:                   2.085\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4770.724\n",
        "Skew:                           2.709   Prob(JB):                         0.00\n",
        "Kurtosis:                      18.097   Cond. No.                         2.47\n",
        "==============================================================================\n",
@@ -226,10 +226,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:20.006285Z",
-     "iopub.status.busy": "2024-10-25T14:49:20.005844Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.304203Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.303527Z"
+     "iopub.execute_input": "2024-10-25T14:50:18.000537Z",
+     "iopub.status.busy": "2024-10-25T14:50:18.000107Z",
+     "iopub.status.idle": "2024-10-25T14:50:18.322478Z",
+     "shell.execute_reply": "2024-10-25T14:50:18.321799Z"
     }
    },
    "outputs": [
@@ -261,10 +261,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:20.306333Z",
-     "iopub.status.busy": "2024-10-25T14:49:20.305968Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.311481Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.310899Z"
+     "iopub.execute_input": "2024-10-25T14:50:18.324776Z",
+     "iopub.status.busy": "2024-10-25T14:50:18.324336Z",
+     "iopub.status.idle": "2024-10-25T14:50:18.330429Z",
+     "shell.execute_reply": "2024-10-25T14:50:18.329850Z"
     }
    },
    "outputs": [
@@ -272,7 +272,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 1639.7735077993912\n"
+      "Causal Estimate is 1639.8254852564378\n"
      ]
     }
    ],
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb
index 98913d2fb..04aa1db90 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:22.052114Z",
-     "iopub.status.busy": "2024-10-25T14:49:22.051922Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.481762Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.481058Z"
+     "iopub.execute_input": "2024-10-25T14:50:20.201015Z",
+     "iopub.status.busy": "2024-10-25T14:50:20.200581Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.726294Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.725675Z"
     }
    },
    "outputs": [],
@@ -43,10 +43,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.484091Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.483770Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.493696Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.493166Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.728836Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.728298Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.738285Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.737783Z"
     }
    },
    "outputs": [
@@ -55,11 +55,11 @@
      "output_type": "stream",
      "text": [
       "         FD0        W0        v0          y\n",
-      "0  -4.604887 -0.308790 -0.488776 -11.455013\n",
-      "1  18.313037  0.774883  3.998435  43.731761\n",
-      "2 -24.094361 -1.699439 -4.952121 -60.329111\n",
-      "3  -9.730324 -0.124735 -2.170977 -22.063332\n",
-      "4  -3.117294 -0.026310 -0.667598  -7.023355\n"
+      "0  -1.622075  0.501889 -0.079710  -0.904602\n",
+      "1   2.467555  0.612379  0.621440   5.619792\n",
+      "2   2.989213  0.035365  0.807377   4.639184\n",
+      "3 -12.350724 -0.415800 -2.783882 -19.974205\n",
+      "4  -0.686259  0.542081 -0.183767   0.643876\n"
      ]
     }
    ],
@@ -88,10 +88,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.495452Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.495256Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.606510Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.605949Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.740185Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.739822Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.851795Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.851243Z"
     }
    },
    "outputs": [
@@ -144,10 +144,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.608796Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.608362Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.622041Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.621584Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.853929Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.853706Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.867512Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.866996Z"
     }
    },
    "outputs": [
@@ -182,10 +182,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.624037Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.623832Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.859053Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.858477Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.869454Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.869234Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.126541Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.125931Z"
     }
    },
    "outputs": [
@@ -233,10 +233,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.861024Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.860815Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.909287Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.908687Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.128567Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.128358Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.177913Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.177366Z"
     }
    },
    "outputs": [
@@ -264,7 +264,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.704622257788243\n",
+      "Mean value: 6.073961318473499\n",
       "\n"
      ]
     }
@@ -295,10 +295,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.911830Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.911524Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.915001Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.914473Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.180981Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.180429Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.184928Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.184397Z"
     }
    },
    "outputs": [
@@ -306,7 +306,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10.704622257788243 10.727800892028872\n"
+      "6.073961318473499 6.05342048266878\n"
      ]
     }
    ],
@@ -329,10 +329,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.917353Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.917141Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.985043Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.984409Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.187562Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.187172Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.254777Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.254115Z"
     }
    },
    "outputs": [
@@ -360,7 +360,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 2.0167870465215287e-05\n",
+      "Mean value: -2.0011491711713347e-05\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb
index 0b0e08816..6b3c2cdf7 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:25.966002Z",
-     "iopub.status.busy": "2024-10-25T14:49:25.965823Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.410743Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.410120Z"
+     "iopub.execute_input": "2024-10-25T14:50:24.426785Z",
+     "iopub.status.busy": "2024-10-25T14:50:24.426586Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.058882Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.058141Z"
     }
    },
    "outputs": [],
@@ -32,10 +32,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.413059Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.412588Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.440737Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.440108Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.061526Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.060956Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.091853Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.091269Z"
     }
    },
    "outputs": [
@@ -74,82 +74,82 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.242831</td>\n",
-       "      <td>1.456611</td>\n",
-       "      <td>-1.029501</td>\n",
-       "      <td>0.110232</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>14.644329</td>\n",
-       "      <td>1.846584</td>\n",
-       "      <td>166.026822</td>\n",
+       "      <td>-0.344602</td>\n",
+       "      <td>0.831968</td>\n",
+       "      <td>0.392958</td>\n",
+       "      <td>0.684496</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.484254</td>\n",
+       "      <td>6.531018</td>\n",
+       "      <td>142.642648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.516329</td>\n",
-       "      <td>-1.044592</td>\n",
-       "      <td>0.063713</td>\n",
-       "      <td>0.596371</td>\n",
-       "      <td>0</td>\n",
+       "      <td>-2.110287</td>\n",
+       "      <td>2.776796</td>\n",
+       "      <td>0.422762</td>\n",
+       "      <td>0.292303</td>\n",
        "      <td>3</td>\n",
-       "      <td>13.124141</td>\n",
-       "      <td>0.848538</td>\n",
-       "      <td>135.730916</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.442877</td>\n",
+       "      <td>4.339151</td>\n",
+       "      <td>97.496255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.598166</td>\n",
-       "      <td>0.939163</td>\n",
-       "      <td>0.589856</td>\n",
-       "      <td>-1.435998</td>\n",
-       "      <td>3</td>\n",
+       "      <td>-0.275387</td>\n",
+       "      <td>-0.917356</td>\n",
+       "      <td>1.883795</td>\n",
+       "      <td>1.311776</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
-       "      <td>14.440459</td>\n",
-       "      <td>4.869747</td>\n",
-       "      <td>249.820705</td>\n",
+       "      <td>12.374288</td>\n",
+       "      <td>10.632938</td>\n",
+       "      <td>-331.699923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.445804</td>\n",
-       "      <td>2.104228</td>\n",
-       "      <td>0.324483</td>\n",
-       "      <td>1.304685</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0.253720</td>\n",
+       "      <td>-0.052694</td>\n",
+       "      <td>0.016866</td>\n",
+       "      <td>0.523128</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>15.488781</td>\n",
-       "      <td>7.546051</td>\n",
-       "      <td>1193.870840</td>\n",
+       "      <td>4.865552</td>\n",
+       "      <td>1.071125</td>\n",
+       "      <td>69.992638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.422895</td>\n",
-       "      <td>0.541271</td>\n",
-       "      <td>-1.408185</td>\n",
-       "      <td>2.583083</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14.132825</td>\n",
-       "      <td>-1.508823</td>\n",
-       "      <td>133.706932</td>\n",
+       "      <td>1.641157</td>\n",
+       "      <td>0.437726</td>\n",
+       "      <td>0.322939</td>\n",
+       "      <td>1.671700</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16.240063</td>\n",
+       "      <td>8.003803</td>\n",
+       "      <td>1513.835137</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         X0        X1        W0        W1 W2 W3         v0        v1  \\\n",
-       "0 -1.242831  1.456611 -1.029501  0.110232  3  1  14.644329  1.846584   \n",
-       "1  0.516329 -1.044592  0.063713  0.596371  0  3  13.124141  0.848538   \n",
-       "2 -0.598166  0.939163  0.589856 -1.435998  3  2  14.440459  4.869747   \n",
-       "3  0.445804  2.104228  0.324483  1.304685  2  0  15.488781  7.546051   \n",
-       "4 -0.422895  0.541271 -1.408185  2.583083  0  2  14.132825 -1.508823   \n",
+       "         X0        X1        W0        W1 W2 W3         v0         v1  \\\n",
+       "0 -0.344602  0.831968  0.392958  0.684496  0  2   4.484254   6.531018   \n",
+       "1 -2.110287  2.776796  0.422762  0.292303  3  0  11.442877   4.339151   \n",
+       "2 -0.275387 -0.917356  1.883795  1.311776  1  2  12.374288  10.632938   \n",
+       "3  0.253720 -0.052694  0.016866  0.523128  1  0   4.865552   1.071125   \n",
+       "4  1.641157  0.437726  0.322939  1.671700  3  1  16.240063   8.003803   \n",
        "\n",
        "             y  \n",
-       "0   166.026822  \n",
-       "1   135.730916  \n",
-       "2   249.820705  \n",
-       "3  1193.870840  \n",
-       "4   133.706932  "
+       "0   142.642648  \n",
+       "1    97.496255  \n",
+       "2  -331.699923  \n",
+       "3    69.992638  \n",
+       "4  1513.835137  "
       ]
      },
      "execution_count": 2,
@@ -174,10 +174,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.443263Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.442712Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.448805Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.448297Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.095424Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.094280Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.101708Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.101163Z"
     }
    },
    "outputs": [],
@@ -192,10 +192,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.450739Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.450518Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.617673Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.617108Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.104357Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.103873Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.284253Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.283730Z"
     }
    },
    "outputs": [
@@ -231,10 +231,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.619689Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.619410Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.640202Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.639709Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.287038Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.286481Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.309908Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.309231Z"
     }
    },
    "outputs": [
@@ -248,9 +248,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -284,10 +284,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.642411Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.642015Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.704053Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.703473Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.312339Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.312100Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.375794Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.374729Z"
     }
    },
    "outputs": [
@@ -304,16 +304,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
+      "b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 38.48325274778242\n",
+      "Mean value: 45.69097368298819\n",
       "\n"
      ]
     }
@@ -339,10 +339,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.706639Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.706397Z",
-     "iopub.status.idle": "2024-10-25T14:49:28.055774Z",
-     "shell.execute_reply": "2024-10-25T14:49:28.055118Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.378381Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.378107Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.735794Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.735127Z"
     }
    },
    "outputs": [
@@ -359,43 +359,43 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
+      "b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 38.48325274778242\n",
+      "Mean value: 45.69097368298819\n",
       "### Conditional Estimates\n",
       "__categorical__X0  __categorical__X1            \n",
-      "(-4.434, -1.311]   (-3.4379999999999997, 0.0683]    -72.917557\n",
-      "                   (0.0683, 0.65]                   -41.452919\n",
-      "                   (0.65, 1.155]                    -18.899074\n",
-      "                   (1.155, 1.72]                     -0.874103\n",
-      "                   (1.72, 5.039]                     30.952455\n",
-      "(-1.311, -0.738]   (-3.4379999999999997, 0.0683]    -36.759444\n",
-      "                   (0.0683, 0.65]                    -3.655510\n",
-      "                   (0.65, 1.155]                     15.870641\n",
-      "                   (1.155, 1.72]                     35.822712\n",
-      "                   (1.72, 5.039]                     69.750816\n",
-      "(-0.738, -0.221]   (-3.4379999999999997, 0.0683]    -14.294039\n",
-      "                   (0.0683, 0.65]                    18.609205\n",
-      "                   (0.65, 1.155]                     37.836481\n",
-      "                   (1.155, 1.72]                     57.871563\n",
-      "                   (1.72, 5.039]                     90.660415\n",
-      "(-0.221, 0.384]    (-3.4379999999999997, 0.0683]      7.522082\n",
-      "                   (0.0683, 0.65]                    41.146692\n",
-      "                   (0.65, 1.155]                     61.207314\n",
-      "                   (1.155, 1.72]                     80.871746\n",
-      "                   (1.72, 5.039]                    113.599925\n",
-      "(0.384, 3.11]      (-3.4379999999999997, 0.0683]     44.351338\n",
-      "                   (0.0683, 0.65]                    76.905999\n",
-      "                   (0.65, 1.155]                    100.413341\n",
-      "                   (1.155, 1.72]                    118.424617\n",
-      "                   (1.72, 5.039]                    149.087826\n",
+      "(-3.509, -0.768]   (-3.3369999999999997, -0.439]    -97.545075\n",
+      "                   (-0.439, 0.148]                  -56.362835\n",
+      "                   (0.148, 0.662]                   -36.699819\n",
+      "                   (0.662, 1.244]                   -16.351319\n",
+      "                   (1.244, 4.414]                    18.098515\n",
+      "(-0.768, -0.193]   (-3.3369999999999997, -0.439]    -43.268273\n",
+      "                   (-0.439, 0.148]                   -9.193659\n",
+      "                   (0.148, 0.662]                    13.083896\n",
+      "                   (0.662, 1.244]                    34.970122\n",
+      "                   (1.244, 4.414]                    69.062313\n",
+      "(-0.193, 0.328]    (-3.3369999999999997, -0.439]    -10.999394\n",
+      "                   (-0.439, 0.148]                   23.879168\n",
+      "                   (0.148, 0.662]                    44.791434\n",
+      "                   (0.662, 1.244]                    66.708583\n",
+      "                   (1.244, 4.414]                   101.492109\n",
+      "(0.328, 0.911]     (-3.3369999999999997, -0.439]     22.816677\n",
+      "                   (-0.439, 0.148]                   56.431745\n",
+      "                   (0.148, 0.662]                    78.275033\n",
+      "                   (0.662, 1.244]                    99.508186\n",
+      "                   (1.244, 4.414]                   134.902504\n",
+      "(0.911, 3.758]     (-3.3369999999999997, -0.439]     71.878946\n",
+      "                   (-0.439, 0.148]                  109.508433\n",
+      "                   (0.148, 0.662]                   128.741806\n",
+      "                   (0.662, 1.244]                   150.536299\n",
+      "                   (1.244, 4.414]                   188.398120\n",
       "dtype: float64\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb
index e94415bd3..1796af42a 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:29.923858Z",
-     "iopub.status.busy": "2024-10-25T14:49:29.923672Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.362428Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.361758Z"
+     "iopub.execute_input": "2024-10-25T14:50:28.558426Z",
+     "iopub.status.busy": "2024-10-25T14:50:28.558184Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.126055Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.125351Z"
     }
    },
    "outputs": [],
@@ -48,10 +48,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:31.364814Z",
-     "iopub.status.busy": "2024-10-25T14:49:31.364514Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.382356Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.381807Z"
+     "iopub.execute_input": "2024-10-25T14:50:30.128557Z",
+     "iopub.status.busy": "2024-10-25T14:50:30.128225Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.146005Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.145461Z"
     }
    },
    "outputs": [
@@ -96,10 +96,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:31.384401Z",
-     "iopub.status.busy": "2024-10-25T14:49:31.384044Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.392877Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.392380Z"
+     "iopub.execute_input": "2024-10-25T14:50:30.147933Z",
+     "iopub.status.busy": "2024-10-25T14:50:30.147729Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.172392Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.171817Z"
     }
    },
    "outputs": [
@@ -107,9 +107,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for initializing model = 0.0045223236083984375\n",
-      "Time taken for vanilla identification = 0.0002155303955078125\n",
-      "Time taken for optimized backdoor identification = 0.00012540817260742188\n"
+      "Time taken for initializing model = 0.005246877670288086\n",
+      "Time taken for vanilla identification = 0.013742208480834961\n",
+      "Time taken for optimized backdoor identification = 0.0015587806701660156\n"
      ]
     }
    ],
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb
index e3d35060e..1e1b06fd3 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:33.133469Z",
-     "iopub.status.busy": "2024-10-25T14:49:33.133289Z",
-     "iopub.status.idle": "2024-10-25T14:49:34.557871Z",
-     "shell.execute_reply": "2024-10-25T14:49:34.557255Z"
+     "iopub.execute_input": "2024-10-25T14:50:32.024076Z",
+     "iopub.status.busy": "2024-10-25T14:50:32.023615Z",
+     "iopub.status.idle": "2024-10-25T14:50:33.548126Z",
+     "shell.execute_reply": "2024-10-25T14:50:33.547503Z"
     }
    },
    "outputs": [],
@@ -80,10 +80,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:34.560159Z",
-     "iopub.status.busy": "2024-10-25T14:49:34.559871Z",
-     "iopub.status.idle": "2024-10-25T14:49:34.742983Z",
-     "shell.execute_reply": "2024-10-25T14:49:34.742414Z"
+     "iopub.execute_input": "2024-10-25T14:50:33.550770Z",
+     "iopub.status.busy": "2024-10-25T14:50:33.550250Z",
+     "iopub.status.idle": "2024-10-25T14:50:33.583876Z",
+     "shell.execute_reply": "2024-10-25T14:50:33.583178Z"
     }
    },
    "outputs": [
@@ -346,10 +346,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:34.744997Z",
-     "iopub.status.busy": "2024-10-25T14:49:34.744624Z",
-     "iopub.status.idle": "2024-10-25T14:49:35.612847Z",
-     "shell.execute_reply": "2024-10-25T14:49:35.612191Z"
+     "iopub.execute_input": "2024-10-25T14:50:33.585942Z",
+     "iopub.status.busy": "2024-10-25T14:50:33.585579Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.469445Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.468765Z"
     }
    },
    "outputs": [
@@ -516,10 +516,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:35.614978Z",
-     "iopub.status.busy": "2024-10-25T14:49:35.614538Z",
-     "iopub.status.idle": "2024-10-25T14:49:35.859803Z",
-     "shell.execute_reply": "2024-10-25T14:49:35.859168Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.471656Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.471186Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.720594Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.719917Z"
     }
    },
    "outputs": [
@@ -575,10 +575,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:35.862084Z",
-     "iopub.status.busy": "2024-10-25T14:49:35.861735Z",
-     "iopub.status.idle": "2024-10-25T14:49:36.012077Z",
-     "shell.execute_reply": "2024-10-25T14:49:36.011400Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.723201Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.722825Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.861826Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.861131Z"
     }
    },
    "outputs": [
@@ -634,10 +634,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:36.014657Z",
-     "iopub.status.busy": "2024-10-25T14:49:36.014240Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.895768Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.895210Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.863972Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.863767Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.871948Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.871282Z"
     }
    },
    "outputs": [
@@ -651,13 +651,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x ↪\n",
+      "────────────(E[y_factual|x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5 ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14])\n",
+      "↪ ,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14,U) = P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7,U) = P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -688,10 +688,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.897670Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.897342Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.910044Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.909544Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.874267Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.873866Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.887686Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.887155Z"
     }
    },
    "outputs": [
@@ -705,9 +705,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "   d                                                \n",
-      "────────(E[re78|hisp,age,nodegr,married,educ,black])\n",
+      "────────(E[re78|nodegr,hisp,age,married,black,educ])\n",
       "d[treat]                                            \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,age,nodegr,married,educ,black,U) = P(re78|treat,hisp,age,nodegr,married,educ,black)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,nodegr,hisp,age,married,black,educ,U) = P(re78|treat,nodegr,hisp,age,married,black,educ)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -745,10 +745,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.912109Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.911603Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.941315Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.940594Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.889830Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.889453Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.920278Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.919673Z"
     }
    },
    "outputs": [
@@ -756,7 +756,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The Causal Estimate is 4.028748218390157\n"
+      "The Causal Estimate is 4.028748218389992\n"
      ]
     }
    ],
@@ -781,10 +781,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.944033Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.943751Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.975513Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.975028Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.923151Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.922616Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.956053Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.955359Z"
     }
    },
    "outputs": [
@@ -792,7 +792,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The Causal Estimate is 1639.8231995062642\n"
+      "The Causal Estimate is 1639.8090120835514\n"
      ]
     }
    ],
@@ -831,10 +831,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.978051Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.977697Z",
-     "iopub.status.idle": "2024-10-25T14:50:31.924467Z",
-     "shell.execute_reply": "2024-10-25T14:50:31.923793Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.959369Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.958331Z",
+     "iopub.status.idle": "2024-10-25T14:51:31.083429Z",
+     "shell.execute_reply": "2024-10-25T14:51:31.082738Z"
     }
    },
    "outputs": [
@@ -843,8 +843,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:4.028748218390156\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:4.028748218389992\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -872,10 +872,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:31.926474Z",
-     "iopub.status.busy": "2024-10-25T14:50:31.926261Z",
-     "iopub.status.idle": "2024-10-25T14:50:32.893080Z",
-     "shell.execute_reply": "2024-10-25T14:50:32.892503Z"
+     "iopub.execute_input": "2024-10-25T14:51:31.085425Z",
+     "iopub.status.busy": "2024-10-25T14:51:31.085203Z",
+     "iopub.status.idle": "2024-10-25T14:51:32.060341Z",
+     "shell.execute_reply": "2024-10-25T14:51:32.059746Z"
     }
    },
    "outputs": [
@@ -884,9 +884,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:-0.4567346745113159\n",
-      "p value:0.02\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:-0.47212791991095493\n",
+      "p value:0.06\n",
       "\n"
      ]
     }
@@ -914,10 +914,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:32.894976Z",
-     "iopub.status.busy": "2024-10-25T14:50:32.894764Z",
-     "iopub.status.idle": "2024-10-25T14:50:33.768531Z",
-     "shell.execute_reply": "2024-10-25T14:50:33.767949Z"
+     "iopub.execute_input": "2024-10-25T14:51:32.062498Z",
+     "iopub.status.busy": "2024-10-25T14:51:32.062067Z",
+     "iopub.status.idle": "2024-10-25T14:51:32.953005Z",
+     "shell.execute_reply": "2024-10-25T14:51:32.952292Z"
     }
    },
    "outputs": [
@@ -926,9 +926,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:4.026103742836523\n",
-      "p value:0.96\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:4.027300878025508\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
@@ -962,10 +962,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:33.770528Z",
-     "iopub.status.busy": "2024-10-25T14:50:33.770304Z",
-     "iopub.status.idle": "2024-10-25T14:50:34.661229Z",
-     "shell.execute_reply": "2024-10-25T14:50:34.660655Z"
+     "iopub.execute_input": "2024-10-25T14:51:32.955148Z",
+     "iopub.status.busy": "2024-10-25T14:51:32.954947Z",
+     "iopub.status.idle": "2024-10-25T14:51:33.888811Z",
+     "shell.execute_reply": "2024-10-25T14:51:33.888209Z"
     }
    },
    "outputs": [
@@ -974,8 +974,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:1639.8231995062638\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:1639.809012083551\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -1003,10 +1003,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:34.663129Z",
-     "iopub.status.busy": "2024-10-25T14:50:34.662921Z",
-     "iopub.status.idle": "2024-10-25T14:50:35.602892Z",
-     "shell.execute_reply": "2024-10-25T14:50:35.602291Z"
+     "iopub.execute_input": "2024-10-25T14:51:33.891036Z",
+     "iopub.status.busy": "2024-10-25T14:51:33.890585Z",
+     "iopub.status.idle": "2024-10-25T14:51:34.875052Z",
+     "shell.execute_reply": "2024-10-25T14:51:34.874337Z"
     }
    },
    "outputs": [
@@ -1015,9 +1015,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:-177.44064343403886\n",
-      "p value:0.8\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:-189.7811505416216\n",
+      "p value:0.76\n",
       "\n"
      ]
     }
@@ -1045,10 +1045,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:35.604984Z",
-     "iopub.status.busy": "2024-10-25T14:50:35.604547Z",
-     "iopub.status.idle": "2024-10-25T14:50:36.463055Z",
-     "shell.execute_reply": "2024-10-25T14:50:36.462391Z"
+     "iopub.execute_input": "2024-10-25T14:51:34.877427Z",
+     "iopub.status.busy": "2024-10-25T14:51:34.876941Z",
+     "iopub.status.idle": "2024-10-25T14:51:35.754559Z",
+     "shell.execute_reply": "2024-10-25T14:51:35.753814Z"
     }
    },
    "outputs": [
@@ -1057,9 +1057,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:1595.963972683793\n",
-      "p value:0.94\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:1658.6773045421505\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb
index 2d12635ca..6f47d1906 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb
@@ -16,10 +16,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:43.270472Z",
-     "iopub.status.busy": "2024-10-25T14:50:43.270290Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.729758Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.729120Z"
+     "iopub.execute_input": "2024-10-25T14:51:43.301755Z",
+     "iopub.status.busy": "2024-10-25T14:51:43.301575Z",
+     "iopub.status.idle": "2024-10-25T14:51:44.927089Z",
+     "shell.execute_reply": "2024-10-25T14:51:44.926426Z"
     }
    },
    "outputs": [],
@@ -44,10 +44,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.732271Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.731785Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.879933Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.879289Z"
+     "iopub.execute_input": "2024-10-25T14:51:44.930025Z",
+     "iopub.status.busy": "2024-10-25T14:51:44.929635Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.081574Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.080866Z"
     }
    },
    "outputs": [],
@@ -68,10 +68,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.882541Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.882327Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.897724Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.897121Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.084881Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.084201Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.098906Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.098147Z"
     }
    },
    "outputs": [
@@ -111,87 +111,87 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.671234</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.572346</td>\n",
-       "      <td>2.147272</td>\n",
-       "      <td>-1.008626</td>\n",
-       "      <td>-0.636455</td>\n",
-       "      <td>-0.726492</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>18.593792</td>\n",
+       "      <td>2.584789</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.416842</td>\n",
+       "      <td>0.943169</td>\n",
+       "      <td>-0.211056</td>\n",
+       "      <td>-1.037660</td>\n",
+       "      <td>1.462029</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>16.889292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.964685</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.517017</td>\n",
-       "      <td>-0.643717</td>\n",
-       "      <td>-0.120277</td>\n",
-       "      <td>0.304531</td>\n",
-       "      <td>-1.707153</td>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8.122055</td>\n",
+       "      <td>-0.421155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.746773</td>\n",
+       "      <td>0.760769</td>\n",
+       "      <td>-0.948665</td>\n",
+       "      <td>-2.609936</td>\n",
+       "      <td>2.330821</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-3.205946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.311971</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.152409</td>\n",
-       "      <td>-0.436145</td>\n",
-       "      <td>0.677291</td>\n",
-       "      <td>1.221427</td>\n",
-       "      <td>0.819015</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2.264079</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.712215</td>\n",
+       "      <td>0.601335</td>\n",
+       "      <td>-0.642743</td>\n",
+       "      <td>-1.544072</td>\n",
+       "      <td>-0.253958</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>19.239614</td>\n",
+       "      <td>26.358782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.299439</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.687731</td>\n",
-       "      <td>1.184121</td>\n",
-       "      <td>-0.530793</td>\n",
-       "      <td>-0.577695</td>\n",
-       "      <td>1.606986</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.807192</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.741959</td>\n",
+       "      <td>2.537230</td>\n",
+       "      <td>0.848927</td>\n",
+       "      <td>-2.727143</td>\n",
+       "      <td>0.768600</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>20.986311</td>\n",
+       "      <td>30.392742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.232770</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.154202</td>\n",
-       "      <td>-1.499937</td>\n",
-       "      <td>-0.320013</td>\n",
-       "      <td>-0.834136</td>\n",
-       "      <td>2.449783</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.514467</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.331064</td>\n",
+       "      <td>-0.036820</td>\n",
+       "      <td>-0.617192</td>\n",
+       "      <td>0.271595</td>\n",
+       "      <td>0.782916</td>\n",
+       "      <td>2</td>\n",
        "      <td>True</td>\n",
-       "      <td>13.098742</td>\n",
+       "      <td>22.997458</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         X0   Z0        Z1        W0        W1        W2        W3 W4    v0  \\\n",
-       "0  1.671234  1.0  0.572346  2.147272 -1.008626 -0.636455 -0.726492  2  True   \n",
-       "1  0.964685  1.0  0.517017 -0.643717 -0.120277  0.304531 -1.707153  0  True   \n",
-       "2  0.311971  1.0  0.152409 -0.436145  0.677291  1.221427  0.819015  0  True   \n",
-       "3  1.299439  1.0  0.687731  1.184121 -0.530793 -0.577695  1.606986  1  True   \n",
-       "4  1.232770  1.0  0.154202 -1.499937 -0.320013 -0.834136  2.449783  0  True   \n",
+       "         X0   Z0        Z1        W0        W1        W2        W3 W4     v0  \\\n",
+       "0  2.584789  0.0  0.416842  0.943169 -0.211056 -1.037660  1.462029  3  False   \n",
+       "1 -0.421155  0.0  0.746773  0.760769 -0.948665 -2.609936  2.330821  1  False   \n",
+       "2  2.264079  0.0  0.712215  0.601335 -0.642743 -1.544072 -0.253958  3   True   \n",
+       "3  0.807192  0.0  0.741959  2.537230  0.848927 -2.727143  0.768600  3   True   \n",
+       "4  0.514467  0.0  0.331064 -0.036820 -0.617192  0.271595  0.782916  2   True   \n",
        "\n",
        "           y  \n",
-       "0  18.593792  \n",
-       "1   8.122055  \n",
-       "2  19.239614  \n",
-       "3  20.986311  \n",
-       "4  13.098742  "
+       "0  16.889292  \n",
+       "1  -3.205946  \n",
+       "2  26.358782  \n",
+       "3  30.392742  \n",
+       "4  22.997458  "
       ]
      },
      "execution_count": 3,
@@ -231,10 +231,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.901070Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.900269Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.907081Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.906572Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.101339Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.101101Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.107555Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.106802Z"
     }
    },
    "outputs": [],
@@ -253,10 +253,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.909537Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.909333Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.078909Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.078261Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.110111Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.109609Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.281474Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.280707Z"
     }
    },
    "outputs": [
@@ -280,10 +280,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.081502Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.081227Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.088033Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.087482Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.283837Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.283609Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.288784Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.288238Z"
     },
     "scrolled": true
    },
@@ -329,10 +329,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.090235Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.090032Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.260750Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.260177Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.291014Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.290636Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.463887Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.463349Z"
     }
    },
    "outputs": [
@@ -346,9 +346,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -391,10 +391,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.262760Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.262421Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.583717Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.583059Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.466110Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.465769Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.811564Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.810854Z"
     },
     "scrolled": true
    },
@@ -412,16 +412,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.3093616611939\n",
+      "Mean value: 13.111841490138564\n",
       "\n"
      ]
     }
@@ -444,10 +444,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.585673Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.585419Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.856249Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.855671Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.813867Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.813485Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.107675Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.106952Z"
     }
    },
    "outputs": [
@@ -464,18 +464,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.225338634909248\n",
+      "Mean value: 13.037286029881079\n",
       "\n",
-      "Causal Estimate is 12.225338634909248\n"
+      "Causal Estimate is 13.037286029881079\n"
      ]
     }
    ],
@@ -500,10 +500,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.858245Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.857870Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.861052Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.860599Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.110002Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.109540Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.112976Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.112504Z"
     },
     "scrolled": true
    },
@@ -523,10 +523,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.862837Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.862415Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.004591Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.003956Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.114807Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.114512Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.260743Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.260115Z"
     }
    },
    "outputs": [
@@ -550,10 +550,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.007016Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.006596Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.011381Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.010855Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.263443Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.263138Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.268183Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.267691Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.013455Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.013060Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.126355Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.125792Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.270116Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.269905Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.428174Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.427576Z"
     }
    },
    "outputs": [],
@@ -610,10 +610,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.128472Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.128088Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.396440Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.395799Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.430434Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.429980Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.710661Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.710063Z"
     }
    },
    "outputs": [
@@ -630,18 +630,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.3093616611939\n",
+      "Mean value: 13.111841490138564\n",
       "\n",
-      "Causal Estimate is 12.3093616611939\n"
+      "Causal Estimate is 13.111841490138564\n"
      ]
     }
    ],
@@ -685,17 +685,17 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.398426Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.398237Z",
-     "iopub.status.idle": "2024-10-25T14:51:13.224614Z",
-     "shell.execute_reply": "2024-10-25T14:51:13.223939Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.712825Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.712432Z",
+     "iopub.status.idle": "2024-10-25T14:52:14.800399Z",
+     "shell.execute_reply": "2024-10-25T14:52:14.799738Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "595c9871fb114acba558d6a7db002403",
+       "model_id": "ea8cb893ee004fa397825bf601616bce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -711,8 +711,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.3093616611939\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:13.111841490138568\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -735,17 +735,17 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:51:13.226834Z",
-     "iopub.status.busy": "2024-10-25T14:51:13.226434Z",
-     "iopub.status.idle": "2024-10-25T14:51:39.854390Z",
-     "shell.execute_reply": "2024-10-25T14:51:39.853760Z"
+     "iopub.execute_input": "2024-10-25T14:52:14.802601Z",
+     "iopub.status.busy": "2024-10-25T14:52:14.802396Z",
+     "iopub.status.idle": "2024-10-25T14:52:40.458714Z",
+     "shell.execute_reply": "2024-10-25T14:52:40.458023Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f219d973cfd4696933afeb56f1a47e2",
+       "model_id": "e3afa9e9c4ef4f7f9b8a7d006e220466",
        "version_major": 2,
        "version_minor": 0
       },
@@ -761,9 +761,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:0.01173367700295361\n",
-      "p value:0.94\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:-0.011250168352115359\n",
+      "p value:1.0\n",
       "\n"
      ]
     }
@@ -786,17 +786,17 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:51:39.856297Z",
-     "iopub.status.busy": "2024-10-25T14:51:39.856108Z",
-     "iopub.status.idle": "2024-10-25T14:52:04.297987Z",
-     "shell.execute_reply": "2024-10-25T14:52:04.297336Z"
+     "iopub.execute_input": "2024-10-25T14:52:40.460682Z",
+     "iopub.status.busy": "2024-10-25T14:52:40.460477Z",
+     "iopub.status.idle": "2024-10-25T14:53:06.480478Z",
+     "shell.execute_reply": "2024-10-25T14:53:06.479816Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dabd0980e25e4488bba07e4eb3defa12",
+       "model_id": "16a7b6646f684210a403e65627e64dc8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -812,9 +812,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.277462311024122\n",
-      "p value:0.82\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.856005739426717\n",
+      "p value:0.02\n",
       "\n"
      ]
     }
@@ -841,17 +841,17 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:04.300093Z",
-     "iopub.status.busy": "2024-10-25T14:52:04.299721Z",
-     "iopub.status.idle": "2024-10-25T14:52:16.797936Z",
-     "shell.execute_reply": "2024-10-25T14:52:16.797225Z"
+     "iopub.execute_input": "2024-10-25T14:53:06.483001Z",
+     "iopub.status.busy": "2024-10-25T14:53:06.482576Z",
+     "iopub.status.idle": "2024-10-25T14:53:19.834952Z",
+     "shell.execute_reply": "2024-10-25T14:53:19.834126Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "157326ad6e5e496a89bca87f987ebfec",
+       "model_id": "2650a6a2475a49bfb7f8241fa41ced30",
        "version_major": 2,
        "version_minor": 0
       },
@@ -873,70 +873,70 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.1s\n"
+      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.5s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.5s\n"
+      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.9s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.3s\n"
+      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.7s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    4.8s\n"
+      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    5.3s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.0s\n"
+      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    6.8s\n"
+      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.2s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.0s\n"
+      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    8.9s\n"
+      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    9.6s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.4s\n"
+      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.9s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   11.7s\n"
+      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   12.3s\n"
      ]
     },
     {
@@ -944,9 +944,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.283713319247354\n",
-      "p value:0.72\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.875409322185174\n",
+      "p value:0.12\n",
       "\n"
      ]
     },
@@ -954,7 +954,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   12.5s finished\n"
+      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   13.3s finished\n"
      ]
     }
    ],
@@ -984,10 +984,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:16.800100Z",
-     "iopub.status.busy": "2024-10-25T14:52:16.799882Z",
-     "iopub.status.idle": "2024-10-25T14:52:17.092581Z",
-     "shell.execute_reply": "2024-10-25T14:52:17.091987Z"
+     "iopub.execute_input": "2024-10-25T14:53:19.837480Z",
+     "iopub.status.busy": "2024-10-25T14:53:19.837078Z",
+     "iopub.status.idle": "2024-10-25T14:53:20.150031Z",
+     "shell.execute_reply": "2024-10-25T14:53:20.149352Z"
     }
    },
    "outputs": [
@@ -996,8 +996,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:11.499907799537318\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.013311802768278\n",
       "\n"
      ]
     }
@@ -1021,16 +1021,16 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:17.094897Z",
-     "iopub.status.busy": "2024-10-25T14:52:17.094320Z",
-     "iopub.status.idle": "2024-10-25T14:52:18.364723Z",
-     "shell.execute_reply": "2024-10-25T14:52:18.364084Z"
+     "iopub.execute_input": "2024-10-25T14:53:20.152158Z",
+     "iopub.status.busy": "2024-10-25T14:53:20.151955Z",
+     "iopub.status.idle": "2024-10-25T14:53:21.472784Z",
+     "shell.execute_reply": "2024-10-25T14:53:21.472014Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Tp+ep9+5fpIaGhnk+CAvSvn17xMbGYuvWrbKyrKws/P777zAyMoKfn5+Cvfn/doOCghASEiJXnpiYiE2bNqFu3br5jjgoO949e/bIXXZ78eJFBAcHy66CSktLw5s3b+T2cXFxgbGxseyy4DZt2sDExASzZ89GZmZmnpiePXtW6Ph79eqF9PR0rFu3DocPH0bPnj3lthf2tezs7FC3bl2sW7dO7nLbY8eO5Zmvkh8DAwNMmDAB4eHhmDBhQr4jGhs3bsTFixcB5LzvFy9eRGBgoGx7amoqVq5cCScnp1KzvlD9+vVhbW2NFStWyF3WfejQIYSHh6NDhw5KeR1F3z9NTc08dbZv357nkvDcRDZ3XhyQMxqzcuVKuXrJycl55hW5u7tDQ0ND1u9u3bpBU1MT06dPz/PaQgi8ePFCkS5TMeGIDKnEoUOHZH+JvqtRo0aoUqWKwu2NHz8e+/btw2effYYBAwbAy8sLqampCAsLw44dOxAVFQUrKyu0aNECffv2xdKlS3Hv3j20bdsWUqkU586dQ4sWLWS3BPDy8sLx48excOFC2Nvbw9nZOd/5DQAwbNgw/PnnnxgwYABCQkLg5OSEHTt24Pz581i8eHGhJzP+18SJE7F9+3Y0a9YMX331Fdzc3PD06VMEBAQgJiYGa9euLVK7isbr6uqKJk2a4JtvvkF6ejoWL14MS0tL2Wm8u3fvomXLlujZsydq1qwJLS0t7N69G3FxcejduzeAnPkQy5cvR9++fVGvXj307t0bFSpUQHR0NA4cOIDGjRvjjz/+KFT89erVg6urK3766Sekp6fLnVZS9LXmzJmDDh06oEmTJhg0aBASEhLw+++/o1atWnITigsyfvx43Lx5EwsWLMCpU6fw+eefw9bWFrGxsdizZw8uXryICxcuAMg5nps3b0a7du0wcuRIWFhYYN26dYiMjMTOnTvzPZWmCtra2vjtt98wcOBA+Pn5wd/fX3b5tZOTE0aPHq2011Lk/fvss88wY8YMDBw4EI0aNUJYWBg2bdqU5/OiVq1aaNiwISZNmiQbed2yZUuepOXkyZP47rvv0KNHD1SrVg1ZWVnYsGEDNDU10b17dwA5SdGsWbMwadIkREVFoUuXLjA2NkZkZCR2796NYcOGFesK41RIKrlWisqt911+DUCsXbtWVleRy6+FyLmUddKkScLV1VXo6OgIKysr0ahRIzF//ny5y1izsrLEvHnzhJubm9DR0REVKlQQ7dq1EyEhIbI6t2/fFs2aNRP6+voCwAcvxY6LixMDBw4UVlZWQkdHR7i7u8v15UN9Ksjjx4/FkCFDRMWKFYWWlpawsLAQn332mQgKCspT18/PT9SqVStPef/+/fNcnluYeHMvv543b55YsGCBcHBwELq6uqJp06bi2rVrsnrPnz8Xw4cPF25ubsLQ0FCYmpoKHx8fsW3btjyxnDp1SrRp00aYmpoKPT094eLiIgYMGCAuX74sF6+hoeF735effvpJABCurq4F1inMawkhxM6dO0WNGjWErq6uqFmzpti1a1e+79n77NixQ7Ru3VpYWFgILS0tYWdnJ3r16iVOnz4tV+/+/fvi888/F2ZmZkJPT094e3uL/fv354kb+VwWnPu7c+nSJbnyqVOnCgDi2bNnsrL8fkfePZ6Feb2tW7cKT09PoaurKywsLMQXX3whdxm+EAUfq9yYCqsw79+bN2/E2LFjhZ2dndDX1xeNGzcWgYGBws/PT/j5+cm1d//+fdGqVSuhq6srbGxsxI8//iiOHTsmd/n1gwcPxKBBg4SLi4vQ09MTFhYWokWLFuL48eN54tu5c6do0qSJMDQ0FIaGhsLNzU0MHz5c3Llzp9B9pOIjEaKYZwESERERFZPSMZZJREREVARMZIiIiEhtMZEhIiIitcVEhoiIiNQWExkiIiJSW0xkiIiISG1xQbx8SKVSPH36FMbGxkW+IzIREREpRgiBV69ewd7evtCLRDKRycfTp0/z3CGWiIiISsajR49QqVKlQtVlIpOP3CXaHz16JLvVPBERERWv5ORkODg4KHRrFyYy+cg9nWRiYsJEhoiIqIQpMq2Dk32JiIhIbXFE5j0yMjKQkZGRp1xDQwNaWlpy9QoikUigra1dpLqZmZn53tq+OOsCgI6OTpHqZmVlQSqVKqWutra2LCMvrrrZ2dnIzs5WSl0tLS3ZxLTSUFcqlea52++7NDU1oampWWrqCiGQmZmplLrv/n4WV13g/b/L/IzIvy4/I/gZ8aG67/v5LwgTmfdYsGAB9PT08pRXrVoVffr0kT2fP39+gR+Ajo6OGDBggOz5kiVLkJaWlm9de3t7DB06VPZ82bJlSEpKyrduhQoV8O2338qer1q1Cs+ePcu3rqmpKUaNGiV7HhAQgKdPn+Zb18DAAOPHj5c937RpEx4+fJhvXW1tbfz444+y59u2bcO9e/fyrQsAU6dOlf1/9+7duHXrVoF1J02aJPtQ279/P65du1Zg3XHjxsHQ0BAAcOTIEVy+fLnAut9//z3MzMwAACdOnEBgYGCBdb/55htYW1sDAM6dO4czZ84UWHfIkCGoWLEiACAoKAjHjx8vsG7//v3h5OQEAAgJCcGhQ4cKrOvv749q1aoBAMLCwrB3794C637++eeoVasWACA8PBw7duwosG7nzp1Rt25dAEBERAQ2b95cYN127drB29sbABAdHY1169YVWLdVq1Zo3LgxACAmJgarV68usK6fnx+aN28OAHj27BmWL19eYF1fX1+0bt0aAJCUlIQlS5YUWLd+/fro0KEDACAtLQ3z588vsK6Hhwe6dOkCIOcLec6cOQXWrVmzJnr06CF7/r66/IzIwc+I/8fPiBwf+ox48+ZNgfsWhKeWiIiISG1JxPvGBMup5ORkmJqa4tmzZ/lO9uWwcf51OWzMYWOeWlK8Lj8jilaXnxEfV7c0/N7nVzc5ORkVKlRAUlJSoS+2YSKTj9xERpE3koiIiD5OUb5/eWqJiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFRIaIiIjUFhMZIiIiUltMZIiIiEhtMZEpQcEPXiAhVfH7SBAREVH+eK+lEvI8JR3DNoRAQwL81KEmuterqNBtyomIiCgvjsiUkMS0DNiZ6uFlWibGbb+GPquC8eBZiqrDIiIiUmtMZEqIq7Ux/hnRBBPaukFPWwOBD16g7eJzWHL8HtKzCr43BhERERWMiUwJ0tbUwDfNXXB0lB+aVauAjGwpFh2/i/ZLziH4wQtVh0dERKR2mMioQGVLA6wb2AC/+3vCykgX95+lotfKIPyw4xpecjIwERFRoTGRURGJRIKOHvY4MdYPfXwqAwC2XX6MlgvPYNeVx+BNyYmIiD6MiYyKmeprY3ZXd+z8xhfVbIyQkJqBMduu4cs1wYh8nqrq8IiIiEo1JjKlhJejBfaPaIof2laHrpYGzke8QJvFZ/H7iXvIyJKqOjwiIqJSiYlMKaKjpYFvm7vi6OhmaFrVChlZUiw4dhftl57DxcgEVYdHRERU6jCRKYUcLQ2xfpA3lvSuCysjHUTEp6Dnn4GYuPM6EtM4GZiIiCgXE5lSSiKRoHPdijgxpjn8vR0AAFsuPULLBWew5+oTTgYmIiICE5lSz9RAG3O61cH2r31R1doIL1IzMGprKPr9dREPX3AyMBERlW9MZNREAycLHBjZFOPbVIeOlgbO3XuO1ovOYtmpCE4GJiKickuliczZs2fRsWNH2NvbQyKRYM+ePbJtmZmZmDBhAtzd3WFoaAh7e3v069cPT58+/WC7y5Ytg5OTE/T09ODj44OLFy8WYy9Kjo6WBoa3cMXRUc3QxNUK6VlSzDtyBx2WnsOlKE4GJiKi8qdIicyGDRvQuHFj2Nvb4+HDhwCAxYsXY+/evQq1k5qaCg8PDyxbtizPtrS0NFy5cgVTpkzBlStXsGvXLty5cwedOnV6b5tbt27FmDFjMHXqVFy5cgUeHh5o06YN4uPjFYqtNHOyMsSGwd5Y3KsuLA11cC8+BT1WBGLSrutISstUdXhEREQlRiIUnDW6fPly/Pzzzxg1ahR++eUX3LhxA1WqVEFAQADWrVuHU6dOFS0QiQS7d+9Gly5dCqxz6dIleHt74+HDh6hcuXK+dXx8fNCgQQP88ccfAACpVAoHBweMGDECEydOLFQsycnJMDU1RVJSEkxMTBTuS0lKTMvAr4duY8ulRwAAKyMdTPmsJjp55IxyERERqYuifP8qPCLz+++/Y9WqVfjpp5+gqakpK69fvz7CwsIUbU4hSUlJkEgkMDMzy3d7RkYGQkJC0KpVK1mZhoYGWrVqhcDAwALbTU9PR3JystxDXZgZ6ODX7nWwdVhDuFQwxPOUDHy/hZOBiYiofFA4kYmMjISnp2eecl1dXaSmFt8X55s3bzBhwgT4+/sXmKU9f/4c2dnZsLGxkSu3sbFBbGxsgW3PmTMHpqamsoeDg4NSYy8JPlUscfD7phj7aTVOBiYionJD4UTG2dkZoaGhecoPHz6MGjVqKCOmPDIzM9GzZ08IIbB8+XKltz9p0iQkJSXJHo8ePVL6a5QEXS1NjGhZFUdGNUMjF0vZZOCOv/+LkIecDExERGWPlqI7jBkzBsOHD8ebN28ghMDFixexefNmzJkzB6tXr1Z6gLlJzMOHD3Hy5Mn3njOzsrKCpqYm4uLi5Mrj4uJga2tb4H66urrQ1dVVWsyq5mxliE1DfLD76hPMOhCOO3Gv0H15IPr4VMaENm4wNdBWdYhERERKofCIzJAhQ/Dbb79h8uTJSEtLQ58+fbB8+XIsWbIEvXv3VmpwuUnMvXv3cPz4cVhaWr63vo6ODry8vHDixAlZmVQqxYkTJ+Dr66vU2Eo7iUSCbvUq4cQYP/SsXwkA8HdwNFouPIN9155yZWAiIioTFL5q6V1paWlISUmBtbV1kfZPSUlBREQEAMDT0xMLFy5EixYtYGFhATs7O3z++ee4cuUK9u/fLzfvxcLCAjo6OgCAli1bomvXrvjuu+8A5Fx+3b9/f/z555/w9vbG4sWLsW3bNty+fTvP3JmCqNNVS4UV9OAFftwdhgfPcuYx+VWrgFldasPBwkDFkREREeUoyvevwonM69evIYSAgUHOF+DDhw+xe/du1KxZE61bt1Yo4NOnT6NFixZ5yvv3749p06bB2dk53/1OnTqF5s2bAwCcnJwwYMAATJs2Tbb9jz/+wLx58xAbG4u6deti6dKl8PHxKXRcZTGRAYD0rGysOP0gZwJwthR62hr4vmU1DGnqDG1NLvJMRESqVSKJTOvWrdGtWzd8/fXXSExMRPXq1aGjo4Pnz59j4cKF+Oabb4oUfGlSVhOZXPefpWDy7hsIfPACAOBma4xfurrDy9FcxZEREVF5ViLryFy5cgVNmzYFAOzYsQO2trZ4+PAh1q9fj6VLlyraHKmASwUj/D3UB/N7eMDcQBu3Y1/h8xUXMHlPGJJec2VgIiJSHwonMmlpaTA2NgYAHD16FN26dYOGhgYaNmwou10BlX4SiQSfe1XCibHN8blXJQgBbAyKRquFZ7D/OicDExGRelA4kXF1dcWePXvw6NEjHDlyRDYvJj4+vkyehinrLAx1ML+HB/4e6oMqVoZ49iod3/19FYMCLuFRQpqqwyMiInovhROZn3/+GePGjYOTkxN8fHxklzUfPXo03xV/ST00crHCwe+b4vuWVaGjqYFTd57h00Vn8OeZ+8jM5srARERUOhXp8uvY2FjExMTAw8MDGho5udDFixdhYmICNzc3pQdZ0sr6ZN8PiYhPwU+7wxAcmbMasJutMeZ0c4dnZU4GJiKi4lMiVy2VB+U9kQEAIQS2hzzG7IPhSEzLhEQC9G3oiHFtqsNEjysDExGR8pVIItOiRQtIJJICt588eVKR5kolJjL/70VKOn45GI5dV54AAKyNdTGtUy20q2373p8DIiIiRRXl+1fhey3VrVtX7nlmZiZCQ0Nx48YN9O/fX9HmqJSzNNLFwp518Xm9Svhpzw1EPk/Ft5uu4BM3a0zvVIsrAxMRkUop7dTStGnTkJKSgvnz5yujOZXiiEz+3mRm43+nIrD8zH1kZgvoa2ti9KdVMaixM7S4MjAREX0klc6RiYiIgLe3NxISEpTRnEoxkXm/iPhX+HHXDVyMyjnWNexMMKebO+o6mKk2MCIiUmslsrJvQQIDA6Gnp6es5qgUc7U2xpZhDfFbd3eY6msjPCYZXf93HlP33sCrN1wZmIiISo7Cc2S6desm91wIgZiYGFy+fBlTpkxRWmBUumloSNCrQWW0rGGDXw6EY/fVJ1gX+BCHb8ZiWsdaaMvJwEREVAIUPrU0cOBAuecaGhqoUKECPvnkE4Xvfl1a8dSS4v699xyT94Qh6kXOasCtalhjeufaqGimr+LIiIhIXXAdGSVhIlM0bzKzsexUBFa8nQxsoKOJMZ9Ww4BGTpwMTEREH6TSOTJEetqaGNu6Og6ObIoGTuZIy8jGrAPh6LzsPK4/TlR1eEREVAYpPCKTnZ2NRYsWYdu2bYiOjkZGRobcdl61RAAglQpsu/wIsw+GI/lNFjQkQD9fJ4xtXQ3GXBmYiIjyUSIjMtOnT8fChQvRq1cvJCUlYcyYMejWrRs0NDQwbdo0RZujMkpDQ4Le3pVxYmxzdKlrD6kAAi5E4dOFZ3H4RqyqwyMiojJC4REZFxcXLF26FB06dICxsTFCQ0NlZUFBQfj777+LK9YSwxEZ5Tt37xkm77mBh28nA39a0wbTO9WCPScDExHRWyUyIhMbGwt3d3cAgJGREZKSkgAAn332GQ4cOKBoc1RONK1aAUdGNcPwFi7Q0pDg2K04tFp4Bmv+jURWtlTV4RERkZpSOJGpVKkSYmJiAOSMzhw9ehQAcOnSJejq6io3OipT9LQ1Mb6NGw5+3xRejjmTgWfuv4Uu/zuPsMdJqg6PiIjUkMKJTNeuXXHixAkAwIgRIzBlyhRUrVoV/fr1w6BBg5QeIJU91WyMsf0rX8zu6g4TPS3ceJKMzsv+xYx/biElPUvV4RERkRr56HVkgoKCcOHCBVStWhUdO3ZUVlwqxTkyJSf+1RvM2h+OfdeeAgDsTPUwvVMttK5lq+LIiIiopHFBPCVhIlPyztx9hsl7wvAo4TUAoHVNG0zvXAt2ppwMTERUXpTIZN85c+bgr7/+ylP+119/4bffflO0OSIAgF+1Cjg6yg/fNM+ZDHz0VhxaLTiDtecjkS1lrk1ERPlTOJH5888/4ebmlqe8Vq1aWLFihVKCovJJX0cTE9q6Yf/IJqhX2QypGdmY/s8tdP3fedx4wsnARESUV5Euv7azs8tTXqFCBdnVTEQfw83WBDu+boRZXWrDWE8L1x8nodMf/2Lm/ltI5WRgIiJ6h8KJjIODA86fP5+n/Pz587C3t1dKUEQaGhJ82dARJ8b44bM6dpAKYM2/kfh04RkcuxWn6vCIiKiU0FJ0h6FDh2LUqFHIzMzEJ598AgA4ceIEfvjhB4wdO1bpAVL5Zm2ihz/61EN3r3hM2XMDj1++xtD1l9G2li2mdaoFW1M9VYdIREQqpPBVS0IITJw4EUuXLpXdMFJPTw8TJkzAzz//XCxBljRetVQ6vc7IxuITd7H6XM4EYCNdLYxrXQ19fZ2gqSFRdXhERPSRSvTy65SUFISHh0NfXx9Vq1YtU6v6MpEp3cJjkjFpVxhCHyUCADwqmWJ2N3fUsjdVbWBERPRRuI6MkjCRKf2ypQJ/X4zG3EO38So9C5oaEgxq7IRRrarBUFfhM6ZERFQKlMg6MkSlgaaGBH0bOuL4WD90cLdDtlRg1blItF50FifCORmYiKi8YCJDas3GRA/LvqiHvwbUR0UzfTxJfI3B6y7j200hiEt+o+rwiIiomDGRoTLhEzcbHBvTDMOaVYGmhgQHw2LRcsEZbAiMgpQrAxMRlVlMZKjMMNDRwo/ta2Dfd43h4WCGlPQsTNl7E71XBSHyeaqqwyMiomJQpMm+9+7dw6lTpxAfHw+pVCq3TZFLsM+ePYt58+YhJCQEMTEx2L17N7p06SLbvmvXLqxYsQIhISFISEjA1atXUbdu3fe2GRAQgIEDB8qV6erq4s2bwp9m4GRf9ZctFVgfGIW5h+/gdWY2dLU0MK51dQxq4sxLtYmISqmifP8qfHnHqlWr8M0338DKygq2traQSP7/S0EikSiUyKSmpsLDwwODBg1Ct27d8t3epEkT9OzZE0OHDi10uyYmJrhz545cXFS+aGpIMLCxM1rVsMHEXddxPuIFfjkYjv1hMZj3eR1UszFWdYhERKQECicys2bNwi+//IIJEyZ89Iu3a9cO7dq1K3B73759AQBRUVEKtSuRSGBra/sxoVEZ4WBhgI2DfbD10iP8ciAc1x4losPScxj5SVV83dwF2po8u0pEpM4U/hR/+fIlevToURyxKE1KSgocHR3h4OCAzp074+bNm++tn56ejuTkZLkHlR0SiQS9vSvj6JhmaOlmjcxsgQXH7qLTH7yrNhGRulM4kenRoweOHj1aHLEoRfXq1fHXX39h79692LhxI6RSKRo1aoTHjx8XuM+cOXNgamoqezg4OJRgxFRS7Ez1sbp/fSzuVRdmBtoIj0lG52XnMffwbbzJzFZ1eEREVAQKT/adM2cOFi5ciA4dOsDd3R3a2tpy20eOHFm0QCSSPJN9c0VFRcHZ2blQk33/KzMzEzVq1IC/vz9mzpyZb5309HSkp6fLnicnJ8PBwYGTfcuwZ6/SMW3fTRwIiwEAuFQwxNzP68DL0ULFkRERlV8lMtl35cqVMDIywpkzZ3DmzBm5bRKJpMiJTHHR1taGp6cnIiIiCqyjq6tbpu4VRR9WwVgXy76oh443YjB5z03cf5aKz1cEYmAjZ4xrUw0GOrzNARGROlD40zoyMrI44ig22dnZCAsLQ/v27VUdCpVCbWvboWEVS8zYfwu7rjzBX+cjcTw8Dr92c0cjVytVh0dERB/wUX925p6VKurlzSkpKXIjJZGRkQgNDYWFhQUqV66MhIQEREdH4+nTpwAgu6Ta1tZWdlVSv379ULFiRcyZMwcAMGPGDDRs2BCurq5ITEzEvHnz8PDhQwwZMqTI/aSyzcxABwt71kVHD3v8uCsM0Qlp6LM6GP7elTGpvRtM9LQ/3AgREalEka49Xb9+Pdzd3aGvrw99fX3UqVMHGzZsULidy5cvw9PTE56engCAMWPGwNPTU7YWzb59++Dp6YkOHToAAHr37g1PT0+sWLFC1kZ0dDRiYmJkz1++fImhQ4eiRo0aaN++PZKTk3HhwgXUrFmzKF2lcqRFdWscHd0MX/hUBgBsvhiNNovO4tTteBVHRkREBVF4su/ChQsxZcoUfPfdd2jcuDEA4N9//8WyZcswa9YsjB49ulgCLUlc2ZcC77/AhJ3XEZ2QBgDo5lkRP3esCTMDHRVHRkRUdhXl+1fhRMbZ2RnTp09Hv3795MrXrVuHadOmqd0cmvwwkSEASMvIwoKjd/HX+UgIAVgZ6WJm51po526n6tCIiMqkonz/KnxqKSYmBo0aNcpT3qhRI7lTPETqzkBHC1M+q4kdXzeCq7URnqek45tNV/DtphA8e5X+4QaIiKjYKZzIuLq6Ytu2bXnKt27diqpVqyolKKLSxMvRHPtHNMHwFi7Q1JDgYFgsPl10BruvPkYR7rlKRERKpPCppZ07d6JXr15o1aqVbI7M+fPnceLECWzbtg1du3YtlkBLEk8tUUFuPEnC+B3XER6TcxuLT9ys8UvX2rAz1VdxZERE6q9E5sgAQEhICBYtWoTw8HAAQI0aNTB27FjZ1UfqjokMvU9mthR/nrmPpScikJEthbGuFn7sUAO9GzjwTutERB+hxBKZso6JDBXG3bhXGL/jOq49SgQANHa1xK/d6sDBwkC1gRERqakSTWTi4+MRHx8PqVQqV16nTp2iNFeqMJGhwsqWCqw9H4l5R+4gPUsKfW1N/NC2Ovr7OkFDg6MzRESKKJFEJiQkBP3790d4eHieiY4SiQTZ2ep/F2EmMqSoqOepmLDzOoIjEwAA9R3N8dvndeBSwUjFkRERqY8SSWQ8PDzg4uKCCRMmwMbGJs+cAEdHR0WaK5WYyFBRSKUCmy5G49eD4UjNyIaOlgZGt6qGoU2doaVZpEW0iYjKlRJJZIyNjXH16lW4uroWKUh1wESGPsbjl2n4cfcNnL37DADgXtEUcz+vgxp2/FkiInqfElkQr2XLlrh27ZrCwRGVF5XMDbBuYAPM+7wOTPS0EPYkCR1//xeLjt1FRpb0ww0QEVGhKTwi8/z5c/Tv3x/e3t6oXbs2tLXl7wzcqVMnpQaoChyRIWWJT36Dn/bcwLFbcQCA6jbGmPt5HXg4mKk2MCKiUqhETi39888/6Nu3L5KTk/M2xsm+RHkIIbD/egym7ruJhNQMaEiAoU2rYPSn1aCnranq8IiISo0SObU0YsQIfPnll4iJiYFUKpV7lIUkhkjZJBIJOnrY49joZujkYQ+pAP48+wDtlpzDpagEVYdHRKTWijTZNzQ0FC4uLsUVk8pxRIaK07FbcfhpdxjiX6VDIgH6NXTED23dYKirperQiIhUqkRGZLp164ZTp04pHBwR5fi0pg2OjfFDr/oOEAJYF/gQbRafxb/3nqs6NCIitaPwn4DVqlXDpEmT8O+//8Ld3T3PZN+RI0cqLTiisspUXxu/fV4Hn3nYYeLOMDx++RpfrglGr/oO+LFDDZjqa3+4ESIiUvzUkrOzc8GNSSR48ODBRwelajy1RCUpNT0Lcw/fxrrAhwAAGxNd/NLFHa1q2qg4MiKiksWbRioJExlShYuRCZiw8zoin6cCADrXtcfUjrVgYaij4siIiEpGicyRIaLi4e1sgUPfN8VXzapAQwLsDX2KTxeewf7rT/Pc14yIiHIoPCIjhMCOHTtw6tSpfO9+vWvXLqUGqAockSFVu/YoEeN3XMPduBQAQJtaNpjZuTasTfRUHBkRUfEpkRGZUaNGoW/fvoiMjISRkRFMTU3lHkT08TwczPDPiCYY2bIqtDQkOHIzDq0WnsGOkMccnSEieofCIzIWFhbYuHEj2rdvX1wxqRxHZKg0ufU0GT/svIYbT3JW0/arVgGzu7mjopm+iiMjIlKuEhmRMTU1RZUqVRQOjoiKpqa9CfZ82xg/tK0OHS0NnLn7DK0XnsHGoIeQSjk6Q0Tlm8KJzLRp0zB9+nS8fv26OOIhonxoaWrg2+auODiyKbwczZGakY3Je27Af1UQot5e5UREVB4pfGrp9evX6Nq1K86fPw8nJ6c8C+JduXJFqQGqAk8tUWmWLRVYHxiFuYfv4HVmNvS0NTCudXUMbOwMTQ2JqsMjIiqyonz/Kryyb//+/RESEoIvv/wSNjY2kEj4wUlUkjQ1JBjY2Bkt3Wwwcdd1XLj/ArMOhONAWAzmdq+DqjbGqg6RiKjEKDwiY2hoiCNHjqBJkybFFZPKcUSG1IUQAlsuPcIvB8KRkp4FHU0NjGzpiq/8XKCtyWWiiEi9lMhkXwcHB365E5USEokE/t6VcWxMM7SoXgEZ2VLMP3oXnf84jxtPklQdHhFRsVM4kVmwYAF++OEHREVFFUM4RFQUdqb6+GtAAyzq5QEzA23ciklG52XnMf/IHaRnZas6PCKiYqPwqSVzc3OkpaUhKysLBgYGeSb7JiQkKDVAVeCpJVJnz16lY+q+GzgYFgsAcLU2wtzP66BeZXMVR0ZE9H4lMtl38eLFiu5CRCWogrEu/veFFw6FxWDK3huIiE9B9+UXMLixM8a2rg59HU1Vh0hEpDS8+3U+OCJDZcXL1AzM3H8Lu64+AQA4Whrg12514OtiqeLIiIjyKsr3b5ESmezsbOzZswfh4eEAgFq1aqFTp07Q1Cwbf+kxkaGy5tTtePy4OwwxSW8AAF/4VMbEdm4w1tP+wJ5ERCWnRBKZiIgItG/fHk+ePEH16tUBAHfu3IGDgwMOHDgAFxcXxSMvZZjIUFn06k0m5hy6jb+DowEA9qZ6mN3NHc2rW6s4MiKiHCVy+fXIkSPh4uKCR48e4cqVK7hy5Qqio6Ph7OyMkSNHKtTW2bNn0bFjR9jb20MikWDPnj1y23ft2oXWrVvD0tISEokEoaGhhWp3+/btcHNzg56eHtzd3XHw4EGF4iIqi4z1tDG7qzv+HuqDyhYGeJr0BgPWXsLYbdeQmJah6vCIiIpE4UTmzJkzmDt3LiwsLGRllpaW+PXXX3HmzBmF2kpNTYWHhweWLVtW4PYmTZrgt99+K3SbFy5cgL+/PwYPHoyrV6+iS5cu6NKlC27cuKFQbERlVSMXKxwe1RSDGjtDIgF2XnmMTxedxeEbsaoOjYhIYQqfWrKwsMD+/fvRqFEjufLz58+jY8eORb78WiKRYPfu3ejSpUuebVFRUXB2dsbVq1dRt27d97bTq1cvpKamYv/+/bKyhg0bom7dulixYkWhYuGpJSovQh4m4Icd13H/Wc6NJzvUscP0TrVgZaSr4siIqDwqkVNLn332GYYNG4bg4GAIISCEQFBQEL7++mt06tRJ4aCVLTAwEK1atZIra9OmDQIDAwvcJz09HcnJyXIPovLAy9ECB0Y2xbfNXaCpIcGB6zH4dOEZ7A19Al7QSETqQOFEZunSpXBxcYGvry/09PSgp6eHxo0bw9XVFUuWLCmOGBUSGxsLGxsbuTIbGxvExhY8bD5nzhyYmprKHg4ODsUdJlGpoaetiR/aumHv8MZwszXGy7RMfL8lFEPXX0bs26uciIhKK4UTGTMzM+zduxd3797Fjh07sGPHDty5cwe7d++GqalpccRY7CZNmoSkpCTZ49GjR6oOiajE1a5oin3fNcGYT6tBW1OC4+Hx+HTRGWy9FM3RGSIqtRRe2TeXq6srXF1dlRmLUtja2iIuLk6uLC4uDra2tgXuo6urC11dzgkg0tHSwMiWVdGmli1+2HEN1x4nYcLOMPxzLQZzurnDwcJA1SESEclReESme/fu+V5FNHfuXPTo0UMpQX0MX19fnDhxQq7s2LFj8PX1VVFEROqnuq0xdn7TCD+2d4Oulgb+jXiONovPYt2FKEilHJ0hotJD4UTm7NmzaN++fZ7ydu3a4ezZswq1lZKSgtDQUNn6MJGRkQgNDUV0dM6CXQkJCQgNDcWtW7cA5Cy8FxoaKjffpV+/fpg0aZLs+ffff4/Dhw9jwYIFuH37NqZNm4bLly/ju+++U7SrROWalqYGhjVzweFRzeDtZIG0jGxM3XcTvVYG4sGzFFWHR0QEoAiJTEpKCnR0dPKUa2trK3y1z+XLl+Hp6QlPT08AwJgxY+Dp6Ymff/4ZALBv3z54enqiQ4cOAIDevXvD09NT7jLq6OhoxMTEyJ43atQIf//9N1auXAkPDw/s2LEDe/bsQe3atRXtKhEBcLYyxJZhDTGzcy0Y6GjiUtRLtFtyDn+euY+sbKmqwyOick7hdWS8vb3x2WefyZKNXNOmTcM///yDkJAQpQaoClxHhih/jxLS8OPuMJy79xwAUKeSKeZ+Xgdutvw9IaKPV5TvX4Un+06ZMgXdunXD/fv38cknnwAATpw4gc2bN2P79u2KNkdEasTBwgDrB3lje8hjzNx/C9cfJ6Hj7//iuxZV8U1zF+hoKTzIS0T0UYp09+sDBw5g9uzZCA0Nhb6+PurUqYOpU6fCz8+vOGIscRyRIfqwuOQ3+Gn3DRwPz7lK0M3WGPM+94B7JfVchoGIVK9E7n5dHjCRISocIQT+uR6DaftuIiE1A5oaEgxtWgWjWlWFnramqsMjIjVTIrcoICLKJZFI0MnDHsdGN0NHD3tkSwVWnLmP9kvP4XJU0e67RkSkCCYyRPTRLI108bu/J1b29YK1sS4ePEtFjz8DMW3fTaRlZKk6PCIqw5jIEJHStK5li2Oj/dCzfiUIAQRciEKbxWdxPuK5qkMjojKKiQwRKZWpgTbmfu6B9YO8UdFMH48SXuOL1cGYtOs6kt9kqjo8IipjipzIZGRk4M6dO8jK4rAxEeXVrFoFHBndDP18HQEAmy8+QuuFZ3HydtwH9iQiKjyFE5m0tDQMHjwYBgYGqFWrlux2AiNGjMCvv/6q9ACJSH0Z6WphRufa2DqsIZwsDRCb/AaDAi5j9NZQvEzNUHV4RFQGKJzITJo0CdeuXcPp06ehp6cnK2/VqhW2bt2q1OCIqGzwqWKJQ983w7BmVaAhAXZffYJPF53BwbCYD+9MRPQeCicye/bswR9//IEmTZpAIpHIymvVqoX79+8rNTgiKjv0dTTxY/sa2PVtY1SzMcLzlAx8u+kKvtkYgvhXb1QdHhGpKYUTmWfPnsHa2jpPeWpqqlxiQ0SUn7oOZvhnRBOM/MQVWhoSHLoRi08XnsWuK4/B9TmJSFEKJzL169fHgQMHZM9zk5fVq1fD19dXeZERUZmlq6WJMa2rY993TVDL3gRJrzMxZts1DAq4hKeJr1UdHhGpEYVvGjl79my0a9cOt27dQlZWFpYsWYJbt27hwoULOHPmTHHESERlVE17E+wZ3hgrzz7AkuP3cOrOM7RedBY/tq8Bf28HjvIS0QcpPCLTpEkThIaGIisrC+7u7jh69Cisra0RGBgILy+v4oiRiMowbU0NDG/hioPfN0G9ymZISc/Cj7vD0GdVMB6+SFV1eERUyvGmkfngTSOJVCNbKhBwIQrzjtzGm0wp9LQ1ML6NGwY0coKmBkdniMq6Erv7tVQqRUREBOLj4yGVSuW2NWvWTNHmSh0mMkSq9fBFKibuDEPggxcAgHqVzbCwZ104WRmqODIiKk4lksgEBQWhT58+ePjwYZ4rDCQSCbKzsxVprlRiIkOkelKpwJZLjzD7YDhS0rNgZaSLLcN84GptrOrQiKiYFOX7V+E5Ml9//TXq16+PGzduICEhAS9fvpQ9EhISFA6aiCg/GhoS9PGpjKOjm6GmnQmep6Sj98pgRMSnqDo0IipFFB6RMTQ0xLVr1+Dq6lpcMakcR2SISpeXqRnoszoY4THJqGCsiy3DGsKlgpGqwyIiJSuRERkfHx9EREQoHBwRUVGZG+pg0xAfuNka49mrdPivDML9ZxyZIaJCjshcv35d9v/79+9j8uTJGD9+PNzd3aGtrS1Xt06dOsqPsoRxRIaodEpIzUCfVUG4HfsK1m9HZqpwZIaozCi2yb4aGhqQSCQFLh+eu42TfYmouL1ISUefVcG4E/cKNia62DLMF868momoTCi2RObhw4eFDsLR0bHQdUsrJjJEpdvzlHT0WRWEu3EpsDXRw5ZhDXlpNlEZUGxzZBwdHWWPhw8fomLFinJljo6OqFixokIJDxFRUVkZ6eLvoQ1R1doIsclv4L8qiKsAE5VTCk/2bdGiRb6XWSclJaFFixZKCYqI6EPeTWZikt6g90omM0TlkcKJTO5cmP968eIFDA05tEtEJaeCcU4y4/o2mfFfGYToF2mqDouISlCh737drVs3ADkTewcMGABdXV3ZtuzsbFy/fh2NGjVSfoRERO+Rk8z4vL0kOxX+q4KwZVhDOFgYqDo0IioBhR6RMTU1hampKYQQMDY2lj03NTWFra0thg0bho0bNxZnrERE+bI21sPmoQ1RpYIhniS+Ru+VQXiUwJEZovJA4ZV9p0+fjnHjxpXp00i8aolIPcUn58yVefA8FRXN9DkyQ6RmSuzu12UdExki9RX3NpmJfJ6KSuY5yUwlcyYzROqgRG5RQERUmtmY5JxmcrYyxOOXr+G/KghPEl+rOiwiKiZMZIiozLE1zUlmnCwN8CjhNXqvDMRTJjNEZRITGSIqk2xN9bB5WEM4ypKZICYzRGUQExkiKrPsTPWxeWhDVLYwQHRCGvxXBSEmickMUVmi8GTfpUuX5t+QRAI9PT24urqiWbNm0NTU/GBbZ8+exbx58xASEoKYmBjs3r0bXbp0kW0XQmDq1KlYtWoVEhMT0bhxYyxfvhxVq1YtsM1p06Zh+vTpcmXVq1fH7du3C9dBcLIvUVnz9O0l2dEJaXCyNMCWYb6wNdVTdVhE9B9F+f4t9IJ4uRYtWoRnz54hLS0N5ubmAICXL1/CwMAARkZGiI+PR5UqVXDq1Ck4ODi8t63U1FR4eHhg0KBBsgX33jV37lwsXboU69atg7OzM6ZMmYI2bdrg1q1b0NMr+EOoVq1aOH78+P93UkvhbhJRGWJvpo/Nwxqi98pARL1IQ++VgUxmiMoIhU8tzZ49Gw0aNMC9e/fw4sULvHjxAnfv3oWPjw+WLFmC6Oho2NraYvTo0R9sq127dpg1axa6du2aZ5sQAosXL8bkyZPRuXNn1KlTB+vXr8fTp0+xZ8+e97arpaUFW1tb2cPKykrRbhJRGVPRLOc0UyVzfUS9yDnNFJf8RtVhEdFHUjiRmTx5MhYtWgQXFxdZmaurK+bPn49JkyahUqVKmDt3Ls6fP/9RgUVGRiI2NhatWrWSlZmamsLHxweBgYHv3ffevXuwt7dHlSpV8MUXXyA6Ovq99dPT05GcnCz3IKKyp5K5ATYPbYiKZvqIfJ4K/5VMZojUncKJTExMDLKysvKUZ2VlITY2FgBgb2+PV69efVRguW3Z2NjIldvY2Mi25cfHxwcBAQE4fPgwli9fjsjISDRt2vS98cyZM0fulgsfOiVGROrLwcIAW4blJDMPnufcmymeyQyR2lI4kWnRogW++uorXL16VVZ29epVfPPNN/jkk08AAGFhYXB2dlZelApo164devTogTp16qBNmzY4ePAgEhMTsW3btgL3mTRpEpKSkmSPR48elWDERFTS5JKZtzeajH/FZIZIHSmcyKxZswYWFhbw8vKCrq4udHV1Ub9+fVhYWGDNmjUAACMjIyxYsOCjArO1tQUAxMXFyZXHxcXJthWGmZkZqlWrhoiIiALr6OrqwsTERO5BRGWbg0XOaSZ7U72cu2avDMKzV+mqDouIFKRwImNra4tjx47h1q1b2L59O7Zv345bt27h6NGjstNALVq0QOvWrT8qMGdnZ9ja2uLEiROysuTkZAQHB8PX17fQ7aSkpOD+/fuws7P7qHiIqOyp/PZSbLvcZGYVkxkidVPkBfHc3NzQqVMndOrUCdWrVy9SGykpKQgNDUVoaCiAnAm+oaGhiI6OhkQiwahRozBr1izs27cPYWFh6NevH+zt7eXWmmnZsiX++OMP2fNx48bhzJkziIqKwoULF9C1a1doamrC39+/qF0lojIsJ5lpCDtTPUTEp6DPqiA8T2EyQ6QuFF5gJTs7GwEBAThx4gTi4+MhlUrltp88ebLQbV2+fBktWrSQPR8zZgwAoH///ggICMAPP/yA1NRUDBs2DImJiWjSpAkOHz4st4bM/fv38fz5c9nzx48fw9/fHy9evECFChXQpEkTBAUFoUKFCop2lYjKCUdLQ2we2hC9Vwbh3ttk5u+hDWFlpKvq0IjoAxRe2fe7775DQEAAOnToADs7O0gkErntixYtUmqAqsCVfYnKp8jnqei9MhBxyemobmOMv4f6wJLJDFGJKcr3r8KJjJWVFdavX4/27dsXKUh1wESGqPyKfJ6KXn8GIv5VOtxsjbFpCJMZopJSlO9fhefI6OjowNXVVeHgiIjUgbOVIbYMawhrY13cjn2FL1YHIyE1Q9VhEVEBFE5kxo4diyVLlkDBgRwiIrVRpYIRNg9riApvk5k+q4LwkskMUamk8Kmlrl274tSpU7CwsECtWrWgra0tt33Xrl1KDVAVeGqJiAAgIj5Fdkl2DTsT/D3EB+aGOqoOi6jMKpFTS2ZmZujatSv8/PxgZWUlt7S/qampwkETEZVWrtZG2Pz26qXwmGR8sToYiWkcmSEqTRQekSkPOCJDRO+KiH+F3iuD8DwlA7XsTbBpiA/MDDgyQ6RsJTIiQ0RU3rhaG78dmdHBzafJ+HINR2aISotCjcjUq1cPJ06cgLm5OTw9PfOsHfOuK1euKDVAVeCIDBHl527cK/ivDMKL1Ay4VzTFxsE+MDXQ/vCORFQoRfn+LdTKvp07d4aubs46Cu/eHoCIqDypZmOMv4c2RJ9VQQh7koS+fwVjw2AfmOozmSFSFc6RyQdHZIjofW7HJqPPqpz1ZTwqmWI9kxkipeAcGSKiEuBmmzPh19xAG9ceJ6HfXxeR/CZT1WERlUuFGpExNzd/77yYdyUkJHx0UKrGERkiKozwmOScxfLSMlHXwQzrB3vDRI8jM0RFVWxzZBYvXiz7/4sXLzBr1iy0adMGvr6+AIDAwEAcOXIEU6ZMUTxqIiI1VcPOBJuGNESf1UEIfZSI/n9dxPpB3jBmMkNUYhSeI9O9e3e0aNEC3333nVz5H3/8gePHj2PPnj3KjE8lOCJDRIq4+TTp7WJ5mahX2QzrmMwQFUmJzJE5cuQI2rZtm6e8bdu2OH78uKLNERGpvVr2by/F1tfGlehEDFh7CSnpWaoOi6hcUDiRsbS0xN69e/OU7927F5aWlkoJiohI3dSuaIpNQ3KSmZCHL9H/r4tMZohKQKHmyLxr+vTpGDJkCE6fPg0fHx8AQHBwMA4fPoxVq1YpPUAiInVR++0ieV+sDkLIw5cY8NdFBAzyhpGuwh+1RFRICo/IDBgwAOfPn4eJiQl27dqFXbt2wcTEBP/++y8GDBhQDCESEakP90qm2DjEByZ6Wrj88CUGrr2IVI7MEBUbLoiXD072JaKPde1RIr5cE4xXb7Lg7WSBtQMbwJAjM0TvVZTv30IlMsnJyYUOoix88TORISJlCH2UiL6rg/EqPQvezhYIGNgABjpMZogKUmyJjIaGRqEXxMvOzi5UvdKMiQwRKcu7yUzDKhb4awCTGaKCFNuCeKdOnZL9PyoqChMnTsSAAQPkFsRbt24d5syZU4SwiYjKrtwVf/uuuYigBwkYHHAZfw1oAH0dTVWHRlQmKDxHpmXLlhgyZAj8/f3lyv/++2+sXLkSp0+fVmZ8KsERGSJStivRL9FvTc4l2b5VLJnMEOWjRBbECwwMRP369fOU169fHxcvXlS0OSKicqFeZXOsG+QNQx1NBD54gcHrLuF1hvqfiidSNYUTGQcHh3zXi1m9ejUcHByUEhQRUVnk5WiO9YNzkpkL919g6PrLeJPJZIboYyh8aungwYPo3r07XF1dZQviXbx4Effu3cPOnTvRvn37Ygm0JPHUEhEVp8tRCej310WkZWSjaVUrrOpXH3raPM1EVCKnltq3b4979+6hU6dOSEhIQEJCAjp27Ii7d++WiSSGiKi41XeywLpB3jDQ0cS5e885MkP0EbggXj44IkNEJeFiZAIGrM0ZmWlWrQJW9vXiyAyVa8W2jkx+0tLSEB0djYyMDLnyOnXqFKW5UoWJDBGVlOAHLzBg7SW8zsyGX7UK+JPJDJVjJZLIPHv2DAMHDsShQ4fy3c4F8YiIFBP04AUGvk1mmlfPSWZ0tZjMUPlTInNkRo0ahcTERAQHB0NfXx+HDx/GunXrULVqVezbt0/hoImIyruGb9eV0dPWwOk7z/D1hhCkZ6n/H4VEJUHhRObkyZNYuHAh6tevDw0NDTg6OuLLL7/E3LlzubIvEVER+br8fzJz6s4zfLPxCpMZokJQOJFJTU2FtbU1AMDc3BzPnj0DALi7u+PKlSvKjY6IqBxp5GKFv/o3gK6WBk7ejse3TGaIPkjhRKZ69eq4c+cOAMDDwwN//vknnjx5ghUrVsDOzk7pARIRlSeNXK3w14CcZObE7XgM33QFGVlSVYdFVGopnMh8//33iImJAQBMnToVhw4dQuXKlbF06VLMnj1b6QESEZU3jV2tsObtyMzx8HgM/5vJDFFBFE5kvvzySwwYMAAA4OXlhYcPH+LSpUt49OgRevXqpVBbZ8+eRceOHWFvbw+JRII9e/bIbRdC4Oeff4adnR309fXRqlUr3Lt374PtLlu2DE5OTtDT04OPjw/vAUVEaqfJ2xV/dbQ0cOxWHL5jMkOUL4UTmf8yMDBAvXr1YGVlpfC+qamp8PDwwLJly/LdPnfuXCxduhQrVqxAcHAwDA0N0aZNG7x586bANrdu3YoxY8Zg6tSpuHLlCjw8PNCmTRvEx8crHB8RkSo1q1YBq98mM0dvxWHE5ivIzGYyQ/SuUrOyr0Qiwe7du9GlSxcAOaMx9vb2GDt2LMaNGwcASEpKgo2NDQICAtC7d+982/Hx8UGDBg3wxx9/AACkUikcHBwwYsQITJw4sVCxcB0ZIipNztx9hqHrLyMjS4q2tWzxex9PaGt+9N+hRKVOiawjU1IiIyMRGxuLVq1aycpMTU3h4+ODwMDAfPfJyMhASEiI3D4aGhpo1apVgfsAQHp6OpKTk+UeRESlhd/b2xfoaGrg8M1YjNx8lSMzRG+V2kQmNjYWAGBjYyNXbmNjI9v2X8+fP0d2drZC+wDAnDlzYGpqKns4ODh8ZPRERMrVvLo1/nybzBy6EYvvtzCZIQIUTGSysrIwY8YMPH78uLjiUYlJkyYhKSlJ9nj06JGqQyIiyqOFmzVW9K0HHU0NHAyLxagtochiMkPlnEKJjJaWFubNm4esrKziikfG1tYWABAXFydXHhcXJ9v2X1ZWVtDU1FRoHwDQ1dWFiYmJ3IOIqDT6xM0Gy7+sB21NCQ6ExWDUViYzVL4pfGrpk08+wZkzZ4ojFjnOzs6wtbXFiRMnZGXJyckIDg6Gr69vvvvo6OjAy8tLbh+pVIoTJ04UuA8RkbppWcMGy7/wgramBPuvx2D0tmtMZqjc0lJ0h3bt2mHixIkICwuDl5cXDA0N5bZ36tSp0G2lpKQgIiJC9jwyMhKhoaGwsLBA5cqVMWrUKMyaNQtVq1aFs7MzpkyZAnt7e9mVTQDQsmVLdO3aFd999x0AYMyYMejfvz/q168Pb29vLF68GKmpqRg4cKCiXSUiKrVa1bTB/77wwrebQvDPtaeQAFjY0wNavJqJyhmFE5lvv/0WALBw4cI82yQSCbKzC39fkMuXL6NFixay52PGjAEA9O/fHwEBAfjhhx+QmpqKYcOGITExEU2aNMHhw4ehp6cn2+f+/ft4/vy57HmvXr3w7Nkz/Pzzz4iNjUXdunVx+PDhPBOAiYjU3ac1bbCsTz18u+kK9l17CokEWNizLjQ1JKoOjajElJp1ZEoTriNDROrkyM1YDN90BVlSga6eFTG/hweTGVJLJb6OzPtW2CUiopLRppYt/uhTD1oaEuy++gTjt19DtpR/o1L5oHAik52djZkzZ6JixYowMjLCgwcPAABTpkzBmjVrlB4gERF9WNvatvjd3xOaGhLsuvoE43cwmaHyQeFE5pdffkFAQADmzp0LHR0dWXnt2rWxevVqpQZHRESF187d7v+TmStP8MOO60xmqMxTOJFZv349Vq5ciS+++AKampqycg8PD9y+fVupwRERkWLau9thae+cZGbnlceYuPM6pExmqAxT+KqlJ0+ewNXVNU+5VCpFZmamUoIiIqKi61DHDgIC328JxfaQx5BIgF+71YEGJwBTGaTwiEzNmjVx7ty5POU7duyAp6enUoIiIqKP81kdeyzuVRcaEmDb5ceYtCuMIzNUJik8IvPzzz+jf//+ePLkCaRSKXbt2oU7d+5g/fr12L9/f3HESERERdDRwx4CwKgtV7H18iNIJMDsru4cmaEyReERmc6dO+Off/7B8ePHYWhoiJ9//hnh4eH4559/8OmnnxZHjEREVESdPOyx6O3IzJZLj/DTnhscmaEyhQvi5YML4hFRWbPn6hOM2RYKqQD6+FTGrM61OTJDpU6JLIhXpUoVvHjxIk95YmIiqlSpomhzRERUArp4VsSCnh6QSIC/g6MxZe8N8O9YKgsUTmSioqLyvZ9Seno6njx5opSgiIhI+bp6VsKCHjnJzCYmM1RGFHqy7759+2T/P3LkCExNTWXPs7OzceLECTg5OSk1OCIiUq5u9SpBKoDxO65hY1A0NCQSTO9UCxIJTzOReip0ItOlSxfZ//v37y+3TVtbG05OTliwYIHSAiMiouLxuVclCCHww87rWB/4EBIA05jMkJoqVCJz/fp1ZGZmQlNTE87Ozrh06RKsrKyKOzYiIiomPeo7QACYsPM61gU+hEQiwdSONZnMkNop1BwZT09PJCQkAAAkEgl/0ImIyoCe9R3wW7c6AICAC1GYsf8W58yQ2ilUImNmZia7y/XDhw8hlUqLNSgiIioZPRs44Lfu7gCAteejMHN/OJMZUiuFOrXUvXt3+Pn5wc7ODgBQv359uRtGvis34SEiIvXQq0FlSAUwaVcY/jofCQ0J8FOHGhx9J7VQqERm5cqV6NatGyIiIjBy5EgMHToUxsbGxR0bERGVEH/vyhAC+HF3GFb/GwmJBPixPZMZKv0KfdVS27ZtAQAhISH4/vvvmcgQEZUxfXwqQ0Dgp903sOpcJCQSCSa1c2MyQ6WawjeNXLt2bXHEQUREpcAXPo6QCmDKnhtYefYBJBJgYlsmM1R6KZzIAMDly5exbds2REdHIyMjQ27brl27lBIYERGpRt+GjoAQmLL3Jv488wASSDChbXUmM1QqKXyLgi1btqBRo0YIDw/H7t27kZmZiZs3b+LkyZNyq/0SEZH66uvrhBmdawEAVpy5j3lH7vBqJiqVFE5kZs+ejUWLFuGff/6Bjo4OlixZgtu3b6Nnz56oXLlyccRIREQq0M/XCdM75SQz/zt9H/OPMpmh0kfhROb+/fvo0KEDAEBHRwepqamQSCQYPXo0Vq5cqfQAiYhIdfo3csLUjjUBAMtO3cfCY3eZzFCponAiY25ujlevXgEAKlasiBs3bgAAEhMTkZaWptzoiIhI5QY2dsbPn+UkM7+fjMAiJjNUiig82bdZs2Y4duwY3N3d0aNHD3z//fc4efIkjh07hpYtWxZHjEREpGKDmjhDKgRmHQjH0pMROSPxn1ZTdVhEiicyf/zxB968eQMA+Omnn6CtrY0LFy6ge/fumDx5stIDJCKi0mFI0yoAgFkHwrHkxD1IJMCoVkxmSLUkguODeSQnJ8PU1BRJSUkwMTFRdThERKXKqrMP8MvBcADA6FbV8H2rqiqOiMqKonz/KjxHhoiIyrehzapgUjs3AMCi43fx+4l7Ko6IyjMmMkREpLCv/Fww8W0ys+DYXfxxkskMqQYTGSIiKpKv/VwwoW1OMjP/6F0sOxWh4oioPGIiQ0RERfZNcxeMb1MdADDvyB387zSTGSpZCicygwYNkq0j867U1FQMGjRIKUEREZH6GN7CVZbMzD18ByvO3FdxRFSeKJzIrFu3Dq9fv85T/vr1a6xfv14pQRERkXoZ3sIVY9+uK/Prodv4k8kMlZBCryOTnJwMIQSEEHj16hX09PRk27Kzs3Hw4EFYW1sXS5BERFT6jWhZFQLAwmN3MefQbUgkwLBmLqoOi8q4Qo/ImJmZwcLCAhKJBNWqVYO5ubnsYWVlhUGDBmH48OFKD/DVq1cYNWoUHB0doa+vj0aNGuHSpUsF1j99+jQkEkmeR2xsrNJjIyIieSNbVsWot+vKzD54G6vPPVBxRFTWFXpE5tSpUxBC4JNPPsHOnTthYWEh26ajowNHR0fY29srPcAhQ4bgxo0b2LBhA+zt7bFx40a0atUKt27dQsWKFQvc786dO3KL6XC0iIioZIxqVQ1CAEtO3MOsAzkL5+WuCkykbAqv7Pvw4UNUrlwZEomkuGKSef36NYyNjbF3717ZHbcBwMvLC+3atcOsWbPy7HP69Gm0aNECL1++hJmZWZFelyv7EhF9HCEEFh27i6Unc65imvJZTQxu4qziqKi0K5GVfU+ePIkdO3bkKd++fTvWrVunaHPvlZWVhezsbLn5OACgr6+Pf//997371q1bF3Z2dvj0009x/vz599ZNT09HcnKy3IOIiIou96aSIz5xBQDM3H8La89HqjgqKosUTmTmzJkDKyurPOXW1taYPXu2UoLKZWxsDF9fX8ycORNPnz5FdnY2Nm7ciMDAQMTExOS7j52dHVasWIGdO3di586dcHBwQPPmzXHlypUCX2fOnDkwNTWVPRwcHJTaDyKi8kgikWDMp9XwXYucZGb6P7cQwGSGlEzhU0t6enq4ffs2nJyc5MqjoqJQo0aNfC/N/hj379/HoEGDcPbsWWhqaqJevXqoVq0aQkJCEB4eXqg2/Pz8ULlyZWzYsCHf7enp6UhPT5c9T05OhoODA08tEREpgRDi7WJ5OZdkT+9UC/0bOak2KCqVSuTUkrW1Na5fv56n/Nq1a7C0tFS0uQ9ycXHBmTNnkJKSgkePHuHixYvIzMxElSqFnzjm7e2NiIiCV5vU1dWFiYmJ3IOIiJRDIpFgfJvq+KZ5zqXYU/fdxPrAKNUGRWWGwomMv78/Ro4ciVOnTiE7OxvZ2dk4efIkvv/+e/Tu3bs4YgQAGBoaws7ODi9fvsSRI0fQuXPnQu8bGhoKOzu7YouNiIjeTyKR4Ic21fGVX84foT/vvYkNQQ9VHBWVBYW+/DrXzJkzERUVhZYtW0JLK2d3qVSKfv36KX2ODAAcOXIEQghUr14dERERGD9+PNzc3DBw4EAAwKRJk/DkyRPZqsKLFy+Gs7MzatWqhTdv3mD16tU4efIkjh49qvTYiIio8CQSCSa2dQME8OfZB5iy5wYkAL5s6Kjq0EiNKZzI6OjoYOvWrZg5cyauXbsGfX19uLu7w9GxeH4Qk5KSMGnSJDx+/BgWFhbo3r07fvnlF2hrawMAYmJiEB0dLaufkZGBsWPH4smTJzAwMECdOnVw/PhxtGjRoljiIyKiwpNIJJjYzg1SIbDqXCQm77kBDYkEfXwqqzo0UlMKT/bNlZGRgcjISLi4uMhGZsoKriNDRFS8hBD45UA4Vv+bcxXTnG7u8PdmMlPelchk37S0NAwePBgGBgaoVauWbDRkxIgR+PXXXxVtjoiIyiGJRIKfOtSQLZI3aVcYtlyM/sBeRHkpnMhMmjQJ165dw+nTp+UWqmvVqhW2bt2q1OCIiKjskkgkmNyhBgY2dgIATNwVhm2XHqk2KFI7Cp8T2rNnD7Zu3YqGDRvK3aagVq1auH+ft20nIqLCk0gk+PmzmhACCLgQhQm7rgMSoGd9LkxKhaNwIvPs2bN8b8CYmppaIvdfIiKiskUikWBqx5oA3iYzO69DAqAHkxkqBIVPLdWvXx8HDhyQPc9NXlavXg1fX1/lRUZEROVGbjLTz9cRQgA/7LyOHSGPVR0WqQGFR2Rmz56Ndu3a4datW8jKysKSJUtw69YtXLhwAWfOnCmOGImIqByQSCSY3qkWhAA2BD3E+B3XIAHQ3auSqkOjUkzhEZkmTZogNDQUWVlZcHd3x9GjR2FtbY3AwEB4eXkVR4xERFROSCQSzOhcC182rAwhgHE7rmH3VY7MUMEKNSIzZswYzJw5E4aGhjh79iwaNWqEVatWFXdsRERUDkkkEszoVBtSAfwdHI2x265BAgm6eFZUdWhUChVqROb3339HSkoKAKBFixZISEgo1qCIiKh809CQYFbn2vD3rgypAMZsC8Xe0CeqDotKoUKNyDg5OWHp0qVo3bo1hBAIDAyEubl5vnWbNWum1ACJiKh80tCQ4JcutQEIbL74CKO3hgIAOtflyAz9v0LdomDPnj34+uuvER8fD4lEgoJ2kUgkyM7OVnqQJY23KCAiKj2kUoEfd4dhy6VH0JAAi3t7opOHvarDomJQlO9fhe61lJKSAhMTE9y5cyfftWQAwNTUtLDNlVpMZIiIShepVGDiruvYdvkxNCTAkt6e6MhkpswptnstjRkzBqmpqTAyMsKpU6fg7OwMU1PTfB9ERETKpqEhwa/d6qCHVyVIBTBqaygOXI9RdVhUCig82feTTz7hZF8iIipxGhoS/Na9Dj73qoRsqcDILVdxMIzJTHnHyb5ERKQ2cpMZqRDYdeUJRmy+CgmAdu52qg6NVISTffPBOTJERKVbtlRg/PZr2HX1CbQ0JPijjyfa1mYyo+442VdJmMgQEZV+2VKBcduvYbcsmamHtrVtVR0WfYSifP8qdK+ldyf7amkpfJsmIiIipdHUkGB+Dw8IIbAn9Cm++/sKln1RD21qMZkpTwp9r6Vt27YhIyMDfn5+0NLSwuPHjyGVSmXb09LSMHfu3GIJkoiIKD+aGhIs6FkXnevaI0sqMHzTFRy9GavqsKgEFTqR8ff3R2Jioux5zZo1ERUVJXv+6tUrTJo0SZmxERERfZCmhgQLenigo8fbZObvKzh+K07VYVEJKXQi89+pNApMrSEiIipWWpoaWNTTA5/VsUNmtsA3m0JwIpzJTHlQ6ESGiIioNNPS1MDiXnXRITeZ2XgFJ28zmSnrmMgQEVGZoaWpgSW96qKDux0ysqX4esMVnLodr+qwqBgpdOnRkSNHZJdXS6VSnDhxAjdu3AAAufkzREREqqKlqYHFvetCKgQO3YjFVxtC8Gc/L7Sonv+yIaTeCr2OjIbGhwdvuCAeERGVFpnZUozcfBWHbsRCR0sDK/t6oTmTmVKt2G4aCeSMwHzoURaSGCIiKhu0NTWw1N8TbWrZICNLimEbQnDm7jNVh0VKxjkyRERUZmlrauB3/3poXTMnmRm6/jLOMpkpU5jIEBFRmaajpYE/+tTDp+8kM+fuMZkpK5jIEBFRmaejpYFlfeqhVQ0bpGdJMWTdZfx777mqwyIlYCJDRETlgo6WBv73RT20qmGN9CwpBq+7hPMRTGbUHRMZIiIqN3S0NLDsi3r4xO3/k5kLTGbUGhMZIiIqV3S1NLH8y5xk5k2mFIPWXULg/ReqDouKqFDryJibm0MikRSqwYSEhI8OStW4jgwRUdmXnpWNrzeE4NSdZ9DX1sTagQ3QsIqlqsMq14ry/VuolX0XL14s+/+LFy8wa9YstGnTBr6+vgCAwMBAHDlyBFOmTFE8aiIiIhXIGZnxwldv15cZuPYSkxk1VOiVfXN1794dLVq0wHfffSdX/scff+D48ePYs2ePMuNTCY7IEBGVH28yszFsQwjO3s0ZmQkY2AA+TGZUolhX9s115MgRtG3bNk9527Ztcfz4cUWb+6BXr15h1KhRcHR0hL6+Pho1aoRLly69d5/Tp0+jXr160NXVhaurKwICApQeFxERlQ162ppY2dcLTata4XVmNgYGXMLFSPWfJlFeKJzIWFpaYu/evXnK9+7dC0tL5WewQ4YMwbFjx7BhwwaEhYWhdevWaNWqFZ48eZJv/cjISHTo0AEtWrRAaGgoRo0ahSFDhuDIkSNKj42IiMoGPW1NrOpXH02rWiEtIxsD1l7EpSgmM+pA4VNLAQEBGDJkCNq1awcfHx8AQHBwMA4fPoxVq1ZhwIABSgvu9evXMDY2xt69e9GhQwdZuZeXF9q1a4dZs2bl2WfChAk4cOCA7K7cANC7d28kJibi8OHDhXpdnloiIiqf3mRm5yyWF/EchjqaWDfIG/WdLFQdVrlRIqeWBgwYgPPnz8PExAS7du3Crl27YGJign///VepSQwAZGVlITs7G3p6enLl+vr6+Pfff/PdJzAwEK1atZIra9OmDQIDAwt8nfT0dCQnJ8s9iIio/MkdmWnsaonUjGz0/+siQh5yZKY0K9I6Mj4+Pti0aROuXLmCK1euYNOmTbLRGWUyNjaGr68vZs6ciadPnyI7OxsbN25EYGAgYmJi8t0nNjYWNjY2cmU2NjZITk7G69ev891nzpw5MDU1lT0cHByU3hciIlIP+jqaWN2vARq55CYzlxDy8KWqw6ICFCmRuX//PiZPnow+ffogPj4eAHDo0CHcvHlTqcEBwIYNGyCEQMWKFaGrq4ulS5fC398fGhrKW8tv0qRJSEpKkj0ePXqktLaJiEj96OtoYk3/BvCtYomU9Cz0/+sirkQzmSmNFM4Gzpw5A3d3dwQHB2Pnzp1ISUkBAFy7dg1Tp05VeoAuLi44c+YMUlJS8OjRI1y8eBGZmZmoUqVKvvVtbW0RFxcnVxYXFwcTExPo6+vnu4+uri5MTEzkHkREVL7p62hizYD6aFjFIieZWXMRV5nMlDoKJzITJ07ErFmzcOzYMejo6MjKP/nkEwQFBSk1uHcZGhrCzs4OL1++xJEjR9C5c+d86/n6+uLEiRNyZceOHZMt3kdERFRYBjpa+GtAA/g4W+BVehb6rbmI0EeJqg6L3qFwIhMWFoauXbvmKbe2tsbz58q/8daRI0dw+PBhREZG4tixY2jRogXc3NwwcOBAADmnhfr16yer//XXX+PBgwf44YcfcPv2bfzvf//Dtm3bMHr0aKXHRkREZZ+BjhbWDmwA77fJTN81wbjGZKbUUDiRMTMzy3ei7dWrV1GxYkWlBPWupKQkDB8+HG5ubujXrx+aNGmCI0eOQFtbGwAQExOD6OhoWX1nZ2ccOHAAx44dg4eHBxYsWIDVq1ejTZs2So+NiIjKBwMdLawd0ADeThZ49SYLX64JxvXHiaoOi1CEdWTGjRuH4OBgbN++HdWqVcOVK1cQFxeHfv36oV+/fsUyT6akcR0ZIiLKT2p61tvF8l7CRE8Lm4Y0hHslU1WHVWaUyDoys2fPhpubGxwcHJCSkoKaNWuiWbNmaNSoESZPnqxw0EREROrCUFcLawd6o76jOZLfjszceJKk6rDKNYVHZHI9evQIYWFhSElJgaenJ6pWrars2FSGIzJERPQ+uZdkhzx8CVN9bWwa4oPaFTky87FKZERmxowZSEtLg4ODA9q3b4+ePXuiatWqeP36NWbMmKFw0EREROrGSFcLAQMboF5lMyS9zsSXa4Jx8ylHZlRB4REZTU1NxMTEwNraWq78xYsXsLa2RnZ2tlIDVAWOyBARUWG8epOJfn9dxNXoRJgZ5IzM1LLnyExRlciIjBACEokkT/m1a9dgYcEbaxERUflhrKeNdYO8UdfBDIlpmfhidTBuPeX9+kpSoRMZc3NzWFhYQCKRoFq1arCwsJA9TE1N8emnn6Jnz57FGSsREVGpY6KnjfWDveEhS2aCEB7DZKakFPrU0rp16yCEwKBBg7B48WKYmv7/0JmOjg6cnJzKzOq5PLVERESKSnqdiX5rgnHtcRIsDHXw91AfuNnyO0QRRfn+VXiOzJkzZ9CoUSPZgnRlERMZIiIqiqTXmei7JhjX3yYzm4c2RHVbY1WHpTZKZI6Mn5+fLIl58+YNkpOT5R5ERETllam+NjYM8oF7RVMkpGagz6og3I17peqwyjSFE5m0tDR89913sLa2hqGhIczNzeUeRERE5ZmpgTY2DvZB7YomePE2mbnHZKbYKJzIjB8/HidPnsTy5cuhq6uL1atXY/r06bC3t8f69euLI0YiIiK1kpvM1LI3wfOUDPgzmSk2Cs+RqVy5MtavX4/mzZvDxMQEV65cgaurKzZs2IDNmzfj4MGDxRVrieEcGSIiUobEtAz0WRWMWzHJsDLSxZZhPnC15pyZgpTIHJmEhARUqVIFAGBiYoKEhAQAQJMmTXD27FlFmyMiIiqzzAx0sGmID2rameB5Sjp6rwxGRHyKqsMqUxROZKpUqYLIyEgAgJubG7Zt2wYA+Oeff2BmZqbU4IiIiNSduWFOMlPjbTLjvyoI958xmVEWhROZgQMH4tq1awCAiRMnYtmyZdDT08Po0aMxfvx4pQdIRESk7nKTGTdbYzx7lQ7/lUxmlKXId7/O9fDhQ4SEhMDV1RV16tRRVlwqxTkyRERUHHIvyb4d+wrWxrrYMqwhqlQwUnVYpUaJLIhXHjCRISKi4vIiJR19VgXjTtwr2JjoYsswXzhbGao6rFKhxBKZS5cu4dSpU4iPj4dUKpXbtnDhQkWbK3WYyBARUXF6npL+drG8FNia6GHLsIZwYjJTpO9fLUVfZPbs2Zg8eTKqV68OGxsbuTth53dXbCIiIpJnZaSLv4c2hP/KINyLT4H/qiBsGdYQjpZMZhSl8IiMjY0NfvvtNwwYMKCYQlI9jsgQEVFJePYqZ2TmXnwK7Ez1yn0yUyLryGhoaKBx48YKB0dERETyKhjnjMy4WhshJukN/FcGIfpFmqrDUisKJzKjR4/GsmXLiiMWIiKicicnmfGBSwVDPE16A/9VQXiUwGSmsBQ+tSSVStGhQwfcvXsXNWvWlN0JO9euXbuUGqAq8NQSERGVtPjkN+i9KggPnqWiopk+tgxrCAcLA1WHVaJK5NTSyJEjcerUKVSrVg2WlpYwNTWVexAREZHirE30sGVoQ1SxMsSTxNfovZIjM4Wh8IiMsbExtmzZgg4dOhRXTCrHERkiIlKVuOQ36L0yCJHPU1HJPGdkppJ5+RiZKZERGQsLC7i4uCgcHBEREX2YjYkeNg9tCGcrQzx++Rr+q4LwJPG1qsMqtRROZKZNm4apU6ciLY3DXURERMXB1jQnmXGyNMCjhNfovTIQT5nM5EvhU0uenp64f/8+hBBwcnLKM9n3ypUrSg1QFXhqiYiISoOYpJy5Mg9fpKGyhQG2DGsIezN9VYdVbEpkZd8uXboougsREREVgZ2pPjYPbYjeK4MQnZAmWwHYzrTsJjOK4k0j88ERGSIiKk2evr2KKTohDY6WBtg6zBe2pnqqDkvpSmSyLxEREZUsezN9bB7WEA4W+nj4Ig29VwYiNumNqsMqFQqVyFhYWOD58+cAAHNzc1hYWBT4ICIiIuWraJZzmqmSuT6iXuScZopLZjJTqDkyixYtgrGxsez/vMs1ERFRyatkbiCbMxP5PBX+K4OweVhD2JiUvdNMhcU5MvngHBkiIirNHiWkoffKnPVlqlQwxJahDWFdBpKZEpkjo6mpifj4+DzlL168gKampqLNERERkYIc3l6KXdFMHw+epcJ/VRDiX5XP00wKJzIFDeCkp6dDR0fnowN6V3Z2NqZMmQJnZ2fo6+vDxcUFM2fOLDAGADh9+jQkEkmeR2xsrFJjIyIiUiUHi5zTTPamerj/LOc0U3lMZgq9jszSpUsBABKJBKtXr4aRkZFsW3Z2Ns6ePQs3NzelBvfbb79h+fLlWLduHWrVqoXLly9j4MCBMDU1xciRI9+77507d+SGpaytrZUaGxERkapVtjTAlmG+6LUyEPefpaLPqmBsHtoQFYx1VR1aiSl0IrNo0SIAOSMyK1askDuNpKOjAycnJ6xYsUKpwV24cAGdO3eW3aDSyckJmzdvxsWLFz+4r7W1NczMzJQaDxERUWmTk8zkTACOiE9Bn1VB+LscJTOFPrUUGRmJyMhI+Pn54dq1a7LnkZGRuHPnDo4cOQIfHx+lBteoUSOcOHECd+/eBQBcu3YN//77L9q1a/fBfevWrQs7Ozt8+umnOH/+/HvrpqenIzk5We5BRESkLhwtDbF5aEPYmujhXnwKvlgdhOcp6aoOq0QoPEfm1KlTMDc3lz3Pzs5GaGgoXr58qdTAAGDixIno3bs33NzcoK2tDU9PT4waNQpffPFFgfvY2dlhxYoV2LlzJ3bu3AkHBwc0b978vfeAmjNnDkxNTWUPBwcHpfeFiIioODlZGb69FFsXd+NS8MWqYLwoB8mMwpdfjxo1Cu7u7hg8eDCys7PRrFkzBAYGwsDAAPv370fz5s2VFtyWLVswfvx4zJs3D7Vq1UJoaChGjRqFhQsXon///oVux8/PD5UrV8aGDRvy3Z6eno709P8/2MnJyXBwcODl10REpHYin6ei15+BiH+VDjdbY2wa4gNLI/U4zVQil19v374dHh4eAIB//vkHUVFRuH37NkaPHo2ffvpJ0ebea/z48bJRGXd3d/Tt2xejR4/GnDlzFGrH29sbERERBW7X1dWFiYmJ3IOIiEgdOVsZYsuwhrA21sXt2Ff4YnUwElIzVB1WsVE4kXnx4gVsbW0BAAcPHkSPHj1QrVo1DBo0CGFhYUoNLi0tDRoa8iFqampCKpUq1E5oaCjs7OyUGRoREVGpVaWCETa/k8z0WRWEl2U0mVE4kbGxscGtW7eQnZ2Nw4cP49NPPwWQk3Qoe0G8jh074pdffsGBAwcQFRWF3bt3Y+HChejatauszqRJk9CvXz/Z88WLF2Pv3r2IiIjAjRs3MGrUKJw8eRLDhw9XamxERESlmUsFI9nVS7djX6HP6uAymcwU+vLrXAMHDkTPnj1hZ2cHiUSCVq1aAQCCg4OVvo7M77//jilTpuDbb79FfHw87O3t8dVXX+Hnn3+W1YmJiUF0dLTseUZGBsaOHYsnT57AwMAAderUwfHjx9GiRQulxkZERFTauVobye7NFB6TjC9WB2PTEB+YGyp3AVtVKtK9lnbs2IFHjx6hR48eqFSpEgBg3bp1MDMzQ+fOnZUeZEnjvZaIiKgsiYh/hd4rg/A8JQO17E2waYgPzAxKXzJTlO9f3jQyH0xkiIiorLkX9wr+q3KSmdoVTbBxcOlLZor1qqX27dsjKSlJ9vzXX39FYmKi7PmLFy9Qs2bNwkdLREREJaaqjTH+HtoQloY6uPEkGX3XXERSWqaqw/pohU5kjhw5IrfWyuzZs5GQkCB7npWVhTt37ig3OiIiIlKaau8kM2FPktD3r2AkvVbvZKbQicx/z0DxjBQREZH6qW6bk8xYGOrg+uMk9Fuj3smMwpdfExERkXrLSWZ8YG6gjWuPk9Dvr4tIfqOeyUyhExmJRAKJRJKnjIiIiNSPm60J/h7aMCeZeZSIvmvUM5kp9DoyQggMGDAAuro592t48+YNvv76axgaGgKA3PwZIiIiKv1q2Jlg05CG6LM6CNceJaLfmovYMNgbxnraqg6t0Ap9+fXAgQML1eDatWs/KqDSgJdfExFReXLzaRK+WB2MxLRMeFY2w/pBqklmuI6MkjCRISKi8ubGk5xkJul1JupVNsP6wT4w0lX4BgAfpUTufk1ERERlT+2Kptg0xAem+tq4Ep2I/n9dREp6lqrD+iAmMkRERAQgJ5nZONgHJnpaCHn4EgPUIJlhIkNEREQy7pVMsXFITjJz+eFLDFx7EamlOJlhIkNERERy6lQyw8YhPjDW08KlqJcYuPZSqU1mmMgQERFRHnUqmWHjYB8Y62rhYlQCBgZcQlpG6UtmmMgQERFRvjwczLBhyNtkJjIBA9eWvmSGiQwREREVqK6DGdYP9oaRrhaCIxMwKOASXmdkqzosGSYyRERE9F6elc1lyUzQg9KVzDCRISIiog+qV9kc6wZ5w1BHE4EPXmDwutKRzDCRISIiokLxcswZmTHU0cSF+y8w78gdVYfERIaIiIgKz8vRAusGeaOxqyW+b1VV1eEU/u7XRERERABQ38kCGwf7QCKRqDoUjsgQERGR4kpDEgMwkSEiIiI1xkSGiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFRIaIiIjUFhMZIiIiUltMZIiIiEhtMZEhIiIitcVEhoiIiNQWExkiIiJSW0xkiIiISG0xkSEiIiK1xUSGiIiI1JaWqgMojYQQAIDk5GQVR0JERFR+5H7v5n4PFwYTmXy8evUKAODg4KDiSIiIiMqfV69ewdTUtFB1JUKRtKeckEqlePr0KYyNjSGRSFQdjtIlJyfDwcEBjx49gomJiarDKTbloZ/loY9A+ehneegjUD76yT4WnRACr169gr29PTQ0Cjf7hSMy+dDQ0EClSpVUHUaxMzExKbO/ZO8qD/0sD30Eykc/y0MfgfLRT/axaAo7EpOLk32JiIhIbTGRISIiIrXFRKYc0tXVxdSpU6Grq6vqUIpVeehneegjUD76WR76CJSPfrKPJYuTfYmIiEhtcUSGiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFREYNLFu2DE5OTtDT04OPjw8uXrz43vrbt2+Hm5sb9PT04O7ujoMHD8ptF0Lg559/hp2dHfT19dGqVSvcu3dPtj0qKgqDBw+Gs7Mz9PX14eLigqlTpyIjI0OujkQiyfMICgpSm34CgJOTU54+/Prrr3J1rl+/jqZNm0JPTw8ODg6YO3eu2vTx9OnT+R4niUSCS5cuAVD+sVR2H3ft2oXWrVvD0tISEokEoaGhedp48+YNhg8fDktLSxgZGaF79+6Ii4uTqxMdHY0OHTrAwMAA1tbWGD9+PLKysorUR1X0MyEhASNGjED16tWhr6+PypUrY+TIkUhKSpKrl9+x3LJli1r0EQCaN2+eJ/6vv/5aro66H8uCfuckEgm2b98uq1daj2VmZiYmTJgAd3d3GBoawt7eHv369cPTp0/l2khISMAXX3wBExMTmJmZYfDgwUhJSZGro5TPV0Gl2pYtW4SOjo7466+/xM2bN8XQoUOFmZmZiIuLy7f++fPnhaamppg7d664deuWmDx5stDW1hZhYWGyOr/++qswNTUVe/bsEdeuXROdOnUSzs7O4vXr10IIIQ4dOiQGDBggjhw5Iu7fvy/27t0rrK2txdixY2VtREZGCgDi+PHjIiYmRvbIyMhQm34KIYSjo6OYMWOGXB9SUlJk25OSkoSNjY344osvxI0bN8TmzZuFvr6++PPPP9Wij+np6XJ9i4mJEUOGDBHOzs5CKpUKIZR7LIujj+vXrxfTp08Xq1atEgDE1atX87Tz9ddfCwcHB3HixAlx+fJl0bBhQ9GoUSPZ9qysLFG7dm3RqlUrcfXqVXHw4EFhZWUlJk2apHAfVdXPsLAw0a1bN7Fv3z4REREhTpw4IapWrSq6d+8uVw+AWLt2rdyxfPdnvjT3UQgh/Pz8xNChQ+XiT0pKkm0vC8cyKysrz+/l9OnThZGRkXj16pWsXmk9lomJiaJVq1Zi69at4vbt2yIwMFB4e3sLLy8vuXbatm0rPDw8RFBQkDh37pxwdXUV/v7+su3K+nxlIlPKeXt7i+HDh8ueZ2dnC3t7ezFnzpx86/fs2VN06NBBrszHx0d89dVXQgghpFKpsLW1FfPmzZNtT0xMFLq6umLz5s0FxjF37lzh7Owse5775ZffB1FRqKqfjo6OYtGiRQXG9b///U+Ym5uL9PR0WdmECRNE9erVFeqfEKXjWGZkZIgKFSqIGTNmyMqUeSyV3cd3FRRnYmKi0NbWFtu3b5eVhYeHCwAiMDBQCCHEwYMHhYaGhoiNjZXVWb58uTAxMZE7tqW5n/nZtm2b0NHREZmZmbIyAGL37t2F68h7qKqPfn5+4vvvvy8wrrJ6LOvWrSsGDRokV6YOxzLXxYsXBQDx8OFDIYQQt27dEgDEpUuXZHUOHTokJBKJePLkiRBCeZ+vPLVUimVkZCAkJAStWrWSlWloaKBVq1YIDAzMd5/AwEC5+gDQpk0bWf3IyEjExsbK1TE1NYWPj0+BbQJAUlISLCws8pR36tQJ1tbWaNKkCfbt26dQ/3Kpup+//vorLC0t4enpiXnz5skNUQcGBqJZs2bQ0dGRe507d+7g5cuXatPHXPv27cOLFy8wcODAPNs+9lgWRx8LIyQkBJmZmXLtuLm5oXLlyrJ2AgMD4e7uDhsbG7nXSU5Oxs2bNwv9WoDq+pmfpKQkmJiYQEtL/rZ5w4cPh5WVFby9vfHXX39BKLhcmKr7uGnTJlhZWaF27dqYNGkS0tLS5F6nrB3LkJAQhIaGYvDgwXm2qcuxTEpKgkQigZmZmawNMzMz1K9fX1anVatW0NDQQHBwsKyOMj5fedPIUuz58+fIzs6W+4UFABsbG9y+fTvffWJjY/OtHxsbK9ueW1ZQnf+KiIjA77//jvnz58vKjIyMsGDBAjRu3BgaGhrYuXMnunTpgj179qBTp05q08+RI0eiXr16sLCwwIULFzBp0iTExMRg4cKFsnacnZ3ztJG7zdzcvNT38V1r1qxBmzZt5G6KqqxjWRx9LIzY2Fjo6OjIPkDza6eg18ndpghV9TO/OGbOnIlhw4bJlc+YMQOffPIJDAwMcPToUXz77bdISUnByJEjFWpbVX3s06cPHB0dYW9vj+vXr2PChAm4c+cOdu3a9d7Xyd2miNJyLNesWYMaNWqgUaNGcuXqcizfvHmDCRMmwN/fX3YDydjYWFhbW8vV09LSgoWFhdzvpTI+X5nI0Hs9efIEbdu2RY8ePTB06FBZuZWVFcaMGSN73qBBAzx9+hTz5s1TOJFRpXf7UKdOHejo6OCrr77CnDlzSsXS28r0+PFjHDlyBNu2bZMrLyvHsjxJTk5Ghw4dULNmTUybNk1u25QpU2T/9/T0RGpqKubNm6fQl58qvZuYubu7w87ODi1btsT9+/fh4uKiwsiKx+vXr/H333/LHbdc6nAsMzMz0bNnTwghsHz5cpXEwFNLpZiVlRU0NTXzXH0RFxcHW1vbfPextbV9b/3cfwvT5tOnT9GiRQs0atQIK1eu/GC8Pj4+iIiI+GC9/1J1P//bh6ysLERFRb33dd59jcIoDX1cu3YtLC0tC5WcFOVYFkcfC8PW1hYZGRlITEwssB1lHUdAdf3M9erVK7Rt2xbGxsbYvXs3tLW131vfx8cHjx8/Rnp6eqFfQ9V9fJePjw8AyH4ey9KxBIAdO3YgLS0N/fr1+2Dd0nYsc5OYhw8f4tixY7LRmNw24uPj5epnZWUhISFB6b+XTGRKMR0dHXh5eeHEiROyMqlUihMnTsDX1zfffXx9feXqA8CxY8dk9Z2dnWFraytXJzk5GcHBwXJtPnnyBM2bN4eXlxfWrl0LDY0P/6iEhobCzs5OoT4Cqu1nfn3Q0NCQDYn6+vri7NmzyMzMlHud6tWrF3rYszT0UQiBtWvXol+/fh/84gOKdiyLo4+F4eXlBW1tbbl27ty5g+joaFk7vr6+CAsLk/tgzf3grVmzZqFfC1BdP4Gc49u6dWvo6Ohg37590NPT++A+oaGhMDc3V2iEUZV9/K/cS5dzfx7LyrHMtWbNGnTq1AkVKlT4YN3SdCxzk5h79+7h+PHjsLS0zNNGYmIiQkJCZGUnT56EVCqVJafK+nzlVUul3JYtW4Surq4ICAgQt27dEsOGDRNmZmayGft9+/YVEydOlNU/f/680NLSEvPnzxfh4eFi6tSp+V6ya2ZmJvbu3SuuX78uOnfuLHfJ7uPHj4Wrq6to2bKlePz4sdylf7kCAgLE33//LcLDw0V4eLj45ZdfhIaGhvjrr7/Upp8XLlwQixYtEqGhoeL+/fti48aNokKFCqJfv36yNhITE4WNjY3o27evuHHjhtiyZYswMDAo8uXXJd3HXMePHxcARHh4eJ64lHksi6OPL168EFevXhUHDhwQAMSWLVvE1atX5X4ev/76a1G5cmVx8uRJcfnyZeHr6yt8fX1l23Mv2W3durUIDQ0Vhw8fFhUqVPioS3ZLup9JSUnCx8dHuLu7i4iICLnfy6ysLCGEEPv27ROrVq0SYWFh4t69e+J///ufMDAwED///LNa9DEiIkLMmDFDXL58WURGRoq9e/eKKlWqiGbNmsnaKAvHMte9e/eERCIRhw4dyhNXaT6WGRkZolOnTqJSpUoiNDRU7mfx3SuQ2rZtKzw9PUVwcLD4999/RdWqVeUuv1bW5ysTGTXw+++/i8qVKwsdHR3h7e0tgoKCZNv8/PxE//795epv27ZNVKtWTejo6IhatWqJAwcOyG2XSqViypQpwsbGRujq6oqWLVuKO3fuyLavXbtWAMj3kSsgIEDUqFFDGBgYCBMTE+Ht7S13+as69DMkJET4+PgIU1NToaenJ2rUqCFmz54t3rx5I9fOtWvXRJMmTYSurq6oWLGi+PXXX9Wmj7n8/f3l1lV5l7KPpbL7WNDP49SpU2V1Xr9+Lb799lthbm4uDAwMRNeuXfN8aURFRYl27doJfX19YWVlJcaOHSt32XJp7+epU6cK/L2MjIwUQuRc3lq3bl1hZGQkDA0NhYeHh1ixYoXIzs5Wiz5GR0eLZs2aCQsLC6GrqytcXV3F+PHj5daREUL9j2WuSZMmCQcHh3yPT2k+lrmXlef3OHXqlKzeixcvhL+/vzAyMhImJiZi4MCBcuvkCKGcz1eJEApey0VERERUSnCODBEREaktJjJERESktpjIEBERkdpiIkNERERqi4kMERERqS0mMkRERKS2mMgQERGR2mIiQ0RERGqLiQwRgObNm2PUqFGqDgNCCAwbNgwWFhaQSCSy+8woYsCAAejSpYvSYyMiKo2YyJBa69ixI9q2bZvvtnPnzkEikeD69eslHFXRHT58GAEBAdi/fz9iYmJQu3btPHVOnz4NiUSS527PuZYsWYKAgIDiDfQjxcbGYsSIEahSpQp0dXXh4OCAjh075rlRXXEqzoRPVYnxtGnTIJFI3vsoDlFRUUVOvIuLk5MTFi9erOowqAQwkSG1NnjwYBw7dgyPHz/Os23t2rWoX78+6tSpo4LIiub+/fuws7NDo0aNYGtrCy0tLYXbMDU1hZmZmfKDU1BGRka+5VFRUfDy8sLJkycxb948hIWF4fDhw2jRogWGDx9ewlGWLePGjUNMTIzsUalSJcyYMUOu7F0FHSMitaLw3ZmISpHMzExhY2MjZs6cKVf+6tUrYWRkJJYvXy6eP38uevfuLezt7YW+vr6oXbu2+Pvvv+Xq+/n5ie+//172HIDYvXu3XB1TU1Oxdu1a2fPo6GjRo0cPYWpqKszNzUWnTp1kN+8ryOnTp0WDBg2Ejo6OsLW1FRMmTJDd7K5///5yN19zdHTMt43cmwe+fPky3+39+/cXnTt3luvbiBEjxPjx44W5ubmwsbHJc/O6ly9fisGDBwsrKythbGwsWrRoIUJDQ2XbIyIiRKdOnYS1tbUwNDQU9evXF8eOHZNrw9HRUcyYMUP07dtXGBsb57kJXa527dqJihUripSUlDzb3u3Tw4cPRadOnYShoaEwNjYWPXr0kN2tVwghpk6dKjw8PMT69euFo6OjMDExEb169RLJycmyOtu3bxe1a9cWenp6wsLCQrRs2VKkpKSIqVOnFnizux9++EFUrVpV6OvrC2dnZzF58mSRkZFR6Nf973HEOzd1/K+EhATRt29fYWZmJvT19UXbtm3F3bt3ZdvXrl0rTE1NxeHDh4Wbm5swNDQUbdq0EU+fPs23vf9ydHQUixYtkj338/MTw4cPF99//72wtLQUzZs3F0IIERYWJtq2bSsMDQ2FtbW1+PLLL8WzZ89k+x06dEg0btxYmJqaCgsLC9GhQwcREREh2/7f/vr5+cnei86dO4tffvlFWFtbC1NTUzF9+nSRmZkpxo0bJ8zNzUXFihXz3Gn9Q79bue3OmzdP2NraCgsLC/Htt9/KjpOfn1+BN7ylsocjMqTWtLS00K9fPwQEBEC8c//T7du3Izs7G/7+/njz5g28vLxw4MAB3LhxA8OGDUPfvn1x8eLFIr9uZmYm2rRpA2NjY5w7dw7nz5+HkZER2rZtW+BfuU+ePEH79u3RoEEDXLt2DcuXL8eaNWswa9YsADmnhGbMmIFKlSohJiYGly5dKnJ8/7Vu3ToYGhoiODgYc+fOxYwZM3Ds2DHZ9h49eiA+Ph6HDh1CSEgI6tWrh5YtWyIhIQEAkJKSgvbt2+PEiRO4evUq2rZti44dOyI6OlrudebPnw8PDw9cvXoVU6ZMyRNHQkICDh8+jOHDh8PQ0DDP9tyRJKlUis6dOyMhIQFnzpzBsWPH8ODBA/Tq1Uuu/v3797Fnzx7s378f+/fvx5kzZ/Drr78CAGJiYuDv749BgwYhPDwcp0+fRrdu3SCEwLhx49CzZ0+0bdtWNlLRqFEjAICxsTECAgJw69YtLFmyBKtWrcKiRYsK/bpLliyBr68vhg4dKmvbwcEh3+MyYMAAXL58Gfv27UNgYCCEEGjfvj0yMzNlddLS0jB//nxs2LABZ8+eRXR0NMaNG5dve4Wxbt066Ojo4Pz581ixYgUSExPxySefwNPTE5cvX8bhw4cRFxeHnj17yvZJTU3FmDFjcPnyZZw4cQIaGhro2rUrpFIpAMh+l44fP46YmBjs2rVLtu/Jkyfx9OlTnD17FgsXLsTUqVPx2WefwdzcHMHBwfj666/x1VdfyUZVC/u7derUKdy/fx+nTp3CunXrEBAQIDulumvXrjyjUVSGqTiRIvpo4eHheW4f37RpU/Hll18WuE+HDh3E2LFjZc8VHZHZsGGDqF69upBKpbLt6enpQl9fXxw5ciTf1/zxxx/z7LNs2TJhZGQksrOzhRBCLFq0qMCRmFxFGZFp0qSJXJ0GDRqICRMmCCGEOHfunDAxMRFv3ryRq+Pi4iL+/PPPAuOoVauW+P3332XPHR0dRZcuXd4be3BwsAAgdu3a9d56R48eFZqamiI6OlpWdvPmTQFAXLx4UQiRMzJiYGAgNwIzfvx44ePjI4QQIiQkRAAQUVFR+b7Gf9+ngsybN094eXnJnn/odYXI+/OUn7t37woA4vz587Ky58+fC319fbFt2zYhRM6IDAC50Y9ly5YJGxubD8YtRP4jMp6ennJ1Zs6cKVq3bi1X9ujRIwFA3LlzJ992nz17JgCIsLAwIYQQkZGRAoC4evWqXL3+/fsLR0dH2c+3EEJUr15dNG3aVPY8KytLGBoais2bNwshCve7ldtuVlaWrE6PHj1Er169Cuw7lV0ckSG15+bmhkaNGuGvv/4CAERERODcuXMYPHgwACA7OxszZ86Eu7s7LCwsYGRkhCNHjuQZTVDEtWvXEBERAWNjYxgZGcHIyAgWFhZ48+YN7t+/n+8+4eHh8PX1lZtw2bhxY6SkpOQ7x0eZ/jtPyM7ODvHx8QBy+pKSkgJLS0tZX4yMjBAZGSnrS0pKCsaNG4caNWrAzMwMRkZGCA8Pz/Me1q9f/71xiHdGzd4nPDwcDg4OciMZNWvWhJmZGcLDw2VlTk5OMDY2zrdfHh4eaNmyJdzd3dGjRw+sWrUKL1++/OBrb926FY0bN4atrS2MjIwwefLkPP183+sWVnh4OLS0tODj4yMrs7S0RPXq1eX6aGBgABcXl496rXd5eXnJPb927RpOnTold+zd3NwAQHb87927B39/f1SpUgUmJiZwcnICgEL9DtWqVQsaGv//VWNjYwN3d3fZc01NTVhaWsr9PBbmd6tWrVrQ1NSUPf/Y94XUl+IzCYlKocGDB2PEiBFYtmwZ1q5dCxcXF/j5+QEA5s2bhyVLlmDx4sVwd3eHoaEhRo0a9d6JjhKJJM+X7rvD/SkpKfDy8sKmTZvy7FuhQgUl9Up5tLW15Z5LJBLZaYGUlBTY2dnh9OnTefbLPdUzbtw4HDt2DPPnz4erqyv09fXx+eef53kP8ztd9K6qVatCIpHg9u3bRe/MO97XL01NTRw7dgwXLlzA0aNH8fvvv+Onn35CcHAwnJ2d820vMDAQX3zxBaZPn442bdrA1NQUW7ZswYIFCwr9usqW32sVNiHMz3+PUUpKCjp27Ijf/q+d+wtpqo0DOP7VREhKXDWrWW0YMyaTakWwxPUHaxTVqov+qIQyJPtnubwJR0EXQYRdjCTKi6CL0Isp3gwKilhuMFuWBZmZNB1R0Sq7iaCW74V0YDZzW73v2+T3gXOxcw7P83t2dji/58/Z+fM/nbtw4UJg/O1ArVZLa2srGo2G79+/YzQaE1osHC/+qX6Pidxb/+U1EH83SWTEtLBnzx6OHz/OjRs3uH79OocOHVJGPnw+HzabjaqqKmB8/cXz588pLi6etDy1Wh0zrz44OMjnz5+VzyaTifb2dvLz88nNzU0oRoPBgNvtZmxsLCa22bNns2jRoqTb/KeYTCbevHlDVlaW0tOeyOfzUV1dza5du4Dxh00oFEq6rjlz5mC1WmlpaaG+vv6nh+ro6Ch5eXkYDAbC4TDhcFgZlXn69Cmjo6O/vG4TZWRkUFpaSmlpKadPn0ar1dLZ2YnD4SA7O5toNBpzvt/vR6vV0tTUpOwbHh5Oup3xyp7IYDDw7ds3AoGAsj7n/fv3DAwMJNXG32UymXC73eh0urhvyf2IqbW1lbKyMgC6u7tjzsnOzgaYss2JxpPsvRVPItdATA8ytSSmhVmzZrF3715OnTrF69evqa6uVo7p9XqlZ97f38/Bgwd5+/btL8vbuHEjly5d4uHDhwSDQerq6mJ6gJWVlcybNw+bzca9e/d4+fIld+/epb6+ftJposOHDxMOhzl27BjPnj2jq6uLM2fO4HA4YobeE/XkyRMePXqkbH19fUmXAVBeXo7ZbGbnzp3cunWLUCiE3++nqamJYDAIjH+HHR0dSj0VFRUp935bWlqIRqOsWbMGt9vN4OAg/f39uFwuzGazElNJSQmVlZX09vbS09PDgQMHWLdu3ZTTVz8EAgHOnTtHMBhkZGSEjo4O3r17h8FgAManhx4/fszAwACRSISvX7+i1+sZGRmhra2NoaEhXC4XnZ2dSbdRp9MRCAQIhUJEIpG435Ver8dms1FbW0t3dzd9fX1UVVVRUFCAzWZLus5UHTlyhA8fPrB//37u37/P0NAQN2/epKamhmg0ikqlYu7cuVy9epUXL15w584dHA5HTBn5+fnMnDlTWSj86dOnlONJ5d6KR6fT4fV6efXqFZFIJOV4xN9PEhkxbdjtdj5+/IjVakWj0Sj7nU4nJpMJq9XK+vXrWbBgwZR/hNbc3MzixYspKyujoqKCxsZGcnJylOM5OTl4vV6WLFnC7t27MRgM2O12vnz5MmkvsqCgAI/HQ09PD8uXL6eurg673Y7T6UypvRaLhZUrVyrbxLUPicrIyMDj8WCxWKipqaGoqIh9+/YxPDzM/PnzAbh48SIqlYq1a9eyfft2rFYrJpMppfoKCwvp7e1lw4YNnDx5EqPRyKZNm7h9+zaXL19WYurq6kKlUmGxWCgvL6ewsJD29vaE68nNzcXr9bJ161aKiopwOp00NzezZcsWAGpra1m2bBmrV69GrVbj8/nYsWMHDQ0NHD16lBUrVuD3++O+fTWVxsZGZsyYQXFxMWq1etK1JNeuXWPVqlVs27YNs9nM2NgYHo/np2mTf5NGo8Hn8xGNRtm8eTMlJSWcOHGCvLw8MjMzyczMpK2tjQcPHmA0GmloaODChQsxZWRlZeFyubhy5Qoajea3ErFU7q14zp49SygUYunSpX/ldK/4czLGfmeyVQghhBDifyQjMkIIIYRIW5LICCGEECJtSSIjhBBCiLQliYwQQggh0pYkMkIIIYRIW5LICCGEECJtSSIjhBBCiLQliYwQQggh0pYkMkIIIYRIW5LICCGEECJtSSIjhBBCiLT1D9UtKYwpYl8IAAAAAElFTkSuQmCC",
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YsmRJuXJP/2VraGhY7sOyMv369UNSUhJ27NghO1ZcXIyvv/4aRkZG8Pf3V7A1/6v3/PnzuHz5stzxjIwM/PLLL2jdunWFPRfKjjcoKEhuyvHFixdx4cIF2eyuvLw85Ofnyz3G3d0dxsbGsinRvXv3homJCZYtW4aioqJyMT169KjK8b/++usoKCjA1q1bcfjwYYwYMULufFWfy87ODq1bt8bWrVvlphofO3as3PiZihgYGGD+/Pm4ffs25s+fX2HPyM8//4yLFy8CKH3dL168iLCwMNn53NxcfP/993B1da0z6y+1b98e1tbW+Pbbb+WmtB86dAi3b99G//79lfI8ir5+mpqa5crs2rWr3HT4smS3bJweUNqr8/3338uVy8rKKjfOycvLCxoaGrJ2Dx06FJqamliyZEm55xZCIC0tTZEmUw1hzw7VWYcOHZL9Rfu0Tp06oXHjxgrX9/7772P//v149dVXMW7cOLRr1w65ubm4ceMGdu/ejdjYWFhZWSEgIACjR4/GV199hcjISPTp0wdSqRSnT59GQECAbHuGdu3aITg4GGvWrIG9vT3c3NwqHG8BAFOmTMF3332HcePG4fLly3B1dcXu3btx9uxZrFu3rsoDMP/rww8/xK5du9C1a1e8/fbbaNasGRISEhAYGIjExERs2bKlWvUqGq+Hhwf8/PzwzjvvoKCgAOvWrYOlpaXskuHdu3fRo0cPjBgxAi1atICWlhb27t2L5ORkvPHGGwBKx2ds2rQJo0ePRtu2bfHGG2+gUaNGiI+Px8GDB9G5c2ds2LChSvG3bdsWHh4e+L//+z8UFBTIXcJS9LmWL1+O/v37w8/PDxMmTEB6ejq+/vprtGzZUm4QdGXef/99/PPPP1i9ejVCQ0Px2muvwdbWFklJSQgKCsLFixdx7tw5AKXv52+//Ya+fftixowZsLCwwNatWxETE4M//vijwst2qqCtrY0vv/wS48ePh7+/P0aOHCmbeu7q6orZs2cr7bkUef1effVVLF26FOPHj0enTp1w48YN/PLLL+U+L1q2bIkOHTpgwYIFsh7c33//vVxiExISgvfeew/Dhw9HkyZNUFxcjO3bt0NTUxPDhg0DUJo4ffbZZ1iwYAFiY2MxePBgGBsbIyYmBnv37sWUKVNqdCV3qiKVzAEjeoZnTT0HILZs2SIrq8jUcyFKp/EuWLBAeHh4CB0dHWFlZSU6deokVq1aJTeFt7i4WKxcuVI0a9ZM6OjoiEaNGom+ffuKy5cvy8rcuXNHdO3aVejr6wsAz52GnpycLMaPHy+srKyEjo6O8PLykmvL89pUmQcPHohJkyYJBwcHoaWlJSwsLMSrr74qzp8/X66sv7+/aNmyZbnjY8eOLTc1uSrxlk09X7lypVi9erVwcnISurq6okuXLuLatWuycqmpqWLatGmiWbNmwtDQUJiamgpfX1+xc+fOcrGEhoaK3r17C1NTU6Gnpyfc3d3FuHHjxKVLl+TiNTQ0fObr8n//938CgPDw8Ki0TFWeSwgh/vjjD9G8eXOhq6srWrRoIfbs2VPha/Ysu3fvFr169RIWFhZCS0tL2NnZiddff12cOHFCrlx0dLR47bXXhJmZmdDT0xM+Pj7iwIED5eJGBVOiy353/v77b7njixYtEgDEo0ePZMcq+h15+v2syvPt2LFDtGnTRujq6goLCwvx1ltvyS1BIETl71VZTFVVldcvPz9fzJ07V9jZ2Ql9fX3RuXNnERYWJvz9/YW/v79cfdHR0aJnz55CV1dX2NjYiI8++kgcO3ZMbur5vXv3xIQJE4S7u7vQ09MTFhYWIiAgQAQHB5eL748//hB+fn7C0NBQGBoaimbNmolp06aJiIiIKreRao5EiBoelUhERESkQnWjT5SIiIiohjDZISIiIrXGZIeIiIjUGpMdIiIiUmtMdoiIiEitMdkhIiIitcZFBatJKpUiISEBxsbG1d4Jm4iIiBQjhEB2djbs7e2rvNAmk51qSkhIKLczMBEREdWO+/fvw9HRsUplmexUU9ly+ffv34eJiYmKoyEiImoYsrKy4OTkpNA2O0x2qqns0pWJiQmTHSIiolqmyBASDlAmIiIitcZkh4iIiNQakx0iIiJSa0x2iIiISK0x2SEiIiK1xmSHiIiI1BqTHSIiIlJrTHaIiIhIrTHZISIiIrXGZIeIiIjUGpMdIiIiUmtMdoiIiEitMdkhIiIitcZdz19QYWEhCgsLyx3X0NCAlpaWXLnKSCQSaGtrV6tsUVERhBC1WhYAdHR0qlW2uLgYUqlUKWW1tbVlu97WVNmSkhKUlJQopayWlhY0NDTqTFmpVIri4uJKy2pqakJTU7POlBVCoKioSClln/79rKmywLN/l/kZUXFZfkbwM+J5ZZ/1818ZJjsvaPXq1dDT0yt33NPTE2+++abs/qpVqyr9kHRxccG4ceNk99evX4+8vLwKy9rb22Py5Mmy+xs3bkRmZmaFZRs1aoR3331Xdv+HH37Ao0ePKixramqKWbNmye4HBgYiISGhwrIGBgZ4//33Zfd/+eUXxMXFVVhWW1sbH330kez+zp07ERkZWWFZAFi0aJHs/3v37sWtW7cqLbtgwQLZB9+BAwdw7dq1SsvOmzcPhoaGAIAjR47g0qVLlZadOXMmzMzMAADHjx9HWFhYpWXfeecdWFtbAwBOnz6NkydPVlp20qRJcHBwAACcP38ewcHBlZYdO3YsXF1dAQCXL1/GoUOHKi07cuRINGnSBABw48YN7Nu3r9Kyr732Glq2bAkAuH37Nnbv3l1p2UGDBqF169YAgKioKPz222+Vlu3bty98fHwAAPHx8di6dWulZXv27InOnTsDABITE/Hjjz9WWtbf3x/dunUDADx69AibNm2qtGzHjh3Rq1cvAEBmZibWr19fadn27dujf//+AIC8vDysWrWq0rLe3t4YPHgwgNIv7eXLl1datkWLFhg+fLjs/rPK8jOiFD8j/oefEaWe9xmRn59f6WMrw8tYREREpNYk4ll9i1SprKwsmJqa4tGjRzAxMSl3nl3UFZdlFzW7qHkZS/Gy/IyoXll+RrxY2brwe19R2aysLDRq1AiZmZkVfv9WhMlONZUlO4q82ERERPRiqvP9y8tYREREpNaY7BAREZFaY7JDREREao3JDhEREak1JjtERESk1pjsEBERkVpjskNERERqjckOERERqTUmO0RERKTWmOwQERGRWqtWsrN9+3Z07twZ9vb2sp1s161b98ydVImIiIhUQeFkZ9OmTZgzZw769euHjIwM2YZiZmZmWLdunbLjIyIiInohCic7X3/9NX744Qf83//9n2xXUgBo3749bty4odTgiIiIiF6UwslOTEwM2rRpU+64rq4ucnNzlRIUERERkbIonOy4ubkhPDy83PHDhw+jefPmyoiJiIiISGkUTnbmzJmDadOmYceOHRBC4OLFi/j888+xYMECfPDBBwrVderUKQwYMAD29vaQSCQICgqSO7948WI0a9YMhoaGMDc3R8+ePXHhwoXn1rtx40a4urpCT08Pvr6+uHjxotz5/Px8TJs2DZaWljAyMsKwYcOQnJysUOxERERUPyic7EyaNAlffvklPv74Y+Tl5eHNN9/Epk2bsH79erzxxhsK1ZWbmwtvb29s3LixwvNNmjTBhg0bcOPGDZw5cwaurq7o1asXHj16VGmdO3bswJw5c7Bo0SJcuXIF3t7e6N27N1JSUmRlZs+ejT///BO7du3CyZMnkZCQgKFDhyoUOxEREdUPEiGEqO6D8/LykJOTA2tr6xcPRCLB3r17MXjw4ErLZGVlwdTUFMHBwejRo0eFZXx9ffHyyy9jw4YNAACpVAonJydMnz4dH374ITIzM9GoUSP8+uuveO211wAAd+7cQfPmzREWFoYOHTpUWG9BQQEKCgrkYnFyckJmZiZMTEyq2WoiIiJSRFkuoMj3r8I9O0+ePEFeXh4AwMDAAE+ePMG6detw9OhRRatSSGFhIb7//nuYmprC29u70jKXL19Gz549Zcc0NDTQs2dPhIWFAQAuX76MoqIiuTLNmjWDs7OzrExFli9fDlNTU9nNyclJSS0jIiKimqRwsjNo0CBs27YNAJCRkQEfHx+sXr0agwYNwqZNm5Qe4IEDB2BkZAQ9PT2sXbsWx44dg5WVVYVlU1NTUVJSAhsbG7njNjY2SEpKAgAkJSVBR0cHZmZmlZapyIIFC5CZmSm73b9//8UaRkRERLVC4WTnypUr6NKlCwBg9+7dsLW1RVxcHLZt24avvvpK6QEGBAQgPDwc586dQ58+fTBixAi58Te1RVdXFyYmJnI3IiIiqvsUTnby8vJgbGwMADh69CiGDh0KDQ0NdOjQQbZ1hDIZGhrCw8MDHTp0wObNm6GlpYXNmzdXWNbKygqamprlZlYlJyfD1tYWAGBra4vCwkJkZGRUWoaIiIjUh8LJjoeHB4KCgnD//n0cOXIEvXr1AgCkpKTUSm+HVCqVGyj8NB0dHbRr1w7Hjx+XK3/8+HF07NgRANCuXTtoa2vLlYmIiEB8fLysDBEREakPLUUf8Mknn+DNN9/E7Nmz0aNHD1mCcPTo0QpXVn6WnJwcREVFye7HxMQgPDwcFhYWsLS0xOeff46BAwfCzs4Oqamp2LhxIx4+fIjhw4fLHtOjRw8MGTIE7733HoDSdYDGjh2L9u3bw8fHB+vWrUNubi7Gjx8PADA1NcXEiRMxZ84cWFhYwMTEBNOnT0fHjh0rnYlFRERE9ZfCyc5rr70GPz8/JCYmys2KKks6FHHp0iUEBATI7s+ZMwcAMHbsWHz77be4c+cOtm7ditTUVFhaWuLll1/G6dOn0bJlS9ljoqOjkZqaKrv/+uuv49GjR/jkk0+QlJSE1q1b4/Dhw3KDlteuXQsNDQ0MGzYMBQUF6N27N7755htFXwoiIiKqB15onZ2GrDrz/ImIiOjFVOf7V+GenYCAAEgkkkrPh4SEKFolERERUY1RONlp3bq13P2ioiKEh4fj5s2bGDt2rLLiIiIiIlIKhZOdtWvXVnh88eLFyMnJeeGAiIiIiJRJ4annlRk1ahR++uknZVVHREREpBRKS3bCwsKgp6enrOqIiIiIlELhy1hDhw6Vuy+EQGJiIi5duoSFCxcqLTAiIiIiZVA42TE1NZW7r6GhgaZNm2Lp0qWy1ZSpenIKijFhy98Y28kVfVvZQkOj8llvREREVDUKJztbtmypiTgIwLawWFyMTcfF2HQ0szXG7FeaoFcLm2dO9SciIqJnU9qYHXpxozq4YFZPTxjrauFOUjbe3n4ZAzecReidFHDtRyIioupReAXlkpISrF27Fjt37kR8fDwKCwvlzqenpys1wLqqJldQzsgrxA+n72HL2VjkFZYAANo4m2HOK03g52HFnh4iImqwqvP9q3DPzpIlS7BmzRq8/vrryMzMxJw5czB06FBoaGhg8eLFilZHFTAz0MH7vZvh9AcBeLtrY+hpa+BqfAZGb76I1787j7DoNFWHSEREVG8o3LPj7u6Or776Cv3794exsTHCw8Nlx86fP49ff/21pmKtU2pzb6yU7Hx8e+Iefr4Qh8JiKQCgk7sl5vZqgnYuFjX63ERERHVJrfTsJCUlwcvLCwBgZGSEzMxMAMCrr76KgwcPKlodVYG1sR4+GdACp94PwOgOLtDWlOBcdBqGbQrDmJ8uIvx+hqpDJCIiqrMUTnYcHR2RmJgIoLSX5+jRowCAv//+G7q6usqNjuTYmurh08GtcOL9AIz0cYKWhgSn7j7C4I1nMWnr37j5MFPVIRIREdU5Cic7Q4YMwfHjxwEA06dPx8KFC+Hp6YkxY8ZgwoQJSg+QynMw08fyoS8hZG43DGvrCA0JEHw7Ba9+fQZTt19GRFK2qkMkIiKqMxQes/Nf58+fx7lz5+Dp6YkBAwYoK646rzbH7DzPvUc5WH88EvuvJUAIQCIB+nvZYVbPJvCwNlJpbERERMpUne/fF052Gqq6lOyUuZucjXXBd/HXjSQAgIYEGNzaATN6eMLVylDF0REREb24Wkl2li9fDhsbm3KXrH766Sc8evQI8+fPV6S6eqsuJjtlbiVkYW3wXRy7lQwA0NSQYFhbB0zv7gknCwMVR0dERFR9tTIb67vvvkOzZs3KHW/ZsiW+/fZbRaujGtDC3gQ/jGmPP9/zQ0DTRiiRCuy89AABq07go703kJDxRNUhEhER1ZpqTT23s7Mrd7xRo0ayWVpUN3g5mmLLeB/88U4ndPG0QrFU4NcL8ei28gQW7/8HKVn5qg6RiIioximc7Dg5OeHs2bPljp89exb29vZKCYqUq52LObZP9MWOKR3g62aBwhIpAs/FosuKUHx24BZScwpUHSIREVGNUXjX88mTJ2PWrFkoKipC9+7dAQDHjx/HBx98gLlz5yo9QFIe38aW+H1KB5yLTsPqoxG4Ep+BH8/E4NeL8RjbyRVTujSGuaGOqsMkIiJSKoUHKAsh8OGHH+Krr76SbQKqp6eH+fPn45NPPqmRIOuiujxAuSqEEDh59xHWHLuL6w9KFyM00tXChM6umNilMUz1tVUcIRERUXm1OvU8JycHt2/fhr6+Pjw9PRvc6sn1PdkpI4RA8O0UrDl2F7cTswAAxnpamNylMcZ3doWxHpMeIiKqO7jOTi1Sl2SnjFQqcOSfJKwNvou7yTkAADMDbbzd1R1jO7nAQEfhK55ERERKx2SnFqlbslNGKhU4cCMR64Lv4t6jXACApaEO3unmjlEdXKCnraniCImIqCFjslOL1DXZKVNcIsW+8ASsPx6J+PQ8AEAjY11M6+aOkb7O0NVi0kNERLWPyU4tUvdkp0xRiRR7rjzAV8ej8PDfxQjtTPXwXncPDG/nBB0thVcvICIiqjYmO7WooSQ7ZQqLpdhx6T42hkQh6d/FCB3N9TGjuyeGtnWAliaTHiIiqnm1luxERkYiNDQUKSkpkEqlcucayvTzhpbslMkvKsFvF+OxMTRathihq6UBZvTwxKDWDtDUkKg4QiIiUme1kuz88MMPeOedd2BlZQVbW1tIJP/7cpNIJLhy5YpiUddTDTXZKfOksAQ/n4/DppPRSM8tXW/JvZEhZvVsgv5edtBg0kNERDWgVpIdFxcXvPvuuw1md/PKNPRkp0xuQTECz8Xi+1P3kPmkCADQ1MYYs1/xRO+W8skwERHRi6qVZMfExATh4eFo3LhxtYJUF0x25GXnF+GnM7H48fQ9ZBcUAwBa2ptgds8m6NHcmkkPEREpRXW+fxUeVTp8+HAcPXpU4eBIvRnraWNmT0+cmd8d07t7wFBHE/8kZGHStksY/M05nLz7CBwLT0REqqBwz87y5cuxZs0a9O/fH15eXtDWlt9OYMaMGUoNsK5iz86zpecW4rtT0dh2Lg5PikoAAO1dzDHnlSbo5GGl4uiIiKi+qpXLWG5ubpVXJpHg3r17ilRXbzHZqZpH2QX49mQ0fj4fh4Li0pl7HRpbYG6vpnjZ1ULF0RERUX3DdXZqEZMdxSRn5WNjaBR+v3gfhSWlSU8XTyvMeaUJ2jibqzg6IiKqL2plzM7ThBAvNA7j1KlTGDBgAOzt7SGRSBAUFCQ7V1RUhPnz58PLywuGhoawt7fHmDFjkJCQ8Mw6XV1dIZFIyt2mTZsmK9OtW7dy56dOnVrtdtDz2ZjoYemgVgh9vxtG+jhDS0OC05GpGPLNOUwI/Bs3HmSqOkQiIlJT1Up2tm3bBi8vL+jr60NfXx8vvfQStm/frnA9ubm58Pb2xsaNG8udy8vLw5UrV7Bw4UJcuXIFe/bsQUREBAYOHPjMOv/++28kJibKbseOHQNQOrD6aZMnT5Yrt2LFCoXjJ8U5mOlj+VAvhMzthuHtHKGpIUHInRQM2HAGU7Zdwu3ELFWHSEREakZL0QesWbMGCxcuxHvvvYfOnTsDAM6cOYOpU6ciNTUVs2fPrnJdffv2Rd++fSs8Z2pqKktUymzYsAE+Pj6Ij4+Hs7NzhY9r1KiR3P0vvvgC7u7u8Pf3lztuYGAAW1vbKsdaUFCAgoIC2f2sLH4pvwhnSwOsHO6NdwM8sD74LvZdS8DRW8k4eisZ/b3sMKunJzxtjFUdJhERqQGFe3a+/vprbNq0CV9++SUGDhyIgQMHYsWKFfjmm2/w1Vdf1USMMpmZmZBIJDAzM6tS+cLCQvz888+YMGFCuXVefvnlF1hZWaFVq1ZYsGAB8vLynlnX8uXLYWpqKrs5OTlVtxn0FDcrQ6x7ow2OzuqK/i/ZAQAO3khEr3WnMOv3q4hJzVVxhEREVN8pPEBZT08PN2/ehIeHh9zxyMhIeHl5IT8/v3qBSCTYu3cvBg8eXOH5/Px8dO7cGc2aNcMvv/xSpTp37tyJN998E/Hx8bC3t5cd//777+Hi4gJ7e3tcv34d8+fPh4+PD/bs2VNpXRX17Dg5OXGAspLdTszC2mN3cfRWMgBAU0OCIW0cMLOHJ5wsDFQcHRERqVp1BigrfBnLw8MDO3fuxEcffSR3fMeOHfD09FS0uiopKirCiBEjIITApk2bqvy4zZs3o2/fvnKJDgBMmTJF9n8vLy/Y2dmhR48eiI6Ohru7e4V16erqQldXt3oNoCprbmeC78e0x40HmVgbfBchd1Kw+/IDBF19iOHtnfBedw84mOmrOkwiIqpHFE52lixZgtdffx2nTp2Sjdk5e/Ysjh8/jp07dyo9wLJEJy4uDiEhIVXO4uLi4hAcHPzM3poyvr6+AICoqKhKkx2qXV6Opvhp3Mu4Ev8Ya4/dxenIVPx2MR5/XH6AN3ycMC3AAzYmeqoOk4iI6gGFx+wMGzYMFy5cgJWVFYKCghAUFAQrKytcvHgRQ4YMUWpwZYlOZGQkgoODYWlpWeXHbtmyBdbW1ujfv/9zy4aHhwMA7Ozsqhsq1ZC2zubYPtEXO9/uiA6NLVBYIsW2sDh0XRGKpX/ewqPsgudXQkREDZpKFxXMyclBVFQUAKBNmzZYs2YNAgICYGFhATs7O7z22mu4cuUKDhw4ABsbG9njLCwsoKOjAwDo0aMHhgwZgvfee092XiqVws3NDSNHjsQXX3wh95zR0dH49ddf0a9fP1haWuL69euYPXs2HB0dcfLkySrHzkUFVeNcVCpWH7uLy3GPAQD62poY08kFb3d1h4WhjoqjIyKimlarKyinpKQgJSUFUqlU7vhLL71U5TpOnDiBgICAcsfHjh2LxYsXV7o1RWhoKLp16wagdBHBcePGYfHixbLzR48eRe/evREREYEmTZrIPfb+/fsYNWoUbt68idzcXDg5OWHIkCH4+OOPFUpamOyojhACpyJTseZoBK79uxihoY4mxnd2w+QujWFqoP2cGoiIqL6qlWTn8uXLGDt2LG7fvl1u9WSJRIKSkhJFqqu3mOyonhACIXdSsObYXfyTULrukbGuFiZ2ccMEPzeY6DHpISJSN7WS7Hh7e8Pd3R3z58+HjY1NufVrXFxcFKmu3mKyU3cIIXDknySsPRaJiORsAICpvjamdG2McZ1cYair8Dh8IiKqo2ol2TE2NsbVq1fLrbPT0DDZqXukUoGDNxKxLvguoh+VLkZoYaiDqf6NMbqDK/R1NFUcIRERvaha2Qi0R48euHbtmsLBEdU0DQ0JBnjb4+hsf6x93RuulgZIzy3Esr/uoOvKUGw5G4P8ooZxmZWIiP5H4Z6d1NRUjB07Fj4+PmjVqhW0teXHRTxvo051wZ6duq+4RIo9Vx5i/fFIPMx4AgCwNdHDtO4eeL29E3S0qrUPLhERqVCtXMb6888/MXr06Ao3wuQAZaqLCoul2HX5PjaERCExs3Q7EwczfUzv7oFh7Ryhrcmkh4iovqiVZMfV1RWvvvoqFi5cKLf2TUPDZKf+yS8qwe8X47HxRLRsMUJnCwN8NbINWjuZqTY4IiKqkloboBweHt7gt1VgslN/5ReV4Ofzcdh0IhppuYVwb2SIo7P9oakhef6DiYhIpWplgPLQoUMRGhqqcHBEdYWetiYmdWmM43P9YaqvjehHudh79aGqwyIiohqi8AIkTZo0wYIFC3DmzBl4eXmVG6A8Y8YMpQVHVJPMDHTwTjd3fHHoDtYeu4sB3nbQ1eL0dCIidaPwZazKtnAASgco37t374WDqg94GUs9PCksQdeVoXiUXYClg1piTEdXVYdERETPUJ3vX4V7dmJiYhQOjKiu0tfRxPTuHvhk3z/4OiQKw9s5cfFBIiI1wzm31OC98bIzHM318Si7AFvDYlUdDhERKZnCPTtCCOzevRuhoaEV7nq+Z88epQVHVBt0tDQwq2cTzNt1DZtORONNX2duIkpEpEYU7tmZNWsWRo8ejZiYGBgZGcHU1FTuRlQfDWnjAA9rI2Q+KcKPpxrGuDMiooZC4QHKFhYW+Pnnn9GvX7+aiqle4ABl9XPoRiLe+eUKDHQ0ceqDAFgZ6ao6JCIi+o9aWWfH1NQUjRs3Vjg4orquTytbeDmYIq+wBJtORKs6HCIiUhKFk53FixdjyZIlePLkSU3EQ6QyEokE83o3BQBsPx+HhAz+jBMRqQOFk50RI0bg8ePHsLa2hpeXF9q2bSt3I6rPunpawcfNAoXFUnwdEqnqcIiISAkUno01duxYXL58GaNGjYKNjQ0kEu4nROpDIpHg/d5NMfzbMOy89ABTurrDzcpQ1WEREdELUDjZOXjwII4cOQI/P7+aiIdI5V52tUBA00YIjXiEdcF3sf6NNqoOiYiIXoDCl7GcnJw4+4jU3txepWN39l9LwO3ELBVHQ0REL0LhZGf16tX44IMPEBsbWwPhENUNrRxM0d/LDkIAq4/eVXU4RET0AhS+jDVq1Cjk5eXB3d0dBgYG5XY9T09PV1pwRKo0+5UmOHQzEcG3k3El/jHaOpurOiQiIqoGhZOddevW1UAYRHWPh7URhrV1xK7LD7DqSAR+ndxB1SEREVE1VGs2FlFDMbOnJ4LCH+JcdBrORqWis4eVqkMiIiIFKZzsAEBJSQmCgoJw+/ZtAEDLli0xcOBAaGpqKjU4IlVzNDfAW74uCDwXixVHIhDkbsnlFoiI6hmFByhHRUWhefPmGDNmDPbs2YM9e/Zg1KhRaNmyJaKjucQ+qZ93A9yhr62Ja/czcOxWsqrDISIiBSmc7MyYMQPu7u64f/8+rly5gitXriA+Ph5ubm6YMWNGTcRIpFLWxnoY39kVQOnMrBKpQnvnEhGRiimc7Jw8eRIrVqyAhYWF7JilpSW++OILnDx5UqnBEdUVb3d1h7GeFiKSs/HntQRVh0NERApQONnR1dVFdnZ2ueM5OTnQ0dFRSlBEdY2pgTbe7toYALA2+C6KSqQqjoiIiKpK4WTn1VdfxZQpU3DhwgUIISCEwPnz5zF16lQMHDiwJmIkqhPGd3aDpaEO4tLysOvSA1WHQ0REVaRwsvPVV1/B3d0dHTt2hJ6eHvT09NC5c2d4eHhg/fr1NREjUZ1gqKuFaQEeAICvjkciv6hExREREVFVKDz13MzMDPv27UNUVJRs6nnz5s3h4eGh9OCI6po3fZ3x4+l7SMjMx8/n4zCpS2NVh0RERM8hEUJwakk1ZGVlwdTUFJmZmdwYtYHZ8Xc85v9xA+YG2jj1QQCM9bSf/yAiIlKK6nz/KnwZa9iwYfjyyy/LHV+xYgWGDx+uaHVE9c6wto5wszLE47wi/HQmVtXhEBHRcyic7Jw6dQr9+vUrd7xv3744deqUUoIiqsu0NDUw55UmAIAfTt/D49xCFUdERETPonCyU9kUc21tbWRlZSklKKK6rr+XHZrbmSCnoBjfnuTK4UREdZnCyY6Xlxd27NhR7vjvv/+OFi1aKFTXqVOnMGDAANjb20MikSAoKEh2rqioCPPnz4eXlxcMDQ1hb2+PMWPGICHh2Qu6LV68GBKJRO7WrFkzuTL5+fmYNm0aLC0tYWRkhGHDhiE5mdsAUNVpaEjwfu/S3p3Ac7FIzspXcURERFQZhWdjLVy4EEOHDkV0dDS6d+8OADh+/Dh+++037Nq1S6G6cnNz4e3tjQkTJmDo0KFy5/Ly8nDlyhUsXLgQ3t7eePz4MWbOnImBAwfi0qVLz6y3ZcuWCA4Olt3X0pJv5uzZs3Hw4EHs2rULpqameO+99zB06FCcPXtWofipYQtoao12Lua4HPcYX4dE4rPBXqoOiYiIKlCt2VgHDx7EsmXLEB4eDn19fbz00ktYtGgR/P39qx+IRIK9e/di8ODBlZb5+++/4ePjg7i4ODg7O1dYZvHixQgKCkJ4eHiF5zMzM9GoUSP8+uuveO211wAAd+7cQfPmzREWFoYOHTpUKV7OxiIACItOw8gfzkNLQ4KQud3gbGmg6pCIiNRarczGAoD+/fvj7NmzyM3NRWpqKkJCQl4o0amqzMxMSCQSmJmZPbNcZGQk7O3t0bhxY7z11luIj4+Xnbt8+TKKiorQs2dP2bFmzZrB2dkZYWFhldZZUFCArKwsuRtRR3dLdPG0QrFUYN3xu6oOh4iIKlCtZEcV8vPzMX/+fIwcOfKZmZyvry8CAwNx+PBhbNq0CTExMejSpYtsP6+kpCTo6OiUS5hsbGyQlJRUab3Lly+Hqamp7Obk5KSUdlH9N69XUwDA3qsPcTe5/L5xRESkWvUi2SkqKsKIESMghMCmTZueWbZv374YPnw4XnrpJfTu3Rt//fUXMjIysHPnzheKYcGCBcjMzJTd7t+//0L1kfrwdjJD75Y2EAJYc5S9O0REdU2dT3bKEp24uDgcO3ZM4fExZmZmaNKkCaKiogAAtra2KCwsREZGhly55ORk2NraVlqPrq4uTExM5G5EZeb2agqJBDj8TxKu3c9QdThERPSUOp3slCU6kZGRCA4OhqWlpcJ15OTkIDo6GnZ2dgCAdu3aQVtbG8ePH5eViYiIQHx8PDp27Ki02KlhaWJjjCGtHQAAq45GqDgaIiJ6WrWTncLCQkRERKC4uLjaT56Tk4Pw8HDZzKmYmBiEh4cjPj4eRUVFeO2113Dp0iX88ssvKCkpQVJSEpKSklBY+L8Va3v06IENGzbI7s+bNw8nT55EbGwszp07hyFDhkBTUxMjR44EAJiammLixImYM2cOQkNDcfnyZYwfPx4dO3as8kwsoorM6tkEWhoSnI5MRVh0mqrDISKifymc7OTl5WHixIkwMDBAy5YtZTOdpk+fji+++EKhui5duoQ2bdqgTZs2AIA5c+agTZs2+OSTT/Dw4UPs378fDx48QOvWrWFnZye7nTt3TlZHdHQ0UlNTZfcfPHiAkSNHomnTphgxYgQsLS1x/vx5NGrUSFZm7dq1ePXVVzFs2DB07doVtra22LNnj6IvBZEcZ0sDvOFTOnB91dEIcI9dIqK6QeF1dmbOnImzZ89i3bp16NOnD65fv47GjRtj3759WLx4Ma5evVpTsdYpXGeHKpKclY+uK0JRUCzFT+Pao3szG1WHRESkVmplnZ2goCBs2LABfn5+kEgksuMtW7ZEdDT3CKKGzcZED+M6uQIAVh25C6mUvTtERKqmcLLz6NEjWFtblzuem5srl/wQNVRT/d1hpKuFW4lZ+OtmoqrDISJq8BROdtq3b4+DBw/K7pclOD/++CNnMxEBMDfUwaQubgBK190pLpGqOCIiooZN4Y1Aly1bhr59++LWrVsoLi7G+vXrcevWLZw7dw4nT56siRiJ6p2Jfm7Yei4W91JzsefKQ4x4mStuExGpisI9O35+fggPD0dxcTG8vLxw9OhRWFtbIywsDO3atauJGInqHWM9bbzbzQMAsC74LgqKS1QcERFRw1WtXc+Js7Ho+fKLSuC/MhTJWQVYNKAFxnd2U3VIRET1XnW+fxW+jAUAUqkUUVFRSElJgVQqPx6ha9eu1amSSO3oaWtiRg9P/N/em9gYGoUR7Z1gqFutXzkiInoBCn/ynj9/Hm+++Sbi4uLKLZomkUhQUsLueqIyI9o74buT9xCfnofAc7GYFuCh6pCIiBochcfsTJ06Fe3bt8fNmzeRnp6Ox48fy27p6ek1ESNRvaWtqYE5rzQBAHx7MhqZeUUqjoiIqOFRONmJjIzEsmXL0Lx5c5iZmcHU1FTuRkTyBnjbo6mNMbLzi/HdKS68SURU2xROdnx9fREVFVUTsRCpJU0NCeb2Ku3d2XI2Fo+yC1QcERFRw1KlMTvXr1+X/X/69OmYO3cukpKS4OXlBW1tbbmyL730knIjJFIDr7SwgbeTGa7dz8DG0CgsHthS1SERETUYVZp6rqGhAYlEUukuzmXnGtIAZU49J0WdiUzFqM0XoKOpgZB5/nA0N1B1SERE9U6NTT2PiYl5ocCICOjsYYmOjS0Rdi8NXx2PxIrXvFUdEhFRg1ClMTsuLi6yW1xcHBwcHOSOubi4wMHBAXFxcTUdL1G9JZFIMK93UwDA7ssPEP0oR8URERE1DAoPUA4ICKhwinlmZiYCAgKUEhSRumrnYo6eza0hFcCaY3dVHQ4RUYOgcLJTNjbnv9LS0mBoaKiUoIjU2dxepb07B68n4ubDTBVHQ0Sk/qq8gvLQoUMBlHbFjxs3Drq6urJzJSUluH79Ojp16qT8CInUTHM7Ewz0tsf+awlYfTQCW8b7qDokIiK1VuVkp2zBQCEEjI2Noa+vLzuno6ODDh06YPLkycqPkEgNzX6lCQ7eSERoxCNcik1He1cLVYdERKS2qpzsbNmyBQDg6uqKefPm8ZIV0QtwszLEiPaO+O3ifaw4EoEdUzpUeHmYiIhenMJjdhYtWsREh0gJpnf3hI6WBi7GpON0ZKqqwyEiUlsKJztEpBz2ZvoY5esCAFh5JKLSRTuJiOjFMNkhUqF3A9xhoKOJGw8zceSfJFWHQ0SklpjsEKmQlZEuJvq5AQBWHb2LEil7d4iIlI3JDpGKTerSGKb62ohKyUHQ1YeqDoeISO1UeTZWma+++qrC4xKJBHp6evDw8EDXrl2hqan5wsERNQSm+tqY6u+OLw/fwdrguxjgbQ8dLf4dQkSkLAonO2vXrsWjR4+Ql5cHc3NzAMDjx49hYGAAIyMjpKSkoHHjxggNDYWTk5PSAyZSR2M7ueCnszF48PgJdvwdj9EdXVUdEhGR2lD4z8dly5bh5ZdfRmRkJNLS0pCWloa7d+/C19cX69evR3x8PGxtbTF79uyaiJdILRnoaGF6dw8AwFchUXhSWKLiiIiI1IdEKDjf1d3dHX/88Qdat24td/zq1asYNmwY7t27h3PnzmHYsGFITExUZqx1SlZWFkxNTZGZmQkTExNVh0NqoLBYiu6rT+DB4yf4sG8zTPV3V3VIRER1TnW+fxXu2UlMTERxcXG548XFxUhKKp06a29vj+zsbEWrJmrQdLQ0MKtnEwDAtyejkZVfpOKIiIjUg8LJTkBAAN5++21cvXpVduzq1at455130L17dwDAjRs34ObmprwoiRqIIW0c4GFthIy8Ivx4OkbV4RARqQWFk53NmzfDwsIC7dq1g66uLnR1ddG+fXtYWFhg8+bNAAAjIyOsXr1a6cESqTtNDQnmvFLau7P59D2k5RSoOCIiovpP4TE7Ze7cuYO7d+8CAJo2bYqmTZsqNbC6jmN2qKZIpQIDN57BzYdZmOTnho9fbaHqkIiI6ozqfP9WO9lp6JjsUE06EZGCcVv+ho6WBk6+3w12pvqqDomIqE6ozvevwuvslJSUIDAwEMePH0dKSgqkUqnc+ZCQEEWrJKL/8G/SCD6uFrgYm46vjkdh+VAvVYdERFRvKTxmZ+bMmZg5cyZKSkrQqlUreHt7y92I6MVJJBLM6116aXjnpfuITc1VcURERPWXwj07v//+O3bu3Il+/frVRDxE9C8fNwt0a9oIJyIeYW3wXax/o42qQyIiqpcU7tnR0dGBh4eHUp781KlTGDBgAOzt7SGRSBAUFCQ7V1RUhPnz58PLywuGhoawt7fHmDFjkJCQ8Mw6ly9fjpdffhnGxsawtrbG4MGDERERIVemW7dukEgkcrepU6cqpU1EyjSvV2nvzv5rCbidmKXiaIiI6ieFk525c+di/fr1UMa45tzcXHh7e2Pjxo3lzuXl5eHKlStYuHAhrly5gj179iAiIgIDBw58Zp0nT57EtGnTcP78eRw7dgxFRUXo1asXcnPlLwNMnjwZiYmJstuKFSteuD1EytbKwRT9vewgBLD66F1Vh0NEVC8pfBnrzJkzCA0NxaFDh9CyZUtoa2vLnd+zZ0+V6+rbty/69u1b4TlTU1McO3ZM7tiGDRvg4+OD+Ph4ODs7V/i4w4cPy90PDAyEtbU1Ll++jK5du8qOGxgYwNbWtsqxEqnK7Fea4NDNRATfTsbV+Mdo42yu6pCIiOoVhXt2zMzMMGTIEPj7+8PKygqmpqZyt5qUmZkJiUQCMzMzhR4DABYWFnLHf/nlF1hZWaFVq1ZYsGAB8vLynllPQUEBsrKy5G5EtcHD2gjD2joCAFYdjXhOaSIi+i+Fe3a2bNlSE3E8V35+PubPn4+RI0dWeV69VCrFrFmz0LlzZ7Rq1Up2/M0334SLiwvs7e1x/fp1zJ8/HxEREc/slVq+fDmWLFnywu0gqo4ZPTwRFP4QZ6PScDYqFZ09rFQdEhFRvVFnFhWUSCTYu3cvBg8eXO5cUVERhg0bhgcPHuDEiRNVTnbeeecdHDp0CGfOnIGjo2Ol5UJCQtCjRw9ERUXB3b3inaYLCgpQUPC/pfuzsrLg5OTERQWp1izadxNbw+LQ2skMe9/tBIlEouqQiIhqXY0tKti2bVscP34c5ubmaNOmzTM/ZK9cuVK1aKuoqKgII0aMQFxcHEJCQqrcsPfeew8HDhzAqVOnnpnoAICvry8APDPZKdsHjEhVpnX3wI5L9xF+PwPBt1PwSgsbVYdERFQvVCnZGTRokOyLvqKel5pSluhERkYiNDQUlpaWz32MEALTp0/H3r17ceLEiSrtvh4eHg4AsLOze9GQiWqMtbEexnd2w6YT0Vh1JAI9mllDQ4O9O0REz6PSy1g5OTmIiooCALRp0wZr1qxBQEAALCwsYGdnh9deew1XrlzBgQMHYGPzv79iLSwsoKOjAwDo0aMHhgwZgvfeew8A8O677+LXX3/Fvn375DYnNTU1hb6+PqKjo/Hrr7+iX79+sLS0xPXr1zF79mw4Ojri5MmTVY6de2ORKmTkFaLLilBk5xdj/RutMai1g6pDIiKqVfVuI9ATJ04gICCg3PGxY8di8eLFlfbKhIaGolu3bgAAV1dXjBs3DosXLwaASi+xbdmyBePGjcP9+/cxatQo3Lx5E7m5uXBycsKQIUPw8ccfK5S0MNkhVdkQEolVR+/CxdIAwXP8oa2p8KRKIqJ6q8aSHXNz8yoPhkxPT69SufqOyQ6pSm5BMbquCEVabiGWDfHCm74VrzlFRKSOamyA8rp162T/T0tLw2effYbevXujY8eOAICwsDAcOXIECxcuVDxqIlKIoa4WpgV4YOmBW/jqeCSGtnWAnramqsMiIqqzFL6MNWzYMAQEBMjGyJTZsGEDgoOD5fa3Umfs2SFVyi8qQfdVJ5CQmY+P+zfHpC6NVR0SEVGtqM73r8IX+48cOYI+ffqUO96nTx8EBwcrWh0RVYOetiZm9vQEAHxzIho5BcUqjoiIqO5SONmxtLTEvn37yh3ft29flaaGE5FyDGvrCDcrQ6TnFuKnMzGqDoeIqM5SeLuIJUuWYNKkSThx4oRsMb4LFy7g8OHD+OGHH5QeIBFVTEtTA7NfaYIZv13FD6fuYXQHF5gb6qg6LCKiOkfhnp1x48bh7NmzMDExwZ49e7Bnzx6YmJjgzJkzGDduXA2ESESVedXLDs1sjZFdUIxvT0WrOhwiojqpzuyNVd9wgDLVFcdvJ2Pi1kvQ09bAyfcDYGOip+qQiIhqTI1NPc/KyqpyEPziJ6pd3ZtZo62zGa7EZ2BDSBQ+HdxK1SEREdUpVUp2zMzMqryoYElJyQsFRESKkUgkeL93M4z84Tx+uxiPyV0aw9nSQNVhERHVGVVKdkJDQ2X/j42NxYcffohx48bJLSq4detWLF++vGaiJKJn6uhuiS6eVjgdmYp1x+9izYjWqg6JiKjOUHjMTo8ePTBp0iSMHDlS7vivv/6K77//HidOnFBmfHUWx+xQXXPtfgYGbTwLDQlwZFZXeNoYqzokIiKlq5VFBcPCwtC+fftyx9u3b4+LFy8qWh0RKYm3kxl6t7SBVABrjt1VdThERHWGwsmOk5NThevp/Pjjj3ByclJKUERUPXN7NYVEAhy6mYTrDzJUHQ4RUZ2g8KKCa9euxbBhw3Do0CHZooIXL15EZGQk/vjjD6UHSERV18TGGINbO2Dv1YdYdfQutk3wUXVIREQqp3DPTr9+/RAZGYmBAwciPT0d6enpGDBgAO7evYt+/frVRIxEpIBZPT2hpSHBqbuPcP5emqrDISJSOS4qWE0coEx12f/tvYFfLsSjvYs5dk3tWOWlI4iI6roaW1SwInl5eYiPj0dhYaHc8Zdeeqm6VRKRkkzv7ondlx/gUtxjnIh4hIBm1qoOiYhIZRROdh49eoTx48fj0KFDFZ7nooJEqmdrqoexnVzx/al7WHkkAv5NGkFDg707RNQwKTxmZ9asWcjIyMCFCxegr6+Pw4cPY+vWrfD09MT+/ftrIkYiqoap/u4w0tXCrcQs/HUzUdXhEBGpjMLJTkhICNasWYP27dtDQ0MDLi4uGDVqFFasWMEVlInqEAtDHUzq4gagdN2d4hKpiiMiIlINhZOd3NxcWFuXXv83NzfHo0ePAABeXl64cuWKcqMjohcy0c8N5gbauPcoF3uuPlR1OEREKqFwstO0aVNEREQAALy9vfHdd9/h4cOH+Pbbb2FnZ6f0AImo+oz1tPFuNw8AwPrgSBQUc0wdETU8Cic7M2fORGJi6fX/RYsW4dChQ3B2dsZXX32FZcuWKT1AInoxozu6wMZEFw8znuC3C/GqDoeIqNa98Do7eXl5uHPnDpydnWFlZaWsuOo8rrND9cnP5+PwcdBNWBnp4NQHATDQqfaqE0REKlUrG4H+l4GBAdq2bdugEh2i+mZEeyc4WxggNacQW87GqjocIqJa9cLJDhHVfTpaGpj9iicA4LuT0cjMK1JxREREtYfJDlEDMdDbAU1sjJCVX4zvT0erOhwiolrDZIeogdDUkGBur6YAgJ/OxOJRdoGKIyIiqh0KJTvFxcVYunQpHjx4UFPxEFEN6tXCBt6OpnhSVIKNoVGqDoeIqFYolOxoaWlh5cqVKC4urql4iKgGSSQSvN+7GQDg1wvxeJjxRMURERHVPIUvY3Xv3h0nT56siViIqBZ09rBEx8aWKCyR4qvgSFWHQ0RU4xRebKNv37748MMPcePGDbRr1w6GhoZy5wcOHKi04IhI+SQSCeb1bophm85h95UHmOLfGO6NjFQdFhFRjVF4UUENjco7gyQSCUpKGsZy9FxUkOq7SVv/RvDtFLz6kh02vNlW1eEQEVVJrSwqKJVKK701lESHSB3MeaV0ZtaB64n4JyFTxdEQEdWcF5p6np+fr6w4iKiWtbA3wQBvewDA6qN3VRwNEVHNUTjZKSkpwaeffgoHBwcYGRnh3r17AICFCxdi8+bNSg+QiGrO7J6e0NSQIOROCi7Fpqs6HCKiGqFwsvP5558jMDAQK1asgI6Ojux4q1at8OOPPyo1OCKqWY0bGWF4O0cAwIojEXjBfYGJiOokhZOdbdu24fvvv8dbb70FTU1N2XFvb2/cuXNHobpOnTqFAQMGwN7eHhKJBEFBQbJzRUVFmD9/Pry8vGBoaAh7e3uMGTMGCQkJz61348aNcHV1hZ6eHnx9fXHx4kW58/n5+Zg2bRosLS1hZGSEYcOGITk5WaHYidTFjB6e0NHUwMWYdJyOTFV1OERESqdwsvPw4UN4eHiUOy6VSlFUpNjmgrm5ufD29sbGjRvLncvLy8OVK1ewcOFCXLlyBXv27EFERMRzp7bv2LEDc+bMwaJFi3DlyhV4e3ujd+/eSElJkZWZPXs2/vzzT+zatQsnT55EQkIChg4dqlDsROrC3kwfozq4AABWHWXvDhGpIaGgtm3biu3btwshhDAyMhLR0dFCCCGWLFki/Pz8FK1OBoDYu3fvM8tcvHhRABBxcXGVlvHx8RHTpk2T3S8pKRH29vZi+fLlQgghMjIyhLa2tti1a5eszO3btwUAERYWVuV4MzMzBQCRmZlZ5ccQ1VWPsvNF84WHhMv8A+LQjURVh0NEVKnqfP8qvKjgJ598grFjx+Lhw4eQSqWyHpdt27bhwIEDSk7F5GVmZkIikcDMzKzC84WFhbh8+TIWLFggO6ahoYGePXsiLCwMAHD58mUUFRWhZ8+esjLNmjWDs7MzwsLC0KFDhwrrLigoQEHB/zZOzMrKUkKLiOoGKyNdTPRzw9chUVh9NAKvtLCBpoZE1WERESmFwpexBg0ahD///BPBwcEwNDTEJ598gtu3b+PPP//EK6+8UhMxAigdZzN//nyMHDmy0kWEUlNTUVJSAhsbG7njNjY2SEpKAgAkJSVBR0enXML0dJmKLF++HKamprKbk5PTizWIqI6Z1KUxTPW1EZmSg33hD1UdDhGR0lRrnZ0uXbrg2LFjSElJQV5eHs6cOYNevXopOzaZoqIijBgxAkIIbNq0qcae51kWLFiAzMxM2e3+/fsqiYOoppjqa2OqvzsAYG3wXRQWS1UcERGRciic7DRu3BhpaWnljmdkZKBx48ZKCeppZYlOXFwcjh079syloa2srKCpqVluZlVycjJsbW0BALa2tigsLERGRkalZSqiq6sLExMTuRuRuhnbyQVWRrq4n/4EOy4xoSci9aBwshMbG1vhthAFBQV4+FC5Xd9liU5kZCSCg4NhaWn5zPI6Ojpo164djh8/LjsmlUpx/PhxdOzYEQDQrl07aGtry5WJiIhAfHy8rAxRQ2Wgo4Xp3UtnW359PBJPCrkFDBHVf1UeoLx//37Z/48cOQJTU1PZ/ZKSEhw/fhyurq4KPXlOTg6ioqJk92NiYhAeHg4LCwvY2dnhtddew5UrV3DgwAGUlJTIxtRYWFjIFjTs0aMHhgwZgvfeew8AMGfOHIwdOxbt27eHj48P1q1bh9zcXIwfPx4AYGpqiokTJ2LOnDmwsLCAiYkJpk+fjo4dO1Y6OJmoIXnDxwnfn7qHhxlPsC0sFm//e2mLiKjequq0LYlEUulNR0dHNGnSRPz5558KTR8LDQ0VAMrdxo4dK2JiYio8B0CEhobK6nBxcRGLFi2Sq/frr78Wzs7OQkdHR/j4+Ijz58/LnX/y5Il49913hbm5uTAwMBBDhgwRiYmKTbfl1HNSZzv/jhcu8w8I7yVHROaTQlWHQ0QkU53vX4kQz19B7Pr162jZsiU0NTXh5uaGv//+G1ZWVjWUftUP1dlinqi+KC6Rove6U4h+lIsZPTwx55Umqg6JiAhA9b5/qzRmp02bNkhPL90kUCKRQCLh+htE6kxLUwNzezUFAGw+fQ/puYUqjoiIqPqqlOyYmZnJdjePi4uDVMopqUTqrk9LW7RyMEFuYQk2nYh6/gOIiOqoKg1QHjZsGPz9/WFnZwcAaN++vdwmoE8rS4qIqH7T0JBgXq+mGLflb2wNi8MEPzfYmeqrOiwiIoVVKdn5/vvvMXToUERFRWHGjBmYPHkyjI2Nazo2IlIx/yaN4ONqgYux6fg6JArLhnipOiQiIoVVeep5nz59AJTuLTVz5kwmO0QNgEQiwbzeTTHiuzDs/Ps+pnRpDFcrQ1WHRUSkEIUXFdyyZQsTHaIGxMfNAv5NGqFYKrAu+K6qwyEiUpjCu54DwKVLl7Bz507Ex8ejsFB+lsaePXuUEhgR1R3zejXFybuPsO9aAqZ2c0czWy63QET1h8I9O7///js6deqE27dvY+/evSgqKsI///yDkJAQuVWViUh9eDmaop+XLYQAVh9l7w4R1S8KJzvLli3D2rVr8eeff0JHRwfr16/HnTt3MGLECDg7O9dEjERUB8x5pQk0JMCxW8m4Gv9Y1eEQEVWZwslOdHQ0+vfvD6B0483c3FxIJBLMnj0b33//vdIDJKK6wcPaGEPbOgIAVh2NUHE0RERVp3CyY25ujuzsbACAg4MDbt68CQDIyMhAXl6ecqMjojplZg9PaGtKcDYqDeeiUlUdDhFRlSic7HTt2hXHjh0DAAwfPhwzZ87E5MmTMXLkSPTo0UPpARJR3eFkYYA3fUovV688GoEqbK1HRKRyVdoI9Gnp6enIz8+Hvb09pFIpVqxYgXPnzsHT0xMff/wxzM3NayrWOoUbgVJDlZKdj64rQpFfJMWPY9qjZwsbVYdERA1Idb5/FU52qBSTHWrIvjx8B5tORKOZrTH+mtEFGhrcHJiIakeN7XpORPS0t7s2hrGeFu4kZePP6wmqDoeI6JmY7BCRwswMdDClS2MAwNpjd1FUIlVxRERElWOyQ0TVMt7PDZaGOohNy8Puyw9UHQ4RUaWY7BBRtRjpauHdAA8AwPrgSOQXlag4IiKiiimc7EyYMEG2zs7TcnNzMWHCBKUERUT1w1u+zrAz1UNSVj5+Ph+n6nCIiCqkcLKzdetWPHnypNzxJ0+eYNu2bUoJiojqBz1tTczs4QkA2HQiGjkFxSqOiIiovConO1lZWcjMzIQQAtnZ2cjKypLdHj9+jL/++gvW1tY1GSsR1UHD2jnCzcoQabmF2HImRtXhEBGVo1XVgmZmZpBIJJBIJGjSpEm58xKJBEuWLFFqcERU92lramD2K00w47er+P7UPYzu6AIzAx1Vh0VEJFPlZCc0NBRCCHTv3h1//PEHLCwsZOd0dHTg4uICe3v7GgmSiOq2V73s8E1oFO4kZePbk/fwYd9mqg6JiEhG4RWU4+Li4OzsDImkYa+YyhWUieQdv52MiVsvQU9bA6feD4C1iZ6qQyIiNVQrKyiHhIRg9+7d5Y7v2rULW7duVbQ6IlIT3ZtZo62zGfKLpNgQGqXqcIiIZBROdpYvXw4rK6tyx62trbFs2TKlBEVE9Y9EIsG83k0BAL9djMf99DwVR0REVErhZCc+Ph5ubm7ljru4uCA+Pl4pQRFR/dTJ3Qp+HlYoKhFYFxyp6nCIiABUI9mxtrbG9evXyx2/du0aLC0tlRIUEdVfZb07e68+QGRy+QVIiYhqm8LJzsiRIzFjxgyEhoaipKQEJSUlCAkJwcyZM/HGG2/URIxEVI+0djJDrxY2kApgzbG7qg6HiKjqU8/LfPrpp4iNjUWPHj2gpVX6cKlUijFjxnDMDhEBAOb2aopjt5Nx6GYSbjzIhJejqapDIqIGTOGp52Xu3r2La9euQV9fH15eXnBxcVF2bHUap54TPdvsHeHYe/Uh/Js0wtYJPqoOh4jURHW+fxXu2Snj6uoKIQTc3d1lPTxERGVm9fTEn9cScPLuI1y4lwbfxhzTR0SqofCYnby8PEycOBEGBgZo2bKlbAbW9OnT8cUXXyg9QCKqn1wsDfH6y04AgFVHI1DNTmQiohemcLKzYMECXLt2DSdOnICe3v9WSO3Zsyd27Nih1OCIqH6b3t0Tuloa+Dv2MU7cfaTqcIiogVI42QkKCsKGDRvg5+cnt2VEy5YtER0drdTgiKh+szXVw9hOrgCAVUciIJWyd4eIap/Cyc6jR49gbW1d7nhubm6D3y+LiMqb6u8OI10t/JOQhUM3k1QdDhE1QAonO+3bt8fBgwdl98sSnB9//BEdO3ZUXmREpBYsDHUw0a901fXVxyJQXCJVcURE1NAonOwsW7YMH330Ed555x0UFxdj/fr16NWrF7Zs2YLPP/9cobpOnTqFAQMGwN7eHhKJBEFBQXLn9+zZg169esHS0hISiQTh4eHPrbNbt26QSCTlbv3795eVGTduXLnzffr0USh2Iqq6SV3cYGagjXuPcrHn6kNVh0NEDYzCyY6fnx/Cw8NRXFwMLy8vHD16FNbW1ggLC0O7du0Uqis3Nxfe3t7YuHFjpef9/Pzw5ZdfVrnOPXv2IDExUXa7efMmNDU1MXz4cLlyffr0kSv322+/KRQ7EVWdsZ423u3mDgBYHxyJguISFUdERA1JlRbImTNnDj799FMYGhri1KlT6NSpE3744YcXfvK+ffuib9++lZ4fPXo0ACA2NrbKdVpYWMjd//3332FgYFAu2dHV1YWtrW3VgyWiFzKmoys2n4nBw4wn+P3ifdnAZSKimlalnp2vv/4aOTk5AICAgACkp6fXaFDKtHnzZrzxxhswNDSUO37ixAlYW1ujadOmeOedd5CWlvbMegoKCpCVlSV3I6Kq09PWxPTungCAr0OikFdYrOKIiKihqFLPjqurK7766iv06tULQgiEhYXB3Ny8wrJdu3ZVaoAv4uLFi7h58yY2b94sd7xPnz4YOnQo3NzcEB0djY8++gh9+/ZFWFgYNDU1K6xr+fLlWLJkSW2ETaS2RrR3wven7iE+PQ+B52LxbjcPVYdERA1AlfbGCgoKwtSpU5GSkgKJRFLpSqgSiQQlJdW7Fi+RSLB3714MHjy43LnY2Fi4ubnh6tWraN26dZXrfPvttxEWFobr168/s9y9e/fg7u6O4OBg9OjRo8IyBQUFKCgokN3PysqCk5MT98YiUtDeqw8we8c1mOhp4fT87jDV11Z1SERUj1Rnb6wqXcYaPHgwkpKSkJWVBSEEIiIi8Pjx43K3unR5Kzc3F7///jsmTpz43LKNGzeGlZUVoqKiKi2jq6sLExMTuRsRKW6gtwOa2BghK78YP5y6p+pwiKgBqFKyM2fOHOTm5sLIyAihoaFwc3ODqalphbe6YteuXSgoKMCoUaOeW/bBgwdIS0uDnZ1dLURG1LBpakgwt1dTAMBPZ2PwKLvgOY8gInoxCg9Q7t69u9J6cHJychAeHi5bPycmJgbh4eGyzUXT09MRHh6OW7duAQAiIiIQHh6OpKT/rcI6ZswYLFiwoFzdmzdvxuDBg2FpKb/Tck5ODt5//32cP38esbGxOH78OAYNGgQPDw/07t1bKe0iomfr1cIG3o6myCsswTcnKu9RJSJSBpUOUL506RICAgJk9+fMmQMAGDt2LAIDA7F//36MHz9edv6NN94AACxatAiLFy8GAMTHx0NDQz5ni4iIwJkzZ3D06NFyz6mpqYnr169j69atyMjIgL29PXr16oVPP/0Uurq6VY6diKpPIpFgXu+mGL35In45H49JXRrDwUxf1WERkZqqMwOU65vqDJAiov8RQmDkD+dx/l46Xm/vhC9fe0nVIRFRPcABykRUb0gkErzfu3Tszu4rD3DvUY6KIyIidaXQdhH1aYAyEdV97Vws0KOZNUqkAmuDI1UdDhGpqSonOzt37kRhYSH8/f2hpaWFBw8eQCr93+7FeXl5WLFiRY0ESUTqq2xm1p/XEnArgSuTE5HyVTnZGTlyJDIyMmT3W7RoIbdnVXZ2doWzooiInqWFvQkGeNsDAFYfjVBxNESkjqqc7Px3UHIVxjUTEVXJ7J6e0NSQ4PidFFyO49g/IlIuhcbsEBHVhMaNjDC8nSMAYMXhCP4xRURKxWSHiOqEGT08oaOpgQsx6TgTlarqcIhIjVRpUcEyR44ckc24kkqlOH78OG7evAkAcuN5iIgUZW+mj7c6OGPL2VisPBIBPw8rSCQSVYdFRGqgSosKAii3SnGFlXFRQSJ6AY+yC+C/MhR5hSX4dlQ79Gllq+qQiKiOqbFFBYHSnpzn3RpKokNENaORsS4mdHYDUDozq0TKsTtE9OI4ZoeI6pTJXRvDRE8LkSk52H/toarDISI1wGSHiOoUU31tTO3mDgBYeywShcXS5zyCiOjZmOwQUZ0zrpMrrIx0EZ+eh52X7qs6HCKq55jsEFGdY6CjhendPQAAXx2PRH4RxwMSUfUx2SGiOukNHyc4mOkjJbsA28JiVR0OEdVjTHaIqE7S1dLErJ6eAIBvTkQjO79IxRERUX1VpUUFzc3Nq7y4V3o697UhIuUY0sYB356MRvSjXPx4OgazX2mi6pCIqB6qUrKzbt062f/T0tLw2WefoXfv3ujYsSMAICwsDEeOHMHChQtrJEgiapi0NDUw55WmmPbrFfx4+h7GdnKFhaGOqsMionqmyisolxk2bBgCAgLw3nvvyR3fsGEDgoODERQUpMz46iyuoExUO6RSgQEbzuCfhCxM7uKG/+vfQtUhEZEK1egKymWOHDmCPn36lDvep08fBAcHK1odEdEzaWhIMK93UwDAtrA4JGXmqzgiIqpvFE52LC0tsW/fvnLH9+3bB0tLS6UERUT0tG5NGuFlV3MUFEvxdUikqsMhonpGoV3PAWDJkiWYNGkSTpw4AV9fXwDAhQsXcPjwYfzwww9KD5CISCKR4P3ezTDiuzDs+Ps+pnRtDBdLQ1WHRUT1hMI9O+PGjcPZs2dhYmKCPXv2YM+ePTAxMcGZM2cwbty4GgiRiAjwcbOAf5NGKJYKrAtm7w4RVZ3CA5SpFAcoE9W+Gw8yMWDDGUgkwOGZXdHU1ljVIRFRLauVAcoAEB0djY8//hhvvvkmUlJSAACHDh3CP//8U53qiIiqxMvRFP28bCEEsPpohKrDIaJ6QuFk5+TJk/Dy8sKFCxfwxx9/ICcnBwBw7do1LFq0SOkBEhE9bc4rTaAhAY7eSkb4/QxVh0NE9YDCyc6HH36Izz77DMeOHYOOzv8W9+revTvOnz+v1OCIiP7Lw9oYQ9s6AgBWHWHvDhE9n8LJzo0bNzBkyJByx62trZGamqqUoIiInmVmD09oa0pwJioV56L4uUNEz6ZwsmNmZobExMRyx69evQoHBwelBEVE9CxOFgYY6eMMAFh5NAKcZ0FEz6JwsvPGG29g/vz5SEpKgkQigVQqxdmzZzFv3jyMGTOmJmIkIirnvQAP6Glr4Gp8BkLupKg6HCKqwxROdpYtW4ZmzZrByckJOTk5aNGiBbp27YpOnTrh448/rokYiYjKsTbRw7hObgCAlUciIJWyd4eIKlbtdXbu37+PGzduICcnB23atIGnp6eyY6vTuM4Okepl5BWiy5ehyC4oxlcj22Cgt72qQyKiGlYr6+wsXboUeXl5cHJyQr9+/TBixAh4enriyZMnWLp0qcJBExFVl5mBDqZ0bQwAWHM0AkUlUhVHRER1kcI9O5qamkhMTIS1tbXc8bS0NFhbW6OkpESpAdZV7NkhqhtyCorhvyIUabmFsDLSRWcPS3T2sEJnDys4mOmrOjwiUrLqfP8qvBGoEAISiaTc8WvXrsHCwkLR6oiIXoiRrhY+G9wKc3ZeQ2pOAfaFJ2BfeAIAwM3KsDT5cbdCR3dLmBnoPKc2IlJHVU52zM3NIZFIIJFI0KRJE7mEp6SkBDk5OZg6dWqNBElE9Cx9vezQvbk1rsRl4GxUKs5Gp+La/QzEpOYiJjUXP5+Ph0QCtLI3RWcPK/h5WKG9qzn0tDVVHToR1YIqX8baunUrhBCYMGEC1q1bB1NTU9k5HR0duLq6omPHjjUWaF3Dy1hEdVtWfhEu3EvH2ahUnIlKRVRKjtx5HS0NtHcxl13y8nIwhaZG+V5rIqpbqvP9q/CYnZMnT6JTp07Q1tauVpBPO3XqFFauXInLly8jMTERe/fuxeDBg2Xn9+zZg2+//RaXL19Geno6rl69itatWz+zzsDAQIwfP17umK6uLvLz82X3hRBYtGgRfvjhB2RkZKBz587YtGmTQjPKmOwQ1S/JWfmlvT5RaTgblYqkrHy58yZ6WujQ2BJ+nlbo5G4F90aGFV6yJyLVqpUxO/7+/rL/5+fno7CwUO68Il/8ubm58Pb2xoQJEzB06NAKz/v5+WHEiBGYPHlyles1MTFBRMT/9sz57wfWihUr8NVXX2Hr1q1wc3PDwoUL0bt3b9y6dQt6enpVfh4iqj9sTPQwtK0jhrZ1hBAC0Y9ycS46FWciUxF2Lw1Z+cU4eisZR28lAwBsTfT+7fUpHfBsY8LPBqL6SuFkJy8vDx988AF27tyJtLS0cucVmY3Vt29f9O3bt9Lzo0ePBgDExsYqFKNEIoGtrW2F54QQWLduHT7++GMMGjQIALBt2zbY2NggKCgIb7zxhkLPRUT1j0QigYe1ETysjTCmoyuKS6S4mZD1b89PKi7FPkZSVj7+uPIAf1x5AADwtDaSXfLybWwBE70X790motqhcLLz/vvvIzQ0FJs2bcLo0aOxceNGPHz4EN999x2++OKLmohRYTk5OXBxcYFUKkXbtm2xbNkytGzZEgAQExODpKQk9OzZU1be1NQUvr6+CAsLqzTZKSgoQEFBgex+VlZWzTaCiGqNlqYGWjuZobWTGaYFeCC/qASXYh/jzL/Jz82ETESm5CAyJQeB52KhqSHBS46m8PMoveTV1sUMuloc7ExUVymc7Pz555/Ytm0bunXrhvHjx6NLly7w8PCAi4sLfvnlF7z11ls1EWeVNW3aFD/99BNeeuklZGZmYtWqVejUqRP++ecfODo6IikpCQBgY2Mj9zgbGxvZuYosX74cS5YsqdHYiahu0NPWhJ+nFfw8rQCUrtQcFp1Wust6dBpiUnNxNT4DV+Mz8HVIFPS0NfCyqwX8/u35aWFnAg0OdiaqMxROdtLT09G4cemKpSYmJkhPTwcA+Pn54Z133lFudNXQsWNHuVlhnTp1QvPmzfHdd9/h008/rXa9CxYswJw5c2T3s7Ky4OTk9EKxElH9YGagg75edujrZQcAeJjxRHbJ62xUGlJzCnA6MhWnI1MBAOYG2ujkbiUb8+NsYcDBzkQqpHCy07hxY8TExMDZ2RnNmjXDzp074ePjgz///BNmZmY1EOKL0dbWRps2bRAVFQUAsrE8ycnJsLOzk5VLTk5+5kwvXV1d6Orq1misRFQ/OJjpY0R7J4xo7wQhBO4m55T2+kSl4vy9NDzOK8LBG4k4eCMRAOBoro/O7lbo7GmFTu6WsDLiZwlRbVI42Rk/fjyuXbsGf39/fPjhhxgwYAA2bNiAoqIirFmzpiZifCElJSW4ceMG+vXrBwBwc3ODra0tjh8/LktusrKycOHChTrRM0VE9YtEIkFTW2M0tTXGRD83FJVIce1+hmyK+9X7j/Hg8RPsuHQfOy7dBwA0szWWXfLycbOAoa7CH8VEpACFf8Nmz54t+3/Pnj1x584dXL58GR4eHnjppZcUqisnJ0fW4wKUDh4ODw+HhYUFnJ2dkZ6ejvj4eCQklC79Xjad3NbWVtZDM2bMGDg4OGD58uUASjcq7dChAzw8PJCRkYGVK1ciLi4OkyZNAlD6wTRr1ix89tln8PT0lE09t7e3l1vjh4ioOrQ1NdDe1QLtXS0ws6cncguKcTE2HWcjU3E2Og23E7NwJykbd5Ky8eOZGGhpSNDW2RydPCzh52EFbyczaGsqvEczET3DC/854eLiAhcXl2o99tKlSwgICJDdLxsTM3bsWAQGBmL//v1yCwSWzZRatGgRFi9eDACIj4+Hhsb/PhgeP36MyZMnIykpCebm5mjXrh3OnTuHFi1ayMp88MEHyM3NxZQpU5CRkQE/Pz8cPnyYa+wQkdIZ6mohoKk1ApqWbp6cmlOAsOg02crODx4/wcXYdFyMTce64EgY6mjCt7GlbLxPUxtjjvchekEKr6AMAH///TdCQ0ORkpICqVQqd64uXsqqCVxBmYiUIT4tr3SKe3TpmJ/HeUVy562MdNHJvbTXp7Mnd3InqpXtIpYtW4aPP/4YTZs2hY2NjdxfHBKJBCEhIYpFXU8x2SEiZZNKBW4lZpWu7ByVhosxacgvkv+D0tXSQLaZKXdyp4aoVpIdGxsbfPnllxg3blx1YlQbTHaIqKYVFJfganyGbJr7tQeZKJH+7yO7bCf3svE+L7tacCd3Unu1kuzY2dnh1KlTCm2aqY6Y7BBRbXt6J/ezUamIrGAn93bO5v9uZmoJLwdTaHGwM6mZWkl2VqxYgYSEBKxbt646MaoNJjtEpGrJWfn/bmaahnPRqUjMlN/J3VhPCx1lg525kzuph1pJdqRSKfr374+7d++iRYsW0NaW3wxvz549ilRXbzHZIaK6RAiBe6m5OPfvLK9z0WnIzi+WK2Nroie75MWd3Km+qs73r8JTz2fMmIHQ0FAEBATA0tKSfyUQEdUBEokE7o2M4N7ICKM7uqJEKnDzYea/iU8q/v53J/c9Vx5iz5WHAAAPayN0di/t+engbsmd3EltKdyzY2xsjN9//x39+/evqZjqBfbsEFF9UraT+9no0vE+Nx5m4ulPfw0J8JKjmazXhzu5U11VK5exXFxccOTIETRr1qxaQaoLJjtEVJ9l5BXi/L1/d3KPSsO91Fy582U7uZdNc+dO7lRX1Eqys2XLFhw+fBhbtmyBgYFBtQJVB0x2iEidlO3kXjrmp3Qn96eZG2ij47+XvDq7W8HFkju5k2rUSrLTpk0bREdHQwgBV1fXcgOUr1y5okh19RaTHSJSV0IIRKbk4Exk6SWvCzHpyCmQH+zsYKYPPw8rdPKwRCd3KzQy5k7uVDtqZYAyN8skIlJvEokETWyM0cTGGBP+3cn9+oPSndzPRKXiavxjPMwov5N72SUv7uROdU219sYi9uwQUcOVV1iMizFlixum4VZiltx5LQ0J2jibydb3ac2d3EmJauUyFpViskNEVCotpwDnotP+3dMrFffTn8idN9TRhI/bv4OdPa24kzu9kBpLdiwsLHD37l1YWVnB3Nz8mT+k6enpVY+4HmOyQ0RUsfi0PJz9N/EJi05Dem6h3HkrIx10creSjflxNG+4k11IcTU2Zmft2rUwNjaW/Z8ZORERVcbZ0gDOls4Y6eMMqVTgdlIWzv073udiTDpScwqx/1oC9l9LAFC6k3unsp3cG1vC3JA7uZNy8TJWNbFnh4hIcYXFUlyNf4yz/25rUdFO7i3tTWRT3F92tYC+Dhc3pP+plTE7mpqaSExMhLW1tdzxtLQ0WFtbo6SkRJHq6i0mO0RELy67bCf3f1d2vpv8n53cNTXQzsUcnT1K1/jhTu5UK8mOhoYGkpKSyiU7CQkJcHd3x5MnTyp5pHphskNEpHwpWfk4F116yetsVAU7uetqoYN72WamlnBvZMShFQ1Mja6z89VXXwEoXX/hxx9/hJGRkexcSUkJTp061eC3kCAiohdjbaKHwW0cMLiNA4QQiEnNlU1xPxediqz8Yhy7lYxjt5IBADYmuujsbiWb5m5ryp3cqbwq9+y4ubkBAOLi4uDo6AhNzf9dQ9XR0YGrqyuWLl0KX1/fmom0jmHPDhFR7SqRCvyTkCnr9fk79jEKi6VyZdwbGco2M+VO7uqpVi5jBQQEYM+ePTA3N69WkOqCyQ4RkWrlF5Xgctzjf3t+UnG9kp3cy8b7tHU2h542BzvXdypZVLCkpAQ3btyAi4tLg0qAmOwQEdUtmXlFCLuXVpr8RKfi3iP5ndx1tTRkixt2drdCC3sTaHIn93qnVpKdWbNmwcvLCxMnTkRJSQm6du2KsLAwGBgY4MCBA+jWrVt1Yq93mOwQEdVtCWU7uf874PlRtvxO7mYG2ujkbilb4JA7udcPtZLsODg4YN++fWjfvj2CgoIwbdo0hIaGYvv27QgJCcHZs2erFXx9w2SHiKj+EEIgKiVHNt7n/L2Kd3Ivu+TFndzrrlpJdvT09BAVFQVHR0dMmTIFBgYGWLduHWJiYuDt7Y2srKznV6IGmOwQEdVfxSVSXHuQKRvvcyX+MYpK5L8Om9kal/b6eFrCx80SRtzJvU6o0annZWxsbHDr1i3Y2dnh8OHD2LRpEwAgLy9PboYWERFRXaX172KF7VzMMaOHJ/IKi/F37L8rO0em4lZiFu4kZeNOUjZ+OhsDLQ0JWjuZyTYz5U7u9YvCyc748eMxYsQI2NnZQSKRoGfPngCACxcucJ0dIiKqlwx0tODfpBH8mzQCULqTe+lg59IBz/HpebgU9xiX4h5j/fFIGOhowrdssLNH6U7uGhzsXGdVazbW7t27cf/+fQwfPhyOjo4AgK1bt8LMzAyDBg1SepB1ES9jERE1HPfT82T7eYVFpyGtgp3cO7pbwc+jdMCzkwV3cq8pKpl63lAx2SEiapikUoE7SdmyKe4X7qXjSZH8vpAulgayKe6d3LmTuzLVaLLTr18//PbbbzA1NQUAfPHFF5g6dSrMzMwAlG4E2qVLF9y6dat60dczTHaIiAgo3ck9/H6GbKZX+P2Mcju5t7Azka3szJ3cX0yNJjv/3e3cxMQE4eHhaNy4MQAgOTkZ9vb23PWciIgatOz8IlyMSZeN94lIzpY7r6OpgbYuZqV7enla4SXu5K6QGp2N9d+ciFe/iIiIyjPW00aP5jbo0dwGAJCSnY+w6DSciSzt+UnIzMf5e+k4fy8dq4/dhbGuFnwbW8LPwxJ+nlbcyb0GcNEAIiKiGmRtrIdBrR0wqHXpTu6xaXk4E5WKc/+u7pz5pAjBt5MRfDv53/K6skte3MldOaqc7EgkknKZJjNPIiKiqpNIJHCzMoSblSFGd3BBiVTgVkLWUzu5pyMluwB7rj7EnqsPAZTu5F6W+HRobAlTfe7krqgqj9nR0NBA3759oatbunz2n3/+ie7du8PQ0BAAUFBQgMOHD3PMDhERUTXlF5XgStzj0uQnOg03HmRA+p+d3L0czeDnYYnO7lZo69LwdnKv0QHK48ePr1KFW7ZsqVK5+o7JDhER1bTMJ0U4/+9O7meiKt/JvWwz04awkzvX2alFTHaIiKi2JWY+wdmoNJz7N/lJ+c9O7qb6pTu5l132clXDndyr8/2r0rlup06dwoABA2Bvbw+JRIKgoCC583v27EGvXr1gaWkJiUSC8PDw59b5ww8/oEuXLjA3N4e5uTl69uyJixcvypUZN26cbAxS2a1Pnz5KbBkREZHy2Znq47V2jljzemtc+KgHjs3uisUDWqBncxsY62oh80kRDt1MwsdBNxGw6gT8vgzF+7uuYV/4Qzz6T2LUkKh0NlZubi68vb0xYcIEDB06tMLzfn5+GDFiBCZPnlylOk+cOIGRI0eiU6dO0NPTw5dffolevXrhn3/+gYODg6xcnz595C65lY1FIiIiqg8kEgk8bYzhaWOMcZ3dUFwixfWHmTgbWbqy85W4DDzMeIJdlx9g1+UHAICmNsb/bmbasHZyrzOXsSQSCfbu3YvBgweXOxcbGws3NzdcvXoVrVu3VqjekpISmJubY8OGDRgzZgyA0p6djIyMcj1JiuBlLCIiqsueFJbg79h02XifW4lZePobv2wn904epeN9WjuZQUer7i9uWKOLCtZXeXl5KCoqgoWFhdzxEydOwNraGubm5ujevTs+++wzWFpaVlpPQUEBCgr+1wWYlZVVYzETERG9KH0dTXRt0ghd/93JPT23sHRxw6hUnItORVza/3Zy/+rfndx93Czg52GFTu5WaGarPju5q32yM3/+fNjb26Nnz56yY3369MHQoUPh5uaG6OhofPTRR+jbty/CwsKgqVnxFL7ly5djyZIltRU2ERGRUlkY6qD/S3bo/5IdgNKd3M9Fp+LMvwOe03ILcSLiEU5EPAIAWBrqoJOHFTr/O+C5Pu/krtaXsb744gusWLECJ06cwEsvvVRpuXv37sHd3R3BwcHo0aNHhWUq6tlxcnLiZSwiIqr3pFKBiOR/d3KPSsWFmHTkFcqvm+dsUbqTu5+HFTq6W8JCRTu58zLWU1atWoUvvvgCwcHBz0x0AKBx48awsrJCVFRUpcmOrq4uBzETEZFa0tCQoLmdCZrbmWBSl8ayndzPPrWTe3x6HuIvxuO3i/EAgJb2JrIp7j51fCd3tUx2VqxYgc8//xxHjhxB+/btn1v+wYMHSEtLg52dXS1ER0REVLfp/LtYoY+bBWa/0gQ5BcW4GJOGM5FpOBedijtJ2fgnIQv/JGTh+1P3oKOpgTbOZqV7etXBndxVmuzk5OQgKipKdj8mJgbh4eGwsLCAs7Mz0tPTER8fj4SEBABAREQEAMDW1ha2trYAgDFjxsDBwQHLly8HAHz55Zf45JNP8Ouvv8LV1RVJSUkAACMjIxgZGSEnJwdLlizBsGHDYGtri+joaHzwwQfw8PBA7969a7P5RERE9YKRrha6N7NB92alO7k/yi7AuejUf3t+0vAw4wkuxKTjQoz8Tu6dPSwxwNseVkaqvTKi0jE7J06cQEBAQLnjY8eORWBgIAIDAyvcpmLRokVYvHgxAKBbt25wdXVFYGAgAMDV1RVxcXGVPubJkycYPHgwrl69ioyMDNjb26NXr1749NNPYWNjU+XYOfWciIgIEEIgrmwn9+jSndwz8opk54/M6oqmtsZKez5uF1GLmOwQERGVV7aT+9noVNx4kIkNb7ZR6pYVHKBMREREKqWpIYGXoym8HE1VHYpM3Rk9RERERFQDmOwQERGRWmOyQ0RERGqNyQ4RERGpNSY7REREpNaY7BAREZFaY7JDREREao3JDhEREak1JjtERESk1pjsEBERkVpjskNERERqjckOERERqTUmO0RERKTWmOwQERGRWmOyQ0RERGpNS9UB1FdCCABAVlaWiiMhIiJqOMq+d8u+h6uCyU41ZWdnAwCcnJxUHAkREVHDk52dDVNT0yqVlQhFUiOSkUqlSEhIgLGxMSQSiarDUbqsrCw4OTnh/v37MDExUXU4NaYhtJNtVB8NoZ0NoY1Aw2hnTbVRCIHs7GzY29tDQ6Nqo3HYs1NNGhoacHR0VHUYNc7ExERtfxGf1hDayTaqj4bQzobQRqBhtLMm2ljVHp0yHKBMREREao3JDhEREak1JjtUIV1dXSxatAi6urqqDqVGNYR2so3qoyG0syG0EWgY7axLbeQAZSIiIlJr7NkhIiIitcZkh4iIiNQakx0iIiJSa0x2iIiISK0x2VETGzduhKurK/T09ODr64uLFy8+s/yuXbvQrFkz6OnpwcvLC3/99ZfceSEEPvnkE9jZ2UFfXx89e/ZEZGSk7HxsbCwmTpwINzc36Ovrw93dHYsWLUJhYaFcGYlEUu52/vz5etFGAHB1dS0X/xdffCFX5vr16+jSpQv09PTg5OSEFStWVKt9qmrniRMnKnyfJBIJ/v77bwB1/73cs2cPevXqBUtLS0gkEoSHh5erIz8/H9OmTYOlpSWMjIwwbNgwJCcny5WJj49H//79YWBgAGtra7z//vsoLi6uVhtV0c709HRMnz4dTZs2hb6+PpydnTFjxgxkZmbKlavovfz999/rRRsBoFu3buXinzp1qlwZZb6Xtd3Gyn7fJBIJdu3aJSunzPdR2e0sKirC/Pnz4eXlBUNDQ9jb22PMmDFISEiQqyM9PR1vvfUWTExMYGZmhokTJyInJ0eujFI+YwXVe7///rvQ0dERP/30k/jnn3/E5MmThZmZmUhOTq6w/NmzZ4WmpqZYsWKFuHXrlvj444+Ftra2uHHjhqzMF198IUxNTUVQUJC4du2aGDhwoHBzcxNPnjwRQghx6NAhMW7cOHHkyBERHR0t9u3bJ6ytrcXcuXNldcTExAgAIjg4WCQmJspuhYWF9aKNQgjh4uIili5dKhd/Tk6O7HxmZqawsbERb731lrh586b47bffhL6+vvjuu+8UbqOq2llQUCDXvsTERDFp0iTh5uYmpFKpEKLuv5fbtm0TS5YsET/88IMAIK5evVqunqlTpwonJydx/PhxcenSJdGhQwfRqVMn2fni4mLRqlUr0bNnT3H16lXx119/CSsrK7FgwQKF26iqdt64cUMMHTpU7N+/X0RFRYnjx48LT09PMWzYMLlyAMSWLVvk3sunf+7rchuFEMLf319MnjxZLv7MzEzZeWW+l6poY3FxcbnfySVLlggjIyORnZ0tK6es97Em2pmRkSF69uwpduzYIe7cuSPCwsKEj4+PaNeunVw9ffr0Ed7e3uL8+fPi9OnTwsPDQ4wcOVJ2XlmfsUx21ICPj4+YNm2a7H5JSYmwt7cXy5cvr7D8iBEjRP/+/eWO+fr6irffflsIIYRUKhW2trZi5cqVsvMZGRlCV1dX/Pbbb5XGsWLFCuHm5ia7X/YFWdGHlaJU1UYXFxexdu3aSuP65ptvhLm5uSgoKJAdmz9/vmjatKlC7StTF97LwsJC0ahRI7F06VLZsbr8Xj6tsjgzMjKEtra22LVrl+zY7du3BQARFhYmhBDir7/+EhoaGiIpKUlWZtOmTcLExETu/a3L7azIzp07hY6OjigqKpIdAyD27t1btYY8g6ra6O/vL2bOnFlpXMp8L+vK+9i6dWsxYcIEuWPKeh+FqNl2lrl48aIAIOLi4oQQQty6dUsAEH///beszKFDh4REIhEPHz4UQijvM5aXseq5wsJCXL58GT179pQd09DQQM+ePREWFlbhY8LCwuTKA0Dv3r1l5WNiYpCUlCRXxtTUFL6+vpXWCQCZmZmwsLAod3zgwIGwtraGn58f9u/fr1D7ANW38YsvvoClpSXatGmDlStXynWFh4WFoWvXrtDR0ZF7noiICDx+/LhetbPM/v37kZaWhvHjx5c7Vxffy6q4fPkyioqK5Opp1qwZnJ2dZfWEhYXBy8sLNjY2cs+TlZWFf/75p8rPBaiunRXJzMyEiYkJtLTkt0KcNm0arKys4OPjg59++glCwSXXVN3GX375BVZWVmjVqhUWLFiAvLw8uedRxnup6jaWuXz5MsLDwzFx4sRy5170fQRqr52ZmZmQSCQwMzOT1WFmZob27dvLyvTs2RMaGhq4cOGCrIwyPmO5EWg9l5qaipKSErlfagCwsbHBnTt3KnxMUlJSheWTkpJk58uOVVbmv6KiovD1119j1apVsmNGRkZYvXo1OnfuDA0NDfzxxx8YPHgwgoKCMHDgwHrRxhkzZqBt27awsLDAuXPnsGDBAiQmJmLNmjWyetzc3MrVUXbO3Ny8XrTzaZs3b0bv3r3lNrqty+9lVSQlJUFHR0f2IVtRPZU9T9k5RaiqnRXF8emnn2LKlClyx5cuXYru3bvDwMAAR48exbvvvoucnBzMmDFDobpV1cY333wTLi4usLe3x/Xr1zF//nxERERgz549z3yesnNVVVfex82bN6N58+bo1KmT3HFlvI9A7bQzPz8f8+fPx8iRI2WbgiYlJcHa2lqunJaWFiwsLOR+L5XxGctkh17Yw4cP0adPHwwfPhyTJ0+WHbeyssKcOXNk919++WUkJCRg5cqVCn1BqtLT8b/00kvQ0dHB22+/jeXLl9eJJdCV7cGDBzhy5Ah27twpd1wd3suGJisrC/3790eLFi2wePFiuXMLFy6U/b9NmzbIzc3FypUrFf6SVJWnkzcvLy/Y2dmhR48eiI6Ohru7uwojU74nT57g119/lXvPytSX97GoqAgjRoyAEAKbNm1SSQy8jFXPWVlZQVNTs9yskuTkZNja2lb4GFtb22eWL/u3KnUmJCQgICAAnTp1wvfff//ceH19fREVFfXcck9TdRv/G39xcTFiY2Of+TxPP0dV1YV2btmyBZaWllVKYOrKe1kVtra2KCwsREZGRqX11PX3UhHZ2dno06cPjI2NsXfvXmhraz+zvK+vLx48eICCgoIqP4eq2/g0X19fAJD9PCrrvawLbdy9ezfy8vIwZsyY55atzvsI1Gw7yxKduLg4HDt2TNarU1ZHSkqKXPni4mKkp6cr/feSyU49p6Ojg3bt2uH48eOyY1KpFMePH0fHjh0rfEzHjh3lygPAsWPHZOXd3Nxga2srVyYrKwsXLlyQq/Phw4fo1q0b2rVrhy1btkBD4/k/TuHh4bCzs6s3bawofg0NDVnXa8eOHXHq1CkUFRXJPU/Tpk0VuoRVF9ophMCWLVswZsyY5345AnXnvayKdu3aQVtbW66eiIgIxMfHy+rp2LEjbty4IffhW/bh3KJFiyo/F6C6dgKl72+vXr2go6OD/fv3Q09P77mPCQ8Ph7m5uUK9laps43+VTd0u+3lU1ntZF9q4efNmDBw4EI0aNXpu2eq8j0DNtbMs0YmMjERwcDAsLS3L1ZGRkYHLly/LjoWEhEAqlcoSWKV9xio0nJnqpN9//13o6uqKwMBAcevWLTFlyhRhZmYmm4kwevRo8eGHH8rKnz17VmhpaYlVq1aJ27dvi0WLFlU4XdnMzEzs27dPXL9+XQwaNEhuuvKDBw+Eh4eH6NGjh3jw4IHc1McygYGB4tdffxW3b98Wt2/fFp9//rnQ0NAQP/30U71o47lz58TatWtFeHi4iI6OFj///LNo1KiRGDNmjKyOjIwMYWNjI0aPHi1u3rwpfv/9d2FgYPBCU89ru51lgoODBQBx+/btcnHV9fcyLS1NXL16VRw8eFAAEL///ru4evWq3M/j1KlThbOzswgJCRGXLl0SHTt2FB07dpSdL5uu3KtXLxEeHi4OHz4sGjVq9EJTz2u7nZmZmcLX11d4eXmJqKgoud/L4uJiIYQQ+/fvFz/88IO4ceOGiIyMFN98840wMDAQn3zySb1oY1RUlFi6dKm4dOmSiImJEfv27RONGzcWXbt2ldWhzPdSVT+vQggRGRkpJBKJOHToULm4lPk+1kQ7CwsLxcCBA4Wjo6MIDw+X+1l8emZVnz59RJs2bcSFCxfEmTNnhKenp9zUc2V9xjLZURNff/21cHZ2Fjo6OsLHx0ecP39eds7f31+MHTtWrvzOnTtFkyZNhI6OjmjZsqU4ePCg3HmpVCoWLlwobGxshK6urujRo4eIiIiQnd+yZYsAUOGtTGBgoGjevLkwMDAQJiYmwsfHR27qb11v4+XLl4Wvr68wNTUVenp6onnz5mLZsmUiPz9frp5r164JPz8/oaurKxwcHMQXX3xR7Taqop1lRo4cKbfuzNPq+ntZ2c/jokWLZGWePHki3n33XWFubi4MDAzEkCFDyn25xMbGir59+wp9fX1hZWUl5s6dKzdlu663MzQ0tNLfy5iYGCFE6dTe1q1bCyMjI2FoaCi8vb3Ft99+K0pKSupFG+Pj40XXrl2FhYWF0NXVFR4eHuL999+XW2dHCOW+l6r4eRVCiAULFggnJ6cK3xtlv4/KbmfZtPqKbqGhobJyaWlpYuTIkcLIyEiYmJiI8ePHy60lJIRyPmMlQlRjnhoRERFRPcExO0RERKTWmOwQERGRWmOyQ0RERGqNyQ4RERGpNSY7REREpNaY7BAREZFaY7JDREREao3JDhEREak1JjtEVdStWzfMmjVL1WFACIEpU6bAwsICEolEti+QIsaNG4fBgwcrPTYiorqIyQ6pvQEDBqBPnz4Vnjt9+jQkEgmuX79ey1FV3+HDhxEYGIgDBw4gMTERrVq1KlfmxIkTkEgk5Xb5LrN+/XoEBgbWbKAvKCkpCdOnT0fjxo2hq6sLJycnDBgwoNzmgzWpJpNCVSXPixcvhkQieeatJsTGxlY7Oa8prq6uWLdunarDoFrAZIfU3sSJE3Hs2DE8ePCg3LktW7agffv2eOmll1QQWfVER0fDzs4OnTp1gq2tLbS0tBSuw9TUFGZmZsoPTkGFhYUVHo+NjUW7du0QEhKClStX4saNGzh8+DACAgIwbdq0Wo5SvcybNw+JiYmym6OjI5YuXSp37GmVvUdE9YrCu2kR1TNFRUXCxsZGfPrpp3LHs7OzhZGRkdi0aZNITU0Vb7zxhrC3txf6+vqiVatW4tdff5Ur7+/vL2bOnCm7D0Ds3btXroypqanYsmWL7H58fLwYPny4MDU1Febm5mLgwIGyDRkrc+LECfHyyy8LHR0dYWtrK+bPny/bwHDs2LFyG+q5uLhUWEfZhpCPHz+u8PzYsWPFoEGD5No2ffp08f777wtzc3NhY2NTblPCx48fi4kTJworKythbGwsAgICRHh4uOx8VFSUGDhwoLC2thaGhoaiffv24tixY3J1uLi4iKVLl4rRo0cLY2PjchsLlunbt69wcHAQOTk55c493aa4uDgxcOBAYWhoKIyNjcXw4cNluzQLIcSiRYuEt7e32LZtm3BxcREmJibi9ddfF1lZWbIyu3btEq1atRJ6enrCwsJC9OjRQ+Tk5IhFixZVuoHhBx98IDw9PYW+vr5wc3MTH3/8sSgsLKzy8/73fcRTG3X+V3p6uhg9erQwMzMT+vr6ok+fPuLu3buy81u2bBGmpqbi8OHDolmzZsLQ0FD07t1bJCQkVFjff7m4uIi1a9fK7vv7+4tp06aJmTNnCktLS9GtWzchhBA3btwQffr0EYaGhsLa2lqMGjVKPHr0SPa4Q4cOic6dOwtTU1NhYWEh+vfvL6KiomTn/9tef39/2WsxaNAg8fnnnwtra2thamoqlixZIoqKisS8efOEubm5cHBwED/99JNc3M/73Sqrd+XKlcLW1lZYWFiId999V/Y++fv7V7qJMakf9uyQ2tPS0sKYMWMQGBgI8dS+t7t27UJJSQlGjhyJ/Px8tGvXDgcPHsTNmzcxZcoUjB49GhcvXqz28xYVFaF3794wNjbG6dOncfbsWRgZGaFPnz6V/rX88OFD9OvXDy+//DKuXbuGTZs2YfPmzfjss88AlF5+Wrp0KRwdHZGYmIi///672vH919atW2FoaIgLFy5gxYoVWLp0KY4dOyY7P3z4cKSkpODQoUO4fPky2rZtix49eiA9PR0AkJOTg379+uH48eO4evUq+vTpgwEDBiA+Pl7ueVatWgVvb29cvXoVCxcuLBdHeno6Dh8+jGnTpsHQ0LDc+bIeKalUikGDBiE9PR0nT57EsWPHcO/ePbz++uty5aOjoxEUFIQDBw7gwIEDOHnyJL744gsAQGJiIkaOHIkJEybg9u3bOHHiBIYOHQohBObNm4cRI0agT58+sh6PTp06AQCMjY0RGBiIW7duYf369fjhhx+wdu3aKj/v+vXr0bFjR0yePFlWt5OTU4Xvy7hx43Dp0iXs378fYWFhEEKgX79+KCoqkpXJy8vDqlWrsH37dpw6dQrx8fGYN29ehfVVxdatW6Gjo4OzZ8/i22+/RUZGBrp37442bdrg0qVLOHz4MJKTkzFixAjZY3JzczFnzhxcunQJx48fh4aGBoYMGQKpVAoAst+l4OBgJCYmYs+ePbLHhoSEICEhAadOncKaNWuwaNEivPrqqzA3N8eFCxcwdepUvP3227Le2ar+boWGhiI6OhqhoaHYunUrAgMDZZdv9+zZU65Xi9SYipMtolpx+/Ztub/MhRCiS5cuYtSoUZU+pn///mLu3Lmy+4r27Gzfvl00bdpUSKVS2fmCggKhr68vjhw5UuFzfvTRR+Ues3HjRmFkZCRKSkqEEEKsXbu20h6dMtXp2fHz+/927j2myasP4Pi3lJmABbkMEZxrB0Ms0DiLm0FecG5qs2UONDoHGMU0Zu7ijbEsC2SLM1myMJaJI8aRzMREB0vA+A+JmG0ELKYMZYxo5RYqZNlFNl3SLCSjnv1BfF4LBQr6vnH190n445yennN+zyXPec455T8+ZZ5++mn17rvvKqWUam1tVZGRkWp0dNSnTHJysjp+/PiU/UhPT1dHjx7V0kajUeXn50/bd6fTqQDV0NAwbbmmpial1+vV0NCQlnflyhUFqPb2dqXU+AxLeHi4z0zOO++8o1atWqWUUurSpUsKUG63228bE4/TVCoqKlRmZqaWnqldpSZfT/709vYqQDkcDi1vZGREhYWFqa+//lopNT6zA/jMolRXV6v4+PgZ+62U/5mdFStW+JQ5fPiw2rBhg0/e8PCwAlRPT4/fem/cuKEA1d3drZRSanBwUAGqs7PTp9zOnTuV0WjUrm+llEpNTVU5OTlaemxsTM2fP1999dVXSqnA7q079Y6NjWlltm7dqrZt2zZl7CJ4ycyOeCgsW7aM1atX8+WXXwLQ399Pa2srdrsdAK/Xy+HDh7FYLMTExGAwGDh37tykWYnZ6Orqor+/n4iICAwGAwaDgZiYGEZHRxkYGPD7HZfLRVZWls8m0ezsbDwej989R/fTxH1LCQkJ/Pbbb8B4LB6Ph9jYWC0Wg8HA4OCgFovH46G0tBSz2UxUVBQGgwGXyzXpGK5cuXLafqi7Zt+m43K5WLJkic+MSFpaGlFRUbhcLi3PZDIRERHhN67ly5fz/PPPY7FY2Lp1KzU1Ndy8eXPGtuvq6sjOzmbRokUYDAbKy8snxTldu4FyuVyEhoayatUqLS82NpbU1FSfGMPDw0lOTr6ntu6WmZnpk+7q6uK7777zOffLli0D0M5/X18fBQUFJCUlERkZiclkAgjoHkpPTyck5L+Po/j4eCwWi5bW6/XExsb6XI+B3Fvp6eno9Xotfa/HRfx7zX5noxD/Una7nb1791JdXc2JEydITk5mzZo1AFRUVHDkyBE+++wzLBYL8+fP58CBA9NuztTpdJMezHcvLXg8HjIzMzl16tSk78bFxd2nqO6fRx55xCet0+m0JQiPx0NCQgLNzc2TvndnWam0tJTz58/zySef8OSTTxIWFsaWLVsmHUN/S1N3S0lJQafTce3atbkHc5fp4tLr9Zw/f562tjaampo4evQoZWVlOJ1OnnjiCb/1Xbx4kaKiIg4dOoTNZmPBggXU1tZSWVkZcLv3m7+2Ah00+jPxHHk8HjZu3MjHH388qWxCQgIw/qtHo9FITU0NiYmJ3L59m4yMjIA2OPvr/0zXYyD31v/zHIgHmwx2xEPjlVdeYf/+/Zw+fZqTJ0/y+uuvazMoDoeDvLw8tm/fDozvB+nt7SUtLW3K+uLi4nzW+fv6+vjrr7+0tNVqpa6ujoULFxIZGRlQH81mM/X19SilfPoWERHBY489NuuY7xer1covv/xCaGio9sY+kcPhoLi4mE2bNgHjDyS32z3rtmJiYrDZbFRXV7Nv375JD95bt24RFRWF2WxmeHiY4eFhbXbn6tWr3Lp1a9rzNpFOpyM7O5vs7Gzef/99jEYjZ86coaSkhHnz5uH1en3Kt7W1YTQaKSsr0/KuX78+6zj91T2R2WxmbGwMp9Op7Rf6/fff6enpmVWM98pqtVJfX4/JZPL76787faqpqSEnJweACxcu+JSZN28ewIwxB9qf2d5b/gRyDkRwkGUs8dAwGAxs27aN9957j59//pni4mLts5SUFO0N3+Vy8dprr/Hrr79OW99zzz3H559/TmdnJx0dHezZs8fnTbKoqIhHH32UvLw8WltbGRwcpLm5mX379k25JPXGG28wPDzM3r17uXbtGmfPnuWDDz6gpKTEZ5o/UN3d3fzwww/aX1dX16zrAFi3bh1ZWVnk5+fT1NSE2+2mra2NsrIyOjo6gPFj2NDQoLVTWFg457fo6upqvF4vzzzzDPX19fT19eFyuaiqqiIrK0vrk8VioaioiMuXL9Pe3s6OHTtYs2bNjEtldzidTj766CM6OjoYGhqioaGBGzduYDabgfGlqB9//JGenh5GRkb4+++/SUlJYWhoiNraWgYGBqiqquLMmTOzjtFkMuF0OnG73YyMjPg9VikpKeTl5bF7924uXLhAV1cX27dvZ/HixeTl5c26zbl68803+eOPPygoKOD7779nYGCAc+fOsWvXLrxeL9HR0cTGxvLFF1/Q39/Pt99+S0lJiU8dCxcuJCwsTNvc/Oeff865P3O5t/wxmUy0tLTw008/MTIyMuf+iAefDHbEQ8Vut3Pz5k1sNhuJiYlafnl5OVarFZvNxrPPPsuiRYtm/GdylZWVLFmyhJycHAoLCyktLSU8PFz7PDw8nJaWFh5//HE2b96M2WzGbrczOjo65dvo4sWLaWxspL29neXLl7Nnzx7sdjvl5eVzijc3N5cVK1ZofxP3YgRKp9PR2NhIbm4uu3btYunSpbz66qtcv36d+Ph4AD799FOio6NZvXo1GzduxGazYbVa59ReUlISly9fZu3atbz99ttkZGSwfv16vvnmG44dO6b16ezZs0RHR5Obm8u6detISkqirq4u4HYiIyNpaWnhxRdfZOnSpZSXl1NZWckLL7wAwO7du0lNTWXlypXExcXhcDh4+eWXOXjwIG+99RZPPfUUbW1tfn9VNpPS0lL0ej1paWnExcVNubflxIkTZGZm8tJLL5GVlYVSisbGxklLNP9LiYmJOBwOvF4vGzZswGKxcODAAaKioggJCSEkJITa2louXbpERkYGBw8epKKiwqeO0NBQqqqqOH78OImJifc0WJvLveXPhx9+iNvtJjk5+YFcWhb3j07dy8KuEEIIIcQDTmZ2hBBCCBHUZLAjhBBCiKAmgx0hhBBCBDUZ7AghhBAiqMlgRwghhBBBTQY7QgghhAhqMtgRQgghRFCTwY4QQgghgpoMdoQQQggR1GSwI4QQQoigJoMdIYQQQgS1fwBX3IhM97q2fQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ]
@@ -1043,8 +1043,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(8.216459566237983, 12.175990977887663)\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:(11.221001774229803, 12.921868423571057)\n",
       "\n"
      ]
     }
@@ -1070,16 +1070,16 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:18.366818Z",
-     "iopub.status.busy": "2024-10-25T14:52:18.366433Z",
-     "iopub.status.idle": "2024-10-25T14:52:22.758493Z",
-     "shell.execute_reply": "2024-10-25T14:52:22.757843Z"
+     "iopub.execute_input": "2024-10-25T14:53:21.475117Z",
+     "iopub.status.busy": "2024-10-25T14:53:21.474722Z",
+     "iopub.status.idle": "2024-10-25T14:53:26.072615Z",
+     "shell.execute_reply": "2024-10-25T14:53:26.071953Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x500 with 2 Axes>"
       ]
@@ -1092,8 +1092,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(4.633438858979389, 12.180186652249047)\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:(5.738666451726319, 13.008344040681948)\n",
       "\n"
      ]
     }
@@ -1118,10 +1118,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:22.760646Z",
-     "iopub.status.busy": "2024-10-25T14:52:22.760412Z",
-     "iopub.status.idle": "2024-10-25T14:52:50.114991Z",
-     "shell.execute_reply": "2024-10-25T14:52:50.113951Z"
+     "iopub.execute_input": "2024-10-25T14:53:26.075637Z",
+     "iopub.status.busy": "2024-10-25T14:53:26.075038Z",
+     "iopub.status.idle": "2024-10-25T14:53:50.166694Z",
+     "shell.execute_reply": "2024-10-25T14:53:50.166085Z"
     }
    },
    "outputs": [
@@ -1135,7 +1135,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
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@@ -1148,8 +1148,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(6.999875970223543, 12.397733619781969)\n",
+      "Estimated effect:13.111841490138564\n",
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diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_401k_analysis.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_401k_analysis.ipynb
index 280df6fe2..76a3aa53c 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_401k_analysis.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_401k_analysis.ipynb
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@@ -369,10 +369,10 @@
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@@ -481,7 +481,7 @@
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-      "Fitting causal mechanism of node e401:   6%|▌         | 1/18 [00:01<00:21,  1.26s/it]"
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@@ -489,7 +489,7 @@
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-      "Fitting causal mechanism of node net_tfa:   6%|▌         | 1/18 [00:01<00:21,  1.26s/it]"
+      "Fitting causal mechanism of node net_tfa:   6%|▌         | 1/18 [00:01<00:22,  1.29s/it]"
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@@ -497,7 +497,7 @@
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-      "Fitting causal mechanism of node net_tfa:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node net_tfa:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node age:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]    "
+      "Fitting causal mechanism of node age:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]    "
      ]
     },
     {
@@ -513,7 +513,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node inc:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node inc:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -521,7 +521,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node fsize:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node fsize:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -529,7 +529,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node educ:  11%|█         | 2/18 [00:06<01:00,  3.76s/it] "
+      "Fitting causal mechanism of node educ:  11%|█         | 2/18 [00:06<01:02,  3.88s/it] "
      ]
     },
     {
@@ -537,7 +537,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node male:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node male:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -545,7 +545,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node db:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node db:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -553,7 +553,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node marr:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node marr:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -561,7 +561,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node twoearn:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node twoearn:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -569,7 +569,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node pira:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]   "
+      "Fitting causal mechanism of node pira:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]   "
      ]
     },
     {
@@ -577,7 +577,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hown:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hown:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -585,7 +585,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hval:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hval:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -593,7 +593,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hequity:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hequity:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -601,7 +601,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hmort:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node hmort:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -609,7 +609,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node nohs:  11%|█         | 2/18 [00:06<01:00,  3.76s/it] "
+      "Fitting causal mechanism of node nohs:  11%|█         | 2/18 [00:06<01:02,  3.88s/it] "
      ]
     },
     {
@@ -617,7 +617,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hs:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node hs:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -625,7 +625,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node smcol:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node smcol:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -633,7 +633,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06<00:00,  2.65it/s]"
+      "Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06<00:00,  2.57it/s]"
      ]
     },
     {
@@ -660,10 +660,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:05.236194Z",
-     "iopub.status.busy": "2024-10-25T14:53:05.235831Z",
-     "iopub.status.idle": "2024-10-25T14:53:05.240191Z",
-     "shell.execute_reply": "2024-10-25T14:53:05.239738Z"
+     "iopub.execute_input": "2024-10-25T14:54:06.687912Z",
+     "iopub.status.busy": "2024-10-25T14:54:06.687532Z",
+     "iopub.status.idle": "2024-10-25T14:54:06.691938Z",
+     "shell.execute_reply": "2024-10-25T14:54:06.691470Z"
     }
    },
    "outputs": [],
@@ -691,10 +691,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:05.241983Z",
-     "iopub.status.busy": "2024-10-25T14:53:05.241639Z",
-     "iopub.status.idle": "2024-10-25T14:53:48.725196Z",
-     "shell.execute_reply": "2024-10-25T14:53:48.724542Z"
+     "iopub.execute_input": "2024-10-25T14:54:06.693728Z",
+     "iopub.status.busy": "2024-10-25T14:54:06.693391Z",
+     "iopub.status.idle": "2024-10-25T14:54:52.986976Z",
+     "shell.execute_reply": "2024-10-25T14:54:52.986291Z"
     }
    },
    "outputs": [
@@ -711,7 +711,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   1%|          | 1/100 [00:00<00:44,  2.21it/s]"
+      "Estimating bootstrap interval...:   1%|          | 1/100 [00:00<00:46,  2.14it/s]"
      ]
     },
     {
@@ -719,7 +719,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   2%|▏         | 2/100 [00:00<00:43,  2.26it/s]"
+      "Estimating bootstrap interval...:   2%|▏         | 2/100 [00:00<00:48,  2.04it/s]"
      ]
     },
     {
@@ -727,7 +727,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   3%|▎         | 3/100 [00:01<00:46,  2.09it/s]"
      ]
     },
     {
@@ -735,7 +735,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   4%|▍         | 4/100 [00:01<00:45,  2.12it/s]"
      ]
     },
     {
@@ -743,7 +743,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   5%|▌         | 5/100 [00:02<00:44,  2.13it/s]"
      ]
     },
     {
@@ -751,7 +751,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   6%|▌         | 6/100 [00:02<00:44,  2.12it/s]"
      ]
     },
     {
@@ -759,7 +759,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   7%|▋         | 7/100 [00:03<00:40,  2.28it/s]"
+      "Estimating bootstrap interval...:   7%|▋         | 7/100 [00:03<00:44,  2.11it/s]"
      ]
     },
     {
@@ -767,7 +767,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   8%|▊         | 8/100 [00:03<00:43,  2.10it/s]"
      ]
     },
     {
@@ -775,7 +775,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:   9%|▉         | 9/100 [00:04<00:43,  2.10it/s]"
      ]
     },
     {
@@ -783,7 +783,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  10%|█         | 10/100 [00:04<00:42,  2.10it/s]"
      ]
     },
     {
@@ -791,7 +791,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  11%|█         | 11/100 [00:05<00:42,  2.11it/s]"
      ]
     },
     {
@@ -799,7 +799,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  12%|█▏        | 12/100 [00:05<00:41,  2.12it/s]"
      ]
     },
     {
@@ -807,7 +807,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  13%|█▎        | 13/100 [00:06<00:40,  2.13it/s]"
      ]
     },
     {
@@ -815,7 +815,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  14%|█▍        | 14/100 [00:06<00:40,  2.13it/s]"
      ]
     },
     {
@@ -823,7 +823,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  15%|█▌        | 15/100 [00:07<00:39,  2.13it/s]"
      ]
     },
     {
@@ -831,7 +831,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  16%|█▌        | 16/100 [00:07<00:39,  2.13it/s]"
      ]
     },
     {
@@ -839,7 +839,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  17%|█▋        | 17/100 [00:08<00:38,  2.14it/s]"
      ]
     },
     {
@@ -847,7 +847,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  18%|█▊        | 18/100 [00:08<00:37,  2.16it/s]"
      ]
     },
     {
@@ -855,7 +855,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  19%|█▉        | 19/100 [00:08<00:37,  2.18it/s]"
      ]
     },
     {
@@ -863,7 +863,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  20%|██        | 20/100 [00:08<00:34,  2.30it/s]"
+      "Estimating bootstrap interval...:  20%|██        | 20/100 [00:09<00:36,  2.17it/s]"
      ]
     },
     {
@@ -871,7 +871,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  21%|██        | 21/100 [00:09<00:34,  2.30it/s]"
+      "Estimating bootstrap interval...:  21%|██        | 21/100 [00:09<00:36,  2.15it/s]"
      ]
     },
     {
@@ -879,7 +879,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  22%|██▏       | 22/100 [00:10<00:36,  2.15it/s]"
      ]
     },
     {
@@ -887,7 +887,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  23%|██▎       | 23/100 [00:10<00:35,  2.15it/s]"
      ]
     },
     {
@@ -895,7 +895,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  24%|██▍       | 24/100 [00:11<00:35,  2.16it/s]"
      ]
     },
     {
@@ -903,7 +903,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  25%|██▌       | 25/100 [00:11<00:34,  2.15it/s]"
      ]
     },
     {
@@ -911,7 +911,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  26%|██▌       | 26/100 [00:12<00:34,  2.14it/s]"
      ]
     },
     {
@@ -919,7 +919,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  27%|██▋       | 27/100 [00:12<00:34,  2.14it/s]"
      ]
     },
     {
@@ -927,7 +927,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  28%|██▊       | 28/100 [00:13<00:33,  2.14it/s]"
      ]
     },
     {
@@ -935,7 +935,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  29%|██▉       | 29/100 [00:13<00:33,  2.14it/s]"
      ]
     },
     {
@@ -943,7 +943,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  30%|███       | 30/100 [00:14<00:32,  2.14it/s]"
      ]
     },
     {
@@ -951,7 +951,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  31%|███       | 31/100 [00:14<00:32,  2.11it/s]"
      ]
     },
     {
@@ -959,7 +959,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  32%|███▏      | 32/100 [00:15<00:32,  2.09it/s]"
      ]
     },
     {
@@ -967,7 +967,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  33%|███▎      | 33/100 [00:15<00:32,  2.04it/s]"
      ]
     },
     {
@@ -975,7 +975,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  34%|███▍      | 34/100 [00:16<00:32,  2.06it/s]"
      ]
     },
     {
@@ -983,7 +983,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  35%|███▌      | 35/100 [00:16<00:30,  2.10it/s]"
      ]
     },
     {
@@ -991,7 +991,7 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...:  36%|███▌      | 36/100 [00:16<00:30,  2.12it/s]"
      ]
     },
     {
@@ -999,7 +999,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  37%|███▋      | 37/100 [00:17<00:29,  2.15it/s]"
      ]
     },
     {
@@ -1007,7 +1007,7 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...:  38%|███▊      | 38/100 [00:17<00:28,  2.15it/s]"
      ]
     },
     {
@@ -1015,7 +1015,7 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...:  39%|███▉      | 39/100 [00:18<00:28,  2.15it/s]"
      ]
     },
     {
@@ -1023,7 +1023,7 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...:  40%|████      | 40/100 [00:18<00:27,  2.16it/s]"
      ]
     },
     {
@@ -1031,7 +1031,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  41%|████      | 41/100 [00:19<00:27,  2.17it/s]"
      ]
     },
     {
@@ -1039,7 +1039,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  42%|████▏     | 42/100 [00:19<00:26,  2.17it/s]"
      ]
     },
     {
@@ -1047,7 +1047,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  43%|████▎     | 43/100 [00:20<00:26,  2.16it/s]"
      ]
     },
     {
@@ -1055,7 +1055,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  44%|████▍     | 44/100 [00:20<00:25,  2.18it/s]"
      ]
     },
     {
@@ -1063,7 +1063,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  45%|████▌     | 45/100 [00:21<00:24,  2.20it/s]"
      ]
     },
     {
@@ -1071,7 +1071,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1079,7 +1079,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  47%|████▋     | 47/100 [00:21<00:24,  2.19it/s]"
      ]
     },
     {
@@ -1087,7 +1087,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1095,7 +1095,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  49%|████▉     | 49/100 [00:22<00:23,  2.14it/s]"
      ]
     },
     {
@@ -1103,7 +1103,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1111,7 +1111,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1119,7 +1119,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1127,7 +1127,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  53%|█████▎    | 53/100 [00:24<00:21,  2.18it/s]"
      ]
     },
     {
@@ -1135,7 +1135,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1143,7 +1143,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1151,7 +1151,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1159,7 +1159,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1167,7 +1167,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1175,7 +1175,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1183,7 +1183,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1191,7 +1191,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1199,7 +1199,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1207,7 +1207,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1215,7 +1215,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1223,7 +1223,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1231,7 +1231,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1239,7 +1239,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1247,7 +1247,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1255,7 +1255,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1263,7 +1263,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1271,7 +1271,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1279,7 +1279,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1287,7 +1287,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1295,7 +1295,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1303,7 +1303,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1311,7 +1311,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1319,7 +1319,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1327,7 +1327,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1335,7 +1335,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1343,7 +1343,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1351,7 +1351,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1359,7 +1359,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1367,7 +1367,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1375,7 +1375,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1383,7 +1383,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1391,7 +1391,7 @@
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      ]
     },
     {
@@ -1399,7 +1399,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1407,7 +1407,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1415,7 +1415,7 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...:  89%|████████▉ | 89/100 [00:41<00:04,  2.22it/s]"
      ]
     },
     {
@@ -1423,7 +1423,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1431,7 +1431,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1439,7 +1439,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1447,7 +1447,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1455,7 +1455,7 @@
      "output_type": "stream",
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      ]
     },
     {
@@ -1463,7 +1463,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1471,7 +1471,7 @@
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      ]
     },
     {
@@ -1479,7 +1479,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1487,7 +1487,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1495,7 +1495,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -1503,7 +1503,7 @@
      "output_type": "stream",
      "text": [
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      ]
     },
     {
@@ -1511,15 +1511,15 @@
      "output_type": "stream",
      "text": [
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+      "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:46<00:00,  2.16it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'ate': 6579.210517369218, '0%-20%': 3941.620563059555, '20%-40%': 3472.827070248335, '40%-60%': 5551.872539342875, '60%-80%': 7535.200629399749, '80%-100%': 12227.572489811779}\n",
-      "{'ate': array([4859.15619427, 8363.63710589]), '0%-20%': array([2425.3999363 , 5605.11577024]), '20%-40%': array([1463.60903336, 5794.94940105]), '40%-60%': array([3306.12004311, 8036.63811783]), '60%-80%': array([ 4810.31782312, 10781.24257438]), '80%-100%': array([ 5546.67902188, 18650.67127681])}\n"
+      "{'ate': 6599.824328516486, '0%-20%': 4039.699222520601, '20%-40%': 3339.4679058647803, '40%-60%': 5794.671860700217, '60%-80%': 7659.605099728334, '80%-100%': 11995.431482001522}\n",
+      "{'ate': array([5071.87036132, 8545.70698262]), '0%-20%': array([2461.44199557, 5631.10422336]), '20%-40%': array([1300.76540705, 5646.82387151]), '40%-60%': array([3720.3467779 , 8486.96668456]), '60%-80%': array([ 4765.22282317, 11213.38070644]), '80%-100%': array([ 6000.76420438, 18819.65878719])}\n"
      ]
     },
     {
@@ -1569,10 +1569,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:48.727280Z",
-     "iopub.status.busy": "2024-10-25T14:53:48.726895Z",
-     "iopub.status.idle": "2024-10-25T14:53:48.821294Z",
-     "shell.execute_reply": "2024-10-25T14:53:48.820697Z"
+     "iopub.execute_input": "2024-10-25T14:54:52.988838Z",
+     "iopub.status.busy": "2024-10-25T14:54:52.988637Z",
+     "iopub.status.idle": "2024-10-25T14:54:53.114015Z",
+     "shell.execute_reply": "2024-10-25T14:54:53.113314Z"
     }
    },
    "outputs": [
@@ -1580,13 +1580,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_4021/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n",
+      "/tmp/ipykernel_4017/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n",
       "  plt.plot((x, x), (ci[0], ci[1]), 'ro-', color='orange')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb
index 626e24c1d..96a76c547 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb
@@ -25,10 +25,10 @@
    "id": "f22337ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:51.740493Z",
-     "iopub.status.busy": "2024-10-25T14:53:51.740087Z",
-     "iopub.status.idle": "2024-10-25T14:53:52.022190Z",
-     "shell.execute_reply": "2024-10-25T14:53:52.021668Z"
+     "iopub.execute_input": "2024-10-25T14:54:57.030287Z",
+     "iopub.status.busy": "2024-10-25T14:54:57.030106Z",
+     "iopub.status.idle": "2024-10-25T14:54:57.374207Z",
+     "shell.execute_reply": "2024-10-25T14:54:57.373587Z"
     }
    },
    "outputs": [],
@@ -51,10 +51,10 @@
    "id": "0367caeb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:52.024540Z",
-     "iopub.status.busy": "2024-10-25T14:53:52.024176Z",
-     "iopub.status.idle": "2024-10-25T14:53:53.148136Z",
-     "shell.execute_reply": "2024-10-25T14:53:53.147513Z"
+     "iopub.execute_input": "2024-10-25T14:54:57.376878Z",
+     "iopub.status.busy": "2024-10-25T14:54:57.376521Z",
+     "iopub.status.idle": "2024-10-25T14:54:58.797105Z",
+     "shell.execute_reply": "2024-10-25T14:54:58.796415Z"
     }
    },
    "outputs": [],
@@ -87,10 +87,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:53.150607Z",
-     "iopub.status.busy": "2024-10-25T14:53:53.150113Z",
-     "iopub.status.idle": "2024-10-25T14:53:53.160107Z",
-     "shell.execute_reply": "2024-10-25T14:53:53.159512Z"
+     "iopub.execute_input": "2024-10-25T14:54:58.800006Z",
+     "iopub.status.busy": "2024-10-25T14:54:58.799409Z",
+     "iopub.status.idle": "2024-10-25T14:54:58.811767Z",
+     "shell.execute_reply": "2024-10-25T14:54:58.811053Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -129,33 +129,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.216788</td>\n",
-       "      <td>0.410497</td>\n",
-       "      <td>0.318602</td>\n",
+       "      <td>-1.004880</td>\n",
+       "      <td>-0.701488</td>\n",
+       "      <td>-2.403116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.476031</td>\n",
-       "      <td>-1.903199</td>\n",
-       "      <td>-4.402106</td>\n",
+       "      <td>-0.734725</td>\n",
+       "      <td>0.451607</td>\n",
+       "      <td>1.933812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.656258</td>\n",
-       "      <td>1.192341</td>\n",
-       "      <td>4.106536</td>\n",
+       "      <td>-0.233025</td>\n",
+       "      <td>-0.405104</td>\n",
+       "      <td>-2.811089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-1.751869</td>\n",
-       "      <td>-4.711298</td>\n",
-       "      <td>-13.801311</td>\n",
+       "      <td>-1.204772</td>\n",
+       "      <td>-1.922767</td>\n",
+       "      <td>-6.003365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.945124</td>\n",
-       "      <td>-0.398115</td>\n",
-       "      <td>0.019949</td>\n",
+       "      <td>1.501223</td>\n",
+       "      <td>3.122603</td>\n",
+       "      <td>10.429561</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -163,11 +163,11 @@
       ],
       "text/plain": [
        "          X         Y          Z\n",
-       "0 -0.216788  0.410497   0.318602\n",
-       "1 -0.476031 -1.903199  -4.402106\n",
-       "2  0.656258  1.192341   4.106536\n",
-       "3 -1.751869 -4.711298 -13.801311\n",
-       "4 -0.945124 -0.398115   0.019949"
+       "0 -1.004880 -0.701488  -2.403116\n",
+       "1 -0.734725  0.451607   1.933812\n",
+       "2 -0.233025 -0.405104  -2.811089\n",
+       "3 -1.204772 -1.922767  -6.003365\n",
+       "4  1.501223  3.122603  10.429561"
       ]
      },
      "execution_count": 3,
@@ -214,10 +214,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:53.161979Z",
-     "iopub.status.busy": "2024-10-25T14:53:53.161651Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.936099Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.934878Z"
+     "iopub.execute_input": "2024-10-25T14:54:58.814011Z",
+     "iopub.status.busy": "2024-10-25T14:54:58.813573Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.026608Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.025470Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -245,10 +245,10 @@
    "id": "4f1493d8-9a0b-4ed5-97be-7b5f39cbd7d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.938962Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.938727Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.946490Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.945891Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.030360Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.029764Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.039636Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.039033Z"
     }
    },
    "outputs": [
@@ -282,19 +282,19 @@
       "Node Y is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Y := f(X) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 0.9742765024277729\n",
+      "LinearRegression: 1.0448453469461705\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 0.9800239824248347\n",
-      "HistGradientBoostingRegressor: 1.0913790854666052\n",
+      "                ('linearregression', LinearRegression)]): 1.0478728496088803\n",
+      "HistGradientBoostingRegressor: 1.1349141690770985\n",
       "\n",
       "--- Node: Z\n",
       "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Z := f(Y) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 0.9500102471330812\n",
+      "LinearRegression: 1.0165052626309083\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 0.9504305607115737\n",
-      "HistGradientBoostingRegressor: 1.4228887507027141\n",
+      "                ('linearregression', LinearRegression)]): 1.017072889305317\n",
+      "HistGradientBoostingRegressor: 1.421134931479623\n",
       "\n",
       "===Note===\n",
       "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n",
@@ -322,10 +322,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.948598Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.948395Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.951785Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.951216Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.041966Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.041541Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.045490Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.044929Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -369,10 +369,10 @@
    "id": "fe5f99f0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.953803Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.953383Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.966843Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.966277Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.047672Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.047251Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.060839Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.060164Z"
     }
    },
    "outputs": [
@@ -413,7 +413,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 404.57it/s]"
+      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 427.09it/s]"
      ]
     },
     {
@@ -450,10 +450,10 @@
    "id": "671cd8d8-75b0-4019-b503-8aa83ad91615",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.969009Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.968611Z",
-     "iopub.status.idle": "2024-10-25T14:53:58.016098Z",
-     "shell.execute_reply": "2024-10-25T14:53:58.015530Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.063207Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.062967Z",
+     "iopub.status.idle": "2024-10-25T14:55:04.196416Z",
+     "shell.execute_reply": "2024-10-25T14:55:04.195750Z"
     }
    },
    "outputs": [
@@ -470,7 +470,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3684.60it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 2787.53it/s]"
      ]
     },
     {
@@ -493,7 +493,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.21it/s]"
+      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.18it/s]"
      ]
     },
     {
@@ -501,7 +501,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.64it/s]"
+      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.15it/s]"
      ]
     },
     {
@@ -509,7 +509,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.68it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.08it/s]"
      ]
     },
     {
@@ -521,7 +521,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -547,21 +547,21 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node X\n",
-      "- The KL divergence between generated and observed distribution is 0.017201478484168996.\n",
+      "- The KL divergence between generated and observed distribution is 0.04486035655302496.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Y\n",
-      "- The MSE is 0.976443943612139.\n",
-      "- The NMSE is 0.4483833647839692.\n",
-      "- The R2 coefficient is 0.7985032436842728.\n",
-      "- The normalized CRPS is 0.2555275355528893.\n",
+      "- The MSE is 1.0467465612232283.\n",
+      "- The NMSE is 0.47323453236211643.\n",
+      "- The R2 coefficient is 0.7742617784338882.\n",
+      "- The normalized CRPS is 0.272445898490441.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Z\n",
-      "- The MSE is 0.9526589247617239.\n",
-      "- The NMSE is 0.1451098038253918.\n",
-      "- The R2 coefficient is 0.9789264378438249.\n",
-      "- The normalized CRPS is 0.08325239327425824.\n",
+      "- The MSE is 1.0162458498820515.\n",
+      "- The NMSE is 0.15398077945269872.\n",
+      "- The R2 coefficient is 0.9762621570148147.\n",
+      "- The normalized CRPS is 0.08788013934735178.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
@@ -569,13 +569,13 @@
       "--- The model assumption for node Y is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Z is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Z is not rejected with a p-value of 0.5259643809464394 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.012872322533934848\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.016592406474661824\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -637,10 +637,10 @@
    "id": "52452496",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:58.018245Z",
-     "iopub.status.busy": "2024-10-25T14:53:58.017880Z",
-     "iopub.status.idle": "2024-10-25T14:53:58.029343Z",
-     "shell.execute_reply": "2024-10-25T14:53:58.028872Z"
+     "iopub.execute_input": "2024-10-25T14:55:04.198725Z",
+     "iopub.status.busy": "2024-10-25T14:55:04.198484Z",
+     "iopub.status.idle": "2024-10-25T14:55:04.211526Z",
+     "shell.execute_reply": "2024-10-25T14:55:04.210903Z"
     }
    },
    "outputs": [
@@ -673,33 +673,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.396680</td>\n",
+       "      <td>0.501525</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.897156</td>\n",
+       "      <td>7.645349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.075865</td>\n",
+       "      <td>0.999885</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.658478</td>\n",
+       "      <td>7.319836</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.565186</td>\n",
+       "      <td>-0.464415</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.775646</td>\n",
+       "      <td>5.997534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-2.294770</td>\n",
+       "      <td>0.903415</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>5.928846</td>\n",
+       "      <td>8.018945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.059750</td>\n",
+       "      <td>-0.751885</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.726993</td>\n",
+       "      <td>6.773647</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -707,11 +707,11 @@
       ],
       "text/plain": [
        "          X     Y         Z\n",
-       "0  0.396680  2.34  6.897156\n",
-       "1 -1.075865  2.34  6.658478\n",
-       "2  0.565186  2.34  6.775646\n",
-       "3 -2.294770  2.34  5.928846\n",
-       "4 -0.059750  2.34  6.726993"
+       "0  0.501525  2.34  7.645349\n",
+       "1  0.999885  2.34  7.319836\n",
+       "2 -0.464415  2.34  5.997534\n",
+       "3  0.903415  2.34  8.018945\n",
+       "4 -0.751885  2.34  6.773647"
       ]
      },
      "execution_count": 9,
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
index 4dd608208..73e16fefe 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
@@ -34,10 +34,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:02.795937Z",
-     "iopub.status.busy": "2024-10-25T14:54:02.795760Z",
-     "iopub.status.idle": "2024-10-25T14:54:03.032257Z",
-     "shell.execute_reply": "2024-10-25T14:54:03.031648Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.027157Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.026967Z",
+     "iopub.status.idle": "2024-10-25T14:55:09.268293Z",
+     "shell.execute_reply": "2024-10-25T14:55:09.267663Z"
     }
    },
    "outputs": [
@@ -128,10 +128,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:03.034275Z",
-     "iopub.status.busy": "2024-10-25T14:54:03.033923Z",
-     "iopub.status.idle": "2024-10-25T14:54:03.575119Z",
-     "shell.execute_reply": "2024-10-25T14:54:03.574518Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.270377Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.270048Z",
+     "iopub.status.idle": "2024-10-25T14:55:09.826985Z",
+     "shell.execute_reply": "2024-10-25T14:55:09.826344Z"
     }
    },
    "outputs": [
@@ -181,10 +181,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:03.577584Z",
-     "iopub.status.busy": "2024-10-25T14:54:03.577313Z",
-     "iopub.status.idle": "2024-10-25T14:54:07.021109Z",
-     "shell.execute_reply": "2024-10-25T14:54:07.020379Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.829344Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.828744Z",
+     "iopub.status.idle": "2024-10-25T14:55:13.551143Z",
+     "shell.execute_reply": "2024-10-25T14:55:13.550341Z"
     }
    },
    "outputs": [
@@ -227,7 +227,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Vision:  67%|██████▋   | 2/3 [00:00<00:00, 19.92it/s]"
+      "Fitting causal mechanism of node Vision:  67%|██████▋   | 2/3 [00:00<00:00, 14.92it/s]"
      ]
     },
     {
@@ -235,7 +235,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Condition:  67%|██████▋   | 2/3 [00:00<00:00, 19.92it/s]"
+      "Fitting causal mechanism of node Condition:  67%|██████▋   | 2/3 [00:00<00:00, 14.92it/s]"
      ]
     },
     {
@@ -243,7 +243,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 29.25it/s]"
+      "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 21.91it/s]"
      ]
     },
     {
@@ -279,10 +279,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:07.023466Z",
-     "iopub.status.busy": "2024-10-25T14:54:07.023059Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.811677Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.811016Z"
+     "iopub.execute_input": "2024-10-25T14:55:13.553797Z",
+     "iopub.status.busy": "2024-10-25T14:55:13.553300Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.674607Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.673945Z"
     }
    },
    "outputs": [
@@ -299,7 +299,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3549.48it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3090.87it/s]"
      ]
     },
     {
@@ -322,7 +322,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.90it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 88.61it/s]"
      ]
     },
     {
@@ -368,15 +368,15 @@
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Vision\n",
-      "- The MSE is 0.003261806190982879.\n",
-      "- The NMSE is 0.18253366556893805.\n",
-      "- The R2 coefficient is 0.9666790551530827.\n",
-      "- The normalized CRPS is 0.10648111139984254.\n",
+      "- The MSE is 0.0032631168083106163.\n",
+      "- The NMSE is 0.1826058582974253.\n",
+      "- The R2 coefficient is 0.9666536471105938.\n",
+      "- The normalized CRPS is 0.10639145853760663.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
       "\n",
-      "--- The model assumption for node Vision is not rejected with a p-value of 0.5421442804573395 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Vision is not rejected with a p-value of 0.6092815181807543 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
@@ -420,10 +420,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.813689Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.813324Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.821322Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.820845Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.676818Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.676383Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.685502Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.684844Z"
     }
    },
    "outputs": [
@@ -506,17 +506,17 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.823085Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.822906Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.980064Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.979584Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.687467Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.687264Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.882040Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.881367Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb03f528a00>"
+       "<matplotlib.legend.Legend at 0x7f5db9719730>"
       ]
      },
      "execution_count": 6,
@@ -620,10 +620,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.982139Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.981767Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.994410Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.993955Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.884610Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.884162Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.897187Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.896585Z"
     }
    },
    "outputs": [],
@@ -663,10 +663,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.996243Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.995897Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.999893Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.999272Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.899641Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.899143Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.903356Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.902747Z"
     }
    },
    "outputs": [],
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
index 1e184367b..2cd02e645 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
@@ -27,10 +27,10 @@
    "id": "6e926b16-37e2-4921-9afb-1c99db9c9feb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:25.520831Z",
-     "iopub.status.busy": "2024-10-25T14:54:25.520651Z",
-     "iopub.status.idle": "2024-10-25T14:54:25.848014Z",
-     "shell.execute_reply": "2024-10-25T14:54:25.847349Z"
+     "iopub.execute_input": "2024-10-25T14:55:32.597217Z",
+     "iopub.status.busy": "2024-10-25T14:55:32.597029Z",
+     "iopub.status.idle": "2024-10-25T14:55:32.952010Z",
+     "shell.execute_reply": "2024-10-25T14:55:32.951360Z"
     },
     "tags": []
    },
@@ -78,10 +78,10 @@
    "id": "2ca5daf9-be47-4c12-bd02-bb5b01014e0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:25.850525Z",
-     "iopub.status.busy": "2024-10-25T14:54:25.850074Z",
-     "iopub.status.idle": "2024-10-25T14:54:27.023943Z",
-     "shell.execute_reply": "2024-10-25T14:54:27.023282Z"
+     "iopub.execute_input": "2024-10-25T14:55:32.954668Z",
+     "iopub.status.busy": "2024-10-25T14:55:32.954456Z",
+     "iopub.status.idle": "2024-10-25T14:55:34.280242Z",
+     "shell.execute_reply": "2024-10-25T14:55:34.279594Z"
     }
    },
    "outputs": [],
@@ -117,10 +117,10 @@
    "id": "39dcb294-25df-4358-81cb-3ee5a41e0419",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:27.026388Z",
-     "iopub.status.busy": "2024-10-25T14:54:27.025867Z",
-     "iopub.status.idle": "2024-10-25T14:54:30.544102Z",
-     "shell.execute_reply": "2024-10-25T14:54:30.543491Z"
+     "iopub.execute_input": "2024-10-25T14:55:34.282920Z",
+     "iopub.status.busy": "2024-10-25T14:55:34.282579Z",
+     "iopub.status.idle": "2024-10-25T14:55:38.116672Z",
+     "shell.execute_reply": "2024-10-25T14:55:38.115975Z"
     },
     "tags": []
    },
@@ -138,7 +138,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.80it/s]"
+      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]"
      ]
     },
     {
@@ -146,7 +146,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.80it/s]"
+      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]"
      ]
     },
     {
@@ -216,10 +216,10 @@
    "id": "f7febdc3-8c1e-46cf-816b-4a62251d641b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:30.546273Z",
-     "iopub.status.busy": "2024-10-25T14:54:30.545915Z",
-     "iopub.status.idle": "2024-10-25T14:54:30.560456Z",
-     "shell.execute_reply": "2024-10-25T14:54:30.559904Z"
+     "iopub.execute_input": "2024-10-25T14:55:38.119038Z",
+     "iopub.status.busy": "2024-10-25T14:55:38.118631Z",
+     "iopub.status.idle": "2024-10-25T14:55:38.134115Z",
+     "shell.execute_reply": "2024-10-25T14:55:38.133627Z"
     },
     "tags": []
    },
@@ -250,10 +250,10 @@
    "id": "5f9e1bad-ae6e-432d-bbb8-1f65ab7bbab6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:30.562493Z",
-     "iopub.status.busy": "2024-10-25T14:54:30.562190Z",
-     "iopub.status.idle": "2024-10-25T14:54:32.816150Z",
-     "shell.execute_reply": "2024-10-25T14:54:32.815490Z"
+     "iopub.execute_input": "2024-10-25T14:55:38.136192Z",
+     "iopub.status.busy": "2024-10-25T14:55:38.135826Z",
+     "iopub.status.idle": "2024-10-25T14:55:40.456788Z",
+     "shell.execute_reply": "2024-10-25T14:55:40.456029Z"
     },
     "tags": []
    },
@@ -344,10 +344,10 @@
    "id": "e6d0b582-20c6-476b-a07c-9d336ccda3fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:32.818347Z",
-     "iopub.status.busy": "2024-10-25T14:54:32.817945Z",
-     "iopub.status.idle": "2024-10-25T14:54:37.351192Z",
-     "shell.execute_reply": "2024-10-25T14:54:37.350630Z"
+     "iopub.execute_input": "2024-10-25T14:55:40.459066Z",
+     "iopub.status.busy": "2024-10-25T14:55:40.458724Z",
+     "iopub.status.idle": "2024-10-25T14:55:45.057653Z",
+     "shell.execute_reply": "2024-10-25T14:55:45.056946Z"
     },
     "tags": []
    },
@@ -396,10 +396,10 @@
    "id": "95ec0312-84c2-4405-a823-740c98482090",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:37.353346Z",
-     "iopub.status.busy": "2024-10-25T14:54:37.352954Z",
-     "iopub.status.idle": "2024-10-25T14:54:37.503915Z",
-     "shell.execute_reply": "2024-10-25T14:54:37.503268Z"
+     "iopub.execute_input": "2024-10-25T14:55:45.060083Z",
+     "iopub.status.busy": "2024-10-25T14:55:45.059636Z",
+     "iopub.status.idle": "2024-10-25T14:55:45.216677Z",
+     "shell.execute_reply": "2024-10-25T14:55:45.215910Z"
     },
     "tags": []
    },
@@ -491,10 +491,10 @@
    "id": "a5b5dbf8-046b-4d9d-848c-694eb742aafb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:37.506385Z",
-     "iopub.status.busy": "2024-10-25T14:54:37.505988Z",
-     "iopub.status.idle": "2024-10-25T14:54:37.669177Z",
-     "shell.execute_reply": "2024-10-25T14:54:37.668647Z"
+     "iopub.execute_input": "2024-10-25T14:55:45.218935Z",
+     "iopub.status.busy": "2024-10-25T14:55:45.218489Z",
+     "iopub.status.idle": "2024-10-25T14:55:45.388096Z",
+     "shell.execute_reply": "2024-10-25T14:55:45.387506Z"
     },
     "tags": []
    },
@@ -571,10 +571,10 @@
    "id": "8b977dca-89e4-406c-80a9-6d24dd8a3aca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:37.671217Z",
-     "iopub.status.busy": "2024-10-25T14:54:37.670853Z",
-     "iopub.status.idle": "2024-10-25T14:54:37.863014Z",
-     "shell.execute_reply": "2024-10-25T14:54:37.862478Z"
+     "iopub.execute_input": "2024-10-25T14:55:45.390214Z",
+     "iopub.status.busy": "2024-10-25T14:55:45.390006Z",
+     "iopub.status.idle": "2024-10-25T14:55:45.748267Z",
+     "shell.execute_reply": "2024-10-25T14:55:45.747600Z"
     },
     "tags": []
    },
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb
index 419a71c88..65b15d5da 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb
@@ -29,10 +29,10 @@
    "id": "f22337ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:42.167628Z",
-     "iopub.status.busy": "2024-10-25T14:54:42.167428Z",
-     "iopub.status.idle": "2024-10-25T14:54:42.398255Z",
-     "shell.execute_reply": "2024-10-25T14:54:42.397619Z"
+     "iopub.execute_input": "2024-10-25T14:55:50.307983Z",
+     "iopub.status.busy": "2024-10-25T14:55:50.307806Z",
+     "iopub.status.idle": "2024-10-25T14:55:50.566746Z",
+     "shell.execute_reply": "2024-10-25T14:55:50.566061Z"
     }
    },
    "outputs": [
@@ -65,33 +65,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.121542</td>\n",
-       "      <td>1.657360</td>\n",
-       "      <td>3.616523</td>\n",
+       "      <td>1.202890</td>\n",
+       "      <td>3.041920</td>\n",
+       "      <td>8.717822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.503374</td>\n",
-       "      <td>1.251506</td>\n",
-       "      <td>4.587577</td>\n",
+       "      <td>-1.050020</td>\n",
+       "      <td>-3.235161</td>\n",
+       "      <td>-8.177542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.473361</td>\n",
-       "      <td>1.553348</td>\n",
-       "      <td>4.065011</td>\n",
+       "      <td>0.048552</td>\n",
+       "      <td>-0.068086</td>\n",
+       "      <td>0.254594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.865151</td>\n",
-       "      <td>1.475716</td>\n",
-       "      <td>3.616999</td>\n",
+       "      <td>0.065656</td>\n",
+       "      <td>-1.161772</td>\n",
+       "      <td>-2.940748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.641329</td>\n",
-       "      <td>2.701932</td>\n",
-       "      <td>11.109621</td>\n",
+       "      <td>-1.968502</td>\n",
+       "      <td>-3.431138</td>\n",
+       "      <td>-10.633668</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -99,11 +99,11 @@
       ],
       "text/plain": [
        "          X         Y          Z\n",
-       "0  1.121542  1.657360   3.616523\n",
-       "1  1.503374  1.251506   4.587577\n",
-       "2 -0.473361  1.553348   4.065011\n",
-       "3  0.865151  1.475716   3.616999\n",
-       "4  0.641329  2.701932  11.109621"
+       "0  1.202890  3.041920   8.717822\n",
+       "1 -1.050020 -3.235161  -8.177542\n",
+       "2  0.048552 -0.068086   0.254594\n",
+       "3  0.065656 -1.161772  -2.940748\n",
+       "4 -1.968502 -3.431138 -10.633668"
       ]
      },
      "execution_count": 1,
@@ -136,10 +136,10 @@
    "id": "0367caeb",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-10-25T14:54:42.400081Z",
-     "iopub.status.idle": "2024-10-25T14:54:47.481474Z",
-     "shell.execute_reply": "2024-10-25T14:54:47.480788Z"
+     "iopub.execute_input": "2024-10-25T14:55:50.568793Z",
+     "iopub.status.busy": "2024-10-25T14:55:50.568551Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.040184Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.039494Z"
     }
    },
    "outputs": [
@@ -180,7 +180,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 423.32it/s]"
+      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 459.01it/s]"
      ]
     },
     {
@@ -220,10 +220,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:47.484135Z",
-     "iopub.status.busy": "2024-10-25T14:54:47.483406Z",
-     "iopub.status.idle": "2024-10-25T14:54:47.495226Z",
-     "shell.execute_reply": "2024-10-25T14:54:47.494671Z"
+     "iopub.execute_input": "2024-10-25T14:55:56.042918Z",
+     "iopub.status.busy": "2024-10-25T14:55:56.042179Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.054149Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.053578Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -262,33 +262,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.481421</td>\n",
-       "      <td>1.807229</td>\n",
-       "      <td>6.236546</td>\n",
+       "      <td>0.377113</td>\n",
+       "      <td>0.796241</td>\n",
+       "      <td>2.942921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.010589</td>\n",
-       "      <td>-0.653087</td>\n",
-       "      <td>-2.223590</td>\n",
+       "      <td>0.029839</td>\n",
+       "      <td>0.995039</td>\n",
+       "      <td>3.948314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.327882</td>\n",
-       "      <td>0.178439</td>\n",
-       "      <td>0.210575</td>\n",
+       "      <td>1.537772</td>\n",
+       "      <td>3.331072</td>\n",
+       "      <td>9.241248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.234144</td>\n",
-       "      <td>-0.272059</td>\n",
-       "      <td>-0.227509</td>\n",
+       "      <td>0.077872</td>\n",
+       "      <td>-1.938379</td>\n",
+       "      <td>-7.001653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.925122</td>\n",
-       "      <td>3.424511</td>\n",
-       "      <td>9.974717</td>\n",
+       "      <td>0.892098</td>\n",
+       "      <td>1.337169</td>\n",
+       "      <td>5.719637</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -296,11 +296,11 @@
       ],
       "text/plain": [
        "          X         Y         Z\n",
-       "0  0.481421  1.807229  6.236546\n",
-       "1  0.010589 -0.653087 -2.223590\n",
-       "2 -0.327882  0.178439  0.210575\n",
-       "3 -0.234144 -0.272059 -0.227509\n",
-       "4  1.925122  3.424511  9.974717"
+       "0  0.377113  0.796241  2.942921\n",
+       "1  0.029839  0.995039  3.948314\n",
+       "2  1.537772  3.331072  9.241248\n",
+       "3  0.077872 -1.938379 -7.001653\n",
+       "4  0.892098  1.337169  5.719637"
       ]
      },
      "execution_count": 3,
@@ -332,10 +332,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:47.497131Z",
-     "iopub.status.busy": "2024-10-25T14:54:47.496943Z",
-     "iopub.status.idle": "2024-10-25T14:54:48.212265Z",
-     "shell.execute_reply": "2024-10-25T14:54:48.211666Z"
+     "iopub.execute_input": "2024-10-25T14:55:56.056555Z",
+     "iopub.status.busy": "2024-10-25T14:55:56.056028Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.816691Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.816041Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -358,7 +358,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.23it/s]"
+      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 11.49it/s]"
      ]
     },
     {
@@ -366,7 +366,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.19it/s]"
+      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 11.45it/s]"
      ]
     },
     {
@@ -374,7 +374,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.14it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 17.05it/s]"
      ]
     },
     {
@@ -401,7 +401,7 @@
       "Evaluated and the overall average KL divergence between generated and observed distribution and the graph structure. The results are as follows:\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.0133688297726812\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.019128396107247297\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb
index d024eec82..9f2f66ba4 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb
@@ -18,10 +18,10 @@
    "id": "13a5f7f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:52.411936Z",
-     "iopub.status.busy": "2024-10-25T14:54:52.411500Z",
-     "iopub.status.idle": "2024-10-25T14:54:53.813945Z",
-     "shell.execute_reply": "2024-10-25T14:54:53.813345Z"
+     "iopub.execute_input": "2024-10-25T14:56:01.147169Z",
+     "iopub.status.busy": "2024-10-25T14:56:01.146746Z",
+     "iopub.status.idle": "2024-10-25T14:56:02.697341Z",
+     "shell.execute_reply": "2024-10-25T14:56:02.696670Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "id": "08d67fd1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:53.816412Z",
-     "iopub.status.busy": "2024-10-25T14:54:53.815902Z",
-     "iopub.status.idle": "2024-10-25T14:54:53.924049Z",
-     "shell.execute_reply": "2024-10-25T14:54:53.923441Z"
+     "iopub.execute_input": "2024-10-25T14:56:02.700190Z",
+     "iopub.status.busy": "2024-10-25T14:56:02.699632Z",
+     "iopub.status.idle": "2024-10-25T14:56:02.819904Z",
+     "shell.execute_reply": "2024-10-25T14:56:02.819206Z"
     }
    },
    "outputs": [
@@ -105,10 +105,10 @@
    "id": "953753d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:53.926517Z",
-     "iopub.status.busy": "2024-10-25T14:54:53.926093Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.608516Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.607875Z"
+     "iopub.execute_input": "2024-10-25T14:56:02.822376Z",
+     "iopub.status.busy": "2024-10-25T14:56:02.821922Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.089046Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.088372Z"
     }
    },
    "outputs": [
@@ -125,7 +125,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 1/20 [00:01<00:25,  1.33s/it]"
+      "Test permutations of given graph:   5%|▌         | 1/20 [00:01<00:26,  1.40s/it]"
      ]
     },
     {
@@ -133,7 +133,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 2/20 [00:02<00:24,  1.35s/it]"
+      "Test permutations of given graph:  10%|█         | 2/20 [00:02<00:24,  1.37s/it]"
      ]
     },
     {
@@ -141,7 +141,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 3/20 [00:03<00:22,  1.31s/it]"
+      "Test permutations of given graph:  15%|█▌        | 3/20 [00:04<00:22,  1.34s/it]"
      ]
     },
     {
@@ -149,7 +149,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 4/20 [00:04<00:18,  1.15s/it]"
+      "Test permutations of given graph:  20%|██        | 4/20 [00:04<00:18,  1.17s/it]"
      ]
     },
     {
@@ -157,7 +157,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 5/20 [00:05<00:15,  1.06s/it]"
+      "Test permutations of given graph:  25%|██▌       | 5/20 [00:05<00:16,  1.08s/it]"
      ]
     },
     {
@@ -165,7 +165,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 6/20 [00:06<00:13,  1.00it/s]"
+      "Test permutations of given graph:  30%|███       | 6/20 [00:06<00:14,  1.02s/it]"
      ]
     },
     {
@@ -173,7 +173,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 7/20 [00:07<00:14,  1.09s/it]"
+      "Test permutations of given graph:  35%|███▌      | 7/20 [00:08<00:14,  1.12s/it]"
      ]
     },
     {
@@ -181,7 +181,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 8/20 [00:08<00:12,  1.02s/it]"
+      "Test permutations of given graph:  40%|████      | 8/20 [00:09<00:12,  1.05s/it]"
      ]
     },
     {
@@ -189,7 +189,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 9/20 [00:10<00:12,  1.11s/it]"
+      "Test permutations of given graph:  45%|████▌     | 9/20 [00:10<00:12,  1.14s/it]"
      ]
     },
     {
@@ -197,7 +197,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 10/20 [00:11<00:11,  1.17s/it]"
+      "Test permutations of given graph:  50%|█████     | 10/20 [00:11<00:11,  1.19s/it]"
      ]
     },
     {
@@ -205,7 +205,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 12/20 [00:12<00:06,  1.21it/s]"
+      "Test permutations of given graph:  60%|██████    | 12/20 [00:12<00:06,  1.18it/s]"
      ]
     },
     {
@@ -213,7 +213,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:13<00:05,  1.18it/s]"
+      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:13<00:06,  1.16it/s]"
      ]
     },
     {
@@ -221,7 +221,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 14/20 [00:14<00:05,  1.17it/s]"
+      "Test permutations of given graph:  70%|███████   | 14/20 [00:14<00:05,  1.14it/s]"
      ]
     },
     {
@@ -229,7 +229,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  85%|████████▌ | 17/20 [00:14<00:01,  1.78it/s]"
+      "Test permutations of given graph:  85%|████████▌ | 17/20 [00:15<00:01,  1.75it/s]"
      ]
     },
     {
@@ -237,7 +237,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 18/20 [00:15<00:01,  1.59it/s]"
+      "Test permutations of given graph:  90%|█████████ | 18/20 [00:16<00:01,  1.56it/s]"
      ]
     },
     {
@@ -245,7 +245,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  95%|█████████▌| 19/20 [00:16<00:00,  1.45it/s]"
+      "Test permutations of given graph:  95%|█████████▌| 19/20 [00:17<00:00,  1.42it/s]"
      ]
     },
     {
@@ -253,7 +253,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00,  1.19it/s]"
+      "Test permutations of given graph: 100%|██████████| 20/20 [00:17<00:00,  1.17it/s]"
      ]
     },
     {
@@ -317,10 +317,10 @@
    "id": "529de3f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.610385Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.610207Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.613608Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.613101Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.091337Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.090939Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.094668Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.094056Z"
     }
    },
    "outputs": [
@@ -350,10 +350,10 @@
    "id": "4623dbc3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.615360Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.615024Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.708019Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.707473Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.096406Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.096202Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.193214Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.192560Z"
     }
    },
    "outputs": [
@@ -382,10 +382,10 @@
    "id": "fcd847fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.710076Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.709865Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.500191Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.499450Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.195920Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.195506Z",
+     "iopub.status.idle": "2024-10-25T14:56:45.926358Z",
+     "shell.execute_reply": "2024-10-25T14:56:45.925587Z"
     }
    },
    "outputs": [
@@ -402,7 +402,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:46,  2.46s/it]"
+      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:48,  2.57s/it]"
      ]
     },
     {
@@ -410,7 +410,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 2/20 [00:04<00:44,  2.46s/it]"
+      "Test permutations of given graph:  10%|█         | 2/20 [00:05<00:46,  2.56s/it]"
      ]
     },
     {
@@ -418,7 +418,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:35,  2.06s/it]"
+      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:36,  2.15s/it]"
      ]
     },
     {
@@ -426,7 +426,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 4/20 [00:08<00:30,  1.91s/it]"
+      "Test permutations of given graph:  20%|██        | 4/20 [00:08<00:31,  1.99s/it]"
      ]
     },
     {
@@ -434,7 +434,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 5/20 [00:09<00:26,  1.80s/it]"
+      "Test permutations of given graph:  25%|██▌       | 5/20 [00:10<00:28,  1.87s/it]"
      ]
     },
     {
@@ -442,7 +442,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:23,  1.65s/it]"
+      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:23,  1.70s/it]"
      ]
     },
     {
@@ -450,7 +450,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 7/20 [00:12<00:19,  1.53s/it]"
+      "Test permutations of given graph:  35%|███▌      | 7/20 [00:12<00:20,  1.57s/it]"
      ]
     },
     {
@@ -458,7 +458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 8/20 [00:13<00:17,  1.44s/it]"
+      "Test permutations of given graph:  40%|████      | 8/20 [00:14<00:18,  1.51s/it]"
      ]
     },
     {
@@ -466,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 9/20 [00:15<00:15,  1.41s/it]"
+      "Test permutations of given graph:  45%|████▌     | 9/20 [00:15<00:16,  1.46s/it]"
      ]
     },
     {
@@ -474,7 +474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 12/20 [00:16<00:06,  1.18it/s]"
+      "Test permutations of given graph:  60%|██████    | 12/20 [00:16<00:07,  1.13it/s]"
      ]
     },
     {
@@ -482,7 +482,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:17<00:05,  1.18it/s]"
+      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:17<00:06,  1.12it/s]"
      ]
     },
     {
@@ -490,7 +490,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:18<00:03,  1.44it/s]"
+      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:18<00:03,  1.37it/s]"
      ]
     },
     {
@@ -498,7 +498,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.11it/s]"
+      "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.06it/s]"
      ]
     },
     {
@@ -557,10 +557,10 @@
    "id": "3fe68e25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.502196Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.502006Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.595059Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.594431Z"
+     "iopub.execute_input": "2024-10-25T14:56:45.928545Z",
+     "iopub.status.busy": "2024-10-25T14:56:45.928282Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.032809Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.032125Z"
     }
    },
    "outputs": [
@@ -604,10 +604,10 @@
    "id": "03e8ffa3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.597521Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.597116Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.767492Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.766953Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.035589Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.035182Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.069198Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.068419Z"
     }
    },
    "outputs": [],
@@ -624,10 +624,10 @@
    "id": "3d30a01b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.769719Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.769347Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.895092Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.894430Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.072783Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.072257Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.217919Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.217241Z"
     }
    },
    "outputs": [
@@ -660,10 +660,10 @@
    "id": "072544c1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.897463Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.897092Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.901575Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.901005Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.221083Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.220461Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.224798Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.224229Z"
     }
    },
    "outputs": [],
@@ -692,10 +692,10 @@
    "id": "bbfe658d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.904014Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.903524Z",
-     "iopub.status.idle": "2024-10-25T15:07:50.708246Z",
-     "shell.execute_reply": "2024-10-25T15:07:50.707586Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.227173Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.226755Z",
+     "iopub.status.idle": "2024-10-25T15:09:10.934004Z",
+     "shell.execute_reply": "2024-10-25T15:09:10.933345Z"
     }
    },
    "outputs": [
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   1%|          | 1/100 [00:10<17:50, 10.81s/it]"
+      "Test permutations of given graph:   1%|          | 1/100 [00:11<18:28, 11.20s/it]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 2/100 [00:21<17:42, 10.84s/it]"
+      "Test permutations of given graph:   2%|▏         | 2/100 [00:22<18:27, 11.30s/it]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   3%|▎         | 3/100 [00:32<17:12, 10.65s/it]"
+      "Test permutations of given graph:   3%|▎         | 3/100 [00:33<17:55, 11.08s/it]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 4/100 [00:42<17:07, 10.71s/it]"
+      "Test permutations of given graph:   4%|▍         | 4/100 [00:44<17:41, 11.05s/it]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 5/100 [00:53<17:08, 10.83s/it]"
+      "Test permutations of given graph:   5%|▌         | 5/100 [00:55<17:37, 11.14s/it]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 6/100 [01:03<16:19, 10.42s/it]"
+      "Test permutations of given graph:   6%|▌         | 6/100 [01:05<16:48, 10.73s/it]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   7%|▋         | 7/100 [01:11<14:46,  9.53s/it]"
+      "Test permutations of given graph:   7%|▋         | 7/100 [01:13<15:08,  9.76s/it]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 8/100 [01:22<15:19,  9.99s/it]"
+      "Test permutations of given graph:   8%|▊         | 8/100 [01:24<15:37, 10.19s/it]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   9%|▉         | 9/100 [01:33<15:37, 10.30s/it]"
+      "Test permutations of given graph:   9%|▉         | 9/100 [01:35<15:58, 10.54s/it]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 10/100 [01:43<15:34, 10.38s/it]"
+      "Test permutations of given graph:  10%|█         | 10/100 [01:46<15:51, 10.57s/it]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  11%|█         | 11/100 [01:54<15:26, 10.41s/it]"
+      "Test permutations of given graph:  11%|█         | 11/100 [01:57<15:41, 10.58s/it]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 12/100 [02:04<15:17, 10.42s/it]"
+      "Test permutations of given graph:  12%|█▏        | 12/100 [02:07<15:29, 10.57s/it]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  13%|█▎        | 13/100 [02:13<14:34, 10.05s/it]"
+      "Test permutations of given graph:  13%|█▎        | 13/100 [02:16<14:45, 10.18s/it]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 14/100 [02:21<13:25,  9.37s/it]"
+      "Test permutations of given graph:  14%|█▍        | 14/100 [02:24<13:38,  9.52s/it]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 15/100 [02:32<13:43,  9.69s/it]"
+      "Test permutations of given graph:  15%|█▌        | 15/100 [02:35<13:57,  9.85s/it]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 16/100 [02:42<13:52,  9.91s/it]"
+      "Test permutations of given graph:  16%|█▌        | 16/100 [02:46<14:05, 10.07s/it]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  17%|█▋        | 17/100 [02:49<12:36,  9.12s/it]"
+      "Test permutations of given graph:  17%|█▋        | 17/100 [02:53<12:45,  9.22s/it]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 18/100 [03:00<12:56,  9.47s/it]"
+      "Test permutations of given graph:  18%|█▊        | 18/100 [03:03<13:09,  9.63s/it]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  19%|█▉        | 19/100 [03:10<13:06,  9.71s/it]"
+      "Test permutations of given graph:  19%|█▉        | 19/100 [03:14<13:23,  9.92s/it]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 20/100 [03:18<12:12,  9.16s/it]"
+      "Test permutations of given graph:  20%|██        | 20/100 [03:22<12:26,  9.33s/it]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  21%|██        | 21/100 [03:28<12:30,  9.50s/it]"
+      "Test permutations of given graph:  21%|██        | 21/100 [03:33<12:47,  9.72s/it]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 22/100 [03:38<12:41,  9.76s/it]"
+      "Test permutations of given graph:  22%|██▏       | 22/100 [03:43<12:57,  9.96s/it]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  23%|██▎       | 23/100 [03:45<11:18,  8.81s/it]"
+      "Test permutations of given graph:  23%|██▎       | 23/100 [03:50<11:29,  8.95s/it]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 24/100 [03:53<11:01,  8.71s/it]"
+      "Test permutations of given graph:  24%|██▍       | 24/100 [03:58<11:09,  8.80s/it]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 25/100 [04:04<11:26,  9.15s/it]"
+      "Test permutations of given graph:  25%|██▌       | 25/100 [04:08<11:33,  9.25s/it]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 26/100 [04:12<11:07,  9.01s/it]"
+      "Test permutations of given graph:  26%|██▌       | 26/100 [04:17<11:14,  9.12s/it]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  27%|██▋       | 27/100 [04:20<10:34,  8.70s/it]"
+      "Test permutations of given graph:  27%|██▋       | 27/100 [04:25<10:40,  8.78s/it]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 28/100 [04:30<10:41,  8.91s/it]"
+      "Test permutations of given graph:  28%|██▊       | 28/100 [04:35<10:47,  8.99s/it]"
      ]
     },
     {
@@ -936,7 +936,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  29%|██▉       | 29/100 [04:40<10:59,  9.29s/it]"
+      "Test permutations of given graph:  29%|██▉       | 29/100 [04:45<11:06,  9.39s/it]"
      ]
     },
     {
@@ -944,7 +944,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 30/100 [04:47<10:01,  8.60s/it]"
+      "Test permutations of given graph:  30%|███       | 30/100 [04:52<10:07,  8.68s/it]"
      ]
     },
     {
@@ -952,7 +952,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  31%|███       | 31/100 [04:54<09:29,  8.25s/it]"
+      "Test permutations of given graph:  31%|███       | 31/100 [05:00<09:33,  8.32s/it]"
      ]
     },
     {
@@ -960,7 +960,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 32/100 [05:03<09:21,  8.26s/it]"
+      "Test permutations of given graph:  32%|███▏      | 32/100 [05:08<09:25,  8.32s/it]"
      ]
     },
     {
@@ -968,7 +968,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 33/100 [05:10<09:05,  8.14s/it]"
+      "Test permutations of given graph:  33%|███▎      | 33/100 [05:16<09:07,  8.18s/it]"
      ]
     },
     {
@@ -976,7 +976,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 34/100 [05:18<08:42,  7.92s/it]"
+      "Test permutations of given graph:  34%|███▍      | 34/100 [05:23<08:44,  7.95s/it]"
      ]
     },
     {
@@ -984,7 +984,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 35/100 [05:27<08:59,  8.30s/it]"
+      "Test permutations of given graph:  35%|███▌      | 35/100 [05:32<09:00,  8.31s/it]"
      ]
     },
     {
@@ -992,7 +992,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 36/100 [05:36<09:02,  8.48s/it]"
+      "Test permutations of given graph:  36%|███▌      | 36/100 [05:41<09:01,  8.45s/it]"
      ]
     },
     {
@@ -1000,7 +1000,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  37%|███▋      | 37/100 [05:44<08:37,  8.21s/it]"
+      "Test permutations of given graph:  37%|███▋      | 37/100 [05:49<08:37,  8.22s/it]"
      ]
     },
     {
@@ -1008,7 +1008,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 38/100 [05:49<07:36,  7.36s/it]"
+      "Test permutations of given graph:  38%|███▊      | 38/100 [05:54<07:41,  7.44s/it]"
      ]
     },
     {
@@ -1016,7 +1016,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  39%|███▉      | 39/100 [05:59<08:23,  8.25s/it]"
+      "Test permutations of given graph:  39%|███▉      | 39/100 [06:05<08:27,  8.32s/it]"
      ]
     },
     {
@@ -1024,7 +1024,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 40/100 [06:07<08:11,  8.19s/it]"
+      "Test permutations of given graph:  40%|████      | 40/100 [06:13<08:15,  8.26s/it]"
      ]
     },
     {
@@ -1032,7 +1032,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  41%|████      | 41/100 [06:13<07:25,  7.55s/it]"
+      "Test permutations of given graph:  41%|████      | 41/100 [06:19<07:28,  7.60s/it]"
      ]
     },
     {
@@ -1040,7 +1040,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 42/100 [06:23<07:49,  8.10s/it]"
+      "Test permutations of given graph:  42%|████▏     | 42/100 [06:28<07:53,  8.17s/it]"
      ]
     },
     {
@@ -1048,7 +1048,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  43%|████▎     | 43/100 [06:28<06:56,  7.31s/it]"
+      "Test permutations of given graph:  43%|████▎     | 43/100 [06:34<07:00,  7.38s/it]"
      ]
     },
     {
@@ -1056,7 +1056,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 44/100 [06:36<06:50,  7.33s/it]"
+      "Test permutations of given graph:  44%|████▍     | 44/100 [06:41<06:54,  7.40s/it]"
      ]
     },
     {
@@ -1064,7 +1064,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 45/100 [06:44<06:55,  7.55s/it]"
+      "Test permutations of given graph:  45%|████▌     | 45/100 [06:50<06:59,  7.63s/it]"
      ]
     },
     {
@@ -1072,7 +1072,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 46/100 [06:47<05:33,  6.17s/it]"
+      "Test permutations of given graph:  46%|████▌     | 46/100 [06:53<05:36,  6.23s/it]"
      ]
     },
     {
@@ -1080,7 +1080,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  47%|████▋     | 47/100 [06:55<05:58,  6.75s/it]"
+      "Test permutations of given graph:  47%|████▋     | 47/100 [07:01<06:00,  6.80s/it]"
      ]
     },
     {
@@ -1088,7 +1088,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 48/100 [07:01<05:45,  6.65s/it]"
+      "Test permutations of given graph:  48%|████▊     | 48/100 [07:07<05:47,  6.68s/it]"
      ]
     },
     {
@@ -1096,7 +1096,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  49%|████▉     | 49/100 [07:10<06:18,  7.42s/it]"
+      "Test permutations of given graph:  49%|████▉     | 49/100 [07:16<06:20,  7.45s/it]"
      ]
     },
     {
@@ -1104,7 +1104,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 50/100 [07:15<05:31,  6.63s/it]"
+      "Test permutations of given graph:  50%|█████     | 50/100 [07:21<05:33,  6.66s/it]"
      ]
     },
     {
@@ -1112,7 +1112,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  51%|█████     | 51/100 [07:24<05:50,  7.16s/it]"
+      "Test permutations of given graph:  51%|█████     | 51/100 [07:30<05:52,  7.19s/it]"
      ]
     },
     {
@@ -1120,7 +1120,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 52/100 [07:33<06:12,  7.77s/it]"
+      "Test permutations of given graph:  52%|█████▏    | 52/100 [07:39<06:15,  7.83s/it]"
      ]
     },
     {
@@ -1128,7 +1128,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  53%|█████▎    | 53/100 [07:38<05:32,  7.08s/it]"
+      "Test permutations of given graph:  53%|█████▎    | 53/100 [07:44<05:35,  7.14s/it]"
      ]
     },
     {
@@ -1136,7 +1136,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 54/100 [07:45<05:26,  7.10s/it]"
+      "Test permutations of given graph:  54%|█████▍    | 54/100 [07:52<05:29,  7.16s/it]"
      ]
     },
     {
@@ -1144,7 +1144,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  55%|█████▌    | 55/100 [07:54<05:35,  7.46s/it]"
+      "Test permutations of given graph:  55%|█████▌    | 55/100 [08:00<05:38,  7.53s/it]"
      ]
     },
     {
@@ -1152,7 +1152,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 56/100 [07:58<04:50,  6.61s/it]"
+      "Test permutations of given graph:  56%|█████▌    | 56/100 [08:05<04:53,  6.67s/it]"
      ]
     },
     {
@@ -1160,7 +1160,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  57%|█████▋    | 57/100 [08:05<04:43,  6.60s/it]"
+      "Test permutations of given graph:  57%|█████▋    | 57/100 [08:11<04:46,  6.66s/it]"
      ]
     },
     {
@@ -1168,7 +1168,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  58%|█████▊    | 58/100 [08:12<04:41,  6.71s/it]"
+      "Test permutations of given graph:  58%|█████▊    | 58/100 [08:18<04:44,  6.78s/it]"
      ]
     },
     {
@@ -1176,7 +1176,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  59%|█████▉    | 59/100 [08:16<04:06,  6.02s/it]"
+      "Test permutations of given graph:  59%|█████▉    | 59/100 [08:23<04:09,  6.08s/it]"
      ]
     },
     {
@@ -1184,7 +1184,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 60/100 [08:24<04:21,  6.54s/it]"
+      "Test permutations of given graph:  60%|██████    | 60/100 [08:30<04:20,  6.52s/it]"
      ]
     },
     {
@@ -1192,7 +1192,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  61%|██████    | 61/100 [08:29<03:52,  5.95s/it]"
+      "Test permutations of given graph:  61%|██████    | 61/100 [08:35<03:52,  5.97s/it]"
      ]
     },
     {
@@ -1200,7 +1200,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 62/100 [08:34<03:43,  5.89s/it]"
+      "Test permutations of given graph:  62%|██████▏   | 62/100 [08:41<03:44,  5.92s/it]"
      ]
     },
     {
@@ -1208,7 +1208,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  63%|██████▎   | 63/100 [08:40<03:38,  5.91s/it]"
+      "Test permutations of given graph:  63%|██████▎   | 63/100 [08:47<03:39,  5.93s/it]"
      ]
     },
     {
@@ -1216,7 +1216,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 64/100 [08:47<03:46,  6.28s/it]"
+      "Test permutations of given graph:  64%|██████▍   | 64/100 [08:54<03:49,  6.37s/it]"
      ]
     },
     {
@@ -1224,7 +1224,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 65/100 [08:53<03:32,  6.08s/it]"
+      "Test permutations of given graph:  65%|██████▌   | 65/100 [09:00<03:35,  6.15s/it]"
      ]
     },
     {
@@ -1232,7 +1232,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 66/100 [08:58<03:13,  5.70s/it]"
+      "Test permutations of given graph:  66%|██████▌   | 66/100 [09:05<03:17,  5.80s/it]"
      ]
     },
     {
@@ -1240,7 +1240,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 67/100 [09:04<03:12,  5.85s/it]"
+      "Test permutations of given graph:  67%|██████▋   | 67/100 [09:11<03:16,  5.94s/it]"
      ]
     },
     {
@@ -1248,7 +1248,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 68/100 [09:08<02:51,  5.37s/it]"
+      "Test permutations of given graph:  68%|██████▊   | 68/100 [09:16<02:55,  5.49s/it]"
      ]
     },
     {
@@ -1256,7 +1256,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  69%|██████▉   | 69/100 [09:16<03:05,  5.98s/it]"
+      "Test permutations of given graph:  69%|██████▉   | 69/100 [09:23<03:09,  6.11s/it]"
      ]
     },
     {
@@ -1264,7 +1264,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 70/100 [09:25<03:27,  6.92s/it]"
+      "Test permutations of given graph:  70%|███████   | 70/100 [09:32<03:32,  7.09s/it]"
      ]
     },
     {
@@ -1272,7 +1272,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  71%|███████   | 71/100 [09:30<03:07,  6.45s/it]"
+      "Test permutations of given graph:  71%|███████   | 71/100 [09:38<03:13,  6.67s/it]"
      ]
     },
     {
@@ -1280,7 +1280,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 72/100 [09:37<03:04,  6.59s/it]"
+      "Test permutations of given graph:  72%|███████▏  | 72/100 [09:45<03:09,  6.77s/it]"
      ]
     },
     {
@@ -1288,7 +1288,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  73%|███████▎  | 73/100 [09:41<02:34,  5.71s/it]"
+      "Test permutations of given graph:  73%|███████▎  | 73/100 [09:49<02:37,  5.85s/it]"
      ]
     },
     {
@@ -1296,7 +1296,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 74/100 [09:47<02:34,  5.94s/it]"
+      "Test permutations of given graph:  74%|███████▍  | 74/100 [09:55<02:37,  6.05s/it]"
      ]
     },
     {
@@ -1304,7 +1304,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  75%|███████▌  | 75/100 [09:52<02:19,  5.58s/it]"
+      "Test permutations of given graph:  75%|███████▌  | 75/100 [10:00<02:21,  5.67s/it]"
      ]
     },
     {
@@ -1312,7 +1312,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 76/100 [09:57<02:07,  5.33s/it]"
+      "Test permutations of given graph:  76%|███████▌  | 76/100 [10:05<02:09,  5.39s/it]"
      ]
     },
     {
@@ -1320,7 +1320,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  77%|███████▋  | 77/100 [10:02<02:02,  5.33s/it]"
+      "Test permutations of given graph:  77%|███████▋  | 77/100 [10:10<02:03,  5.38s/it]"
      ]
     },
     {
@@ -1328,7 +1328,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 78/100 [10:08<01:58,  5.40s/it]"
+      "Test permutations of given graph:  78%|███████▊  | 78/100 [10:16<02:00,  5.47s/it]"
      ]
     },
     {
@@ -1336,7 +1336,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  79%|███████▉  | 79/100 [10:12<01:48,  5.16s/it]"
+      "Test permutations of given graph:  79%|███████▉  | 79/100 [10:21<01:49,  5.22s/it]"
      ]
     },
     {
@@ -1344,7 +1344,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 80/100 [10:19<01:52,  5.61s/it]"
+      "Test permutations of given graph:  80%|████████  | 80/100 [10:27<01:53,  5.65s/it]"
      ]
     },
     {
@@ -1352,7 +1352,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  81%|████████  | 81/100 [10:23<01:41,  5.32s/it]"
+      "Test permutations of given graph:  81%|████████  | 81/100 [10:32<01:42,  5.38s/it]"
      ]
     },
     {
@@ -1360,7 +1360,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 82/100 [10:28<01:32,  5.14s/it]"
+      "Test permutations of given graph:  82%|████████▏ | 82/100 [10:37<01:33,  5.19s/it]"
      ]
     },
     {
@@ -1368,7 +1368,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  83%|████████▎ | 83/100 [10:33<01:24,  4.96s/it]"
+      "Test permutations of given graph:  83%|████████▎ | 83/100 [10:41<01:25,  5.02s/it]"
      ]
     },
     {
@@ -1376,7 +1376,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 84/100 [10:40<01:31,  5.75s/it]"
+      "Test permutations of given graph:  84%|████████▍ | 84/100 [10:49<01:32,  5.80s/it]"
      ]
     },
     {
@@ -1384,7 +1384,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  85%|████████▌ | 85/100 [10:45<01:23,  5.54s/it]"
+      "Test permutations of given graph:  85%|████████▌ | 85/100 [10:54<01:23,  5.59s/it]"
      ]
     },
     {
@@ -1392,7 +1392,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 86/100 [10:53<01:25,  6.13s/it]"
+      "Test permutations of given graph:  86%|████████▌ | 86/100 [11:02<01:26,  6.18s/it]"
      ]
     },
     {
@@ -1400,7 +1400,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  87%|████████▋ | 87/100 [10:59<01:21,  6.25s/it]"
+      "Test permutations of given graph:  87%|████████▋ | 87/100 [11:08<01:21,  6.27s/it]"
      ]
     },
     {
@@ -1408,7 +1408,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 88/100 [11:07<01:18,  6.54s/it]"
+      "Test permutations of given graph:  88%|████████▊ | 88/100 [11:15<01:19,  6.59s/it]"
      ]
     },
     {
@@ -1416,7 +1416,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  89%|████████▉ | 89/100 [11:10<01:02,  5.70s/it]"
+      "Test permutations of given graph:  89%|████████▉ | 89/100 [11:19<01:03,  5.77s/it]"
      ]
     },
     {
@@ -1424,7 +1424,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 90/100 [11:16<00:57,  5.79s/it]"
+      "Test permutations of given graph:  90%|█████████ | 90/100 [11:25<00:58,  5.84s/it]"
      ]
     },
     {
@@ -1432,7 +1432,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  91%|█████████ | 91/100 [11:20<00:45,  5.05s/it]"
+      "Test permutations of given graph:  91%|█████████ | 91/100 [11:29<00:45,  5.09s/it]"
      ]
     },
     {
@@ -1440,7 +1440,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 92/100 [11:25<00:41,  5.23s/it]"
+      "Test permutations of given graph:  92%|█████████▏| 92/100 [11:34<00:42,  5.29s/it]"
      ]
     },
     {
@@ -1448,7 +1448,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  93%|█████████▎| 93/100 [11:32<00:38,  5.55s/it]"
+      "Test permutations of given graph:  93%|█████████▎| 93/100 [11:41<00:39,  5.60s/it]"
      ]
     },
     {
@@ -1456,7 +1456,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 94/100 [11:35<00:29,  4.86s/it]"
+      "Test permutations of given graph:  94%|█████████▍| 94/100 [11:44<00:29,  4.92s/it]"
      ]
     },
     {
@@ -1464,7 +1464,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  95%|█████████▌| 95/100 [11:43<00:28,  5.70s/it]"
+      "Test permutations of given graph:  95%|█████████▌| 95/100 [11:52<00:28,  5.76s/it]"
      ]
     },
     {
@@ -1472,7 +1472,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 96/100 [11:47<00:21,  5.40s/it]"
+      "Test permutations of given graph:  96%|█████████▌| 96/100 [11:57<00:21,  5.46s/it]"
      ]
     },
     {
@@ -1480,7 +1480,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  97%|█████████▋| 97/100 [11:53<00:16,  5.44s/it]"
+      "Test permutations of given graph:  97%|█████████▋| 97/100 [12:02<00:16,  5.51s/it]"
      ]
     },
     {
@@ -1488,7 +1488,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  98%|█████████▊| 98/100 [11:55<00:08,  4.41s/it]"
+      "Test permutations of given graph:  98%|█████████▊| 98/100 [12:04<00:08,  4.47s/it]"
      ]
     },
     {
@@ -1496,7 +1496,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  99%|█████████▉| 99/100 [12:01<00:04,  5.00s/it]"
+      "Test permutations of given graph:  99%|█████████▉| 99/100 [12:11<00:05,  5.08s/it]"
      ]
     },
     {
@@ -1504,7 +1504,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 100/100 [12:03<00:00,  4.09s/it]"
+      "Test permutations of given graph: 100%|██████████| 100/100 [12:13<00:00,  4.17s/it]"
      ]
     },
     {
@@ -1512,7 +1512,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 100/100 [12:03<00:00,  7.24s/it]"
+      "Test permutations of given graph: 100%|██████████| 100/100 [12:13<00:00,  7.33s/it]"
      ]
     },
     {
@@ -1580,10 +1580,10 @@
    "id": "58650ea8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:07:50.710303Z",
-     "iopub.status.busy": "2024-10-25T15:07:50.710116Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.286854Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.286195Z"
+     "iopub.execute_input": "2024-10-25T15:09:10.936127Z",
+     "iopub.status.busy": "2024-10-25T15:09:10.935770Z",
+     "iopub.status.idle": "2024-10-25T15:09:35.872512Z",
+     "shell.execute_reply": "2024-10-25T15:09:35.871810Z"
     }
    },
    "outputs": [
@@ -1600,7 +1600,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:46,  2.43s/it]"
+      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:48,  2.54s/it]"
      ]
     },
     {
@@ -1608,7 +1608,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 2/20 [00:04<00:44,  2.49s/it]"
+      "Test permutations of given graph:  10%|█         | 2/20 [00:05<00:46,  2.56s/it]"
      ]
     },
     {
@@ -1616,7 +1616,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:35,  2.10s/it]"
+      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:37,  2.18s/it]"
      ]
     },
     {
@@ -1624,7 +1624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 4/20 [00:07<00:28,  1.77s/it]"
+      "Test permutations of given graph:  20%|██        | 4/20 [00:08<00:29,  1.87s/it]"
      ]
     },
     {
@@ -1632,7 +1632,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 5/20 [00:10<00:30,  2.01s/it]"
+      "Test permutations of given graph:  25%|██▌       | 5/20 [00:10<00:31,  2.13s/it]"
      ]
     },
     {
@@ -1640,7 +1640,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:22,  1.63s/it]"
+      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:24,  1.72s/it]"
      ]
     },
     {
@@ -1648,7 +1648,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 7/20 [00:12<00:21,  1.62s/it]"
+      "Test permutations of given graph:  35%|███▌      | 7/20 [00:13<00:22,  1.72s/it]"
      ]
     },
     {
@@ -1656,7 +1656,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 8/20 [00:13<00:16,  1.38s/it]"
+      "Test permutations of given graph:  40%|████      | 8/20 [00:14<00:17,  1.46s/it]"
      ]
     },
     {
@@ -1664,7 +1664,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  55%|█████▌    | 11/20 [00:14<00:06,  1.33it/s]"
+      "Test permutations of given graph:  55%|█████▌    | 11/20 [00:15<00:07,  1.27it/s]"
      ]
     },
     {
@@ -1672,7 +1672,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:15<00:04,  1.51it/s]"
+      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:16<00:04,  1.45it/s]"
      ]
     },
     {
@@ -1680,7 +1680,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 14/20 [00:16<00:04,  1.42it/s]"
+      "Test permutations of given graph:  70%|███████   | 14/20 [00:17<00:04,  1.34it/s]"
      ]
     },
     {
@@ -1688,7 +1688,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:17<00:03,  1.34it/s]"
+      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:18<00:03,  1.28it/s]"
      ]
     },
     {
@@ -1696,7 +1696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 16/20 [00:18<00:03,  1.05it/s]"
+      "Test permutations of given graph:  80%|████████  | 16/20 [00:21<00:05,  1.30s/it]"
      ]
     },
     {
@@ -1704,7 +1704,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.06it/s]"
+      "Test permutations of given graph: 100%|██████████| 20/20 [00:21<00:00,  1.05s/it]"
      ]
     },
     {
@@ -1763,10 +1763,10 @@
    "id": "ab8ef360",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:13.288895Z",
-     "iopub.status.busy": "2024-10-25T15:08:13.288505Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.376022Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.375468Z"
+     "iopub.execute_input": "2024-10-25T15:09:35.875021Z",
+     "iopub.status.busy": "2024-10-25T15:09:35.874524Z",
+     "iopub.status.idle": "2024-10-25T15:09:35.960051Z",
+     "shell.execute_reply": "2024-10-25T15:09:35.959317Z"
     }
    },
    "outputs": [
@@ -1800,10 +1800,10 @@
    "id": "073a54c4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:13.378137Z",
-     "iopub.status.busy": "2024-10-25T15:08:13.377924Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.479623Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.479014Z"
+     "iopub.execute_input": "2024-10-25T15:09:35.962346Z",
+     "iopub.status.busy": "2024-10-25T15:09:35.962106Z",
+     "iopub.status.idle": "2024-10-25T15:09:36.042389Z",
+     "shell.execute_reply": "2024-10-25T15:09:36.041649Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb
index 5d2433943..8435550ba 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb
@@ -46,10 +46,10 @@
    "id": "6bfa09f9-67e2-4c65-a27c-903c2742fe0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:18.048145Z",
-     "iopub.status.busy": "2024-10-25T15:08:18.047729Z",
-     "iopub.status.idle": "2024-10-25T15:08:19.590651Z",
-     "shell.execute_reply": "2024-10-25T15:08:19.589971Z"
+     "iopub.execute_input": "2024-10-25T15:09:40.817638Z",
+     "iopub.status.busy": "2024-10-25T15:09:40.817437Z",
+     "iopub.status.idle": "2024-10-25T15:09:42.539565Z",
+     "shell.execute_reply": "2024-10-25T15:09:42.538950Z"
     }
    },
    "outputs": [
@@ -101,10 +101,10 @@
    "id": "04f25d22-df49-45c8-9e4d-6fe05bc30e88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:19.593146Z",
-     "iopub.status.busy": "2024-10-25T15:08:19.592799Z",
-     "iopub.status.idle": "2024-10-25T15:08:23.679118Z",
-     "shell.execute_reply": "2024-10-25T15:08:23.678382Z"
+     "iopub.execute_input": "2024-10-25T15:09:42.543235Z",
+     "iopub.status.busy": "2024-10-25T15:09:42.542808Z",
+     "iopub.status.idle": "2024-10-25T15:09:47.069123Z",
+     "shell.execute_reply": "2024-10-25T15:09:47.068392Z"
     }
    },
    "outputs": [
@@ -153,7 +153,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 23.46it/s]"
+      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 23.83it/s]"
      ]
     },
     {
@@ -161,7 +161,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 23.46it/s]       "
+      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 23.83it/s]       "
      ]
     },
     {
@@ -169,7 +169,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 28.62it/s]"
+      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 28.99it/s]"
      ]
     },
     {
@@ -200,10 +200,10 @@
    "id": "ec3eadc5-e305-4291-864a-fab105c27443",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:23.681422Z",
-     "iopub.status.busy": "2024-10-25T15:08:23.681042Z",
-     "iopub.status.idle": "2024-10-25T15:08:24.784489Z",
-     "shell.execute_reply": "2024-10-25T15:08:24.783852Z"
+     "iopub.execute_input": "2024-10-25T15:09:47.071599Z",
+     "iopub.status.busy": "2024-10-25T15:09:47.071092Z",
+     "iopub.status.idle": "2024-10-25T15:09:48.315271Z",
+     "shell.execute_reply": "2024-10-25T15:09:48.314678Z"
     }
    },
    "outputs": [
@@ -220,7 +220,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 429.87it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 3254.93it/s]"
      ]
     },
     {
@@ -252,35 +252,35 @@
       "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "--- Node displacement\n",
-      "- The MSE is 1066.8802205998074.\n",
-      "- The NMSE is 0.3150091474285043.\n",
-      "- The R2 coefficient is 0.9002474266291071.\n",
-      "- The normalized CRPS is 0.17522937047621454.\n",
+      "- The MSE is 1060.1157400952736.\n",
+      "- The NMSE is 0.31274920184264293.\n",
+      "- The R2 coefficient is 0.9015843974287858.\n",
+      "- The normalized CRPS is 0.17714313434646875.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node weight\n",
-      "- The MSE is 79949.61282051282.\n",
-      "- The NMSE is 0.3359350937278292.\n",
-      "- The R2 coefficient is 0.8852962623585189.\n",
-      "- The normalized CRPS is 0.18398226376156224.\n",
+      "- The MSE is 77927.59152872444.\n",
+      "- The NMSE is 0.3293392447432768.\n",
+      "- The R2 coefficient is 0.8909086867528749.\n",
+      "- The normalized CRPS is 0.18431008611167252.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node horsepower\n",
-      "- The MSE is 209.29113924050634.\n",
-      "- The NMSE is 0.38098501624901415.\n",
-      "- The R2 coefficient is 0.8513378579863071.\n",
-      "- The normalized CRPS is 0.20296699411876892.\n",
+      "- The MSE is 207.14212917883805.\n",
+      "- The NMSE is 0.37926574409578384.\n",
+      "- The R2 coefficient is 0.8557617347249853.\n",
+      "- The normalized CRPS is 0.20152482768695754.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node mpg\n",
-      "- The MSE is 15.699538297818407.\n",
-      "- The NMSE is 0.5134945988653445.\n",
-      "- The R2 coefficient is 0.7353328271586502.\n",
-      "- The normalized CRPS is 0.2830433213216262.\n",
+      "- The MSE is 15.620289636388742.\n",
+      "- The NMSE is 0.5121045268547029.\n",
+      "- The R2 coefficient is 0.7344865131314628.\n",
+      "- The normalized CRPS is 0.2793091596647558.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.9578045815757854\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.9394536430054833\n",
       "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "==== NOTE ====\n",
@@ -307,16 +307,16 @@
    "id": "6257a9e2-a1f4-4c16-b629-b5a13bbf32dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:24.786752Z",
-     "iopub.status.busy": "2024-10-25T15:08:24.786284Z",
-     "iopub.status.idle": "2024-10-25T15:08:25.400709Z",
-     "shell.execute_reply": "2024-10-25T15:08:25.400051Z"
+     "iopub.execute_input": "2024-10-25T15:09:48.317389Z",
+     "iopub.status.busy": "2024-10-25T15:09:48.317064Z",
+     "iopub.status.idle": "2024-10-25T15:09:48.907181Z",
+     "shell.execute_reply": "2024-10-25T15:09:48.906448Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -352,10 +352,10 @@
    "id": "9cbc1002-57e4-4b79-8b8b-cab7dff25d4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:25.403081Z",
-     "iopub.status.busy": "2024-10-25T15:08:25.402865Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.536283Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.535521Z"
+     "iopub.execute_input": "2024-10-25T15:09:48.909438Z",
+     "iopub.status.busy": "2024-10-25T15:09:48.909205Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.195167Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.194428Z"
     }
    },
    "outputs": [
@@ -372,7 +372,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.36it/s]"
+      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.29it/s]"
      ]
     },
     {
@@ -380,7 +380,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.87it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.88it/s]"
      ]
     },
     {
@@ -388,7 +388,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.79it/s]"
+      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.82it/s]"
      ]
     },
     {
@@ -396,7 +396,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  1.74it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  1.75it/s]"
      ]
     },
     {
@@ -404,7 +404,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.13it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.15it/s]"
      ]
     },
     {
@@ -433,10 +433,10 @@
    "id": "885195c7-f9d7-449e-a96f-894ce09562d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.538879Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.538499Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.542131Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.541519Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.197880Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.197489Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.201286Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.200692Z"
     }
    },
    "outputs": [],
@@ -452,16 +452,16 @@
    "id": "a80ca005-23aa-46b0-889d-4de7144adb33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.544226Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.543793Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.659374Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.658767Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.203221Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.202925Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.328892Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.328110Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -530,10 +530,10 @@
    "id": "c3cb5016-edca-4314-ae34-dc64e3686ce5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.662052Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.661658Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.753150Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.752586Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.331920Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.331681Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.428376Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.427696Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "id": "5c5a439d-4b43-4a38-bb78-975b0694a2c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.755606Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.755076Z",
-     "iopub.status.idle": "2024-10-25T15:08:54.025050Z",
-     "shell.execute_reply": "2024-10-25T15:08:54.024439Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.430879Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.430518Z",
+     "iopub.status.idle": "2024-10-25T15:10:17.910638Z",
+     "shell.execute_reply": "2024-10-25T15:10:17.910003Z"
     }
    },
    "outputs": [
@@ -647,7 +647,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 369.38it/s]"
+      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 379.97it/s]"
      ]
     },
     {
@@ -670,7 +670,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 188.24it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 190.31it/s]"
      ]
     },
     {
@@ -678,7 +678,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 240.35it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 247.72it/s]"
      ]
     },
     {
@@ -690,7 +690,7 @@
     },
     {
      "data": {
-      "image/png": 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rafDgwapbt64uXryogwcP6uDBg7pw4YLq16+vl19+2RUZAQAAsuXwiMyhQ4e0f/9+FS9e3HasePHimjp1qpo2berUcAAAADlxeESmRo0aio2NzXL8ypUrqlatmlNCAQAA5EauikxCQoLtMW3aNA0fPlxr1qzRxYsXdfHiRa1Zs0YjR47UjBkzXJ0XAADAxmK1Wq13e5GHh0eWRfAk2Y799XlGRoYrct6zhIQE+fv7Kz4+Xn5+fs59c7/ezn0/d5ew0ugEAAA3k6s5MhEREa7OAQAA4LBcFZm2bdu6OgcAAIDDclVkoqKiVLduXXl4eCgqKirH19avX98pwQAAAO4mV0XmoYceUkxMjAICAvTQQw/JYrEou6k1eXGODAAAcF+5umvp3LlzKl26tO3rs2fP6ty5c1keZ8+edeiHz58/X/Xr15efn5/8/PwUGhqq77//3nY+OTlZQ4cOVcmSJVW0aFH17Nkz21u/AQBA/pSrIlOxYkVZLBalpaVp8uTJyszMVMWKFbN9OKJ8+fKaPn26Dhw4oP3796tDhw568skndfToUUnSqFGjtH79en355Zfatm2bLl26pB49ejj+fyUAAHBLubr9+q/8/f116NAhVa5c2SWBSpQooffff19PP/20SpcurRUrVujpp5+WJP3666+qVauWdu/erebNm+fq/bj9Og/h9msAgJM5vLJv9+7dtXbtWqcHycjI0MqVK5WUlKTQ0FAdOHBAaWlp6tixo+01ISEhqlChgnbv3n3H90lJSbFbwC8hIcHpWQEAQN7g8F5L1atX15QpU7Rr1y41btxYPj4+dueHDx/u0Pv98ssvCg0NVXJysooWLapvvvlGtWvX1qFDh+Tl5aVixYrZvT4wMFAxMTF3fL9p06Zp8uTJDmUAAADm5PClpZwuKVksFocn/Kampio6Olrx8fFas2aNFi9erG3btunQoUMaMGCAUlJS7F7frFkztW/f/o7bIaSkpNh9T0JCgoKDg7m0lBdwaQkA4GQOj8icO3fOqQG8vLxsm002btxY+/bt04cffqhnn31WqampiouLsxuViY2NVVBQ0B3fz9vbW97e3k7NCAAA8iaH58hMmTJFt27dynL8jz/+0JQpU+47UGZmplJSUtS4cWN5enoqPDzcdu7EiROKjo5WaGjoff8cAABgfg5fWipQoIAuX76sgIAAu+PXr19XQECAQwviTZgwQV26dFGFChV08+ZNrVixQjNmzNAPP/ygRx99VK+++qq+++47hYWFyc/PT8OGDZMkRUZG5vpncNdSHsKlJQCAkzl8aclqtdrthP2nw4cPq0SJEg6915UrV9SvXz9dvnxZ/v7+ql+/vq3ESNKsWbPk4eGhnj17KiUlRZ06ddLHH3/saGQAAOCmcj0iU7x4cVksFtvIxl/LTEZGhhITEzVkyBDNmzfPZWHvBSMyeQgjMgAAJ8v1iMzs2bNltVo1cOBATZ48Wf7+/rZzXl5eqlSpEnNXAADAA5XrItO/f39Jt2+/btGihTw9PV0WCgAAIDccniPTtm1bZWZm6uTJk7py5YoyMzPtzrdp08Zp4QAAAHLicJHZs2ePnnvuOZ0/f17/O73GYrE4dNcSAADA/XC4yAwZMkRNmjTRhg0bVKZMmWzvYAIAAHgQHC4yp06d0po1a2yr8QIAABjF4ZV9H374YZ0+fdoVWQAAABzi8IjMsGHDNGbMGMXExKhevXpZ7l6qX7++08IBAADkxOEtCjw8sg7iWCwW24q/eW2yLwvi5SEsiAcAcDLDd78GAAC4Vw4XmYoVK7oiBwAAgMMcLjKSdObMGc2ePVvHjx+XJNWuXVsjRoxQ1apVnRoOAAAgJw7ftfTDDz+odu3a+umnn1S/fn3Vr19fe/fuVZ06dfTjjz+6IiMAAEC2HJ7s27BhQ3Xq1EnTp0+3Oz5+/Hht2rRJBw8edGrA+8Vk3zyEyb4AACdzeETm+PHjGjRoUJbjAwcO1LFjx5wSCgAAIDccLjKlS5fWoUOHshw/dOiQAgICnJEJAAAgVxye7PvSSy/p5Zdf1tmzZ9WiRQtJ0q5duzRjxgyNHj3a6QEBAADuxOE5MlarVbNnz9YHH3ygS5cuSZLKli2rsWPHavjw4XluE0nmyOQhzJEBADiZw0Xmr27evClJ8vX1dVogZ6PI5CEUGQCAk93Tyr7p6emqXr26XYE5deqUPD09ValSJWfmAwAAuCOHJ/u++OKLioyMzHJ87969evHFF52RCQAAIFccLjI///yzWrZsmeV48+bNs72bCQAAwFUcLjIWi8U2N+av4uPj89zO1wAAwL05XGTatGmjadOm2ZWWjIwMTZs2Ta1atXJqOAAAgJw4PNl3xowZatOmjWrWrKnWrVtLknbs2KGEhARt2bLF6QEBAADuxOERmdq1aysqKkq9evXSlStXdPPmTfXr10+//vqr6tat64qMAAAA2bqvdWTMgHVk8hDWkQEAOJnDIzIAAAB5BUUGAACYFkUGAACYFkUGAACY1j0VmfT0dG3evFkLFy60LY536dIlJSYmOjUcAABAThxeR+b8+fPq3LmzoqOjlZKSokcffVS+vr6aMWOGUlJStGDBAlfkBAAAyMLhEZkRI0aoSZMm+u9//6vChQvbjj/11FMKDw93ajgAAICcODwis2PHDkVGRsrLy8vueKVKlfT77787LRgAAMDdODwik5mZme3mkBcvXpSvr69TQgEAAOSGw0Xmscce0+zZs23PLRaLEhMTNXHiRHXt2tWZ2QAAAHLk8BYFFy9eVKdOnWS1WnXq1Ck1adJEp06dUqlSpbR9+3YFBAS4Kus9YYuCPIQtCgAATubwHJny5cvr8OHDWrVqlQ4fPqzExEQNGjRIffv2tZv8CwAA4GpsGnk/GJFxDCMyAAAnc3iOzLRp07R06dIsx5cuXaoZM2Y4JRQAAEBuOFxkFi5cqJCQkCzH69Spw2J4AADggXK4yMTExKhMmTJZjpcuXVqXL192SigAAIDccLjIBAcHa9euXVmO79q1S2XLlnVKKAAAgNxw+K6ll156SSNHjlRaWpo6dOggSQoPD9frr7+uMWPGOD0gAADAnThcZMaOHavr16/r73//u1JTUyVJhQoV0rhx4zRhwgSnBwQAALiTe779OjExUcePH1fhwoVVvXp1eXt7OzubU3D7dR7C7dcAACdzeETmT0WLFlXTpk2dmQUAAMAhDheZpKQkTZ8+XeHh4bpy5YoyMzPtzp89e9Zp4QAAAHLicJEZPHiwtm3bphdeeEFlypSRxWJxRS4AAIC7crjIfP/999qwYYNatmzpijwAAAC55vA6MsWLF1eJEiVckQUAAMAhDheZf/3rX3r77bd169at+/7h06ZNU9OmTeXr66uAgAB1795dJ06csHtNcnKyhg4dqpIlS6po0aLq2bOnYmNj7/tnAwAA83P49uuGDRvqzJkzslqtqlSpkjw9Pe3OHzx4MNfv1blzZ/Xu3VtNmzZVenq6/vnPf+rIkSM6duyYfHx8JEmvvvqqNmzYoLCwMPn7++u1116Th4dHtqsLZ4fbr/MQbr8GADiZw3Nkunfv7rQfvnHjRrvnYWFhCggI0IEDB9SmTRvFx8dryZIlWrFihW0V4WXLlqlWrVras2ePmjdv7rQsAADAfBwuMhMnTnRFDklSfHy8JNnm4Bw4cEBpaWnq2LGj7TUhISGqUKGCdu/eTZEBACCfu+cF8ZwtMzNTI0eOVMuWLVW3bl1Jt3fa9vLyUrFixexeGxgYqJiYmGzfJyUlRSkpKbbnCQkJLssMAACM5fBk34yMDP373/9Ws2bNFBQUpBIlStg97tXQoUN15MgRrVx5f/Mopk2bJn9/f9sjODj4vt4PAADkXQ4XmcmTJ2vmzJl69tlnFR8fr9GjR6tHjx7y8PDQpEmT7inEa6+9pv/7v/9TRESEypcvbzseFBSk1NRUxcXF2b0+NjZWQUFB2b7XhAkTFB8fb3tcuHDhnjIBAIC8z+Ei85///EeffPKJxowZo4IFC6pPnz5avHix3n77be3Zs8eh97JarXrttdf0zTffaMuWLapcubLd+caNG8vT01Ph4eG2YydOnFB0dLRCQ0OzfU9vb2/5+fnZPQAAgHtyeI5MTEyM6tWrJ+n2xpF/TtB9/PHH9dZbbzn0XkOHDtWKFSv07bffytfX1zbvxd/fX4ULF5a/v78GDRqk0aNHq0SJEvLz89OwYcMUGhrKRF8AAOD4iEz58uV1+fJlSVLVqlW1adMmSdK+ffvk7e3t0HvNnz9f8fHxateuncqUKWN7rFq1yvaaWbNm6fHHH1fPnj3Vpk0bBQUF6euvv3Y0NgAAcEMOL4g3fvx4+fn56Z///KdWrVql559/XpUqVVJ0dLRGjRql6dOnuyrrPWFBvDyEBfEAAE7mcJH5X7t379bu3btVvXp1PfHEE87K5TQUmTyEIgMAcLL7XkcmNDT0jhNvAQAAXClXRWbdunXq0qWLPD09tW7duhxf261bN6cEAwAAuJtcXVry8PBQTEyMAgIC5OFx5/nBFotFGRkZTg14v7i0lIdwaQkA4GS5GpHJzMzM9msAAAAjOTRHJi0tTZ07d9aCBQtUvXp1V2UCcsZImGMYCQPgxhxaR8bT01NRUVGuygIAAOAQhxfEe/7557VkyRJXZAEAAHCIw7dfp6ena+nSpdq8ebMaN24sHx8fu/MzZ850WjgAAICcOFxkjhw5okaNGkmSTp48aXfOYrE4JxUAAEAuOFxkIiIiXJEDAADAYQ7PkQEAAMgr7mmLgv3792v16tWKjo5Wamqq3Tl2pgYAAA+KwyMyK1euVIsWLXT8+HF98803SktL09GjR7Vlyxb5+/u7IiMAAEC2HC4y7777rmbNmqX169fLy8tLH374oX799Vf16tVLFSpUcEVGAACAbDlcZM6cOaO//e1vkiQvLy8lJSXJYrFo1KhRWrRokdMDAgAA3InDRaZ48eK6efOmJKlcuXI6cuSIJCkuLk63bt1ybjoAAIAcODzZt02bNvrxxx9Vr149PfPMMxoxYoS2bNmiH3/8UY888ogrMgIAAGQr10XmyJEjqlu3rubOnavk5GRJ0htvvCFPT09FRkaqZ8+eevPNN10WFAAA4H/lusjUr19fTZs21eDBg9W79+3dhz08PDR+/HiXhQMAAMhJrufIbNu2TXXq1NGYMWNUpkwZ9e/fXzt27HBlNgAAgBzlusi0bt1aS5cu1eXLl/XRRx/pt99+U9u2bVWjRg3NmDFDMTExrswJAACQhcN3Lfn4+GjAgAHatm2bTp48qWeeeUbz5s1ThQoV1K1bN1dkBAAAyNZ97bVUrVo1/fOf/9Sbb74pX19fbdiwwVm5AAAA7uqe9lqSpO3bt2vp0qX66quv5OHhoV69emnQoEHOzAYAAJAjh4rMpUuXFBYWprCwMJ0+fVotWrTQnDlz1KtXL/n4+LgqIwAAQLZyXWS6dOmizZs3q1SpUurXr58GDhyomjVrujIbAABAjnJdZDw9PbVmzRo9/vjjKlCggCszAQAA5Equi8y6detcmQMAAMBh9zzZF0A+49fb6ATmkbDS6ARAvnFft18DAAAYiSIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMq6CRP3z79u16//33deDAAV2+fFnffPONunfvbjtvtVo1ceJEffLJJ4qLi1PLli01f/58Va9e3bjQAAD359fb6ATmkbDS0B9v6IhMUlKSGjRooHnz5mV7/r333tOcOXO0YMEC7d27Vz4+PurUqZOSk5MfcFIAAJAXGToi06VLF3Xp0iXbc1arVbNnz9abb76pJ598UpL06aefKjAwUGvXrlXv3rRlAADyuzw7R+bcuXOKiYlRx44dbcf8/f318MMPa/fu3Xf8vpSUFCUkJNg9AACAe8qzRSYmJkaSFBgYaHc8MDDQdi4706ZNk7+/v+0RHBzs0pwAAMA4ebbI3KsJEyYoPj7e9rhw4YLRkQAAgIvk2SITFBQkSYqNjbU7HhsbazuXHW9vb/n5+dk9AACAe8qzRaZy5coKCgpSeHi47VhCQoL27t2r0NBQA5MBAIC8wtC7lhITE3X69Gnb83PnzunQoUMqUaKEKlSooJEjR+qdd95R9erVVblyZb311lsqW7as3VozAAAg/zK0yOzfv1/t27e3PR89erQkqX///goLC9Prr7+upKQkvfzyy4qLi1OrVq20ceNGFSpUyKjIAAAgDzG0yLRr105Wq/WO5y0Wi6ZMmaIpU6Y8wFQAAMAs8uwcGQAAgLuhyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANMqaHQAAEAO/HobncA8ElYanQAGYEQGAACYlimKzLx581SpUiUVKlRIDz/8sH766SejIwEAgDwgzxeZVatWafTo0Zo4caIOHjyoBg0aqFOnTrpy5YrR0QAAgMHyfJGZOXOmXnrpJQ0YMEC1a9fWggULVKRIES1dutToaAAAwGB5erJvamqqDhw4oAkTJtiOeXh4qGPHjtq9e3e235OSkqKUlBTb8/j4eElSQkKC8wNa05z/nu7MWf8b8Lk7hs/9wXPm7xs+99zjczeGK/59/QtfX19ZLJY7ns/TRebatWvKyMhQYGCg3fHAwED9+uuv2X7PtGnTNHny5CzHg4ODXZIRDvD/2ugE+ROf+4PHZ24MPndjuPhzj4+Pl5+f3x3P5+kicy8mTJig0aNH255nZmbqxo0bKlmyZI6Nzl0kJCQoODhYFy5cyPF/eDgXn7sx+NyNwedujPz6ufv6+uZ4Pk8XmVKlSqlAgQKKjY21Ox4bG6ugoKBsv8fb21ve3t52x4oVK+aqiHmWn59fvvoPPa/gczcGn7sx+NyNweduL09P9vXy8lLjxo0VHh5uO5aZmanw8HCFhoYamAwAAOQFeXpERpJGjx6t/v37q0mTJmrWrJlmz56tpKQkDRgwwOhoAADAYHm+yDz77LO6evWq3n77bcXExOihhx7Sxo0bs0wAxm3e3t6aOHFilstrcC0+d2PwuRuDz90YfO7Zs1itVqvRIQAAAO5Fnp4jAwAAkBOKDAAAMC2KDAAAMC2KDABTsFqtio6OVnJystFRAOQhFBkApmC1WlWtWjVduHDB6Cj5zpEjR+54bu3atQ8uCJANigxwj+Li4rR48WJNmDBBN27ckCQdPHhQv//+u8HJ3JOHh4eqV6+u69evGx0l3+nUqZPOnTuX5fhXX32lvn37GpAo/1i2bJlu3bpldIw8jSID3IOoqCjVqFFDM2bM0L///W/FxcVJkr7++mu73drhXNOnT9fYsWNzHCGA8w0ePFgdO3ZUTEyM7diqVavUr18/hYWFGRcsHxg/fryCgoI0aNAgRUZGGh0nT2IdGTdx6tQpRURE6MqVK8rMzLQ79/bbbxuUyn117NhRjRo10nvvvSdfX18dPnxYVapUUWRkpJ577jn99ttvRkd0S8WLF9etW7eUnp4uLy8vFS5c2O78nyNjcL5hw4YpIiJC27dv18aNGzV48GB99tln6tmzp9HR3Fp6errWr1+vsLAwff/996pSpYoGDBig/v3733HPwfyGIuMGPvnkE7366qsqVaqUgoKC7Hb5tlgsOnjwoIHp3JO/v78OHjyoqlWr2hWZ8+fPq2bNmkxIdZHly5fneL5///4PKEn+1LdvX+3bt0+///67VqxYoSeffNLoSPlKbGysPv/8cy1fvly//vqrOnfurEGDBumJJ56Qh0f+vcCS57cowN298847mjp1qsaNG2d0lHzD29tbCQkJWY6fPHlSpUuXNiBR/kBReXDWrVuX5ViPHj20Y8cO9enTRxaLxfaabt26Peh4+VJgYKBatWqlkydP6uTJk/rll1/Uv39/FS9eXMuWLVO7du2MjmgIRmTcgJ+fnw4dOqQqVaoYHSXfGDx4sK5fv67Vq1erRIkSioqKUoECBdS9e3e1adNGs2fPNjqi20hISJCfn5/t65z8+Trcv9z+hW+xWJSRkeHiNPlbbGysPvvsMy1btkxnz55V9+7dNWjQIHXs2FFJSUmaMmWKVq5cqfPnzxsd1RAUGTcwaNAgNW3aVEOGDDE6Sr4RHx+vp59+Wvv379fNmzdVtmxZxcTEKDQ0VN999518fHyMjug2ChQooMuXLysgIEAeHh52l07/ZLVa+QcVbumJJ57QDz/8oBo1amjw4MHq16+fSpQoYfeaK1euKCgoKMv8yPyCS0tuoFq1anrrrbe0Z88e1atXT56ennbnhw8fblAy9+Xv768ff/xRu3bt0uHDh5WYmKhGjRqpY8eORkdzO1u2bLH94o6IiDA4DfBgBQQEaNu2bQoNDb3ja0qXLp3t7fH5BSMybqBy5cp3PGexWHT27NkHmAaAuxk+fLiqVauW5Y+iuXPn6vTp01xKdZG0tDR17txZCxYsUPXq1Y2Ok2fl32nObsJqtWrr1q06duyYzp07l+VBiXGN4cOHa86cOVmOz507VyNHjnzwgfKRHTt26Pnnn1eLFi1siw9+9tln2rlzp8HJ3NdXX32lli1bZjneokULrVmzxoBE+YOnp6eioqKMjpHnUWRMzmq1qnr16rp48aLRUfIVfrEb46uvvlKnTp1UuHBhHTx4UCkpKZJuz1l69913DU7nvq5fvy5/f/8sx/38/HTt2jUDEuUfzz//vJYsWWJ0jDyNOTIm99dl2xl6fHD4xW6Md955RwsWLFC/fv20cuVK2/GWLVvqnXfeMTCZe6tWrZo2btyo1157ze74nwu0wXXS09O1dOlSbd68WY0bN85yI8HMmTMNSpZ3UGTcwJ/Lts+fP19169Y1Ok6+wC92Y5w4cUJt2rTJctzf39+2TQScb/To0Xrttdd09epVdejQQZIUHh6uDz74gPkxLnbkyBE1atRI0u11qv4quzv48iOKjBvo16+fbt26pQYNGrBs+wPCL3ZjBAUF6fTp06pUqZLd8Z07d1IgXWjgwIFKSUnR1KlT9a9//UuSVKlSJc2fP1/9+vUzOJ174069u+OuJTfAsu3GmD9/vqZOnapLly5Juv2LfdKkSfxid6Fp06bp888/19KlS/Xoo4/qu+++0/nz5zVq1Ci99dZbGjZsmNER3d7Vq1dVuHBhFS1a1OgogCSKDHDf+MX+4FitVr377ruaNm2abt26Jen2dhH/+Mc/bCMFcJ2rV6/qxIkTkqSQkBCVKlXK4ETur3379jleQtqyZcsDTJM3UWTcREZGhtauXavjx49LkurUqaNu3bqpQIECBicD7t+5c+fs1ktKTU3V6dOnlZiYqNq1a1MiXSwpKUnDhg3Tp59+als9tkCBAurXr58++ugjFSlSxOCE7mvUqFF2z9PS0nTo0CEdOXJE/fv314cffmhQsryDIuMGTp8+ra5du+r3339XzZo1Jd2eFBkcHKwNGzaoatWqBid0T2vWrNHq1asVHR2t1NRUu3PsOO5cHh4eqlixotq3b68OHTqoffv2KleunNGx8o1XXnlFmzdv1ty5c23LDuzcuVPDhw/Xo48+qvnz5xucMP+ZNGmSEhMT9e9//9voKIajyLiBrl27ymq16j//+Y9tKffr16/r+eefl4eHhzZs2GBwQvczZ84cvfHGG3rxxRe1aNEiDRgwQGfOnNG+ffs0dOhQTZ061eiIbmXr1q22x969e5WamqoqVarYSk379u0VGBhodEy3VapUKa1ZsybL7soRERHq1auXrl69akywfOz06dNq1qwZN3OIIuMWfHx8bPss/dXhw4fVsmVLJSYmGpTMfYWEhGjixInq06ePfH19dfjwYVWpUkVvv/22bty4oblz5xod0W0lJycrMjLSVmx++uknpaWlKSQkREePHjU6nlsqUqSIDhw4oFq1atkdP3r0qJo1a6akpCSDkuVfn332mcaNG2e72SA/4/ZrN+Dt7a2bN29mOZ6YmCgvLy8DErm/6OhotWjRQpJUuHBh2+f/wgsvqHnz5hQZFypUqJA6dOigVq1aqX379vr++++1cOFC/frrr0ZHc1uhoaGaOHGiPv30UxUqVEiS9Mcff2jy5Mk5bmaI+9ejRw+751arVZcvX9b+/fv11ltvGZQqb6HIuIHHH39cL7/8spYsWaJmzZpJkvbu3ashQ4aoW7duBqdzT0FBQbpx44YqVqyoChUqaM+ePWrQoIHOnTsnBjldIzU1VXv27FFERITtElNwcLDatGmjuXPnqm3btkZHdFsffvihOnXqpPLly6tBgwaSbo/4ent7a9OmTQanc2//u4K4h4eHatasqSlTpuixxx4zKFXewqUlNxAXF6f+/ftr/fr18vT0lHR7Wetu3bopLCws26X0cX8GDx6s4OBgTZw4UfPmzdPYsWPVsmVL7d+/Xz169GBvFCfr0KGD9u7dq8qVK6tt27Zq3bq12rZtqzJlyhgdLd+4deuW/vOf/9hGvmrVqqW+fftmWYATeNAoMm7k1KlTdr9kqlWrZnAi95WZmanMzEwVLHh7UHPlypWKjIxU9erV9corr3BJz8k8PT1VpkwZde/eXe3atVPbtm1VsmRJo2Ple5cvX9bUqVO5lPoA7N+/37a8Ru3atdW4cWODE+UdFBkAeV5SUpJ27NihrVu3KiIiQocOHVKNGjXUtm1bW7EpXbq00THd0tGjRxURESEvLy/16tVLxYoV07Vr1zR16lQtWLBAVapUYZK1C128eFF9+vTRrl27VKxYMUm3R+FbtGihlStXqnz58sYGzAMoMm4gIyNDYWFhCg8P15UrV2wLVv2JlR+dIyoqKtevrV+/vguT4ObNm9q5c6dtvszhw4dVvXp1HTlyxOhobmXdunV6+umnlZ6eLkmqUqWKPvnkE/Xq1UuNGzfWyJEj1blzZ4NTurfOnTsrLi5Oy5cvt1snbMCAAfLz89PGjRsNTmg8iowbeO211xQWFqa//e1vKlOmTJblrGfNmmVQMvfi4eEhi8Vy18m8FotFGRkZDyhV/pSZmal9+/YpIiJCERER2rlzp5KTk/ncnaxZs2Zq2bKl/vWvf2nx4sUaPXq06tSpo6VLl6pp06ZGx8sXChcurMjISDVs2NDu+IEDB9S6dWvbVh35GUXGDZQqVUqffvqpunbtanQUt3b+/Plcv7ZixYouTJL/ZGZmav/+/bZLS7t27VJSUpLKlStnWxCvffv2fO5O5u/vrwMHDqhatWrKyMiQt7e3Nm7cqI4dOxodLd+oUaOGPv/8c9sdqX/66aef9Nxzz+n06dMGJcs7uP3aDXh5eTGx9wH46z+SKSkpSk9Pl4+Pj4GJ8o9ixYopKSlJQUFBat++vWbNmqV27dqx/YaL3bx5U35+fpJu761UuHBhValSxeBU+cv777+vYcOGad68eWrSpImk2xN/R4wYwfYE/x8jMm7ggw8+0NmzZzV37twcd0nF/bt69ar69eunzZs3KzMzU02bNtXnn39OkXSxhQsXqn379qpRo4bRUfIVDw8PLV++3LaEQ58+fTR79uws20GwXpVzFS9e3O53eVJSktLT0213Sf75tY+PD1sUiCJjWv+72uOWLVtUokQJ1alTx7aWzJ++/vrrBxnNrQ0cOFDff/+9hg8frkKFCmnhwoUqU6aMIiIijI4GOJ2Hh8ddX8OcMOdbvnx5rl/bv39/FyYxB4qMSQ0YMCDXr122bJkLk+QvwcHBWrx4sTp16iTp9to9tWrVUlJSkry9vQ1OBwD5D0UGcECBAgX0+++/KygoyHbMx8dHR48eVaVKlYwLBsAtHTx4UJ6enrZNgb/99lstW7ZMtWvX1qRJk1h8U9Ldxw2R53Xo0EFxcXFZjickJKhDhw4PPpCbK1CgQJbn/D0AwBVeeeUVnTx5UpJ09uxZPfvssypSpIi+/PJLvf766wanyxsYkXEDHh4eiomJUUBAgN3xK1euqFy5ckpLSzMomfvx8PCQv7+/3US8uLg4+fn52c0nYAKeayQlJXGnGPIVf39/HTx4UFWrVtWMGTO0ZcsW/fDDD9q1a5d69+6tCxcuGB3RcNx+bWJ/XWn22LFjiomJsT3PyMjQxo0bVa5cOSOiuS3mGxkrMDBQvXr10sCBA9WqVSuj4wAuZ7Vabau1b968WY8//rik2/P1rl27ZmS0PIMRGRP7c6VZSdle2ihcuLA++ugjDRw48EFHA1xi7dq1CgsL03fffadKlSpp4MCB6tevn8qWLWt0NMAlOnTooODgYHXs2FGDBg3SsWPHVK1aNW3btk39+/fXb7/9ZnREw1FkTOz8+fOyWq2qUqWKfvrpJ7tN87y8vBQQEJBlPgfgDq5evarPPvtMYWFhOn78uDp16qSBAweqW7dutrU24DwXLlyQxWKxbVD4008/acWKFapdu7Zefvllg9O5t6ioKPXt21fR0dEaPXq0Jk6cKEkaNmyYrl+/rhUrVhic0HgUGQCm9tFHH2ns2LFKTU1VqVKlNGTIEI0fP15FihQxOprbaN26tV5++WW98MILiomJUc2aNVWnTh2dOnVKw4YN09tvv210xHwnOTlZBQoUyLJuWH5EkXETp06dUkRERLa7X/NLBu4mNjZWy5cvV1hYmM6fP6+nnnpKgwYN0sWLFzVjxgyVLVtWmzZtMjqm2yhevLj27NmjmjVras6cOVq1apV27dqlTZs2aciQITp79qzREd1aXFyc1qxZozNnzmjs2LEqUaKEDh48qMDAQOZBism+buGTTz7Rq6++qlKlSikoKMjujhqLxUKRgdv4+uuvtWzZMv3www+qXbu2/v73v+v5559XsWLFbK9p0aKFatWqZVxIN5SWlmZb8HHz5s22LQlCQkJ0+fJlI6O5vaioKD3yyCMqVqyYfvvtN7300ksqUaKEvv76a0VHR+vTTz81OqLhWEfGDbzzzjuaOnWqYmJidOjQIf3888+2x8GDB42O53bS0tJUtWpVHT9+3Ogo+c6AAQNUtmxZ7dq1S4cOHdJrr71mV2IkqWzZsnrjjTeMCeim6tSpowULFmjHjh368ccf1blzZ0nSpUuXVLJkSYPTubfRo0drwIABOnXqlAoVKmQ73rVrV23fvt3AZHkHIzJu4L///a+eeeYZo2PkG56enkpOTjY6Rr50+fLlu859KVy4sG1CJJxjxowZeuqpp/T++++rf//+atCggSRp3bp1atasmcHp3Nu+ffu0cOHCLMfLlStnt+RGfsaIjBt45plnmA/wgA0dOlQzZsxQenq60VHylSJFiujMmTN688031adPH125ckWS9P333+vo0aMGp3Nf7dq107Vr13Tt2jUtXbrUdvzll1/WggULDEzm/ry9vZWQkJDl+MmTJ+3uVM3PGJFxA9WqVdNbb72lPXv2qF69ellmsQ8fPtygZO5r3759Cg8P16ZNm1SvXr0sq82y47hrbNu2TV26dFHLli21fft2TZ06VQEBATp8+LCWLFmiNWvWGB3RbVmtVh04cEBnzpzRc889J19fX3l5eXF3mIt169ZNU6ZM0erVqyXdnvcYHR2tcePGqWfPnganyxu4a8kNVK5c+Y7nLBYLdxS4wN12H2cFYNcIDQ3VM888o9GjR8vX11eHDx+2raPUo0cPXbx40eiIbun8+fPq3LmzoqOjlZKSopMnT6pKlSoaMWKEUlJSGJVxofj4eD399NPav3+/bt68qbJlyyomJkahoaH67rvv2LJDFBkAJlK0aFH98ssvqly5sl2R+e233xQSEsLcJRfp3r27fH19tWTJEpUsWdL2uW/dulUvvfSSTp06ZXREt7dz505FRUUpMTFRjRo1UseOHY2OlGdwacmNpKam6ty5c6patSqrmz4A6enp2rp1q91Q+6VLl+Tn56eiRYsaHc8tFStWTJcvX84yCvnzzz+znoYL7dixQ5GRkfLy8rI7XqlSJf3+++8GpcpfWrVqxf5id8C/dm7g1q1bGjZsmJYvXy5JtmHfYcOGqVy5cho/frzBCd3P/w61P/roo/L19dWMGTMYaneh3r17a9y4cfryyy9lsViUmZmpXbt26R//+If69etndDy3lZmZqYyMjCzHL168KF9fXwMSubc5c+bk+rXMgeTSklsYMWKEdu3apdmzZ6tz586KiopSlSpV9O2332rSpEn6+eefjY7odhhqN0ZqaqqGDh2qsLAwZWRkqGDBgsrIyNBzzz2nsLAw9hZzkWeffVb+/v5atGiRfH19FRUVpdKlS+vJJ59UhQoVmBPmZDnNe/wr5kDeRpFxAxUrVtSqVavUvHlzu3kDp0+fVqNGjbK9dQ/3p2TJkoqMjFTNmjWzzNWoXbu2bt26ZXREtxYdHa0jR44oMTFRDRs2VPXq1Y2O5NYuXryoTp06yWq16tSpU2rSpIlOnTqlUqVKafv27QoICDA6IvIxLi25gatXr2b7iyQpKcluuwI4D0PtxqpQoYIqVKhgdIx8o3z58jp8+LBWrlxpm3A6aNAg9e3bV4ULFzY6HvI5iowbaNKkiTZs2KBhw4ZJkq28LF68WKGhoUZGc1uPPfaYZs+erUWLFkm6/ZknJiZq4sSJ6tq1q8Hp3Mvo0aNz/dqZM2e6MEn+VrBgQT3//PNGx8h3rFar1qxZc8dNgVmziiLjFt5991116dJFx44dU3p6uj788EMdO3ZMkZGR2rZtm9Hx3NIHH3ygTp06qXbt2kpOTtZzzz1nG2r/4osvjI7nVnI7x4vRR+dat25drl/75yaScL6RI0dq4cKFat++vQIDA/nvPBvMkXETZ86c0fTp03X48GHbOgPjxo1TvXr1jI7mttLT0+2G2hs1asRQO9yGh0fudrCxWCzZXmaFc5QoUUKff/45I705oMiYWG4n8fr5+bk4CQDAFSpXrqzvv/9eISEhRkfJsygyJubh4ZHjMKPVauWvJSdiqN0YPXr0yPVrmS8Ad7N8+XJt3LhRS5cuZbT3DpgjY2IRERG2r61Wq7p27arFixezwqmLdO/e3e65xWLR//4d8GexpDw6j7+/v9ERICk8PFyzZs3S8ePHJUm1atXSyJEjWSrfxXr16qUvvvhCAQEBqlSpUpZNgQ8ePGhQsryDERk38tf1TOBamzdv1rhx4/Tuu+/a7gzbvXu33nzzTb377rt69NFHDU4IOM/HH3+sESNG6Omnn7b9975nzx6tWbNGs2bN0tChQw1O6L569eqliIgIPf3009lO9p04caJByfIOiowbocg8OHXr1tWCBQuy7H2yY8cOvfzyy7a/WgF3UL58eY0fP16vvfaa3fF58+bp3XffZb8lF/Lx8dEPP/zAPks54NIScA/OnDmjYsWKZTnu7++v33777YHnyU/WrFmj1atXKzo6WqmpqXbnGGZ3jbi4OHXu3DnL8ccee0zjxo0zIFH+ERwczA0bd5G7++tgGqwx8GA0bdpUo0ePVmxsrO1YbGysxo4dq2bNmhmYzL3NmTNHAwYMUGBgoH7++Wc1a9ZMJUuW1NmzZ9WlSxej47mtbt266Ztvvsly/Ntvv9Xjjz9uQKL844MPPtDrr7/OH0g54NKSif3v3Rzr169Xhw4d5OPjY3ecOzmc7/Tp03rqqad08uRJBQcHS5IuXLig6tWra+3atapWrZrBCd1TSEiIJk6cqD59+thdSn377bd148YNzZ071+iIbuOvOzAnJCTo3//+t1q2bGk3R2bXrl0aM2aM3nzzTaNiur3ixYvr1q1bSk9PV5EiRbJM9r1x44ZByfIOioyJDRgwIFevY2da17Barfrxxx/166+/Srp9F0fHjh0ZFXOhIkWK6Pjx46pYsaICAgL0448/qkGDBjp16pSaN2+u69evGx3RbbADc96wfPnyHM/379//ASXJuygyAEyjSpUq+uqrr9SwYUM1adJEL730kl555RVt2rRJvXv35q9TIB9isi+QS38dar+b4cOHuzBJ/tWhQwetW7dODRs21IABAzRq1CitWbNG+/fvd2jhPMCMkpOTs0xwZyIwIzJArv3vUPvVq1d169Yt291LcXFxKlKkiAICAhhqd5HMzExlZmaqYMHbf4OtXLlSkZGRql69ul555RV5eXkZnNA9sQOzcZKSkjRu3DitXr0620unLL7JiAyQa+fOnbN9vWLFCn388cdasmSJatasKUk6ceKE7VIHXMPDw8NuM8PevXurd+/eBibKH9iB2Tivv/66IiIiNH/+fL3wwguaN2+efv/9dy1cuFDTp083Ol6ewIgMcA+qVq2qNWvWqGHDhnbHDxw4oKefftqu9MC54uLi9NNPP2U7MtCvXz+DUrk3dmA2ToUKFfTpp5+qXbt28vPz08GDB1WtWjV99tln+uKLL/Tdd98ZHdFwjMgA9+Dy5ctKT0/PcjwjI8NubRk41/r169W3b18lJibKz8/PbmTAYrFQZFzE39+fFcMNcuPGDdtn7+fnZ5vQ3qpVK7366qtGRsszWBAPuAePPPKIXnnlFbuVZA8cOKBXX32VTfRcaMyYMRo4cKASExMVFxen//73v7YHdyy5zqRJkzR58mT98ccfRkfJd6pUqWIb4Q0JCdHq1asl3S712a0unh9xaQm4B1evXlX//v21ceNG2wJV6enp6tSpk8LCwhQQEGBwQvfk4+OjX375hdGBB+yPP/7QU089pV27drED8wM2a9YsFShQQMOHD9fmzZv1xBNPyGq1Ki0tTTNnztSIESOMjmg4igxwH06ePKnjx4/LYrEoJCRENWrUMDqSW+vRo4d69+6tXr16GR0lX2EH5rzj/PnzOnDggKpVq6b69esbHSdPoMgA9+nP/xfiTg7XWLdune3rq1evasqUKRowYIDq1auXZWSgW7duDzpevsAOzA/e7t27df36dbu9rD799FNNnDhRSUlJ6t69uz766CN5e3sbmDJvoMgA9+jTTz/V+++/r1OnTkmSatSoobFjx+qFF14wOJl7+evt1jmxWCysqeEif87NYATgwenSpYvatWtn2138l19+UaNGjfTiiy+qdu3aeu+99/TKK69o0qRJxgbNA5jsC9yDmTNn6tVXX1XXrl21evVqrV69Wp07d9aQIUM0a9Yso+O5lT8XwbvbgxLjOuzA/OAdOnRIjzzyiO35ypUr9fDDD+uTTz7RqFGjNGfOHNvE3/yOERngHlSuXFmTJ0/Ocrvv8uXLNWnSJNaRcbJz587lehNDOB87MD94hQoV0qlTpxQcHCzp9u3WXbp00RtvvCFJ+u2331SvXj3dvHnTyJh5AuvIAPfg8uXLatGiRZbjLVq00OXLlw1I5N6qVq2qihUrqn379rZH+fLljY6Vb8yePdvoCPlOYGCgzp07p+DgYKWmpurgwYOaPHmy7fzNmzezFMr8iiID3INq1app9erV+uc//2l3fNWqVapevbpBqdzXli1btHXrVm3dulVffPGFUlNTVaVKFXXo0MFWbAIDA42O6bb69+9vdIR8p2vXrho/frxmzJihtWvXqkiRImrdurXtfFRUlKpWrWpgwryDS0vAPfjqq6/07LPPqmPHjmrZsqUkadeuXQoPD9fq1av11FNPGZzQfSUnJysyMtJWbH766SelpaUpJCRER48eNTqeW4qOjs7xfIUKFR5Qkvzj2rVr6tGjh3bu3KmiRYtq+fLldr9XHnnkETVv3lxTp041MGXeQJEB7tGBAwc0a9YsHT9+XJJUq1YtjRkzJsv+S3CN1NRU7dq1S99//70WLlyoxMREJvy6iIeHR47LC/C5u058fLyKFi2qAgUK2B2/ceOGihYtyo7vosgAMInU1FTt2bNHERER2rp1q/bu3avg4GC1adNGbdq0Udu2bRkZcJHDhw/bPU9LS9PPP/+smTNnaurUqerRo4dByQCKDOCQhISEXL3Oz8/PxUnylw4dOmjv3r2qXLmy2rZtq9atW6tt27YqU6aM0dHytQ0bNuj999/X1q1bjY6CfIwiAzjgbkPsVquVhdlcwNPTU2XKlFH37t3Vrl07tW3bViVLljQ6Vr53+vRpNWjQQElJSUZHQT7GXUuAAyIiImxfW61Wde3aVYsXL1a5cuUMTOX+4uLitGPHDm3dulUzZsxQnz59VKNGDbVt29ZWbEqXLm10TLf1vyORVqtVly9f1qRJk7hLD4ZjRAa4D76+vjp8+DC7MT9gN2/e1M6dO23zZQ4fPqzq1avryJEjRkdzS9mNRFqtVgUHB2vlypUKDQ01KBnAiAwAE/Lx8VGJEiVUokQJFS9eXAULFrTdPQbn++tIpHS72JQuXVrVqlVTwYL8MwJj8V8ggDwvMzNT+/fv19atWxUREaFdu3YpKSlJ5cqVU/v27TVv3jy1b9/e6Jhuq23btkZHAO6IIgPcp5wm/8I5ihUrpqSkJAUFBal9+/aaNWuW2rVrx8qmLrZu3bq7vqZgwYIKCgpS3bp1WdMEhmCODOCA/10vY/369erQoYN8fHzsjn/99dcPMpbbW7hwodq3b68aNWoYHSVf8fDwyPVrg4KCtGrVKrtl9IEHgSIDOGDAgAG5et2yZctcnATIG6xWq2JjY/XOO+8oMjJSBw8eNDoS8hmKDADgvv32228KCQlRcnKy0VGQz1BkAABOER8fL39/f6NjIJ+hyAAAANPK/UwuAACAPIYiAwAATIsiAwDI0dtvv62IiAgm8iJPYo4MACBHjz76qHbv3q309HQ1bdrUtllny5YtVbhwYaPjIZ+jyAAA7io9PV179+7V9u3btW3bNkVGRiolJUVNmzbVzp07jY6HfIwtCgAAd1WwYEG1bNlSpUuXVokSJeTr66u1a9fq119/NToa8jlGZAAAOVq0aJG2bt2qbdu2KSUlRa1bt1a7du3Url071a9fn/3GYCiKDAAgRx4eHipdurTGjBmjv//97ypatKjRkQAbigwAIEdr167V9u3btXXrVh0/flwNGza0jci0atVKRYoUMToi8jGKDAAg1+Lj47Vjxw59+eWX+uKLL+Th4cFt2TAUk30BAHd1/fp1bdu2TVu3btXWrVt19OhRFS9eXK1btzY6GvI5RmQAADmqV6+ejh8/ruLFi6tNmzZq166d2rZtq/r16xsdDWBEBgCQsyFDhqht27aqW7eu0VGALBiRAQDkSmpqqs6dO6eqVauqYEH+DkbewF5LAIAc/fHHHxo0aJCKFCmiOnXqKDo6WpI0bNgwTZ8+3eB0yO8oMgCAHI0fP16HDx/W1q1bVahQIdvxjh07atWqVQYmA5gjAwC4i7Vr12rVqlVq3ry53Sq+derU0ZkzZwxMBjAiAwC4i6tXryogICDL8aSkJLYngOEoMgCAHDVp0kQbNmywPf+zvCxevFihoaFGxQIkcWkJAHAX7777rrp06aJjx44pPT1dH374oY4dO6bIyEht27bN6HjI5xiRAQDkqFWrVjp06JDS09NVr149bdq0SQEBAdq9e7caN25sdDzkc6wjAwAATIsRGQAAYFrMkQEAZMvDw+OudyVZLBalp6c/oERAVhQZAEC2vvnmmzue2717t+bMmaPMzMwHmAjIijkyAIBcO3HihMaPH6/169erb9++mjJliipWrGh0LORjzJEBANzVpUuX9NJLL6levXpKT0/XoUOHtHz5ckoMDEeRAQDcUXx8vMaNG6dq1arp6NGjCg8P1/r161W3bl2jowGSmCMDALiD9957TzNmzFBQUJC++OILPfnkk0ZHArJgjgwAIFseHh4qXLiwOnbsqAIFCtzxdV9//fUDTAXYY0QGAJCtfv36sSkk8jxGZAAAgGkx2RcAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJjW/wOOtFhk41jIFAAAAABJRU5ErkJggg==",
+      "image/png": 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NmjTRs88+64qMAAAA+XJ4RGbfvn3avXu3ypUrZztWrlw5TZkyRa1atXJqOAAAgII4PCJTt25dJSYm5jl+8eJF1a5d26H3+vDDD9WkSRP5+/vL399f4eHh+u6772zn09PTNWLECFWoUEFlypRRnz598v3ZAACgeCpUkUlJSbE9pk6dqlGjRmnlypU6d+6czp07p5UrV2rMmDGaPn26Qz/8rrvu0rRp07Rnzx7t3r1bXbp00cMPP6yDBw9KksaOHau1a9fqyy+/VExMjM6fP6/evXs7/k8JAADcksVqtVpv9iIPD488i+BJsh376/OcnJzbClS+fHm98847evTRRxUYGKilS5fq0UcflSQdOXJE9evX1/bt29W6detCvV9KSooCAgKUnJwsf3//28qWh38/576fu0tZZnQCAICbKdQcmejoaFfnUE5Ojr788kulpaUpPDxce/bsUVZWliIiImyvCQsLU9WqVQssMhkZGcrIyLA9T0lJcXl2AABgjEIVmY4dO7oswC+//KLw8HClp6erTJky+vrrr9WgQQPt27dPXl5eKlu2rN3rg4ODlZCQcMP3mzp1qiZNmuSyvAAAoOgoVJGJi4tTo0aN5OHhobi4uAJf26RJE4cC1KtXT/v27VNycrJWrlypQYMGKSYmxqH3+Kvx48crMjLS9jwlJUWhoaG3/H4AAKDoKlSRufvuu5WQkKCgoCDdfffdslgsym9qza3MkfHy8rLd7dSiRQvt2rVL7733nh5//HFlZmYqKSnJblQmMTFRISEhN3w/b29veXt7O5QBAACYU6GKzOnTpxUYGGj72pVyc3OVkZGhFi1ayNPTU1FRUerTp48k6ejRo4qPj1d4eLhLMwAAAHMoVJGpVq2aJCkrK0uTJk3S66+/rho1atz2Dx8/fry6d++uqlWr6urVq1q6dKk2bdqk77//XgEBARo6dKgiIyNVvnx5+fv7a+TIkQoPDy/0HUsAAMC9ObSyr6enp7766iu9/vrrTvnhFy9e1MCBA3XhwgUFBASoSZMm+v7773X//fdLkmbOnCkPDw/16dNHGRkZ6tq1qz744AOn/GwAAGB+hVpH5q8GDRqku+++W2PHjnVVJqdiHZkihHVkAABO5vBeS3Xq1NHkyZO1bds2tWjRQr6+vnbnR40a5bRwAAAABXF4RKaguTEWi0WnTp267VDOxIhMEcKIDADAyRwekXH1XUsAAACF5fDu15MnT9a1a9fyHP/jjz80efJkp4QCAAAoDIcvLZUoUUIXLlxQUFCQ3fHLly8rKCjotjeNdDYuLRUhXFoCADiZwyMyVqvVbifsP+3fv1/ly5d3SigAAIDCKPQcmXLlyslischisahu3bp2ZSYnJ0epqakaPny4S0ICAADkp9BFZtasWbJarRoyZIgmTZqkgIAA2zkvLy9Vr16drQMAAMAdVegiM2jQIEnXb79u06aNPD09XRYKAACgMBy+/bpjx47Kzc3VsWPHdPHiReXm5tqd79Chg9PCAQAAFMThIrNjxw498cQTOnPmjP73hieLxVLk7loCAADuy+EiM3z4cLVs2VLr1q1TpUqV8r2DCQAA4E5wuMgcP35cK1euVO3atV2RBwAAoNAcXkfm3nvv1YkTJ1yRBQAAwCEOj8iMHDlSL774ohISEtS4ceM8dy81adLEaeEAAAAK4vAWBR4eeQdxLBaLbcXfojbZly0KihC2KAAAOBm7XwMAANNyuMhUq1bNFTkAAAAc5nCRkaSTJ09q1qxZOnz4sCSpQYMGGj16tGrVquXUcAAAAAVx+K6l77//Xg0aNNBPP/2kJk2aqEmTJtq5c6caNmyoH3/80RUZAQAA8uXwZN9mzZqpa9eumjZtmt3xV155RT/88IP27t3r1IC3i8m+RQiTfQEATubwiMzhw4c1dOjQPMeHDBmiQ4cOOSUUAABAYThcZAIDA7Vv3748x/ft26egoCBnZAIAACgUhyf7PvPMM3r22Wd16tQptWnTRpK0bds2TZ8+XZGRkU4PCAAAcCMOz5GxWq2aNWuW3n33XZ0/f16SVLlyZb300ksaNWpUkdtEkjkyRQhzZAAATuZwkfmrq1evSpL8/PycFsjZKDJFCEUGAOBkt7Syb3Z2turUqWNXYI4fPy5PT09Vr17dmfkAAABuyOHJvk8//bRiY2PzHN+5c6eefvppZ2QCAAAoFIeLzM8//6y2bdvmOd66det872YCAABwFYeLjMVisc2N+avk5OQit/M1AABwbw4XmQ4dOmjq1Kl2pSUnJ0dTp05Vu3btnBoOAACgIA5P9p0+fbo6dOigevXqqX379pKkLVu2KCUlRRs3bnR6QAAAgBtxeESmQYMGiouLU9++fXXx4kVdvXpVAwcO1JEjR9SoUSNXZAQAAMjXba0jYwasI1OEsI4MAMDJHB6RAQAAKCooMgAAwLQoMgAAwLQoMgAAwLRuqchkZ2drw4YNmj9/vm1xvPPnzys1NdWp4QAAAAri8DoyZ86cUbdu3RQfH6+MjAzdf//98vPz0/Tp05WRkaF58+a5IicAAEAeDo/IjB49Wi1bttR///tf+fj42I4/8sgjioqKcmo4AACAgjg8IrNlyxbFxsbKy8vL7nj16tX122+/OS0YAADAzTg8IpObm5vv5pDnzp2Tn5+fU0IBAAAUhsNF5oEHHtCsWbNszy0Wi1JTUzVhwgT16NHDmdkAAAAK5PAWBefOnVPXrl1ltVp1/PhxtWzZUsePH1fFihW1efNmBQUFuSrrLWGLgiKELQoAAE7m8ByZu+66S/v379fy5cu1f/9+paamaujQoRowYIDd5F8AAABXY9PI28GIjGMYkQEAOJnDc2SmTp2qhQsX5jm+cOFCTZ8+3SmhAAAACsPhIjN//nyFhYXlOd6wYUMWwwMAAHeUw0UmISFBlSpVynM8MDBQFy5ccEooAACAwnC4yISGhmrbtm15jm/btk2VK1d2SigAAIDCcPiupWeeeUZjxoxRVlaWunTpIkmKiorSyy+/rBdffNHpAQEAAG7E4SLz0ksv6fLly/r73/+uzMxMSVKpUqU0btw4jR8/3ukBAQAAbuSWb79OTU3V4cOH5ePjozp16sjb29vZ2ZyC26+LEG6/BgA4mcMjMn8qU6aMWrVq5cwsAAAADnF4sm9aWppef/11tWnTRrVr11bNmjXtHo6YOnWqWrVqJT8/PwUFBalXr146evSo3WvS09M1YsQIVahQQWXKlFGfPn2UmJjoaGwAAOCGHB6RGTZsmGJiYvTUU0+pUqVKslgst/zDY2JiNGLECLVq1UrZ2dn65z//qQceeECHDh2Sr6+vJGns2LFat26dvvzySwUEBOiFF15Q7969871zCgAAFC8Oz5EpW7as1q1bp7Zt2zo9zKVLlxQUFKSYmBh16NBBycnJCgwM1NKlS/Xoo49Kko4cOaL69etr+/btat269U3fkzkyRQhzZAAATubwpaVy5cqpfPnyrsii5ORkSbK9/549e5SVlaWIiAjba8LCwlS1alVt37493/fIyMhQSkqK3QMAALgnh4vMv/71L73xxhu6du2aU4Pk5uZqzJgxatu2rRo1aiTp+irCXl5eKlu2rN1rg4ODlZCQkO/7TJ06VQEBAbZHaGioU3MCAICiw+E5Mu+++65Onjyp4OBgVa9eXZ6ennbn9+7de0tBRowYoQMHDmjr1q239P1/Gj9+vCIjI23PU1JSKDMAALgph4tMr169nB7ihRde0P/93/9p8+bNuuuuu2zHQ0JClJmZqaSkJLtRmcTERIWEhOT7Xt7e3kV2TRsAAOBcDheZCRMmOO2HW61WjRw5Ul9//bU2bdqkGjVq2J1v0aKFPD09FRUVpT59+kiSjh49qvj4eIWHhzstBwAAMKdbXhDPGUaMGKGlS5fqm2++kZ+fn23eS0BAgHx8fBQQEKChQ4cqMjJS5cuXl7+/v0aOHKnw8PBC3bEEAADcm8O3X+fk5GjmzJlasWKF4uPjbfst/enKlSuF/+E3WINm0aJFevrppyVdXxDvxRdf1BdffKGMjAx17dpVH3zwwQ0vLf0vbr8uQrj9GgDgZA7ftTRp0iTNmDFDjz/+uJKTkxUZGanevXvLw8NDEydOdOi9rFZrvo8/S4x0fUPKuXPn6sqVK0pLS9OqVasKXWIAAIB7c7jI/Oc//9FHH32kF198USVLllT//v318ccf64033tCOHTtckREAACBfDheZhIQENW7cWNL1jSP/XMTuwQcf1Lp165ybDgAAoAAOF5m77rpLFy5ckCTVqlVLP/zwgyRp165d3PYMAADuKIeLzCOPPKKoqChJ0siRI/X666+rTp06GjhwoIYMGeL0gAAAADfi8F1L/2v79u3avn276tSpo4ceeshZuZyGu5aKEO5aAgA42W2vIxMeHs7idAAAwBCFKjJr1qxR9+7d5enpqTVr1hT42p49ezolGAAAwM0U6tKSh4eHEhISFBQUJA+PG0+rsVgsysnJcWrA28WlpSLEWZeW+NwdwyU9AG6sUCMyubm5+X4NoBihQBYe5RG4Yxy6aykrK0v33Xefjh8/7qo8AAAAheZQkfH09FRcXJyrsgAAADjE4XVknnzySX3yySeuyAIAAOAQh2+/zs7O1sKFC7Vhwwa1aNFCvr6+dudnzJjhtHAAAAAFcbjIHDhwQM2bN5ckHTt2zO6cxWJxTioAAIBCcLjIREdHuyIHAACAwxyeIwMAAFBU3NIWBbt379aKFSsUHx+vzMxMu3OrVq1ySjAAAICbcXhEZtmyZWrTpo0OHz6sr7/+WllZWTp48KA2btyogIAAV2QEAADIl8NF5q233tLMmTO1du1aeXl56b333tORI0fUt29fVa1a1RUZAQAA8uVwkTl58qT+9re/SZK8vLyUlpYmi8WisWPHasGCBU4PCAAAcCMOF5ly5crp6tWrkqQqVarowIEDkqSkpCRdu3bNuekAAAAK4PBk3w4dOujHH39U48aN9dhjj2n06NHauHGjfvzxR913332uyAgAAJCvQheZAwcOqFGjRpozZ47S09MlSa+++qo8PT0VGxurPn366LXXXnNZUAAAgP9V6CLTpEkTtWrVSsOGDVO/fv0kSR4eHnrllVdcFg4AAKAghZ4jExMTo4YNG+rFF19UpUqVNGjQIG3ZssWV2QAAAApU6CLTvn17LVy4UBcuXND777+vX3/9VR07dlTdunU1ffp0JSQkuDInAABAHg7fteTr66vBgwcrJiZGx44d02OPPaa5c+eqatWq6tmzpysyAgAA5Ou29lqqXbu2/vnPf+q1116Tn5+f1q1b56xcAAAAN3VLey1J0ubNm7Vw4UJ99dVX8vDwUN++fTV06FBnZgMAACiQQ0Xm/PnzWrx4sRYvXqwTJ06oTZs2mj17tvr27StfX19XZQQAAMhXoYtM9+7dtWHDBlWsWFEDBw7UkCFDVK9ePVdmAwAAKFChi4ynp6dWrlypBx98UCVKlHBlJgAAgEIpdJFZs2aNK3MAAAA47LbuWgIAADASRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQYAAJiWoUVm8+bNeuihh1S5cmVZLBatXr3a7rzVatUbb7yhSpUqycfHRxERETp+/LgxYQEAQJFjaJFJS0tT06ZNNXfu3HzPv/3225o9e7bmzZunnTt3ytfXV127dlV6evodTgoAAIqikkb+8O7du6t79+75nrNarZo1a5Zee+01Pfzww5KkTz/9VMHBwVq9erX69et3J6MCAIAiqMjOkTl9+rQSEhIUERFhOxYQEKB7771X27dvNzAZAAAoKgwdkSlIQkKCJCk4ONjueHBwsO1cfjIyMpSRkWF7npKS4pqAAAD35c+of6GlLDP0xxfZEZlbNXXqVAUEBNgeoaGhRkcCAAAuUmSLTEhIiCQpMTHR7nhiYqLtXH7Gjx+v5ORk2+Ps2bMuzQkAAIxTZItMjRo1FBISoqioKNuxlJQU7dy5U+Hh4Tf8Pm9vb/n7+9s9AACAezJ0jkxqaqpOnDhhe3769Gnt27dP5cuXV9WqVTVmzBi9+eabqlOnjmrUqKHXX39dlStXVq9evYwLDQAAigxDi8zu3bvVuXNn2/PIyEhJ0qBBg7R48WK9/PLLSktL07PPPqukpCS1a9dO69evV6lSpYyKDAAAihBDi0ynTp1ktVpveN5isWjy5MmaPHnyHUwFAADMosjOkQEAALgZigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADCtkkYHAAAUwL+f0QnMI2WZ0QlgAFOMyMydO1fVq1dXqVKldO+99+qnn34yOhIAACgCinyRWb58uSIjIzVhwgTt3btXTZs2VdeuXXXx4kWjowEAAIMV+SIzY8YMPfPMMxo8eLAaNGigefPmqXTp0lq4cKHR0QAAgMGK9ByZzMxM7dmzR+PHj7cd8/DwUEREhLZv357v92RkZCgjI8P2PDk5WZKUkpLi/IDWLOe/pztz1v8GfO6O4XO/85z5+4bPvfD43I3hiv++/oWfn58sFssNzxfpIvP7778rJydHwcHBdseDg4N15MiRfL9n6tSpmjRpUp7joaGhLskIBwSsMjpB8cTnfufxmRuDz90YLv7ck5OT5e/vf8PzRbrI3Irx48crMjLS9jw3N1dXrlxRhQoVCmx07iIlJUWhoaE6e/Zsgf/Dw7n43I3B524MPndjFNfP3c/Pr8DzRbrIVKxYUSVKlFBiYqLd8cTERIWEhOT7Pd7e3vL29rY7VrZsWVdFLLL8/f2L1b/oRQWfuzH43I3B524MPnd7RXqyr5eXl1q0aKGoqCjbsdzcXEVFRSk8PNzAZAAAoCgo0iMykhQZGalBgwapZcuWuueeezRr1iylpaVp8ODBRkcDAAAGK/JF5vHHH9elS5f0xhtvKCEhQXfffbfWr1+fZwIwrvP29taECRPyXF6Da/G5G4PP3Rh87sbgc8+fxWq1Wo0OAQAAcCuK9BwZAACAglBkAACAaVFkAACAaVFkAJiC1WpVfHy80tPTjY4CoAihyAAwBavVqtq1a+vs2bNGRyl2Dhw4cMNzq1evvnNBgHxQZIBblJSUpI8//ljjx4/XlStXJEl79+7Vb7/9ZnAy9+Th4aE6dero8uXLRkcpdrp27arTp0/nOf7VV19pwIABBiQqPhYtWqRr164ZHaNIo8gAtyAuLk5169bV9OnT9e9//1tJSUmSpFWrVtnt1g7nmjZtml566aUCRwjgfMOGDVNERIQSEhJsx5YvX66BAwdq8eLFxgUrBl555RWFhIRo6NChio2NNTpOkcQ6Mm7i+PHjio6O1sWLF5Wbm2t37o033jAolfuKiIhQ8+bN9fbbb8vPz0/79+9XzZo1FRsbqyeeeEK//vqr0RHdUrly5XTt2jVlZ2fLy8tLPj4+duf/HBmD840cOVLR0dHavHmz1q9fr2HDhumzzz5Tnz59jI7m1rKzs7V27VotXrxY3333nWrWrKnBgwdr0KBBN9xzsLihyLiBjz76SM8//7wqVqyokJAQu12+LRaL9u7da2A69xQQEKC9e/eqVq1adkXmzJkzqlevHhNSXWTJkiUFnh80aNAdSlI8DRgwQLt27dJvv/2mpUuX6uGHHzY6UrGSmJiozz//XEuWLNGRI0fUrVs3DR06VA899JA8PIrvBZYiv0UBbu7NN9/UlClTNG7cOKOjFBve3t5KSUnJc/zYsWMKDAw0IFHxQFG5c9asWZPnWO/evbVlyxb1799fFovF9pqePXve6XjFUnBwsNq1a6djx47p2LFj+uWXXzRo0CCVK1dOixYtUqdOnYyOaAhGZNyAv7+/9u3bp5o1axodpdgYNmyYLl++rBUrVqh8+fKKi4tTiRIl1KtXL3Xo0EGzZs0yOqLbSElJkb+/v+3rgvz5Oty+wv6Fb7FYlJOT4+I0xVtiYqI+++wzLVq0SKdOnVKvXr00dOhQRUREKC0tTZMnT9ayZct05swZo6MagiLjBoYOHapWrVpp+PDhRkcpNpKTk/Xoo49q9+7dunr1qipXrqyEhASFh4fr22+/la+vr9ER3UaJEiV04cIFBQUFycPDw+7S6Z+sViv/QYVbeuihh/T999+rbt26GjZsmAYOHKjy5cvbvebixYsKCQnJMz+yuODSkhuoXbu2Xn/9de3YsUONGzeWp6en3flRo0YZlMx9BQQE6Mcff9S2bdu0f/9+paamqnnz5oqIiDA6mtvZuHGj7Rd3dHS0wWmAOysoKEgxMTEKDw+/4WsCAwPzvT2+uGBExg3UqFHjhucsFotOnTp1B9MAcDejRo1S7dq18/xRNGfOHJ04cYJLqS6SlZWlbt26ad68eapTp47RcYqs4jvN2U1YrVZt2rRJhw4d0unTp/M8KDGuMWrUKM2ePTvP8Tlz5mjMmDF3PlAxsmXLFj355JNq06aNbfHBzz77TFu3bjU4mfv66quv1LZt2zzH27Rpo5UrVxqQqHjw9PRUXFyc0TGKPIqMyVmtVtWpU0fnzp0zOkqxwi92Y3z11Vfq2rWrfHx8tHfvXmVkZEi6PmfprbfeMjid+7p8+bICAgLyHPf399fvv/9uQKLi48knn9Qnn3xidIwijTkyJvfXZdsZerxz+MVujDfffFPz5s3TwIEDtWzZMtvxtm3b6s033zQwmXurXbu21q9frxdeeMHu+J8LtMF1srOztXDhQm3YsEEtWrTIcyPBjBkzDEpWdFBk3MCfy7Z/+OGHatSokdFxigV+sRvj6NGj6tChQ57jAQEBtm0i4HyRkZF64YUXdOnSJXXp0kWSFBUVpXfffZf5MS524MABNW/eXNL1dar+Kr87+IojiowbGDhwoK5du6amTZuybPsdwi92Y4SEhOjEiROqXr263fGtW7dSIF1oyJAhysjI0JQpU/Svf/1LklS9enV9+OGHGjhwoMHp3Bt36t0cdy25AZZtN8aHH36oKVOm6Pz585Ku/2KfOHEiv9hdaOrUqfr888+1cOFC3X///fr222915swZjR07Vq+//rpGjhxpdES3d+nSJfn4+KhMmTJGRwEkUWSA28Yv9jvHarXqrbfe0tSpU3Xt2jVJ17eL+Mc//mEbKYDrXLp0SUePHpUkhYWFqWLFigYncn+dO3cu8BLSxo0b72Caooki4yZycnK0evVqHT58WJLUsGFD9ezZUyVKlDA4GXD7Tp8+bbdeUmZmpk6cOKHU1FQ1aNCAEuliaWlpGjlypD799FPb6rElSpTQwIED9f7776t06dIGJ3RfY8eOtXuelZWlffv26cCBAxo0aJDee+89g5IVHRQZN3DixAn16NFDv/32m+rVqyfp+qTI0NBQrVu3TrVq1TI4oXtauXKlVqxYofj4eGVmZtqdY8dx5/Lw8FC1atXUuXNndenSRZ07d1aVKlWMjlVsPPfcc9qwYYPmzJljW3Zg69atGjVqlO6//359+OGHBicsfiZOnKjU1FT9+9//NjqK4SgybqBHjx6yWq36z3/+Y1vK/fLly3ryySfl4eGhdevWGZzQ/cyePVuvvvqqnn76aS1YsECDBw/WyZMntWvXLo0YMUJTpkwxOqJb2bRpk+2xc+dOZWZmqmbNmrZS07lzZwUHBxsd021VrFhRK1euzLO7cnR0tPr27atLly4ZE6wYO3HihO655x5u5hBFxi34+vra9ln6q/3796tt27ZKTU01KJn7CgsL04QJE9S/f3/5+flp//79qlmzpt544w1duXJFc+bMMTqi20pPT1dsbKyt2Pz000/KyspSWFiYDh48aHQ8t1S6dGnt2bNH9evXtzt+8OBB3XPPPUpLSzMoWfH12Wefady4cbabDYozbr92A97e3rp69Wqe46mpqfLy8jIgkfuLj49XmzZtJEk+Pj62z/+pp55S69atKTIuVKpUKXXp0kXt2rVT586d9d1332n+/Pk6cuSI0dHcVnh4uCZMmKBPP/1UpUqVkiT98ccfmjRpUoGbGeL29e7d2+651WrVhQsXtHv3br3++usGpSpaKDJu4MEHH9Szzz6rTz75RPfcc48kaefOnRo+fLh69uxpcDr3FBISoitXrqhatWqqWrWqduzYoaZNm+r06dNikNM1MjMztWPHDkVHR9suMYWGhqpDhw6aM2eOOnbsaHREt/Xee++pa9euuuuuu9S0aVNJ10d8vb299cMPPxiczr397wriHh4eqlevniZPnqwHHnjAoFRFC5eW3EBSUpIGDRqktWvXytPTU9L1Za179uypxYsX57uUPm7PsGHDFBoaqgkTJmju3Ll66aWX1LZtW+3evVu9e/dmbxQn69Kli3bu3KkaNWqoY8eOat++vTp27KhKlSoZHa3YuHbtmv7zn//YRr7q16+vAQMG5FmAE7jTKDJu5Pjx43a/ZGrXrm1wIveVm5ur3NxclSx5fVBz2bJlio2NVZ06dfTcc89xSc/JPD09ValSJfXq1UudOnVSx44dVaFCBaNjFXsXLlzQlClTuJR6B+zevdu2vEaDBg3UokULgxMVHRQZAEVeWlqatmzZok2bNik6Olr79u1T3bp11bFjR1uxCQwMNDqmWzp48KCio6Pl5eWlvn37qmzZsvr99981ZcoUzZs3TzVr1mSStQudO3dO/fv317Zt21S2bFlJ10fh27Rpo2XLlumuu+4yNmARQJFxAzk5OVq8eLGioqJ08eJF24JVf2LlR+eIi4sr9GubNGniwiS4evWqtm7dapsvs3//ftWpU0cHDhwwOppbWbNmjR599FFlZ2dLkmrWrKmPPvpIffv2VYsWLTRmzBh169bN4JTurVu3bkpKStKSJUvs1gkbPHiw/P39tX79eoMTGo8i4wZeeOEFLV68WH/7299UqVKlPMtZz5w506Bk7sXDw0MWi+Wmk3ktFotycnLuUKriKTc3V7t27VJ0dLSio6O1detWpaen87k72T333KO2bdvqX//6lz7++GNFRkaqYcOGWrhwoVq1amV0vGLBx8dHsbGxatasmd3xPXv2qH379ratOooziowbqFixoj799FP16NHD6Chu7cyZM4V+bbVq1VyYpPjJzc3V7t27bZeWtm3bprS0NFWpUsW2IF7nzp353J0sICBAe/bsUe3atZWTkyNvb2+tX79eERERRkcrNurWravPP//cdkfqn3766Sc98cQTOnHihEHJig5uv3YDXl5eTOy9A/76H8mMjAxlZ2fL19fXwETFR9myZZWWlqaQkBB17txZM2fOVKdOndh+w8WuXr0qf39/Sdf3VvLx8VHNmjUNTlW8vPPOOxo5cqTmzp2rli1bSro+8Xf06NFsT/D/MSLjBt59912dOnVKc+bMKXCXVNy+S5cuaeDAgdqwYYNyc3PVqlUrff755xRJF5s/f746d+6sunXrGh2lWPHw8NCSJUtsSzj0799fs2bNyrMdBOtVOVe5cuXsfpenpaUpOzvbdpfkn1/7+vqyRYEoMqb1v6s9bty4UeXLl1fDhg1ta8n8adWqVXcymlsbMmSIvvvuO40aNUqlSpXS/PnzValSJUVHRxsdDXA6Dw+Pm76GOWHOt2TJkkK/dtCgQS5MYg4UGZMaPHhwoV+7aNEiFyYpXkJDQ/Xxxx+ra9eukq6v3VO/fn2lpaXJ29vb4HQAUPxQZAAHlChRQr/99ptCQkJsx3x9fXXw4EFVr17duGAA3NLevXvl6elp2xT4m2++0aJFi9SgQQNNnDiRxTcl3XzcEEVely5dlJSUlOd4SkqKunTpcucDubkSJUrkec7fAwBc4bnnntOxY8ckSadOndLjjz+u0qVL68svv9TLL79scLqigREZN+Dh4aGEhAQFBQXZHb948aKqVKmirKwsg5K5Hw8PDwUEBNhNxEtKSpK/v7/dfAIm4LlGWload4qhWAkICNDevXtVq1YtTZ8+XRs3btT333+vbdu2qV+/fjp79qzREQ3H7dcm9teVZg8dOqSEhATb85ycHK1fv15VqlQxIprbYr6RsYKDg9W3b18NGTJE7dq1MzoO4HJWq9W2WvuGDRv04IMPSro+X+/33383MlqRwYiMif250qykfC9t+Pj46P3339eQIUPudDTAJVavXq3Fixfr22+/VfXq1TVkyBANHDhQlStXNjoa4BJdunRRaGioIiIiNHToUB06dEi1a9dWTEyMBg0apF9//dXoiIajyJjYmTNnZLVaVbNmTf300092m+Z5eXkpKCgoz3wOwB1cunRJn332mRYvXqzDhw+ra9euGjJkiHr27GlbawPOc/bsWVksFtsGhT/99JOWLl2qBg0a6NlnnzU4nXuLi4vTgAEDFB8fr8jISE2YMEGSNHLkSF2+fFlLly41OKHxKDIATO3999/XSy+9pMzMTFWsWFHDhw/XK6+8otKlSxsdzW20b99ezz77rJ566iklJCSoXr16atiwoY4fP66RI0fqjTfeMDpisZOenq4SJUrkWTesOKLIuInjx48rOjo6392v+SUDd5OYmKglS5Zo8eLFOnPmjB555BENHTpU586d0/Tp01W5cmX98MMPRsd0G+XKldOOHTtUr149zZ49W8uXL9e2bdv0ww8/aPjw4Tp16pTREd1aUlKSVq5cqZMnT+qll15S+fLltXfvXgUHBzMPUkz2dQsfffSRnn/+eVWsWFEhISF2d9RYLBaKDNzGqlWrtGjRIn3//fdq0KCB/v73v+vJJ59U2bJlba9p06aN6tevb1xIN5SVlWVb8HHDhg22LQnCwsJ04cIFI6O5vbi4ON13330qW7asfv31Vz3zzDMqX768Vq1apfj4eH366adGRzQc68i4gTfffFNTpkxRQkKC9u3bp59//tn22Lt3r9Hx3E5WVpZq1aqlw4cPGx2l2Bk8eLAqV66sbdu2ad++fXrhhRfsSowkVa5cWa+++qoxAd1Uw4YNNW/ePG3ZskU//vijunXrJkk6f/68KlSoYHA69xYZGanBgwfr+PHjKlWqlO14jx49tHnzZgOTFR2MyLiB//73v3rssceMjlFseHp6Kj093egYxdKFCxduOvfFx8fHNiESzjF9+nQ98sgjeueddzRo0CA1bdpUkrRmzRrdc889Bqdzb7t27dL8+fPzHK9SpYrdkhvFGSMybuCxxx5jPsAdNmLECE2fPl3Z2dlGRylWSpcurZMnT+q1115T//79dfHiRUnSd999p4MHDxqczn116tRJv//+u37//XctXLjQdvzZZ5/VvHnzDEzm/ry9vZWSkpLn+LFjx+zuVC3OGJFxA7Vr19brr7+uHTt2qHHjxnlmsY8aNcqgZO5r165dioqK0g8//KDGjRvnWW2WHcddIyYmRt27d1fbtm21efNmTZkyRUFBQdq/f78++eQTrVy50uiIbstqtWrPnj06efKknnjiCfn5+cnLy4u7w1ysZ8+emjx5slasWCHp+rzH+Ph4jRs3Tn369DE4XdHAXUtuoEaNGjc8Z7FYuKPABW62+zgrALtGeHi4HnvsMUVGRsrPz0/79++3raPUu3dvnTt3zuiIbunMmTPq1q2b4uPjlZGRoWPHjqlmzZoaPXq0MjIyGJVxoeTkZD366KPavXu3rl69qsqVKyshIUHh4eH69ttv2bJDFBkAJlKmTBn98ssvqlGjhl2R+fXXXxUWFsbcJRfp1auX/Pz89Mknn6hChQq2z33Tpk165plndPz4caMjur2tW7cqLi5Oqampat68uSIiIoyOVGRwacmNZGZm6vTp06pVqxarm94B2dnZ2rRpk91Q+/nz5+Xv768yZcoYHc8tlS1bVhcuXMgzCvnzzz+znoYLbdmyRbGxsfLy8rI7Xr16df32228GpSpe2rVrx/5iN8B/7dzAtWvXNHLkSC1ZskSSbMO+I0eOVJUqVfTKK68YnND9/O9Q+/333y8/Pz9Nnz6doXYX6tevn8aNG6cvv/xSFotFubm52rZtm/7xj39o4MCBRsdzW7m5ucrJyclz/Ny5c/Lz8zMgkXubPXt2oV/LHEguLbmF0aNHa9u2bZo1a5a6deumuLg41axZU998840mTpyon3/+2eiIboehdmNkZmZqxIgRWrx4sXJyclSyZEnl5OToiSee0OLFi9lbzEUef/xxBQQEaMGCBfLz81NcXJwCAwP18MMPq2rVqswJc7KC5j3+FXMgr6PIuIFq1app+fLlat26td28gRMnTqh58+b53rqH21OhQgXFxsaqXr16eeZqNGjQQNeuXTM6oluLj4/XgQMHlJqaqmbNmqlOnTpGR3Jr586dU9euXWW1WnX8+HG1bNlSx48fV8WKFbV582YFBQUZHRHFGJeW3MClS5fy/UWSlpZmt10BnIehdmNVrVpVVatWNTpGsXHXXXdp//79WrZsmW3C6dChQzVgwAD5+PgYHQ/FHEXGDbRs2VLr1q3TyJEjJclWXj7++GOFh4cbGc1tPfDAA5o1a5YWLFgg6fpnnpqaqgkTJqhHjx4Gp3MvkZGRhX7tjBkzXJikeCtZsqSefPJJo2MUO1arVStXrrzhpsCsWUWRcQtvvfWWunfvrkOHDik7O1vvvfeeDh06pNjYWMXExBgdzy29++676tq1qxo0aKD09HQ98cQTtqH2L774wuh4bqWwc7wYfXSuNWvWFPq1f24iCecbM2aM5s+fr86dOys4OJh/z/PBHBk3cfLkSU2bNk379++3rTMwbtw4NW7c2Ohobis7O9tuqL158+YMtcNteHgUbgcbi8WS72VWOEf58uX1+eefM9JbAIqMiRV2Eq+/v7+LkwAAXKFGjRr67rvvFBYWZnSUIosiY2IeHh4FDjNarVb+WnIihtqN0bt370K/lvkCcDdLlizR+vXrtXDhQkZ7b4A5MiYWHR1t+9pqtapHjx76+OOPWeHURXr16mX33GKx6H//DvizWFIenScgIMDoCJAUFRWlmTNn6vDhw5Kk+vXra8yYMSyV72J9+/bVF198oaCgIFWvXj3PpsB79+41KFnRwYiMG/nreiZwrQ0bNmjcuHF66623bHeGbd++Xa+99preeust3X///QYnBJzngw8+0OjRo/Xoo4/a/n3fsWOHVq5cqZkzZ2rEiBEGJ3Rfffv2VXR0tB599NF8J/tOmDDBoGRFB0XGjVBk7pxGjRpp3rx5efY+2bJli5599lnbX62AO7jrrrv0yiuv6IUXXrA7PnfuXL311lvst+RCvr6++v7779lnqQBcWgJuwcmTJ1W2bNk8xwMCAvTrr7/e8TzFycqVK7VixQrFx8crMzPT7hzD7K6RlJSkbt265Tn+wAMPaNy4cQYkKj5CQ0O5YeMmCnd/HUyDNQbujFatWikyMlKJiYm2Y4mJiXrppZd0zz33GJjMvc2ePVuDBw9WcHCwfv75Z91zzz2qUKGCTp06pe7duxsdz2317NlTX3/9dZ7j33zzjR588EEDEhUf7777rl5++WX+QCoAl5ZM7H/v5li7dq26dOkiX19fu+PcyeF8J06c0COPPKJjx44pNDRUknT27FnVqVNHq1evVu3atQ1O6J7CwsI0YcIE9e/f3+5S6htvvKErV65ozpw5Rkd0G3/dgTklJUX//ve/1bZtW7s5Mtu2bdOLL76o1157zaiYbq9cuXK6du2asrOzVbp06TyTfa9cuWJQsqKDImNigwcPLtTr2JnWNaxWq3788UcdOXJE0vW7OCIiIhgVc6HSpUvr8OHDqlatmoKCgvTjjz+qadOmOn78uFq3bq3Lly8bHdFtsANz0bBkyZICzw8aNOgOJSm6KDIATKNmzZr66quv1KxZM7Vs2VLPPPOMnnvuOf3www/q168ff50CxRCTfYFC+utQ+82MGjXKhUmKry5dumjNmjVq1qyZBg8erLFjx2rlypXavXu3QwvnAWaUnp6eZ4I7E4EZkQEK7X+H2i9duqRr167Z7l5KSkpS6dKlFRQUxFC7i+Tm5io3N1clS17/G2zZsmWKjY1VnTp19Nxzz8nLy8vghO6JHZiNk5aWpnHjxmnFihX5Xjpl8U1GZIBCO336tO3rpUuX6oMPPtAnn3yievXqSZKOHj1qu9QB1/Dw8LDbzLBfv37q16+fgYmKB3ZgNs7LL7+s6Ohoffjhh3rqqac0d+5c/fbbb5o/f76mTZtmdLwigREZ4BbUqlVLK1euVLNmzeyO79mzR48++qhd6YFzJSUl6aeffsp3ZGDgwIEGpXJv7MBsnKpVq+rTTz9Vp06d5O/vr71796p27dr67LPP9MUXX+jbb781OqLhGJEBbsGFCxeUnZ2d53hOTo7d2jJwrrVr12rAgAFKTU2Vv7+/3ciAxWKhyLhIQEAAK4Yb5MqVK7bP3t/f3zahvV27dnr++eeNjFZksCAecAvuu+8+Pffcc3Yrye7Zs0fPP/88m+i50IsvvqghQ4YoNTVVSUlJ+u9//2t7cMeS60ycOFGTJk3SH3/8YXSUYqdmzZq2Ed6wsDCtWLFC0vVSn9/q4sURl5aAW3Dp0iUNGjRI69evty1QlZ2dra5du2rx4sUKCgoyOKF78vX11S+//MLowB32xx9/6JFHHtG2bdvYgfkOmzlzpkqUKKFRo0Zpw4YNeuihh2S1WpWVlaUZM2Zo9OjRRkc0HEUGuA3Hjh3T4cOHZbFYFBYWprp16xodya317t1b/fr1U9++fY2OUqywA3PRcebMGe3Zs0e1a9dWkyZNjI5TJFBkgNv05/+FuJPDNdasWWP7+tKlS5o8ebIGDx6sxo0b5xkZ6Nmz552OVyywA/Odt337dl2+fNluL6tPP/1UEyZMUFpamnr16qX3339f3t7eBqYsGigywC369NNP9c477+j48eOSpLp16+qll17SU089ZXAy9/LX260LYrFYWFPDRf6cm8EIwJ3TvXt3derUyba7+C+//KLmzZvr6aefVoMGDfT222/rueee08SJE40NWgQw2Re4BTNmzNDzzz+vHj16aMWKFVqxYoW6deum4cOHa+bMmUbHcyt/LoJ3swclxnXYgfnO27dvn+677z7b82XLlunee+/VRx99pLFjx2r27Nm2ib/FHSMywC2oUaOGJk2alOd23yVLlmjixImsI+Nkp0+fLvQmhnA+dmC+80qVKqXjx48rNDRU0vXbrbt3765XX31VkvTrr7+qcePGunr1qpExiwTWkQFuwYULF9SmTZs8x9u0aaMLFy4YkMi91apVS9WqVVPnzp1tj7vuusvoWMXGrFmzjI5Q7AQHB+v06dMKDQ1VZmam9u7dq0mTJtnOX716NU+hLK4oMsAtqF27tlasWKF//vOfdseXL1+uOnXqGJTKfW3cuFGbNm3Spk2b9MUXXygzM1M1a9ZUly5dbMUmODjY6Jhua9CgQUZHKHZ69OihV155RdOnT9fq1atVunRptW/f3nY+Li5OtWrVMjBh0cGlJeAWfPXVV3r88ccVERGhtm3bSpK2bdumqKgorVixQo888ojBCd1Xenq6YmNjbcXmp59+UlZWlsLCwnTw4EGj47ml+Pj4As9XrVr1DiUpPn7//Xf17t1bW7duVZkyZbRkyRK73yv33XefWrdurSlTphiYsmigyAC3aM+ePZo5c6YOHz4sSapfv75efPHFPPsvwTUyMzO1bds2fffdd5o/f75SU1OZ8OsiHh4eBS4vwOfuOsnJySpTpoxKlChhd/zKlSsqU6YMO76LIgPAJDIzM7Vjxw5FR0dr06ZN2rlzp0JDQ9WhQwd16NBBHTt2ZGTARfbv32/3PCsrSz///LNmzJihKVOmqHfv3gYlAygygENSUlIK9Tp/f38XJyleunTpop07d6pGjRrq2LGj2rdvr44dO6pSpUpGRyvW1q1bp3feeUebNm0yOgqKMYoM4ICbDbFbrVYWZnMBT09PVapUSb169VKnTp3UsWNHVahQwehYxd6JEyfUtGlTpaWlGR0FxRh3LQEOiI6Otn1ttVrVo0cPffzxx6pSpYqBqdxfUlKStmzZok2bNmn69Onq37+/6tatq44dO9qKTWBgoNEx3db/jkRarVZduHBBEydO5C49GI4RGeA2+Pn5af/+/ezGfIddvXpVW7dutc2X2b9/v+rUqaMDBw4YHc0t5TcSabVaFRoaqmXLlik8PNygZAAjMgBMyNfXV+XLl1f58uVVrlw5lSxZ0nb3GJzvryOR0vViExgYqNq1a6tkSf4zAmPxbyCAIi83N1e7d+/Wpk2bFB0drW3btiktLU1VqlRR586dNXfuXHXu3NnomG6rY8eORkcAbogiA9ymgib/wjnKli2rtLQ0hYSEqHPnzpo5c6Y6derEyqYutmbNmpu+pmTJkgoJCVGjRo1Y0wSGYI4M4ID/XS9j7dq16tKli3x9fe2Or1q16k7Gcnvz589X586dVbduXaOjFCseHh6Ffm1ISIiWL19ut4w+cCdQZAAHDB48uFCvW7RokYuTAEWD1WpVYmKi3nzzTcXGxmrv3r1GR0IxQ5EBANy2X3/9VWFhYUpPTzc6CooZigwAwCmSk5MVEBBgdAwUMxQZAABgWoWfyQUAAFDEUGQAAIBpUWQAAAV64403FB0dzUReFEnMkQEAFOj+++/X9u3blZ2drVatWtk262zbtq18fHyMjodijiIDALip7Oxs7dy5U5s3b1ZMTIxiY2OVkZGhVq1aaevWrUbHQzHGFgUAgJsqWbKk2rZtq8DAQJUvX15+fn5avXq1jhw5YnQ0FHOMyAAACrRgwQJt2rRJMTExysjIUPv27dWpUyd16tRJTZo0Yb8xGIoiAwAokIeHhwIDA/Xiiy/q73//u8qUKWN0JMCGIgMAKNDq1au1efNmbdq0SYcPH1azZs1sIzLt2rVT6dKljY6IYowiAwAotOTkZG3ZskVffvmlvvjiC3l4eHBbNgzFZF8AwE1dvnxZMTEx2rRpkzZt2qSDBw+qXLlyat++vdHRUMwxIgMAKFDjxo11+PBhlStXTh06dFCnTp3UsWNHNWnSxOhoACMyAICCDR8+XB07dlSjRo2MjgLkwYgMAKBQMjMzdfr0adWqVUslS/J3MIoG9loCABTojz/+0NChQ1W6dGk1bNhQ8fHxkqSRI0dq2rRpBqdDcUeRAQAU6JVXXtH+/fu1adMmlSpVynY8IiJCy5cvNzAZwBwZAMBNrF69WsuXL1fr1q3tVvFt2LChTp48aWAygBEZAMBNXLp0SUFBQXmOp6WlsT0BDEeRAQAUqGXLllq3bp3t+Z/l5eOPP1Z4eLhRsQBJXFoCANzEW2+9pe7du+vQoUPKzs7We++9p0OHDik2NlYxMTFGx0Mxx4gMAKBA7dq10759+5Sdna3GjRvrhx9+UFBQkLZv364WLVoYHQ/FHOvIAAAA02JEBgAAmBZzZAAA+fLw8LjpXUkWi0XZ2dl3KBGQF0UGAJCvr7/++obntm/frtmzZys3N/cOJgLyYo4MAKDQjh49qldeeUVr167VgAEDNHnyZFWrVs3oWCjGmCMDALip8+fP65lnnlHjxo2VnZ2tffv2acmSJZQYGI4iAwC4oeTkZI0bN061a9fWwYMHFRUVpbVr16pRo0ZGRwMkMUcGAHADb7/9tqZPn66QkBB98cUXevjhh42OBOTBHBkAQL48PDzk4+OjiIgIlShR4oavW7Vq1R1MBdhjRAYAkK+BAweyKSSKPEZkAACAaTHZFwAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmNb/AxyEB50w8Ci1AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb
index dcadf0581..c3ed8ced9 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb
@@ -53,10 +53,10 @@
    "id": "3e2cc4f9-539c-415d-98a5-3a35ebe226a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:58.985667Z",
-     "iopub.status.busy": "2024-10-25T15:08:58.985154Z",
-     "iopub.status.idle": "2024-10-25T15:08:58.996389Z",
-     "shell.execute_reply": "2024-10-25T15:08:58.995815Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.352828Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.352401Z",
+     "iopub.status.idle": "2024-10-25T15:10:23.364146Z",
+     "shell.execute_reply": "2024-10-25T15:10:23.363558Z"
     }
    },
    "outputs": [
@@ -136,10 +136,10 @@
    "id": "49665408-6239-4d17-ab3f-fca7a8bfbbd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:58.998409Z",
-     "iopub.status.busy": "2024-10-25T15:08:58.997995Z",
-     "iopub.status.idle": "2024-10-25T15:08:59.282289Z",
-     "shell.execute_reply": "2024-10-25T15:08:59.281746Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.366220Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.365794Z",
+     "iopub.status.idle": "2024-10-25T15:10:23.684446Z",
+     "shell.execute_reply": "2024-10-25T15:10:23.683677Z"
     }
    },
    "outputs": [],
@@ -175,10 +175,10 @@
    "id": "47243f27-c8ef-40e2-9d88-ac9418a96393",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:59.284777Z",
-     "iopub.status.busy": "2024-10-25T15:08:59.284199Z",
-     "iopub.status.idle": "2024-10-25T15:09:00.560857Z",
-     "shell.execute_reply": "2024-10-25T15:09:00.560207Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.687080Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.686600Z",
+     "iopub.status.idle": "2024-10-25T15:10:25.215492Z",
+     "shell.execute_reply": "2024-10-25T15:10:25.214845Z"
     }
    },
    "outputs": [
@@ -213,10 +213,10 @@
    "id": "9d4f5efd-5873-46ed-9c3a-d5b644380f84",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:00.563239Z",
-     "iopub.status.busy": "2024-10-25T15:09:00.562874Z",
-     "iopub.status.idle": "2024-10-25T15:09:00.580409Z",
-     "shell.execute_reply": "2024-10-25T15:09:00.579799Z"
+     "iopub.execute_input": "2024-10-25T15:10:25.219018Z",
+     "iopub.status.busy": "2024-10-25T15:10:25.217893Z",
+     "iopub.status.idle": "2024-10-25T15:10:25.236761Z",
+     "shell.execute_reply": "2024-10-25T15:10:25.236200Z"
     }
    },
    "outputs": [
@@ -371,10 +371,10 @@
    "id": "ffe4eb1d-3bf5-486b-947d-5d25115b5ab3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:00.582576Z",
-     "iopub.status.busy": "2024-10-25T15:09:00.582343Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.411247Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.410581Z"
+     "iopub.execute_input": "2024-10-25T15:10:25.239330Z",
+     "iopub.status.busy": "2024-10-25T15:10:25.238940Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.701678Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.700871Z"
     }
    },
    "outputs": [],
@@ -404,10 +404,10 @@
    "id": "941a24d8-cb6c-442e-81d9-0d64bb5ec933",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.413577Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.413250Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.423905Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.423268Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.704482Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.703980Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.715632Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.714981Z"
     }
    },
    "outputs": [
@@ -438,67 +438,67 @@
       "Node Shopping Event? is a root node. Therefore, assigning 'Empirical Distribution' to the node representing the marginal distribution.\n",
       "\n",
       "--- Node: Unit Price\n",
-      "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
+      "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n",
       "This represents the causal relationship as Unit Price := f(Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 138.94088619006752\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 138.9540857127003\n",
-      "HistGradientBoostingRegressor: 423.7141382351113\n",
+      "                ('linearregression', LinearRegression)]): 144.66690660144045\n",
+      "LinearRegression: 144.67363407450733\n",
+      "HistGradientBoostingRegressor: 423.78543034447205\n",
       "\n",
       "--- Node: Ad Spend\n",
       "Node Ad Spend is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Ad Spend := f(Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 15987.54106716137\n",
+      "LinearRegression: 16128.70662145468\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 16077.092422884758\n",
-      "HistGradientBoostingRegressor: 81230.60985063826\n",
+      "                ('linearregression', LinearRegression)]): 16140.622755139828\n",
+      "HistGradientBoostingRegressor: 80441.31192441305\n",
       "\n",
       "--- Node: Page Views\n",
       "Node Page Views is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the discrete causal relationship as Page Views := f(Ad Spend,Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 80093.59713032305\n",
+      "LinearRegression: 89538.34945420861\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 103808.892264037\n",
-      "HistGradientBoostingRegressor: 1422131.7607968026\n",
+      "                ('linearregression', LinearRegression)]): 96004.15431450964\n",
+      "HistGradientBoostingRegressor: 1466488.6168275173\n",
       "\n",
       "--- Node: Sold Units\n",
       "Node Sold Units is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the discrete causal relationship as Sold Units := f(Page Views,Shopping Event?,Unit Price) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 8897.337998967068\n",
+      "LinearRegression: 8558.37003116247\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 15093.623441629079\n",
-      "HistGradientBoostingRegressor: 259744.19440229205\n",
+      "                ('linearregression', LinearRegression)]): 14786.380409459769\n",
+      "HistGradientBoostingRegressor: 234793.98953644396\n",
       "\n",
       "--- Node: Revenue\n",
       "Node Revenue is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n",
       "This represents the causal relationship as Revenue := f(Sold Units,Unit Price) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 6.992361408303774e-19\n",
-      "LinearRegression: 72430705.45956144\n",
-      "HistGradientBoostingRegressor: 149689722723.5923\n",
+      "                ('linearregression', LinearRegression)]): 4.717022054539674e-19\n",
+      "LinearRegression: 74897935.84700143\n",
+      "HistGradientBoostingRegressor: 124795953359.75322\n",
       "\n",
       "--- Node: Operational Cost\n",
       "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Operational Cost := f(Ad Spend,Sold Units) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 38.74700556676281\n",
+      "LinearRegression: 38.7763118955903\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 38.84743309762714\n",
-      "HistGradientBoostingRegressor: 14171555085.146936\n",
+      "                ('linearregression', LinearRegression)]): 39.52633219472173\n",
+      "HistGradientBoostingRegressor: 14356916125.0501\n",
       "\n",
       "--- Node: Profit\n",
       "Node Profit is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Profit := f(Operational Cost,Revenue) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 1.8102834561483138e-18\n",
+      "LinearRegression: 1.2133410476877163e-18\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 1.1168538088104242e-06\n",
-      "HistGradientBoostingRegressor: 25059619203.962067\n",
+      "                ('linearregression', LinearRegression)]): 1.0878901127713091e-05\n",
+      "HistGradientBoostingRegressor: 26792154395.605186\n",
       "\n",
       "===Note===\n",
       "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n",
@@ -540,10 +540,10 @@
    "id": "2b032fa2-9348-418c-b62b-9ae77f49c342",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.425886Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.425513Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.450071Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.449545Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.717999Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.717611Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.747801Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.747122Z"
     }
    },
    "outputs": [
@@ -624,7 +624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 408.02it/s]"
+      "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 333.22it/s]"
      ]
     },
     {
@@ -653,10 +653,10 @@
    "id": "ab315928-4d60-4008-abb9-a9030e4cad44",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.451941Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.451505Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.649430Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.648824Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.749937Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.749592Z",
+     "iopub.status.idle": "2024-10-25T15:11:04.741859Z",
+     "shell.execute_reply": "2024-10-25T15:11:04.741199Z"
     }
    },
    "outputs": [
@@ -673,7 +673,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 1109.35it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 785.23it/s]"
      ]
     },
     {
@@ -696,7 +696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 1/50 [00:00<00:28,  1.69it/s]"
+      "Test permutations of given graph:   2%|▏         | 1/50 [00:00<00:25,  1.94it/s]"
      ]
     },
     {
@@ -704,7 +704,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 2/50 [00:01<00:26,  1.81it/s]"
+      "Test permutations of given graph:   4%|▍         | 2/50 [00:01<00:26,  1.82it/s]"
      ]
     },
     {
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 3/50 [00:01<00:25,  1.86it/s]"
+      "Test permutations of given graph:   6%|▌         | 3/50 [00:01<00:26,  1.75it/s]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 4/50 [00:02<00:21,  2.12it/s]"
+      "Test permutations of given graph:   8%|▊         | 4/50 [00:02<00:24,  1.89it/s]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 5/50 [00:02<00:22,  2.03it/s]"
+      "Test permutations of given graph:  10%|█         | 5/50 [00:02<00:23,  1.91it/s]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 6/50 [00:02<00:20,  2.10it/s]"
+      "Test permutations of given graph:  12%|█▏        | 6/50 [00:03<00:22,  1.98it/s]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 7/50 [00:03<00:21,  2.05it/s]"
+      "Test permutations of given graph:  14%|█▍        | 7/50 [00:03<00:21,  1.97it/s]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 8/50 [00:03<00:20,  2.04it/s]"
+      "Test permutations of given graph:  16%|█▌        | 8/50 [00:04<00:21,  1.97it/s]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 9/50 [00:04<00:19,  2.08it/s]"
+      "Test permutations of given graph:  18%|█▊        | 9/50 [00:04<00:19,  2.11it/s]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 10/50 [00:05<00:20,  1.99it/s]"
+      "Test permutations of given graph:  20%|██        | 10/50 [00:04<00:17,  2.27it/s]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 11/50 [00:05<00:17,  2.22it/s]"
+      "Test permutations of given graph:  22%|██▏       | 11/50 [00:05<00:17,  2.23it/s]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 12/50 [00:05<00:15,  2.41it/s]"
+      "Test permutations of given graph:  24%|██▍       | 12/50 [00:05<00:15,  2.43it/s]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 13/50 [00:05<00:13,  2.70it/s]"
+      "Test permutations of given graph:  26%|██▌       | 13/50 [00:06<00:14,  2.50it/s]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 14/50 [00:06<00:14,  2.53it/s]"
+      "Test permutations of given graph:  28%|██▊       | 14/50 [00:06<00:14,  2.54it/s]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 15/50 [00:06<00:13,  2.57it/s]"
+      "Test permutations of given graph:  30%|███       | 15/50 [00:06<00:13,  2.64it/s]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 16/50 [00:07<00:12,  2.70it/s]"
+      "Test permutations of given graph:  32%|███▏      | 16/50 [00:07<00:11,  3.00it/s]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 17/50 [00:07<00:13,  2.49it/s]"
+      "Test permutations of given graph:  34%|███▍      | 17/50 [00:07<00:11,  2.98it/s]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 18/50 [00:07<00:12,  2.55it/s]"
+      "Test permutations of given graph:  36%|███▌      | 18/50 [00:07<00:11,  2.69it/s]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 19/50 [00:08<00:12,  2.55it/s]"
+      "Test permutations of given graph:  38%|███▊      | 19/50 [00:08<00:11,  2.64it/s]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 20/50 [00:08<00:11,  2.63it/s]"
+      "Test permutations of given graph:  40%|████      | 20/50 [00:08<00:12,  2.45it/s]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 21/50 [00:08<00:10,  2.88it/s]"
+      "Test permutations of given graph:  42%|████▏     | 21/50 [00:09<00:12,  2.36it/s]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 22/50 [00:09<00:10,  2.79it/s]"
+      "Test permutations of given graph:  44%|████▍     | 22/50 [00:09<00:11,  2.49it/s]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 23/50 [00:09<00:09,  2.74it/s]"
+      "Test permutations of given graph:  46%|████▌     | 23/50 [00:09<00:10,  2.50it/s]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 24/50 [00:10<00:10,  2.58it/s]"
+      "Test permutations of given graph:  48%|████▊     | 24/50 [00:10<00:09,  2.83it/s]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 25/50 [00:10<00:08,  2.88it/s]"
+      "Test permutations of given graph:  50%|█████     | 25/50 [00:10<00:08,  3.07it/s]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:10<00:07,  3.06it/s]"
+      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:10<00:06,  3.71it/s]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:11<00:07,  2.89it/s]"
+      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:10<00:05,  3.88it/s]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:11<00:07,  2.85it/s]"
+      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:10<00:04,  4.57it/s]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 30/50 [00:11<00:04,  4.17it/s]"
+      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:11<00:04,  5.00it/s]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:12<00:05,  3.72it/s]"
+      "Test permutations of given graph:  60%|██████    | 30/50 [00:11<00:03,  5.42it/s]"
      ]
     },
     {
@@ -936,7 +936,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:12<00:04,  3.79it/s]"
+      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:11<00:04,  4.74it/s]"
      ]
     },
     {
@@ -944,7 +944,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:12<00:05,  3.39it/s]"
+      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:11<00:04,  4.00it/s]"
      ]
     },
     {
@@ -952,7 +952,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:12<00:03,  4.03it/s]"
+      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:12<00:04,  3.93it/s]"
      ]
     },
     {
@@ -960,7 +960,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 35/50 [00:13<00:03,  3.97it/s]"
+      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:12<00:04,  3.81it/s]"
      ]
     },
     {
@@ -968,7 +968,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:13<00:03,  4.64it/s]"
+      "Test permutations of given graph:  70%|███████   | 35/50 [00:12<00:04,  3.45it/s]"
      ]
     },
     {
@@ -976,7 +976,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 37/50 [00:13<00:03,  3.70it/s]"
+      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:12<00:03,  3.72it/s]"
      ]
     },
     {
@@ -984,7 +984,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 38/50 [00:13<00:03,  3.75it/s]"
+      "Test permutations of given graph:  74%|███████▍  | 37/50 [00:13<00:03,  3.75it/s]"
      ]
     },
     {
@@ -992,7 +992,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 39/50 [00:14<00:02,  4.11it/s]"
+      "Test permutations of given graph:  76%|███████▌  | 38/50 [00:13<00:03,  3.53it/s]"
      ]
     },
     {
@@ -1000,7 +1000,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 40/50 [00:14<00:02,  4.29it/s]"
+      "Test permutations of given graph:  78%|███████▊  | 39/50 [00:13<00:03,  3.58it/s]"
      ]
     },
     {
@@ -1008,7 +1008,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 41/50 [00:14<00:01,  4.98it/s]"
+      "Test permutations of given graph:  80%|████████  | 40/50 [00:13<00:02,  4.22it/s]"
      ]
     },
     {
@@ -1016,7 +1016,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 42/50 [00:14<00:01,  4.93it/s]"
+      "Test permutations of given graph:  82%|████████▏ | 41/50 [00:14<00:02,  4.10it/s]"
      ]
     },
     {
@@ -1024,7 +1024,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 43/50 [00:14<00:01,  5.76it/s]"
+      "Test permutations of given graph:  84%|████████▍ | 42/50 [00:14<00:01,  4.29it/s]"
      ]
     },
     {
@@ -1032,7 +1032,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 44/50 [00:14<00:01,  5.43it/s]"
+      "Test permutations of given graph:  86%|████████▌ | 43/50 [00:14<00:01,  4.36it/s]"
      ]
     },
     {
@@ -1040,7 +1040,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 45/50 [00:14<00:00,  6.00it/s]"
+      "Test permutations of given graph:  88%|████████▊ | 44/50 [00:14<00:01,  4.96it/s]"
      ]
     },
     {
@@ -1048,7 +1048,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 46/50 [00:15<00:00,  6.45it/s]"
+      "Test permutations of given graph:  90%|█████████ | 45/50 [00:14<00:00,  5.48it/s]"
      ]
     },
     {
@@ -1056,7 +1056,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 47/50 [00:15<00:00,  6.67it/s]"
+      "Test permutations of given graph:  94%|█████████▍| 47/50 [00:15<00:00,  5.55it/s]"
      ]
     },
     {
@@ -1064,7 +1064,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 48/50 [00:15<00:00,  4.98it/s]"
+      "Test permutations of given graph:  96%|█████████▌| 48/50 [00:15<00:00,  5.34it/s]"
      ]
     },
     {
@@ -1072,7 +1072,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  6.68it/s]"
+      "Test permutations of given graph:  98%|█████████▊| 49/50 [00:15<00:00,  5.74it/s]"
      ]
     },
     {
@@ -1080,7 +1080,15 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.17it/s]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  4.98it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.15it/s]"
      ]
     },
     {
@@ -1092,7 +1100,7 @@
     },
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -1118,67 +1126,67 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node Shopping Event?\n",
-      "- The KL divergence between generated and observed distribution is 0.01006454331352404.\n",
+      "- The KL divergence between generated and observed distribution is 0.019379875172409668.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Unit Price\n",
-      "- The MSE is 139.41387612465752.\n",
-      "- The NMSE is 0.5616885215223719.\n",
-      "- The R2 coefficient is 0.6669227941580237.\n",
-      "- The normalized CRPS is 0.10313819496164527.\n",
+      "- The MSE is 184.91813678408536.\n",
+      "- The NMSE is 1.1255525660980945.\n",
+      "- The R2 coefficient is -1.0000963529058176.\n",
+      "- The normalized CRPS is 0.18132262173243546.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Ad Spend\n",
-      "- The MSE is 16157.220051300485.\n",
-      "- The NMSE is 0.4764603364312297.\n",
-      "- The R2 coefficient is 0.7629774538505525.\n",
-      "- The normalized CRPS is 0.27307929207717874.\n",
+      "- The MSE is 15986.775126115423.\n",
+      "- The NMSE is 0.5271546552545335.\n",
+      "- The R2 coefficient is 0.6967044526958617.\n",
+      "- The normalized CRPS is 0.30524001266694467.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Page Views\n",
-      "- The MSE is 77696.70136986303.\n",
-      "- The NMSE is 0.3061734788455607.\n",
-      "- The R2 coefficient is 0.8202746841221045.\n",
-      "- The normalized CRPS is 0.153542404367848.\n",
+      "- The MSE is 80358.36712328767.\n",
+      "- The NMSE is 0.16122920792518886.\n",
+      "- The R2 coefficient is 0.9721511406886085.\n",
+      "- The normalized CRPS is 0.06207081187872663.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Sold Units\n",
-      "- The MSE is 8761.94794520548.\n",
-      "- The NMSE is 0.28035764227531546.\n",
-      "- The R2 coefficient is 0.816290015483671.\n",
-      "- The normalized CRPS is 0.12098919626226576.\n",
+      "- The MSE is 8843.898630136986.\n",
+      "- The NMSE is 0.12925595992014843.\n",
+      "- The R2 coefficient is 0.9816809669321052.\n",
+      "- The normalized CRPS is 0.0563944284124813.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Revenue\n",
-      "- The MSE is 8.381096663330535e-16.\n",
-      "- The NMSE is 1.875736833919074e-14.\n",
+      "- The MSE is 1.6686495222103205e-16.\n",
+      "- The NMSE is 1.1067668020372116e-14.\n",
       "- The R2 coefficient is 1.0.\n",
-      "- The normalized CRPS is 4.907414383535057e-15.\n",
+      "- The normalized CRPS is 2.8144957901556516e-15.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Operational Cost\n",
-      "- The MSE is 38.73103242267744.\n",
-      "- The NMSE is 1.945709517377356e-05.\n",
-      "- The R2 coefficient is 0.9999999995726991.\n",
-      "- The normalized CRPS is 1.0734009606877667e-05.\n",
+      "- The MSE is 39.15134109515263.\n",
+      "- The NMSE is 1.8022101153815417e-05.\n",
+      "- The R2 coefficient is 0.9999999996670821.\n",
+      "- The normalized CRPS is 9.99642316493373e-06.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Profit\n",
-      "- The MSE is 1.2873786891923771e-18.\n",
-      "- The NMSE is 4.5101982377797984e-15.\n",
+      "- The MSE is 1.1732032891789207e-18.\n",
+      "- The NMSE is 4.050097390974477e-15.\n",
       "- The R2 coefficient is 1.0.\n",
-      "- The normalized CRPS is 3.650963853919452e-16.\n",
+      "- The normalized CRPS is 3.9305545120030674e-16.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 1.0693674038028511\n",
+      "The overall average KL divergence between the generated and observed distribution is 1.0386964291919851\n",
       "The estimated KL divergence indicates some mismatches between the distributions.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -1187,9 +1195,9 @@
       "+-------------------------------------------------------------------------------------------------------+\n",
       "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |\n",
       "| equivalence class of the given DAG (p-value: 0.00).                                                   |\n",
-      "| The given DAG violates 6/18 LMCs and is better than 74.0% of the permuted DAGs (p-value: 0.26).       |\n",
+      "| The given DAG violates 6/18 LMCs and is better than 80.0% of the permuted DAGs (p-value: 0.20).       |\n",
       "| Based on the provided significance level (0.2) and because the DAG is informative,                    |\n",
-      "| we reject the DAG.                                                                                    |\n",
+      "| we do not reject the DAG.                                                                             |\n",
       "+-------------------------------------------------------------------------------------------------------+\n",
       "\n",
       "==== NOTE ====\n",
@@ -1235,10 +1243,10 @@
    "id": "74ed17eb-45ec-444d-9364-2aa850911a05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.651772Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.651268Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.667304Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.666740Z"
+     "iopub.execute_input": "2024-10-25T15:11:04.744096Z",
+     "iopub.status.busy": "2024-10-25T15:11:04.743727Z",
+     "iopub.status.idle": "2024-10-25T15:11:04.760025Z",
+     "shell.execute_reply": "2024-10-25T15:11:04.759543Z"
     }
    },
    "outputs": [
@@ -1278,111 +1286,111 @@
        "      <th>0</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,249.40</td>\n",
-       "      <td>11648</td>\n",
-       "      <td>2274</td>\n",
-       "      <td>$2,271,726.00</td>\n",
-       "      <td>$1,638,259.65</td>\n",
-       "      <td>$633,466.35</td>\n",
+       "      <td>$1,176.23</td>\n",
+       "      <td>11531</td>\n",
+       "      <td>2318</td>\n",
+       "      <td>$2,315,682.00</td>\n",
+       "      <td>$1,660,178.10</td>\n",
+       "      <td>$655,503.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,188.04</td>\n",
-       "      <td>11516</td>\n",
-       "      <td>2274</td>\n",
-       "      <td>$2,271,726.00</td>\n",
-       "      <td>$1,638,193.71</td>\n",
-       "      <td>$633,532.29</td>\n",
+       "      <td>$1,390.23</td>\n",
+       "      <td>11805</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>$2,353,644.00</td>\n",
+       "      <td>$1,679,398.64</td>\n",
+       "      <td>$674,245.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,164.94</td>\n",
-       "      <td>11533</td>\n",
-       "      <td>2270</td>\n",
-       "      <td>$2,267,730.00</td>\n",
-       "      <td>$1,636,166.52</td>\n",
-       "      <td>$631,563.48</td>\n",
+       "      <td>$1,156.13</td>\n",
+       "      <td>11678</td>\n",
+       "      <td>2296</td>\n",
+       "      <td>$2,293,704.00</td>\n",
+       "      <td>$1,649,158.30</td>\n",
+       "      <td>$644,545.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,394.90</td>\n",
-       "      <td>11856</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>$2,021,976.00</td>\n",
-       "      <td>$1,513,408.87</td>\n",
-       "      <td>$508,567.13</td>\n",
+       "      <td>$1,100.22</td>\n",
+       "      <td>11592</td>\n",
+       "      <td>2312</td>\n",
+       "      <td>$2,309,688.00</td>\n",
+       "      <td>$1,657,100.84</td>\n",
+       "      <td>$652,587.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,254.98</td>\n",
-       "      <td>11657</td>\n",
-       "      <td>2342</td>\n",
-       "      <td>$2,339,658.00</td>\n",
-       "      <td>$1,672,262.08</td>\n",
-       "      <td>$667,395.92</td>\n",
+       "      <td>$1,449.15</td>\n",
+       "      <td>11905</td>\n",
+       "      <td>2417</td>\n",
+       "      <td>$2,414,583.00</td>\n",
+       "      <td>$1,709,959.83</td>\n",
+       "      <td>$704,623.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,290.24</td>\n",
-       "      <td>11718</td>\n",
-       "      <td>2295</td>\n",
-       "      <td>$2,292,705.00</td>\n",
-       "      <td>$1,648,803.22</td>\n",
-       "      <td>$643,901.78</td>\n",
+       "      <td>$1,171.43</td>\n",
+       "      <td>11635</td>\n",
+       "      <td>2292</td>\n",
+       "      <td>$2,289,708.00</td>\n",
+       "      <td>$1,647,174.03</td>\n",
+       "      <td>$642,533.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>False</td>\n",
-       "      <td>$999.00</td>\n",
-       "      <td>$1,183.30</td>\n",
-       "      <td>11720</td>\n",
-       "      <td>2360</td>\n",
-       "      <td>$2,357,640.00</td>\n",
-       "      <td>$1,681,187.43</td>\n",
-       "      <td>$676,452.57</td>\n",
+       "      <td>True</td>\n",
+       "      <td>$913.46</td>\n",
+       "      <td>$2,499.94</td>\n",
+       "      <td>20426</td>\n",
+       "      <td>5966</td>\n",
+       "      <td>$5,449,707.95</td>\n",
+       "      <td>$3,485,503.60</td>\n",
+       "      <td>$1,964,204.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,458.28</td>\n",
-       "      <td>11713</td>\n",
-       "      <td>2345</td>\n",
-       "      <td>$2,342,655.00</td>\n",
-       "      <td>$1,673,960.31</td>\n",
-       "      <td>$668,694.69</td>\n",
+       "      <td>$1,111.98</td>\n",
+       "      <td>11715</td>\n",
+       "      <td>2355</td>\n",
+       "      <td>$2,352,645.00</td>\n",
+       "      <td>$1,678,623.63</td>\n",
+       "      <td>$674,021.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>False</td>\n",
-       "      <td>$999.00</td>\n",
-       "      <td>$1,379.30</td>\n",
-       "      <td>11727</td>\n",
-       "      <td>2292</td>\n",
-       "      <td>$2,289,708.00</td>\n",
-       "      <td>$1,647,380.83</td>\n",
-       "      <td>$642,327.17</td>\n",
+       "      <td>True</td>\n",
+       "      <td>$913.46</td>\n",
+       "      <td>$2,690.98</td>\n",
+       "      <td>20295</td>\n",
+       "      <td>5894</td>\n",
+       "      <td>$5,383,938.77</td>\n",
+       "      <td>$3,449,695.95</td>\n",
+       "      <td>$1,934,242.81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,326.60</td>\n",
-       "      <td>11694</td>\n",
-       "      <td>2387</td>\n",
-       "      <td>$2,384,613.00</td>\n",
-       "      <td>$1,694,832.18</td>\n",
-       "      <td>$689,780.82</td>\n",
+       "      <td>$1,296.94</td>\n",
+       "      <td>11796</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>$2,366,631.00</td>\n",
+       "      <td>$1,685,800.08</td>\n",
+       "      <td>$680,830.92</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1390,28 +1398,28 @@
       ],
       "text/plain": [
        "   Shopping Event?  Unit Price  Ad Spend  Page Views  Sold Units  \\\n",
-       "0            False     $999.00 $1,249.40       11648        2274   \n",
-       "1            False     $999.00 $1,188.04       11516        2274   \n",
-       "2            False     $999.00 $1,164.94       11533        2270   \n",
-       "3            False     $999.00 $1,394.90       11856        2024   \n",
-       "4            False     $999.00 $1,254.98       11657        2342   \n",
-       "5            False     $999.00 $1,290.24       11718        2295   \n",
-       "6            False     $999.00 $1,183.30       11720        2360   \n",
-       "7            False     $999.00 $1,458.28       11713        2345   \n",
-       "8            False     $999.00 $1,379.30       11727        2292   \n",
-       "9            False     $999.00 $1,326.60       11694        2387   \n",
+       "0            False     $999.00 $1,176.23       11531        2318   \n",
+       "1            False     $999.00 $1,390.23       11805        2356   \n",
+       "2            False     $999.00 $1,156.13       11678        2296   \n",
+       "3            False     $999.00 $1,100.22       11592        2312   \n",
+       "4            False     $999.00 $1,449.15       11905        2417   \n",
+       "5            False     $999.00 $1,171.43       11635        2292   \n",
+       "6             True     $913.46 $2,499.94       20426        5966   \n",
+       "7            False     $999.00 $1,111.98       11715        2355   \n",
+       "8             True     $913.46 $2,690.98       20295        5894   \n",
+       "9            False     $999.00 $1,296.94       11796        2369   \n",
        "\n",
-       "        Revenue  Operational Cost      Profit  \n",
-       "0 $2,271,726.00     $1,638,259.65 $633,466.35  \n",
-       "1 $2,271,726.00     $1,638,193.71 $633,532.29  \n",
-       "2 $2,267,730.00     $1,636,166.52 $631,563.48  \n",
-       "3 $2,021,976.00     $1,513,408.87 $508,567.13  \n",
-       "4 $2,339,658.00     $1,672,262.08 $667,395.92  \n",
-       "5 $2,292,705.00     $1,648,803.22 $643,901.78  \n",
-       "6 $2,357,640.00     $1,681,187.43 $676,452.57  \n",
-       "7 $2,342,655.00     $1,673,960.31 $668,694.69  \n",
-       "8 $2,289,708.00     $1,647,380.83 $642,327.17  \n",
-       "9 $2,384,613.00     $1,694,832.18 $689,780.82  "
+       "        Revenue  Operational Cost        Profit  \n",
+       "0 $2,315,682.00     $1,660,178.10   $655,503.90  \n",
+       "1 $2,353,644.00     $1,679,398.64   $674,245.36  \n",
+       "2 $2,293,704.00     $1,649,158.30   $644,545.70  \n",
+       "3 $2,309,688.00     $1,657,100.84   $652,587.16  \n",
+       "4 $2,414,583.00     $1,709,959.83   $704,623.17  \n",
+       "5 $2,289,708.00     $1,647,174.03   $642,533.97  \n",
+       "6 $5,449,707.95     $3,485,503.60 $1,964,204.36  \n",
+       "7 $2,352,645.00     $1,678,623.63   $674,021.37  \n",
+       "8 $5,383,938.77     $3,449,695.95 $1,934,242.81  \n",
+       "9 $2,366,631.00     $1,685,800.08   $680,830.92  "
       ]
      },
      "execution_count": 9,
@@ -1453,10 +1461,10 @@
    "id": "efe77b22-e99c-43e9-876f-4f7198d5b112",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.669278Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.668944Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.931709Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.931099Z"
+     "iopub.execute_input": "2024-10-25T15:11:04.761998Z",
+     "iopub.status.busy": "2024-10-25T15:11:04.761643Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.074470Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.073753Z"
     }
    },
    "outputs": [
@@ -1499,10 +1507,10 @@
    "id": "b3ff0966-ddaf-4b19-b9ba-1fe854092f43",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.933806Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.933380Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.979244Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.978767Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.077024Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.076587Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.126806Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.126213Z"
     }
    },
    "outputs": [
@@ -1539,16 +1547,16 @@
    "id": "ecef5abd-f551-444b-8ec4-53e224f6d529",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.981093Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.980768Z",
-     "iopub.status.idle": "2024-10-25T15:09:39.307324Z",
-     "shell.execute_reply": "2024-10-25T15:09:39.306770Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.128900Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.128534Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.422439Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.421744Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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o1WrlQHbkyBHa2tqAj1bIuFzOsSRJIhQKcerUKerq6rBYLIwbNw6LxTLYTbsg8vBbX18f27dvx2w2ExsbS2Fh4XkfEwwGWbduHQsWLKCoqOjSNXYI8Pl87N69m40bN9Le3k4oFEKn0wEffWdaWlqQJInu7m7Gjx9Pdnb2ILf4dDqdTlmfNjs7m46ODqWHrqamhqqqKlavXo3b7SYnJ4fMzEwSEhLEyZpwyYkeO+GS6H8w37p1K8uWLWP37t1MnDiRmTNncs8995CdnT2kz3IlSUKSJF566SVeffVV1q9fzze/+U1mz57NxIkTiYqKorOzk4qKCtavX8/vf/971Go1V1xxBWvWrDnr+b773e+yYcMGqqqqqKmpwWq1XhYlFSRJwu128/vf/56XXnqJzMxM/vCHP5CVlTXYTbtgPT09VFRUcOWVV5KZmcn8+fP53e9+d977O51OcnNz+d73vsd1111Hfn7+JWztxSMX/VWr1Uqu2Zmampp44IEHOHbsGJMmTeL+++9n2rRp9PX1UVZWxgsvvMAbb7zBjBkzWLRoEY899hgwtEsUSZKEy+Viz549rFy5kueeew6r1cqECRO4/fbbuf766zEYDKjV6iH9OoSRZegeRYURxeVy8dJLL/H3v/+drq4uUlJSeP311xk3bhxxcXHo9fphcWZbVlbGxo0b2bNnD1/+8pf52te+RlpamrJ6hTxLd9KkSfT29rJ9+/bBbvKQJOcthcNhJWAWhq+vfOUrlJaWkpuby6uvvvqxva8PPPAAU6dOZebMmWi1WiwWC9HR0RQVFVFSUkJJSQnd3d3cddddREVFDemTPZVKhcViYdasWUydOpUHHniAZcuWsW3bNh588EFuuukmvvrVrzJlyhTMZvNgN1e4TAzdb4ww7MkH70OHDrFt2zZeeeUVUlJSuPLKK5k1axaTJk0iKioKo9E42E391BoaGuju7kalUpGWlobFYlGGk+CjHb1arcZsNjNt2jROnjxJOBwexBYPTQaDgeuvv54xY8Zgs9mIi4sb7CZdEkajkaeffpqxY8eSkJAw2M0ZMC6XS0lBOF+QHhERwbe+9S0SExOJjY1Vvjfy0n82m42kpCTa29txuVw0NjZit9uHdGAH/26/RqMhNTWVm2++mTFjxrB79262bdvG0qVLOXLkCLfffjtRUVHD4gRWGN6G9jdGGLbknXtNTQ27du1i9erV9PT0cO211zJr1ixmzZo1LIcnXC4Xfr//E3uZVCoVY8eOpbi4GJfLdQlbOPTJB8Lx48czfvz4wW7OJVFVVUU4HCYiIoLbbrtt2G33A8FsNnPllVee8zaVSoVGo8FoNKLRaAiFQrhcrmF1UiSf0I0fP57k5GSSk5OVepzd3d1kZ2czY8YMLBbLZZFyIQweEdgJF40kSfzlL39h69atNDc388c//pEZM2bgcDgGu2mfWVRUFCaTCa/Xy549e7jnnnvOCvDkXrsLDVzkYFF+vjMP/mdePvP/9r98rsf2v/189z3f9fLf/UtAfFK7Pq4N53rPzvRZ23iu+11omwc68Prf//1fvF4vV111Fbfddhtarfa878X5/v/53rMzP9sz73u+7eizfpYf93j5cv+A7NPMaJe3+6amJlwuF3a7nYiIiGHbuxUTE8OMGTP4xz/+wZNPPsm6dev42te+xjvvvENOTo4y018QLgYR2AkXRVtbG6+99hrvvPMOU6dO5b/+679YsGCBMnN0uCoqKiI7Oxuj0cjWrVt57LHHmDVrFnPnzqW4uPgzHYjC4TBtbW0sXbqUPXv2cPjwYdRqNampqRQWFvK1r32NpKSk04Z84aPyC93d3fzzn//kwIED1NTU0NHRgclkIjk5mfz8fG677TbGjx+PxWLB6/Xy0ksv8corr3Dq1Cml+PNXv/pVsrKy+Ne//sWxY8fw+XxotVoKCgq44447KCwsJCMjA4B169axceNGXnnlFQDsdjt5eXk88cQT/OMf/6C8vJzGxkZCoRC5ublMnDiRxYsXM378eDQaDd3d3ezevZsf/OAHdHV1Ka9l+fLlFBQUIEkSW7Zs4emnn+bIkSPK7d///veZNGkS//rXv9izZw8+nw+DwcDYsWO57777GDVqFDExMWe9t9XV1axdu5atW7dy8OBBgsEgUVFRZGRkcO+997J69WoOHz7M8ePHAZg2bRpz5szhP/7jPy74c7xQoVAIp9PJlVdeSXt7+2m3TZw4kUWLFnHvvfciSRIrV67k0UcfVW6Pioriiiuu4Oc//zkOh4NwOEx3dzevvvoqBw4coKysjLa2NkwmEzExMYwaNYr777+fzMxMHA4HkiSxbds23nrrLd555x3lea+55hp+8IMf8Mwzz3Do0CHa2trQaDQUFhayaNEi5syZQ2JiIiqViiNHjrB+/XqeeeYZWlpa8Pv9dHR0UFBQoARzGo2GrVu3kpyc/LHvRUtLC1u2bKGyshKDwUBWVhY5OTno9fqBfdMvIZVKRWxsLI899hhXXHEFP/rRj/jhD3/Ifffdx5IlS4bliIUwPIjAThhwLpeLuro6li9fTn5+PtOmTaO4uBiTyTTsd2RWq5Vp06bR1tbG22+/zcGDB+ns7KSsrIzc3FxSUlKIi4sjOTmZ3NzcTzUpJBgMsnLlStRqNRMnTmTMmDE0NzdTUVHBtm3bsNvtPPjgg0otPAC3282xY8f44IMP2LZtGwkJCUyfPh2bzYbf76elpYWamhr++te/8uUvf5kxY8aQmJhIYWEhgUCAffv2sXLlSiorK9m8eTNtbW0UFhZSUFBAX18fnZ2dHDx4kFdeeYXq6mpuuOEGUlNTSU9PZ+bMmZhMJp5//nnlgP7OO++QmJhIYmIiwWBQWed3586dtLS08O1vf5ukpCQMBgMZGRnceeed7Ny5k9LSUiorK/H5fMBHw1lpaWncdNNNpKamsnnzZk6cOMH27duRJInc3FySk5Pp6emhtbWV3bt3ExUVxYIFC/jCF76gvD/9g97Dhw/T2dnJjBkziIuLU2Ypbt68mZKSEiorK2lra+O73/0uubm5n7nMRnNzMzt27ODHP/7xWbft3buXpKSk066TS+TccccdbNmyhdLSUurq6rjzzjuZOnUqhYWFyvclKyuLe+65hxdffJG0tDRGjRrFggULMBqN+Hw+Wltbef7559m3bx9arZbJkycTFRWF3++nu7ub6upq/v73v7No0SKmTp1KfHw8ycnJzJ07F5PJxPLly6murqa0tJRVq1aRkZFBYmIiLpeL1tZWdu3aBXw0q/eBBx4APuqVmjBhAvfddx8vvfQSjY2NxMTEcMcddygBmVqtxmq1nvP9CofDdHR0cOLECQ4dOsTKlSsJhUJMnz6da665ZlgHdfDR56vVaomPj2f8+PHcfvvtvPbaaxw4cIC8vDzGjRs32E0URigR2AkDrquri1OnTrFz505+/OMfc8UVV5CamjrYzfrcVCoVer2eyZMno9frqaiooLa2liNHjnDgwAFMJhMFBQXk5OQoQ7BxcXHYbLaPnREXDAbZtWsXN910E+PGjSMlJYWysjL++te/sm7dOl599VVuu+220xKv5YPts88+i9Vq5corr2TevHmMGjWKzs5ONm7cyNq1a3nllVdwOByo1WqSk5OZOnUq06ZNY+PGjezbt4/KykoqKyuJjo7mgQceIDs7G6fTyalTp6iqqmLNmjW0t7eTnp5OSkoK+fn55Ofns3DhQjZt2kRFRQVdXV3s37+fRx99lLy8PBwOBzU1NfzpT39i586d7Nixg9mzZzNz5kwlKBk1ahQvvPACPT09VFZWKu+FVqtV/se4ceNoaGigsrKSU6dOkZiYyJe//GWlhtjBgwdZsWIFK1euxGKxsHDhQqUXRF6e7sUXX0StVjNq1CjuuusuRo8eTTAYpKGhgd/97nc0NjbS29uL0Wjk8ccfx2azfebto6urC6/Xq9Qm7K+tre2s59ZoNJjNZr797W8TGxtLIBCgrq6O66+/nmnTpinfGZVKxahRo3jsscfYvHkzCxYsYO7cucycORP4qLervLycv/3tb1gsFubMmcMtt9xCcXExnZ2dHDp0iOeff55XXnkFvV5PVFQUCQkJZGVlkZWVxcyZMykvL6elpYX29nZKSkr42te+Rnp6OoBSvmf79u309vZy3333oVarlUB+zpw57N69G6fTSUZGBt///vfPG8z1Jxcp//DDD9m/fz87duwgNTWV+fPnc8011wz7k0CZXq8nNTWV22+/nddff53jx4+ze/duEdgJF40I7IQBt2/fPjZu3Eh0dDS33HILmZmZg92kAZWSkkJycjJXXnkla9asoaSkhH379rF9+3a2bdvGli1bAHA4HNx111184Qtf4Kqrrjrv8+n1er74xS8yffp04uPjAZg8eTJHjx6lubmZVatW0draSnx8vFJG4pVXXmH16tW0trby//7f/2PevHnKGrpWq5W77rqLqVOnsnXrVv75z3/i8/koKioiNjb2tP/t8Xi47rrruP3228nLywM+Gl6Ni4vjt7/9LXfccQclJSX8+te/5pprrjkr6TsQCBAZGcn//M//MHr0aGWofezYsXzve9/jrbfe4gc/+AG/+tWv+NWvfvWZ1/mdNGkSixcvpri4WGmjzWZj3rx5bNy4kVOnTtHd3a3kb9bU1PDb3/6WlpYWvvzlL3P77bczY8YM5fliYmL43e9+x+233059ff2ApAiMHj2a+fPn89vf/vas2x5++GG8Xu95Hzt16lT8fj+rVq3i7bffRqVSnXYy1NXVxeHDh2lqaiI3N5dp06Ypt23bto2XX36ZhoYGfvrTn3L11VczefJk4KNVTeLi4hg3bhxTpkxRimrPnj37nO1IT0/nlltuYcKECcpnHRsby7x589iyZQvHjh2jq6uLyMjIzz1bta+vjxtvvBGj0Uh6ejp33303jzzyCImJiSOuNIjRaCQ3N5fFixdz4MAB3nvvPaXnUxAGmgjshAFXX19PY2MjM2fOxGw2j5gzb5n8evR6PXPmzGHSpEncdddd9PT0cPz4cY4fP87OnTv58MMPefXVVzl58iRpaWlkZWWdlScHHw1XyWU/+r9XVqtVCVQ6OztxuVxKYHfgwAGOHj2KRqNh+vTpxMbGnvZYjUaDzWZj8uTJbNmyhbq6Ovbt28fChQtPu5+cj5eenn7a9VqtlpycHBISEqivr+fEiRO0tbURGxt7WhCk0+mw2+3k5+ej1+tPe46UlBTS09OJjo7m1KlTNDY20t3dTWRk5AW/50lJSWRlZZ31GuPj49HpdHi9Xtrb24mKikKlUilLVwUCATIyMhg/fvxpj1Wr1URFRZGSkkJsbCy9vb0X3Kbz+Szbe1paGhMnTiQ/P5+dO3eSmZnJ1VdfrWwTNTU1vPbaa9x4441kZ2efFmBXVlaye/duVCoVxcXF5OTknNYGvV5PTEwMmZmZVFZWcuLECVpaWoiOjj4rOIuIiGDUqFGn5X9JkkRsbCwWi4Xe3l7a2tqwWq0DUoZEkiQefvhhFixYQE5OjlIGZaTuM6644goqKio4fvw4TqdTzJAVLgoR2AkDzuVy4Xa7lZUkRtpOGv49009eYkieCRgXF0dGRgZJSUnKMOOJEyc4cuQIKSkp5wzs5Oc58zatVqtcFwgElGXHwuEwXV1dyhBidHT0WT1OckmR+Ph41Gq1kit15n2MRiMmk+msWoJy6QabzYZOp8Pj8eB0OomKijorsDOZTOfsYTEYDFgsFmw2Gx0dHTidTtxu92cK7OS2nNl+eX3dUCiE3+8HPsrd8vl8tLe3o1KpsFqtZ/3P/rXTzGbzgAZ25zJr1iwCgcBZQZfMaDQSGxvLtGnTWLlyJVVVVZSVlSkrMzQ2NnL48GG+973vkZCQoMyEldctlidfbNq0iaqqqrO2pVAoRGdnJ263G6fTSXt7+zl73fR6PZGRkWe10WAwKDN55XI/AyUpKYmcnJwRka7xSeQcT4/Hg8vlwmQyicBOGHAisBMGXCAQIBAIjIjJEv3Jy6L5fD5CoRB2u125TZ4BmJaWRlpaGlOmTKGtrY0333xTyVuaP3/+OSvyywHKmTv4/mUi+h9Ig8Gg0gatVqtMBjiTnLiuVquVBczPPCDLdcMkSTrnZ2UymZQDUV9fH8Fg8LTb5f9/vhIn8soCKpUKn8+Hx+M553v7SfR6/TmHS+UVP+Df71EoFCIQCODz+dDr9ej1+nMG1PLznu+2gXTHHXd84n2sViuLFi1i06ZNyqSWKVOm0NraSk1NDQ0NDUybNu20STRyQCtPPvn73//+sT1pOp0Oi8VCZ2fnOWvEabVaTCbTWdf3f5+HU225ocZoNCoBstvtFu+lcFEMzyJBwpAWGRlJREQEJ0+eJBAIDHZzBlRpaSnf/e53ueGGGz6210KtVjNt2jTi4+MJBoN0d3cP2E5cr9djsVjQ6/X4fL5zBlzwUQDY1tZGKBRSyl6cWQ+uq6tLCQrOpbu7m76+PlQqFTExMWfNVHS73R/b2+Xz+ejo6ECSJGw2GxEREZ/hFV8YrVaL0WjEarUSCATweDznDSh7enpwOp0XvU2fhs1mY/HixWRnZ1NfX8/LL7+Mz+dj+fLlHD9+nNtvv/2sXjadTofZbMZut6PRaFi1ahUNDQ00Nzef92fHjh3MmDFjQFd8+TS16oSPClX39PRgMBjOWcJIEAaCCOyEAZeVlUV2djZ79uyhtbUVt9s92E0aMOFwGKfTSXNzM8ePHz9vQrwkSVRVVSnDpVlZWQOSkyQfQMeOHUtubi6hUIj9+/fT2dl52v3kyv2lpaUEg0ESExMZN27cWQffQCBAU1MTtbW1p10fDAaprq6mra0NlUpFRkYGDofjrMAuFArR19dHRUXFWUF8c3MzDQ0NdHV1kZaWRnx8/Gm9nBeLSqXCbrczceJEtFotdXV1HD169LT7yMPZzc3NZ713F5PX6+V73/ser7zyCocOHTqr3Tqdjvnz55OTk0NLSwvvvvsu69ato6uri+uvv/601AZ5W0hLS6O4uJhwOExdXR0dHR1KT6X8o9VqWb16NT//+c/5+c9/PuBr88pLavU/waisrOShhx7i7bff5uTJk2c9xmq18tZbb7Fo0aJz1iAcaSRJYvPmzfj9fsaOHXtWTqogDBQR2AkDLi0tjdzcXHp6ejh48CANDQ0jZqF3SZKUHrgNGzZQVVVFR0cHXq+XUCiE1+uls7OTU6dOsW/fPtxuN7GxsRQUFAxoXa7i4mLGjRuHwWBg69atVFRU0NnZSTAYxOVyUVVVRWlpKQ0NDWRkZJCXl3dWHTX4qGexsrKSgwcP0tXVhd/vx+1209raytatW+ns7MThcDBlypTzDhd7vV52795NS0sLLpdLqSFXUlJCWVkZoVCIqVOnkpiYeMlqk0VFRTFr1izMZjOVlZXs3LmTtrY2fD4fXq+Xrq4u9uzZQ1dXF6FQ6JK0CT4KhHfs2EFFRcVZRYnlFUsmT55MdnY2Ho+HVatWKZMV8vPzzznknpWVxRVXXIHJZGL//v0cPHiQtrY2AoEAfr8fp9OpFGo+cuTIOcuxfF42mw2j0YjT6cTj8dDb20tdXR0rV66kvb1dyX+U9fX1UV9fj0qloqOj46K0aSjxer3U1dVx6NAhTCaTUsxcBHbCxSBy7IQBl5eXh8/nIzY2lpdffhlJksjMzPzYXK7hQq1Wo9Vq6enp4cknn8Tj8TBhwgRycnKIjIykq6uL6upqtm3bxhtvvEFsbCyTJk1i/vz56PV6JcANBoOEQiHlst/vV3o75Jw4+T7wUQ+afJ1Go2HhwoWEw2F2797NCy+8gFarJRgMMm7cOFpbW1m9ejWrVq3C6XRy8803s2DBgrOGQVUqFWazmZKSEoLBIA6Hg9zcXHp7ezl+/Di//e1v6erqYvbs2dx7773n7HGUh4OXLVumzI6NiYnh5MmTPP/88+zfvx+r1cp9991Hbm7uaa8/GAwqw9NyXqYc3Mh5cvLJgPz6NRoNGo2GcDiM3+9X3sNwOKy8P1qtlsTERO666y7efPNN9u/fT1tbG9nZ2YwfP16pF/fHP/6Rnp4erFbrxw5Hn4/cZrmdcjvODGL68/v9nzgkP3PmTI4dO8bbb7/NsmXLuPrqqxk3btxpuXX9FRcXY7Vaeffdd3nnnXdobm5Wil1LkkRzczMffvghb731FkuWLOGmm25S3kP5fQ6Hw6e1X6vVKkFk//e5/2cXCoWUQD8lJQWHw0FlZSVNTU0EAgGOHz9Oe3s7OTk5xMXFndbm+vp61qxZw49//GOuvvpqrr32Wu67774LefuHjXA4TGdnJ6tXr+bIkSPcfvvtLFq0aLCbJYxkkiAMsHA4LHV1dUkffPCBlJ2dLc2bN0/69a9/LYVCISkcDg928z6zcDgs+Xw+6ciRI9Ibb7whfeMb35CuvvpqadKkSVJeXp4UExMjJSYmSrm5udLMmTOl//zP/5Q2bdoktbe3S+FwWAqHw1J9fb303nvvSVFRUZLBYJA0Go2kUqmkyMhI6atf/ar08ssvS36/X8rOzpYsFouk1+slQLJYLNL48eOlr33ta1IoFJJCoZDU29srlZeXS9/4xjekuXPnSrm5uVJsbKyUnJwsTZw4Ubrtttukd999V2ptbZUCgcBpr2XDhg3SuHHjpOTkZOmXv/yltHz5cmnx4sVSdna2lJiYKCUnJ0uzZs2S/vnPf0oHDx6U/H7/aZ9dIBCQ5syZI2VnZ0tXXHGFtHfvXunee++Vpk2bJqWkpEhxcXHSpEmTpIceekhas2aN5PF4pFAoJHV0dEgrVqyQsrKyJLPZLGm1WgmQbDabNGnSJOnrX/+6tGvXLunqq6+WbDabpNPpJEAym83SqFGjpJ/97GdSZ2en9OSTT0qRkZGS0WiUVCqVpNPpJIfDIX3xi1+Udu3aJYXDYSkQCEgHDhyQfvrTn0oLFiyQEhMTpcTERKmwsFBavHixtGrVKunOO++Uxo4dK0VHR0u9vb0XtD3cddddUnx8vGSz2SRA0mg0ksFgkKKios77ExkZKWk0Gulb3/qWtGHDhvNuZxs3bpTuv/9+SaVSSX/84x+lEydOfOJ2WVVVJf30pz+VlixZIo0ZM0aKjY2V0tPTpfHjx0vXX3+99Morr0gVFRXKZ3no0CHp29/+thQZGSnpdDpJpVJJer1eiouLk/77v/9bKisrk7Zu3SpFRUVJRqNR0mg0klqtlux2u7Ro0SJp6dKlShtOnDghPffcc9K0adOk5ORkKSsrS5o1a5b05JNPSp2dnVIoFDqtzUePHpV+97vfSVarVbr55pulf/zjHxf03g8nR48elf70pz9JDodDeuCBB6S1a9dKgUBgWO8LhaFN9NgJA07uCSoqKuKLX/wiR44c4b333iMmJoaFCxcSFxc3bKf463Q6UlNTsdvtpKSk0NbWhsfjUYb41Go1er0eq9WqzJCNiIhQeikjIiIYO3Ysv/zlL8967qysLNLS0lCr1fz4xz8+K3/PbrcrpS7k91iuaD9//nx6e3vxer1KDTuHw8GoUaM+tpisvF5scXExDzzwAO3t7QSDQdRqNdHR0YwZM0apLXY+BoOB7Oxs7rjjDqWsiSRJREZGkpCQoKz5KZdQKSgo4Mc//vFZvWQREREkJiaSnp7Oww8/zJIlS0673Ww2M2rUKEwmE/PmzTur/Im8uoZck0+j0ZCVlcX1119PUVERbW1thMNhTCYTERERjBs3jldeeYVAIKDM3L0Qd955J7Nnz/5MQ7mFhYXKyg5nkleauPvuu5k8eTKzZs1SClef7/46nY6EhASuv/56pkyZQnt7Oy6XS5lIEhERQUFBAdHR0eh0OiRJIjExkRtvvFEpTC1Tq9UUFhaSkJBARETEObfVxMREcnJylMsJCQnMmjULh8NBR0cHWq0Wm81GXl4eFovlrCHkhIQEpRc5JSXlMxeuHspCoRAlJSW8++67lJaWMnr0aG6++WYKCgoGJN9WEM5HJUkjIPFJGJKk/1to/N1332Xt2rVkZWVx//33U1BQQGJiIkajcVgPyw5nGzdu5LHHHqOzs5Mnn3ySL33pSxf0+GAwyJVXXkl9fT0ZGRmsX7/+IrX0swmHwwSDQY4fP058fPxZBZyl/xv+/spXvkJJSQmRkZFs2bLlnOVoBOFCyBOXWlpaeP3111m/fj19fX0sWrSIxx577LQTPUG4GMRpg3DRqFQqZs2aRWJiIkVFRTz66KMcP36cK6+8kocffpjRo0efdl9BGCiBQIC2tjZuuukmHnvsMR588MHTeklCoRBNTU00NDQAMHHixHNOTBCET6N//4jX62Xfvn3885//5PXXX2fhwoXcdtttPPTQQ4DY1wkXnwjshIsuPT1dWdLoL3/5Czt27GDNmjUsWbKExYsXM2bMmM+0GoEgfBKn08k777xDX18fd911F3a7nd7eXiorK3nyySepr6+nqKiIr33ta5dsxq4w8kiShMvl4oMPPuD111/n5MmTeDwennrqKWbOnElWVtZgN1G4jIjATrjo5PVECwoKuPnmmzly5AiHDx9m//79OJ1OsrOzKS4uPm9JDWFgBAIBSkpK2L9/P/v27aOlpQWPx8Pbb79Nc3Mz48ePZ9q0aedceUB2/PhxTpw4QUlJCTU1NfT29iJJEj/96U+VtU6zs7Mv4as6NznPcMmSJfT29lJeXs5zzz2nrNTQ19eH3+9n0aJFTJo0iYyMDNFjJ1wwSZKUtZQPHjzIhx9+SE9PD3l5eRQWFjJnzhzS0tIuSf1GQZCJwE64JORF12+88UYmT57Mvn37eOqpp1i3bh3bt2+nqqqKyMhI4uPjsVqtyuLYYthi4ASDQQ4fPsybb77JqVOnMBgMGAwGdu/eTXV1NcFgkOLi4o8N7Gpqati2bRuvvPIK8FGR2WAwyLPPPovX68VisQyJwE6r1WK327nnnnvYvn07paWlrF69mvb2djQaDXa7nSlTpnDLLbeQn59PVFTUYDdZGAakfuWJfD4fLpeLffv2sWXLFjZv3kx3dzcLFixg9uzZXHfddTgcDrEPEy45MXlCuKT6b25ut5v333+fDRs28Nprr2EymZg6dSqzZs3i1ltvJSEhQQyPDaD+7/25vvb9VzT4pOf4rI+/lM5s45mX+7dzqLRZGNokScLj8bBv3z527tzJa6+9Rnl5ORkZGcycOZPHH3+c5OTk05ZrE9uWcKmJwE4YFNL/FUPt7u6mo6OD6upqVq5cSXl5OU1NTeh0OqZOncr48eOZOHEixcXFYj1KQRAGRVtbG9XV1Xz44YesXbtWWYM5JyeHefPmkZ+fT1ZWFklJSUppH0EYLGIoVhgUcp2x6OhoIiIiSEhIIBAIkJyczMmTJzlx4gSnTp2ip6eHyspKTp06RVJSEtHR0cTFxREVFSV2noIgXBTysmhtbW3U1dVRVVVFXV2dsj50WloaCQkJTJgwgRkzZpCYmCiG84UhQ/TYCUNKKBSip6eHLVu28NZbb3HkyBGqqqpISEhg2rRpFBYWMmXKFMaNG6ecGWu1WmXChejREwThQsh5c6FQiFAoRDAYpKWlhZMnT7Jv3z7Wr1/PiRMn8Pv9JCUlcfvttzNz5kwKCgrEbH5hSBKBnTCkyJujvI5le3s71dXVvP3222zfvp3a2lo6OjpIT09n8uTJFBUVMWfOHCXQE4GdIAgXQvq/9XSPHDnC/v37lTVdvV4vZrOZefPmMWvWLMaNG8ekSZOUE0mRGiIMVSKwE4Y0v9+Py+WisbGR9vZ2WltbqaqqoqysjNbWVlwuF2q1mvT0dFJTU0lNTWX06NHk5uYSERGB2Wwe7JcgCMIQEQ6HCQQCVFRUUFNTo5QqOXHiBL29vQSDQeLi4sjMzCQlJUXJm4uJiSEiIkIZbhUBnTCUicBOGFacTicNDQ3s2rWLY8eOUV1dTW1trbI2aUxMDGlpaeTl5REbG0t0dDRRUVHYbDYsFgtWqxW1Wi12zIIwwsllSTweD11dXfT29tLX10d3dzdHjx6loaGBtrY2mpqacDqd2Gw24uLimDBhAsXFxWRkZJCRkYFWqxX7C2FYEYGdMKy5XC4qKirYsGEDZWVllJeXU1paSigUIjIykvT0dGbNmsX48ePJzc2loKAAs9msDKOcazhF7MQFYfg4swSPnDMXDodpa2ujqqqKbdu2UVpaysmTJykvL8fn8xEfH09aWhqTJk1i7ty5jBkzhqysLHQ6ndgHCMOaCOyEYU1Oevb7/QSDQYLBIE6nk6NHj1JZWcnx48fZt28fdXV1uFwuDAYDGRkZZGdnk5mZydixYxk1ahSJiYnExsYCIrAThOEkGAzidrs5ceIElZWVVFVVcfDgQcrKyujo6MDlcmGz2cjPzycjI4MxY8YwY8YM4uPjsdls6HQ6dDodGo1GTMISRgQR2AkjiiRJBINBOjo66O7upr29ncbGRpqammhra6OxsZGenh4CgQDBYFDp2YuKiiImJobU1FQSExNxOBzExsaSmJiIyWRCp9MN9ksThMuWJEkEAgHa29uV73VdXR0tLS10dnbS2tpKZ2cnoVAISZLQ6/UYDAZsNhsOh4OMjAxiYmKU73lKSgomk0kUQBdGJBHYCZeFnp4e2traqKio4OjRo9TW1tLQ0EBVVRV9fX2o1WqMRiPp6emkpKQQFxdHUlISWVlZREVFYTKZlCW49Ho9Op1O+Vvk7AnC5ycHb/KPz+fD5/Ph9/vx+/04nU7q6+tpbW2lsbGR6upqmpqa6OzspLu7G5PJpNS5zM/PJzc3l9TUVNLT08nKyhKzWIXLhgjshMtWOBzG5/NRVlZGVVUVJ0+eZP/+/ZSVlSk9AQARERHExMSQnZ3NmDFjSEtLIyUlhby8PLKysjCbzZ9YLFkcUITL2ac9zNTX1ysnXRUVFRw/fpza2lrq6+upqakhFAqh1WqxWCwUFRUxatQoMjMzKSgoYMKECURGRoqZ8MJlTwR2wmWr/4LeZ/YSuN1uuru7OXnypDKM29TURH19Pe3t7TidTlQqFSaTCYfDoSRix8fHEx0dTUJCAunp6cTFxREREYHFYhnslysIgyYYDOLz+WhsbKSxsZGOjg5aW1upra2lubmZ9vZ2Ghoa6O7uJhAIoFKpcDgcJCcnExMTQ2xsLGlpacqQakxMDEajUek91+v1GI1G1Gq1WJFGuOyJwE4QziEQCOD1emlpaVFKJXR0dNDW1nba5Z6eHkKhkLJEmjzco9FoiIyMxG63Y7FYiIiIIDIyUim5EhERgdVqxWQyYTKZsNlsmEwmtFqxyp8wfMgnRi6XC6/Xi8vlwul00tfXh8vlore3l56eHlwuF319fXR2dtLT04PP5yMYDBIOh5UfSZKwWCzY7XbsdjvR0dHExsYq353Y2FhiYmKwWq1YLBYxtCoI5yECO0G4QKFQCK/XS21tLVVVVTQ1NdHU1ERtbe1phZQ9Hg+hUAi1Wo3FYiE5OZno6Giio6OVWbjyxI2kpCQiIyMxGo3K7Dy59+HMy/J14sAmDDT5cBAKhZRgS15qSw7A+l8OhULKSU5XVxft7e20tLTQ0dFBZ2cnzc3NNDU10d3djcvlQqVSodPpsFqtOBwO0tPTSUhIIC4ujpSUFHJzc5U1oUUvtyB8NiKwE4QLdK6vTP/r5INhfX09TU1NtLa2UlNTQ1VVFc3NzbS0tFBdXU1rayt+v59wOAyAXq/HYrEQFxenDEE5HA4SExNPCwpjY2OJjY3FbDZjNBov2esWRr5QKKTMPpVnlre1tVFfX09HRwcdHR00NDQokxY6Ojro7e09bfuXUxPi4uJIS0sjMzOThIQE4uPjyczMJC0tDYvFctZMc1FPUhAGhgjsBGGAyV8peUafnLvn9XqVy16vF6/Xi9vtVmbsdnd309fXh9PpVIavPB4Pvb29OJ1OwuEwarVaqbul0+kwGo1ER0djtVqVId7IyEjlcmRkpJLjJ19nsVjQ6/Wi1MMIJfekOZ1O3G43Ho8Hl8tFZ2enMkTa3d2tbG99fX1KeoHb7aavr++0ckDBYBCLxYLZbMZisWCz2bDb7cplh8OBw+HAbrcTFRWlbF/yzPH+uXBGoxGDwSBy4QThIhKBnSAMklAohM/nU3KP5CWPenp66OzsVHKV5IOwx+NRAkKfz0coFAJQDpparVY5mGq1WrRaLXq9HrPZrOTyWSwWpX6XXK6l/33lgLH/ZXnRc71erwwJy9f1/63VasUB+wLIk3fkwtryEGf/vwOBgPK3vL3I9w8EAkphbvnv/uVC5BMDn8+nnCDIt/W/bzAYVC7LQ6z9S/sYjUYiIyOx2WxYrVaioqKUkwe5TpzdbsdqtWK329Hr9aK3TRAGkQjsBGGIk7+iLpeLnp4eZZjM6XTS3d1NS0sLPT09OJ3O04bQenp66OjowOfzKflS8vPJPX9yb57ZbFaS1uWeGPlgbjQaT5vgodfrMZlM5w0Y5Qkg8sG9/0H+Qv8+l897u+zT7vrOd78zh98/zW3nWvaqr68Pj8ej/LjdbmV2tjzRQA7O5Fw1t9uN0+mkt7cXl8ulbBtOp1MJ/vvnYKpUKrRaLTabTQnGYmNjlc88Pj5emezjcDiUYr7yJAaxzJYgDB8isBOEYeB862Ge7+/+l+WDv9PppKurSxmOk4d8+/r6lEBBHgKWh+t6e3uVQKGnpwf4+IBMntghD/0ajUalDIU8JCf/yGUqNBoNZrNZmRBit9uVQORcNQJtNtvHFoWWey7Pl38o94p5PJ6PDe76+vqUlQxkgUAAj8dDOBzG6/UqPV7ysLvcm+Z2u08bEgWU2aPyDFK/34/P5zvtcz3X3/LwpclkUmZWm81mbDabEpjLM68jIiKUwDwqKkoZerfZbMTExCgL2p8Z9J0ZhJ9rHWUR2AnC8CACO0EY4eTgo/8wnTzMd67LcnDSf3hOvl0OXs5cFUAeHpYfK/+4XC7a2to4ePAgOTk5xMfHYzAYTpt1KQdA/Ycm4aNcMflvWf/7ni/QCAaDpwW3Z5KvNxgMZ90mv1ZJkjAYDMrQs6z/zGQ5uJQD0v73lQMoQOntkoNeechao9Gg0+lO6+nUaDSsW7eOiooKmpubmT17Ntdeey2pqalKYCwPe/cfNj/X5f731Wq1GAwGMZNaEC4DomiWIIxw8gHfZDJ97ufy+XynTQaRgzs5/08O/jweD+3t7Zw6dYrGxkaMRiM5OTnk5+ej1+uVAFHuPZOHi8PhsNKTFgqF8Hg8yv+Wg0Cv16vMJD6XQCBAR0cHhw4dIjExkejoaOx2+2n3UalU5yynIQ9l19bWMm/ePOx2+2mzN/vnGvYPouTASg7aTCaTEkTJNde0Wq3Sgyn3WhoMBsxmMwaDQVmIXqvVEhcXx9GjR7HZbERHR5OdnU12drbSmykIgnA+osdOEIQBJe9Sjhw5wnvvvceTTz7Jddddx3/8x38wbdq0SzK54sMPP+Tuu+/m7rvv5rrrrmPixImf6nEbN25k7dq1PPPMM+zZs4fs7OwBCYgv1IkTJ9i2bRv/+Z//icFgYNq0aTz66KNMnTpVCTRFgCcIwrmI6WuCIAyoYDDI0aNH+a//+i/eeOMN5s2bx69//WsmTpx4yYKRQCBAZ2cndrv9nEOu5zNx4kRmz56NSqVi7dq1VFRUXMRWnl9mZia33norGzduZPHixdTU1HDbbbfxs5/9jAMHDpw1RC0IgiATQ7GCIAwYt9tNc3Mzf/rTn+jq6mLMmDF86UtfIiYm5pLNrJTzAb1er1JT7dOyWCzEx8czfvx4SkpKyM3NZfz48Rextecm59tlZGRwyy23kJuby7Zt29i4cSNdXV1MmjSJa6+9lujoaDQazSVvnyAIQ5fosRMEYUAEAgGam5spKSnhgw8+ICYmhpkzZ7Jw4cJLWi6jf522c61w8HG0Wi1RUVFMnjyZ8vJy6uvr8fl8n7o0ykBSqVSYzWZmzpzJjTfeyOLFi/H5fOzYsYPly5ezb98+WlpacLvdHztZRBCEy4sI7ARB+NwkSaK9vZ033niD73//+yQmJvLd736Xe++995IXrJVLuQBERERc0FAsQHR0NDfeeCN1dXUcPXqUU6dOXYxmfmoqlYrU1FS+9KUvsWzZMmbPns3x48f54he/yD/+8Q+OHDkyqO0TBGFoEUOxgiB8LsFgkK6uLh5//HFOnTpFamoqzzzzDFlZWYOybJm8TJZKpSIiIuKC19O1Wq1MmjSJjIwM6uvrWbduHaNHj75Irf30VCoV6enpfO9732PJkiW8++67vPzyy2zevJnJkyfzne98B4fDcUE9lIIgjDwisBME4TMLBoO0tbXx9ttvU1FRQVJSEnPmzCE3N1dZE/RS83q9SpkUq9V6wYGOWq3GYrFQWFhIU1MT+/fvJxQKKfXqBotKpcJgMJCQkIDJZCIQCOB2u6mpqWHfvn08++yzzJs3j8zMTBITEwetnYIgDC4xFCsIwgWTc7p6e3upqKjgb3/7GwDTp0/nK1/5CiaTadCS+uWluYALzrGDf6+6MG3aNDQaDYcOHcLlcn1s7bxLSa1WExUVxbx583jkkUeYNm0afX19/PGPf2TlypUcOnSI7u7u05aREwTh8iF67ARB+EwCgQDLli3jzTffpKOjg5deeolx48YRFRU1qO3q6+vD6XSi0Wiw2WwXnGMnu+666zh8+DD79+9nzZo1XHXVVYP+2s5UUFBAbm4uX/3qV/nNb37DP//5T1577TVmzZrF//7v/xIRETEow+GCIAwe0WMnCMIFkVeFeOqpp3j//fdxuVz88pe/pKCgQFkZYTCHLN1uNz6fD7vd/rl6DR0OB3l5eeTk5LBy5Up6e3sHsJWfn/w+63Q64uLi+PrXv87//M//MHv2bPbs2cNDDz3E8uXLqaysHOymCoJwCYkeO0EQLkhvby/Hjx9nw4YNhEIhiouLmT17NlFRUUMicV9e2sxqtSrruV4oOZ8tPT2d3Nxc9u7dS2dnJwkJCZ+5B/BiUavVGI1GcnNzkSQJs9lMIBDg8OHDrF+/ntbWVqZPn05BQcElLTsjCMLgED12giB8asFgkNraWpYuXcqBAwcoKirigQceICMjY0gEdfBRjp3P58Nms33uICY7O5tJkyZRVlZGfX39kOu1O1NeXh4333wzP/3pTykoKGDNmjU8/fTTLF26lPb2dmVNXkEQRi7RYycIwqe2fPlyVq1axSuvvMJvfvMb5s6dS15e3mA36zR9fX14vV6io6M/d2CXl5eHWq0mJiaGjRs3otPpWLhw4QC19OIwGo2kpqby7LPPsm3bNjZs2KD8fffdd3PttdcOuc9MEISBIwI7QRA+UTAYZPv27bzzzjvU1NRwxx13MHfuXFJSUobcklYejwe/34/NZvvc5VY0Gg12u50ZM2ZQVlZGWloaV1999aDnEX4cuW1Go5Fx48YRGRlJSkoKr7/+OitWrGD//v3ccMMNXHXVVcpwtSAII4cI7ARB+Fg+n4/Ozk7Wrl1LVVUVNpuN66+/nqysrCGXbwan59h93uBLpVJhMpmYNm0aL730EjU1NfT09BAZGTkwjb3I4uLiiIiIIDExkdraWkpKSjhy5AgajYaoqCiys7OJj4/HYDAM2UBVEIQLI07VBEE4L0mSaG1tZceOHTz99NOkpaVxxx13cP311w/JoA4+WlLM5/MRERExIMGKxWLh+uuvJxAIcOrUKQ4ePDis6sMZDAaSkpL45S9/yQ9/+EOuvPJKXnnlFb73ve/x8ssv09TUJNaaFYQRRAR2giCcV21tLe+88w7f//73WbBgAQ8++CBLliwZ7GZ9rL6+PjweD1FRUQMyzKjT6cjOzmb8+PH4/X7eeeedYTkBQaVSMWPGDL7zne+wcuVKHA4HL774IjfffDPLli2jra1tsJsoCMIAEIGdIAhnCYfD9Pb28uabb7Jt2zZsNht33nkn+fn5mM3mwW7ex/J6vUqO3UD02Mm14oqKirBarZSUlOB2uwmFQgPQ2ktDzrszGAw4HA7Gjh3LPffcw7XXXktsbCzLli3jpZdeYsuWLbhcLtF7JwjDmMixEwThNOFwGJ/PR3l5OStXrsTpdDJhwgS+8IUvYDKZhnwulsfjIRAIYLFYBrStRUVFVFZWsn37dlpbW0lKShryQe65aLVaYmJi+OIXv0h6ejp2u52lS5fi8/loa2vDarWSm5uL2WxGqxWHCEEYbkSPnSAIpwkEAtTV1fHQQw/R3NzMnDlz+M1vfoPZbB7yQR18lGPn9XqJiooa0PbOmjWLcePGEQ6HWblyJa2trQP23Jea3As5ffp0vv/97/Pcc8+hVqt54YUXWLJkiVLYWBCE4UecjgmCAHw0USIcDrNu3TreeustWltb+e///m9mzJiB3W4HGNKBnSRJeDwevF4vkiQRGRk5oKU8jEYjWVlZTJ8+nffee4+pU6eSkZExYM9/qcmfpV6vZ8qUKfz+979n//79vP/++3zve99j9uzZzJ49my996UsYjcYh/dkLgvBvIrATBEGxe/duPvzwQ44dO8bChQuZNGkSaWlpQ65W3blIkoTb7SYYDKLRaAak3El/arWahIQEJkyYwHPPPUdjYyM9PT1EREQM2P8YDGq1Whl+1ev1qFQqvF4v9fX1rF27llAoxIIFC4iNjcVqtQ52cwVB+AQisBMEgXA4jNfr5a233mL37t14PB4eeeQR8vPzsVgsg928T83pdCqB3UBNnugvMTGRqVOn8otf/IKqqioaGxuHfWAnMxgM5OXlkZWVRXR0NEuXLqWkpIQDBw5gsVgoKioiPT0dk8kEDO3eW0G4nInAThAE2traeOONN3jppZeYP38+d999N0VFRcPq4C1JEl1dXfj9fnQ6HQ6HY8DbHx8fz5QpU8jJyWHfvn1YLBZGjx49oP9jsGk0GhYuXMjUqVMpLS3lt7/9LQ899BDTpk1j4cKFfP3rXxeTKgRhCBOTJwThMldfX8+OHTv461//ylVXXcUNN9zA5MmTh/SyWeciSRLd3d0EAgG0Wi0REREDvlyWSqVCr9dz1VVXUVdXx/79+/H5fCOqPIhKpUKtVmO32ykuLuanP/0pjz76KBqNhmXLlvGtb32L0tJSuru7B7upgiCcgzjtEoTLlDzZoLS0lO3bt+N0Opk9ezbjx48nOjp6sJt3wSRJwuVyEQqFMBgMF20Wr1arZerUqezatYv6+noaGhpIT08fFnmIF0Kn0xEVFUVkZCR9fX1otVr27t3L3r17SU5OZvz48YwZM0aZQDKcTgIEYSQTPXaCcBmSl5BqbGzkrbfe4t1332X69OksXrx42A4tSpJEb28vkiSh1+svWs09nU7HvHnziIyMpK2tjV27dhEMBgf8/wwFcu/dnDlzePTRR3n88cfRarU89dRTPPXUU6xcuRKv10s4HB5RvZaCMJyJwE4QLkOhUIju7m4efPBBDh48yKRJk/jDH/5ATEzMYDftM5Mkic7OTgwGAzab7aL9H5VKRWxsLJMnTyY6OpqXX34Zv99/0f7fUBETE8PMmTNZuXIlP/nJT7BYLDzxxBPcfvvtbNmyRQzNCsIQIYZiBeEyI0kSlZWVvP3229TX13Pttddy1VVXKXXfhuuQmiRJ9PX1odPpLuqKEHLuYXFxMR0dHbz//vs0NTWh0WiG5UoUn5a8bdjtdq6++mqSkpIYM2YMa9eu5U9/+hPjxo3j2muvZcKECWJyhSAMIvHtE4TLTGtrK0eOHGHVqlUkJCQwbdo0pk+fPiIOxi6XC61Wq5TkuJjy8vI4deoUnZ2dnDx5EpvNNqIDO/h3UJubm4vD4SA5OZlTp05x8uRJuru70Wq1WCwWkpKSsNvtIy7vUBCGg+G/JxcE4VORc6BWrVrFqlWrOHDgAG+++Sbjx4/H4XAMcus+P3lWrE6nuyS190aPHk1TUxOxsbGsXr2aqKgoEhIShm2P54WKjo7G4XDwr3/9i3/+85+sXr2an/zkJ+zbt4/77ruPWbNmDYsVSwRhpBE5doJwmQgEAqxbt46lS5dSXV3NT37yE6ZNmzas8+r6kwM7vV5/UXPsZCqViqSkJJYsWcKqVauoqKggEAhc9P871BgMBu6++25+/etf8/e//51jx47xX//1Xzz44IPs2rXrssg/FIShRAR2gnAZ8Hq9NDU1sWzZMtRqNePHj2fBggVYLJYRM1wmSRJOpxOdTndJhmJVKhUxMTHMnj0bp9NJVVUVp06duuj/dyiRZ83abDbS09OZMWMG9913H/n5+bS2tvLnP/+Z5cuXc/z4cYLBoJg5KwiXgBiKFYQRTpIkOjo6OHLkCKtXr+aaa65hzpw5jB8/frCbNuD6+vqIiIi4JIEdQEREBBMnTsRgMFBTU0NZWdmwLRfzeVksFnJzc7nvvvuIjIxkw4YNrF+/nkAggMvlQq/Xk5SUhE6nG/DC0YIg/Jv4dgnCCOf3+9m4cSM//vGPSUhI4Ktf/Spf/OIXB7tZA+5SD8UC6PV64uLimDVrFg0NDaxZs+aS/N+hLCoqivvvv58//OEP/OpXv2LXrl088cQT3HfffRw9ehSv1zvYTRSEEU0EdoIwwv3zn//k3Xffxel08utf/5rCwkJ0Ot1gN2vAXerJE/DvocgbbrgBjUbDwYMHqaysvCxz7eDfs2ZVKhVRUVFcd911PPfcc9xxxx14PB6++tWv8swzz7Bt2zZR1FgQLhIxFCsII5TP5+PUqVNs2bKFnp4errjiCoqKii7KGqqDTV5Jw+VyXbIcO5lKpaKwsJDY2Fjq6+s5cOAAcXFxIzJ4vhBarRaHw0FxcTHhcBiz2czGjRvZuXMnXV1ddHZ2Mnv2bCwWy2X/XgnCQBKBnSCMQOFwmN7eXtasWcO+ffsoLi7mrrvuIjY2dsQFdbJQKKQEdpeynpxKpSI/P5/MzEzKy8vZuHEjs2bNwmKxXPZlPuSeuwULFjBp0iTi4+P561//SllZGfv27SMhIYHMzEwiIiLQ6/WD3VxBGBFG5h5eEC5zLS0tSm7TggUL+MpXvsKCBQtGbKDh9/vp7e0lGAxisVguWY6dTKVSMWXKFKZOncqrr75Ke3v7ZTscez4RERHcf//9vPLKKzz66KO0tbWxaNEifvGLX7Bx48bBbp4gjBiix04QRhBJknC73axatYrly5eTk5PDzTffTFFR0YjtqYOPavQ5nU4kScJsNl+yHDv4d/HdgoICuru7efbZZ9m3bx96vZ6cnJxL1o6hTH6PNBoNqamp3HDDDYwaNYqXXnqJPXv2cPToUUpLS7n33nuJjIwUvXeC8DmM3D29IFxm5ET00tJS9u7dS2VlJQsWLKCgoIDY2NhBbt3FJZfUgI8K5hqNxkvehri4ODIyMoiJieHw4cPU1dVd8jYMBxaLhfT0dGbOnMk111xDeno6brebjRs3snbtWsrKyujo6BjsZgrCsCUCO0EYISRJIhAI8I9//IN9+/YRGRnJd7/7XeLj40d0bx18FNj19vYOSo+dzGw2k5iYyBVXXMHu3bs5fvy4mPl5HhqNBqvVyl133cUPfvADbrnlFk6ePMnjjz/OCy+8QGlpKYFAQJkUIwjCpzey9/aCcBlpaGjg97//PatXr2bixIn84he/IC4uDq125Gdc+Hw+urq6ALBarZc8x04WHR3NXXfdRVVVFQcPHrzsVqL4LMaMGcO9997Lxo0bWbRoEevXr+e+++7j8ccfp7a2VuQqCsIFGvl7fEG4DDQ2NlJSUsIbb7zBnDlzmDNnDqNHj1Zqio10co6dXq/HYDAMWvkMk8lEcXExcXFxtLa2sn37dpFn9wm0Wq3S23nPPfcwevRoDh06xNatWwmFQsyYMYPp06eTkpJyWWzLgvB5icBOEIYxSZIIBoMcO3aMvXv3Ul1dzX333UdRURExMTGD3bxLJhAI4Ha7laBusNa/1el0JCUlkZWVhdPpZO/evdx5551oNBoRlHwMtVqNwWBgxowZREdHk5CQQHV1Ndu2bcPj8QAwffp0YmNjMRgM4r0UhI8hhmIFYZiS8486Ojp46aWXeOutt1iwYAG33HLLZbdeqd/vp6enZ9Drock9pAsXLsRgMLB+/Xr6+voIhUKD1qbhZtSoUdx66628+uqrFBcXs337dr72ta/xhz/8gbq6OkKhkMi9E4SPIXrsBGGYklda+Pa3v82JEycoKCjg5z//OXa7fbCbdsnJdewiIyOHxCoGixYtorKykpKSEpYvX861115LXFzcYDdr2NBqtcTGxvLLX/6S0tJStmzZwtKlSykpKWHOnDl85StfIS0tTfTcCcI5iMBOEIappqYmdu/eTWlpKZMmTWL27NkkJycP2jDkYJLLnVit1iExWcThcJCRkUF6ejqbNm1ShhFFIPLpqFQqNBoNsbGxFBYWYjKZcLvdHDp0iF27duF0Olm8eDGjRo0iOjpavK+C0M/g7wEFQbhgHo+HkydPsnz5crxeLzNmzOCaa67BYDAMdtMGxVAK7FQqFQaDgfT0dAoLC9m4cSPt7e2kp6eLwrsXQA7WEhMTiY2NJTExkWeeeYa9e/fy6quvKj2zo0ePJiIiQuQxCsL/EYGdIAxDu3btYvny5bz++uv86U9/Yv78+aSkpAx2swaN3++nq6trSK1aMG7cOMLhMH//+9/Zv38/UVFRjBo1arCbNSxpNBqys7P53e9+x+HDh3njjTd46qmnWLVqFbNnz+b73/8+8fHxl2VvtSCcSQR2gjCMhEIhmpqaePbZZ6mpqWHJkiUsXLjwsh+OknPsUlJShkSOHXxU0y4/P5+xY8eye/dubDabCOw+o/5LkuXl5fHQQw+Rn5/PihUr+PDDDzly5AiPPfYY48ePJzU19VM9ZzgcBhjxxbuFy48I7ARhmJDXgV2zZg1VVVVERkayaNEiYmNjh0wwM1jkcicWi2XQh2JlOp2OiIgIJk+ezKlTp6ioqMDtdmMymS7rIPzzUKlUWCwWTCYTs2fPxufzUVJSwqFDh3jvvfeor69n3LhxTJw4EZ1Od86gTZIkPB4PR48exe12M336dDGMK4woQ2MPKAjCJ/L7/bS1tfH3v/8dgOLiYpYsWTJkApnBFAgE6Ovrw2q1Dqkg12g0ctVVV7Fz506OHz9Oa2urmM05ANRqNZmZmdx9993MmjWLpUuX8vzzz7Nv3z6mTJlCamoqMTEx6PX604Zn5TIpzc3NvPnmmzQ3N1NQUEBkZKQYxhVGDNEHLQjDxM6dO/nd737HsWPHePTRR3nooYcGZbH7oUheUiwqKmrIBXY33HAD6enptLS08Prrr4uadgNIr9eTn5/Pz3/+c1566SXGjRvHBx98wPz583nnnXeoqqo66zGBQIDHH3+cZcuWsWbNGn7yk5/Q3d196RsvCBeJCOwEYYiTJImysjI2bdrEli1buP/++ykuLlbKZ1zuvT/hcBi/34/b7cZmsw2pwE6eITtlyhQiIyNZu3Ytfr9fye8SPh+VSoVarUar1VJcXMxDDz3Ej370I1JSUvjrX//K//7v//Liiy/i8/kIh8O0tLSwevVq9u3bR3t7O52dnXzwwQfs2LGD+vr6wX45gjAgxBiOIAxh4XCYQCDAjh07OHbsGOFwmIULF5KcnIzJZBrs5g0JgUAAv99PMBgcUjl28O96bOPHj6epqYnVq1fT1NREUlISZrN5sJs3YqhUKmJjY7FarSQkJNDS0sK6des4fvw4breb5ORkMjIyqK+vZ/Xq1TQ3N+Pz+QCorq5m48aNmEwmYmJixJJlwrA3dPaAgiCcRpIkQqEQ7e3t/POf/0SSJObOncuVV14pZvL143K58Hg8SJKE3W4fUj12shkzZlBXV8err77Kzp07mTt3rgjsLgKTyURmZiZPPPEEY8eOZcWKFbz33nscPXqUu+66i46ODp577jn8fr/yGJVKxT/+8Q90Oh15eXmkpqYiSZII7oRhSxwdBGGIkiSJuro6Hn30UXp7e7n66qt54oknxAHnDL29vbhcLlQqFQ6HY8jUsesvNjaWMWPGMGPGDJYtW0Z1dfVgN2nE+8IXvsAvfvELli9fjt1uZ9myZbzyyitnDYOHw2F8Ph/Lly/nRz/6kciBFIY9EdgJwhB17NgxNmzYwIEDB/jCF77A7NmziYqKGuxmDTlutxuv14tKpcJmsw2poViZWq0mKSmJ2bNnc+zYMaqqqmhvbx/sZo1oBoOB6OhosrOzycvLw+Vy0dbWds78Rrk+ZGlpKWvXrqWnp2cQWiwIA0MEdoIwxMj16g4ePMiHH34IwIIFCygsLESv14seuzP0D+yGWo5df7GxsUyePJmuri6qq6upq6sb7CaNeHKOo0ajwe1243K5zhnYSZJEX18ftbW1fPDBBzQ1NSk5eIIw3AzNPaAgXKbkOlvl5eW899577Ny5k29961tMmTIFh8Mx2M0bkvr6+nC73ahUKiIiIoZkjh18FNhNnTqV9PR0Dh06REREBEVFRQAiWL8IJEmis7OTQ4cOsXTp0tPy6s53/56eHv7yl79QXFyMTqcjOztbfDbCsCN67ARhCAmFQnR3d/Of//mfNDQ0MG/ePL761a8SEREx2E0bsnp7ewkEAkqpk6F8INbr9XzpS1+iubmZLVu24HQ6kSRpsJs1Ivl8PjZs2MAPf/hDwuHwp94uVCoVTz75JEuXLhWfjzAsicBOEIaQtrY2Vq5cSXV1Nbm5uSxcuJDIyEgxC/ZjeDwepdTJUK7rJw8Lzpw5E6vVSmtrKwcOHCAYDA5200akXbt2sWvXLk6ePEk4HP7UAZokSTQ2NnLgwAHWrl0rag4Kw444WgjCEOHxeKisrOS1115Dr9czbdo0rrnmmiEdrAwFbrebUCiE1Wod7KZ8Io1Gw8SJE0lKSsLr9bJlyxb8fr/oFboISkpKOHHiBOFw+Lzrxp6LvJbssWPHePPNN3E6nSL4FoYVkWMnCEPE5s2bWblyJWvXruXll19WVisQPp7T6SQUChEVFTUsAmCLxcKMGTPweDz861//4sEHH8RsNg+Ltg8nV199NWlpaUyaNIl169ZRUVFBT08PkiSh1WoJhULn7Y0LhUJUV1fT1tbGG2+8wTXXXENaWtolfgWC8NmIwE4QBlkoFKKmpobXX3+d8vJyvvKVrzBt2jRiYmLEwf5T6OvrIxgMYrfbB7spn0j+PKdPn47L5WLFihXs2rWLiRMnkpqaOsitG1mys7NJSkpi6tSp3HLLLfT09NDR0UF1dTV79uyhrKyMuro6urq6UKvVSq9p/95Tj8fDU089hc1mw2g0EhcX96n+tyRJhMNhpcaix+Ohr6+Pnp4eXC4XLpcLp9OJ1+vF7/fj8/lwuVz4/X5lJRWPx4PH4yEQCAAf9UyfOaQcCoUIBAIEAgHlenmZNb1ef9ZEIpVKpZxEaLVaDAYDRqMRvV6PXq/HZDJhMpmUx5pMJmw2GyaTCYvFgt1ux2azYTabsVgsREREoNVq0Wg0n+uzEgaWCOwEYRCFw2G8Xi9r166lpqYGq9XKtddeqyxtJHwyj8dDKBRScuyGg8TERLKysoiOjmbfvn0kJiaKwG6Amc1mzGYz0dHRpKWl4ff7cTqdZGdnEx8fz5gxY2hoaKCpqYm2tja6urro6uqitbX1tJ68EydO8OGHH2K1WlmwYAEejwefz4fP58Pj8eB2u5XLcukdv9+P3+9XZmzLt/X19REIBAgGgwSDQaXXMBwOEwwGlb/l64PBoBKwyQFef/Is+nOV+JGXIzzX9XJ6h1qtRqPRoFarlTV35ctyTqhOp0Oj0aDVapUAz2g0YjabsVqtpwWFZrMZg8GAXq/HbDYrQaLRaMRisWAwGNBoNMPmezpcicBOEAaR3++nvb2dP//5z9jtdmbOnMkNN9wgzoAvgMvlIhQKYbPZBrspn1pkZCTp6elMnDiRrVu3UlBQwKRJk8TnfhHp9Xqio6OJjo6msLAQAK/XS21tLTt27ODw4cOUlZWxe/duZYk6tVqN1+vl/fffp6enh1GjRtHS0kJHRwddXV20tLTQ3NysBIXNzc20t7fjdDpxOp1KANM/kLJYLFitViIjI5Vgx2g0Kr2Cer0eg8GA1WpVAiO1Wo3RaFSCLJkcmFksltNeayAQwOfznZUbKPciyveRewV9Ph9erxeXy3VaoOpyueju7lZ6GPvng8pBpcFgwGw2ExsbS0JCAlFRUURGRpKQkEBsbCxRUVHExMSQnJxMdHQ0JpNJCe7kAFIOLPvnE4vg77MTgZ0gDKJ9+/bx3HPP0dTUxDe/+U0WLlwoDu4XSC534nA4htXBICkpifvuu4+vfOUr7N+/nwkTJpCXlzesXsNwZzAYyMnJITMzk87OTlpaWjh+/Lgym/bkyZNUV1fT2NjIq6++yltvvXVaL1l0dDTx8fFERUXhcDi44ooriI2NJSIigoiICGJiYrDb7VitVux2Ow6HA61We94A5nx/n+vyZ3XmRJ3+l8/1d//fvb29dHd309fXR29vL52dnfT09NDb20t7eztNTU10dnbS0NDA+vXraW9vx+fzKb2EBoMBu91OSkoKmZmZJCUlER8fT0ZGBunp6SQkJBAXFzcklwUcTkRgJwiD5ODBg2zZsoXt27fzwAMPMHHiRKKjo8WB/QK5XC6CweCwmBXbn81mY8KECaSnp1NXV8eWLVvIy8sb7GaNWPJs146ODmpqamhpaaG9vZ2Ghgbq6uro6emhr6+Pvr4+/H4/arWaqKgo0tLSMJvNqNVq/H4/U6ZMIS4ujoiICGVYsv9wpMFgQKfTKdfpdDq0Wq1y3XCd5S5JEjqdDpvNRjAYVHIB5Rw/eWhaHob2eDx4vV48Hg9Op5P29nal90/uATxy5AglJSV4vV6MRqOS05eSkkJiYiIxMTEkJCSQnp5OXFycUqtS+HgisBOES0ySJHw+H7t27aKsrAz4aAZfamoqRqNxkFs3/AzHHDv4aGgwLi6O0aNH09XVRUlJCXffffcFleYQziZPXOjr68Pj8eByuZSgrbe3l5aWFqqrq5Xepo6ODjo6OgiHw2g0GkwmEwkJCURGRhIdHU1ycjI2mw2NRoPf72f69OkkJCQM6VVOLgaVSoVOp7ug1xwOh/H5fDidTlpbW2lvb6erq4u2tjYaGhqUIe3W1lbcbjdOp5OmpiZaW1txOBzY7XYluE5ISFCus9vtWCwWzGYzdrsdrVYrvjP9iMBOEC4hSZIIBoM0Nzfz0ksv4fV6ueGGG5gxY8ZldZAYCPIQUV9fH8CwmBV7JpVKxbXXXsuyZcvYsWMHnZ2dxMTEiIPUp3Tm0KEc1Hm9XsrKyjh16hTl5eXs2bOH8vJyOjs78fl86HQ6UlJSSE5OJj8/n0WLFpGWlkZqaio5OTlYLBaREjEA1Gq1MtP2fDOKJUkiEAhQW1tLY2MjNTU1HDlyhIqKCg4dOkRVVRW9vb3KWtCZmZlMnjyZvLw8cnNzmTRpEg6HQ9l/ijw9EdgJwiXX3t7OY489RldXF1deeSXf/e53h+zC9UNdIBDA7XZjMpmGTR27/lQqFddddx2lpaVUVlaybNkyvvrVrxITEzPYTRsW5Lyv48ePs337dioqKqisrOTo0aN4PB6MRiORkZGMGzeOe+65h5SUFLKyssjLy8NoNKLVapUfeTaomLV56el0OjIyMkhLS2Py5MnceOONhEIhpZxLbW0tNTU11NfXc+LECQ4fPsyGDRuUUjXyY3Nzc5k+fTrjx48nLi4Ok8k02C9tUIijiSBcQpWVlezdu5cDBw5www03MG/ePBwOx2A3a1iSh9uCwSBarXZYzYrtz2q1kpeXR05ODuvWrWPJkiU4HA7Ra3cOPp+Prq4u6uvrOX78OLW1tbS0tNDQ0IDb7Uar1RIREcGCBQuUWZjR0dEkJibicDiIiIggMjISh8MhArghQv4MznVyK/fIysPjo0ePZsKECcyZM4euri46Ozupqamht7cXn8/HkSNHKCsrIy4ujri4OFJTU8nPzyc1NVWZoXs5EIGdIFwCkiTh9/spLy9n8+bNBAIB5syZw8SJE8UQ7GckSRJ9fX2EQiF0Ot2wy7EDlEKxOTk5jB07lqVLl9LS0kJcXNywmwwy0OShVTkpv7e3l46ODhoaGigvL6e0tJSqqiq6uroIh8NkZGSQkpJCRkYGY8eOZcyYMcTExBARETHYL0X4jOTvszzLuD+5LmFZWRnHjh2jpqaGyspKysrKKC8vx2g0kpKSQk1NDfn5+aSnp5OWlqYUWDaZTMoM5ZFGBHaCcInU1NSwevVqli9fzte//nWuuOIKkpKSBrtZw1Y4HKarq4tgMIherx/WZ+MTJ05EpVLx1FNP8eGHH6LX65k4ceJgN+uSO7MUh8fj4fjx45SUlPDuu++yZ88euru7UavVTJ06lSuuuIKxY8cyffp0cnJyRErDZUSuSzh79mxmz54NfLRP6OzspKSkhCNHjrB7926WLl1KT08ParWa3Nxcrr/+eqZOnaqs2dx/mxspQZ5KEqtPC8JFJVeAf+CBB6iursZut/Pcc88RFRUleus+B6/Xy65du3jkkUcYN24czzzzzLBdhk1em/TRRx8lFApx00038fDDDw92sy4puYfu5MmTHD9+nC1btrBjxw46OjoIBoPKMFxubi6jR48mNjYWo9GIwWBQCvkOx89eGDiSJBEKhU5bGcTpdFJdXc2pU6fYu3cv+/fvx+PxoNfrKSoqYubMmYwdO5aioiKsVuuI2IbE6Y0gXGS9vb3s3r2bsrIyMjMzmT9//rANQIYSOccuHA6j0+kwm82D3aTPTKPRYLfbmTNnDi+//DKVlZU0NzcTHx8/4reTQCCA0+mktraW0tJSTpw4QV1dHW1tbdjtdjIyMkhMTKSgoIC8vDwSExNJSkrCaDSO+PdGuDByaoNWq8VisSizpB0Oh1IbLzc3l8bGRlpbW+np6WHTpk0cPnyYXbt2UVhYSHZ2tlKncLgSgZ0gXESBQIDm5mZef/11+vr6GDduHDfeeOOwLVI6lEiShNPpBFAWLB/O5LVIly5dSk1NDRUVFcTHxw92sy4KeU1UucZcbW0t27dv56233qKtrQ1Jkhg1ahTXXHMNEyZMoLCwcFjOehYGl7zerTyZoqCggMWLF1NfX095eTkrV65kx44d7Nmzh1AoxNy5c5k/fz6FhYVkZmZisVjOWsZtOBCBnSBcRBUVFWzcuJHnn3+e3/zmN8yfP5/ExERxgBoAcj6NvDD5cH9PjUYjkyZNoqioiPb2dl588UVmzJgx7A4qn4bb7aapqYm//vWvrF27ltraWlQqFTfffDNXX301kydPJiMjAxg5eU/C4JPXpU1LSyMtLY2rrrqKvr4+jh8/zrp163j++ed57733MJlMTJs2jYceeojCwkJSUlIGu+kXRAR2gnARyDM2ly9fzoYNG5g1axZXX3016enp4kA1QMLhMD09PcpSRMP9fZXbf+2117JmzRq2b99OfX098fHxI2JFknA4jNPpZNWqVezdu5c9e/bgdrsZN24ct956K1OmTCEjIwOHw4HNZhPlXoSL4sz1eC0WC6NGjSI+Pp758+dz+PBhysvLOXDgAD/84Q/Jzc2lqKiIW2+9ldTUVAwGwyC2/tMRgZ0gXAThcJg9e/Zw6NAhenp6uPXWW0lJSbnsS1gMJDnHzmAwjIjAR1ZUVERZWRnbt2/n4MGDTJ8+fdi+PnlChLyE1KFDh/jwww9pbGxEkiSmTp3KpEmTGD16NOPGjcNsNg/7AF0YXjQaDTabDavVSkpKCjExMaSmpmK329m5cyft7e3s2rULjUbDhAkTlLI6QznHUwR2gjDA5PURly5dyqlTp0hOTub+++8XQd0Ak3Ps5B67kaKwsJCCggIiIyN5//33yc3NJTo6esgeRM5HTlx3uVzs37+fdevW8eKLL2IymSguLubee+/l9ttvx2AwiN45YdDJec85OTnk5ORwzTXXsHPnTlasWMGGDRv4n//5H+bNm8fVV1/NddddR1pamrJayVAjAjtBGGB1dXWsX7+eFStW8MADD7BkyZJhXWNtqAqFQnR2dmI2m7FYLIPdnAGjVqspLCzk5ptv5qmnnuK6664jJSVl2K2s4fF4OHHiBP/5n/9JRUUFgUCAhx56iNtuu420tDTMZrOoOycMaXK9u29+85usX7+ev/3tbzz99NP86U9/4kc/+hFXXXUViYmJg93Ms4hvlSAMoK6uLg4dOsQLL7zA7NmzmT59OqNGjRp2vS3DgbxOqN1uH1E9diqVivT0dObOncszzzxDSUkJDoeDmTNnDnbTPpE89Lp//35KSkp477336Onp4corr2TKlClMmTKF9PR0zGbziJwUIowc8oxatVqNw+Fgzpw5REdHs3//fnbv3s1f/vIXDh06xNSpU7n66qux2+1DZj8vAjtBGAByne+KigoOHDhARUUFjz/+OKNHjxZrwV4kco5dTEzMsEhovhCRkZHk5uaSmprK8ePHSUpK4oorrkCtVg+Zg8eZ5OKw5eXlbN26lT179lBZWcnMmTNZsGABs2fPJi4ubsi2XxDORaVSodfrSU1NJT4+nri4OAwGA6+++ip79uyhr68Pu93OFVdcMWR6oQe/BYIwQgQCAV566SV2795NSkoKd911lxiCvYjkWbEmk2lE9djBRwui2+12brjhBlasWIHRaOSuu+4asq9TDuqcTie/+MUv2Lt3LxqNhocffpi7776biIiIIRHQySdg4XAY+Hf5i4F4LtlnCb77LwAl93qei/y8Z/4eyc73Pn+ez+6zkpf6KywsZNq0aTz55JOsW7eOTZs28eKLL5Kbm0tkZOSgfy5DL+tPEIYhj8fDH//4R7Zu3UpUVBRPPfUUERERQzKxdqQ4s47dSGMymbjnnnuIiori1KlTvPvuuwSDwcFu1jm5XC42b97MV77yFbZu3cqSJUv4y1/+wv3334/dbh/s5incbjfHjx9n1KhRzJgxg+985zuf+bl6e3tZvnw5kyZNIjU1ldTUVLKzs9m9e/dner5wOMz69ev59a9/zeLFixk7dqySyD9x4kRuv/12nnjiCV599VXq6urw+/2fue3DSTgc5oMPPuCLX/yi8j5nZWXx5z//edDapNPpmDJlCn/84x/56U9/SnR0NHfeeSfPPvssZWVlg9YumeixE4TPSV4O6b333iM9PZ0pU6YwatQoNBrNoJ+5jWRyrcCRVu5EplariY+PZ8yYMRw/fpy1a9dy4403otVqh8x2JUkSwWCQFStWsG3bNurq6rj77rtZsGABBQUFQy7g7uzsZNeuXTQ0NNDW1obNZqOlpYWYmJgLzvkzGAyMGjWKhx56iO3bt7N9+3YaGxs/U8Dl8/l46623WLduHV1dXSQlJTF58mT0ej1qtZpAIEBDQwPl5eWUlJSwe/duHnvsMaWI80imUqnIy8vjtttuIycnh3fffZfGxkb6+voGtU16vZ74+HimTJnCAw88wLPPPsuHH35Id3c3jzzyCHFxcYM2LCsCO0H4HMLhMK2trRw4cIDDhw/z8MMPM2vWLGJiYga7aSOaPPTncrkwGAwjLscOPjp4mM1mxo0bpwQk3d3daLVa9Hr9YDcP+Gj7b2hoYNWqVRw9epTY2FjuuOMOUlNTh9wsXkmS6OjoYNeuXWi1Wvr6+mhoaODUqVNERERccGBnNBoZNWoUo0aNwuFwUF1dTWNj4wW3KxgM0tvby+uvv87hw4dJTk5m8eLFXHXVVUqvf2dnJ6tXr2b16tXs3r2bPXv2cNddd10WgZ1arSYvL4+8vDwKCwvZv38/LS0tg90s4KOh2czMTOLj4zl69ChbtmyhtbWV6dOnM2fOHMxm86CM2ohxIkH4HHw+Hxs3buRnP/sZkyZN4qabbmL27NmD3awRLxAI4PF48Pl8WCyWIdczNJCuvfZapk2bRk1NDVu2bKG5uXmwm6RwuVz8/Oc/Z/fu3aSlpfG3v/2NUaNGDbmgDj4K7BobG3n//ffJysoiIiKCrq4u3nnnHdxu96C1q6Ojg4MHD7Jy5Ury8/P50pe+xD333KOUuLFYLKSmpvLAAw9w//33c8MNNwyZHlvhowLHVquVX/3qV9xyyy0YDAYeeeQRqqqq8Hq9g9ImEdgJwufw4osvsmbNGgKBAE888QQ5OTmD3aTLgtfrpaenBwCbzYbZbB7kFl08CQkJjB49mqlTp/Laa69x6tSpwW4SgJL39+6777J48WIeeughUlJShmzQcfToUSoqKjAYDPzgBz+guLiY7u5uVqxYQV9f31nJ+ZdKT08PVVVVSJKkzLqUi+We+ZOdnS1OHIcotVrNLbfcwte+9jUkSeJ3v/sdW7ZsGZy2DMp/FYRhLhAIUFdXx5YtW+jt7WXmzJmMGjVKLIl0ifj9ftxuN5IkYTabR2SOnUyn05GUlMTMmTMpLy+nqqqK9vb2QW1TMBjk5MmTbNiwgbS0NCZPnkxBQQF6vX7Ibv/l5eU0NTVRUFBAcXExGRkZmM1mGhsbqaqqoqura1Da1X92Z1dXF93d3ee9b2xsLBMmTOChhx4iPj7+ErVQ+CRy4J2QkEBhYSFXX301hw8f5vDhw8ryeZeSyLEThAskSRIej4eSkhL27t3LmDFjuOWWW4iKihKzYC8Rv9+vJE+P9MAOID4+ngULFvDHP/6RiooKqqurBzWPs6+vj2PHjrF582a+/OUvU1xcTEpKyqC15+PI5UMOHz5Ma2srs2bNIj09nYyMDBISEjh69CiHDx8mOjqa6Ojocz5HKBQiGAzi8XgIBAJIkqQUsDWbzYRCoc/cPqPRqOw7ampqOHnyJN3d3ZhMJrRa7WnlUxwOBw6Hg6KiotNen8fjwePxKLOm1Wo1JpMJnU6H2+0mGAwqwYVOp8NoNKLT6c5K7pffK7/fr/z0Lw3T/7FyTmIgEDjt+yj/f4fDgdvtJhAIEAwGlfdLp9NhMpnOObksHA4rubPBYFD531qtFqPROGRnhcuMRiPp6encfffdrFmzhrKyMo4dO3bJV6cQgZ0gXCCfz0dlZSXf/va3ycnJ4Qtf+AKLFi0asj0VI5HH41F6Nux2+4jOsQOIjo5m2rRpFBcXc+jQIXQ6HZMmTRq09mzatImSkhJCoRBf//rXh/RkIUmS6O7uZvfu3Wi1Wm6++WalXEV7eztHjx5lxYoVxMTEMHbs2HM+R1NTEwcOHOC5555jx44d+Hw+ZVbsY489Rm1t7WduX3x8PJMnTyYuLo7y8nKam5s5duwYjzzyCIWFhSQkJHzsvsXv9/OPf/yDpUuXcuTIEeCj4fsHHniAiRMn8vvf/57Dhw/jdrvRarUUFRVx7733MnnyZPLz8896vq6uLjZs2MDatWvZtGkTbW1tqFQqbDYbU6dO5fbbb2fChAlkZWUBUFZWxsaNG3n88ceV50hKSmLLli387W9/Y+fOnRw5cgSNRkNeXh7Tpk3j4YcfJj09/azAsru7m8rKSn71q1+xf/9+Ojs7gY/WT7711luH9HYms1qtTJ8+nRkzZtDa2srrr7/O/PnzL2kbRGAnCBdox44drF69GrfbzZ133sn06dNFT90l5vP56OvrQ6vVYjKZRuSs2P5UKhVarZYbb7yR9957jz179tDY2DhoJRX279+P2+1m4sSJREVFDYlq++cTCATYtGkTVquVlJQUEhMTUavVjBkzho6ODjQaDQcOHGDWrFn09PSctjRUOBymubmZP/zhD+zYsYPOzk6+8Y1vkJ6eTmRkJB6Ph61bt3L8+PHPPFNTq9XicDj40Y9+xAsvvMCJEyfYsWMHdXV1xMXFER8fz+jRoykqKiInJ4e0tLTThrx1Oh033XQTU6dOZe/evTz99NN0dnayfv162trauPvuu4mMjMTlctHY2MiyZcv4wx/+wOTJk3nooYcYM2YMGo2GQCBAT08PP/zhDzlx4gSBQIBHHnmE9PR04KNSMW+88QZ/+MMfGDt2LI8++ih5eXlkZ2cTFRXFuHHjePrppzl06BDd3d38+c9/ZtSoUUydOhWDwUBFRQXLly9n7dq1dHd389vf/paIiAjlfWhpaeGDDz5g2bJlNDU1cdNNNzFq1CiSk5NxuVyUlZWxY8cOmpqaPlcP6cUmfy5z5sxh9erVlJaW4vP5lNI1l8LQ/TYKwhAjSRLt7e0cOHCAkpISioqKKCoqIjk5ebCbdtmRc+zkYaHLIbBWq9VMmTKFLVu2UFtbS2lpKfPmzRuUoKqmpoZAIEBubu4lPWB9FoFAgN27dxMdHU12drayekd0dDRpaWkkJyfT3NxMXV0d1dXVjBs3TnlsKBRi79697Nq1i+rqaoqLi5k/fz4ZGRlYrVY6Ozuprq7m5MmTymSeC6VWqzEajcydO5fOzk4OHjxIZWUlTU1NNDQ0oNfrqaqqora2VilYXFxcTHx8PAaDAbVaTVpaGmlpaUiShNVqpbm5GY1Gg8PhYPLkySQlJeHz+WhoaODYsWOsWrWKXbt2MXr0aPLz89FoNPT19VFSUsKWLVswGo0UFBQwd+5csrKylH1fVVUV77zzDrt27aKwsJDMzExsNhs2m420tDTefPNNTp06RXd3t7J9yIFfamoq+/bto7a2lg8//JC+vj6sVqsypHv48GF27drF/v37mT59OjNnzmT8+PEkJibS3t6O2+2moaGBzs7OQZvociHy8/P58MMPaW9vp6uri5iYmEv2PRm630ZBGELk3JPS0lJ27tzJqVOneOihh0hPTx+yyzyNZD6fD7fbjd1uH1IFey8mtVrN5MmTlZnXb7/9Ni6X65InZgPU1dURCoXIyckZ0u+9JEn4fD42bdpEWloa48ePV24zmUzExcUxZcoUtFotlZWV7Ny587TH+/1+XnnlFU6cOEFMTAz33nsvEyZMIDExUQlm7r77biZOnEhvb+9nbqdWq2XUqFF873vf41e/+hXf+MY3mDNnDomJiXg8Hvbs2cM//vEPnnjiCb7zne+wadMmenp6zhvg+P1+pk6dyh133EF+fj42m42YmBjGjBnDN7/5TZKSkjh58iT/+te/lJzBtrY2XnzxRerr6xkzZgx33HEHxcXFREZGEhUVRXZ2Nt/85jfJzMyktraWpUuX4vP5zrn9GY1GrrzySgoLC0lOTsZsNlNYWKjkYsrBn8/nUx6zYsUKdu3ahSRJ3HvvvcycOZOsrCxMJhOpqaksXryYxYsX09PTMyjb/IXKzs7G4XDg9Xqpqak57bVebCKwE4RPIRQK0d3dzRNPPEFHRwc333wz11133ZBaLuly4vV6cTqdOByOIT0MONA0Gg1XXXUVc+fO5bXXXqO6unpQKvDLB9fzTTYYKjo7Ozl27BiVlZWMGzfurLzE6OhovvSlL2GxWDh8+DDvvffeabcHg0E2b95Me3s7UVFRzJ07F51Op9yuUqmIiYkhNTVVGbL8PLRaLenp6XzpS19SVjIoLS3lrbfe4s477yQjI4OqqioeeeQR1q9fT0NDwzmfRw6Gziy/JAeQKSkpqNVqjh49SnNzM16vl66uLlatWoXP5yM3N5cZM2ac9liVSkVsbCxZWVnY7XYOHTpEQ0PDOWsA6nQ6iouLz8p9dTgcxMTEKIFk/8fu3r2bkydPotfrmTt3LpGRkac9NiIigqSkJLKysobFdz46Ohqz2Uw4HKajo+OSDh8P/XdHEIaApqYm3nzzTTo7O/nCF77ATTfddNn0FA1FPp8Pl8uFzWa74BUDhit5WxszZgydnZ3861//YvPmzWg0GoqLiy9pW3Q6HSqVasivV1pfX8+2bdtwuVw8/fTTvPrqq6dtL36/n9bWVpxOJ5IkUV1dTWVlJcnJyej1egKBAH19fUiShMFgwGKxnPWdl4dSB+IkTy6boVarlRmoBoOBiRMnEh0dzahRo5RF5w8dOkRSUhKpqalnPY/ZbEav15/13ZBnplqtVkwmEx6Ph56eHhwOBz6fD6fTSTgcZvny5Zw8efKcAdTevXtpbW0lFArR3NxMbGzsWQGcWq3GarWeNfSo0WiUNoVCIWUkJBgMKjNo9Xo9FovlnG3X6XRERkZ+rskql4rf7ycUCqFSqTAYDJf0WCECO0H4BE6nk6qqKj744AMSEhIYP378aeUGhEtPzrGz2WxDOr/rYoiPjyc3N5fMzEx2795NZmYmY8eOvaQnGnKA83mGHy82SZKU2az5+fm4XC5qamrOed+4uDi6urro6OigrKwMh8OBTqdTAo/+Ade5qNXqz9yL5Pf7cblcnDhxgry8vNN6quSgIDExkcTEREKhEJ2dnWzcuJGGhgZaW1vP+Zxye+W2n6u98msJhUKEw2EkSVKGdnt6eqipqTnnY+WyMBqN5rxL28lB2JnvV///CyhDqv3/d/+CzOd63uFyQt3b24vX61VmFF/KE1AR2AnCecg7nWPHjrFlyxY2b97Mv/71L6ZPn47Vah3k1l3evF4vLpeLyMjIYTEsM5C0Wi0JCQncdttt/Pa3v2X06NHMnTsXh8NxydqQlJREb2+vsmLC+QKIwRQMBqmurmbfvn08//zzFBYWnjW8Bx99z3/2s5+xcuVKjhw5wsqVK5X76vV6zGazkg/mdrvP6rWT8/icTudnamdHRwelpaXcd999vPTSS8ybN++872VCQoJSksXtdp93ySqn06nkv53ZVvl2l8uFSqUiKioKs9mMwWDAZrPhcrl48MEHefzxx89Za65/MDYQn7lKpVLeZ41Gg9/vx+PxoNFoTvtuy+tD9/b2DovJE9XV1XR2dqLT6UhPT7+k6ztfXqe6gnCBurq6+Ne//sV7773Hl7/8ZRYsWHDOoQ/h0vJ4PPT29g75UhsXS0xMDHfccQfR0dEcPnyYN99885L+/4kTJ6LT6di8efOQLRq7a9cuampqiImJYeLEiR87VHr99dczduxYPB4Py5cvp6WlhWAwiFarZc6cOcTExNDV1cXWrVsJBALK4yRJoqenh6ampvP2Bn4Subeqo6NDyQc8n9bWVo4dOwZAVlbWeWfk+3w+amtrqaioOO36UCjE8ePHaWhoQJIkRo8eTXx8vFIkeeHCheh0Ourq6jh8+PA5n/uDDz7ghz/8IbNmzaKrq2vAJjJMmTKFnJwc/H4/W7ZsOWsFDpfLRWtrK5WVlUN2m+tPnuCSl5dHTEzMJd1PicBOEM4jGAwquSZWq5Wbb775suwhGorks/rLKceuP61WS1RUFFOmTMHr9fLhhx/i9XovWU9GUVERCQkJtLa2cuDAgSE5JHvgwAFcLhcTJkxQyoKcjzz5QR6SraiooL6+Hq1Wy3XXXUd6ejrd3d289dZblJeXK+U3WltbWbFiBQcPHvxcKQFyb9TatWt59913OXjwIN3d3Xi9Xnw+H11dXezatYsNGzawfft2kpKSKCoqUooEn8lgMFBeXs769eupra3F7XYrxX9ffvllWlpaSE5O5oYbblBq4jkcDhYvXkxCQgLHjx/n7bffpqKigt7eXjweD06nkz179rBmzRqOHTtGUVGRkms5EObNm8f48eNRqVS8/fbbHDhwgIaGBnw+H62trWzevJn169efthLHUBQMBunp6aGkpAS1Ws3EiRPP2fN5MYkjlCCcg9/vp6uri3feeQe3201BQQFXXnnlJe1OF85PzrE7V4L25UCtVqPX65k1axbvvPMOpaWltLS0kJiYeEmKNY8ZM4b09HTC4TBbtmwhNjb2tJpkgyUUCuHz+aivr2fv3r0EAgHy8/Opq6sjPj7+rNJEcuK+XIMuIiKCpqYmSkpKMJvNSJLE/Pnz2bVrF9u2bWPbtm1MnjyZvLw8oqKi6O7uZtu2bdTX12OxWPB4PNTX11NVVYXFYvlUtcu0Wi1ms5nU1FTKy8sJBoOYzWb6+vqUYd/u7m527NjBnj17qK2tZfLkyRQVFZ139MBoNNLe3k5ZWRkZGRkkJSXhcrmorq7m/fffR6vVMn78eL7whS8oJ6qRkZEsWLCAN998k7q6OrZu3arUzTOZTAQCAbZv387Ro0ex2+1cf/31yjJfbreblpYWenp68Pv9BAIBTp48qWwXFouFyspKmpublSHr+vp6oqKigI9yHKdPn05DQwNHjhxh27ZtZGVl4Xa7ycjIoLOzk927d3P48GGsViter5f29nZOnjyJzWZTciKHAnllovLyciZPnsyUKVMufSMkQRDOcvLkSekvf/mLZDQapSeeeEI6cOCAFA6HpXA4PNhNEyRJ+t3vfifl5eVJTz/9tNTY2DjYzRkU4XBY6unpkf7rv/5LysjIkH7+859fsvciHA5LGzdulO69914pISFBevPNN6X29vZL8r8/TldXl7Rp0yYJUH60Wq00ZcoUadeuXWfd3+v1SpWVlVJKSsppjwEkk8kk5ebmSl6vV6qvr5dWrFgh3XDDDVJ0dLRksVikqKgoafLkydLzzz8v/eQnP5FmzpypPDYrK0t6+OGHJY/H84ltDofDktfrlbZt2yb9+te/lu655x5pypQpUkpKimS1WiW9Xi/ZbDZp/Pjx0p133in94Q9/kPr6+qRgMHjW/mjHjh3SuHHjpJSUFOlnP/uZtGHDBumaa66R4uLiJIvFItntdmn27NnSCy+8IB07duy0fZr8d2dnp/Tmm29KDz74oJSVlSWZzWbJYrFICQkJ0lVXXSX99a9/PW1/eOrUKem555476/0DpLvvvlt68803pZ6eHslkMp11+5gxY6SHH35Yea6Ojg5p37590k033SSlp6dLVqtVMpvN0oQJE6Sf//zn0t///ndp/vz5ksFgkADJZrNJ9913n1RdXT0wG9AAKC8vl2666SYpLy9PeuqppySv13vJjxuix04QziAPe/z5z39myZIlzJs3j/z8/CHd/X+5kP4vn+dynjzRn8ViYcKECdTU1PDiiy9y9dVXEx0dfdF7llUqFUVFRVitVnbu3Mlzzz1HVVUV3/jGNwZ0eO5C2Ww2Jk2axIEDB05rq8lkOmc+mk6nIykpidWrV5+WOyc/Tq/Xo9PpiIuLY9asWYwZM0YpCi3PWI2JicHv9/PlL38Zl8sFgF6vx2azferPQafTUVRURG5uLl6vF6/XSyAQUEqCyG0xmUyYzWZMJtN5Z47KoqOjmTx5Mn/605/weDzKML3JZFJqrPV/vDyL1mazsWDBAqZMmcI3v/lNJZ9NrVZjMpmIiIg4bQKJPKTb/z2XRUVFKfffuXPnWfl4RqPxtOey2+2MHj2a3/72t3g8HuV/GwwG5bs+b9485TNQq9VERkaSkJDwqd7ni23Hjh1s3LiRzZs388QTTzBv3rxB6Um8fPeIgnAGeaezd+9eSkpKcDqdLFy4UKl+LgwNfr9fqRF1uQ7Fwr9rkuXm5jJ16lRWrFjB8ePHiYmJISMj46L/f5vNRlZWFtdddx07duxg9+7dbNq0iblz5562lumlJNdo+7TliNRqNQaDgYKCgk+8X0RExGlrm57pswYXcoBmsVjOqgf3eeh0OmW5rwtpi1arJTIy8pwziM/FYDBgMBg+sVh1/1U/zker1aLVasnMzDzvfYZiUexwOMzJkyfZvHkze/fuZfTo0UydOlUpBH2picBOEPrxeDy8//77lJaWkpKSIlaXGII8Hg8+n49wOHzZTp7oLzs7m1AohFarZe/evURHRyurIFzM4EoOAB566CFOnDjB8ePHWbp0KWPGjCEmJgaj0Sh6uYURLxgM4vF42LRpE6tXr6a9vZ2HH36YgoKCQTt2XJ6nuoJwDi6XixdffJF169Zhs9l48sknsdvtl22P0FDV09ODx+NBrVZfdkuKnYs8zHj77bezadMmNm3adNaw4sWiVqvJysriRz/6Ebfeeitr167l3nvvZd26dedcakoQRppTp07x+9//nu985ztERkby6KOP8h//8R8X1FM60C7vPaIg/B+5Kv2zzz5Lfn4+c+bMYfTo0UN+av3lyOl04vV6leGxyz2wU6lUWK1Wbr31VrZt20ZFRQVbtmxhwYIFF33blZ8/NzcXnU5HVFQUf/nLX3j22WcpLS3l3nvvJSEh4bL/jC6mQCDA+++/z5o1aygtLaWqqopAIMBTTz3F2rVrWbRoEYsXL/7UQ6vCJ5P+ryj1+++/z9atW1m/fv3/Z++8w+Mqrzz8m96rpNFo1Ea9V1uSu2zLNjY2xkDAoTqBbMiSZAPZZDcJ2UASQno2MSSEULJgCC0UG1fcu2RZ1ZLVu0Zteu9z9w+4N5ZtjPvMSPd9Hj0qM6M5c8v5zncqHnjgAaxZswbl5eVhjyLQdxvNrIbMq9PpdDh16hSGhoZw6623Yu7cuZfMp6EJHy6XCz6f73PnUc5GuFwu8vLykJiYCIfDgWPHjmHp0qVfmGB/vZBKpcjIyACfz6fuoxMnTkCtVmPhwoVISEigWlvQG6XrDzneKz4+HvHx8dTfeTzeTe+hNtMh5+s2NjZi37596OvrQ0xMDG655RbMmTMHSUlJ4RYRDOL8MhUamlkE8Vlj0HfffRcvv/wy9Ho9XnrpJaqzPk1kQRAEDhw4gL///e84evQozp49Cz6fH/YdcqTwy1/+EsePH8fo6CiOHTsGoVB4Uw3fUCgEnU6Hv/zlL9i1axdVKbtq1SpUVlZSFbO0oUETTZBmUjAYxOjoKFpaWvDf//3fsNvtKC0txTe/+U2sXLkyYtYM2mNHM+vZunUrtm3bhu7ubrz++uvIzc2lQ0cRjMVigd/vB5/Pp9o+0HzKPffcA5/Ph+PHj+Pjjz9GdXU1NBrNTXt/BoOBxMREPPnkk3jggQfw5ptv4tVXX8VHH32E0tJS/OAHP6CrzGmijlAoBJvNhldeeQX79+9HfX09Kioq8Nhjj6GsrAzx8fERtWZEjiQ0NDeZQCAAu92OrVu3wmKxYOnSpSgsLLxgyDdNZEH2sBKLxQDo0N65xMfHIycnBwUFBdi6dSuys7OhVqtvmteO9MYJhUKkpKTg7rvvhkKhQGtrKwYGBvA///M/WLhwIYqLi1FeXg6lUkmfP5qIhCAI+P1+dHd3o62tDfv370dPTw9EIhE2btyI9evXo6ioCDExMRHjqSOhDTuaWYvH40FbWxtaWlqQlpaG5cuXIy4ujl5oIhyy2vJ69vyaKYjFYqSlpaGiogLbtm3D0NAQMjIybnriPJPJhEQiQWlpKWQyGRQKBfx+PxobG+F2uzE1NQWfz4f8/HwolUoIBAJ6XB9NRBAKheD1emG1WjE8PIzTp0/j9OnTOHHiBOLj41FQUICVK1eipqYmYvN7acOOZlZCEAQmJibwi1/8AqFQCPPnz8d9990XbrFoLgNy4LxEIqGN8IuQm5uL+++/Hy+++CKOHz8OmUyG5cuXA7j53k0Gg4H09HSkpaVh48aN2LlzJ/76179iy5YteOGFF/DAAw9gw4YNKCwsREJCwrTX0dDcLM4tNfB4PBgZGcHu3bvx5z//GUajERKJBJs2bcJXvvIVJCQkgM/nh1HaL4Y27GhmJY2NjThw4ACOHj2K3//+91i8eDHtMYgSyIHtdPuGiyORSJCZmYnbb78dp06dgt/vx5IlS8KeAyQSibBu3TosXLgQnZ2dOHLkCN5//33s2rULCoUCFRUV2LhxI/Lz8yNmRBTN7MDlcmFiYgLvvvsujhw5goGBATgcDixduhTLly9HRUUFkpKSIBaLw34fXQ6RLyENzXWEIAg4HA6cOnUKJ06cQGlpKcrKypCYmBixbnWa6ZDzOOlQ7MUhZ3quXbsWL730Enp7e9HS0oLS0tKwLUrnjs0iw65isRhKpRJdXV2YmprCwMAAXn31VaSnp0Or1SI3NxfZ2dmQSCTg8XhhkZtmZkKGW/v7+9HT04Ph4WH09PSgt7cXTCYT5eXlyMzMxNy5c5GTk4Pk5GQIhcJwi33Z0IYdzaxjeHgY9fX16OzsxP3334/MzEza+xNFuFwuEARBG3aXgMPhYNmyZfjggw8wOjqKI0eOID8/PyJ6mjGZTKhUKsTFxWHu3Llobm5GU1MTPvnkExw7dgyNjY1Qq9VYtGgR3G43NBoNFAoFRCIR1dqG3oTRXAkEQVBNhZ1OJ1wuF8xmM44dO4ba2lr09fVhaGgIWq0W1dXVqKysxNKlS6N28hDdx45mVhEIBPDwww+jo6MDcXFxePfdd296ry+aq4cgCHz5y19GKBRCQUEBnnrqqbAbKpEIqdb//Oc/Y//+/WhtbcXx48cRFxcXMT3/zl96CIJAa2srDhw4gOPHj2P//v1wuVxITk5GcXExNmzYgCVLlkCtVtPtUmiuiFAoBLvdjsbGRmzduhX19fVoa2uD0+nEnDlzMGfOHKxYsQK33noruFzuNJ0SjfqF9tjRzBqmpqZw8uRJHDp0CKtXr8Ztt91G90GLQmw2G0QiUVhnMUY65DW9atUqhEIhHDx4ELt27cKSJUuQkZERZuk+5WL3XXZ2NhISErB+/XoYjUY0NTWhv78f/f39+NWvfoXnnnsOKpUKGRkZKCsrQ2FhIVJSUuicPJppeL1emEwmdHd3o66uDp2dnRgaGsLo6CgkEgmSk5Px7//+71i6dCk0Gg1kMhmkUill1EX7mkAbdjSzAq/Xi9HRUezatQuxsbEoLCxEcXFxxHgvaC4fp9MJkUhEh2Ivg4SEBGRmZiItLQ1HjhxBamoqtFptRF73ZP87oVAIlUqF1NRUSKVSpKWlIT09HQqFAuPj4/D7/ejv74der8fZs2ehVquh1WqpkK1cLqd6i0X7Ak1zacgQq8Vigc1mg8VigU6nw9TUFPR6PUZGRqDX6+F0OsHlclFSUoKMjAykpaUhMzMT5eXlUVMQcSXMrE9DQ/M5mM1mnD17Fh988AHuu+8+VFRUIDU1Ndxi0VwBZOju/AbFNJ+PRCKhejT+85//RFVVFSoqKiLe28lgMMDhcJCXl4e8vDyEQiFYrVbU1tbi7NmzaG5uxrFjx7B3716wWCwkJiZiwYIFyM7ORkZGBoqLiyGRSMBms8FisajvTCaTNvaiFNKICwaD0768Xi96enowODiIrq4unDx5Et3d3bBarWCz2SgrK0NRUREKCgqwaNEiaLXaGd8Bgc6xo5kV/OY3v8H+/fvR39+Pffv2Qa1W05V2UQap1PPy8lBSUoJNmzZh3bp19EL9BbjdbkxMTGDRokVYtmwZbrvtNmzcuDHcYl0R5DJFEARCoRCVCN/Z2UmF244cOQKdTgeLxQI2m42cnBykp6cjJycHVVVVKCgogEqlglQqDfOnobka/H4/rFYrurq6cOrUKXR3d6OnpwfNzc1wOBzgcDiQy+VYsGABKioqkJ+fj7KyMiiVSqpo6NyvmQztsaOZ0fj9ftTW1uLIkSNwOBx47LHHInIEDM0XEwwGYbfbEQgEwOPx6AX6MuFyuVCpVFi5ciXGxsawe/du3H777eDxeFGzwJFyMhgMMJlMEAQBFouF7OxsaDQalJSU4K677oLZbIbRaMTg4CCmpqZgNptRX1+Pffv2QSwWQywWQ6FQIC0tDWq1GnFxcUhOToZWq4VEIqGLMsJMKBRCIBDAxMQEdDod9Ho9xsbGMDg4iLGxMRiNRpjNZhAEAYlEAoVCgXvuuYfKs9RoNIiNjYVcLodEIoFMJpuVIXnasKOZsQSDQbhcLuzfvx8WiwXx8fFYtmwZ+Hw+XQUbhYRCITidToRCIXA4HDrH7jJhsVjg8/lYsmQJ/vnPf6KrqwsDAwPIyMiI2pAU6XWRSqWQSqXQaDQAPvVO2u12DAwMoL+/H6OjoxgaGkJvby9sNhscDgf0ej1MJhP1WpVKBa1WC6VSCYlEQvXa4/F44PP5VJsVLpcbtccrUggGg/D7/fB4PHA6nfB4PPB6vXC73dTvLpcL4+PjmJiYgNVqhdVqhcFggNVqhd/vB5/Ph1qtRkJCAhITE5GZmYn09HSqhc5s8Mh9EbRhRzNj8Xq9mJycxMsvv4yqqiqsWLECpaWl4RaL5ioJBoOw2WwIhULg8Xh0jt0VwGQycfvtt+P06dPYv38/tm3bhn/7t3+DQqGYUYugQCCAQCCASqVCVVUVgE83BBaLBZ2dnRgeHkZfXx/OnDmDvr4+jI2NYWJigsrZVCqVSE9PR3JyMtRqNRITE5Geno6kpCTExsYiJibmglYY53oTz/1+7nNmMueGyb/ou8vlgtVqxcjICPr6+jA+Po7JyUkMDQ2hv78fU1NTMBqN1PmIjY1FRkYGCgsLkZ6ejpSUFOTl5SElJWXGFTxcT+gjQzNjOXXqFF566SWw2Wzce++9qKmpCbdINNdAIBCA0WhEKBSCQCCgm0pfIQqFAkuXLoXL5cLzzz+PdevWQSQSzfhcUwaDAblcjoqKCsyZM4cK94VCIfh8PlitVnR0dECn02FsbAwjIyMYGRlBU1MTpqamYLfbwWQyKS9xUlISlEollEol1Go14uPjoVAoqN/JPD6pVBqR1cfXG6fTCYfDAYPBgImJCRiNRphMJkxMTECv18NsNsNkMmFkZARmsxkejwehUAgsFguxsbGIjY1FUlISli9fjvj4eGg0GuTn5yMhIYHqMcpisajiF7pB9RdDG3Y0M5LOzk6cOnUKjY2NuPfee5GbmxvxlYA0l4bMsQuFQtRIKprLg/QsFRcXw2azYevWrThx4gSYTCby8vLCLd4NhfzsFzMGQqEQJBIJhEIhcnJy4HA4YLfbYbfb4XK54HQ6YTKZYDKZqL/7/X4EAgE4nU50dnaitbWVKugQCAQQCoVU2FYsFlNhXbKVC+lVFAqF4PF44HA44HA44PP54HA4YLPZYLPZVPj3XIOGrO4lYTKZYDKZ05rqkkVGgUAAwWCQei5ZeOL1egGAqij1+XwIBAIIBALw+XzweDzUZyTDpB6PBx6PBw6HAy6Xa9rv5P9wu91wuVzwer0IBoMQi8Xgcrlgs9mIiYmhGkuLRCIolUrExMRALBZDKBRCIpFAKpVCKBRCLBYjJiYGQqGQ9spdJfRRo5lRkMqrsbERZ8+ehcfjwa233gqNRkMriSgnFArB4XCAwWCAy+XOeE/TjSAlJQVFRUWIjY1FXV0d4uPjkZ2dPWvbgDCZTPD5fCpH73zINiuTk5MwGAyYnJy8IPdLr9dT+Xterxd+vx/BYBChUAhCoRB8Ph98Ph9CoZDK3yNz98437LhcLjgcDmUI8vn8aS1byOeSMBgMygg8F9JIO9ewC4VCCAaDcLvd1HNIo83n81FG3ecZdm63+6KGHSkHh8Oheso5HA5kZ2ejpKQEarUaSqUS8fHxUKlUlJEXHx8fESPuZiL0SkczoyBHx7z22muwWq1Yt24d5s+fTxt1M4BgMAiLxQI+nw+BQEBXNl8FQqEQKSkpuO+++/D6669DpVJhyZIlkMlk4RYtImEymVAoFFAoFJd8XigUgt/vx8TEBKampijDb2pqijL6LBYLTCYTlWNmNpupggGy6IP0/IUTiUQCPp8PHo8HkUgEuVwOsVgMiURCNYEmPWxKpRIymQwymQwqlQp+vx9vvvkm3nrrLUilUnzzm99EYWEhda/SRtzNgV7taGYUFosFv/nNb6DT6bBgwQI88cQTsyLPZTYQCARgNpuphYfm6oiJicEjjzyCHTt2oKWlBa+//jq+/e1vh1usqIb0Ims0GsTHxyMUClEeMvLn87+CwSDVdJeMNPh8PrS2tuKVV16BwWDA+vXrUV5eToVQzw+vklWmZHiVhMlkgsfjXVDFy2KxqGpyDodDeQ/J5woEAsp7S4avyS8Gg0HlupGPkWFi8meCIPDwww+juLgY3//+9/HTn/4Ua9aswcMPP0xvxG4itGFHM2OwWCzo6+vD/v37UVBQgMrKSiQkJIRbLJrrBBmKJXOWaK4ONpuN+Ph4VFVVYWBgAIcOHcL9998PmUxGe7avEtITdX6o9EpxOp3o7+/H5OQk5s2bh6qqKpSWllIFH6QxSEIaiIFA4AJ5OBzOBZtaBoNB3TssFouSl8zrux7tXBITEwEAd955JxoaGnD48GHExsZi/fr1s7KnXDig72KaqIdUdDqdjsqt27RpE6qqquiGozOIYDBIGXZ0P7Grh0y2J8eMnTx5EgMDA8jNzaULUsKM3W7HxMQE+vv78cQTTyAnJyfqNqdisRhpaWl45JFH0N/fj66uLrz11luoqqpCXFxcVDXGjlbommGaGYHX68XOnTuxefNmLFu2DKtXr0ZhYWG4xaK5jpwbiqUN9mvn9ttvR3V1NWQyGX7/+99Dp9OFW6RZz+HDh3H8+HEIhUKsWrUKycnJ4RbpquBwOCgtLcUvf/lL3Hbbbdi/fz9++MMforW1New5hLMB2mNHMyN46623cOLECTAYDPzXf/0X1Go1vSucYZDFE2KxmM6xuw6wWCzMmTMHd999NzZv3ozbbrsNQqEwao2JaIbMszt58iSGhoawZs0aSCSSqA2Nk7o3MzMTGzZsgEQiwXPPPQepVIqRkRHcddddYZZwZkN77GiimkAgAJPJhIMHD8LlcqGkpARFRUW0R2cGQubYkW0haK4eMjk+OTkZlZWV4HA4aGpqQnt7O2Vk0Nw8QqEQdDodBgYG4PP5sGDBgmm96aIVkUgErVaLpUuXoqCgAL29vTh48CDa2trg9/vDLd6MhTbsaKIWgiDgdrvR3d2Nw4cPQyqV4o477oBCoYjanS7N50M2KCYbu9JcO2q1GuXl5SgtLcWJEydw/PhxesENA4FAAI2NjdDpdBAIBKiurp4xOkyhUKCkpASPPvoofD4fDh48iK1bt8LhcEyr8KW5ftCGHU3UEgqF0NfXhyeeeALJyclYs2YN7rjjjnCLRXODIEeK0Tl21xeJRIInn3wSTqcTx48fx969exEKhcIt1qzC6/Viy5YtUCgUqKioQFZW1owam8Vms7Fu3Tp8//vfx/Lly/Hzn/8cb731Fnp6esIt2oxkZmwJaGYlTU1NOHjwIPr7+/HUU0+hsrKS7lk3gwkGg7DZbLTH7jrD4XCQm5uLyspK9Pf3Y8uWLVi6dCnV04zmxuJ2uzE1NYX6+nqsXbsWc+bMmXHHneyBN3fuXLBYLAwPD+ONN96gpmDk5OREfdg5kphZVw/NrIAgCLhcLjQ1NaGhoQFxcXGoqqpCcnIylTtEM7Mg51+6XC66j911hslkQqlUorKyEiqVCg0NDRgaGoLL5Qq3aLMCi8WC3t5emEwmZGRkIDs7O9wi3TDi4+NRVFSEW2+9FWazGY2NjTh27Bjsdjsdlr2O0IYdTVRBJnaPjo5i3759aGxsxEMPPYTs7Gx6LNIMhuyw73K5IBaLacPuBrB27VrMnz8fer0eO3fuxNjYGF1EcYMhCAJDQ0PYu3cvZDIZSkpKkJubG26xbigajQaPPPIIVq1ahc7OTmzevBnd3d3w+Xz09XadoA07mqjD7/fjqaeewvDwMMrKyvCNb3yDbqw6w3E6nbBarSAIAjKZjM6xuwHEx8ejoqICGzduxF/+8he0tLTQXrsbTCgUQmdnJ3bu3ImNGzciKSlpVqSTsFgsPP300/jyl78MiUSCBx98EI2NjbBareEWbUZAG3Y0UYXRaMSJEyfQ1NSE3NxcrF27FmKxmA6/znA8Hg9lZNAeuxsDk8lESkoK1q5dC4/Hg9OnT+PUqVPhFmtG093djd7eXlitVlRXVyMmJmbG6zLy80mlUixfvhz33HMPgsEg3n77bRw9ehRer5f23F0jdPEETdQQCAQwPj6OAwcOwOVyobi4GIsWLZoVO9zZjtfrpQw7oVBI97G7QcTGxlIzljs6OiCXyzF//nx6DNQNoq2tDSMjI+DxeCgpKYFEIgm3SDcFspiisLAQIpEIBw4cwLFjxyAUCpGdnY3s7GwQBEFfc1cJ7bGjiRqsVisaGxvx17/+FXfccQcWL16M9PT0cItFcxNwuVyw2+0APt3p06HYGwOXy4VSqcSjjz6KiYkJbN++HYODg3Ri+3WGzBXeu3cvJiYmsGDBAiQmJs66iSo8Hg/p6enYvHkzeDwe9uzZg1/+8pcIBALhFi2qoQ07mqhhy5Yt+PDDDyGXy/GNb3xjxicZ0/wL0rBjMpmQSqWzbgG8mXA4HHzpS1/CvHnzEAqF8Mwzz8DhcIRbrBlFIBDA8PAwGhoawOFwcOedd864FieXC4fDgUajwVNPPYWqqiocOHAAzz//PEZHR8MtWtRCh2JpIp5AIID+/n6cOnUKFosFa9asQWJiIu21mUV4vV54PB7w+Xyw2exZuwjeLORyOaqqquB0OnHs2DF0dXUhOzsbCoUi3KLNCDweD+rq6uD1eqkWILM17MhgMMDhcFBcXIyJiQmMjY1hx44dSEtLA4PBQFJSUrhFjDpow44moiEIAj6fDydOnEBnZyfi4uLw5S9/GUKhkM6tm0V4PB54PB6IRCKwWKxZuwjeDMhekPPmzYPL5cK7776L2tpaiEQiyOVy+thfIwRBwOl04sCBAxAIBEhNTUVGRka4xQo7iYmJWLhwIYLBIP7zP/8TR44cAZfLhUqlAofDoa+7K4A27GgiGr/fj6mpKfz0pz9FUVERVq5cifnz54dbLJqbjNPphNPphFKppA36m0R6ejo8Hg9uu+02PP/882Cz2cjIyACfz6cX2WsgEAjAYDDgvffew4MPPoiqqiraA/0ZWVlZUKvVaG9vx7Zt29DZ2QmVSoWysjL6vr8C6KuJJqJpa2vDG2+8AZfLhbVr12L16tX0dIlZiNvthsfjgVwupxfBmwQZBnvssccQCoVQV1eHHTt2hFusqKe3txcnT56Ey+XCokWLUFhYSOu0z2AwGBAKhXjsscdQXV0Nu92Op556CuPj4/B6veEWL2qgNSRNxGKz2dDZ2Ynjx4+jqKgI+fn5SExMDLdYNGHA6/XC6/XSPQtvMiKRCPn5+cjNzYXBYMDRo0fhdDrpKtlrYHh4GGfOnIFGo0FKSgqUSmW4RYooWCwWMjIysHTpUmRlZaGrqwuHDh3C+Pg43d/uMqENO5qIg2wFMDg4iMbGRpw+fRoPPPAAMjMzIRQKwy0eTRggPXZSqZT22N1E2Gw25HI5br/9dgQCAezfvx+jo6N0O4qrgJx33NXVhYaGBsyfPx/x8fF0Edh5MBgMMJlM3HHHHVi/fj2EQiFefPFFnDlzhvbaXSa0hqSJSAiCwO9+9zvU1taisrISX/7ylxEfHx9usWjCBJ1jFz4YDAYeeOABLFmyhBoFNTk5GW6xopLu7m60tLRgdHQUmzZtor11l0AkEmHJkiV44YUX0NnZiTfeeANvvfVWuMWKCmjDjibicDgc2L17N9ra2qBWq7Fp0yZwuVw6BDeLIatiaY9deODz+ViwYAHWrFmDY8eOoampie4zdhUcOnQIBoMBiYmJKC4upvsxXgIGgwGxWIzs7Gzcd9990Ov12LZtG1paWuDz+cItXkRDa0iaiCIQCMBisWDv3r0gCAIZGRmYP38+nVw8y6Fz7MIHGRrLzs5GVVUVvF4vTp8+jb6+PoRCITrv6TIgCAKBQAD19fXw+XzIysqCSqUCm003prgU5CSU1atXQy6XY2RkBAcPHoTdbqfTAS4BbdjRRBROpxMDAwP4+9//jgULFqC6uhrJycn0Yj7LcbvdcLvdtMcujCQlJaGiogKLFi3Ctm3bcPjwYfj9/nCLFRUEg0E4HA4cO3YMIpEINTU1AEDrtcuAxWJhzZo1WL16NRITE/HLX/4S/f39cDqd9Kbic6A1JE1EsXPnTjz//POQyWR44IEHsHjx4nCLRBMBnJtjRxt24SM2NhZPP/00AODYsWN4/fXX6cX1MjAYDPjggw/gcDhQUFCAW265JdwiRR133303vvnNb0KhUOBHP/oRtm/fjmAwSF9/F4HWkDQRAUEQ6OnpQUNDA3p7e/GlL30JycnJdBXsLIcgCPj9fni9XgSDQUgkEtqwCyMcDgdarRaLFi0Ci8XCjh07YDAY6JynL8BiseDQoUPIyspCWloa5HJ5uEWKOiQSCbKzs7Fp0yaMjY3hxIkTOHz4cLjFikhoDUkTdgiCQCgUwunTp9HT0wOfz4f169dDoVDQFZA08Hq98Pv9IAgCYrGYNuzCCIvFgkwmw9KlSxEXF4empiZ0dHTA6XSGW7SIxev1wmAw4PTp0ygqKkJqaip4PB4dhr1C2Gw24uPjceedd0IsFqO7uxv79u2Dy+Wi+yqeB60hacJOKBSCy+XCH/7wB9hsNqxZswZLliyBSCQKt2g0EYDZbIbX6wWbzYZCoaANuwjgjjvuwMqVKxETE4Nnn30WAwMD4RYpYhkcHERLSwu6urqwZs0a5ObmhlukqEUgECA7OxtPPPEE+Hw+3njjDZw6dQoulyvcokUUtIakCTujo6P485//jImJCcyfPx8PP/xwuEWiiRAIgoDVaoXX6wWLxaJHikUILBYL8+bNw7e+9S3U1tZi3759aGpqCrdYEUltbS1qa2uRm5uLwsJCxMbGhlukqGfNmjVYu3YtcnNz8eMf/xh9fX108+JzoDUkTVix2WwYGBjA3r17UVxcjKKiIiQlJYVbLJoIwuFwwO/3g8Vi0aHYCIHBYFDD2dPS0tDU1ITa2lp4vV46mf0zCIKA2+1Gd3c3RkdHUVVVBalUCg6HE27RohoGgwGZTIaysjIsW7YMQ0NDOHnyJLq7u8MtWsRAa0iasECODRsbG0NbWxvq6uqwevVqlJSUQCKR0PknNBTnGnZ08UTkIJPJkJmZiZqaGnR0dODIkSPQ6/W0YfcZBEHAaDSit7cXJpMJK1asAI/HC7dYM4aioiKsXbsWAoEA+/btQ11dHb2x+AxaQ9KEDYIgqDExhYWF2LhxI51/QjMNgiBgsVgQCATA5XJpoz/CEIlE+OEPfwitVouuri784Q9/oENinxEMBvH+++9Dp9NBrVbj9ttvpydNXEcEAgEyMjLw61//Gi0tLfjwww9x9OhR2rADbdjRhIlgMIidO3eivr4eoVAI3/3ud+lFm+aiWK1WsFgsiEQi+vqIMMiw2IYNG1BUVIStW7eira0NZrM53KKFFXLSxO7du6FUKlFVVQWBQEBfv9cRBoMBgUCAxYsXo7q6Gm63G5s3b4bVap31Uylow47mphMMBuF0OrF37154vV6kpaWhsrKSngdLcwEEQcDlcoHJZEIoFNKj5SIMBoMBDoeD8vJyFBYWwufz4fDhwxgbG0MoFAq3eGHD4/FgfHwc3d3dUKvVKCoqAovFoq/d6wyLxUJcXByWLFmCxMREdHR0oL6+HhaLJdyihRXasKO56ZBK77333kNCQgJWrFgBrVZL96yjuSh2u53y2NFEJsXFxViwYAEKCwuxZcsWtLe3z+qmxWazGc3NzZiYmEBWVhYqKirCLdKMZsOGDVi8eDFCoRD+7//+D0NDQ7O6tx09gZjmptPc3IwXXngBXC4XGzZswJo1a8ItEk2EQhAEzGYz2Gw2pFJpuMWhuQSFhYV45plnsGbNGuzYsQMAcM8994RZqvAwMDCAt956C+Xl5SguLkZKSkq4RZrRSKVS1NTUgMPh4LHHHkNubi4IgsDcuXPDLVpYoD12NDeVvr4+nD59GrW1tbjvvvuQl5dHjw2juSS0xy46EAqF0Gq1WL16NXQ6HXbt2gWDwTDrPCd2ux3Dw8Nobm7G0qVLodFo6EruGwyDwUB8fDzmz5+PiooK1NfXY8+ePfD5fLOymIK+2mhuCgRBIBgMorm5Ge3t7fB6vVixYgU0Gg3YbNpxTPP5OJ1OKseOJnLhcDiQyWRYsWIFWCwWOjo60NTUNOsW1/HxcQwPD8NgMKCiooJuSHyTEIvFSE1NxdKlS2EwGNDU1IShoaFZmetJG3Y0NwWCIOD1evGPf/wDzc3NWLZsGRYuXEgrPZpLQhAEbDYb1ZyYJrJhsVjYuHEjysvL4fV68atf/WrWJLKTvTmPHz+OlpYWSKVSLFq0CCqVKtyizRp4PB4ee+wxxMfHo7e3F2+99das7G1HG3Y0NwWHw4HNmzejs7MT6enp+OEPf0h3YKe5LOgcu+iCy+Xi3nvvxVe+8hXU1tZi69at6OjoCLdYN4VgMIj9+/dDp9PhS1/6EoRCIR2GvYkwGAzExcXhrrvuQklJCV588UX09fXB4XCEW7SbCn3F0dxwnE4ndDoddu7ciczMTFRVVSE5OZku/ae5LBwOBx2KjRLIdjTJyckoLS1FeXk5PvnkEzQ1NcHpdM5oz0kgEEBHRwdGRkbA4/GwZMkSsNlsWs/dRBgMBlgsFubOnYuKigowmUzs2rULg4OD4RbtpkIbdjQ3DDI0YTQacfbsWbS0tKC8vByVlZUQi8W0wqP5QgiCoAw7gUAQbnFoLhOFQoGMjAysWLECzc3NaG5uxsjISLjFuqH4/X40NDTAZrNBLpejrKyM9taFiaysLJSWliIjIwN79+5Fd3c33G53uMW6adBXHc0N58iRI3juueeQnZ2N9evXY/78+eEWiSaKsFqt4HA4dCg2ykhISMB3v/tdaDQa1NbW4s9//nO4RbqhuN1uvP3224iPj0d5eTmSk5Npwy5MsFgs5Obm4gc/+AHq6upw8OBBNDY2hlusmwZ91dHcUD755BPs378fQ0NDePLJJ5GSkkJ76mgui0AgAIfDAZ/PBz6fD4lEEm6RaK4AMnz++OOPIzExEXv37sWBAwdgs9nCLdpVEQqF4Pf7LxpOttvtGBoaQl1dHcrLyzFv3jxaz4URBoMBuVyOqqoq1NTUoL29Ha+++uqsqdCmDTuaqyYQCODs2bMwmUzw+/3THguFQvB4PDhw4ACmpqaQlZWF8vJyOgRLc9kEAgEqL4vH49E5dlEGg8EAk8lEeXk5cnNzIRKJsG3bNkxNTV2gL0gjvqWlBXq9PkwSXxqTyYT9+/dj9+7daG5uxuTkJJVuMjk5idbWVoRCIWRnZyMtLS3c4s56yIKrW265BQKBAJ2dnWhubobX6w23aDcc2rCjuSoIgoDf78e+ffvQ1dUFvV4Pj8dDKTq/34+pqSns3bsXfr8fK1asQHJyMng8XrhFp4kSAoEA7HY7CIIAn8+nDbsohMFgID09HXPnzkVhYSHee+899Pf3w263U8/x+Xyw2Wzo7+/Hzp07MTg4GJFelfHxcbz55pv461//ip07d6KlpQUmkwkulwu9vb04ceIEVCoVsrOzkZSUFG5xafCp13j9+vVITU2FyWTC7t27Z3wRD0CPFKO5SgiCgNPpxE9/+lMwGAwUFRXh61//Ou644w7weDyMj4/j6aefhtVqxfr16/H1r3893CLTRBl+vx8mkwkEQUAkEkEmk4VbJJqrZNmyZUhMTMSePXvw8ssvY2JiAg899BAAoLa2Fnv27MFf/vIX+P1+yOVylJaWRlw7JL/fD4PBgEOHDmH79u3g8XhITk7Gl7/8ZXR1deHUqVN48MEH6UkTEUZCQgJWr16NUCiE5557DrfffjtEIhH4fH64Rbth0IYdzVXhcDgwMjICp9OJUCiElpYW/PrXv8bx48dRVlaGUCiEvXv34v7778eSJUvo5rI0V4zf74fNZqM8dvRIseiFz+cjMTERjz32GD766CPs3bsXiYmJOHr0KE6ePImenh44HA6EQiHodDoMDAwgOzs73GJPg/QgB4NBKtVkdHQU//jHP+ByueByudDT04OWlhYQBEGHYyMAMu1nzpw58Pv9+Oc//4ldu3bB5/OhoqIizNLdOGjDjuaqcDgcGBsboxKKzWYzLBYLXC4XxsbGIBAI4HA4MHfuXGRkZNBjw2iumGAwCIfDAQaDAR6PN6N32DMdFotFDWrfsWMHent7sWPHDpw4cQJdXV3UdAoGgwG9Xo+RkZGIM+xCoRDcbjcVxiMIAi6XC/39/QA+HanW09ODffv2YXR0FHl5edBqtYiNjYVIJAKLxQqn+LOauLg4ZGVlIScnB/X19UhKSkJxcTG4XO6MzPmmV1uaq8JqtaK3t3faTUEQBPr7+9HX1wcejwetVousrCwolUoEg0EwmcwZeRPR3BhIDwmHwwGPxwOXyw23SDRXCUEQ4HK5qKioQHZ2No4fP44///nPCIVCCAaD1PPYbDbGxsbQ1dWFmpqaMEp8IcFgEC6X64L8LHIWqdfrRXNzMxoaGiCVSpGWloavfvWrqK6uRmZmJu1xDiM8Hg8JCQn40pe+hD/+8Y/QarVYsWIF4uPjwy3aDYFOBKC5KgwGA86cOUN1mic5N0zR19eH9evX4z/+4z+wffv2MEpLE434fD6YzWYoFAq66CbK8Xg8GBgYwDe/+U3s3bsXQ0ND8Pv9CAQC0wwlv9+Pjo4O1NfXh1HaixMMBr8w8Z78PDabDWfOnMF///d/48UXX8Thw4dvoqQ0FyMmJgYPP/wwEhIS0NbWhtdeey3cIt0waI8dzVVhsVjQ09NzgWImIStj9Xo9Dh8+DJ1Oh46ODtx6661ISUmBXC6/+ULTRBWkx04sFtPeuiiFIAi0tbXhxIkTOHbsGOrq6mCxWCgv18WYmprC0NAQvF4vOBxOxBQiBINBqvL/iyAIAgwGA0uWLMGiRYtQWFh4EySkuRRMJhNisRhLly5FW1sbDh06hG984xsQiUQzLlVoZn0amptCKBSCzWbDyMjItDDK+RAEAZ/Ph4GBAYyOjsJoNEKr1UIsFtOGHc0XQnpIhEJhxFVI0nwxgUAAVqsVhw8fxq5du3DkyBG43e5L6gzg0/xdg8EAk8mEuLi4iDHsQqHQZfdA4/F4iImJwerVqzF37lykpKTcYOlovghyjuyCBQug0+lQW1uLwcFBZGZmzjjDLjLuGJqogiyWGBoa+sLnkrvbQCCAlpYWNDY2YmBg4EaLSDMDIKtipVIp7bGLQmw2G44cOYKf/exn2LlzJxwOxxcadcC/Wik1NTXB4/HcBEm/GIIgLiie+DxYLBY0Gg1qamrw6KOPIisr6yZJSXM5rFy5EiUlJQiFQnjvvfdgMBhmXF872rCjuWL6+vqg0+kuGU4hYbFY4PF4SEpKwmuvvYZvfetbWLhw4U2QkibaIXPsZDIZnWMXhchkMtxyyy343e9+h/Xr14PP51+WZ4TFYsHr9eLEiRMRZdgFg8ELJmacD5vNRnZ2Nu644w78+te/piu5IxA+n485c+bg1ltvxeuvv06F/WcSM8v/SHNTGBsbg9FoBIPBuOROh8lkQiKRoKysDNXV1Zg/fz5UKhWt7GguC7LdiUajoUOxUQiLxYJAIMD8+fPB4XCQnJyMrVu3Qq/XX3JmZygUgsvlwpkzZyJmwfX7/fD5fJ/7OBnmUygUWL9+PWpqahAXF3dBcRlNeCHPR1paGhYuXIgtW7bg7NmzUKlUyM3NDbd41w3asKO5Ysh8OSaT+bmhFSaTCZFIhKysLKxevRobN25ESkoKreRoLhtyfqhIJKINuyiFwWAgKysL8fHxyM/Px8jICJqbm2E0GuF0Oi/6GjIU29HRAbfbjVAoFPY8O5/Pd0nDjslkQiAQID8/H+vXr0dZWdmMy9uaSWg0GpSVlUEkEuHMmTNISEhATk4OAMyINYoOxdJcMT09PRgdHf3cxxkMBiQSCSoqKvDaa6/h29/+Nm3U0Vwxfr8fFouFzrGbAUgkEhQVFeGdd97Bt771LcybN++S3iyfz4fe3l4YDAa43e6bLO2FeL3ezw0Lk57J5ORk/P3vf0d5eTkdlYhwuFwuVCoVbr/9dpw6dQpHjx6dUXl2tGFHc8V0d3dfsiK2pKQEDzzwAJ5//nlotVrweDzaqKO5Yvx+P6xWK6RSKZ1jF+WQRhyXy8VDDz2En/zkJ/j5z38OiURyyZYm7e3t0Ol0N1naC/F4PBcNCzMYDLDZbNxyyy349a9/DY1GM2OnGcwkGAwGhEIhNm7ciFAohM7OTtTV1V1W3ng0QPuKaS4bsn2JwWCAzWab9hiDwQCTyURVVRWWLl2KBQsWIDs7m542QXNVEASBQCBAtTuhw1rRD2ncxcfHg81mg8fjYWhoCHV1dRgZGYHFYpnmNWEwGOjv70dubm7Yx4t9Xo4d2atu6dKlKC8vp426KILD4SAnJwdqtRoejwenTp3C3LlzZ8ToN9pjR3PZhEIh2O12mM3mafkxTCYTXC4XCoUCmzZtwle+8hWsXbsWLBaLVnI0VwU5g9jlctENimcgMTExqKiowE9+8hOsWrUK6enpEAqF057DZDLR3d2NiYmJMEn5Ly6WY0cWh23atAlr1qyBRqOh9V0UwWKxkJCQgPz8fHC5XBw6dOhzG+5HG7RhR3PZeL1eNDU1TTPq2Gw2BAIBFixYgLfffhubNm1CZmZmGKWkmQk4nU44HA6EQiF6pNgMhcFgIDExEc888wxeffVVPProoxAIBJRnLxAI4OTJk+jr6wu3qPB4PBfk+iUmJuJ///d/ceuttyI9PT1MktFcKytXrkR6ejoOHz6M8fHxiGmxcy3Q8Q2ay8bn86G7uxter5cq79doNHjwwQexePFilJaWgsPh0LtWmmvG5XLB5XIBAF08MUMh9QSXy0VaWhoeeeQRKBQK7Ny5k2pObDAYMDk5CaPRCKVSGTbd4vP5puXYzZs3D8uWLcPKlSshFotpnRfFlJSUoLe3FxwOB4cOHUJNTQ1SU1PDLdY1QRt2EQKZU+T3+xEMBhEMBhEIBBAMBqmu536/H6FQCKFQiHrOua89H/K5F3Mts9nsC5QRuVMmQ6hMJhNMJhNsNhtMJhNWqxVdXV3wer3g8XhQKBSYP38+lixZgrKyMiiVyhtzcGhmHW63m9o5z8RZjjT/gqyiz83NxapVq+B2u8FkMtHa2gqHw4GJiQl0dXUhPT2dahQcDAYp/RYKhSj9R+rKiyXBX0ofkrruYgYak8lET08PFRKWyWRITU1Feno6gsEgpqamqNefqy9ZLBbYbDb1e7hbttBcnJiYGCQmJiIxMRH19fUoLS2lDTuaf3GuwiB//rzv5//t3Pw1l8sFt9sNm80Gr9dL7Rbtdjs8Hg98Ph/cbjc1d5GcqXk+Xq8Xfr9/mtFHvqdUKr0gSZTJZILD4VAd4smfJRIJeDwenE4nTpw4AYfDAblcjsLCQjz22GNQq9UIBoNwu93T8urOVZLn/+3zvtPQAP/y2JGLPu2xiw6uVgeSPxcUFEAul6OkpARPPfUU+vv70dPTg48//hgVFRXw+XzweDxwuVzTdKPT6QRBEPD7/Z9b6HAxfUjC4XDA5XIv2i+Rx+NhYmICQ0NDVF++mJgYOJ1O7Ny5k7o+SX0pFovB4/HA5/Mhl8up38/fTF+NnqS5/rDZbGg0GsyfPx8HDhzAunXrQBBEVB9z2rC7zgQCAej1elgsFpjNZoyNjcFgMMBqtcJkMsFoNMJms8HhcFCGnMPhgNvtht/vB0EQ075IGAwGlUTO5XLB4/EgFAopz5pEIrlAFtI4O39RDIVCGBsbu2BXGwgE4PV6EQgEqC/SoCSTh8l5j263GwaDAbW1tdROl8FgQC6XQyKRQCQSQSaTQS6XQyQSQSKRID4+HnFxcZDL5VAqlVCr1YiLi4NYLKb7PtFMw263w263AwCdYxdl2Gw2WCwWWK1WGAwGGAwGWCwWWCwWGI1GWCwWOJ1OOJ1OWCwW2O12uFwuOBwOKq8yEAhQzYkbGxtx5swZsNlsEAQBDocDgUBA6UIulwuBQAAWi0V5yS6mT0jv2fnXEkEQsNlsCAQCF+hEgiDgcrkovS0Wi2E0GrFr1y5s374dfr+fkvlcI+z8n9lsNoRCIWQyGUQiEaUfpVIpRCIRxGIx4uLiEBsbC6lUCoVCgfj4eCgUCojFYojF4ht0tmgAIDU1FevXr8ff//53dHR0IC8vL6rzJmnD7jLx+XxwuVyUsrJYLLDZbDCbzTAYDLDb7XA6nZSS8nq9VFPLc13ybDabGj0TFxdHKSOyyaVIJKKUFZ/PB4fDoV7P5XLBYrEoN/+5+WwX82iQ3rPzPXPkzvb8kAQZ5jg3xBEMBikPoc1mQ1dXFzIyMsDlcuH3++H1euF2u6nvZCiZVM7BYJBS6D09PdSsRRaLBZFIBD6fDz6fD5FIRBmFUqkUSqUSMTExkEqlkMlkUKlUEAgEdEhulkDeQ3w+H1wulw5jhRGCIOB2uylj22w2UxtSm80Gk8k0zThzOp3T9F8gEJims8jQJI/HQ2JiIqXTzt2I8ng8yngj9UtqauoFevTc15IbTPK9zocMh17sMbIa8mIh3EAggP7+fng8HsTGxkIsFk/TkT6fj4qckDqR9Bye61kk9eG5oWTyGAaDQbS2tlLpNWRRGo/HozbxMpkMQqEQIpEISqXyAn1JPk5vkq8cqVSK7OxsKBQKDA8Po7Ozkzbsoh3SwCFvRo/HQ4U8SVc/eQMaDAaMjY3BbDZPM+wcDgdcLhecTielcEjXvFwuh0AggFwup1zz5C5NIpFAKBRCKBRSRgyfzwePx6M8dKTCCycejwcWiwXNzc0oLi4Gn8+H1+ulPjP5ndyNn7sQkLtzo9EIg8EAl8sFj8czLSzC5XKpHaxEIqEMO7lcjpiYGCQlJVHH5vwdO2kc0nksMwe32w2fz0d5YujzeuMg+1OSBgkZ7vT5fJROJCMQZrMZU1NTMJvN1P1tMpmm6b9gMDhNBwoEAkrnKZVKSCQSCAQCSucJhULqOeRj5O98Pp+KaOTl5V2ymfGNpLe3F8FgEKmpqRdtuE4adaQDgFw7SP3ndrupSm8yzYD0UJLHzWg0wmq1Uvml526SWSwWZbgJhcILDLuEhARqI0yGfjkcDrUGkb+TIeNoDjPeCHg8HuLi4pCcnIzJyUn09PSEW6RrYtYZdp/Xo4ZMgh0bG0N3dzc6OjowPDyMkZER9Pf3Y3JyksrbYLPZlNs8OTkZ2dnZiIuLQ0xMDNRqNeLj4ynDJC4ubkY06eXz+VCr1Vi9evU1/y/SUB4fH8fU1BRMJhN17KempjA1NYXa2loMDg5SShIAxGIx5HI5EhMTUVBQAK1Wi+TkZGRlZSEvL4/ydn4e0X4OZhNOpxM+nw8KhYI+b9fIF/XlCgQCGBsbw8DAAMbGxiiPxcjICCYnJzE+Pg6r1UrlHbFYLKjVasTGxiI+Ph4pKSlQqVRUFILUhXK5HHFxcRfN5402vqiFE2nI8vl8SKXSa3ov0iCcmJiAyWSiokTj4+MwmUwwmUwYGhpCfX09ld5zrqdPIpEgNTUVGo0GGo0GmZmZyMrKgkajQWJiIpKSkr7wmpht9xwZLq+urkZdXR0aGxupYxSNx2LWGXZ2ux2Tk5NobW1Fb28vxsbGMDQ0hN7eXrhcLipEqdFooFKpkJ6ejsWLF0OlUiEmJgbx8fFITEykkmFJb9q5IdJzQwS0p+FCuFwutdvMyMiYFp44tyI4EAhQHoHR0VFMTk7CZDJBr9djcHAQp0+fhs1mg8/nA4/Hg1KphEqlohRZSkoK0tLSkJube9EqYJrIxeFwwOfzQS6X0+ftGiGr5vv6+jA8PIzR0VH09vZicHAQExMT0Ov1VI4Zl8uFRCJBcnIytFotysrKEBsbC7VajZiYGCiVSsTHx1ORBDJN5Hz9R/5M68Arh8PhUPrx3NQYMgfw3I4JZB70+Pg49Ho9zGYz9Ho9JicnYTAYMDg4iBMnTlCGOYfDgUwmQ1JSElQqFRITE5GbmwutVguNRoOUlJSoN8KvFjabjZUrV6K+vh79/f3o7e1FWlpaVKb/RJ/ElwFZeEAaAiaTCePj49QOyGg0YmJigip9ZzKZyMnJoUIBpKdNJpNBJpNNc3FLJBLI5XJ6qsI1QO76L0eBkOHe5ORkqujEZrNhamqKStAmQ7xk6EKn00Gn04HH40EikSApKQmxsbFQKpWIi4uDRqOhkpTpasvIhMzXlEgk9H12BdhsNqpQa2JigipWMBgMmJiYgM1mg9vtprwRCoUCMTExkEgkUCgUkEgkkMlkiI2NpdJGyL+JxWIIhUJIJJIZEYWIVK5EP5J50SqVispztNvtVA44uTE2Go2w2+3U3wDAarXCarWio6MDUqkUUqkUcXFxUKvVkMvlVAGHSqWizv1MhslkIjs7GzExMRgaGkJrayuSk5Npwy5ckEmvZF4Imcjf3t6OoaEhjIyMoKurC729vZSHRyKRID09HRqNBlqtFtnZ2UhOToZarUZKSgrt4YkQyORhhUJx0ce9Xi8mJyfR29tLGXS9vb3o6uqCyWSCzWajOtyr1Wqkp6cjNzeXOvdk3h6Zr0fmMtLnPryQhh3d/PXikAs6mctFJu2Pjo5Cp9NhaGgI3d3dGB4epsKpACAUCiGXy5GZmYm0tDSo1WokJCQgPT0dycnJkEqlEAgEYf50NJcLGUJUKpWf20eUIAg4nU4qnNvf30+F2UdHR9HZ2UmlPjAYDKSlpSEhIQEJCQnIyMhATk4OFbHi8XiUnuTxeDPKwcFgMJCUlISEhATodDqcPn0aq1atispiFAYxAwajkYn9J06cQH19Pc6cOYOTJ0/CYrFQiaZFRUXIz89Heno6cnJyUFVVRVWZfh4z5YKdyVzq8iUTkk+dOoX29nb09/ejvb0dZ86cQTAYhEgkQnZ2NpYtW4aSkhIUFxejsLBwWrsCmvDw9NNPo7W1FQqFAps3b4ZIJAq3SBGFz+eD0WhEfX09zp49i7Nnz+LYsWMYGxuD3+8Hh8NBeno6MjMzkZKSgqysLFRWViI9PR2xsbGXDI/S1/7M44uWeTLHubW1FWfOnMHg4CAGBwfR3t5OFTHFxMSgsrISZWVlyMvLQ3l5OZKSkmZM6JY8Rq+++ioOHjyI9vZ27N27F7GxsWGW7MqJSsPObDZjfHwcR48eRWtrK0ZGRqDT6eDxeKBSqRAXF4fU1FSUlJRQ1UJkGTj5RXoCaCU2cyFzUMiqNHLe4/j4OAYGBiivRn9/P3w+H5hMJhITE1FYWIjs7GwUFRVRY9LoPKGby/e+9z10dnYiOzsbzzzzzIwPA10Kv98Pk8mE3t5e1NbWoqenBzqdDqOjo/D5fJBKpYiNjYVWq0VmZiaVJE9WlZKV45/XKJeG5tyKXrK9jMfjgc1mQ29vL0ZHRzE8PIze3l7o9Xr4/X7w+Xykp6dTX2VlZSgsLKQq2aOVuro6fPLJJ/jNb36Dw4cPIzMz85oLYm42URGKJVtnkMpsfHwco6Oj6O7uht1uRyAQoHKnkpOTodFoKM+cUqmkmzvOUsg8lXMbkhIEAa1Wi9TUVIyNjSEtLQ09PT2YmpqC0WiE0+lEZ2cnxsfH0dHRgY6ODqqRckJCAlQq1YwKP0QqZLsHkUg06461z+ej+sPpdDpMTExQkw/GxsaoIi+yAj8+Ph5qtRoZGRnQarVU2Iw24GguF7I1yrmecbLoJjExEZOTk9DpdEhPT8fo6CiMRiPV5qa7uxtjY2Po6elBS0sLdf0lJiZGZf/RxMREJCcnw+/3Y3BwkKrsjiYi8miTOXPk7NSJiQkMDg7i4MGDOHjwIHQ6HaxWKzUGpKCgAKWlpaisrKTGYdHQXAxygkd2djays7NRXV0N4NNQxODgII4fP46TJ0+ira0NAwMDEAqFKC0tRWFhIRYtWoTKykqIRCKqJxTt9b0xkG1uZnrxBBkwObfBrdlsxsDAAFpbW3H48GG0tLTAZDIhGAyiqKgIhYWFyMnJQUlJCcrKyiAUCsPe55Jm5sFgMMDhcKDVaqHVaqm/e71eGAwGdHZ2ora2Fm1tbeju7sbHH38MPp8PrVaLvLw8LF68GBUVFYiPj4dEIqGaU0e6zkxISEBqairEYjE6OzuptlrRRESGYr1eL8bGxnDo0CG8+eabVCK8QqFATU0NysvLMWfOHJSXl1/QcRygc0RorozzZ/aSHeHHx8fx8ccf4+TJk+jq6kJ/fz/UajWqqqqwaNEi3HXXXYiPj6cX1RvA3XffDZ1Oh4cffhgPPvjgjB0pRk6BOXv2LJqamvDxxx/j+PHjcDgc4HA4qKiowLx581BUVIQ5c+ZQkxpIfUfPEqW52ZD6kuwqEQqFqJYrBw8eREtLC86cOYOGhgYqvaW8vBx33303KioqkJCQENHOF4Ig0NLSgh/84AeQy+V48MEHsXbt2nCLdUVEjGFHzuM7ePAgGhoaMDg4iPHxcahUKqSlpSE1NRX5+flQq9VQKBTUFAeAVmo0159AIACPx0O1jDAYDBgaGkJDQwN0Oh1sNhtkMhnmzp2LgoICFBUVIS8vL+J3o5EOWe15xx13YGpqCv/5n/+JDRs2zLi2NGRFYkNDA06ePImxsTHY7XbIZDJkZGQgJSUFWq0WSUlJUCqV1PxQPp9PX180EQfZlUKv18NqtcJisVAVt6OjoxgdHYXNZoNarUZycjLKysqwaNEiqNXqiAxz9vb24q9//SsOHjyIb3/72/jKV74SbpGuiLCazaSlT+bO9fT04MiRIxgYGIDX64VCoUBVVRVKSkqQnZ2NrKwsun8SzU2BzWZDLBYjMzMTmZmZ8Hq9mJqagkKhQEtLC7q6ujA8PIyGhgZMTU1Bp9PBZDIhMTGR2ngA9KbjaiBnawKYMTl2BEHA6/XCbrdjZGSEKtppa2tDb28v2Gw25HI5ysrKMG/ePGRkZCA1NRVCoXBGfH6amQ2TyYRQKERqaiqAT9d2p9OJjIwMdHd3o7W1FU1NTVT/UXIkXXp6OjUNQ6lURkz+skAgQFZWFt577z2YTCa43e6oagN00w27c8NePp8Po6Oj2LJlC44cOYLa2lrExcVh2bJlWLBgAW677Tao1eqorrChmRnweDwkJyfj3nvvxd133w2r1YqDBw/igw8+wLFjx/D6668jMzMT9913HxYuXIj58+dT120kKKpogSAI2O12+P1+sNlsyGSycIt01Zyr68iRhc3NzXj11VepqSkpKSlYv349Fi1ahJKSEmg0Gvp6oYl6mEwmJBIJqqqqUFVVhVAohImJCRw5cgR1dXXYtWsX/vnPf1JdCL7yla+guroaUqk0IlIMJBIJiouL4XQ6MTk5iYmJCWi12qi5N296KJYgCIyNjeHw4cPYt28ftm/fDqVSidLSUqxevRpr1qyBQCCgktNpDx1NpEFONiGbwk5NTaG7uxsvvvgimpubwWQykZ+fj29/+9tUyx2ayyMQCKC7uxuPPvoo2Gw2nn/+eeTm5kbl5i4YDMLhcGDnzp14//33qWrrqqoqrFq1CmVlZSguLqYavl7utAEammjj/IJIr9eLkydP4tChQ6irq0NraytycnIwb948rFixAqtXr6by58NBMBiEx+NBcXExFixYgDvvvBMbNmyIGlvkpnnsQqEQrFYrTp06hY8//hjDw8OwWq1YtWoV5s+fj6ysLGRkZFDNM6PlANLMPsg8OnIqBtkm4Bvf+Aba29sxODiIvr4+/OUvf0FhYSFKSkqwatWqGTEM/UZDeuyCwSD4fH5UVsUSBIH+/n60tLTg9OnTOH36NJhMJkpLS3HXXXdh7ty5SE9PR3x8/OdOVKGhmUmcOyaNy+VCJBKhvLwccXFxqKysREtLCzo6OqgUhdOnT2Pp0qXIysoKS0Uqk8kEl8uFVquF1+tFX1/fTZfhWrjhhh1BEHC73TAYDOjr68Pu3btx8OBBCIVCpKWlYcOGDaiurkZMTAzdBJYmKhEIBBAIBEhISEBxcTHa29uxZ88e7Nq1C1NTUxgZGYFSqURBQQHkcjmVqxFtBsvNgCAIOBwOBAIBsFisqJo44ff7qQTy48eP4/jx46ivr0cwGMTChQtRUVGB6upqaLVaWtfRzFpIvafRaKDRaFBaWorS0lJs3boVjY2NaGtrw8jICNWYOxgMQq1WU17tmyUjk8lEamoqLBYLhoeHb8r7Xi9uaCiWDFk1NTXhlVdewf79+zE6OoonnngCa9asQVVVVcQkS9LQXE+CwSBOnDiBV155BYcPH4bJZMIPfvADrFixAnPmzAl7Dkmk4vV6sX37dvziF79AYmIiPvzww6jQEQRBQK/Xo729Hb/73e9w6NAhpKamYuHChXjyySehVqujcuYkDc3NxGKxYHBwEM888wyOHDkCDoeDBQsW4Kc//SmSk5Nv6uzoUCiEP/zhDzh69Cj8fj927NgBIDp09g312A0NDeGTTz7BH/7wB4jFYlRUVOCvf/0r8vPz6bAUzYyGyWRi7ty50Gq16Ovrw9tvv41XXnkFu3fvxrx58/DjH/8YQqGQvgfOgyAImM1mKlwTDccnGAzigw8+wAcffIDm5mbw+Xz86le/QmlpKbKzs6lqPxoamksjkUiQl5eHzZs34/Tp06irq8OOHTtw55134s4770RNTQ1qampumjxpaWk4evQoxsbG4PV6qab0kc51N+zIJMmGhgacOnUKu3fvRnJyMiorKzFnzhyUlJRAJpPdNEXn8XgwNTWFN95446KPazQaLFy4EOnp6ZRMBEHg1KlT6OnpucAFy+Vy8eCDD0Iul0dd09QDBw6goaEBfr//c5/DYDDA5/MRExOD+Ph4FBUVIT4+nl6YrhAGgwGBQACNRgOBQIANGzaAzWajv78ftbW1eO2113DrrbciISEhqsrobzRkKJbD4UAgEES0EvX7/ZicnER9fT3ee+89WK1W5OTkoLq6GkuXLkViYiKUSuVNl6ulpQWffPLJF97nPB4PMpkMcXFxmDt3LmJjY2dcv0DgU8+LXq/H7t27odPppj3GZrMhkUhw//33X9BPjSx+efHFFxEIBKY9tmrVKsydO/eGyz7bIPPwNBoN5syZQ/Wr3bNnD1pbW2EymeByubB06dIbvjFmMBiUp93tdkOv10OtVkdFGsV1NexIo25qagq7d+/GsWPH0N7ejv/6r//CLbfcgpycnJuuqH0+H8bGxvDCCy/A4/FQQ45FIhFEIhEqKiqg0Wig1WqnXSTNzc3Yu3cvTpw4AaPRCD6fDx6PB6VSiVtvvRUikeiaDTsyVO1wOOByuaheVjeqK3dtbS1effVV2O12GI1GAJ+28Tg/gVsgECA+Ph7Z2dkIBAJYuHAhxGLxVctFtnswGAxgsVhUUvxsgMViITY2FrfccguSkpLw7rvvYuvWrXjxxRehUqkwZ84cpKSk0HM9P+N8wy4SIfWcwWBAc3MztmzZgrq6OtTU1GDVqlX48pe/HNbwcUdHB/72t7/BbrfDbDYjEAhQ9/m5MvH5fCiVSqSmplIe5piYmBk3SYU8Vx988AFqa2spfcvn8yGTyZCZmYkNGzZc1LCz2+148cUXYTAYEAgEqEIptVqN4uJiGI1GsNlsCIXCqMoHjQYSExORkJCAiooKcLlcfPTRR9i/fz/MZjOSk5ORmpp6QXuU601CQgLEYjE12jRq7g/iOhIIBAi9Xk+sW7eOSE5OJpYtW0Zs376d8Hq9RCgUIkKh0PV8u8uCfN9gMEjs2LGD+MpXvkIIBALie9/7HtHR0UEEg8GLyka+RqfTEdXV1cSzzz5LHDx48HOff7WyORwO4he/+AVRUFBArFu3jujr67vm/3up93M6nURDQwORlpZGpKenE5s2bSL8fj8RDAYJj8dD6PV64qOPPiJuu+02QqlUEjwej/jHP/5B9Pf3X/X7er1eoqenh8jOziZqamqI3//+99fxU0UPoVCIMJvNRF1dHVFSUkIkJSURX/3qV4mzZ88SwWAw3OJFBHa7nfjud79LrFmzhvj+978fbnEuSjAYJEZHR4mvfvWrRFZWFpGQkEC8//77xPDwMKUbwqHrSEKhEOHz+YjGxkZi8eLFhEqlIu68807CYrEQwWCQ8Pl8hMlkInbu3En827/9GxEbG0twuVziT3/6E3HmzJmwyX2jOHcNOH36NLFp0yaCw+EQa9euJf7yl79QOv1Sr3vmmWeIBx98kPjNb35D2O12wu12Ew0NDURWVhaxatUq4pVXXgnDJ5v5nHs/NTc3E3/84x+JmJgYorCwkPjDH/5ATE1N3bB7LRQKEV6vl3jiiSeIlJQU4v333ycsFssNea/rzXX1Kba0tOAvf/kLmpub8dBDD+G///u/MX/+/LAO/iXfl8lkXuBCPXfe4vmyXWwW46Wef7UQn+3+ySHgxEVqWU6ePImVK1fi0UcfxbFjx676vUi5z0/cJz8Xh8OBTCbDokWL8MADD+DOO++Ez+fDK6+8gvr6+qt+X+Izz+SlPuNsgMFgQCwWIzc3F3/5y19QWVmJ7u5u/OhHP8Lk5CS8Xm+4RQw7BEHAarWCx+NFpAfE4/FAp9Ph8ccfR1tbG4qKivDSSy9h8eLFUKlUF9xj4eBcXUX+Tn5nMplU+HHevHm4++678ZWvfAWBQADvvPMO9uzZEza5bxTnHo+EhASsWbMGHA4HbW1tOHny5CVfR16PHR0dMBqNuOWWW6giGFqn3XjOvZ9Iz+rLL78MkUiEjz76CE899RSsVusFofLrBZvNhkgkglAoxMTExA17n+vNdYv5DQ4O4vTp0zhy5AgKCwuxYMEClJWVhSXHJJpgs9koKirC+vXrERcXd9EQpdlsRm1tLex2+w0dRkwavzExMSgoKMD4+DgIgkB7ezt0Oh18Ph84HM4VL1osFgsSiQQbNmxATEwM8vLybtAniHzIRbW8vBwrV67EkSNH0NTUhAMHDqC6uhqJiYmzOiRLEAScTic4HE7EVZGGQiGMjIzg+PHjOHPmDMrLy7Fo0SLMnz8fcrk8KnJvgE8XSzabDYVCgaysLBiNRhAEge7ubgwMDFz1fR4NSKVSFBQUQCaTwWq1YmhoCOPj44iPj79oqkkoFEJ3dze8Xi9kMhlSUlLAZDLBYrGgVCpxxx13IDY2FpmZmWH4NLMLkUgELpcLoVCImpoaNDQ0oLGxEfv378eyZcsuSDW4VkiDUigUQigUwmKxIBgMXrf/fyO5ZsOO9MYcO3YM+/fvx5kzZ/DSSy+hoqICcXFx10PGGQtZqHD77bfj9ttvD7c409BoNNTcv4mJCRiNRng8nqvKL2Cz2VCr1fjd7353vcWMSsjzfv/990MikaCjowMvvPACNBoN4uPjoyOH4wZBEARsNhvkcjmEQmG4xZmG1+tFQ0MDXnzxRXC5XNx7771YvHgxNRc4GomJiaGMEoPBAL1eD5fLFdWj3C6FWCymmt52d3djYmICzc3NWLZs2UUNu0AggMOHD0MikSA1NZU610wmE2lpabROu8lwOBzExsbiO9/5Dl566SW89dZb2Lx5M7KysiAWi29I8Y9QKIREIqF66kUD17zFDAQCGB8fx+bNmzE+Po7vfOc7WLNmDWJjY6+HfDRh4vzQQrjDSzMRsViMlStX4ve//z1aWlqwbds2HDp0KNxihRWCIKhipUgrsHn33Xfx4YcfYnBwEC+//DKWLFkyIw2gmX6fs1gs3HnnnUhLS4PRaMTbb78Np9N50ef6/X68++67SEtLw7Jly26ypDSfR1xcHB566CE888wzqK+vxxtvvIGjR4/ekPcSCAQQiUSwWq0IhUI35D2uN9fssXM4HNi6dSt8Ph/Ky8uxfv36WVHh19PTg6amJrz33nvU3xITE/GjH/0IH3/8MXp7ezE2NgYmk4nk5GTk5+dj2bJliIuLA5PJhN1ux9mzZ/Hiiy/CbrdT/+PZZ59FWloaQqEQPvnkE+zbtw8NDQ3weDzo6enBr3/9a2zZsgXAp7vGO++8Exs3brzun0+n02FgYADAp5VBMTExYDAYqK+vx+uvv47JyUnquf/2b/8GhUKBI0eOoKOjAy6Xi2q1cO+996Kvrw+nTp2inr948WJ84xvfmBbuIUNwLS0tOHPmDNra2mCxWMBisSAUCqkqtIyMDOTm5lKvDQQCcDqd2LVrF3p6ejA6Ogqr1Uq1bElJScG6deuQkJAQcR4gBoMBmUyG3NxcLFu2DJ2dnRCJRKipqZm1hjTpseNyuRFTFRsKheByubBnzx6YTCbcdtttyMzMhFAojPpzpNfr0dXVBQBQqVRQqVQQCoUIhULo6elBb28vGhsbodPp4PF4QBAEYmNjMW/ePBQUFCAvL++i16rZbMbw8DD27NlDhTLJHLeamho4HA40Nzejo6MDBEEgJiYGZWVleOihhyAQCKhWI5988gm6u7sxMjJC5V4qFAqkpKTgtttug1qthlgsvqLPzGQyUV1djWPHjqGjowMnT56E2WyGTCab5vGxWq3o7++H0WiEVqtFTk4O1XB/z549aGxspJ67bt063HfffdPWPuKz8XinTp1Ce3s7urq6YLFYqLyt9PR0rFy5EsnJyYiJiQEAvPfee2hqakJPTw/1v/l8PrRaLb773e9CoVDA5/Ohq6sLr7zyCpRKJW677TaUlJSAyWRibGwMu3fvxv79++Hz+aj/8fvf/x4ajQZutxs7d+5Ee3s7pqamYLfbweFwoNFokJKSgrlz56KwsBA8Hi9ir21SrtjYWBQUFGD9+vWoq6uDTCbDggULwOfzr6vsHA4HPB4PLpdrdhh25GJ8+PBhKl8jJyfneskW0QQCAbjdbphMJpw9exZWqxXx8fFYs2YNhoeHMTk5CYPBAIfDge7ubuh0OojFYqxevRpMJpMaIm+xWNDV1YXx8XGYzWZ873vfg1arBUEQ8Hg8sFqtsNvtIAgCgUAADocDZrMZwKcKyuPxXLfPRBZxWCwWnDlzBmfOnAGDwUBJSQmSkpLAYrHg9/thtVrR29uLkZERGI1G5OTkIC0tDaOjozAajTCZTDAYDOjo6EBNTQ2sViumpqbQ0dFBNZ/9+te/Pu19fT4f6urqcOLECbS1tcFmsyEYDILNZoPP50Ov18NgMGBiYgJCoRDp6ekIBAIwm804deoUPvnkEypcHAwGwWKxMDk5iYGBAfD5fCxZsgQpKSkRl5BPttBZsmQJPvjgA3R0dMBkMkGpVEasYr2RnJtjFymGXSAQwMjICLq7u6FSqbB8+fKb2ovzekMm/VutVnR1deH06dNgMBjIzc1FRkYGWCwW9Ho96urq0NLSgtHRUWpwO9m7z263Uxuo9PT0af/b4/HgzJkzOH36NHbv3g2/3z/tfDY3N2NsbAxHjx5Fc3MzCgsLwWaz4XK5AHx6vC0WC+rq6vDJJ59gamrqove1QCDAwoULkZaWdkXGHZmIr9FowOfzMTIygtHRUSiVymmRJrPZjM7OTsjlcqjVasTGxlJ62+FwYGxsDN3d3TCbzUhKSsLGjRupcG4wGITX68WJEydw6NAh9PT0wOFwIBQKUYUco6OjYDAYmDNnDubMmQOJRAK3243JyUm0trait7cXarUaqampiImJoYwKn8+H1tZWbN++HfHx8UhJSUFRURGYTCaCwSBcLhd6e3upZP+8vDwEg0EYjUb09/djz549sFqt8Hq9CAQCYDKZsNlsGB8fh8FgQFxcHOLi4iJuI3w+ZFuulStX4g9/+AN6enowNjY27Xq8HrDZbLDZbHi93ugpkrmWktpgMEicPXuWUCgUxH/+538Se/fuvZZ/d8PZtWvXtHYnnZ2dX/iasbExorq6mvjlL39JHDp06ILHA4EA8eCDDxLp6emEVColNmzYQHz88cdET08PYTKZiH379hFVVVWEWq0mFi1aRDidzgvKs1977TWipqaGAEDU1tYSPp9v2uM7duwgxGIxUVVVRWzduvWajoHL5SIaGxupdicPPfQQ4fV6Cb/fT7hcLkKv1xMffvghcdtttxExMTEEn88n3nrrrQvanezatYtYs2YNwWAwiKKiIuLrX/86cfjwYcJqtRLd3d3E66+/TnC5XOLUqVOEw+Eg9Ho9sWHDBkKhUBD33Xcf4Xa7qePg8XiI4eFhory8nEhKSiLmzJlDbN++nRgfHyeMRiMxMDBAPPvss0RGRgZRUlJCbN68mfB6vYTJZCJ2795NpKSkEAkJCcS///u/EwcOHCDsdjvR3d1N/O1vfyMWL15MCAQC4re//S3R1tZ2TcfuRhEIBIienh6ipqaGmDt3LrFv3z7C6/WGW6ybTigUIvR6PSGTyYjvfe97EaNPzGYzsXnzZiI3N5f45je/Sdjt9rC2M7kc/H4/0dzcTLU7ueOOOwiTyUT4/X7C4/EQRqOR2LFjB/G1r32NiIuLI3g8HrF582aira2N8Pv9xI4dO4hFixYRGRkZxK9+9StidHSUMBgMRF9fH/Hcc89R9+lPfvITIhAIUMcjGAwSPT09xAMPPEAolUoiOTmZeOutt4iOjg7CbDYTbW1txCOPPEKUl5cTMTExBI/HI7Zt2zZNdrPZTBw8eJC6rx9++GFi3759hM1mI3p6eoj/+7//I5YsWUIIhULi5z//OdHa2npVx+j3v/89MW/ePAIA8bOf/Yyoq6ujHguFQsSxY8eIr3/968Tjjz9+0ffo7+8n1q9fT4jFYuLxxx+fptOcTifR09ND5OTkEBqNhqiuriZ27txJGAwGYnh4mDhw4ACxbNkyQqlUEhs3biRqa2uplkf9/f3E5s2bCYFAQDzyyCPEzp07p8ml1+uJTZs2EbGxsYRWqyU2bdpEtRQjz8H27duJJ554gvja175GvXb37t3E/fffT8jlcuJ///d/icbGRsLpdBITExPE3//+d+K+++4jGAwG8e677xKDg4NXdUxvNoFAgDCbzcS8efOImpoa4tVXXyUCgcB1fY9//OMfxH333UesW7eOGBgYuK7/+0ZxTR47vV6Pvr4+WCwWzJ07F9nZ2dfH2oxS5HI57rvvPixYsIBqnLh48WIsW7YM+/btQ2trKwwGA+Lj4yNiasXQ0BDluj8XoVAIjUaDDRs24J577sG8efMu6elau3Ytbr31VlRVVVHNOpOSkrBq1SoolUqw2Wy43e7Pff3g4CA2b96M7u5urFu3DnfddRdWrFhBhTVkMhn+4z/+A1NTU5iamqJet337dmzduhU6nQ4///nPUVNTg9LSUrDZbKSlpSEmJgalpaVYvnw5tmzZAr1ej1//+tfXfuCuM0wmE1qtFunp6ejs7ERdXR3mz58/I6cAXArSy+FyuaiE5UjA7Xbj6NGjyMrKQl5eXsR7Ms7HYDBg586dFzSIFwgEiI2NxdKlS/Hwww9jzpw505qV5+TkoKioCN/61reo8JZMJsPDDz+Mvr4+nDhxAlu2bMHjjz8OiUQCNpuNQCCAP/zhD6irq4NCocCPf/xj3HLLLZBIJGAymRCLxXjqqafw9ttv4+mnn76ovHv27MGHH36I0dFRPPXUU1ixYgXmzp0LNpsNrVaLmJgYFBcXY/ny5XjnnXdgMBjwxz/+8YqPS2VlJdVx4OOPP0ZSUhIqKysBfOoV6+3txd69e7FlyxYkJSVd0f8+e/YsXnrpJQwODuKBBx7AunXrsHz5cnA4HMjlcsTExOCFF17APffcg5MnT8LtduP999+nwtW33XYbfvazn6GpqQkKhQJr1qwB8C9v5p49eyAUCuHz+bB//37Y7XbIZDKw2WwQBIGPP/4YKpUK8+fPp2TS6XRoaGhAdnY25s2bR3lK+Xw+Nm7ciIyMDNTV1UWVJ5rJZEIqlWLOnDkYGRnByZMn8eCDD96w94uWKMo1GXYOhwMmkwkEQUClUkV8IvG5uSDEFbhUyed+0Unl8XjIzs6GUCikXPJcLhcxMTGQSCTw+XywWCxQKBQRYdjJ5XKkp6fjy1/+8rRWDXw+H3K5HCqVCvn5+V84s5PsAk4aImSvrMsNpdlsNpw8eRIejwdJSUkoLi6+YCafSCTCXXfdBafTSY046+3tRVtbGwiCQFlZGVJSUqbJIJFIkJiYCLVaDb1ej/7+flgsFsrojhTObT8hEomg1+ujJpfjekKGuAiCgEAgiJhQbDAYxPj4ODIzM6FUKiPq2rkcyNSFe++9d1oLGR6PB4lEgtjYWJSWllKGQSgUQl5eHu69914qH4yEvLdTUlLQ1taG/v5+jI+Pg8vlUq9tamqCXq9HSkoKKioqIBKJKH3IZDIRFxdHhTbPzdUlGRgYQEtLCwiCQElJCbRa7QX3dVJSEtRqNYxGI+VcuNL7Oi0tDYWFhRAIBBgcHMTQ0BCmpqYQFxeHnp4eTExMQCqVIi0t7YqNeYPBgFOnTiEQCCAjI4PKWyPh8/lISkpCQkICjEYjurq6YDabIZfLweFwoFQqkZubi6mpKXR2dsJms0EsFmNqago9PT1Qq9XIysrCxMQEWlpa0NLSgpKSEsjlcjidTpw9exZpaWkoKiqi3lMulyMpKQnt7e3Yv38/HA4HsrKykJiYCD6fj+zsbPzP//wPiouLL5hGFKmQazqp4ycnJ697uJT4rPNHtBh1wDUadl6vl6omkkgkEaOIPw8Wi0XltwUCgcu+APx+PzXD7lKw2WxoNJoLyuaFQiF1bJxOZ8Q0OZTL5cjPz8d3vvOda9qlKZXKq1YExGc5VT09PQiFQoiNjUVycvJFn7to0SLqNQRBYGJiAiMjIwA+vRbHxsag1+unvcbpdEIgEGBiYgJ6vR4mkyliPEHnIxKJwOfzqZzK2Ybf76f0CZ/Pj5g+dqFQiFpYIy1H83IQiUTIzMzEY489dsHYrIvBYDCQkpIChUJB3ZuBQAChUIjacDidTgSDQQQCARgMBmg0GqrZ+uDgIBwOB/h8PtLS0i7Qh3w+H1KpFDExMdM88OR9PTk5Sc3o9nq9GB8fp0Ygkni9XvD5fIyNjWFqagpGoxFisfiKDDuVSoX09HSoVCqMjo5ieHgYQ0NDiIuLw9mzZ2EwGKDVaq94hi7xWQFQT08PlZNnNptx5syZC57LZDIRCAQwNTUFk8lENcMVCAQoLi7G/v37MTw8TOWOjY+Po6urC7m5uaiqqsKZM2dQX1+PxsZGpKSkQCAQYHJyElarFXK5nGpZBXzawqqiogJdXV04deoU7HY7DAYDcnNzIRKJIBAIcMcdd1zxcYwEpFIpOBzOBfr/ekBe99F0TK7JsLtaD1i44PP54HK5lDFxOT1pQqEQ7HY7uFzuF3rZyN3k+RcAmXwJ/Et5zSQu59h8HoFAAB6PBw6HAzweDwKB4LKqmvx+P1wuF5VwvWHDhi98L7fbjeHhYSQnJ0dsuCHadobXE6/XC6vVCuDTVjBXWu14IyGnEMy0e/fzcDgc2L59O44fP46jR49ifHwcTqfzgukoMpmMKmwgi6BI45y8ny92PXM4nIsameff15dT8e/1ejE8PIykpKQrmmdN9kRbvXo1/vGPf6CjowN79uzB3LlzcfjwYbjdbqxbt+6KF3S/3w+Px0Mdh6eeegpPPfXUJV8jlUoxPDyM2NhYCIVCMJlMrFmzBl1dXeju7sbOnTvxyCOPoLu7GydOnMCGDRswd+5cMJlMvPHGG9i1axfmzZsHHo+HPXv2YM6cOdBqtdOcLVVVVSguLkZ+fj52796N3bt347nnngMAZGdno7i4GBs2bMDq1asj3klzM/F6vXC73ZDJZFFj3F2TYSeRSKgmxOPj40hJSYFarb4ugt0INBoNlEolQqEQent7P7d3EUkwGKSMAYVCQZWkh4OZutiTOR5isRgulwtutxtut/tzFwQSchi3SCSC1+vFzp07odFoLnnj8fl8qFSqK1L+NxO9Xg+bzUZVuM02vF4vLBYLCIKIKO8Ym81GYmIipqambohHINLwer34zne+g9OnT4PNZuOee+5BdXU11Q6EwWBg27Zt2LdvH+rr6yljl8lkgsvlQiQSwe12U/mSAoHgguvZ6/XCZDJd8N7n3tcejwcfffQR0tLSLnk/8Hi8q27sTU6P+Oijj9Dd3Q02m40HH3wQZ86cQWZmJlatWnXFm0ByaopEIoHL5cKzzz6LW2+99ZL/h8lkIikpifJSM5lMLFy4ENu2bcPZs2exbds2rFmzBoODg+jv70dNTQ1kMhnS09NRUVGBhoYGDA4OwufzYdu2bXjooYeQkZFxwfvw+XzcdtttWLZsGWV8NjU14fjx4+jo6MB//Md/4Hvf+x6WLVuG4uLiKzuYYWR0dBRutxsajea6/2+XywW73Y709PSIXTvO55qklEqlUKlU4PP5GBoaQlpaWkQbdkqlEnFxcZBKpRgaGoJer4fD4fhcz4DBYEB/fz/YbDbi4uLC1mGezMEKhUKUEiUIAv/85z8hFouh1WqRm5sblcYfg8GASCRCdnY2WltbodfrMTIyctFCnL1798JqtSIuLg4LFy5EQkICUlJS0N3dDbFYjKSkpAvyPAOBAJqamtDX1wepVDotNBEphEIhWK1W6PV6eL1eaLXaiPUo3kjIUCyTyYyoUCyHw0FmZiaampowMTExo0dukWkq9fX1mJiYQE5ODpYvX46CggKIRCLKeKqrq7sg74xs45GWlkZ5Ofr7+5GTkzMtlOl2u2G1WqlRZue+nsFgID4+HlqtFh0dHRCJRNBoNBekegSDQTQ1NaG/v5/q83Y154PP5yM3NxdxcXEYGRnB4OAgPvroI2paztVMTyKLTLKystDa2go+n4/Y2NiLro1tbW0YHh6GXq/H/fffP82AlUgk0Gq1SEhIQE9PD44dOwaj0UgdDzabDZVKhbKyMpw6dQqtra3Q6XQYHx9HTk7OBY6I5uZmNDQ0YMOGDdQGNxAIQCAQQCKRQCKR4Pjx42hqakJubm5UGHakl3hoaAgejwcFBQXX/b70eDxwu90XjcZFKtckpUQiQXx8PGJjY9Hd3Y3h4eGIDlfI5XJqVNbIyAjVi40MJZCyn9sLqKGhAQkJCUhISAhbcQjZpNfn88Hv9yMQCMDn8+HPf/4z3nnnHbS1tYVFruuFTCbD/PnzqZ5SLS0t8Hq9VHgnEAjAarXiH//4B1566SUcPHgQgUAAmZmZKC4uBoPBwNmzZ6HT6eD1eqmcCJ/PB6vViu3bt+Pll1/G1q1bI7IogSAISrkTBIHc3NxZa9g5HA6wWCwqbSIS4PF4KCkpgV6vx/Dw8EU9TTMJsliE+KwZcWlpKaRSKVVx6fP5YLPZLtpDk8lkory8HHFxcXC5XKivr4fD4aBy9Px+PyYmJqi+nRcjPT0dpaWlYDAY6OzsxMjIyLT72u/3w263Y8eOHXjllVfw/vvvX/V9zePxkJKSAq1WC6FQiKmpKfzf//0fVCoVsrKyvjBy8HmQTZzZbDaGh4fR3d09LWRNNlY/ePAg3njjDfztb3+blvdNbuYzMzORm5uLsbEx7NixAxaLBSUlJeBwOGAymYiPj8e8efPAYDBQV1eHY8eOIRAIQKvVXuCIOHbsGH7xi1+gp6cHdrud6hOanp6OFStWYPny5QA+7ZZgsViu6njebEKhEPR6PQYGBhAMBm+YYedyuSKu6O5SXLOUMpkMd911F44ePYra2toLcjAijcrKSnz/+98Hm83G7373O/zwhz/E7t27qea2Xq8Xk5OTeO+99/C///u/ePnll/E///M/SE5ODtsOnRxcPTo6ipGREQwNDaGxsREDAwMQiUQoLS0Ni1zXi9TUVPzgBz9AdnY2Tp48id/85jfYv38/VezQ19eHH//4x2hoaIBSqcRXv/pVcLlcrFu3Dl/72teQkpKCX/7yl/jb3/6GEydOwG63w2KxoLW1FVu2bMEf//hHVFRU4L777otIg8nr9eLvf/87HA4HMjMzqQVhtkE25FYoFBE1L1ckEuGOO+6AXC5HZ2cn3nzzzYjcIFwvOBwOqqqqwOFwqOk4Y2NjcDgcsFgsOHHiBN555x3U19df8Fo2m40nnngCVVVVsFqtePrpp/HJJ5+gt7eXaoj8q1/9Ctu2bfvcsZO33HILHn30UaSmpuL3v/89XnjhBRw7dgw2mw0WiwXt7e3YsmUL/vSnP6GgoACbNm265vt67dq1KCgogM/nQ3NzMxYsWICKioqr/n/5+fn4/ve/j4yMDHz00Ud49tlncejQIZhMJlitVuh0Orz55pt47rnn4HA48L3vfe+CTgAAUF5eThlcW7duRSAQoFqfAJ9OO1qxYgWkUinV3H3Dhg2fmyNnt9vxox/9CAcPHqQMZqPRiB07duDDDz8EQRCYO3fuFbd3CRc2mw0vv/wyLBYLMjIysGbNmutufJGh2NjY2KjRywziGt1rbrcbra2t+Pd//3ckJibi3nvvvaB9RiTh8XhgNpuxc+dOnDx5EuPj4/B4PFRHcDLnKzY2FklJScjMzMTq1asvWGxGRkZQV1eH5557Dl1dXbBarWAymZgzZw5uv/12LF68GBkZGbj77ruh0+lgNBphNptRXFyMkpIS5OfnY968efjd735HldZbLBaUlZUhLy8P5eXlePzxx6lO67W1tfjNb34D4NNdJpfLpXJAVq5ceVkFBy+++CL++c9/wmw2o729HcCnhnlOTg7i4+OxePFifPvb3/7c1/t8PvT09OBXv/oVWlpaoNPpYDKZUFBQAKVSCS6Xiw8++IBKiiYIAg0NDdi9ezd27dpFHSelUkmNWLv77ruRk5MDv9+PkydP4vjx42htbYXZbIbf76fOBzkupqioCBUVFeDxeNSUjKamJnz44YdUR3yfz0cVYUgkEpSWlmLZsmVIS0uLuIHt5NiiL33pS1iyZAmWL1+OBx54AMDMzav8PE6fPo2tW7fivffew4svvojq6upwiwTgX5Mafvvb36K2thY6nQ7/+Mc/kJSUFJE97Xbu3Ik//elPMBqN6O3thcvlokbXkWOYnnnmmc99fTAYxMmTJ7F161ZqtB+Hw6F6C2ZkZMBkMqG1tRWnT59Gfn4+8vLysHTpUnzjG9+Ax+NBU1MTdT7dbjc1eUKtVqOyshKjo6PYu3cvzp49i3feeQfr1q2j3j8QCMBms6GhoQHbtm3D+Pg4bDYbfD4f+Hw+BAIBxGIxSktLsWTJEmRmZl5Tew6CINDR0YHnn38eL7/8MuRyOd555x1UVFRckKYTCoVw6tQpfPTRRzh06BB6e3thsViQkJCA3NxcVFdX48EHH0RycjK8Xi9OnjyJI0eOoKurCyaTiQrj83g8asNeWlqKyspKamzjubjdbjQ2NmLjxo0IBAJ47LHH8J3vfAdSqZQq6HE4HHjooYdQW1uLxMRE/Pa3v8XChQsv8Hi3t7fj+PHjOHbsGOXEcLvdYDKZVEpMeno6HnroISQnJ0d8+zKj0Yj29nY88sgjWLRoEW655RZ86UtfAovFui66k/hsisqPfvQjfPDBB/jTn/6E5cuXX1Zlebi5ZvOTy+UiKysLubm5MBgM2LdvH5YtWwalUhkRvdrOh0ygX758OcRiMQYGBqDT6agBvywWi8pby8vLQ35+PuLi4i64UFgsFkQiERWmPRcyyZjJZEKtVkOlUk17XK1WQyaTgc/nIz4+HkKhECUlJdTj5/cElMlkKC8vx7p166DT6aiE5JqaGuTn5192BZNEIqHmvmZmZk57LCYm5rIuWC6Xi7i4OOTn5yM/P3/aY2w2+wKDnsvlQi6XIzk5eVobExaLBZlMRoUUuFwu5s6dCy6Xi9jYWLS3t1OzYkUiEbKysrBw4UKkpKRQuVdk77d58+ZRY85GR0dhNpshEAggk8mgVqtRU1ODtLS0iLsh/X4/hoaGcODAAfh8PhQUFFCh5dlIIBCAy+WCWCyOqJ0xueGbP38+DAYD2trasGfPHqxevXpa/8ZIgTSgFArFBfe5TCb7QiOIyWSipKQETqcTGo0Gvb29sNls4HA4kEgkSE1NRWFhIVJTU6HVagF8qtMkEgkYDAYEAgHy8/Op3p3d3d3w+XxgsVhITExEcXEx5WHj8XgX7SJApmc4HA709PRgeHgYZrOZapVC3tcXCzleDUlJSVi4cCHsdjukUim0Wu1Fi3cYDAY1r1ar1VKfH/hU15G9AMk80crKSjCZTKhUKnR2dsJsNlNN3BMTE7FkyRKkp6d/rvdSIBAgOTkZX/rSlxAIBFBeXj5Nj5HyrFmzBomJiVAqlcjKyrqoBzM5ORlLly4Fk8nE0NAQzGYzNSuWHJtWXl6OzMzMiFy7SUiD6+zZszh06BDcbjcqKytRUFBw3fWG3W6Hx+MBg8GAXC6PyIjPRbleIyzefvtt4t577yVUKhXx8ccfE5OTk9SIFBoamn8RCoWo0UB/+9vfiOzsbOKWW24hjh8/PqvvmcOHDxNf//rXiaVLlxK1tbXhFucC/H4/8dFHHxHLli0j0tPTibfeeosYHx8ngsFgxI8Yu1mQ17bH4yFcLhf1+7kEg0HinXfeIbKysgitVkt88sknYZKWJtoIhUJEMBgkBgYGiB/+8IdEZmYmsWbNGqKzs/O6685QKES0trYSDz74IJGRkUG0t7cTbrf7ur7HjeK6mbd33nknBAIBHA4Hvva1r+GZZ57B8uXLr/tAXhqaaIf4LHzy5JNPoqGhASwWC5s3b0ZiYuKs9dYB/8qxUyqVEZVjR8JisVBTU4PMzEzcc889ePbZZ3HgwAH85Cc/gUajmdXn7lz0ej2efvppjIyM4IMPPrjAizI5OUmNM7z99tuhVCrDJClNtEE2rH7kkUcwNTUFrVaLl1566aJRtWuFIAj09PTAYrFAKBQiIyMjIvXSxbhuiXBsNhvl5eX4+te/jtTUVCpPprGxMWImLdDQhBun04n+/n48/fTTaGxsRHJyMr773e9Co9FcVp7kTMbn88HhcFBzRyMNMsyYnJyMJ598EmlpaWhvb8eTTz6J+vr6C6YjzFaIzyYvdHV14eWXX8bIyAjsdjucTid6e3vx2muv4eDBgxCLxbjzzjuRmJgYbpFpooChoSHs27cPP/rRj2CxWLB06VJ8+9vfpooaboTuHB0dpVrwRFOLo+umPRkMBhISEiASiVBdXY0jR46gvr4eSqWSmjsqEomi5sDQ0FxPiM+mnfT29qKpqQn79u2DSqXC3LlzsWrVKvrewL9y7L5oNnE4IXNwb7nlFgwPD+PEiRM4deoUsrOz4XK5kJOTQ+XcztbzyeFwqHyvkydPQqlUUsn+Y2NjqK+vp/Ki5s6dG3EFTTSRA/HZmLrR0VHU1tairq4ODQ0NqKioQHV1Naqrqy9aTXy9mJycpOYbnztpK9K5rttiFosFuVyOZ599Fn/+85+xe/duPPXUU/B4PLj99tunddSPlgNEQ3MtEOc0lO7s7MTrr7+ODz74ALGxsfjhD3+I0tLSsE40iSS8Xi/sdjtVVBOpMJlMxMTE4PHHH8eyZcvw/PPP4xe/+AVqampw66234pFHHqE8jrNNzzEYDCiVSmzatAklJSV4//338fTTT8NoNMLpdEImk6GiogLr16/Hhg0bkJKSMuuOEc3lQRp1brcbr732Gt5//31MTU1h7dq1+NnPfoa4uLgbqicIgsDQ0BBYLNbnzi+PVG5IvIPFYuGRRx7BwoULsXXrVrzwwgv45JNPMG/ePHzrW99CUlJSRCtuGprrhd/vx+joKN588028/vrrEIlEWLJkCZ555hmo1eqIma4QCZCzYuVyeVToBx6Ph7KyMvzxj39EeXk5tm/fjmeffRbvvvsuHn/8cZSUlEyrmpxNZGVlQavVYvXq1fD7/dTUHCaTSbX7oK99mkvR2tqK48eP46233kJ3dzfWrFmD5cuX47bbbrtpzYJ7e3svqH6OBq67YUfuvsRiMTIyMrBu3Tp4PB50dnaiubkZv/3tb7F27Vrk5eUhNTWV6qFDQzOTCIVCGBgYQG1tLZqbm1FXV4eioiKUlJSgsrISiYmJVKsXmk8hB8hLJJKIDcWeC4PBAJfLhUKhoJrEtra2oqmpCa+//jpyc3NRWlpK9b6KBmP1esHhcKjedzQ0l4vb7YbRaMS+fftQW1uLsbExsNlsPPLII5g/fz7y8vKuqWfh5UJOnzIajcjOzr6gpVmkc0MzlBUKBSoqKiCRSPDPf/4TR44cwfbt2wF82h8mFAohNjYWYrH4ujUVpKEJJz6fj6ruPHHiBLZv34729nZwOBw88MADWLBgAQoKCgDMvjDdpSBHRXk8HohEoogsnvg8mEwmioqKoNFoUFhYCJ/Ph4MHD2J4eBgDAwNQKBRITU2FXC6HRCIBj8ejzz0NzWecO/5xcnISfX19ePfdd9Hf3081pv/a176G+Pj4i/YWvBEEAgFYLBbYbDYIhcKLzvmNZG649mQymcjLy8OTTz6Jr371q3jzzTfxpz/9Ce+++y4SExPx2GOPYf369VCpVFGxS6ehuRQjIyM4ffo03n33XWzbtg0FBQVYtmwZ/uu//gsJCQnUNU4v7NNxu91wu90IBAIRN1LsclEqlaisrERlZSU1ueGjjz7CO++8g/nz56O6uhp33XUXNQeVhobm081wV1cX/v73v+PEiRNobW1FamoqHnvsMSxZsiQsIzPtdjuam5sRCASQkJCA7Ozsmy7DtXBTtsUMBoMaWEy6VGtra3Hy5EkqJ6W8vBwrV67E0qVLLzrBgIYmUjGZTBgYGMBrr72GU6dOwel0Ii4uDps3b0ZpaSlSU1MRGxtLe6Uvgd1uh9vtpjq8R5PHjoQ8twRBUPl1GzduxCeffIIDBw7g448/xvvvv4/q6mrU1NSgqKgIubm5015LQzPTIQgCBEGgsbERx44dQ319PU6dOgW5XI7c3Fzcf//9WLlyZVg7aTgcDrS0tCA+Ph5qtZqq6o4Wbpr2JPNRYmNjUVJSAoFAgLi4OMTGxmJsbIyapdff34/U1FQkJiYiLS0NIpGINvJoIopQKASv14vR0VH09PRgcHAQ3d3dOHPmDDWTs7y8HEuWLEFSUlLEz1yMBFwuFzW6J9JGil0p5NxNgUAAhUKBUCgEuVyO/v5+DAwMYHR0FLt27UJTUxOysrKQkZGBpKQkxMfHXzCblIZmJkC2e9LpdBgfH8fAwACam5sxOjoKi8VCzUfPzc1FXl4ecnJywpp/73K50NvbC41GA4VCEXX66KZLS+7IKysrKS/dO++8g2PHjmHHjh04cOAAKisrMWfOHKxatYqaDcrhcGZtCwGa8EN8Ngje4/HA4/HAZDLhyJEj2LNnD7q7uzE2NoaCggKsW7cO8+bNQ0VFRbhFjiqcTie8Xi8YDEbENii+UphMJgQCAebOnYu5c+dCr9ejs7MTr7zyCk6dOgWDwQCZTIbly5djwYIFKCkpQUpKCng8HthsNtV0ldZ3NNEG6ZXz+/3w+/3URri2thZNTU04cuQI9Ho9UlJSUFhYiPvuuw8LFiyAWCwO+/VOEARcLhd6enpuWrHG9YZBkI22wsC5Pb5sNht0Oh22bNmCvXv3YmBgAB6PB2vWrEF1dTUqKiqogcrhPvE0sw+n04mhoSF88MEHOHz4MFpaWmCz2VBdXY0FCxZg6dKlmD9/PlgsFn2NXgXHjh3DW2+9hbfffhs9PT2QSqUzwrg7F1LfhUIhDA0NobOzE7t378a2bdtgMpnAYrFQVVWFdevWoaysDAUFBZDJZPS1RBN1BINBWK1WHD9+HKdPn8aRI0dw8uRJiEQiJCcno6amBnfffTfS0tIQGxs7bQMT7us9EAjg8OHD2LBhA37+859TaRPRRFgNu3MJBALUHLjJyUmMjo6isbERjY2NsNvtAIDExERUVFQgNzcX2dnZyM3NpXe0NDcEstT9xIkTOHPmDAYGBtDf349AIACNRgOtVouFCxciLS0NMTExUCqVVMiVvh6vnF27duH999/Hjh070N/fP+PHq7ndbrhcLhgMBkxMTKC3txe9vb1oa2vDxMQE1ey9qKgIBQUFyMjIQE5ODmJjY+nUFJqIw+/3w2g0orGxET09Pejr60NbWxtsNht4PB5UKhXKysqQn58PjUaDuLg4xMfHg8/ng8vlhlv8aYyMjGDfvn149NFHsXXrVpSWltLtTq4WMvSQmZkJrVaLrKwsqFQq8Pl89PX1YXJyEuPj4zh16hRGRkbQ2dmJvr4+qFQqyGQyyOVyxMTEUB4TGprLgdzXeDweOJ1O6PV6GI1G6PV6jI6Oorm5GVNTU3A4HOByucjNzUV+fj7y8/Mxf/78GelZCgdutxvBYBB8Pv+GzX2MJAQCAQQCAZRKJTIyMpCamor09HTI5XKcOXMGBoMBDocD7e3tVAj37Nmz1HOkUinkcjmV/0PrPJqbRTAYhNfrhc1mg9FohMVigcViwfDwMM6ePQuDwQCz2QyPxwONRgONRoPs7GwsXLgQmZmZUCgUEX296vV6TE5Ogs1mIyEhARKJJNwiXTERuSKx2WyoVCqoVCosW7YMer2eGgC8Z88eNDY2YmpqCnFxcZg3bx7y8/NRWlqK+fPnUz2wmEzmtCrEmb5Q0Fwe54bDyJE1gUAAExMTGBgYwNGjR1FXV4e+vj4MDw8jNjYWlZWVWL58Oaqrq1FZWUn3IbsBOBwOhEIhSKXScItyU2EwGGCz2VR3+2XLlsFut2NgYACtra3Yt28f6uvrMT4+Drfbjby8PBQWFiInJwclJSUoLy+HRCIBl8sFk8mk0gBovUdzrZybKnWuvnS73ZiamsLZs2dRV1eHtrY29PX1oa+vDzKZDNnZ2SgqKkJNTQ2qqqoQFxcXVVNGRkdHodPpoFAokJCQcNN6511PIiYUeynIiyoQCCAQCFCG3u7du3H69GkMDQ1Bp9NBIBAgJycHWVlZmDt3LhYtWoSkpCRqFiet5GgIgoDBYEBXVxc6OztRV1eH2tpa6PV6uFwuKJVKlJeXo6CgABUVFVi4cCHlRWKxWHQfuhvE888/j9raWoyOjmLv3r1R2cfuekEuoMFgkPKOTE5Ooq2tDQcPHsTZs2cxPDyMsbEx8Pl8JCcnIy0tDZWVlSgrK0NaWhq0Wi29AaG5JkKhEDweD86ePYu+vj50dHSgtrYW3d3dMJlM8Pv90Gq1yM7ORk5ODubPn4+KigqIxWKq2JF0rkTTdfjb3/4Wx48fB5PJxCuvvAK5XB5V8gMR6rE7H9Jty2KxwOPxwGKxIBaLqYoyk8mEqakp9PX1wWazwW6346OPPsKePXsglUqhUCiQnJyM1NRUqFQqJCQkICMjgw5hzHDcbjcsFgv6+/uh0+kwgTFJrgABAABJREFUNTWF0dFRDA0NwWKxwO12g8ViIS8vD9XV1dBoNMjKykJcXByUSiXi4uIgl8vpYoibgNPpRCgUioiquHDDYDCmbSJ4PB64XC7EYjFSUlJgNpthsVgwOTmJwcFBmM1mOBwOHDt2DIcPH4ZAIIBMJkNCQgLi4+MRExMDtVqN5ORkxMfH0+kDNBRk9Sp5TY2Pj2NkZAQGg4FyoBgMBng8HgQCAUgkEixcuBAxMTHQarVITU2FQqGATCZDfHw8YmNjweFwovIeJo/F6OgojEYjli1bFrVpIVF5d/P5fPD5fMTGxgL4tHO13W7HmTNn0NvbS4UxdDodBgcHwWKxoFarodFoEB8fj6SkJExOTkIqlVL/SygUUomcZOJ2NJ7Q2QR5I3q9Xrjdbni9Xupnl8sFq9WKqakpdHV1YXx8HEajERMTEzCbzeDz+ZBKpUhNTUV5eTnVTyw9PZ0+92HA7XYjFApFZdjjRsNkMiEUCiEUCpGYmAjg02R1m82Gs2fPYnBwEIODg+js7MTg4CAmJiYQCAQgk8kQGxsLuVyOuLg4pKWlITk5GSqVCmKxGAKBAFwuFzwej9KDpJeFZmYRCASotiNkyyafzwev1wu73Y6pqSkYDAaMjIxgfHwcZrMZZrMZExMT1KYiJiYG+fn5yM7ORlpaGnJychAXFzejnCN2ux16vR5utxvZ2dlRuwGKTqnPg8vlIiYmBkuXLsXSpUupnmPt7e3o6elBT08PmpqacOzYMUxNTcFms4HL5SIrKwvJycnIzMxEQUEBtFotEhMTkZGRAS6X+4ULPL3431gulSVAGnUejwc6nY4KT42MjKCjowNtbW0wmUxwOp0QCoXIyspCeno6ampqMHfuXOTk5CAxMZEeUh4h2O12BIPBqOwZFQ44HA5iYmKwePFiLF68mPo76XHp7u5Gc3Mzurq60NLSgr6+PrjdbnC5XEilUiplJTExEUlJScjOzkZ6ejoUCsU045rWf5HNF+lIEpvNRnWb6O7uRn9/P8bHxzE0NIS2tjZqYyUQCFBYWIj09HTMmzcPRUVFKCkpgUqlglKpvBkfKWyEQiF0d3djamoKbDYb5eXlUZsSMiMMu4tBhtiysrIQCATg8/ng9/upEEZLSwsGBgYwOTmJpqYmfPTRR/D7/QA+DX2kpaUhISEBiYmJ0Gq100q0k5OTKcOP5sZBEAQcDgfMZjOGh4cxPj4OvV4PnU6Hnp4ejIyMwGg0Tku8J0ME9957L5KSkpCWloa8vDyqyTWHwwGXy6U9ExGGzWZDMBiMynyWSIJcgMlm2X6/H4FAAB6PB729vRgeHqYiGcPDw2hoaIDJZILH4wGbzYZQKIRCoUBmZial89RqNZKSkhAbGwulUgm1Wh21C95Mgyz+IlNNTCYTJiYmMDo6iomJCSpcbzAY4PP5EAqFwOfzkZCQgNjYWOTl5WH9+vXUGpecnEzpShaLBS6XS/080wkGg6irq4PP54NarUZmZmbUfu4ZadiRCwOXy72gR45SqYRKpUJMTAyMRiOsVivMZjP0ej0sFgtsNhtMJhPlpu7r60Nvby+YTCZ4PB7EYjEUCgWkUinEYjGkUimUSiVEIhGEQiHEYjEkEgkVOiFn3dGL1b8gexbabDY4nU643W44nU7YbDY4HA44nU4qFOBwOOBwOGCxWCjFRB7PlJQUZGRkQCaTQaVSQSqVUuEnMgRFnm86Ty6yIXPs6FDstUHm5vF4POpvZDGGSCRCSkoKrFYrjEYjTCYT7HY77HY71aLC7XbD4/HA7/dTvRsJggCXy6V0GnmfiUQiiMViyOVyKrRL6kAytUUoFEIgEMyocN2NhjTEnU4nXC4X3G433G43bDYblWZCrlVOpxMOhwNWqxUOhwNerxd+v39adXR6ejqKioogEoko/SiTyag89fj4eGpNIzdWs1FXhkIhdHZ2Uk2Uo3nzMiMNu0vB4/HA4/GoSlngX4rPaDTCYDBgYGAAPT09GB8fx/j4OIaHh6HX6ymFFwqFIJFIIJVKqR2tTCajCjVIo4LsrUfueMgee+TX+b+f26KFvDFJhRgJivH8ViHkcbvUVzAYnPYzOZbL4XBQO0yr1QqLxTKtJ9Lk5CSmpqbg8/kAfBp6IhUPWfySlJSEhIQEaLVapKWlQSAQRPXNOJtxuVwIBoO0YXcDIIsxyBZS5xMMBqnCoqmpKeh0OgwMDGBsbAwGgwFTU1Mwm80IBALU/XyuIaBSqSjjjtzonttrTy6XU4Vq5HdSv507qeV8/Xfu30ljI1KNDlIfntsa5HL04/m6kqyCttvtMBqNMJvNsNvtlMOBNMTJnDi73Q6HwzHNoJdKpUhMTKTOd2pqKrRaLeLj46FSqRAXFxeRxzCckOlb3d3dUCgUSElJiepjNOsMu4tBKo+4uDjExcUhLy/vguf4fD5YLBaMjY1hYGAAOp1uWs6CXq+HyWSC0WhEMBikXsdmsylFR3oKSW+fUqmEQqGgvHwKhYLKcSF3w+cWdEQCfr+f2kGe62mz2+2UcUYqG4vFQikjq9WKyclJTE5Owul0UmFv4F9GG1nFp1KpsHTpUkoZaTQapKenQyqV0obbDMVms4HP50Mul4dblFkHk8mEWq2GWq3+3OeQSeV6vR4jIyMYGRmBXq/H1NQUhoaG0NTUBIvFAoPBAJfLNe21LBYLMpkMSqUS8fHxkMvlEIlEkEgkkMvlkEgklDeJ1IcikQhyuZxq5Mzn88Hj8SAQCCIyPOb3+6koj8PhoAoUSMPM6XRSG1gyKmGxWChPm91ux/j4OEwmE9xu9zT9SM4cTkj4f/buOzyu6lr48G96l2bUe7GKLbnIHeNecIxtaoDQIZBCEkLqDUluSCWkh04giUMH4wCmuuHeC+5FclW1ep2Rppfz/cE953PFBdmjst/n0WNLGmnWjGbOWWftvddOxeFwEBcXR1ZWFuPHjychIYHk5GRycnJITU0lNjb2nPOGe3PCcqnIi/B27drFLbfcQnFxcbRD+kJEYvd/zvVi1+l0xMXFYbPZyM3NVTY3llcayT32AoEAbW1tuFwunE4nzc3NyhtbHlaUV2vu378ft9utrFCSJOmMVTw58ZT7A8nzxCwWi/J9eePwEx+HvJvHqcPRcqk/EokoX5P7BPp8PgDlsQUCATweD36/n0AgQCgUOu1KVL6KlyRJGYKRh2Hk4eiEhASGDx+uDOHo9Xr+9a9/0draSkZGBt/61rcYN26cUnWTh9G1Wi06nU5pcyMOSn2LXAWWd/YQe6Nefud6viVJwmKxYDAYSElJYeDAgcrcPfkYIVfzQqGQsmPGzp07efPNNzGbzQwbNozi4mI8Ho9yPGlsbKS8vByfz6esZvf5fKeNaJw6gqHVapUWMCf+Kx//5FXEpz4ujUaj/Oyp5J1PTjwmwmfVTDlRlY91crzyvG154cGJTXxP7L0qTx8xGAzK8VFOUs1mM8nJyWRnZzN+/HglqZUTYXnI1Gw2K8fzE+e+ycfHE+fCiffPhevo6KCsrAy326305uvNRGJ3nuSDjU6nO+sVkfymlq/I5Ku0rq4uPB6P8nW5NYdc8ZLbdMgHjBMPmnJ5PhwOn9QcNxKJ4PF4lDdxV1fXafPIJElShjtOjVGuqvn9frKzs0/6nvz75Z+Xr5gjkYhyYD3xQz6oGAwG5QRgNBqVA5ec6MnD12azGZ1Oh9vtZsuWLXg8Hg4cOMDgwYOVip04OPUP8pVyMBhEo9GIodge6MS+evJ7/Ezk48euXbuU0Yvc3FymTp1KUVERWVlZSmInV7c8Ho9y3PP5fHi9XiVRlP+VP+SFAnJMchInN9JVq9XKMaurq+u0YdszHQ/lr8ujLKfe/sRj4onDrXKCZTQalQts+Tk6ceqNwWBQEi/5YldORuVqpHy8jImJwWKxYDKZsFgsyrFSTgSFS8flcnHkyBFMJhOJiYlKK7XeSiR23Ug+aMTGxiobwp+vSCSiJIIej0eZOCtfGfp8Pjo7O5XP5ZK/fOCR5yiduMT9xAPkidRqNYFAgKamJhobG5k0aZKSsJnNZiWBlZM1m82mzE2UrzRP/JAPSPKeeueTlEmSRGFhIa+99hrLli3jlVdeQa/XM2fOHGUo+nx/l9B7SZKE2+0mFAqh1WqxWq3RDkm4CHJ1qrOzkzVr1rBo0SLq6uqYMWMGDz/8MElJSefc7F0+lsnHPpfLpQxpnpj8nfpvIBDA6/UqownhcJiurq7Tfv+JoxCnOnEl6IlUKhU2m+2kOYAnJlvyopITq4cnXtzKFTe9Xt8jh5CFz7S3t3PgwAGl80Vvb7skErseQqVSYbValRPb2foTnW/fonNZtWoV8+fPZ8mSJfzqV79S9tj9vPgu5Ovnw2AwcP/99zNr1iw++ugjfvOb37Bu3TomT57Mb3/7W6VRtNB3hcNh2traCIVCGAwGHA6H+Jv3Qj6fj08//ZRnn32WRYsWMXfuXL72ta9x1113XVCTV7Vajc1mw2azkZycDFz6Y+G5XEwvP/Ea7l0aGhrYsGEDkyZNOuMCo95GJHY9xKkHgkt5YJAkibS0NOx2O+FwmMbGRrKzsy9ruV9+fPJqvWuuuYZIJMLatWtZtWoVXV1dfOc73yEnJ0epBAp9TyQSwel0EolE0Ov14m/dy4TDYRoaGnjttdfYunUrhw4d4uGHH2bixIkMGjRISerO53gmkiQhGtxuN42NjVRUVHDPPff0iUbMIrHrp+SFIJIk0dTURGpqatR2YTAajWRlZTFz5kxlvuHmzZvJz89n9OjRDBkyhLi4OHGA74PkOanhcBidTifm2PUibreb1tZWli9fzoYNG2hra2Po0KFcffXVFBQU9IkTpND3NTU10dTUhMfjYeDAgcTExEQ7pC8s+s3RhKhITExUViAeP34cv98f1XhUKhWDBg3ivvvu43/+53/QaDT8/e9/Z968eezZs0eZPyP0LZFIBJfLJSp2vYS8eCAcDlNXV8eaNWv42c9+RnV1NaNHj+Yvf/kLY8aMEUmd0GvIeyzr9XqGDBlywfPjeyJRseuHVCoVWq0Wu91OcnIy5eXleL3eaIcFgMPh4Morr+Tjjz/m0UcfZdu2bdx99938+Mc/5uabbyYrKyvaIQrdSJ5jJ682F6v/eoc333yTjz/+mJUrV3LVVVfx4IMPMnToUGJiYkRlXehVdu7cSVNTE2PGjMFms13QnNCeqvc/AuGiyIs1EhMTqauri3rFTiavyE1ISODuu++moKCA9evXs2DBAjo7O7nyyiuZOnUqOp1OnED6ALliJ68yFH/Tnsvv99Pc3Mzbb7/NJ598gkql4qabbuK2226jqKgIm83WI3bIEYTzIVefjx49SmdnJyUlJX2mD6BI7Poxs9lMfHw8jY2NZ2wBEC0qlQqdTse4ceOIjY3FaDTyzDPPsHbtWtxut7J5tWgh0PvJ7U5Er66eS5IkvF4vNTU17N+/n/feew+3283o0aO5/vrrmTJlikjohF5HPvbU1tYSDocpKirqM69jkdj1Y/I2Xps3b8bn8ykNPHsKlUpFcXExeXl55Obm8thjj/Hmm2+ybt06Xn75ZTIyMrBYLD0qZuHChMNhnE6nskuJ0LPI81rLy8v5z3/+wwcffEAgEODxxx9n7Nix5OTkRDdAQbhIwWCQsrIyGhsbycrK4oorrhCJndD7xcbGkp6eTmVl5Wn7O/Yker2eyZMnk5yczNq1a5k3bx533HEH999/PzNnzmTgwIHRDlG4SJFIhLa2NiwWi0jseqDW1lbWr1/P7373OyRJYvjw4TzyyCPk5eWJFcxCr+b3+1m7di1arZbMzEzy8vL6TJFAJHb9mMViITExUdnH1uv19siTq0qlwmw2k5eXhyRJBINB3nvvPdasWUNTUxM33HADQ4YMOWdne6Hnkbe3E0OxPYc892j//v3s2rWLDz/8ELPZzOjRo5kwYQIFBQWYzWYxDULo1YLBIPv37ychIYH09PQ+df4QiV0/ZrFYSEpKIhAI4HQ6cbvdPTKxk8XExDBs2DByc3M5fvw4mzdv5tixY5jNZuLi4khMTFTi7ytXXn2dnNjZ7XaMRmO0w+n35FYmDQ0NrF27lnXr1rFhwwbuuOMObrjhBiZMmNBnJpgL/VckEsHv93PgwAGGDBlCZmZmn3pNi8SuH4uNjVXah9TX19PY2EhiYmKUo/p8Go0Gu93OE088weLFi1myZAmPPPII+/bt49Zbb+Xqq69Gp9NFO0zhPEUiEdrb20lPT+/RFxX9hd/vp6mpiW9+85uUlZWRmJjI008/zXXXXack3n3pBCj0Tz6fj+bmZvbs2cONN97IoEGDoh1StxKJXT9mNBqJj4/HZrPR0tJCc3NztEM6J5VKpSzymDhxItnZ2QwYMIBXXnlF2e/v4Ycfxm6394l+RH1dOBymo6MDs9mMyWSKdjj92tGjR1m/fj1vvPEGDQ0NfPWrX2XixImMGjVKtKIR+pSqqiq2bNmCVqtl6NChFBQURDukbtU3loAIF0Wj0aDX64mLi6Ozs5OOjo5oh3ReVCoVKpUKu93OgAEDmDFjhjJEtG3bNt555x3Ky8vp7OyMdqjCOchbiok5dtETDAY5ePAgH3/8MWvWrKG1tZWZM2cyZcoUhg8fTlxcXJ9ZLSgIAI2NjRw8eJDMzEySkpKwWq3RDqlbiZJGP6fRaEhNTaWrq4v29vZoh3PBLBYLJSUl/PCHP2T+/Pm88847/OEPf0Cv13PFFVdQUFAgmhn3UJIknbR4Qsyxu3zkNibBYJCOjg6WLl3KvHnzCIVCjB07lp///OfY7fY+NaFcEOTXfW1tLQcOHGDEiBHY7fY+N31HXIb1cxqNhry8PNra2mhoaIh2OBctPz+fH/3oR7zxxhsMHDiQX/ziF/zP//wPCxcuJBKJRDs84QwikQjBYBCn04nVahXtMy6zQCDAsmXL+NGPfsTDDz9MSUkJjzzyCP/+979JTEzscyc7QQDo7Ozk2LFj7N+/nzlz5vTJfY1Fxa6f02g0ZGRkUFNTQ2tra7TDuSjy0KzZbCY3N5ff/OY3zJ8/nyNHjvDEE0/Q1NTE3LlzycnJES0aehC/309XVxeSJGG1WkXF7jIJhUK0t7fz/PPPs337dmpqavif//kfZs2aRX5+vlKlE1VuoS/as2cP1dXV6HQ6rrjiCmw2W7RD6nYisevn1Go1ycnJeDwenE4n4XAYtVrdKw/qGo0Gq9XKuHHjaG1txWAwsHr1apYtW0ZMTAxer5fCwkIxNNtDBAIBvF4vACaTScyxuwzcbjfNzc1s2bKFdevWEQgEKCwsZO7cuQwdOpSYmJhohygIl1RZWRlOp5PExMQ+179OJhK7fk6u2Hm9Xtrb2wkEAr26cqJSqdBoNFx33XUMHjyYIUOG8D//8z/U1tYyZcoUfvazn5GUlNRrk9e+xOfzKQtcrFarWBV7icjziiKRCFVVVWzatIlHHnmE5ORkbrnlFu6++26ysrLE+0Ho0+T3waefforP52PYsGFYLJY+uTBIJHb9nDzHzmg00tXVRVVVFQUFBX1iyDInJ4eUlBTy8vJ46qmnWLp0KZs3b+bZZ58lPz+/T86t6E28Xi9OpxP4rKei6GN36YTDYf7zn//w7rvvcvDgQSZNmsQjjzxCTk5On1sRKAhnEolE8Hg8bN26Vemm0FcvZvpeqipcELVaTWpqKkajEb/fT319vXJl09tpNBpMJhPFxcXce++93HLLLahUKp588kk++OADysrKoh1ivybPsQMwm81iKPYSCAQCHDt2jHnz5rFgwQLUajVXX301DzzwALm5uVitVrGThNAveL1e9u7dS1dXF4mJiQwbNizaIV0yomLXz6nVaux2OyaTCY/HQ2NjY59J7OCzx5eQkMD06dNJSkqisbGRtWvXotPpCIfDxMTEkJSUhFarFSe3y8zv9+P1etFqtRgMBrEKsxtJkoTf76eqqoodO3bw/vvv09DQwMyZM5kxYwZXXXVVtEMUhMvK6/WyZ88eNBoNycnJ5OTkRDukS0Ykdv2cSqXCYDAQHx9PMBikurq6T7YHsVqtjB07lhEjRvCnP/2JDz/8kGXLlnHkyBF+/OMfk5CQAIiVgJeTz+ejq6sLm82GXq/vE8P/PYHcH7C6upo//OEPbNq0ia6uLp5//nlGjx5NRkZGtEMUhMtKkiQ6OztZuXIleXl55OXl9cnVsDKR2AkApKam0tnZSWVlZZ+q2J1IpVKh1+t56KGHGDVqFOvWrePVV1+lqqqK6667jmuvvRar1SqSu8vE6/XS1dWFw+EQSV03qqurY9euXTzyyCOoVCrGjRvHt771LYYOHSrmMQr9UjAYpK2tjfXr1/PNb36T/Pz8aId0SYnETgAgLi4Ok8lEY2Njn6zYwf+vxsXFxTF8+HAMBgNtbW2UlpayZMkSXC4XN910U5/sRN4TyUOxVqu1T65Mu5wkSUKSJHbv3s327dtZu3YtZrOZyZMnM3bsWAYPHiyeZ6Hfamxs5NixY7hcLoqKikhPT492SJeUSOwEAOLj4zEajRw7dqzPVuxOlJGRQVxcHHFxcfziF79g9+7dHDlyhIKCAoYMGYLdbheT+S+xQCCAz+fDZrOJit0XEIlECIVCtLa2snz5ctavX8+uXbu48847ueuuuxg0aBBarTjUC/1XdXU1Bw8eRKfTMXDgQJKTk6Md0iUlLt8EANLS0rBarVRUVPTZit2pTCYTI0eO5OWXX+aBBx4gFArx5S9/mVdeeYX9+/dHO7w+z+v10tnZKYZivyCfz8fRo0e57777+Nvf/obX6+UPf/gDf/jDHxg8eLB4boV+b9euXWzdupXJkyeTlpbW56ckiMs4AYCkpCTsdjudnZ20t7ej1+v7fMVKpVIhSRLx8fHceOONDBkyhKeeeoq33nqLTZs2MXfuXO69916xU8Ul4vV6cbvd2O12kXxcBEmSqKys5KOPPmLJkiWUl5fzgx/8gCuuuIKSkhLRxkTo9yRJwul0cujQIaqrq/nWt76F2Wzu8+8LUbETAHA4HMTGxipDOn6/P9ohXRYqlQqdTkdKSgojRozg2muvJScnB6fTyZIlS1i5ciV1dXWEQqFoh9rnyEOxYu7XhfP5fJSXl/PBBx+wadMmWlpamD59OlOnTqWkpITExMQ+f/IShHORJIkjR47Q2NiISqVi5MiRfb5gAaJiJ/yfhIQEHA4HkiRRX19Pampqv9o3UqPREBsby/33309KSgqLFy9m/vz5eDwevv3tbzNhwgTi4uJQqVTihNlN/H4/Ho+HmJgYUbE7D/Lc11AoRHNzM6tWreLJJ58kJiaGkSNH8rvf/Q6HwyEW/gjC/5EkiU2bNtHa2kpcXBwjRowQiZ3QfzgcDhISErBYLFRUVFBQUEBKSkq0w4qKWbNmUVJSwuzZs3nwwQf53//9X0aPHs1vfvMbMjMzxUT0buLxeMQcuwsUDof54IMPWLRoEfPnz+fWW2/l5ptv5qqrrurVezwLwqUQiUT44IMPsNlsjBgxot+8R8T4hwB8NiRpMplITk6mrq4Oj8cT7ZCiRqPRkJCQwOjRo3nssccYNmwYR48e5eGHH2bLli00NTVFO8Q+wefziYrdeQoEAjQ3N/Poo4/y4osvUl5ezkMPPcQDDzzAmDFjlCqEqCYLwme8Xi/V1dUcO3aMlJQUxowZ029GXETpQQA+OyEYjUYSExNpaWnB6/VGO6SoMhgMJCUlMWvWLJxOp9JCYsWKFbjdboYMGUJaWlq/OEh0N7nnWiAQwO/3izl259DV1UVdXR27d+9mxYoVqNVqBgwYwLXXXsvIkSP7TRVCEC5EV1cXhw8fxuv1kpqaSmFhYbRDumxEYicozGYz6enp/b5iJ1OpVCQkJPD1r3+diRMn8sQTT/DEE0+wfft2rr32Wr7+9a8rCYlI8C5MMBjE7/cTCASIjY0VFbszkBPgY8eOsXTpUp5++mlsNhvf/e53mTNnDgMGDIh2iILQI0mSREtLC8uXL8fhcJCfn9/nd5s4kUjsBIXFYiEnJ4ePPvqIzs7OaIfTY+h0OoqLi3nyyScZO3YsH3zwAY899hg7d+7k4YcfJjMzE71eH+0we5WOjg68Xi8ajYa4uDiR2J2Bz+fjnXfe4YUXXqClpYVRo0bxpz/9ifT0dKxWa7TDE4QeS14E+PHHHzNnzhzy8vKiHdJlJRI7QWEymUhJSaGlpQW32004HBYnXD6rxmm1WmJiYpg2bRparZb09HS2bt3KCy+8wKRJk5g0aRJ2u11U7s5TV1cXgUAAtVotdp44RSgU4tChQ2zevJm33noLi8XC8OHDmTNnDtnZ2RiNRvF8CcLnqK2tpby8nKamJsaOHUtqamq/OjaLxE5QGI1GkpKS6OzsxOPx4Pf7+3yH7guhUqkYNGgQRqORhIQE9u3bx4oVK/B6vcTExDBq1ChMJpNYNXseTk3sxBy7z6oMwWCQqqoqNm/ezEcffURFRQU33HAD06ZNY/bs2f1m8rcgfBGVlZVUVFQgSRLFxcXExcVFO6TLSpyBBIXZbCY7OxtJkmhtbaW5uZns7Oxoh9Xj5OTkkJaWxrBhw/j+97/P4sWLWbJkCS+88AKjRo3C4XCIk+85uFwufD4farVa7DzB/0/q6uvr+elPf8r+/fsJhUI8++yzXHHFFcTHx0c7REHoFSRJYuvWrZSWljJs2DByc3P7VU9WEO1OhBNYLBays7NRq9U0NzdTV1cX7ZB6LJ1OR2ZmJk888QQ//elPycvL44EHHuBvf/sbK1asiHZ4PZ7T6SQQCChD3P09sauoqOD999/ny1/+MhUVFVx99dW88sorTJ48GbvdHu3wBKFXiEQiOJ1Otm3bRn19PV/+8pf75fxnUbETFFqtFovFgs1mw+1209raGu2QeiyVSoVeryc7O5sJEyagVqt566232LlzJy6Xi2AwyLRp0zAYDGKY8QzcbjeSJGEymfptUievet21axcbNmzg008/RaVSMXfuXMaPH8+QIUOwWCyi+isI5ykcDrN3716am5sxmUyMHTu2X06N6X+PWDgrtVqNXq8nPj4er9crErvzoNfrGTx4MAMGDCAYDPLKK6+wcuVKWltbld07LBaLSO5O0dXVBaBsyN3fkhd56NXpdLJ06VKWL19OZWUlc+fO5Wtf+xrp6en9stIgCF9EKBRi48aNeDwecnJyKCkpEYmdIKhUKnJzc/F6vdTW1kY7nF7DZDLx7W9/m5EjRyo9xyorK3nooYf40pe+REJCQrRD7DEkSaKjowOVStVvhxl9Ph/79u3jL3/5C8uWLWPatGn84he/4Gtf+1q/S3IFobsEAgEWLFhAYWEhkydPxmKxRDukqBCJnXAStVpNRkYGtbW1Yuus83Tiibi4uJjY2FgKCgp44okn+Ne//sWGDRt48MEHKSgoEFWY/9PZ2amsiO1PIpEItbW1vPHGG2zcuJFDhw7xv//7v1xxxRUUFRWJyq4gXKSWlhZKS0upqKjgpptuYuzYsf32IkkcRYTTJCUlEQgExFDsRYiJiSE3N5fJkyczY8YMDAYDpaWlvPfeexw8eJC2trZoh9gjeDweVCpVv2qn43a7qaqq4oMPPmDr1q10dHQwYcIErrrqKkpKSkhNTY12iILQazU3N7Nnzx6MRiM5OTlkZWVFO6SoEYmdcBKVSkVaWhp+v5+WlhZlgrdw/oxGIxkZGfzyl79k7ty5GAwG/vKXv/DRRx9x+PBh/H5/v3hew+Ew4XCYUCik/D8SiSBJkrKzSV/fQUH+O8utTNatW8djjz1GTU0NI0aM4A9/+AMjR44U7UwE4QuQJInjx4+zdu1aCgsLGTBgAMnJydEOK2rEUKxwEnmDcbVaTXt7O21tbaLP2EWyWCx861vf4pprruGNN97g6aefZvHixVx11VX84he/QKfTRTvES6qsrIyWlhY8Hg9xcXHYbDYsFgsxMTG0tbWhUqmIjY2NdpiXXCQS4aWXXlIWSdx0003cf//9SkPr/jpcJAjdxeVycfDgQdauXcvvfvc7MjIyoh1SVInETjiJSqUiNTUVs9lMa2srdXV1YsuniyCfrHU6HSkpKdx0000EAgF2797N4sWLkSSJO++8k8zMzD47HFldXc3mzZtZu3Yter0enU6HVqtFp9OxY8cONBoNFRUVdHV1YbFYMJlMmM1mbDYbw4cPJzMzk5SUlGg/jIvm9/tpbW3lpZdeYuXKlajVar761a9yxx13UFhYiMlkEnPqBKEbbN++ncOHD6PX65kwYUK/22niVCKxE06iUqlISEjAZDIRDodpbGykoKAg2mH1WvI8sqKiIubOnYtKpWL16tUsXbqUzMxMRo4cSWFhIVartc9VbiRJorGxkfXr15/x+1qtlsbGRuXiwWw2Y7VacTgcxMfH99qhFEmS8Hg8VFdXs3//fpYsWUIkEmHo0KFce+21jB07VlwoCUI3kKez7Nmzh/r6etLS0sjJyemzF8vnSyR2wmkSExOx2WxIkkRNTQ2hUCjaIfV6KpWKcePGkZOTw+TJk/n2t7/N73//e6ZNm8Z3v/tdRowYgVqt/tzk7tQ5eT09EZTnupxNKBQiFArh8XhO+rrBYOCuu+7qMa1QTnzez/Wcy3PqysvLmT9/PvPnz0eSJH7/+98zfvz4z30+BEG4cJIksX79elwuFzNmzMBsNvf7CycxDiCcRqVSkZiYiMPh4NixYyKx60aJiYlMmDCBRYsWMX36dPbu3cttt93GggULzrmFm9/vp62tjYqKCnw+32WK+OLl5OSQm5t73i1N1Go1VquVW265hYKCgh6zsCIYDHLkyBH8fv85b9vR0cG7777L3XffzYcffsiwYcNYtGgR1113Xb9epScIl4LP52PTpk0cOnQIu93OLbfcIqY3ICp2winkioTdbsdms1FfX08kEolyVH2HRqPBYDCQmZnJ7bffTm5uLhs2bOC1116jvr6eMWPGKFuUyX8LuQq0b98+1q9fj9vt5p577iErK6tHV+20Wi0Oh4PCwkL27NlzzgsESZLQaDRcddVVJCYm9ogDdDAYpLGxkWeeeYarr76aoqKi06pukiQpWxnt3LmThQsXkpSURElJCRMnTiQnJwej0djvqwiC0N28Xi+rVq3CbDaTnZ1NTk5Ojz4mXi4isRPOyG63ExMTQ0NDA+FwONrh9ClqtRqTycRVV11FSkoKOp2OF154gVAoREdHBxkZGWRkZKDVapXkpqWlhU8//ZQFCxYQCASYPHkyCQkJPbqzurzqtaioiAMHDhAOhz+3xYtWq8VmszFu3DgcDsdljPTs2tvbOXjwIG+88QbhcJhgMEhqaipGoxGVSkUkEiEUClFVVcW6detYt24d+/fv56tf/SozZ85k/Pjx5xxiFwThwoXDYTo7O1m/fj0pKSnk5eWJtkH/RyR2whklJCTgcDjYu3evGIq9RFQqFUOHDmXQoEEMGDCAp59+mhdffJEdO3bw/PPPk5ycjNFoBOCll15iyZIlbN++HUmSWLJkCZIkMXXq1Og+iHOIj49n7NixvPvuu597O41GQ2pqKmPGjCEvL6/H7O+4fv165s+fT0dHB//6179oamoiPT2dUaNGoVKp8Pv9NDY28u1vf5sjR44QExPD448/ztVXX60k3SKpE4Tu53K5qKysZPXq1fzhD39g3Lhx0Q6px+gZR0+hx0lMTCQuLo6amhoCgQCRSKRHDI31JSe2RLn66qtJTU1l48aNvPzyy9x///3cfvvtjBs3jsrKSl577TUqKiqUYfE333yTrq4uSkpKsNvtPTZ5SEhIYOzYscDpiz9OFA6HGThwIHfccUePeJ1JksTevXtZuXIly5cvV4bD161bR3NzM/Pnz6e9vZ3t27fzr3/9i46ODu6++25mzJjByJEjMZvNPfZvIgh9gdw6Kjk5mfHjxzNo0KBoh9RjiMROOKPY2FjsdjterxeXy4Xf78dkMkU7rD5JHrIsLi5Gq9XS3t7OqlWrWL16NYcPH6axsZGampqTVo82NTVRVlbGunXrmDt3bo+pcJ3KZDKRlpaG1WolGAyetfprs9nIzMxk6NChUU+I5Dlzy5YtY+/evcouGZIk4XQ6OXjwIG+99RbNzc0cP34cv9/PDTfcwPTp0xk6dGi/aLosCNEi7+RSXl7O3r17GT16NMnJyeL8dIKeeTYQos5utxMfH084HKalpYXU1FTxxrnEEhISlL1mq6ur2b59O++//z6BQOC0eY6hUIiKigreffddpk+f3mOX+BsMBhISEkhKSsLr9dLV1XXabeRt7PLy8sjPz49ClCcLhUI4nU4WLlzI4cOHUalUJ20N1tHRwTPPPENHRwfx8fFMnz6d73//+zgcjj6/m4gg9AQul4sjR45w+PBhfvzjHxMbG9sjKv09hXgmhDNKSkoiPT0dlUpFVVUV7e3t0Q6pX5B3qnj88ceZPn26crA6dRgzHA4rid369etpaWmJRrjnRa1WM3XqVNLS0s6YfKpUKr7yla8wefLkKER3urq6Op588kkOHz6M0+k8aVW4JEkEAgFqampIT0/ny1/+Mv/4xz9ITEzssVVTQehrPvjgA/bt24fJZOK2227r9ztNnEokdsIZ6fV6ZReAhoYGXC5XtEPqF+TE4fHHH2fLli1nrNadKBgM8tRTT7F///4eu8hFrVYzfPhw7Hb7aa1z5J055DkyKpUqqkOxzc3N7N27l/nz59PZ2XnWVj/hcJjKykq2b9/OqlWrCIVCUR9CFoS+TpIkIpEIS5cuVRaPxcbG9sjRimgSiZ1wRnK/tfj4eFpbW3G73dEOqV/o7Oxk586drFu3Ttn141yLDnbv3s3+/fupqqq6jJGeP7VaTWFhITExMac9FoPBwMCBA8nMzIzqThPyUGtZWRlbt26lurr6nImy2+2moqKCRYsW0dzcfF4NjAVBuHiBQIC6ujoOHz6M1WplzJgxaLVacVF1CpHYCWel1+tJT0+nublZmUAuXDrhcJiqqir+85//UFpaSltb2zmbQ0ciEVpaWli/fj3r1q0jEol8biIYDWq1miFDhuBwOE46AKvVamJiYrjmmmuIj4+P+lBmMBjkk08+YeHChef9M1VVVfz73/9m3759tLe397jnXhD6ks7OTjZv3kxDQwOZmZlMmTIl2iH1SCKxE87KZDKRl5dHbW2tmGN3Gaxbt47XX3+dV199Fbfbfd5JQiQSYdGiRbz66qtUVVX1yIbSDoeD7Ozsk7bVkiQJm83GvffeG/WGxOFwmNdff51Vq1Zx+PDh8x7WliQJv9/Pgw8+yOLFi8UFkCBcQo2NjcybN48RI0YwatQocnJyoh1SjyQSO+GsRMXu8tLr9cTHxzNkyBD0er2yY8H5DDMEg0GqqqqYN2/eSW1RegL5MeTk5FBYWIhGo0Gj0ZCdnc3IkSNJTk6O6mpSv9+v9KY7evToeSXG8mNSqVSo1Wri4uIIBAK0trZehogFof9pbW2lsrKSffv2MXnyZAoLC8WuLmchlnEJZ6XT6UhOTsbpdNLV1UUoFEKj0Yg30iUSFxdHYWEhM2fOJDY2lo6ODjo7O2lvb8fpdBIOh5XWG6eKRCK0trbyySefcPfdd6PVajGbzVF4FGeXnp6u7OUoSRLZ2dmMGjUKk8kU1deU0+lU9nl1Op3nvL1KpcJmsxEbG6ssMBozZgzJycmXIVpB6J9qa2s5cuQIXV1dDB8+nPT09GiH1GOJxE44K4PBQFZWFl1dXTidTjweDzabLdph9VlFRUUUFRVxww030NnZyZ49e9i1axdLlixh06ZNeDweVCrVGRdUSJKEy+Vi586drFq1iqlTp1JUVNQtCVN3zRvLzc2lsLBQGeYcMmQIs2fPvqj76K5EUJIkDh06xJNPPonH4znrnEa5MgefLSwqLi5mypQpjB49milTphAfHy/6aAnCJSAfGzZv3sz69evJzc1lxIgRpKSkRDmynkskdsJZyXPstFotbW1tVFdXM3jw4GiH1S9YrVbGjh3L8OHDuf3226mtreXQoUPs3r2bJUuWcPToUTweD1qtlmAwqPycJEn8+c9/JhQKkZmZidVq/cJJkLwTg8fjwev1EggECAQCeL1eIpEIoVDojMO/gUDgpN0mvF4vnZ2dqNVqsrKyMJlMtLS0sGHDBuVnVCoVFosFtVqNVqvFaDRiMBiUf00mU7e2Nti6dStLly5lzZo1+Hy+k76n1WqJRCJoNBry8/P50pe+xPDhwykpKSEtLQ2j0YhOp8NgMIgqtiBcQo2NjWzatIkjR47wwAMPYLVaox1SjyYSO+GsdDodiYmJ6PV6PB4Pzc3N0Q6pX5DnbhkMBgwGA1arFbPZjMPhICsri0GDBnHkyBGOHz9OZWUlu3fvxu12K73UGhsb2b59OwMGDGDq1Km43W68Xq/y4ff7lQ+v14vP5yMQCODz+fB6vUoy5vf7CYVChEIhwuEwgUBA+b/8dXkVbiAQOO1xhMNhIpGIMmdNHi6WJImOjg7Wr19PTU3NaY9dr9crFTKtVqt8aDQadDodOp0OjUaDVqvFYDCg0+nQ6/VK4mc0GtHr9RiNRkwmEwaDQfncarUqt1Gr1Sxfvpx169bh8/lQq9VEIhF0Oh0JCQkMHTqUjIwM0tLSlPmBqamppKamir1gBeEyCYfDrFu3jqamJux2OxMmTBAXU+cgEjvhrDQaDbGxsZjNZrxer0jsokSuYpnNZrKyshg9ejQVFRUcPHiQTZs20dzcTFNTE263m0gkQiAQYO/evVitVqUPYWdnJy6Xi87OTtxut5LEdXV1KYmfx+PB7XafVJGTGyRHIpGTFnOcODQJnHEY8sTbysLhMCkpKZjNZlpbW8+4Y4Y8HCr3lpObksr/l5M6nU6nJG4GgwGz2awkwUajEYvFgtVqVRI8+fmIiYnBZrOh1WpZtWoVu3btQqVSYTQa0Wg02Gw28vPzmTFjBiNHjqS4uJjU1FTlMQmCcHnI2/itWrUKn89HdnY2xcXFYuu+cxCJnfC51Go16enphMPh06orwuUXDAapr6+nubmZSCRCVlYW119/PeXl5dTU1FBeXk5LSwt79uxhz549PP/88yf9vMFgICYmBqvVis1mIy4uTkl00tPTcTgcGI1GjEYjNpsNk8mEXq9Hr9ef9LnBYMBisaBSqdBoNMTExJwWq1xdMxgMyteam5tZuXIl06dPR6/Xnza3LhKJ4HK5kCSJUCikVBTlD7fbfVLFUU5UPR4PnZ2dtLW10dTURFdXFy6Xi7a2Nrq6uvB4PCcNWcNnFy7hcBiNRqNMOygsLCQnJ4eCggJycnJISEgAPjvBiKROEC6vYDBIW1sb77//PrNnz2b27NkiqTsPIrETzkpevZiRkUFnZye1tbXRDqlPCwQCdHZ2UldXx7Fjx2hubqatrY36+nrq6+tpbW2lvb1dqaSpVCp0Oh1xcXGYzWYSEhLIzs7GZrOdNLR6/fXXk56eTkxMDGazGY1Gg1qtVoY61Wq18jX5XzlhO7E6J3+cWrE7tXonO1PFLi4ujquvvhqr1YparT7jookTF+hEIpGTqnWnfi5XE+WPcDh80hCw/H85SWxtbaWxsZGamhrWrVuHw+FAp9MhSRJOp5OmpiaqqqpYtGgRwWBQqQ6azWZSUlKIi4sjISGB9PR0kpOTSUxMJCMjg5ycHAwGg9jaSBC6UXV1NR999BEAo0ePZvLkyeIC6zyIxE74XCqVisTERFwuV4/eaL63CAaD+Hw+WltblTYyLpeL9vZ2Ojo6cLlcyhBlMBgkHA4TDAYJBoMYDAaSkpKUIUZ56NFut5/0udVqJRQKKUOq48aNIz4+HrPZrMxfixatVnvOZsSXYgeKSCRCMBjE5XLR0dFBc3MzaWlpWK1WtFot4XAYl8ulDE27XC5lJbg8TA2ftUZxu93U19crizscDgcpKSlKJdRut+NwOJSqaEJCAnq9XiR9gnABQqEQ9fX1rF+/nuLiYnJycoiLi4t2WL2CSOyEc0pOTubYsWM0NTUBYljqXOQq1KkVo1AopAwXlpaWUllZSUNDA7W1tcrz6/V6lT5pSUlJJCUlkZ6eTkZGBomJiSQlJZGVlUVaWprSR004N7VajcFgIDExkcTERAoKChg/fvxZby9JEu3t7bS2ttLc3Ex1dTW1tbW0tLTQ1NSkfO5yufB6vUiSpFTzsrOzycvLUxZaDB48GIfDoVT05IUg8geIuXuCcCq3201VVRWbNm3iwQcfJDs7G71eH+2wegWR2AnnlJ2dzcaNG6mpqVHmJAmfLxQKcfToUSoqKqiqqmLv3r3s2LGD48eP09zcjCRJxMfHk5KSQk5ODjNnziQzM5P09HTy8/MpKChAp9MpJ/yz/StcOg6HA4fDQV5eHuPGjVMS9hP/lauv+/bto6KiguPHj1NRUcHSpUupr69Xqtzx8fGkp6dTVFTE6NGjyc/PV1Y4izlDgnC6FStWsGbNGkKhEHfeeaeygEk4N5HYCeeUmpqKxWLB5/PR1NREYmKiOBmdQJ6btXfvXg4fPszx48eprq6mvb2dUCiEVqslPj6ekpISpk2bRkJCAnl5ecqcN5PJhMViURYtyKs6z3c7MaH7nfi8f97fQK1Wo9PpsFgslJSUKPMa5cUc7e3tHD16VNlvuaWlhVdffRWNRoPBYCA+Pp5BgwaRk5OjrPhLS0sTlQmh35Lnzq5bt46qqipmzZqlTGcQzo9I7IRzSkxMxGw2EwwGaWlpUSac9zfySk25dUhbW5syH66uro7Dhw/T0tKifN9sNpOYmIjD4SA7O5sBAwaQmpqqVOm6u9mucPnJw6lGo/G07wUCAdxuNwUFBVRXV5807N7R0YHH46GpqYlQKERtbS0HDx7k6NGjZGdnY7fbiY2NJSEhQRnGvRRzDwWhpwmHw1RXV3PkyBGCwSCTJ09WWhEJ50ccKYRzSk1NJSYmhnA4zPHjx8nOzsZkMkU7rEtOHnKTV2FGIhE6OjooKyujtLSULVu2sG3bNqWHXFxcHCNGjGDw4MEMHTqU0aNHk5GRgd1uF9tN9UNymxiHw8HIkSMBlNfR8ePHqampobS0lM2bN1NaWsry5cvp6OggISGB3NxcBg8ezIQJExgzZgwpKSnKjhwnrkwWhL5Ebna+atUqjh8/Tl5eHnPmzBEXNRdIPFvCOdlsNhwOBxaLhWPHjjF69Ohoh3TZOJ1Odu/ezb59+1i/fj2bNm3C7Xaj0WgYMGAAkydPprCwkKKiIq644gr0ev1Jk+PFCVg4lVqtVuZTjhkzhrvuukvZlu3gwYPs2LGDI0eOcOjQIf773/+i0WhITEykqKiIadOmceWVV5Kfn4/dbo/2QxGEbud2u/nHP/6h7JyTnp4ujqEXSCR2wueS31AxMTHEx8dTW1t7WqPXvkIeaj148KCys0NpaSktLS34/X5MJhOzZ88mMzOT1NRUpY+Z3N7C4XCIRE74XCcufpH7CMJnrz2z2cyQIUNITk6mo6OD9vZ2qqurOXr0KE1NTbS0tLBgwQJWrlxJSkoKAwcOZNSoUeTm5pKamqpcSAhCb9XQ0MDu3buprq7mxhtvZOzYsWK04yKIxE44L1arFYfDQWNjY59K7CRJwu/343a7cTqdNDQ0sH37do4cOcLhw4epqanBYrGQmJjIoEGDGD9+PIMGDVI2sRcnUqE7qFQqtFotCQkJym4XkUgEt9vNvn37OHz4MHv37uXTTz+loqKCyspKampqaGtro7i4mIKCAhITE4mLi8NoNPbLObBC7yZJErW1tXz66adotVqKiorIz8+Pdli9kkjshPNit9tJSkqisrLyjBu+9zYn7nggH0zWrFnDu+++S1dXFykpKYwaNYqf/exnjB07lqysLMxmcxQjFvobtVqNzWZj/PjxjB8/HkmS6OzsZNeuXezevZtVq1bx1FNPAZ8tcPryl7/MDTfcQF5eHsnJycrvERcfQm8QiUTYv38/7733HjNmzKCoqIjExMRoh9UricROOC9xcXGkpKSwevVq/H5/tMP5wsrLy9m3bx8LFixg+/bthMNh7HY79913H9OmTSM7O5vExERlk3mxIkvoCaxWK1dccQUjRozg9ttvp6amhj179rBjxw4+/vhj/vvf/5KcnMzQoUO5//77KS4uPudOH4LQE6xdu5ZNmzbR2NjIc889R0ZGRrRD6rVEYiecF5vNRnx8PG1tbXi9XoLBYK8b7nG73dTW1rJp0yZKS0uprq6mpaWFMWPGkJGRQW5uLkOGDKGgoAC73X7GFhaCEC3y/E2536HNZsNsNmOz2cjKyqKgoICysjJaWlqoqanhhRdeYOjQoRQUFDBs2DBycnLEBYrQ48j7Pq9fv57GxkYGDhxIXl5ev+i8cKmIxE44L1arlfj4eLq6uujq6sLv9/eKxE5ePu9yuairq2PHjh3Mnz+f48ePI0kSY8eO5eabb2bw4MHk5uaKxQ9Cr6FSqbBarRQWFlJQUMC0adPYsWMH27dvZ+3ataxevZqysjIGDRqkrOR2OByYTCbR7FXoMeS5pJs3byYcDjNu3DgSEhLERcgXIBI74bzExcWRmZkJQF1dHa2trb1in9JwOExpaSkvvPAC69ev5+DBg0yfPp3vfOc7TJkyhaFDh550e5HUCb2VWq1mzJgxjBkzhm984xts27aN+fPns2nTJt58803Gjh3LXXfdxZe+9CUKCwujHa4gANDV1cWSJUsoKytj+vTp3HfffWIl7BckEjvhvMTExJCcnIxWq6WhoYG2tjays7OjHdZZ+Xw+9u/fz/Lly3nrrbcwmUwMHz6cn//854waNYqEhASsVqtI5IQ+4dTXsVarpaSkhMzMTOrr69m3bx9vvvkmL730Eu+++y433HADt956K/Hx8b2i8i70TZIk0dbWxosvvkhRURFjxowhKysr2mH1eiKxE86LVqvFbDYTExNDR0cHLpcr2iGdUSgUoqmpiU2bNimTyuPj4xk1ahQlJSVMmjSJjIwMUeYX+jR5mNZqtZKQkEB8fLyyora2tpYVK1agVqsZNmyYsvpQXOQIl1tLSwtHjx6ltLSUu+++m6KiIjG3uRuIxE44L/Jm53LzVKfTGe2QTiPv47pjxw6eeeYZqqurMZlM/PSnP2X69OlkZGRc9pOXPDE4EAgQDAaJRCLKFmXy1lDyc6vVapUto76oYDBIMBjE5/MB/78hbkxMzHk9B5IkEQwG8Xq9hMNh5XdYLJbznp8ViUQIBAJ4PJ4zfl+v1yt7oMoxSZKEz+cjEAgo9yvTaDRYrVbleRPOj9lsJi8vj+9///ts3bqVtWvX8sILL1BaWsqsWbO4+eabGT9+PHq9XgyBCZeF3G6qoqKC7du309LSwsSJEykuLo5yZH2DeBcL502n05Gfn09TUxPNzc3RDuc0O3fu5LnnnuPWW29Fp9Px3e9+l5UrV3LXXXdFbem8JEk0Njby6quv8pWvfIVx48aRn59PRkYGgwcPZurUqdx7773MmzePHTt2dNvzumXLFh599FFSU1NJSUkhLy+P6dOnEwqFzuvnPR4P77//PuPGjSMlJYXU1FSysrJYtmzZecfQ2trKyy+/TEpKyhk/HnroIdatW3fSz3i9Xv7yl78o93vix7Rp0zh69GifapB9OalUKq644gp+8IMfsH79eqZNm8bGjRu5++67ef3113vke1rouyRJYs2aNbz//vtcf/31DBs2jKSkpGiH1SeIip1w3rRaLRkZGRw5coT29vZohwP8/03VX3vtNdasWcOBAwe45ZZbuPXWWxk0aBDx8fFRrfAsXbqUNWvWsGzZMiZMmMD06dNJT0/HZDLh8XhoaGjgyJEj/Pvf/+aDDz5g4sSJ/PrXv/7C91tcXExCQgITJkzgT3/6EzU1NReUEBkMBiZMmMBzzz3H+vXreeeddzh69OhJjZ3PJSYmhquvvpq3336bLVu28MEHH9DU1MS0adO45ZZbKCgoOC3h1uv13HbbbUyaNIkdO3bw+uuvM378eEpKSigqKiI9Pb1bNgSXK4PPPvssx48fJzk5mR//+McYDIYv/Lt7ohO3MtPr9SQlJfHQQw+xceNG1q5dyzPPPMPx48eZNGkS06ZNE6vDhUtu69at7N+/n66uLu644w7sdrt4zXUTkdgJ502r1ZKcnMyOHTtwuVzKST5ab0ZJknC73Rw6dIhPPvmE+vp6HA4Hc+fOZdy4ccrerdGKzeVysX37dtatW4fT6WTkyJGMGDGC3NxcrFYrTqeTqqoqbDYbK1asYN++fd3WTDY+Pp74+HiKiop49dVXL7gao9VqSU9PJz09Hb/fz+rVqzl69OgF/Q6DwUBOTg4ZGRkEAgHWr19PZ2cn+fn5zJo1i9jY2DPe78CBAxk4cCAGg4ElS5YwdOhQpk6dyqBBgy7o/j+PvC/wjh07OHToELm5uacN/QIcOnSIyspKrFYrJSUlvWIl+Lmo1Wr0ej3Dhg1Do9Gg0WgoLy9n69atRCIRkpKSGDx4sDjJCpeEfDG+adMmmpubiY+PZ+TIkWJuXTcSiZ1w3rRaLZmZmcoG5ZFIJKpzciKRCLW1tTz55JOsWrWKuXPncueddyoVh2iSJImjR48qe3vecMMN3HjjjcTHxyu3SUxMJDExkeLiYsrKyli1alUUI+6fNBoNWq32rItp5s+fz9NPP01RUREvvfRSn2kTIr8/hgwZQl5eHklJSTz66KMsXLiQtrY2/vrXv2I0GqP+PhL6Hnl/7nfffRer1crEiROjMv+5LxOJnXDe5Dl2AE6nk4aGBtLS0qIWz4oVK1i2bBnvvfcejz/+OOPHj2fgwIFRi+dEkiRx5MgRnE4nBoOBvLy8sw4h6nQ6rrnmGkpLSy9zlP2XvGr0n//8J+FwGI1G02873RuNRubOnYvFYmHRokW89tprFBYWMmfOHLEJu9DtGhsbWbZsGUePHuXrX/869913X7RD6nNEYiecN41GQ1paGjqdDrfbTWNjI6mpqVFZaVpXV8eaNWvYuXMn11xzDePGjSMrK6tH9eQym81otVp8Ph+HDx8+68IFjUZDcXEx3//+93tU/H2Z/JrtC0OrX5Q8727YsGF0dnZSU1PDO++8Q0ZGBg6H46QqsyB8EaFQiPr6ej744AOGDh1KcXFxVM4hfZ1I7ITzptFoSEhIwGAw4Pf7aWpqikockUiE/fv3s3//fjo6OvjGN75BXl4eFoslKvGciUqlIiEhAYvFQjAYpLS0lMOHD5OTk4PNZlNilduQpKamkpqaesbfJUkS4XCYrq4unE4nLpdLWQihVqsxm83Y7XasVitms/mCY5XbmjQ1NdHV1UUkEkGlUmEymUhKSsLv91/8E9HN5LmLra2ttLW1KV93OBykpaVRX1+Px+MhGAyiUqkwm83ExcURExOjtGnx+Xx0dXVRWVmp/LxarWbo0KFotVqCwSBtbW3U19dTV1dHOBzG7Xazf/9+pX+jSqUiPz+f2NhYpaWN0+mks7PzrH8fs9ms/H164oksJSWF4cOH09zczCOPPMK+ffvIysoSiZ3QbTo6OqioqGDbtm3cc889FBQUiIurS0AkdsJ502g02O12YmNjCYVC1NTUXNAqye4gT3r/xz/+QUdHB6NHj+b222/vcSdKlUrFsGHDyM7OZvPmzWzevJmf//znXHXVVcyYMYOxY8eiVqtPe/5OfRzy9zs7O9mwYQOLFy/mk08+oampiXA4jMlkYtSoUVx//fVceeWVjBgx4oy/50zk393S0sK+fft47rnn2LJlCx6PB61Wy5AhQ/jOd77T49pgbN26lZdffpn58+crX7v55pt59NFHefTRR9m+fTtNTU1oNBpGjhzJbbfdxlVXXaV0tK+pqWHDhg18/etfJxKJAGAymaiqqiIuLo7m5mbefPNNHnvsMaVf4549e7jpppuU+9NoNHz44YfMmTMH+Cw5XrNmDStXrmTFihXU1dUBYLFYGDVqFNdddx2jRo1i+PDhPbpXXG5uLnfeeSevvfYa69evx+/3M2rUKKBnJqNC7yAfazZv3syqVatwOp3ceuut5OXlRTmyvkkkdsIFS0tLU1Z0Xu7Ezu12U11dzebNm/nKV77Cdddd12NPOGazmXvvvZfc3Fz+/ve/s337dvbu3cu//vUvkpOTGT58OIWFhQwZMoTJkydjNBpPm8QfDodpb2/nu9/9LocPHwbgkUceYeDAgeh0Otrb25k3bx7//Oc/WbBgAd/97ne59tprz3u+WE1NDU888QRLly4lEAjwyCOPUFRURFJSEq2trSxcuJCysrKoJPFnM3HiREpKSvjhD3/IL37xC7Zt28aePXt48sknueOOO/jhD39IOBzm8OHDPPLIIzz33HPs2LGD5557DoCcnBxSUlKYMmUKjz/+OOvWrTtpxW9ycjLf+MY3uPnmm3nyySd55ZVXyM/P589//jM5OTnAZ0lOSkoK8Nnf6E9/+hNr167F6/Xy61//mqKiIlQqFR0dHbz33ns8/fTTDB06lB/96EeMGjWqx75mVSoVRqORb3/72/zrX/9iy5Yt1NTUkJ6eLnZrEb4Qj8fDkiVL2LlzJ/fccw9ZWVk9apSlLxGJnXDBEhIS8Hg8NDY2Xvb77uzs5ODBg0QiEXJzcxk0aFCPPEnKfcByc3PRaDQEg0F27txJbW0tjY2NHDt2jK6uLg4dOsTOnTvZtWsXV1xxBfn5+SftwdvZ2cny5cvZs2cPNpuNsWPHcuWVV5KamopGo8HtdnPjjTfy73//m2PHjvHOO+8wfvx4kpKSzrlDRCQSYdWqVezevZvOzk5mzZrFlVdeSXZ2NlarleTkZGVoc/fu3Zf4GTs/8vCq2WxW/q9Wq7FYLGRlZVFcXKwMHTocDrKysqivr6e0tBSPx4PRaESn06HT6bDZbNjt9tN612m1Wux2O3a7HYfDgVqtxmAwkJmZecYKgyRJ7N69m/b2dlJSUhgzZgzp6enAZyczn8/H1q1b8fv9+Hy+HpMgn4lKpUKj0TB69Gj++9//0tDQwMGDB0lOThaJnXDRwuEwW7dupbKyEp1Ox6xZs5T3rtD9xLMqXLCEhAS0Wi2NjY2X/STlcrkoKyvDarWSkZERtR0lzldCQgLDhw/nhz/8IV/96le59tprlcTM5XJx4MABPvroI/7617+ycOFCdu3aRSAQUJ5Xl8vFBx98QG1tLZmZmVx33XUMHDiQ2NhYrFYrSUlJ3HTTTQwZMoRwOMzHH39MXV0dXq/3nLGFw2GWLl3KsWPHsFgsfPnLX6a4uJjExERMJhPJyclcffXVjBgxQtma7GKdmHxfitdMUlKSsoAmJiaGmJgY8vLyKC4uRq1W09DQgMvlUoZeu1tDQwOSJBEbG6v0ItTpdMTFxfGlL32JUaNGkZOT0ysaIKtUKgoKCkhKSkKSJEpLS897xxJBOJW8peInn3yiXPxMnjz5vLcmFC6cqNgJFyw9PZ1Dhw5x6NChy57YdXV1UVFRwaBBg6LagPhCyKsOZ8+ezezZs5V5grt27WL79u2sWrWK999/nxdeeIG9e/eSk5PDkCFD0Gq1dHZ2smzZMjweD1lZWUyYMOG036/T6Rg0aBDFxcWsWLGCzZs3Yzabz9gA+ESRSISNGzfS2NjIsGHDlOHgEzkcDtLT08nOzqa8vPyinwN59w+5Oen5kBeNnM/OIQ6HgyFDhpxWAUhOTsZisdDV1UVTUxN2u71bdq44kUqlYvz48SxdupSVK1fy85//nJtvvpmBAweSkZGBWq3mmWee6db7vNTUajWZmZnU19dz5MgRkdgJF01eaPfSSy8xa9YsZs2ahd1uj3ZYfZpI7IQLlpqaitFopKmpCa/Xq3SvvxzC4TBerxej0XjS5vE92Zli1Ol0DB48mJycHCZPnkxMTAwrV66kubmZ1atXK/34/H4/XV1dSp+1U+fOndi2IyYmBoD29vZzVuwikQjBYBCPx4MkSRgMBmV489Tfr9frv/CB2GQyodFolBWm53NBIK8EltvGfB6dTnfG+TparVZZpHKpqnUajYaHHnqIkSNHsnv3bkpLS/nlL3+JWq3GarVSXFzMlClTKCoqoqio6JLE0J3k14DBYECn0+H1env08LHQsx07dowPP/wQlUrFxIkTmTJlSq84bvdmIrETLlhcXBwWiwWfz6c04L1ciZ1arUan0xEIBM64BVRPIUkSzc3NlJaWEggE+NKXvqR878RkzGq1YrfbGTlyJNu3b6erq4uGhgal5YjcDgU+S8bOtttHOBxWng85mTmXE3+3nPic7ee+yIldpVLhcDjQ6/WEQiFaW1vPmWRFIhH8fj8dHR3ExMSccwhTnht2pq9/0ZPI+fx8dnY24XCYxMREkpKSqKiooKOjA5fLxcGDB3G73dTW1gIwcODAXjG3KBgMEgqF0Ol04kQsXBSv10tVVRUbNmxg6NChyi4nwqUlEjvhgiUmJmK324lEItTX1xMbG3vZ9vnT6XTExsZy+PBh/H4/kiT1yJOOJEmUl5fzwgsv0N7ezvTp09FoNGeMVa1Wk5+fj8ViweVyKRPsNRoNBoOBmJgY2tvb8fl8uN3uk4ZY5YSrq6uLzs5OJYk616pYOUG2Wq24XC6lMhgTE3NSgiTPj+ns7Lzo50JeQSr39KuuriYQCHxuIun3++ns7KSlpYXk5OSL6s/XXc7UgqarqwudTqdsSdbZ2Ul6ejq5ubnMnDlTaQe0f/9+XnrpJd577z127dpFKBQiPz+/Ryd2cl++zs5OPB6PsoBEEC5US0sLZWVlbN68md/+9rfk5uaKuXWXgXi3ChcsPT2dxMREVCoV5eXluN3uy3bfdrudkpISSktLqauru6z3faHkk2NdXR07duwgEAic8XbhcJi9e/ficrmwWq3K/DoAm83GnDlzsFqtVFZWsn79+tN+PhAIUFpayv79+zEajUyYMEHp2fZ5VCoVkydPJi0tDZfLxerVq09bJOF0Oqmvr6e8vPyiq3YqlYqsrCzy8vKIjY1lx44dHDt2TOkRdyZbtmxh165dJCQkMHDgwKjNydHpdBgMBoLB4EkLWqZMmcLvf/97Vq9eTTAYZMyYMTzzzDNUV1cDn1VNc3JymDNnDi+//DIFBQXU19ezZs2aSzYk3J2cTifl5eW0trYycuTIbp+XKPQPr732GitXriQ9PZ0777zzvI5Lwhcn3q3CBdNqtVitVuLj4zl+/PgXXjF5IeLi4hg1ahQ6nY6ysjK2b9/O1KlTL9v9XyhJkqitreUvf/kL9913H/n5+SQnJyuVsrq6Og4cOMCCBQsIh8MMHz6cadOmKVuLORwO7r33Xvbu3cuxY8d4+eWXSU5OZsCAAeh0Ojo6Ovjvf//L9u3bMZvNfOUrXyEtLe28KqgajYbbbruN1tZWdu7cyb///W8cDgcFBQU4HA7a2tpYuHAh69atw2Aw4PF4Luo5kIdDp06ditfr5d///jdPPPEE06ZN48orr6SgoACTyUQwGMTlcrFr1y7eeustnE4n9957LyaTKWpV2bi4ONLS0qitraW5uRmLxUJdXR3V1dXEx8crbU38fj+rVq0iGAxyxx13kJycrOySsWrVKtra2oiNje2x7XlOFAqFWLlyJa2trcTFxTFy5Eix1Z1wQeSK9caNG/H7/dx+++1YLBbRMucyEYmdcMHUajUmkwmHw0FTU9Nl3XLKZDKRkZFBVlYWNTU17Nixg/Hjx/fIeUDyibylpYWamhq2bt1KfX09CQkJ6PV6urq6lJ52gUCAYcOGMWHCBLKzs5WhL5PJxIgRI5g6dSqlpaXU19ezdu1aysvL0Wq1uFwutmzZouxAMXv2bGJiYnC5XLS0tFBRUUFDQwNut5tAIMDy5cvJyMggMTGRlJQURowYwfjx45W5MGvWrKGyspL4+Hh8Ph81NTX4/X5iY2Pp6upi165dxMbGKpW0C6nkFBcX43a72bNnD5WVlWzbtg2v10tlZSUmk0lZGHP48GG6urpITk5m+vTpJ/1t5SpoTU0NBw4coKmpiWAwSF1dHatWrSI/P5+0tDT8fj+ffvopR48exeVy4fV62bx5M263m8TERCwWCwcOHKC8vByn00k4HGb16tVkZmaSlJSk9KvLzMykpKSE+vp6tm7dyrFjx2hvbyc9PZ2srCySkpJQqVSMGDECr9dLaWkp69evx+FwKEO2mzdvJiEhgaysLEaPHt2jhzXlv8HatWuBz+YOpqWliROycEHk919jYyPZ2dlMmTKl1yx26wtUkljuJFyExYsX8/zzz2O1WvnJT37CyJEjL9t9B4NBfve737FixQosFgtvvvkm8fHx59UW43KR5ykdO3aMw4cPs27dOj799FOam5tpb2+ntbUVg8GAw+EgIyODOXPmcO2115Kfn39atU3eh3TDhg0sWbKE5cuX09zcTCgUwmQyMXLkSK677rqTthRbt24dixcv5u9///tpsd16663MnTuX22+/HYDGxkYOHDigbCnmdrvRaDQUFhZy//3309HRwdKlS9mwYQORSIT09HRmz57NX/7yF2w22wU9L3JT5hdffJEDBw5QVVVFc3MzkiQpFwsjR47khhtuYMSIEQwaNOi052LPnj3885//ZN68eSd9z2Aw8L3vfY8777yT+vp6Zs+efdr9T548mXHjxlFYWMg3v/nN04ZFr7zySr70pS/xyCOPANDc3MyhQ4f45S9/qTTGjo+P58EHH+Saa64hOzubSCRCaWkpa9asYdu2bezatUvZUsxoNJKbm8vcuXMZP348kydPBnrm9lySJOH1eqmrq2Py5MlMmTKFmTNncv/990c7NKEXkSSJxsZG7rrrLnw+H7NmzVLeTz3xdd8XicROuCgbN27kzTffZMeOHfztb39j4sSJl+2+JUmiqamJX//616xbt46SkhKeeuopZd5fTyC/reTVqvIKw0gkoqxAPXHVq8FgQK/Xn3GBhZwkBoNBAoEAwWDwpIREq9ViMBjQarXKkFkgECAQCJyx7Yl8X3ICGYlECIVCeL1eQqGQErtGo8FoNCoLKE7c2F6v12OxWC64+iT38PP5fMpzIz8W+bnQarXo9Xq0Wu1pFUG5t53X6z3jFACTyYTRaCQSiZxxDp+864RGoznjghD5+3LrlEgkorRoCYfDSJKkVKzlv5ccUzAYPOnvfOJjOvXv01NepycKh8OsW7eOp556ir179/L73/+e2bNnKw2XBeF8HDt2jM2bN/O1r32NP//5z8yYMYMhQ4YAPfN13xeJoVjhopjNZpKSkmhoaFBWcV7ON63D4WDmzJloNBo++eQTFi5cyMSJExk8eDAQ/QOIfP9ycvJFdhyQE0CDwXDev0ev16PX67Faree8rZyofd5qte5alapSqZTk6WJ/XqvVYrPZzlktTExM/Nzvn888RLVajVqt/tzFG3JMWq32vPfo7UnkRH7FihWsWLGCsrIy7r33XoYOHXrBFVmh/5IvQEtLS/nkk08oLi5m6NChZGZmRv143N+IxE64KGazmcTERKUZbjgcvmwr5+SmuWPGjCESibB8+XKWLVuGXq/H4XCQmpqq3E4QhLOTK8F1dXV88skn7N69G7PZzHXXXUdWVpZYDStckPb2dsrKyti1axdTp04lNzdX7DIRBeJdK1wUm81GZmYmHo+HtrY2nE6nsvn65ZKVlYXVakWr1fKd73yHyspK9u7dyx//+EelsiWSO0E4M3lYvKmpiZ/+9KesW7eOoUOH8qc//Ylhw4aJBRPCeZOrvkuXLmXTpk24XC6++93vKhfZwuUlEjvhothsNrKzs1GpVDQ2NtLY2HjZEzv4bOXpVVddxT/+8Q8+/PBDFi9eTHl5OT//+c8pKioSV4uCcAaSJNHa2srHH3/M22+/zb59+3jwwQeZOHEiY8aM6dErd4WeJxKJ4PP5ePXVV4lEItx8883k5OSINjlRIhI74aIYDAbsdjsGgwGn00l7e3tU4tBoNFitVkaNGoXP58NoNLJx40befvttRowYwZgxYygsLBQnKkHgs4TO5/Nx7Ngxli9fzo4dO2htbeXmm29m6tSpFBQUnHHPXUH4PG63m61bt1JbW8vIkSOZPn06er1ejJhEiUjshIui1+uVCeydnZ20tbVFLRZ5ZwObzUZeXh5lZWUsWrSIo0eP4vV6cTgcxMTEKKsYBaG/kVfuut1uGhoaWLlyJS+//DIqlYqCggK+//3vk5KS8oUW+Qj9UyQSoa2tjY8//phwOExhYeFl7ZIgnE6UMYSLplarycnJwel00tDQEO1wsNvtjBo1ivfff5+77roLl8vF9773Pe666y6WLl1KY2NjtEMUhKipqqriySef5N577+XHP/4xw4YN45FHHuHFF18kKytL7OEpXJSOjg5KS0v5z3/+w/XXX8+UKVOIiYmJdlj9mqjYCRdNrVaTmZlJV1cXzc3N0Q5H6RlmtVr56le/ypQpU9i9ezevv/46f/7zn0lPT2f69OnceOONxMXFnVe7C0HozZxOJ5WVlaxcuZIPPvgAn89HXFwcL7zwAuPGjSMlJUVpZSOGzYSLsWbNGpYsWUJcXBzXXHMNgwcPFq+lKBOJnXDR1Go1ycnJHDlyhI6OjmiHA/z/nmKZmZnExMQQFxdHY2Mje/fupa2tjdWrVxMOh8nPzycrK4sBAwZgNBrFHDyhzwgGg8rWbIcOHeLYsWPs3LmTcDhMUVERQ4cOZfr06WRmZorJ7cJFkxfg7N27l8OHD3PllVeSk5MjqnU9gEjshIumVqtJS0tjz549tLa2Kkvee8rVWmxsLDExMfz2t7/lk08+Ye3atSxcuJBVq1YxevRoJkyYwO23305qaioGgwGNRtOjtiUThPMlz6ELhUJ0dnZSV1fHSy+9xOrVq2ltbSU+Pp5vfetbTJ8+nUGDBon+dMIXIjcjlnvWyS1zHA6HmMfcA4gtxYSL5vf7+eijj/jzn/9McnIy77zzDgaDoUclRvLLOxKJEAwGcbvdvPXWWyxdupTdu3fT1dXFrFmzmDBhAhMmTGDEiBE9Kn5BOB9er5edO3fy4Ycfsm3bNnbv3o3dbuf6669n0qRJzJgxA4vFomxZJ17jwhchbzN43XXX4fV6KSoq4h//+Ie4MO4hxGWbcNHUajXp6ekYDAa8Xi8NDQ1kZmb2qCs2+SAjV+O0Wi2zZ89m6NChVFZWsmXLFo4dO8bChQv5+OOPGTx4sNIiZdCgQZjNZnGgEnoceb/crVu3cuDAAQ4ePEhZWRkqlYq4uDjuvfdepkyZQm5uLklJSdhsNnHSFbpNS0sL27dv59ChQ8yZM4drrrmmRx33+zuR2AkXTa1Wk5SUhF6vJxAI0NzcTEZGRrTDOit5/t2AAQPIyspi8ODBxMfHs2rVKo4dO0ZdXR1bt27F5XJRXV1NQ0MD6enp2O12rFYrdrtdqXgIwuUUiUQIh8O0trbidDqVlegbN27k6NGj1NXVKXPoiouLGTVqFJMmTUKn04n5o0K3CofDNDY2smbNGvR6PYMGDaKkpCTaYQknEImdcNHkip3ZbKapqYmamppe8wbXarXEx8czd+5c5s6dS0NDA4cPH2b+/PmsWLGCBQsWADBp0iSuvPJKhg8fzoQJE4iJiVFOlHKCJxI9obvJUwjkuUzBYBCXy8WKFSvYvHkze/fuZceOHej1eoYNG8bkyZO59dZblSqzIFwqPp+PI0eO8Prrr3P99dczevRo0tPTox2WcAKR2AlfiMFgIDExEZfLRXl5OeFwONohXZTExETi4uIYPnw4HR0dVFZWsn37dtasWcMbb7zBs88+i16vZ/z48ZSUlDBkyBCuuOIK4uLixMpCodt5PB5aW1vZuHEjW7ZsoaysjNLSUgKBAHl5eRQUFHDbbbcxbdo04uLisFgsGI1GsShCuOTef/99li1bRjgc5tvf/jY5OTnRDkk4hTgKCBdNrlQ5HA5MJhP19fVEIpEoR3VxNBoNGo0GvV6PyWTCbDYTFxdHfn4+tbW1NDQ0UFFRgdPpZNOmTWzdupWPPvqIrKwsUlJSSE1NJS8vj7S0NCwWizjBCudFrsa1tLTQ1NREZWUllZWVNDY20tTURH19PWq1GpvNxsyZMxk4cCDp6ekkJyeTmZlJdna2sqJbEC4leYeJzZs3c/z4ca6++mrS0tIwmUzRDk04hTj7CF+Yw+HAbDbT2NhIX1hkrdPpSEhIICEhgSFDhuD1emlubmbXrl2sXbuWQ4cOUVFRwe7du0lMTCQ1NZXs7GyGDh3KwIEDSUhIwGq1YjKZ0Ov16PV65eQrhm37L3menM/nw+/3EwgE8Pv9tLe3U1VVRWVlJWVlZRw+fJi2tjY6OzuJjY1l6NChDBo0iBEjRjB+/HgsFouoEguXXTgc5tChQ5SVlREIBJg9ezZWq1VcxPZAot2J8IX961//YsOGDZSXl7No0SJiY2OjHdIlFQgEcLlcbN++nU2bNnHgwAEOHDjAoUOHlKRw0KBBjB07lsLCQgYMGMDQoUOx2+1nncguEr6+42yHVDmJ27FjBwcPHqS8vJz9+/eza9cuvF4vWq2WwsJCxo8fT3FxMUOGDGHcuHGiIif0CC6Xi7vvvpv6+nrGjh3Lk08+KS5WeyiRagtfWHJyMjabjcrKyl47FHshdDoddrudiRMnMnLkSPx+P16vl7q6OkpLS6mpqaGqqoo1a9bw/vvv4/f7MRgMZGdnk5GRQVZWFoMGDSInJ4ekpCSSk5MxmUziANkHyBW5yspKGhoaqKuro6ysjCNHjtDQ0EBjYyN+vx+73Y7D4SA7O5svfelL5Ofnk5mZqWzxpdfrMRqNGI1G8boQoq62tpbt27ezbt06vv3tbzNr1ixxsdGDicRO+MLsdjs2m43Ozk48Hg82m61Pl+fltilWqxWr1Qp8NsyWnJxMYmIizc3N1NfXc/z4cVpaWmhvb6elpYVQKKS0qzh06BB2u52YmBjsdjsJCQkntVWJi4vDZrNhNpuxWq2iB1kPIe/w4HK5cLlcuN1unE4nra2tuFwuOjs7aWtro7m5mc7OTtxuN4FAgFAoRFxcHMnJycrrJC4ujpSUFLKzs0lJSSE+Ph6LxSL+zkKPEgqFKC8v55NPPiEtLY1hw4ZRWFgoXqc9WN89+wqXTVxcHLGxsXi9Xjo6OrDb7X06sTsTtVpNbGzsacPQbrebtrY2Dh48yL59+6ioqKCqqoo9e/bQ1dVFMBhEpVKRkpKiTIpPT08nLy+P1NRUEhMTSUlJwWAwoFarlUUe8v9P/FfsKHDxTtyhRP4Ih8OEw+GT/h8Oh/H7/Rw/fpzjx4/T1NTE8ePHKS8vp76+nubmZpqbm9FoNJhMJmJiYigqKqKkpISsrCzy8vIoKSnBarWi1+uj/KgF4dw6Ozs5cOAAn3zyCdOmTWPw4MGkpqZGOyzhc/Svs69wSaSmppKamookSVRWVirtFwQwm82YzWYyMjKYMWMG8P97k9XV1VFfX091dTVlZWVUVFRQW1vLxo0bqampUVrHaDQa0tPTlWHb3NxcMjMzlYUbGRkZJCQkYLPZMBqN0Xy4vZrX66WlpYW2tjYaGxuV4dTGxkaqqqqorq5Wvh8KhVCpVFgsFlJTUykqKmLUqFGkp6dTUFDAwIEDSUpKwuFwAKLnodB7LViwgGXLluH3+/nZz34mkrpeQCR2whcWExODw+HAaDRSW1tLYWFhtEPqMU48kcv/l6tDycnJ2O12cnNzGTNmDF6vV5mv53Q6aWtro729naamJtrb23G73XR1dVFWVsbWrVsJBAKEw2EMBgM6nQ69Xo/VaiUuLo6YmBhsNhsOhwOHw6EMG8fExCg9zywWizLcq9fre/0OBZIkEYlE8Hg8+Hw+3G63Mj3A6/XS2dlJR0cHXV1duFwu2tvbaW9vp7OzE6fTqVRQg8EggUBAaX1jsVhwOBxMnDgRi8VCTEwMKSkpJCYmYrPZlOfQaDRiMBiUn9HpdGIektBrhUIhGhsb+eijj/D5fNxxxx3K6IHQs4nETvjCdDodZrOZ2NhYmpub8Xg80Q6pR5MTPIPBgMFgICYm5qTvS5KE3+9Xto5qaWmhpaUFp9NJR0cHzc3NtLa20tXVpSQufr+fUCiEy+XC7/fT2tqKTqdTkg2dTqd8bjKZlH+tVqtyG/l28nCvvB2VPNx74udqtRqdTqc8Fvk2p5J/16mCweBpq0flxExegBOJRAiFQspwaCQSIRgMnvR5KBQ66cPn8ykJssfjwe12K5/LH3LiJq9SbWpqorq6mpSUFFJSUkhKSsJoNOJwOIiJiSE2NpakpCTi4+OJjY3FbreTlJREXFyc0tJGEPoar9fLxo0bqaurIz8/n2nTpmE0Gnv9BWB/IBI74QtTqVQYjUbS0tJobGwUid0XJD+fRqOR5OTks1ZAA4EAXV1d1NXV0dLSoiR9TU1NJ1X7mpub6ejooKOjA5fLRSAQQJIkVCqV0g9No9EoSYx831ar9aRqYGxsLHq9XvncZrMp8/rkpPHUg75OpzttWF6SJDo7O09bQR0KhZTES/68q6tL6fkmP95TP/d6vXg8Hrq6unA6nSc9j3LiqdFosFqtJCYmKitS5dXcADU1NUyZMoWZM2cydOhQ0tPTT9o+ThD6k0gkQnt7Oy+++CI6nY6SkhKmT58uphL0EiKxE7qFxWJhwIABVFVVnXRyFS4dnU6Hw+FQFmzIc/fO9hEMBtm3bx+/+tWvOHbsGAMHDuQ73/kOoVAIj8dDR0cHTqdTaaArJ1Fyhau6ulr5XP6+XGWTv35iFS4SiRAIBPD5fKfFbrFYlEUfMo1Gg1arVeYJypVgg8GgNHq22WwnJZhWqxWz2axUH+XVxPIwszwMbTKZTqowykmfJEkcOnSIhQsX8uSTT1JeXs706dP54Q9/eCn/dILQox08eJB169axcuVKnnrqKaZOndrvFsT1ZuIvJXQLo9FIamoqmzdvxu12RzucfuHEatS5yHuPvvDCCwQCASZNmsQ999zDkCFDAJS5ZX6/XxnqPHXYMxAInLRiNBQKKUmj/LUTne3rAFqt9rRqmEqlOmlIV61Wo9VqlQRQHv49cTWwVqtVbqPT6ZQKo1arVSqLJ/7MqSRJIjc3l5tvvhm1Ws22bdtYvnw57e3tfPOb3yQ9PV2p6glCXydfAG7fvp0lS5Ywbtw4Ro4cSUZGhqjW9SIisRO6hcFgICkpidbWVrxeL5FIRAxj9RCBQICamhq2bdvG5s2bGTt2LJMnT2batGknVbH6I5VKRWxsLEOGDCESieDz+ZTkrrCwkJEjR5Kfn4/D4ejXz5PQf1RXV7Nv3z6OHj3KzTffTHZ29mnzgIWeTSR2QrcwGo1kZmYqjVlDoZCYVB5l8rBoW1sbS5Ys4emnnyYpKYkHHniAcePGib/PCdRqNcOHDyczM5Ndu3bxq1/9il/84hdce+213HLLLcyePVu5rUjwhL5Irr6//fbb7NixA51Ox7e//W3i4+OjHZpwgURJRegW8hw7SZJoaWmhoaEh2iH1e/IE6Icffpg333yT5ORkXn75ZcaMGSP63Z2Fw+FgwoQJ/Pe//+WrX/0qhw8f5qGHHuLXv/41x48f7xdb5gn9k8/n4/Dhw7zyyitYrVYeeOABEhMTxdy6Xkj8xYRuYTAYSElJQa1W43K5aG1tJSsrK9ph9VuhUIjm5mZeffVVDh8+TGZmJjNnziQzM1PsP/o51Go1RqORlJQUbrjhBhITE9mxYwcrVqxAr9czbtw4xo0bh9VqFc+h0GfIF+TvvvsuarWaIUOGMHHiRLGVYS8lEjuhW+j1euLj49Hr9XR2dtLS0hLtkPqlE4dfy8rKmD9/PlarlTFjxnDTTTdhNpvFgfoc5L2AJ02aREJCAsnJyfztb39j0aJFdHR0EBMTw+DBgzEajaKaIfQJHo+H6upqPvroI3JycigpKaG4uDjaYQkXSRyVhG6hVqsxmUwkJSXh8Xioq6uLdkj9VjAY5L333uPNN9+kvr6el156idGjR5OQkCCSugs0aNAg8vLyGDduHD/5yU947733WLhwIc8//7zynApCb/fpp5+ydOlSduzYwYcffsiIESNEta4XE4md0C3kvmCZmZn4/X6R2EWBJEmEw2Eee+wx1q9fj9Pp5Pnnn2fMmDFiVedFkqt3ubm5/PWvf2XdunUsXLiQhx56iBtuuIGpU6cye/bsk5ohC0JvIUkSTqeTjz76iI0bN3LHHXdQUlJCfHy8eD33YiKxE7qNSqUiOTlZmWMnXD6SJNHe3s727dtZv349RqORadOmMXbsWGJjY8WQ4Rcgz7sbMGCA0rvv3XffZf/+/bjdbiRJYurUqZhMJrE3rNCrhMNh1q1bx9GjR1Gr1cyZMwe73a7sSCP0TuJoL3SrlJQUWltbaW5ujnYo/YrP56OiooKXX36Z8vJybr31Vu644w7S09PFlXc3MRgMDB06lMLCQqxWK//+979Zvnw5NTU1ZGdnk5GRgc1mE0m00CtEIhG8Xi9vv/02TU1N5OTkMHfuXLFivg8QRyCh26hUKnJzc9m5cyfHjx+Pdjj9ysKFC1m0aBHvv/8+L7zwAuPHjycvL08kdZeAXq/njjvuYNiwYaxcuZK///3v3HzzzTzwwANce+21FBQURDtEQTin1tZWdu3axXvvvcfXv/51brzxxtP2dRZ6J5HYCd1GpVKRnp6ORqOhtbUVl8uF1WoVO1BcQsFgkLfffpsFCxbQ1NTET37yE6ZMmUJiYqJI6i4ReT7dgAEDsFgs5Obm8uyzz/L++++zZcsW7rvvPiZPnixOkkKPFQqFKC0t5fnnn2f48OFMmDCBYcOGiWNGHyESO6FbJSYmotfr8fv9tLW1YTKZRGJ3iXR1dVFTU8OSJUtwuVzk5OQwa9YsUlNTxa4Sl4HVasVgMBAXF0dZWRlbtmzh+PHjfPzxxxgMBgoKCkhLSxOrC4Uep6qqigMHDrBv3z5uvfVWCgsLcTgc0Q5L6CbijCt0G5VKRVpaGhaLhUAgwPHjxwmFQtEOq08Kh8NUV1fz4Ycf8t5771FUVMQ999zD+PHjRVJ3Gel0Oux2Ow8//DDf+MY3GDFiBC+++CJPP/00a9asURZXyP0FBSGaJEkiEomwevVq1q5di8fj4Z577iE/Pz/aoQndSCR2QrdKSUnBbrejUqk4duwYwWAw2iH1SWvXrmXevHn88Y9/5Ic//CEPPPAAM2fOjHZY/ZZarWbWrFn86le/4qWXXuLw4cM89thj3H333VRVVeH3+6MdoiAQCoUoKyvj9ddfp7Kykp/85CdkZ2djNpujHZrQjURiJ3QrjUZDbGwsNpuN2tpaUbHrZoFAgN27d/PGG2+wd+9eZsyYwY033khOTo5oURAl8pw7nU6n7DX7ox/9iPHjx1NXV8cvf/lLli9fTkVFRbRDFfqxSCSC2+1m3rx5+Hw+iouLmTVrFjqdTkwV6GPEHDuh28gHh5iYGGw2G42NjYTD4ShH1XfI8xZXrVrF3r17sdvtzJ49m6FDh4qkrofQ6/VkZGQwd+5cZXu9zZs3k5iYiMfjQavVKi1oxMlUuJy6urqorKxk+fLl5OXlMWLECAYOHCjmQPdB4i8qdLu4uDji4+Oprq4WFbtuIkkSDQ0NbNiwgUceeYTk5GRuuukmvva1r4m+aT2MSqUiNTWVO+64g6effpoZM2bw1ltv8fvf/55///vf+P1+Me9OuKwkSaKsrIy3336b8vJybrrpJr785S+j0WjEBUYfJM4IQrdLSEggISGBjRs3ijl23UCSJI4dO8Zbb73FG2+8wZQpU/jpT39KSUlJtEMTPodGoyEpKYk//vGPTJ48mdWrV/Of//yH0tJSvvnNbzJ27Fjsdnu0wxT6OEmSOH78OMuXL2f+/Pl873vfY9y4caSkpEQ7NOESEYmd0O1iY2Ox2+00NTURDAaJRCKi3H+RQqEQHR0dzJ8/n507d2K327nnnnsoLCzEZrOJq+0eTKVSodFoiI+P58orr8RsNmM2m9m0aZNSOZkzZ47S+1EQuptcGV66dCn79+/HYrEwZ84ckpKSRKW/DxN/WaHbyYldW1sbPp+PcDgsEruLEA6Hcbvd7N+/nw8//BCdTseYMWO48cYb0ev1IqnrRfLy8oiPjyc7O5uDBw+yZcsWqquriY+PZ9KkSdjtdrGVk9DtwuEwLpeLJUuW0NzczODBg7niiivEnNw+TpxthW6XkJBAWloawWCQ+vp6XC5XtEPqlVwuF7t27eKrX/0qALfccgt//OMfMRgMIqnrhWJjYxk5ciTvvvsut99+O16vl7vuuovnnnuOffv2RTs8oQ/q6OjgtddeY+vWreTl5fGLX/xCXBT2A6JiJ3S7+Ph40tLSUKlU1NbWkpOTQ3x8fLTD6jUkScLpdPLqq6+yaNEiLBYLv/rVrygpKVGqOuLA3PuoVCokScJisXD//fczYcIE3n33Xd566y12797NxIkT+c53voPZbBZDs8IX5nQ6OXjwIM899xzTp0/n6quvVhoRi+NH3yYSO6HbGY1GrFYrZrOZ1tZWurq6oh1SrxGJRAgEAixevJgtW7bQ0dHBddddR0lJCSkpKeKE38vJ8+6Sk5PR6XR4vV5aWlpoampi3bp1JCYmMn36dBITE8Ves8JFkVdbHzx4kI0bN+JyuZg4cSJDhgzBZDJFOTrhchCJndDtdDodZrMZh8NBS0uLGIo9T5Ik4ff7aWpq4p///Ccul4sBAwbw/e9/n4SEBDHZuQ9RqVTEx8cza9Ys4uLieO2111i+fDl//etfsVqtjBw5kqysLNE8Vrgofr+fzZs3s3jxYjIzM5k2bRoDBgyIdljCZSLm2AmXhMFgIC8vj/r6etra2qIdTq8QCATYsWMH9913HxUVFdx+++08/vjjJCcni0pdHzZ69Gh+85vfMG/ePBwOB9/73vf42c9+xpIlS0SvO+GCSZLEO++8w+LFi2loaODpp58mIyNDHEP6EVECEC4JnU5HRkYGzc3NOJ3OaIfTo0mSRDgc5u2332bVqlVUVlbys5/9jIkTJ5KcnCwqNn2YvANFTEwMgwcP5i9/+Qvz58/n6NGj/OlPf6KyspLrrruOrKwscWIWzikQCCgLJjQaDbNnz6aoqEgsuOpnRMVOuCR0Oh3Jycl0dHSIOXafQ07q9uzZw9q1azly5AjFxcVMnTqV3Nxc0QKjn9DpdNjtdq688kpmzpzJ4MGDkSSJ5cuXs3btWvbt24fH4xEVPOFzuVwutm/fTnl5OWlpaUyePBm73S4uCvoZkdgJl4RcsZPn2IktlM5MkiTcbjfPPfcc69evR6/X88gjj1BQUIDNZot2eMJlJC+suOGGG3jwwQd58MEH+fTTT/nb3/7GvHnzqKurIxwOi/eRcEaRSITq6mqef/55TCYTEyZMYM6cOdEOS4gCMRQrXBIGg4H8/Hw6Oztpa2ujs7NTJCpnsGPHDt566y3effddHnroIWbNmsXo0aPFFXY/l5OTQ2pqKkVFRfz1r39l5cqVLF++nCeeeIIRI0aQmpoa7RCFHmbTpk0sW7aMlStX8tJLL3HFFVdgMBiiHZYQBaJiJ1wSer2etLQ0NBoNnZ2dNDc3RzukHmfNmjV8+OGHrF69mjvuuIPp06dTVFSEVqsV82H6OY1Gg9FoJD8/n/vvv5877riD5ORknn32WV5//XXWr19PJBIR1TsBSZLo7Oxk1apV7Nixg4kTJzJixAgSExPFcaSfEhU74ZLQarXKfoQej4fm5max3P7/hMNhWlpalANxMBjk+uuvZ+jQoSQkJEQ7PKGHUKvV2O12pkyZQkJCAm63m/feew+NRoPb7SYpKYns7Gz0er3Ysq8fi0QiHD58mF27dtHS0sItt9xCRkYGZrM52qEJUSISO+GS0Ol0pKamYjKZcLvd1NbWRjukHkGSJLq6uliwYAGvvvoqWVlZPPzww8ycOVMMvwpnZDQaGTVqFCUlJQwcOJCXXnqJ559/nu3bt/P444+TkZGByWQS1Zl+SO59+be//Y2KigqKior41re+JRZd9XMisRMuGY1GQ1paGpIkUV1dHe1weoSDBw+yYcMGHn30UW644Qauuuoq5syZIyouwjlpNBpuvvlmBg8ezJYtW3j22We5++67ufHGG7n++usZNGiQSO76mePHj7NlyxaWLl3K17/+dWbPno3FYhGvg35OJHbCJSHvi5mYmIjP56OpqSnaIUWVJEmUl5ezatUqVqxYweDBg5k5cyYjR44Ui0qE86JSqbDZbBQUFKDX6+nq6mLx4sVs2rSJtrY2vvzlLzNkyBCsVmu0QxUuA7fbTVlZGR988AEFBQWMHDmSQYMGiYtEQSR2wqWVmJhIbW0tTU1NSJLUL68kI5EIfr+fbdu2sWbNGnbv3s1DDz3E5MmTSUlJiXZ4Qi/jcDiw2WxkZmbS2NjItm3bOHjwICaTCZvNRlZWllK16Y/vt75OXjBTX1/Pnj17WL16NXfddRfDhg0jLS0tytEJPYFKEsuqhEtEkiT+/ve/s2XLFpxOJ8uWLeuXV5NOp5Pt27dz//33k56ezowZM/jd734HIE68wkWRD9uSJLF+/XoWLVrE3//+d2bPns3111/PnXfeKebd9VHy3/7HP/4x27Ztw+VysWrVKmJjY9HpdFGOTugJRMVOuKTS09NRq9XU1tYSiUT6XRWhtraWHTt28Nvf/paBAwdy/fXXc+211wIiqRMunjzVQaVSKX3t0tLSeOutt3jxxRfZsmULv/zlL0lOTsZkMkU7XKEbeTwelixZwtq1a0lMTOQ73/kOMTExaLXidC58pv+VT4TLKiEhAYPBgMfjobOzk3A4HO2QLgtJkujo6GDTpk2sWLECp9PJVVddxRVXXEFGRka0wxP6gBP3mc3Ozmb69OlMnz6d5ORkSktLWbBgAbt27aKhoSHaoQrdxO/309zczKJFizCZTBQXFzN27FjR+1I4iUjxhUsqOTkZq9WK3++npaUFo9HY568s5f1fjx49ynvvvcfWrVsZNmwYd955JykpKf1yOFq4tAwGA8OGDSMlJYVPPvmEefPm8fe//522tjZmzJhBbGys0gJDJAC9jzz82tHRwZEjR3jvvfe44447mDp1Kvn5+VGOTuhp+vYZVoi6rKws4uLiCIVCHD16lISEhD4/NOT1eqmvr+f+++9Ho9EwadIknnzySWw2m0jqhEsqMTGRr3zlK3zpS1/iscce49133+W9997j+uuv55FHHsFms4nErpcKh8N8/PHHvPjii+Tm5nLfffdRUlIS7bCEHkgkdsIlZbFYiImJwWw2U1tbSyAQiHZIl1RXVxe7du1i3rx5SJLEDTfcwMyZM7FarajVanFSFS4plUqFTqfDbrfzjW98g+zsbHbu3MnixYuJRCLMnTuX0aNHExMTE+1QhQu0ZMkS1q9fT0tLCz//+c/Jysrq86MfwsURrwrhkpFPMhaLBZvNRmNjI8FgMNphXRKSJBEKhdi/fz8bNmxg27ZtXHHFFUycOJHhw4eLA7Bw2ahUKvR6PUOGDCEcDivvvU2bNmEymQgEAowZMwa73S52O+kFwuEwXV1drF69mvr6ejIzM5kyZQp2u12MAAhnJF4VwiUXExNDUlISNTU1+P3+aIfT7SRJQpIkXC4X8+bNY/78+eh0On71q18xfvx4sWejEDUlJSXcfvvtPPHEE6hUKt544w1+/etfs2PHDrxeL5FIRHn9Cj2PJEn4fD4OHz7Me++9h06n48477yQnJweDwRDt8IQeSiR2wiXncDjIyMigvLwcn88X7XC6XSgUoqmpifvuu49NmzZRXFzMG2+8QWZmptizUYg6i8XCoEGDePvtt3nwwQex2Wzcdttt/PWvf2Xz5s3RDk/4HPIirO9973ukpKQwZ84cbrvttmiHJfRwYnxIuOSsVivx8fHs3LnzjHPsIpEI4XC4Vy7ZD4fD7Nu3j08++YRDhw4xa9YsJk2aRE5OTq98PELfo1Kp0Gg0JCYmMmvWLFJTU4mLi2Pt2rU0NDRw7NgxbrrpJoxGoxia7WF27tzJ2rVrqamp4Qc/+AFXXHGFuFgUzkkkdsIlIw/vmM1mYmNjaW5uprm5mfr6ekKhED6fj2AwSDgcJhKJUFxc3Gs6p8uPrba2lq1bt7J48WIcDgczZsxg3LhxYv9XoUc5cd5deno6KpWK5557jl27dtHS0kJhYSH5+fnExMSg1+ujHW6fF4lECAaD+Hw+dDrdadM1JEnC7Xazfft2Nm3ahN1uZ/r06RQUFIiLReGcRGIndIuzzdGRJAm9Xo/FYqG5uZmPP/6YLVu20NjYyKFDhzh+/Dgej4eYmBjWrFlDQkLCZY78zDGf6NQDqfz9SCTCM888w/r166mvr+e///0vRUVFYsWh0GOpVCri4uK4/fbbyc/P56233mLBggXcfffdPPbYY1x55ZVKA22RQFw6oVCI6upqysrKSE9PZ+TIkSd9X5Ikdu7cyaJFizh06BC//vWvKSgoEBeMwnkRe8UK3SISiRAKhXjppZc4fvw4zc3NVFdXU1tbi8vlorOzk9bWVuLi4tBqtUQiEdxuN+FwmLi4OMaPH89//vMf7HZ7tB8KFRUVqFQqEhISlM3UTyRJEs3NzTz99NO8/fbbDB06lDvuuIPZs2djMBjESjWhx5MkCb/fT1NTE0eOHOHRRx+lubmZwYMHc/vtt3PttdeKldyXUEdHB2+99RYvvPACdrudadOm8eMf/xiTyYQkSTidTm6++WZlJOPPf/6z0jJJEM5FvHOFbiEnP+Xl5ezevZuamhra2tpob28nGAwqVa729nZlK6RwOIxarcZgMJCVldVjDlqbN2+moaEBi8XC7bffjtlsPukkd+zYMXbs2MHy5csZPHgwkydPZtSoURiNRlHlEHoFlUqF0WgkJSUFo9HIXXfdxZIlS2hvb2fBggUAjB49mtTU1LMmeHKLnyNHjpCVlYXVar2cD6FX8/l87Ny5k+PHjytTU2w2G9OmTUOv17Nnzx5qamq46qqrmDlzphgFEC6ISOyEbqFSqVCr1QSDQWpqaigrKzvj7U5traBSqTCZTGRlZUV94rYkSQSDQVavXs2OHTuAz05uubm5xMbGolar6erq4tNPP2Xx4sWUl5fzta99jYkTJ5KdnR3V2AXhYuj1ehITE/nqV7+KXq9nxYoVLF++nEAggFqtZuzYsSQmJqLRaE67aPH7/XR0dLBmzRquvvpqDAZDr5kjG02RSASv18uuXbvwer34fD7a29uprq4mEolgs9n45JNPMBgMjBs3junTp0c7ZKG3kQShm0QiEamiokK68847JY1GIwHn9VFSUiItW7ZM8vl8UY3f7/dLu3fvlkaMGCGpVCpJq9VKAwYMkF555RWpsbFRCofD0jPPPCNNnjxZSktLk+bNmye1trZKkUgkqnELwhcRiUSUj6qqKumtt96SkpKSpIEDB0r33HOPVF5eLgWDwdN+bvPmzdJPfvITSavVSk899ZR08ODBKETf+7jdbmnbtm2nHSO1Wq2k1Wolk8kkpaSkSPPnz5cqKyvF8UW4YD1j7EvoM1JTU7nyyiuZMmXKeVfgzGYz+fn5Ua/Yeb1e/vvf/9Le3o5arSYcDtPY2MhTTz3FT37yE15//XX++c9/YrVaeeCBB7jmmmuIiYkRw69CryZPjVCpVCQnJzN9+nReeOEFRo4cyYEDB/j617/O6tWrqaurU35m7969LF68mDfffJNwOMzrr7/OggULCAQCotnxOVRXV7N7924ikchJXw+FQoRCIaUSunDhQvbt24fT6YxSpEJvJYZihW6jUqkwGAwMGTKEuro6NmzYQDgc/tyf0Wq1mM1m4uPjozrHLhQK0dnZycaNG3E6ncqQsdz13el04vf7iUQiDB48mGnTppGUlCSSOqFPMRgMxMfHM378eNrb27FarWzfvp2PPvqI5uZmhg0bRnZ2NitXrmTLli3U1dUhSRJHjx5l9+7d7Nu3j+HDh0f9Iq0na2ho4PDhw6hUqjMmwZFIhEAgoPTHdLvdTJw4keTkZLGgRTgv4lUidLvhw4cTCoV49tlnCYVCp12ZnkjucRcbG3sZIzyd1+uloaGBzZs3n7TYIxwO4/F4qKqqorq6mhtvvJEpU6Zw5ZVXiqRO6JPUajXJycncddddjB07lueff5433niDAwcOMHnyZG666SZee+01Dh48qPyM0+mktLSUhQsXMmjQIEwmU49ZDNVTyMeU48ePs2/fvs89fkQiEY4dO8bx48fZsGEDWq2W6dOnY7PZRHInnJN45wndLiYmhoEDB/LNb34Th8PxuQew7OxsBgwYcBmjO7N9+/bx3//+96xX0HKCumjRIpYtW8b27dvFkJPQp+l0OoqLi3n88cf585//jE6n4/HHH+e6667j0KFDJw27RiIRjhw5wrPPPsuuXbtob2+PcvQ9Uzgc5tChQ2zatOmcoxnBYBC3283evXu58847+d3vfsf69esvU6RCbyYSO6HbqVQqHA4HN998s9K37ky0Wi1paWmkp6df5gj/P+n/WjYcOnSI5cuXEw6Hz5qwRSIRfD4fS5cu5YknnmDbtm14PJ7LHLEgXB7ySne9Xs/MmTOZOHEi+fn5NDY24vf7T0tM5KkL//jHP9i/f/85E5f+6OjRozQ0NFzQcUOr1RIXF0dJSQlpaWmXMDqhrxA1XeGSMBqNFBcXU1BQQGdnJw0NDafdRqVSER8fT2JiYhQi/P+am5uprKzk6NGjnztsLEmSMkTicrkoLi7GarWSmZkp+kwJfZrJZEKv16NSqfD5fGd8n0iSRDgcZuPGjUyYMIG8vDxlFwvhM8eOHaOlpeVzjzMnstvtpKSkMGrUKAYPHtwjduYRej6R2AmXhEajwWq1csMNNxCJRPjkk09OO5gFg0GSk5OjWrGDzxoSl5WV4Xa7z3lbeVFFU1MTjz76KAaDgauuuorRo0dfhkgF4fKSX+/r1q1jzZo1Sn/HswmHwxw/fpzVq1djsVi49957AbE9GXz2XG7fvp36+nq0Wi3BYPCMt5MrpQAlJSVcddVV/PCHP8RoNIp5i8J5EYmdcEndcsstNDc3s23bNtrb208b5szIyIh6c9/58+ezbdu287qtfILS6/Xk5OSQkJCAyWS6lOEJQtR4PB4qKip45JFHqK6uPq+fiUQiLFmyhIaGBq688koGDBggGhf/n1WrVnHkyJGzJnUajQadTkdMTAx/+MMfGDduHDk5OeIYI1wQkdgJl5TNZmPw4MHMmDGDd99996TETp47Eh8fH5XY/H4/NTU1HD16lJaWlnMuhlCpVNhsNoqKihgyZIiylVhqauplilgQLp9AIEBlZSX/+te/qKurw+fznffPyu+tN954gx/+8IfY7fZ+XbULBoO0t7dTX19PV1fXWW+Xk5OjHC8nT55MSkoKFovlMkYq9AUisRMuGZVKhUajoaCggOnTp/PRRx8RCASUIVm51Um09pj0er3s2bOHxsbGz53MrNFoMBgMOBwOBgwYwOTJk5k4cSIzZ85ErVb36xOW0HcFg0FaWlrYtGkTkiQpTbvP1n/tRJIk0d7ezkcffcStt96KwWDAbDZfpsh7HjnRdTqdBAKBk74nHyeTk5MZO3YsU6ZM4bbbbhPNz4WLJhI74ZIbNGgQMTEx/P3vf6e2thafz4dKpSIvLw+bzRaVeSOSJNHa2sorr7yCx+NBrVafcUKzWq3GZrNRUFDAXXfdxd13301MTIxowCr0ecFgkNjYWG666Sbef/99ysvLaWlpQafTEQqFTtv3+USRSITOzk727t3LwoULufrqqxk9enS/TVQ6OjpYvXo1oVDopMRYq9Wi0Wiw2+089thjzJw5U6x8Fb4wkdgJl5xKpSI2NpYf/OAHPP7441RUVKDT6Rg8eHDUGhM7nU7Ky8tZs2YNbrf7pKROrVYrO2Lce++9zJgxg0GDBhEfH09MTIyYwCz0CzabjUGDBpGZmcntt99Oa2srdXV1bNmyhbVr11JRUUF9fT0ajeasbYIikQgvvPACkiSRkZFBSkpKv0zu2tvbWbFiBV6vV/maSqWiqKiImTNncvPNN1NUVCSGXYVuIRI74ZKTtxqbMGEC77zzDi0tLfj9fjIzM6M2PFNRUcHevXvxeDxKUqdWq1Gr1RQXFzNgwACKioqYPn06xcXFJCUliY7vQr+i0WjQaDQYjUbi4+NJSkoiPT0du91OTk4OtbW11NbWUlVVRUVFBa2trco+yydW85qbm9mxYwcDBgzgzjvvRJKkfpXcBYNBnE4nhw8fJhgMolKpsFqtjBs3junTpzNmzBgGDx6MzWbrV8+LcOmIM5VwVid2lZcP1JFIRPmQP5e/d6aPEythSUlJJCYmYrPZ8Pl8WCwWXC4XtbW1J92vvCH5qU6slJ04t03+/4mfn/g1jUajtBCQv1ZWVsbmzZuVz/V6PSaTidjYWGbMmMGUKVOYMWMGFotFHGwFgc/mxJrNZlJSUpg0aRKdnZ00NzezevVqNmzYwOHDhzl27BjBYBCfz0cgEFB2bNm9ezcajYZrrrkGnU6nDEfKx4cTjzWyE5PDU/9/6tdO/JkT/z2b87nd2Y5Dpzq1gi//jPyvx+OhpqaGqqoq4LMV9QkJCcydO5drrrmG+Ph4JEmis7NTuc8TP+T7ONPnZ7p/QVBJYl8k4SzkA2drayudnZ3KgbytrQ2Xy4XL5aK1tRW3243P58Pr9dLZ2YnX68Xn89HV1UVXVxfhcJhwOIzL5SIQCOD3+/H7/dhsNvx+P6FQ6KT71Wg0pzX8ValUWCwWdDodarWamJgYzGYzRqMRs9mM1WrFZDJhNBqx2WzY7XZsNhuxsbEkJSUpn9vtdgwGA//7v//LSy+9hNfrRavVMnLkSGbOnMmDDz5IXFwcer3+pPsWBOFkpyZHbreburo6Fi5cyKZNmygtLaW8vFy5fWxsLPfffz/FxcXo9Xq8Xi8ulwuv10sgECAcDivHi1AopCSHwWBQSRbD4TCBQACfz4ckSXg8npNah/j9fiWh/Ly45fv5vNOffLF3rsTJZrMpF48ABoMBnU6HTqdDr9fj8/no6Ojg2LFjxMbGkp2dTVZWFmlpaSddVKrVaqxWK0ajEZPJpPzfYDBgsViw2WzK5/LxzmQy9etFKcKZicSuH5JXrDmdTtrb22lqaqK1tRWn00lbWxuNjY20t7fT2dmprOIKhUKEw2GCwSCSJKHVapUDjnwQO/FD/r5er1euLuXO9ZWVlezYsYNrrrlG+Xn5oChfxZ+6ciwSiRAMBpVq4YkH/GAwqMQYCoXw+/14vV4lgZTnzGk0GuXfiooKmpubsVqtDBo0iAEDBlBYWMiIESNITk7G4XDgcDiIj48XiZ3Qp0mSRCAQoKurC4/Hg8fjweVy4fF48Hq9eDwe5YLN6/UqF2zyxZycnHk8HpxOJy0tLTidTjwez0ktUjQaDSkpKVitVvR6PTqdDoPBoLwn5R5ucsX91M/l44T8dfgs+TpxIZNcmf+8vnny79BoNGdN2iKRiJJgfh75mHiiYDCoJI3BYJAjR47Q1NREJBIhIyMDk8mETqdTRjtCoZDyO+TjmHx8k29zYrzyc3Pi/+WLWvlC1/b/2DvP8Cius2HfK61WfaVV770LCYGoooiODQYb7Dhxiu3EcYkTJ3GcxH7zpr5Oj2PHiePEdhL3ihtgOqYjUSQkhIR671pJq1XdPt8PfzORQGAwGAk493VxsaudnTkze8pznurtrQh+3t7eeHp6nvV+9LGjBUXB1Y8Q7K5BJEnCbDYzNDTEyMgIQ0NDYybqwcFBenp66O/vx2g00tfXp+yO5cnabDYrQpM8CcsTsTyByJOELMBpNJoxr+Xj5AnI1dUVZ2dnGhsbKSoq4sYbb8TLy2tcwe7MnFkOh0OpTyl/LrdRFuDkyVC+Z3khkjUCstBXV1eHWq1W2icvNrKpSavVKtq+wMBAZVKUd80eHh5j7l2YQgSTBVlIkMfE6HE8MjKivDabzZhMJmVTNDw8PEbzLgtuVqtVOacs6MjnkDXxozd+drtdEUJGC2uyRkveGHp4eODr66uUKpM3XfJr+b38XXn+UKlUynGAIhjKjL7m+ZCvc66xKwtcZ24wz8Rut2M2m8do/mTto7wBraqqwmAwKPOJSqVS5rPR15H/Nvr3kzersgApP2P5t5BfjxaO5ecm/02j0YzZ2MrzuDyXe3p6KnOah4eHMoePnu/l+d/NzW3cz4T/8eRCCHZXKWf6mcgTifxar9fT1NREe3s7TU1N1NbW0tbWRkdHB42NjQwNDSm5qdzc3AgKClIcpCMjIwkKClI0VkFBQXh7e+Pt7Y2fnx++vr6XNJCHhobo7e0lPDz8cxeKHA6Hop0cGBigt7eXF198ER8fH7RaLcPDw7S2ttLT00N3dzctLS3KxAzg6elJWFgYISEhREZGEh8fT1hYGKGhoSQkJODv7z9GqzDavw+EGVdw6ZzPh/XMf/Imrre3l+7ubgwGg7KB6+rqor+/n4GBAXp6ehQXC/lvMNY/TBYKZBcGLy8vPD098fHxwcfHB09PTzw9PfHz81M0PrKQIJsM5Q2Qm5sbarWavr4+LBYLOp3uuqkj29nZic1m+0ylE2UBTtaayhtaeZNuMpkYHh5WtK2yBrWvr0/RrBqNRoxGI0NDQ8p7eZMM/+1fTk5Oiv+fVqtFq9Xi7++Pv78/Xl5eaLVaxa1Fq9Xi5+dHYGAgPj4+eHh4jOsfeC6fQTEvfr4Iwe4qRdbKyYJaSUkJp0+fprGxkdraWlpaWnA4HKjVaiUPW1hYGMHBwcTFxRETE0NQUJDyb7Rz7vkG4+UYmPJEcqHOyZfCeI7WcmSa7MB9ZrCHvOh1dHQoz7Kjo4Pm5maqq6sZGBjAbDajUqkICwsjIiKC+Ph4pk6dSnJyMpGRkcTGxuLm5iYmMMElMzg4SH9/P11dXbS2ttLd3a30z7a2NqW/tre3YzQaFfOhvFi7uroq2iJ5Q+Pn50dAQADe3t5jFmwvLy9lA+fh4aG4TwDnnQfO9f+Zr0dzvWi6RwtOn+W753t9If+P9zfZXWVgYGDMxtdgMKDX6xUf6u7ubnp7exW3nK6urjFJ5kf7CMobXlkYlNcbWQCMiIhAp9OJefEKIAS7qwCHw4HNZqO2tpbm5mba2tqora2loaFB2YnZbDbFTOjl5UVISAiBgYH4+voqO6zRPhajHXPd3NwAsYuSh4Js1pUnPnknLGtD5Emura0NvV6v7Jb7+vqU9BA+Pj7ExcUpTtLx8fHExMQo5miBQDZjdnV10dfXR19fH3q9Hr1er/i/6vV6xUQqa2dkk5qsERvt+iBr0WSBzcvLS3Hml327RpviZAd/2cSmVquVc6vV6utG+LrekE24sil+tK/yaBP9meZ82VQvz43y+iNrD2W3F3n+tFgsivA32tzr6+ur+DLLmw554yEHul3v69GlIAS7SYjsc9HT0zNGnX769GlaWlro7OxUdu6jTakxMTGEhIQQERFBUlISoaGhyiARXD5ks0hTUxN1dXW0t7fT2tpKdXU1vb29DA8PY7FYCAwMVDSisbGxpKSkKFoReQKTfVQE1x7yhkzeGMgL5ejAnsHBQdra2ujt7VWCl0ab0fr6+rDb7UpAgKur6xi3iNFauICAACWJtmwulQMVBILLhdyH5c2HbOqV3Vnk97IrwOj+7nA4cHZ2xsvLC51OpygZfH19CQwMxM/PT6kfPtpfW/b9kzczsr+lYHyEYDcJOFNtPjAwQFtbG9u2baOwsJDq6mrKy8sxmUwEBAQQHh7OrFmzmDVrlqIJioyMFB19EtDW1kZraytVVVXk5+dTUlJCQ0MDra2tuLm5ER0dTXJyMvPmzWPRokVERkYSEBAg/E+uQs6VQ01+bTabGRgYoLy8nJqaGtrb22lubqaurm6MX6fsz+bm5kZ4eDgxMTEEBwcTGhqqbNb8/PwU/1eh9RVcLdjtdoaHh+np6aGlpYWuri66u7tpa2tTNsWdnZ00NjYqyeJVKhXe3t5EREQoSbETEhKIjo5WXoeFhY1JMQNnz5vX8zwqBLtJgOy0v2/fPg4dOqRM/K6urqSmpiranlmzZhEcHIxWq1Uik2SzjChGPzkYHbE2OhqxtbWVgoICamtrqauro6ioSHEij4uLY9myZcycOZPY2NjP5GQtuPJIksTIyAhdXV1UVlbS3NxMe3s79fX1VFdXo9fr6evrw2az4e7ujq+vrxKEI2snQkJCiImJISAgAB8fnzFRoXIqizNTW8D1vWgJrh7OTGwvB/nJ2uzRf5fTbnV1ddHU1ERnZ6eSfqupqYne3l5MJhNqtRqdTkdISIgi6MXFxREaGqq8lwParleEYDcBmM1mjEYjlZWVFBcXKwuCXq9Ho9Hg6emJTqcjOTlZcUYNDg4mPDwcT09PYVq5yrDZbAwNDY3R0jQ3N1NbW6ske7ZYLPj6+hIQEEBsbCzTpk0jNjaW4OBgkVtqgpHT67S3t9Pb24ter6e1tZXOzk7FN66np0dxKJdTSsi+a3LEuez3Jpvj5Rxivr6+iqnpel6MBNc3o9Nz9fX1Kf7NowM85IAOecMkb6IdDodispX99XQ6nbJujvY3vx4Qgt0VQs6kLldraGlp4fDhwxw8eBC9Xo/FYiEkJITZs2eTlpZGRkYGWVlZwoH5GkTOXVVWVkZVVRUlJSUcOHBASWLq7+/PwoULmT59OomJiYqWVvY5EXx+yHnb5GAF2S/OYDBQVVWlmNpra2tpbW1VKhjI9VQDAwOJiYlRNAihoaHExcXh7+8vfjuB4BKQ1085fVd7e7uSuaC5uVlJom232wkKCiIgIIDIyEji4uKIjo4mNDSUsLAwJVBI3njJG6prSQsuBLsrRGdnJ0VFRbz++uvk5+fT2tqKi4sLN910E/PmzWPmzJlkZ2efs+6g4NrizGHncDgoLy/n1KlT7Ny5k02bNjEyMoK3tzdLly7ltttuIzMzk4SEhAlq8fVBa2srzc3NnDx5khMnTlBTU0NDQwP19fVIkoSPjw+hoaFkZ2eTmppKTEwMiYmJJCcn4+Hhcc78jmIcCwSXxvlEFYfDQV9fH21tbZSVlVFeXk59fT3l5eWUlpZisVhwcnLC39+frKws4uPjSUhIIDs7m/T0dHx8fK6pJMtCsPuckJ2njx49yqZNmzh16hQ1NTUEBweTmZlJeno6c+fOJTg4eEy5FxCLwPXG6DQrco3d7u5uKisrqaysJC8vj4aGBnQ6HYmJidx6663k5OTg7+8vtLmfEYfDgdFopKKigvr6eiorKzl27BgdHR0MDg4iSRLh4eEEBAQQGBhIYmIiaWlpii+cHJk3uuLKtbbrFwiuFuTE/LK2XU7RYjKZGBoaoqOjg/b2dqqrq2ltbaWjo4POzk6MRqOSxzEuLo7s7GySk5OJiooiOjoauDrXYyHYXUbkR1lbW6skCi4oKKCtrQ1JktDpdEoS25iYGJKSkkTYtuAsZFOtHEVZUlJCYWEhXV1dDA4OEhoayowZM0hISCAxMZHExEQhVJwHuWanXq+npaVlTJWR1tbWMbkK3dzc8PLyIigoSPFv9fPzIzIykoiICCX/o0AgmPzIgRt9fX0YDAZFqJPzRTY1NdHT04PValVSh8k+enIUbmBgIP7+/kRERKBWq6+KeVYIdpcJOdnj4OAgW7Zs4eDBg+Tl5dHd3U1OTg5z585l9erVpKamilQFgotCkiRFc7d79252796Nn58fqamprFy5kltuuUWpW3stmRM+K3Iknlwb2Gq10t/fT0lJCUePHqWmpkaJTpZLKCUmJjJ37lymTJlCXFycEJYFgmscm81Gc3MzRUVFVFdXU1ZWxtGjRzEajYrbhazBS0pKIicnR8lIIdcrvhLVkz4LQrC7TNTV1XHixAn+8Ic/UFVVRVxcHPPnz+f+++8nOjoaT09PkadM8JmRhRWr1UpNTQ1vvfUWhw4d4siRI0yZMoW7776bRYsWkZ6ePtFNnXDkHXppaSlbtmzh2LFjVFRU0NXVRXR0NImJiUyZMoX58+eTlZVFYGAg7u7uF1UOSyAQXN2MV2pNrvBUU1NDYWEhhw4doqamho6ODjQaDfPnz2f69OnMnDmTpUuX4u7uPik300KwuwRkTcDLL7/MsWPHqK+vx93dnVtuuYWUlBRiY2OJiIjAzc1NaOkElwWHw8HIyAgdHR20trZSXl7Ohg0bGBoaQqfTkZuby1e/+lX8/f2vK5Oh0WikpaWF/fv3U1paqqQQcnNzU/JdZWZmEh8fr1RkkKuyyPkgBQLB9YssCsluGf39/UoqIzn4UU5RNTg4SGBgIGlpaSQmJjJ16lRmzJgxafLnTT5R8ypBjsApKChg//79GI1GdDod8+bNY9myZURERFw3OXMEVw4nJyc8PT2Jj49Xysf19PRQWFiIXq9n3759+Pv7K0KMn5/fNal1khOctre309LSQktLCzU1NZw6dUrxmQkICCApKUlJYDp16lRCQkJECTeBQHAW8jwp11sOCAgAPkmzYjQaCQ4O5uTJkzQ2NtLU1IRer1csAY2NjXR2dio+eWFhYRPqPy80dheB/KhsNhvFxcXs3buXp59+Gm9vb26++WZWrVpFbm7uBLdScD1SUFDAgQMHeO655xgZGWHdunWsX7+enJwcxVRwtQt4o8ef1Wqlr6+P7du3s2nTJsrLy2lubiY5OZmcnBymTZvGggULiI+Pn5SmEoFAcHUyMjJCb28vO3fu5ODBg5SVlVFWVoafnx8LFiwgJyeH1atXExQUhEajUXx1r+T8KwS7i0A2gz311FO89957GAwGZs+eza9//WtCQkImrb1dcO1jt9uVRLo/+9nPOH78OEajkXvvvZdvfvObBAcHTwoTwaVgtVrp7e3ljTfe4NChQxQUFChjcObMmSxevJg5c+Yo5hC5FNfVLtAKBILJw+jgLLvdzuDgIG1tbbz55pscPHiQ2tpaLBYLK1asYPHixcybN4/09HQh2E1GHA4HFRUV7NixgzfeeIOoqCjS0tJYsWIF2dnZojC3YMJxOBxYrVZOnDhBfn4+hYWFVFRUsH79ehYsWMC8efOuOkFHkiQsFgulpaWUlJSwZ88eWlpacHd3JygoiOzsbBISEpSs8oGBgVfV/QkEgqsbm83G8PAwdXV1tLS0KAnO6+vrAdBqteTm5rJs2TLCwsLQarWfe5uEeulTkKXz+vp6jh49yrZt23A4HMyaNYuFCxcyd+7cK9qe2tpaOjs7lbqU58LJyQmNRoO7uzt+fn4EBQVddYu64OJwcnLC1dWVuXPnKjVJKysr2bdvH5IkERgYSFJS0lXRD+QE3waDgdraWvLz8ykqKqKoqIioqChSU1PJzs5m+fLl+Pr6Tnq/ueHhYQYHB+nr66O/v1+pbynnzpLrxgYGBuLs7Dzpfx/B58fIyAg9PT00NTUpfcTFxYXMzExcXV0/83ktFgsjIyNKPky5fJ6TkxNqtRq1Wo2rqytarVZJmi+n9BCcG7VajVarJSsriylTpmAwGIiOjmb79u1UVFRQVVXFyMgIarWa5ORkYmNjCQsL+1zLhQqN3acgawx+9rOfsX//furr63nppZeYMWMGQUFBV7w9Dz30EP/5z38YGRkZU2LlzMHn6upKaGgoycnJ3HTTTdx1111jUq4Irn2Gh4cpKCjgoYcewmKxMH36dJ577jnc3d0nrXZZ7tN2u52mpiZ27NjBE088gdFoJDAwkLvuuou7774bf3//SS/MwX/vp7y8nMLCQnbs2EF+fj69vb1YLBY0Gg2xsbFkZGQwY8YMvvKVr6DVaoVwd4GcmbJisuYVuxhqamp49913efzxxxkeHsbFxYWgoCDy8vKIioq6qHONXiM6OjqoqKjglVdeobi4mM7OTvr6+nBzc0On0+Hn50dUVBTz5s1jypQpJCUlXdXVFyYDFRUVHD16lD/+8Y+0tLQQFxfHypUr+f73v49Op/vcBGch2H0Kvb29/Pa3v2Xbtm0kJSVxzz33sGTJkgkzvRoMBnp6eqipqeHrX/86Hh4ezJo1iz/+8Y+oVCocDgcWi4X6+nrefvttjh8/TnNzMw888AC33HILs2bNuuJtFkwMsmn20KFDbNy4kQ8++IA77riDe+65h+Tk5Ilu3rjY7Xba29v55z//yaFDh6ioqGDx4sWsWrWKmTNnEhYWhqen51WhdQTo7+/n2Wef5Z133sHV1ZWcnBxWrVpFUFAQbm5uWCwWysrK2LdvH4WFhQwPD/P4448zffr0i17Er0dMJhNFRUX8+Mc/Jjo6mi9/+cusWrVqopt1ScimvYGBAZ588kk++ugjhoaGPrNgJ0kSzz33HB9//DFFRUXcfPPNLFiwgMjISPz8/BgcHKS9vZ2qqio2b97MyZMncTgcJCYmcuDAAbHJuASsVisWiwWj0cju3bs5ePAge/bswcPDg3vuuYfc3FymTZt22a8rTLHnoauri7KyMj7++GMyMzOZN28e06ZNG5PM9Eqj0+lwdXVlYGAAZ2dn1Go1np6eREREKIKdw+HA29ubwcFB3N3d+fvf/86uXbtIS0sjMzPzuspvdj0jm2bT09Pp6+tjYGCAjz/+mKlTp+Lq6kpMTMxEN3EMRqOR5uZmXn75ZUpKSnBzc+MrX/kKubm5pKWlER4ertRTvhpoaGigqKiITZs2ERYWxpQpU1ixYgXp6elK/jybzYa7uzteXl6EhYXx8ssv884772AwGFizZg3+/v7X5aLa3t7Oc889R2BgIDNmzGD27NnjHidJEiaTifb2djw8PBgZGbnCLb38yKY9+d+lBOQNDg6Sl5fHli1bMJlMSsRmQkICOp0ODw8PzGYzgYGBhISEoNPpePrpp6mrq0Ov1zPRep+ysjI2bNhAcHAwS5cuJSkpaULbc7G4uLigVqvx8PAgJyeHgIAAYmJi2L59O3v27KG5uZmenh4WLlyIi4vLZRvrQrAbB7mgcH19PYcPH6axsZH77ruP3NxcwsPDJ7p558XJyQknJyeCg4NZtGgRNpuN5557jqKiIurr6xkYGBCC3XVGSEgIs2fPRq1Ws3nzZo4cOYJWqyUiImLS7MaHh4dpbGzk8OHDvPXWW8TGxjJ37ly++c1vEhcXN2lNx+MhR8xVVFSwdetWTpw4wf/+7/+yfPly5syZM+ZYFxcX4uLiiIyMJCEhgUOHDrF7926cnJxITU3F399/gu5iYtHr9TzxxBOkpKTg5OR0TsFOpVLh7u5OdHS0os0VfILNZqO3t5cPP/yQ48ePk5OTw2233caSJUvGjHlZORASEkJ6ejpHjx5lZGQEs9k8ga3/hKqqKv785z8zZcoUYmNjrzrBDv5rxk5ISCA6OpqZM2cyODjIrl27aGpqwmq1Eh8fT3Bw8GVTGgnB7hz09vby0Ucf8eKLL3LHHXdwww03TDoNx6fh6+tLSEgIgYGBdHR00NHRQX19PYGBgRPdNMEVJjw8HB8fH770pS/x8ccf09HRwcyZMwkKCpoUgt3Ro0fZsGEDr776KmvWrOHhhx9m6tSpV4Uf3Xi0traye/duXn/9ddLS0li7di2ZmZnnPF6tVhMeHs5PfvIT7rrrLg4ePIjVauWNN964qoTaK42rqyuzZ89m9+7dE92USUdPTw8lJSU8//zzpKamsnDhQtauXXve8e7s7Mzdd99Nf38/eXl5V7C11wdqtZqAgAB++9vfsnjxYrZt28Y//vEPjEYjX//61y9bHlwh2J2Dd999l9LSUoKCgnj44YcJDQ2dFAvgxSC3V1anq9Xqq3ahFFwaKpUKDw8Pvv/979Pc3ExbWxuvvfYa3/nOdyZUg2s2m6muruaPf/wj/f393Hbbbfz0pz8lNDT0spomrjTbt2/n1KlTuLi4KJG757sXWfM0ffp0wsLCKC8v59ixY7S0tBAcHCy07Ofgau0fV4KTJ0+ya9cuHA4Hc+fOJSkp6VP7oCRJpKam8n//938MDw+LvKyXmdHPf+7cuYSGhhIdHc1f/vIXXFxc6O3tZf369Zd8HfGrnYFshj127Bhms5nMzEyioqKuyg4+MjLC4OCgMkC9vb3PKnMm+6hUVlbS0dFBV1eXcrybmxsBAQFkZmbi6+uLu7s7FouFDz/8kJ6enjHn8fX1JTk5malTp+Ls7ExXVxfvvfee8rlarSYqKorZs2ej0+mU69bW1tLe3k5HRwfDw8OKX1hgYCBTpkxBp9Ph5eUFfOJ3I/styQQEBHDDDTdw4sQJurq66O/vR6VS4e/vT2RkJImJiXh7e6NSqTAajVRVVVFUVITNZsPFxQV/f39WrFiBl5cXNpuNtrY28vLy6O3tBT7RCCxevJi4uLgx9+twODCbzZSVldHZ2UlPT4/y3Dw8PPD39ycrKwsfH59Jsyg7OTkp+ReLioo4dOgQ991334SVvnE4HAwMDLBlyxZ6enqIj4/npptuIiYm5qoJjjgXFRUVdHR04OzsTHx8/AX1AScnJ7y8vAgKCqK+vh6j0UhjYyN6vZ6+vj6qqqqAT/pkQEAAc+bMoaioiN7eXoaGhpAkCT8/P8LCwoiPjx9XG2uxWBgYGOD06dO0tLQwMjKC1WpVxkJoaOiYUnQjIyO0t7ezd+/eMaa5VatWYTKZqK+vp729HYvFgsPhwNnZmTvuuAN3d3esViutra3U1NTQ39/P8PAwFosFLy8v/P39iYiIIDU1VfmtJUmitLSU6upqCgsLsVqt6PV6Dhw4MCbqNTExkSVLltDU1ERRURHt7e1Ku2bPnk1WVtaY/iOnzqmurqa9vZ2uri6GhoaUlFD+/v6kpaUREBCg5BhrbW2ltraW0tJS5dyhoaEsWbJEKd83MDCASqUiICCA6Oho4uLilLlGvq4kSXR1ddHZ2UljYyMGgwGr1YokSXh7exMXF0dYWJji4nM5+3xbWxvV1dUAREVFKSWyzoe8AZSjYUcjP8eamhra29vp7Oz81OcI/y3/d/LkSdrb2zEajYyMjODs7IxWq0Wn0xEfH09YWBgajQabzaZUc9i/fz9Wq5XOzk4++ugj6urqlHbOmjWL7Ozsy/S0rjze3t7ExMSQm5vLzp07aW1tZe/eveTm5uLr63tJmvqrT1r5nJGrS5w4cYK4uDgWLVp0VWkORqeL6Orqoq2tjf7+foKCgggJCRmTokWSJKxWKy0tLWzZsoUTJ05QXl6OwWBAo9Hg4+NDfHw83/jGN5gyZQrh4eGYTCb+8Y9/cOLECUwmE1arFS8vL1JTU/nyl79MWloaKpWK1tZWfv7znzMwMICTkxNarZYbb7yR+Ph4fH19sdvttLa2smPHDo4dO0ZpaSkGg0ERQBMTE/na175GRkYGsbGxqNVqmpqa2LlzJ3/7298YGBjAZrORkZFBWloa7733HiUlJTQ1NSFJEtHR0SxcuJDbb79dyfrd29vLvn37+Otf/0p3dzdqtZqpU6cyc+ZMRbCrqanhmWee4dSpUwwPD6PVavnXv/41RrCTI49bWlr44IMPKCkpUYpDu7m54efnR1xcHPfddx/p6ekEBwdPio2BSqVCo9Ewc+ZMent72bhxI0NDQ3h4eExI+2w2G3q9npdeeonY2Fhyc3O55ZZbrnqhDqC+vh69Xo+zszORkZFoNJoL/m5ISAi+vr60trZSVVWF1Wrl1KlTvPPOOxiNRry9vcnMzOQXv/gFr7zyCtXV1XR2dmK1WomKimLOnDmsXr2a3NxcpQqHLGQYDAaqq6t57bXXKCwspLe3l5GRETw8PIiPj2f69OmsXr2aWbNm4eLiwvDwMKdPn+Z3v/sdnZ2dmM1mrFYrb731FoODg+zevZuCggL6+voYHh7G4XCwfPlyAgIC6OvrY9++fezYsYPW1lZ6enqU4umJiYnMnTuXiIgIvLy8UKvVOBwOjh07xrvvvsuRI0ewWCy0tbWxefNmdu3aBXwi/N52220sWrSIyspK/v3vf3P48GFFaPztb3/LlClTlOct+zu2traybds2CgsLOX36tDL+vby8iI2N5Y477mDatGkkJiai0WhoaGhg69atvPDCC0rOt7lz5xIfH8+GDRs4deoUra2tSJJETEwMy5cv5+abbyY9PX3Mb2kymSgtLVVqijc3NzM0NITdbicoKIglS5awYMECfH19L7t/oF6vp6GhQelTPj4+n/lco5/j9u3bKSgooKysTHmOnp6eY55jUlKSkspDrhjz/vvvU1RURGtrq7LGhIWFERcXx4oVK1ixYgU6nQ6r1UpeXh4vvvgilZWVmM1mmpubefXVVxWLk7OzM//zP/9zVQt2gDKWv/jFL/Laa6+xb98+ampqFAXJZ0YSjMFoNEp79+6VQkNDpe9+97tSc3Oz5HA4JrpZYxgaGpJOnDghhYeHS0lJSdI999yjtNFut0sjIyPSyZMnpYceekiaOnWq5ObmJj399NNSaWnpmHsZHByUTp06JSUmJkrh4eHS6tWrpY8++kjS6/VSdXW19P7770u5ubmSTqeTvvOd70hFRUWSw+GQLBaL9OGHH0qLFy+WnJ2dpX/+859SU1OTZLPZJIfDITkcDslut0tms1m69dZbpW9/+9vSu+++K1mtVsnhcEjDw8NSTU2NlJaWJkVEREiLFi2SNm3aJHV0dEi1tbXS9u3bpdzcXCkgIEC68847pYKCAuWcNptNGhwclG6//XYpIiJC8vPzk9avXy9t3bpVqqqqkjo6OqRt27ZJGRkZUkREhLR8+XLJbDZLkiRJDodDstlsksVikR566CEpICBAmjdvntTU1KR8brfbJYvFIm3cuFHKysqSAgICpPfff3/M8zcajVJ+fr4UHx8vhYWFSV/5ylekbdu2ST09PVJFRYX08ssvSwsWLJC0Wq30y1/+Ujp16tQV6hkXRltbm/SPf/xD0mg00tatW6X29vYJacepU6ekZ555RnJ2dpY2btwodXZ2Kv3nasbhcEhz586VvL29paCgICk/P1/q7++/4O8/+uijUnZ2tuTl5SX9+te/lmprayWr1Sr19/dLc+bMkUJCQqSgoCBp/vz50ubNm6Wqqiqps7NTys/Pl9asWSNFR0dLQUFB0u7du6Xu7m6lTR0dHdIPfvADKTY2VgoJCZH+9a9/SSdOnJC6urqk4uJi6etf/7o0ZcoUKSAgQNq7d6/U3d09Zky88MIL0uLFiyVAmjVrlvToo49K+/fvlwwGg1RcXCz99re/lTw8PKTa2lrp6NGj0p///GfJ2dlZ+uEPfyht27ZN6uzslNra2qSXX35ZuuOOOyQvLy/pkUcekUpLS5U22mw2yWq1SoWFhZKHh4c0ffp06ZlnnpHMZrNkNpsli8WizCN2u12yWq1SUVGRdNNNN0murq7Sb3/7W8lkMil9aHBwUCovL5dSUlKkiIgI6YYbbpA2btyozDU7duyQ1q1bJ2m1WumWW26R3n777TFzjcFgkNavX68881tvvVXauXOnVFNTI7W2tkrbtm2TEhMTpejoaOmmm26S7Ha78jtarVZp27Zt0pIlS6S4uDjpN7/5jVRfXy91dHRIlZWV0l/+8hcpKipKmjFjhvSrX/1KstlsZ/WFX/7yl1JaWpoUHh4uNTY2XlQ//OlPfyoFBgZKgPTWW29d9PdHMzQ0JFVWVkqpqalSRESEtGLFCuU51tXVSTt37pTWrVsn+fj4SGvXrpXeeust5TfIy8uTvvGNb0gBAQHSr371KykvL08yGo1SS0uL9Nprr0l333235OzsLL388stSTU3NmHn6nXfekby8vKTZs2dLmzdvHtMPxnteVyMOh0OyWq3Sr3/9a2nu3LnSXXfddclz8sSrESYZZrOZ2tpa3N3dCQgIuCD19UTS0tLC9u3bufHGG5W/ORwOhoaG8PT0ZPr06Tz88MMsXLjwrHJLpaWlbNy4kdbWVm677TaWLl3KrFmz8PHxwdPTEw8PDx599FHuvfde8vPzcXV1VRzak5KSuPnmmzl48CBHjx4lICCAW2+9VTm3yWRSQuYzMjLIyclRIjDlBJzNzc3ceOONLF26lDlz5uDr64tWq8XLy4vHHnuM7373u5w4cYL//Oc/ZGVlKTsYWQuhUqnw8fFh+fLlZGZmotPpUKvVzJgxgxkzZnDs2DEqKysxGo1KdQJnZ2fl35nIyU3lTOzn0hodO3aMrVu30trayt13382yZcuYMWMGWq0WNzc3JeHnXXfdxe7du5EkiSlTplyun/ySkSuReHt709DQQEJCAiEhIVe8HU1NTZw+fZrMzEyio6M/1Q/takL6/xoyuHTzmtwf5X5vtVoJCAjgW9/6FtnZ2fj4+ODi4kJqair33XcfH3zwAa+//jrPPfccjz32GP7+/jgcDl5//XWOHj2Kw+Hg3nvvZeHChYSEhCh9Vq4p/Le//Y3nn3+e733ve8yePVsZE6MjqJctW8aiRYvIyMjAy8uLuLg4vvjFLzJjxgxCQkLo6+vD3d2d9PR0br31VhISEvDx8UGSJJYtW4ZKpaK9vZ2NGzeyePFixVwtj0t5/MnXHk/jOXqsniuDf3l5OR9++CHNzc3ceuutLFmyhLlz5ypzjVar5dFHH6WiooKSkhLMZjPLli1T0ozIzxzA39+f5cuXk5GRgY+PD05OTspcU1RUpMw13t7eigZckiTCw8MJCgriS1/6EiEhITg5OeHt7c0tt9xCcXExZWVlbN68me9973t4eXld1mCZy9UHKyoq+OCDD2hqamLdunUsXbqUuXPn4uPjg4+PD97e3jz22GNUV1dTWlrKSy+9xPLly9FqtfT09HDy5Ekl5U9aWhqenp64urqyZMkSAgIC2LVrF87OzkiShEqlUuZo+TmqVCrUavVFab6vJpydnUlPT6elpYXdu3dfctoeIdidgRwi7u7ujoeHx6TxjzoX8iCQS83IxeCbmpqUBTwyMpLw8PCzBkVLSwt5eXmMjIyQkZHBtGnTlIhZFxcXXFxcmD17Nn5+fnR2dirmV1dXV4KCgpg+fTo+Pj5UVlYSFRXFmjVrFLP18PAwx48fx9fXl7CwsDEm4M7OTg4dOsTw8DApKSnMmDFjzHU1Gg2zZ88mICCAxsZGjh8/zsjIyLgVEzw8PJgyZQr+/v7KbxUQEEBERATl5eW0tLQwMDCAl5fXZQscqaur49ixY5hMJrKzs8nIyFA2AGq1mtDQUFxdXfHx8aGxsZGSkhJGRkZwdXX93ErIXAyurq54eHjg6emJwWDAZDJNSDsMBgOdnZ1ERUXh7e19TU3arq6uSn+TfaouFKvVit1uVwIqxuszWq2WOXPmEBgYqCx+Pj4+ZGdnc/r0aZycnDh27BhdXV1YLBacnJzIz8+nqakJV1dXJXWTh4cH8Mm4y8jIoKqqCnd3d44ePUp7eztms3ncMlZpaWkkJiai0+mAT0xKss8YoPhNrVmzhuTkZOU4QPEBjI+PJy8vTylx9XnMta2treTl5TE8PEx6ejrTp08fM9fIJbSio6MpKSlRfA8TEhLOck/w8vJS5hr5t9VoNERGRlJZWUlrayv9/f2Ka4NKpSIoKIicnBzUavUYdw6NRkN0dDRJSUnU19dTVlaGwWC4rInv5fuD//apz0pbW5ti8k5LSxvzHAHlOUZFRVFcXMzp06dpbm4mISFBWaOGhobQ6/W0t7cTFBSEj48PISEhSJLEmjVriIyMvC7T1chCd3BwMOHh4bS2tmIymbDb7Z+5LwjB7gxkbZeHh8cl1eW7UoSHh7NgwQJeeOEFRaDq6Ojg8ccfZ8+ePRw6dIjQ0FBmzJgxxldQkiTa29s5ceIEKpWKuLg4IiIizlrk3d3dCQsLo6urS/EbCgkJwd/fn8zMTJKSkqipqcHPz4++vj5FwDEYDLz//vvk5OSQnp6udFBJktDr9Rw/flzxhYuJiRn3uqGhoTQ1NSmav9DQ0LM6upubG4mJiWcJbb6+vnh5eSFJEr29vQQEBFxyclt5cW5sbOTkyZOKI3dQUNBZ7ffw8CA8PJzy8nIaGhro7u4mNDR0Ugh28N8kqENDQ1it1glpw9DQEP39/SQkJFxz0dqjk78ajcaLWlQHBwcxmUxKENCZz8bFxWWMEDWa0NBQwsLClE1FV1cXAwMDeHt7U1BQQFdXF8nJyWRkZACM6bcuLi7KRuzUqVO0tbVhNBrHLZ0YHR193rRJMTExREdHs2TJEiXQaLRwq9FoCA8PV3yB9Xr9ZbeOSJJER0cHBQUFSJJEUlISCQkJY46R67CmpqbS1tZGW1sbJ06cIDQ0VBF6ZTw8PEhISDhrDtLpdHh6euJwOOjt7cXf31/ZxE2fPp2srCzlGcjtkpEFHKvVSlNT02UNtvL09FR8NQcGBj7zBk6SJDo7O5XnmJiYSGJi4phjVCqV8hxbW1tpbm6mqKiI0NBQAgICmDZtGi+//DLvvvsuLS0trFixQql9GxgYyDPPPHNN+NZeCr6+vgQEBChBj2az+aw+eKEIwe4MnJyccHd3V5yErzbkZJ1//vOf+fGPf6zkybnhhhvIyMggKChICZoYGRlhYGAAgDvvvPOcDvSys29YWBj19fVKjTs3Nzfuu+8+/vKXv1BbW8srr7zCt7/9bQYGBqisrOTIkSP88pe/JCUlRTnXmdd96KGH+MEPfjDudYeHh7HZbPj4+FBfX4+vr+9Zk54cWXXmhCCbXeETYf1iNCbnw2q1KgXdAW666aZz7qoGBwdxOBwEBwdTV1c3Rrsy0djtdoaHh3Fzc5uwNrm7u+Pp6Ulvb+8laRMmI9OnT6ezs5PTp09TVlZGVlbWGK3V+aitraWjowONRkNOTs5ZkexypYpz4ebmhr+/P52dnfT399PX16dUqzGbzZSWlpKYmDjuImqz2ZQ61F1dXYp2ZbxrnE/DajKZaG5uZsOGDezZs4fOzk6MRqMy7u12OzabDbvdjsVi+VyS4Y4eq66urnh6ep5TaPL398fT0xO73Y5er8dms511jLwZOvO5qdXqMRvX0QwMDLBz507y8/PJy8tTIknl+7VYLFitVqVqhsPhuBy3DnwiXGdlZVFWVkZtbS0pKSmkpqZe9HnkOftCnqOfn58i5HZ1dWG1WsnOziY5OZkZM2Zw+PBhdu/ezXPPPYezszOxsbGkpqaybt06cnNz8fb2vtTbvmoZGhrCaDTi7OyMl5fXJSmWJscqM4mQCy4PDAwoE+HnVaj380D2O/Hx8WHhwoUMDAzw7rvv8uGHHyomVFk1rtFoFCH2gQceICUl5bz36enpSWJiojKo1Wo18+bN4/3336ekpITt27fzjW98g1OnTlFcXKyUUBndQc+87p133kl2dvZ5r+vq6kpKSso5dy+XUvjbZrONK/SNXuBGI5u95TqfjzzyiFLO7Vz4+vqSlJQ0aYQ6s9nM0NAQBoOBwMDACSvTFRAQQFhYGPv27VOinCfLM7pUcnJyqK6upqSkhKNHj7J27dpP/Y7sQtHV1YWHhwfJyckEBQWdpbGzWCxj+uaZfc9qtSrpT9zc3BTToJubGy4uLoSFhfG///u/n6o9njZtGhEREeN+9mnjbdeuXezdu5d9+/aRnZ3NwoULCQ4OVu6lra2N4uJiPvzwwzH+iJcTea5xdXXFarUqm/XxBFJZ2JL93861WRvvvs/1LKxWK88++yx5eXkYjUamT59OcnLyGF/CI0eOcOLECWpqai77M0hKSmLOnDm8+eabnDp1StHSng9JktiyZQsnT57EaDTy8MMP4+fnd1HP0WKxoFKplOfo7OyMt7c3ixcvJjk5md7eXqXeeVNTE83NzTz11FP09/crQuBorpa191Jpbm6mrq6OhIQE3NzcLsm6c23MopcRFxcXwsPDMZvN9PX10dvbOyGO5ZeCLLhlZWWh1+vZsmULhw8fZvr06UydOlUZcF5eXgQGBtLa2srChQtZsmTJWZoAeeduNBqx2WyEhoYqnzk5OREbG0tcXBy1tbWcOnVKUcFXVVWxcOHCsyZJZ2dnPDw8CAoKorW1lTlz5rB+/fpxd2p6vR6j0YjJZCIkJOSyDnB5l20ymc6aUG02G2azWdHKycjPVavVEhAQQHt7O8uXL2fatGlnCZ2y+aKvrw8nJ6dJ1YeMRiO9vb0MDw8TGho6YX4tQUFBxMbG8vrrr9Pc3KxUSbkWSE1NJSkpiYCAAMrKymhrayMsLGxMfq/RyC4gx44do7+/n+DgYGbPno23t/dZE7ws2A0ODuLp6TlmXJhMJgYHBxkYGMDDwwNvb288PT1xdnYmKCiIwcFBdDodt912G1qt9iwBZnh4GIPBgF6vVwJaLgZ5LBUWFrJt2zbq6ur45je/yezZs0lMTFTuv7S0FJVKxcaNG895rvGEq5qaGhwOBzqdjoCAgE+touDp6UlgYCDNzc309fXR398/xuQrSRIOhwO9Xq/koQwJCblk1wDp/6cH2bRpE9XV1YSEhLBw4UJyc3PHmFs1Go0i5Mjfu1xEREQwZcoUIiIiaGhooL6+np6eHiVH4Zk4HA5MJhO7d+9W0s08+OCDwCdmaPk5Go1GjEbjmLEqC+fd3d0MDQ3h4uJCaGgoGo2G3t5empqaSE5OJiYmBpVKhd1u59SpU+Tn53P48GHefPNNkpKS0Ol0YwQ7WVExWviXJImKigrFj/lc93O1IEkSw8PD1NfX09jYyJQpUy45t+jkcPiZRHh4eChF0ltaWigsLJzoJn1mMjIymDdvHomJiZw6dYpjx45x6tQpZZBERkYyb948HA4HNTU1NDY2Kp+N/vfee+/x6KOP8sADD4wxUciRSrm5ucyZM4eenh42bNjApk2bqKqq4otf/OK4Aps8yUmSRH19PbW1teNed+PGjfz85z/na1/72kU7oH8aXl5eeHl5odfrsdvtY65rNBrR6/V0dnaOe82EhARmzZqF3W5XnKbPbLvNZuPNN9/ku9/9Lj/84Q8nlanx9OnTVFRUoFarmTJlCn5+fhPSjuTkZKUfbNy4kfz8/M9Ne3MlUalUhIWFsWjRIr7whS9QVVWl5P4ar5/LrhFtbW08+eSTmM1m5s2bxwMPPHBOc6nRaOT06dPKuJD/NTc3K0JMQkICoaGhivlwwYIFhIaGMjg4yMmTJxW/t9H/6urq+M9//sP69es5cODAZzINOhwOmpqaqK+vx9/fn7Vr15KdnT1GqJWDvM6Fk5MTbm5u2O32MePz5z//OT/60Y/YvHnzuObSM4mIiGDevHmoVCrKysooLy8/654tFgsnT56ks7NTCUo5n6n7QpDngNLSUmw2G/Hx8dx+++1nVRGxWCwXdB+fBZ1OR1paGvfeey+9vb3k5eWxY8eOc/ZBk8lEQ0MDW7ZswW63k5ubS3R0NK6uroSHhzN//nycnJzO+RzNZjMlJSV0dHTg7e3N7Nmz8fLyYseOHaxevZqqqiqGhoaUdWPatGnce++9PPLII6hUKoqKipS8ezJyP7DZbIpLjSRJPPDAA/z6179W8htercj3U15eTl5eHlVVVXzhC1+4ZJO00NidgVqtxt/fnzlz5tDd3c3777/P6tWrJ7pZn5no6GgefvhhHnroIT7++GMsFgtz584FYMaMGXh7e5Ofn88rr7zC6dOnufPOO8nMzESlUtHT00N+fj7PPPMM06ZN4/bbbx/XVLZw4UKGh4fZsGEDzz77LCkpKSxYsIDg4OBxd93p6ek8/PDDHDlyhA8++ICqqiruu+8+MjMzcXZ2ZmBggP379/Pss88SHR3N97///cueJDo+Pp6MjAw2b97Mu+++y8KFC0lPT6e3t5fXXnuN/Px8/Pz8xp10Fy1ahE6no6CggGeeeYbS0lLWr19PZmYmDoeDjo4Ojh49yt/+9jcl8eZkqve5e/duSktLyc3NJTAwcMKChDw8PIiNjeXRRx/llVdewWg04uPjw4IFC67qHbjMjBkziIyMZHh4mM2bN3PkyBGOHTvGzTffrKQZkYWKXbt2kZeXR3d3N48//jhz5sxRqhGciZubG319fTzxxBM8+OCDJCQkoNVqaWxs5Mknn+TYsWN4e3vzv//7v4qTu7OzM9/5znew2Wzs2LGDH//4x/z4xz9mypQphIWF4XA4yM/PZ/fu3bz33ns88sgjpKenfyZzkJOTEwkJCaSkpFBWVsZrr73GggULSE9Px93dncrKSnbu3Mm77757TsHRzc2N9PR0KisraW5uprW1la6uLurq6ggODiY7O/uC2jZ9+nS8vLzIz8/nvffeo76+HpvNxvTp0xkeHqaurk5J8jxz5kzWrVtHQEDAJY9XOZhg5syZlJaWUlFRwQsvvMDq1avRarXY7XbKy8t59913KS4uvqRrnQ9/f3++853vKEl/f/azn9HY2MiSJUuIjo7Gz88Pg8FAeXk5hYWFvPbaa+h0Or785S/zhS98QTlPVlYW3/ve98jPz+fDDz+koaFBeY4jIyPU19fz8ssvU1lZSXZ2NuvWrSMwMFB5jmazmV/+8pd89atfZcaMGcoG4/jx4+zduxdJksjKyjqrHruPjw+pqalKtHJTUxPd3d00NDSQlZV13vrLVwMWi4Xu7m5+9KMfYTabyc3N5aabbrpk9xgh2J2BbG5buHAhH3/8MaWlpZw6dYqEhITPHKFyOdmyZQtHjx5VHKNNJhMnTpzgF7/4BfHx8aSmpjJr1iwAJc/brFmziImJUQS1X/ziF4SGhpKRkUFycjIPPvggxcXF9Pf3884777Bt2zZcXFyUkk/z5s0jJyeHGTNmKOcdjRzmPmPGDA4dOkRcXByzZ88+p7+Up6cnMTExPPDAA5SUlCh+gNu3b1dMIP39/cyYMYOsrCzmzJmDSqWiu7ub8vJydu7cyalTpzAajTgcDh5//HFycnJIS0vDx8eHp59+mmPHjlFTU4PJZOL5558nMzOTKVOmsGTJEuATbebAwABNTU3k5+crC4aXlxeenp5ER0dTVVVFa2srb775JvX19WRkZLBw4UJ8fX1JTU3l29/+NgUFBbS3t/P222+zZcsWXFxcsNvtDA4OsnTpUpYsWUJWVtakEFRsNhvl5eWUlZVhNptZu3bthPqPyv5MK1as4MSJExgMBl577TWl4snV7kjt5uZGcHAwt956Kzqdjp6eHhobG3n55ZeV3GuSJDEyMkJ/fz8xMTEsW7aMnJwcwsPDzzl+XFxc8PPzY+7cuRw5coRDhw4pC0RjY6NSCUHOcScTFhbGqlWr8Pf3p7CwUBEm5UAIOXJy1apVSt5Ls9lMV1cXb7zxhjJOAP75z38SHByMt7c3P/rRjxQhS+5LOTk52O12NBoNR48epa6ujoCAALy9vXF1dUWv1xMVFUVVVRV79uyhu7ub48eP87WvfQ13d3d8fHy4+eabeeONNygrK+Ppp5/GZrMRFRVFVlYWoaGhVFdXc/jwYQoKCqioqMBms7F7926sVitpaWksX74cLy8v4uPjefDBByksLMRkMrFhwwZ27dqFzWZjaGiItrY21qxZw9y5c1mwYIFSEvHUqVN8/PHHlJWVMTg4SF1dHb/+9a+ZN28eU6ZMwcXFhWeffZYjR45QX1/P0NAQ//znP5k6dSpTp05l9uzZfPnLX2bXrl20tLSwc+dOKisr8fLyUnIH+vr64u/vj8Fg4NVXX6W4uJioqCgiIyPZt28fe/fuRa/XYzKZePLJJ8nIyCAlJYWcnJwLGrfOzs74+Phwww03EBISQklJCc3Nzbz33ntoNBqcnJwU0/7w8DBJSUksXLiQOXPmjAmaGe85vvvuu+zatUsJxGpra+Omm25izpw5ynNUqVQkJCTwta99jdbWVg4ePMjJkydxdnbGbrfT09NDX18fa9euZeXKlWf514WFhbFmzRr0er1SlcFms5GdnU1WVtakcnG5WAYHB6mpqWHz5s10dXWRm5vL8uXLL4trjBDszsH8+fOpqKjg+PHj7N+/Xwlhn2jNy/Hjx3n33Xcxm82Kj4PRaOT1119XkgDLgh18ohVJTExk1qxZlJaW0tXVxeuvv05aWhoeHh7k5ORw7733snHjRgoLCxWThJubGz4+PkqZmLS0NCIjI8dtk+xPsWzZMjo6OsjOzj5vqRe1Wo2vry/33HMP27dv59ixYxQWFtLZ2YlGo8Hb25v4+Hhuu+020tPTlbQORqORsrIy3nzzTQDFV+att95SahU6Ozvz2muvKdeKiIhg7969dHd343A4FMEuOTkZDw8PmpqaOHr0KI2NjdjtdpKTk7nrrrvw9/enqakJh8NBYWEh7e3t2O125s6di5eXF6Ghodx3331otVqKioqU5+bh4YFOpyMmJoavfe1rpKWlERwcfAm/+OXB4XAwPDzMvn37FH+2xYsXT3iwgpubGzNmzGDZsmXs3r2bnTt3kpqairOzMzExMYpJbDIIxp8FNzc3RTty6tQpDhw4QH5+Pn19fVgsFlxcXIiOjiY1NZWpU6eyZs0axSfuXKjVaoKCglizZg3PPPMM5eXlSv+UHeZvuOEGYmNjxwhcnp6eLF68mKSkJFxdXTl8+LCSQ06uWzl16lSWLl3KlClTFO25Xq9nw4YNir9oXFwce/bsAT6JgnzkkUfO0p7NmjWLoKAgLBYL+fn5NDY2YjKZ8Pb2Jjs7m8DAQBYsWEBdXR3Nzc0YDAZaWlr40pe+hLu7O1qtljVr1lBdXU15eTk7duxAq9Vy8803M3fuXPz8/Dh16hQ7d+6koKAA+MQ6IacWMhgM5OTk4OPjg7+/P/feey/BwcEUFhZSXFxMV1eXEuWalJTEl7/8ZVJTU5U5rq+vj5MnT/LOO+8AKELO22+/jbu7u6Jxff3114FPBKiQkBA+/vhjenp6cHFxYd68eaxfvx5XV1eOHDmiJCPWaDTodDpmzZpFZmYmGo2GkZERjhw5Qnt7OwkJCUyfPp0333wTs9ms5AjcvHkz7e3tOBwOcnJyLrgPqlQq5s6dq5x3w4YNlJaWotfr6e/vx9XVlZCQEKKjo1m1ahU333zzWb7R8mbim9/8JiEhIRQUFCjPUQ6OSE5O5ktf+hJpaWlERUUp301KSuIb3/gG7733HpWVlRQXF9Pd3Y2rq6tS0/vmm29myZIlZ/mghoaGsnr1aoqLi6mrq6O8vBwvLy9F8zdRbiSXguxTV1dXR35+Pm+++SbBwcGKYHc5UElXu0PL54QkSWzfvp133nmHDz74gFdeeYW5c+dOuHP3hfxcZy6Cl+snPt/iOt41Lvb4T7vu5biPy3EuuWD5xV5zIjEajdTU1LBy5UoWLVrEjTfeyDe+8Q1gcrTP4XBw6NAhXnvtNV588UXWr1/PjTfeyFe+8pXzVgG5WrjY/jbe/ZpMJpYuXUpHRwdTpkzhww8/vOjzXGy/vdDjL+ecc7Hj63Ke63LONZ8nn2U8XGof/Kxz3qVc99O+ezXOCxaLhe3bt/P0009TXV1NeHg4r7zyCuHh4bi7u1+WexIau3OgUqmYPXs2Wq2Wo0eP8vzzz1NfX883vvENvLy8JqxDfZbrXom2Xuw1Jvo+LvVcV9OEMjQ0xJ49e3j11VcJDg7mi1/84qTzY1OpVEybNo2QkBBCQ0P5+OOP+etf/8ru3bv51re+RVJS0oRvqi6Fz+NZX4kx9FnbPZnG10TPNZOFifpNLuW619LvIJdW27FjBx988AExMTHccccd3HHHHURGRl5Wtxgh2J0HHx8fEhMTWbVqFXl5eRw5coTQ0FBuuummSWGWFQjOh8PhwGKxsHfvXg4cOEB9fT033ngj6enpk05IkvNeubm5sXz5cqxWK5WVlTQ0NLBhwwalykl6evq4aToEAoFgsmG32xkZGeH06dMUFRVRXl7O6dOniY2NJTc3l5kzZ5KWlnbZgwOFYHcenJ2d0el0PPjgg9TW1lJaWkpzczPTpk1TSs5MlhJRAsFoHA4HVquVjo4O/v3vf1NXV4enpyff+ta3CAsLm7SCkYuLC/PnzycuLo7CwkJefvllXnnlFWJjY5k+fTr33HOP4h8pByBcS7v68bBYLFgsFiWJs/zb9vb2KkmHr7WSbALB1croNDpDQ0NKcN3mzZsxm81ERkby6KOPMmvWrM/N/1r42H0K8uNpampi06ZNPPXUU+h0On784x+zaNGiSeEYLxCcSW9vL6dPn+a73/0uAwMD3HTTTTz00ENKgtDJLgzJiWNtNhsHDhxgw4YNHDx4kMbGRtatW8fixYtZsGABSUlJk/5eLpV3332XrVu38vrrryt561QqFRqNhm9961usW7eOhQsXTnQzBQIBn1Qc6e/vZ8OGDezcuZMjR45gs9m48847Wbx4MYsWLVIqS3xec5cQ7C4QOXnj0aNHefrpp/Hz8yM9PZ3bb7+dWbNmiR2zYFIgSRLHjh1j165dHDp0iPb2diXzf0ZGBm5ubleVICRJEr29vbS0tFBfX8/+/fspLi5W6mtOnz6defPmkZSURHx8vJJi4Vqira2Nzs5OWltbz/osJiaG0NBQ/P39J6BlAoEAPrGQ1NXVKZHPx48fR6/Xo9PpiIyM5IYbbiA5OZng4GD8/f0/9821MMVeIG5ubkRHR+Pp6UlBQQElJSWcPHkSLy8vNBoNUVFR+Pn5CQFPMCHYbDYGBwdpbGxkx44dFBYW0t3dzbx581i0aBGxsbETVhP2UlCpVPj7++Pn56eMP5VKRW1tLZ2dnRQUFDA0NERdXZ2Skkcu6D4Z8k5eDsLCwggLC2PatGkT3RSBQACKqdVgMNDX14der6e4uJjTp0/T1NRER0cHERERpKenk5WVxYoVK/Dw8LhiLjBCY3eRyKVTXn75ZbZt28bWrVv54he/yK233sqCBQvw9fU9K1mnQPB5IA9dOZF0eXk5zz33HB988IEymTzyyCNKItJrAfme6+vrOXnyJK+//jqHDx9mZGQEPz8/br/9dubPnz+mLqVs8hDjUSAQfBZGz7WSJGG32+nu7iYvL4+CggL27t1LSUkJ4eHhpKSksG7dOtauXYuvry8ajeaKt1cIdheJ/LjMZjNtbW3s37+f3//+91gsFkJDQ7n//vtZs2bNGAFPIPg8kDcZ77zzDu+++y6lpaXY7XYeffRRcnJySE5OVupSXitCzegJVq432traSllZGfv372fbtm0YjUZcXFyIiYlhxYoVZGdnk5aWpiS6FggEgovBZrNhMBg4cOAAxcXFFBUVcfToUSRJIjg4mKysLL7whS+Qnp5OcHAwGo1GSV8yEXOvEOwugeHhYdrb2zlw4AAnTpygtraWkZER5syZw9SpU8nKylKcu6+VhVUwscjDta6ujqqqKqWklEajITg4mHnz5rFw4UJCQkLGlJO6lhkeHqanp4eWlhZqa2tpaGigo6OD9vZ2+vv7lRJVcsm9sLAwwsPDiYqKuiZ98gQCwaVhNpvp6emhurqa5uZmmpubqayspLOzE0mSlHKB8fHxhIaGEhERQVJSEn5+fpPC5UUIdpeIJEkMDQ2Rl5fHwYMH2bRpE66urqSnpzNnzhzmz5+Pv7+/UoMUrh3tieDKIA9Rk8nE4OAger2egoICioqKOHToEFarlYULFzJv3jxWr1593abhkdMMNDY2Ul9fT2FhoRJAIpfNSk9PJyoqipiYGNLT0/H29sbd3R13d3e8vb3RaDSTNhWMQCC4/NhsNqVe7uDgIMPDwxiNRpqbmzl58iQNDQ20trbS3NyMTqdTSgAuWLCAjIwM/P39J8Tcej6EYHcZkSNn//SnP3HkyBGqq6tJS0vjy1/+MvPmzWP27NljFg0h4AnOx+ih6XA4KC8vJy8vj2effZba2lp8fX2ZM2cOP//5z4mOjj6rzuL1jizo1dfXU1lZyc6dO9m3bx8tLS309vbi6enJ1KlTSU5OJisri9zcXKKios6r6RRjViC4OjmXqNPb26sIcbKptampie7ubrRaLSkpKWRkZLBy5UoWLlyITqebdILcmQjB7jIiSRJWq5W+vj7a2tqoqqri7bffpqysDICoqChuvfVW5syZQ2xsrFiIBedlaGiIzs5O9uzZw+bNm2loaMBoNJKdnc2qVatITU0lLi4OnU6Hi4vLdamlOx/y1DZ6Rz4yMoLBYKCjo4OCggIqKytpbm6msbERq9WKn58fISEhpKWlMWXKFKKjo4mMjCQuLk6YbQWCqxiLxUJnZ6cSudrY2MjJkyepq6tjcHAQh8NBYmIicXFxREdHk5aWRlpaGl5eXri6uuLh4YGbm9tVMQ8Iwe5zYnBwkJ6eHo4ePUpJSQktLS20t7fj7u5OREQE4eHhJCYmMmXKFIKDg/H19QWERuB6RpIkhoeHaWtro7S0lIaGBsVvzGQy4evrS0REBLNmzWLq1Klj+o3gwjGZTAwMDCgmFr1eT0dHB42NjRiNRiwWC05OTjg5OeHl5YWPjw8hISHK8/b39yc0NJSAgAA8PT2VABWBQDCxSJKkBDr09fXR3d1Ne3s7PT09GAwGOjs76ezsxGKxYLfbsdvtuLq6otVqCQ4OVvJCBgYGEhYWRkhICGq1+qpbl4VgdwXo6OigrKyMXbt2KVF7rq6uTJkyhZUrV5Kenk50dDRubm64u7uPKRF0tXUowYUjV1ewWq2KNkmv11NUVMTWrVuprKyku7sbHx8fVqxYwbx585g7dy7h4eGiX1xG5MXg9OnTlJeXU1dXx+nTpykrK6O/vx+TyYSTkxMxMTGEhYURGRlJSkoKCQkJBAYG4uvri1qtxtnZGbVajVqtVnz1ROojgeDyIrtYyIKZxWLBZrNhs9mw2+0MDg5SX19Pa2srtbW1VFRU0NzcjF6vZ3BwEA8PD4KCgoiMjCQjI4PMzEzi4+NJSEjAw8PjmhirQrC7AsiPWE5PceLECQoLC9m0aRN5eXk4Ozvj7+/PwoULWb58ORkZGZ9LYWDB5MJut9PZ2UllZSWbNm3i0KFD1NfX09vbS2ZmphIQsWrVqrMCIkS/uHycawqUJAmDwUB7ezuFhYWK0FdVVUVpaSkOhwNXV1f8/f1JSkpSNPGpqalMmTKFsLAwAgMDr4t6tgLBlUKSJIxGIx0dHbS2tlJaWkp5eTmtra20tLRQWVmJxWJBrVbj5+fH1KlTiY2NJSYmhmnTpjFt2jQlUGo8roWxKgS7K4i80xgaGmJwcBCDwUBFRQWVlZVUV1dTWVmJ0WjEzc2NoKAg5syZQ3p6OjExMSQkJKDVaq+JTnc9IkkSg4ODSmmswsJCiouL6ezsxGg0KtFWMTExZGRkkJiYiK+vL1qtVsmJKH77K4/snzc0NMTw8DBms5mRkRG6urpobGykq6uLzs5OOjo66O3tpb+/n76+PsUnx8vLS/HTCwwMJDg4mIiICEXTp9PpRDokgWAUDoeD/v5+Ojs7MRgMdHV10dzcTHt7O3q9nubmZnp7ezGZTNjtdtzd3QkMDMTHxwc/Pz9CQ0OV8RYYGIinpyeurq64ubnh4eGBp6fnGG36tYgQ7CYQSZLQ6/VKeoaysjJqa2vp6+tjaGgInU6Hv78//v7+REZGEh4ejp+fHz4+PgQEBBAQECCc5ichcvJcvV6PwWDAaDTS3d1NW1sb7e3tdHZ20tXVRX9/P2q1Gh8fH5KTk0lISCA6Oprk5GSCgoLE7zpJkSSJkZER2tvb6e3tpauri9bWVsWPp6uri76+PkwmE1arVUmnIi8uPj4+Y4R2rVarHOPl5YWXlxfu7u6KYHgtVQ4RXN/Y7XasVivDw8MMDAwoLiiDg4MMDg4yMjLC8PAwBoOB3t5eBgYGMBqNjIyMYDKZlI2VWq1WUhQFBAQQEhKirJfh4eGEhobi4+Nz3eTyPBMh2E0yWltbqaqqoqCggN27d1NZWUlfXx8Oh4Pk5GRFAMjKyiIrKwutVqtM/PIuRP4H14ZaeTLjcDiUf3a7HYfDgc1mY2hoiIKCAsrLy6mpqaG4uJiamhrsdjseHh5kZmYyf/58MjMzmTZtGlFRUeK3ugZwOBxYLBZqa2vHmIbq6upob2+nra2Nnp4eHA4HAC4uLkRFRREUFERgYKBizg0KCiIkJERJYyO7ZYwuj3bm+9FaXdGXBFeK0a5GcsmtM1/L781mM4ODg7S1tdHQ0EBnZyfd3d20tLTQ1NSEXq+nu7ubgYEBVCoVLi4uuLm5ER8fT1RUFGFhYcTFxZGUlERkZCQhISHCkjUOQrCbZIweEHa7nZGREbq7u6moqCAvL0/R6lVXV6NWq5UIvfT0dJKTk4mNjSU5OZn09PSrIiz7aqelpYXGxkaqq6spLS2lurqapqYm6urqMJlM+Pn5ERYWxvTp05kzZw5paWnEx8fj4+MjhPBrkNGL3Hj/ZDNTR0cHPT09tLe309LSQltbGx0dHdTX19PY2MjQ0BBWqxWVSoVGo8HT0xM/Pz9FCPTz88Pf35+goCBFey8vdK6urkLDJ7hiWK1WTCYT3d3dNDc3093drZhMu7u76e/vx2Aw0NTURGdnJ0NDQ8oa5+3tjU6nIzY2lvDwcPz9/QkMDCQhIYGYmBjFnCqvZeP9AzF3nokQ7CY5cj3MgYEBZZD09fUpaTBkU5/s36NSqfDw8CA4OJjg4GD8/PwICgoiLCwMf39/RdCQ/QwE50aSJAYGBtDr9fT29qLX6xWTm7woGwwGRkZGsFgs+Pr64uPjo6TIiI+PH+P74e/vj7e3N15eXldlCL3g0pEjcGWzkslkYnh4GJPJxMjIiOJ/OzAwQF9fn2Kyl01WIyMjmM1mzGYzQ0NDmEwmnJ2d0Wg0imnK09MTb29vpS96enoqfdLT03PM5x4eHorJV/TH6xtZ2yy7AskVGAYGBhgeHmZwcPCs9/L8J/fJkZERAJydnRV3Ajc3N8W/Tc78oNPpCA4Oxt3dHTc3N8X9QKPRKO89PDxwdXXF1dUVEMLbxSAEu6sQOQlydXW14rdVV1dHU1MTRqORwcFBbDYbWq0WLy8vtFotfn5+6HQ6dDod4eHh6HQ63NzclIHk6uqKi4uL4gek0WhwcXGZ0ELGnwey5kROMdLa2orFYlHuVV5w5V1ob28vnZ2d9Pb20tPTo/h9yJOa7Ouh1WqJi4sjMjKS4OBgIiMjSU5OxsfHR+Q5E1wUDodDWVRlB3JZ0NPr9RiNRvr7++nt7VWcyOX+6nA4UKvVykI62rdPXkTd3Nzw9vbG19dXcSb38fHBxcVFSdni4uJyVgqX0a/P9X60aVhwZZA3C6NdQaxWK3a7XUkDIn9+rvdWq1WZEw0GA0NDQwwNDSn+baM3IfJGVi5xKLsVjA5Q8PLyUhQJcl8LCAhQfErlIAe5zwkuL0Kwu4aw2Wz09PTQ2NhIcXExDQ0Nii9DZWUlAwMDmEwmAMXRVM7nExISoryPiooiMDAQPz8/AgICFOFO5sxJ+1yT+MVM7vKxF9Mdz5em4lzvJUlSdqX19fW89NJL6PV6RbPZ0tJCR0cHer2ezs5Oenp6gE98obRarZLWIiwsjKSkJNLT04mMjCQsLExo4QRXFHlBl01dra2tSnRuT08P3d3dY0xhnZ2dijbGbDaPOZeLiws6nQ4vLy/FPCZr/+QgD1n7Jzuly5pAnU43JgDkfOPgfONDpVIhSdIFjaHLNc7OdZ7LtSxejvnsfJ/JG9X+/n5F6JJ/74GBAUU4MxqNija4r69P+UzeMPT19SnBC6PNm87Ozsrv6+vrS3BwMDqdTrFCBAcHKwF+oaGhhIeHK5o3wcQhBLtriNGJG2022xiHfrvdrphsZT8eWQMlh5H39fXR09OjZN6XHbJ9fHzw9vZWJnzZpCjvvGSVuZubG56enmPU6x4eHspu3tXV9awIPzc3tzG7NrmtQ0NDY+7LYrFgNpuVe5NNBfIuUn4tO+fKE1V/f7+i5ZD/bjQaleciazm8vLzw8/MjJSWFoKAgdDodAQEBxMXFER4eTkBAAP7+/qjVasUvTtZYCD85wURwpj+f3KdH++me6cwua3QGBgbo6elRtH96vV4RDoaGhhQBUI5YHC04DAwMYLVaAcb1eZLNZ6NTvsiaQln4kzU7snlOo9Gg0WiU4CJ5XvDw8FA0gV5eXgBoNBo8PDyA/84fo8efk5MT3t7e5312sgB6Lm2RbJaUN8LnOkbWlo5eRoeHh5X5d3h4WJmz5DlM1pLJ6TpGz21y9Ofo9DqytkzemMtm+NGas/H8OeETk6g8d8saXFkod3d3VwR12UVE1qz5+Pig1WrR6XTKHDc6QGf0/+O9FvPgxCIEu+sIeYKQd3CjJ2r59fDwsCIUjZ5cRgd0yAvI6H/ywgEophm5goY82EdPEDLj/U2uxjCaM69rtVrHmBHgv4uMLHTJO87REcMARUVFtLa2YjQalfO7uLjg5+fHPffcw6xZswgKCsLDwwNfX1+8vb2VBUkguNqRhRZZMDCbzco4t1qtirAgZ/SXj5Hfy/PI6O/KAo5cqulc0ZGj5wt5Phn9WV9f3xhTMPx3XMvVeJydnce8PjNITA44OR/jzTtnIgtk50LWmMrznozValXuUf5c3rDa7XblOGdnZ0VD6eTkhCRJY4IEzox4PpdwpVKpFC2ZHEUqb7Y1Go3yvzwfj34vfy7/TRa+R78XgtrVh3qiGyC4csgD+Hy5feRs+729vYoA2NPTo6jxjUajIvjJ70c7gstC4JlCHzDmtcx4fwPO2kmfqRUYHVGqUqmUXb+sJZB9h2RNnFarxcnJicHBQRobG+nu7h5zftm/xNXVlaSkJGJiYoQgJ7gmcXJyUjRonxWTyaRoy/v7+5VADjkIRBYE5YCQ0e9Hzxey5slgMNDY2EhLSwtOTk54eHgQExNzllAIjHl95vxxpsbqXIwWsM7FmZGXF3rMmdrD0abN0dp+d3f3MSk9nJycFMFrdO7D0QEF8ntZ6zn6M/k3lc3n8vcF1x9CYye4bMg7cHlylyd8eecrmyhGd7nBwcGztHOy+Xc0cpCCbLaV/X1kc8KFpHYpLi7md7/7HR9++CEWi2XcY9RqNb/73e9YtmwZU6dOFTtVgeBzRK7Ec/jwYR577DFOnTqFr68vM2bM4KWXXiIgIACVSqXMH6O1jQBDQ0NjxrKsxZc/P9c1BwYGzrmplHFxccHd3f2cmj1ZKJOPkRk9T8mv5frBsgAnEHyeCMFOcNk4M1Hl6N02jK+dO9fu+szJ78yd8ZmmCfmY89HZ2cnBgwd54IEHMBgMZ5lQZMLCwli+fDnf/e53mTp1qojaEgg+ByRJwmQy8de//pXt27eTn5+Pt7c39913H+vWrSMrK2vM2BtPY3fm/DF6rjkfn/a5zKcJYaPnn9F/Gz1PjdbYCbOm4EogTLGCy8aZJofJhre3N5mZmaxZs4ZDhw5RW1s7rlDZ2dlJQUEBb775ppIAVqQsEQguH3IVgo0bN7J//36qqqpQq9Xcdttt5ObmkpiYqPjYyYgNlkBwYQjBTnDd4OHhQUJCAnfeeaeSKb2vr++s4+x2O5WVlXR1dbFo0SKmTZtGcHCwWFgEgsuArKlrb2/nP//5D8XFxYyMjBAdHc0999xDQkLCdVvjUyC4HEw+tYpA8DmiUqlYtGgR3/3ud3n44YfPeZzNZqO7u5v77ruPrVu30tXVdQVbKRBcuzgcDvLy8vjRj37Exx9/zODgILNnz+all15i6tSpQqgTCC4RobETXFfI5uL09HTc3d1pbW3lrbfeor+//6xjJUlCr9fzwgsvUFNTw49+9CN0Ot2kNDMLBJMd2e3h1VdfZdeuXRw4cAAXFxfuvvtuVq5cSUpKikjyLRBcBsQKJbgu0Wq1xMTEsGrVKjIzMwkMDBz3OKvVSkVFBQcPHmTnzp0MDg5eUKoEgUAwFovFwokTJ/j4448pLCxkYGCA+fPnK+4O3t7eQqgTCC4DIipWcF0jSRJPPfUUW7Zs4cCBA+dMSOrp6UlcXBxvvvkm0dHReHp6ikVIILhAbDYbXV1d/O53v+Odd97BYDDg5+fHCy+8wKxZswgODp7oJgoE1wxCsBNc10iShNVqZevWrbz22mu899575zzWycmJhQsXcv/997Nu3bqzaugKBILx2bt3Lxs3buTvf/87kiSxYMEC7r//ftavX4+Li4sYRwLBZUT42Amue1xcXJg1axbu7u60tLRQVlbG4ODgWcc5HA5OnTrFu+++S39/P/fcc8+nliUSCK5nJEli3759fPjhh2zduhWbzca6detYvnw58+fPF0KdQPA5IAQ7wXWNvKiEhITg7u7O8uXL6e/vp6GhYdzs9T09PRQUFCBJEjk5OcTHxyv1FAUCwX+xWCz09PSwe/du8vPzaWhoID4+nmXLljFv3jwiIiImuokCwTWJEOwEAj4xs/r6+vKTn/yE3t5e9uzZQ0VFxbjHNjY2YjAY8PDw4PHHHycqKkoIdgLBKOSa01u3buW5556jt7cXf39/vvWtb7F+/XpCQkImuokCwTWL8LETCP4/8lDo6Ohg+/btPPHEE5SXl49bnUKlUuHq6sp9993H6tWrWbFixZVurkAwaTl58iT79u3jV7/6FUajkTlz5rBq1Sq+973v4e7uLpJ9CwSfIyLdiUDw/5HrOPr7+zN79mzuu+8+QkNDcXV1PetYOXv+xx9/zLZt2zh8+PAF158UCK5VHA4H1dXVbN68mffff5++vj6ys7O54YYbWLVqFZ6enkKoEwg+Z4QpViA4A41GQ3x8PD4+PuzcuZOioiI6OzvHFdzKyspwdXVFq9WSkpKCVqvFxcVlAlotEEwsNpuN4eFh8vLy2LVrF0ePHsXHx4fFixcrueoEAsHnjzDFCgTnwOFw0NDQwM9//nO2bt2KwWA457FarZYnnniCVatWER4efgVbKRBMDvR6PYWFhXzlK1/BaDSi0+lYvXo1f/rTnwgICBB+qALBFUIIdgLBOZBz3JWUlLBnzx5+85vfMDAwMK7PnZOTE6Ghofzwhz9k8eLFTJ06dQJaLBBMDPX19ezZs4cnnniC6upqkpOTmTdvHj/96U8JCwvD2dlZCHYCwRVCmGIFgnOgUqkUs6zZbGb9+vW89957DA0NnWWWdTgctLW1sXPnTux2OwEBAYSGhoq6soJrGofDQW9vLzt27GD37t00NDQQFhbGkiVLWLZsGeHh4SLXo0BwhRGrjkDwKeh0OjIyMvjWt75FVFQU7u7u4x4nSRK7d+9m48aNnDhxArPZLAIqBNcsDocDs9lMeXk5b7/9Njt37gRgxowZrF27llWrVglNnUAwAQhTrEBwAUiShCRJ7Ny5k+eff55NmzZht9vHPdbFxQWdTsd7771Hamoq/v7+V7i1AsHnj9FopKamhvXr19PZ2YmnpycZGRm89dZb+Pn5iaoSAsEEIUyxAsEFIC9Qs2bNYnh4mKCgIJ577rlxj7XZbBgMBn72s59x3333sWTJElHkXHBNYTAY2LlzJ6+88gqdnZ0EBAQwb948HnroIXQ6HWq1Wgh1AsEEIQQ7geACUalU+Pn5kZWVhcPh4NixY5SXl2MymcYcJwddFBYWsm/fPtzd3Vm5ciWurq7C505wVeNwOLBYLBw+fJi9e/dy/PhxXFxcmDNnDkuXLmXatGloNBoh1AkEE4gQ7ASCiyQuLg6dToder+f3v/89LS0t4/rSDQwM8P7779Pc3MzUqVOJiIhQkiALBFcbkiRhs9nQ6/U888wzFBUVYTAYSElJ4Z577mHu3Ll4enpOdDMFguse4WMnEHwGZK3cU089xbZt29i/f/+4x6lUKjw9PUlLS+OVV14hOjoaNze3K9xageDSMZlM1NTU8MADD1BcXIyTkxPR0dG8/fbbREVF4enpKTYtAsEkQGjsBILPgJwKZe3atbi5uWG1WsnPzz8rx50kSYyMjFBVVcUzzzzDmjVryM3NFeYqwVWDvImRy+eVl5ej0WjIycnhC1/4ApGRkbi7u4v+LBBMEoRgJxBcAikpKQwODtLT00N1dTV9fX1YrdYxx9jtdvr6+ti5cye+vr6EhoaSnp4OIBZDwaRGjgYvLy9n//797N69m/7+fmbPns2iRYtYuXIlXl5eoh8LBJMIYYoVCC4Rh8NBX18fDz74IPv27aOzs/OcxyYlJTFv3jz++c9/KpGDYlEUTEZkoc5kMnHPPfdw5MgRmpub8fPz48UXXyQ7O5uQkJCJbqZAIDgDEaInEFwiKpUKHx8fnnrqKW655RaSkpLOeWxdXR0fffQRjzzyCM3NzVewlQLBxSFJEpWVlTz88MNs376drq4uEhISeOWVV8jJySEwMHCimygQCMZBCHYCwSWiUqlwcnIiMDCQW265hbVr1xIWFjauJs5ms9HX18eePXv46KOPKC4uvvINFgjOg6ypO3r0KJs2bWL//v2YTCZmzJjBHXfcwdSpU/Hy8sLZ2XmimyoQCMZB+NgJBJcBlUqFWq0mNzcXV1dXysvLMRgM45YVs1qtnD59mi1btqBSqYiJicHX11eYZAWTAkmS6O7uZt++fWzfvp2qqiqioqJYtGgR69evJyQkRPRVgWASI3zsBILLiCRJWCwW9Ho9a9eupba2lv7+/nMeP3PmTL7yla/w7W9/G7Va7LMEE4/ZbOYXv/gFH3zwAdXV1bi4uPDvf/+bnJwcYmNjhVAnEExyhGAnEFxmHA4HNpuNvLw8nnnmGXbv3o3RaBz3WA8PD0JCQvjd737H/PnzCQsLu8KtFQj+S2VlJVu2bOGJJ57AYDAQERHB/fffz1e/+lV0Oh2urq4T3USBQPApCBWBQHCZcXJywsXFhYyMDFauXAnARx99hNlsPuvY4eFhWlpaeP/99/Hw8AAQwp1gQqiuriYvL4+tW7fS1dVFamoqOTk5LFmyBH9/f1xcXCa6iQKB4AIQgp1A8DmgUqnw9/dn9erVBAQEUFBQQGtrKzab7axjLRYLGzZsIDg4GBcXFwICAnBxcbmsJi+bzYbFYmFoaOiC2+/i4oKnpydqtVpJvGy327Hb7VgsFqxWq+I/KAeQqNVqNBoNGo1G+fuFIl/DZrNhtVqx2WzYbLYx13B2dsbV1RUXFxfFef/Ma0iShMPhwGw2Kz6O8rmdnZ2V9olC9Z8gSRJms5nDhw+zdetWPv74Yzw9PVm6dCmrVq1i+vTpynFyrViLxYLdbj/r93d2dsbd3R1nZ+fz1kWWfw+TyTTmXKPPI//OnzUlkFwCzWq1jukHcj/SaDS4uLgoLhCiLwiuFYQpViD4HJEkiYGBAUpKSrjzzjtpaWk5K4GxjEajYcaMGfzf//0fCxYsUISjy0FpaSk7duzgJz/5ybh1bc/E3d2dBQsW8OSTT5KcnKwsxBUVFZw+fZq9e/dy8OBBOjs7GRoawsvLi5iYGGbMmMHixYu55ZZbLnpBliscFBYWsnfvXkpKSiguLqa1tRVXV1e8vb2JioriS1/6Ejk5OWRmZgJnL8gOh4OmpiY+/PBDNmzYQHNzM319fajVatLS0sjNzeWGG25g/vz5YjEHhoaGeO+99/jtb39LdXU1arWan/3sZ6xfv56UlBRFQBseHkav17N9+3Z27txJbW0tra2tmEwmAgICCAkJISUlhQcffJD4+Hj8/PzOeU2Hw8Hw8DAvvvgie/fupbKykra2Ntzc3IiKiiIlJYUvfOEL5ObmfuYEyFarlZKSEnbv3s37779PS0sLJpMJNzc3MjMzWbp0KXPmzGHevHmAEOwE1w5CsBMIPmdsNhuDg4O88sorbNq0ib17944rXKlUKnQ6HZmZmTz55JPExsbi6+t7Wdpw6tQptm/fzmOPPXZBgp1Wq+Wee+7h+9//PlFRUTgcDlpbW/nDH/7AyZMnFW2kn58fGo2GgYEBtm/fTnNzMyMjI3z9619nzZo1F2VWHhgYoKmpiXvvvRetVktMTAzz5s1Dp9NhMpno7Oxk9+7dNDY2kpWVxbJly7jjjjuUBVnWKL3zzjvs27ePvLw8li1bRlJSEkFBQZhMJvbt20dzczMWi4Wf/vSnTJs2DZ1O95mf69VOc3MzxcXF/OxnP6O6uprg4GCWLFnCo48+SlhYGJ6ensqxe/fuZffu3WzatIlFixYRHx9PZGQkrq6u6PV6qqqq2Lt3L2q1mi984QssXrxYEb5HYzabOXbsGBs2bKCmpobZs2cTGxtLQEAAg4ODFBQUUFZWRnt7O3/7299ITU09r5B4JrJW8emnn1ai01etWkVgYCBqtZqhoSE++OADOjo6CAkJ4Q9/+AMRERHC1Cy4ZhCmWIHgc0atVuPj48OiRYswGAz09PSMm79OkiT6+vooKChgx44dLFmyhPT09DGL66Xg5OREaGgoSUlJ5xQYh4eHlUjerKwsxe9PkiSGh4cVzcqiRYtYsGABwcHBuLm5YTQaGRoa4qOPPqKwsJBt27Yxd+5cQkJCzmuSG43NZqO/v5+CggLWr1/P9OnTWbhwIcHBwYyMjNDW1sbIyAhHjhzBbDajVqu5/fbbcXZ2RqVSIUkSzc3NHD58mGPHjmGz2Zg7dy6ZmZmEhoZiMpmw2+1s2bKF/fv3s337dsLDw/H29r7uIpIlSVL64fbt2zl16hShoaFMnz6dG2+8kdjY2LOeiV6vp66ujpGRETIzM8nOziYuLg53d3fa2trQ6XS0tLQobgXe3t7jCnbV1dUcPXqUQ4cOMWvWLLKzs5kyZQqhoaEMDAzg6uqK2Wxm+/btGAwGLBbLRd2b2Wzm5MmT7Nu3D4fDwZQpU5g3bx7h4eHKJqS9vZ233nqLyspKhoaGzqrxLBBczVxfs5lAMEGoVCoyMzNRqVT4+fnxwx/+EKvVetaC4nA4GBwc5E9/+hNWqxWdTkdCQoJyjku5vkajYfbs2Yqm6kwkSaK2tpa///3vbNmyheXLl+Pj4zPmHG5ubqSkpPCDH/wAHx8fRWhzc3Pj7rvvZnh4mEOHDrFt2za+853vYLfbL1iwg0+ETw8PD26//XZWrFiBl5eXcn4fHx8SEhJ48803qa6uZu/evVitVpycnFCpVNhsNvbv38+RI0fo7e3l1ltv5cYbb8Tb2xv4RAv5ta99DbvdTklJCS+88AK5ubmEhYUpx1wPyJrNkpIS3n//fV5//XUA5s+fz5o1a7j11lvH/Z6zszO+vr6sXLmS1atXj9HGxsbG4ufnR3h4OB9++CGHDh1CpVLx9a9/HUARvAFFa93T08P//u//EhQUpETburq6csMNNxAaGspLL72kCO0Xc299fX385z//oby8nDVr1vDYY48REBCgnMfDw4P777+fqqoqTp48KUywgmsOIdgJBFeQtLQ0QkNDMZvNPP3007S0tIx7nMFg4O9//zv5+fk8//zzSmDFZ0Wr1RIfH8/IyMgYYe1M3nnnHfr7+1myZAnBwcHKoufk5ER8fDxvvfUWgKLJk5ETNPv4+BAcHExvby9msxmr1XrB7fb19SU7O5umpibc3d3P0hjJwmlISAitra1KcISLiwtOTk7YbDZ27NhBR0cHQUFBLF++fIyfoixcxMbGkpubywsvvMDBgwfx9PRk0aJFF9TGa4GhoSFOnDjBQw89RH19PS4uLtx222089thjpKSknPN7a9euZdWqVQDjpj3RarWkpaXh4eGBwWCgvb19zOcOh4Oenh42bdrE0NAQN99887j9WqPRMH36dJqbm5VAjIu5t4aGBl599VVWr17N3Llz8ff3P+s4Dw8PnnrqKex2O+7u7he1+RAIJjuiNwsEVxBnZ2e0Wi2rV69m9erVZGRkjHucJEkYDAZKS0v585//TFtb27jpUi4Uf39/MjMzFV+jM3E4HJhMJvLy8nB2dmbhwoVjgh/kSEJPT088PT3PCoyQi8UbjUb6+vqIiopCq9VeVACILBxqtdpxo4Jlh/vu7m7UajURERFjFmVJkujt7cVisaBWq/Hz8ztrwVapVLi7u+Pn54ckSTQ0NNDa2nrBbbza6e7upqioiCeffJLm5mZ8fX2ZPXs29957L9HR0ecVwl1cXHB3dz+nIORwOLBarVitVrRaLQEBAWM+N5vNnDhxAoPBgJeXlxKYcebvfGY/uBihq7W1lbKyMsxmM3FxcYSGhp51Dbnvuru7K6XRhNZOcC0hNHYCwRVGo9GQmprKsmXLsNvtdHR0oNfrzzrOYrHQ2dnJpk2bmD9/PtOnTycqKmrcxfDTkAWyiIiIcT+3WCy0trbS3NxMUlISU6dOvaDzOhwO7HY7IyMj1NbW0tzcjMPhYNasWfj5+V2y79ro1CWDg4PU1dXR399PUFAQWVlZaDSaMc9CTsEiC6LjIecZBOjq6qK7u/uS2ng1IEkSIyMjVFRUsH//fnbt2oVarSY5OZmlS5cyf/78S07/Yjab6erqwmq1EhsbS1xc3Fm/TUVFBSMjI7i7uxMcHExzczODg4OYzWZsNpuSYsfLy4uQkJCLFrq6urqora1FkiQCAwNxcnKivr6e3t5ebDabIjT6+vri7++vpPMRCK4lRI8WCCaIW2+9leDgYKxWK6+99hp2u/2sYywWC3V1dTz++OPcdddd3HfffXh4eCj5uC4Hsl/Sli1b8PT0JCEhgfT09E/9DnyymBuNRmpqanjyySdpaGggKiqKn/3sZ8TExFxyuwAGBwdpaWnh1KlT/P3vf0eSJG644Qbuuuuus77j6+uLi4sLVquV3t7ecSOA5TYD9PT0YDAYLqmdkx1JkhTt5H/+8x/ef/99hoeHmTdvHl/+8pe58847L1mokyQJvV7Pvn37sNvtzJ8/n9WrV485xmKxUFlZiclkUvLH/f3vf+fQoUPU19fT19enCOxz5szhgQcewNfX96I2Ml1dXdTX1wOfmFsrKirYsGEDmzdvpre3V8kvuXLlSm677TZmzpyJVqsFRLoTwbWDEOwEgglkxowZREVF0dfXx5EjR+js7Bz3uNOnT/Pyyy/T2NjIH//4x8ueXFev1/PKK69w0003XZC2TpIk/v3vfytRsDabjbi4OG644QZWrVpFSkrKJWtC6uvr2bhxI3//+98ZHh5Go9EQExPDb37zGzIzM88SHNVqNfPnz6e6upre3l727NnD3Llzz/IHa25uJi8vT0nMe7FRl1cbIyMjtLe3c//991NRUYHNZiM7O5s//elPpKSkXJZ8iXI081NPPcVNN93ELbfcwvz588ccY7fbFY3esWPHaG9vZ+rUqXzve98jISEBNzc3Dh06xObNm3nuuefYsWMHzz33HLGxsbi5uV1QO4aGhujt7QXgySefJCoqiqSkJN544w08PT0xGAwUFxfz1FNPkZ+fz8yZM3nyySc/c648gWAyIgQ7gWCCkKNMQ0JC+OpXv4rdbqewsPAsp3P4xIzV2NjIwYMHef/991myZAmBgYGXZTHq7Oykrq6O9vZ2pk2bRlRU1AW1PSMjAycnJzIzM+nv76erq4uWlhbee+89rFYr06ZNu6Q8fD4+PkyfPp27776b/v5+BgYG6O7uZseOHbS1tTFjxgzmzJmjHO/s7ExOTg6HDh2iuLiYAwcOKHns/P39sVqtnDhxgsLCQsX8+mkVEq52hoeHqaqq4p133qGiogJJkkhMTOSee+4hMTERrVZ7yZq6kZERtm7dytGjR/H39+f2228nJSXlLGFMFqTlihAmk4k1a9Ywbdo0goKC0Gg0ODk5odfrGRoaorS0lLy8PFQq1XmDOkYjV1iBT4S8sLAwbrrpJlJTU3F1dVWChw4ePMjp06cpKSnh0KFDLFq06KyAIIHgakUIdgLBBCJHeq5du5ampibMZjM9PT3japH6+vo4ffo0b7zxBhEREbi6up43wvVCaWxspKKiApVKRWpqKsHBwRfU7jlz5jBz5kxMJhMdHR188MEH7N27l507dyJJEkFBQbi7u3/mwvH+/v4sXLiQnJwcent7aWho4KOPPuKll16ioqKC3t5epk6diqurK87Ozjg7O5OVlcX06dPR6/VUVFSwd+9ejEYjUVFRWK1Wdu/eTW1trbKIy2WlrjVk38SWlhaOHDnCK6+8Qn9/P8nJycydO5evfOUreHl5XZJQK1cKqaurY+fOnbS3tzN79mxuvPHGcwpJsruBi4sLfn5+3HTTTbi5uSntSE9Pp7Ozk/7+fg4cOMDhw4eVihYXgsPhUMzvnp6epKamcuONNyq+ep6envj6+rJs2TJaW1tpampiz549zJkzRwh2gmsGIdgJBJMAFxcXHnjgAaZOnUp3dzclJSXj+tyZTCY2b96MWq1mzZo1Sp6wS+Hw4cPk5eWxbt26iy72Luedi4uL45FHHiEhIQGHw8Ff//pXIiMjWbFixTkjfy8UtVpNUFAQQUFBzJo1i+7ubvbv38+zzz5LTk4OM2fOVDSDGo2Ghx9+mNWrV/Pqq6/y3nvv8Y9//IORkRFlQV+8eDF3330369evx8fHR/GxupaQI4T/8Ic/sGvXLlpbW4mPj+fb3/42t91222XJ22ez2ejq6uKee+4hNDSUFStW8N3vfvecQSsqlQoPDw9UKhXBwcHMnj173OjnpKQkRaNaUFBAdnb2BbdJo9EomsKsrCzi4+PPCsBwcnJi4cKF7Nq1i7KyMg4ePIjJZLrY2xcIJi1CsBMIJhh50XFzc2PatGn89a9/5cEHH6ShoYGBgYFxv7Nnzx4GBgZQq9V86Utf+kxaJ0mSaGlpoaSkhLa2Nn7wgx8oqUwutu0yERERzJkzhx07dlBSUkJUVNQlCXZnnl+SJGbOnEljYyNtbW0cPHiQlJQUfH19xzzHpKQkHn74Yb7xjW8oiaCdnZ3x9vbGaDRSWVkJQFRU1EWVPbsakANHHnnkEQ4dOoTBYCA8PJwnnniCadOm4ePjc8kmfLvdzsGDB3nnnXfw9/fnm9/8JtnZ2YpQN9751Wo1YWFhaDQaNBqN0o4zj/X09FQ00f39/YyMjFxwu7RarZLOx9vbG3d393HTqeh0OlxdXbHZbBgMhgsqsycQXC0IwU4gmCQ4OTmh1WrJyMjg5ptvZteuXZSUlIy7sBmNRioqKvjoo4+UQIKLNcs6HA6KiooYGBhAp9ORmJh4yWZJNzc3RXvW19enRJ9eTnx9fXF3dx/jjD8aWYvo4eExbnqXsrIyOjo6kCSJmJgYIiMjL3sbJwqbzUZ9fb2ihTUajYSFhbFy5UpmzJhBQEDAZUnvUVBQQEFBAa2trdxwww1MmTJlTELrI0eO0NPTo1zT2dkZtVpNVFQUGo0Gu91+zqAVu92OzWYDPhEGLyZBsa+vryKoW61W5TxnYrPZlLQ4Z6bMEQiudoRgJxBMIuTErA8++CA2m43u7m7q6urGrWXZ0tLCxo0byc7OZvXq1Xh4eFywYCZJEna7nd27dwOQkpJyXs2V7CTvcDhwdnbG3d39U69hsVjOubCOh9VqxWKxIEkSbm5u5xRA5PQdsjO+rG2R/ybX/pQjHc9MpNzd3U1VVRUajYaUlBTi4uIuuI2TFbl/9Pf3c+TIEZ599lkaGxsJCwtj9uzZPPLII4SGhl6UkDQeciLrjRs3UldXh6+vL9/85jfH+MkBfPDBBxQXF/OrX/0KX19fnJ2dcXFxITU1FQ8PDywWCz09PTgcjrNS9wwNDSkbAp1Od1G+b0FBQcTHx6NSqejr6xtTB1a+hmymlquW+Pv7X9MBNILrDyHYCQSTkJCQEL797W8zY8YM7r33XoxG4zlzsv3sZz+js7OTtWvXkpube0Hnt9lsGI1GPvzwQ26++WbWrFlz3uNNJhO/+c1v6O7uJiEhgR/96EfjHtfU1MShQ4eQJImEhASio6MvqD3wiRbo/SCjlr8AADRvSURBVPffR6/X84Mf/GDcAvIA+fn51NfXo9FoyMrKwtPTE/hvtQ75eb366qsEBwcrwows9BUXF7NlyxbWrl1LZmbmBQWLXA1YrVb+7//+j71791JaWopOp+OnP/0py5cvv6BI5wuht7eXZ599luPHj7N06VK+9a1vXZCQD+Du7s7SpUuJjY2lpKRE8W2TA19kKioqyM/PB2DBggUkJSVdcPsSExORJInQ0FBOnDhBRkYGZrN5TACPw+Fgz549tLa24uPjww033HDB9yAQXA0IwU4gmGTImoWAgABmzJjBr371K/70pz/R3t4+rgbMYrGwceNGOjs78fb2ZsqUKZ+am6yrq4uDBw8qqSQuJHed3W6nuLiY6upqIiIimD9/Pr6+vqjVavr7+9m/fz87duwgPz+fhIQEcnNzx5zXaDRy6tQpfv/73yNJEmvWrOHGG28kKioKlUqlaIM+/vhjoqOj6ejoYOrUqfj6+jIyMkJnZyeHDx9mx44dDAwMkJqaysqVK8ekVJEjNWtra3n66ae59957CQoKwtnZmZaWFt566y1OnDhBcHAwDz74IBEREVe9Gc7hcGAwGHjuuefYvXs3ra2t6HQ6fvGLX5Cbm0toaOhluceqqiqOHTvGa6+9Rm9vL83Nzezbt29cLWBFRQU6nW7M32Sz55e//GV8fX3ZuXMnf/rTn7jxxhtJSEjA3d2doqIiNm7cyMGDB0lOTmbVqlVjkmX39vZy8OBBXnjhBSRJ4q677mLevHmEh4cDn6SvCQ4O5nvf+x7/+Mc/OHz4ME8//bQSBTwwMMDp06d55513GB4eJisri1tuuUVExAquKYRgJxBMUtzc3AgODmbFihUcPnyYgoKCc5pl6+vrUalU7Nixg5CQEPz8/M6Z1FWSJHp6esjLyyMiIoKIiIhxC6WPxsnJiYSEBCorK+nr6+PYsWNYrVY8PDxwcnJiYGCA48eP09raqqQpSUtLG1OX1mKx0N7eztatW5Ekifj4eBYuXKh87uvrS0JCAoGBgdTX1+Pk5ERPTw/u7u6MjIzQ09PDiRMncHZ2Ji0tjRkzZhAbG6uYn2XBITk5mb6+Pk6dOsWePXvQ6XQ4OzvT09NDdXU13t7epKamMm3aNEXbdzUi94OOjg7KysrYtm0bzc3NaLVapk2bxrJly4iMjLxs2qjm5mYKCwuprq4GPqnaIQehjMeZ1UvkMm8zZ85kYGCA9vZ2iouLcXNzo6amBldXVyorK2ltbUWr1TJz5kxSU1Px8/NTzmE2m2loaFD60IIFC5g2bZryuZOTE15eXixdupQTJ07Q09PD8ePHCQgIwMPDg6GhIRoaGrDZbGRkZDBr1izi4uKuyZQ3gusXIdgJBJMYOcLze9/7Hq+++iovvvjiuKkZHA4H9fX1/P73vyczM5OsrCzCwsLOqanp6upi79693HzzzURFRX2q75VGo+H2228nMjKSoqIiDh06xPbt2zEYDAwPD+Pi4kJMTAzp6elKGhY5v9xonJyccHV1RZIkNBrNGD+6xMREgoKC8PX15eDBg+zbt4/XXnuNzs5OJSAiOjqaW265hdzcXObOnTvG8V2lUqHVarnrrrvIyMhg48aN/OEPf1AiiyMiIli5ciWLFi0iNzcXV1fXq15bZ7PZOHz4MG+//TaHDh3Cw8ODuXPn8v3vf5/k5OTL6jvW1NTEqVOnLrhSxXipTABSU1Px9/cnPT2dv/zlL7z88stK7saoqChmzpzJqlWruPPOO3F1dR1zD7JweK4+BJ+MmezsbH7yk5+wf/9+tmzZwv/8z/9gNptxc3MjNDSUm2++mZtuuonMzMzPnGdRIJisqKTxtv8CgWDSIAc6lJSU8PHHH/M///M/4+a4g08WvoiICO677z6++tWvEh0dPW7KEJvNpghk4y2O47UBPhEk5H+jk8HCJ0KbHP0oL5ajr+1wOLDZbAwODgKfLMDywq1SqZTgBzma0W63K8718rmcnJyUOqNym8+8P/m7FosFu92ufF/+rvz98b57NSFJEk888QQfffQRR44cweFw8Pjjj7NkyRKmT59+Vv62S8VsNmM2my84IMbZ2RlPT89x2+FwOLDb7ZhMprN+o0/rQxaLheHhYQAlYGi8jYncT8+MjnVyclISU1/uZyQQTAaEYCcQXCUYDAbq6+t58skn2bVrF11dXeMe5+rqSnZ2NosXL+bhhx/Gx8fnsqS4EEweenp6OHDgAE8++SRVVVWYzWa+9KUv8fWvf53ExMQx5kuBQHB9IWZ7geAqQafT4ebmxi233EJjYyMWi4W+vr6zjjObzZw8eZLh4WEWL15MdnY23t7el5zqQjA5MBqN1NbWsnnzZoqKihS/wvXr15OSknJZyswJBIKrF6GxEwiuQv71r3+xZcsWPvzww3Meo9FoSEhI4MUXXyQ9PV2J/BOmp6sTearetWsXW7Zs4a9//SsqlYqbb76ZL33pS9x+++3itxUIBEKwEwiuRvr7+zl27Bgvvvgi77zzzjn9nlxcXJg/fz633XYbd99997gllgRXB3a7nY8++oh//etfHDx4EKPRyIMPPsiaNWuYP3/+RZeDEwgE1ybCFCsQXIVotVrS0tK47bbbOH36NHV1dfT39591nNVqpaysDJ1Oh4+PD1/4whfOGa0omLz09/fT0NDA22+/TUlJCZIkkZubq+R58/LymugmCgSCSYIQ7ASCq5TQ0FBuuOEGDhw4gMVioaamZtz6m11dXeTn5zMyMsL8+fMJDg6+JlJ9XC+YzWZaW1vZt28fGzduRJIk4uLiWLduHQsWLECr1U50EwUCwSRCmGIFgqsUeehaLBaeeOIJNm7cyPHjx895vKurK7fccguPPfYYmZmZoj7mVUJBQQGbN2/m97//PRaLhWXLlnHjjTfy8MMPA8JnUiAQjEVo7ASCqxQ595uLiwtf/epXiY+P5ze/+Q0VFRXnLD22a9cufH19WblyJevWrZuAVgsuFKvVSklJCU888QT5+fnY7XZWrFjB17/+dXJzc4VAJxAIxkVs2QWCqxg5aW9YWBjZ2dnccsstBAUFjZtNX5Ikent7yc/PZ+/evZw8efKciY4FE8vw8DBtbW188MEHFBQUYDAYiImJYe3atUyfPp2QkJCJbqJAIJikCI2dQHANIJf0+u53v8vx48cpKSmho6Nj3LqyJSUlmEwmdDodMTExeHl5iRx3kwi73Y5er6ewsJC//e1vmEwmwsPDWbhwIXfeeaeIfhUIBOdF+NgJBNcI8lBuamri2Wef5dVXX6W9vX3cY1UqFa6urjz++OOsWrWKtLS0K9lUwXmoqanhjTfe4O9//ztdXV1MmzaNm266iR//+Md4enoCwq9OIBCcG6GxEwiuEWSfu+DgYNavX4+/vz+//e1vGRgYGFPTFT4RAs1mMy+99JJSn3Xq1KkT1HIBfOJT193dzV/+8hcOHz5Mf38/ycnJ3HvvvcyfPx93d3dACHUCgeD8CMFOILiGUKlUuLm5kZKSglqtZu/eveTn59Pf33+WWVaSJE6fPs2BAwdwc3MjPDwcnU4nzLITgNVqxWAwsH//fvbt20draytarZZly5Yxf/58kpOTxe8iEAguCGGKFQiuUUZGRigtLeX++++nvLwck8k07nHOzs4kJyfzxz/+kUWLFuHh4SG0QlcIefo1GAyUlJTw9a9/nba2NnQ6HRkZGbz11ltotVpcXFwmuKUCgeBqQQh2AsE1iiRJ2Gw2Dhw4wDvvvMOLL76I1Wod91gXFxf8/f15/vnnmT59OuHh4Ve4tdcnkiRhMBh4/vnnefPNNyktLSU8PJzbb7+dBx98kJiYGFQqlRC0BQLBBSNMsQLBNYpKpcLFxYUpU6ZgNBoZHh7mjTfeOMvfDv7r3/Xiiy/S19fHDTfcQEBAwDkFCkmScDgcmM1m7HY73t7en/ftXHPY7XbMZjP/+te/2LlzJ83Nzfj4+HD33XezePFiQkNDhVAnEAguGiHYCQTXOMHBwWRnZ+NwODh06BAdHR3jmmVtNhsff/wxvr6+hIaGsmDBAlxcXM6qUCFJEpIk0dTUxNDQEM7OzqSmpl6p27kmcDgcDA0NUVNTwwcffEBtbS0Oh4Pp06ezZs0akpOTlWAJgUAguBiEKVYguE4YGRnh5Zdf5m9/+xunT58+53He3t4kJyezYcMGQkND0Wg0itZIFupMJhPf+c53sFgsxMXF8atf/QoQEZufxugycIWFhTz22GMcOXIEd3d3MjIyeOWVV4iIiECj0UxwSwUCwdWKEOwEgusEh8NBf38/r7zyCjt27GDr1q3jHufk5IS7uzszZ87kd7/7HVOmTMHLywsAk8lEaWkpTz/9NFu3bkWSJNLT09m0aRNarVZEbn4Kst/j888/z7Zt29izZw+urq7ceeed3H777cyaNQu1Wi0EZIFA8JkRpliB4DrByckJX19fFixYAEBnZyfFxcVnlRVzOByMjIxw6tQpNm7cyMDAAAsXLsRms7Fv3z4OHjxIfn4+fX19ynmKioqYPXu2kkD3euLMvfH5/BKtVitbtmxhz549nDp1CkmSWLduHUuWLFFS1AihTiAQXApCsBMIrjOmTZuGm5sber2e2tpaBgYGxhXuenp6eO+997BYLCQkJDA4OMi7777L3r17aWxsVI41Go0cOHCAjIyM6zZVysjICGazGQBfX19grIAnJ4TW6/W89tprHDlyBIPBQHh4OHfddRdpaWn4+/tPRNMFAsE1hjDFCgTXIbKg8eCDD7Jv3z7q6+vPeWxQUBDx8fF0d3fT1tbG0NDQmM9dXFwICwtj586dJCQknBVsca3jcDjYvn07+fn52O12fvWrX52leZMkifz8fJ577jneeOMNnJycmD59Or/73e+YO3fuGD9GgUAguBSExk4guE7RaDT86Ec/ws/Pj+3bt1NWVjbucQaDgdLSUqxWKxaL5azPbTYbHR0dnDhxAo1GQ0xMzOfc8smFw+Hgww8/5Pjx44yMjGC32/n+979PYGAgavUnU+x7773Hzp072bZtGwB33HEHK1euZNq0abi4uAihTiAQXDaEYCcQXIfIgkRSUhKLFi3CarXS2dlJb2/vWXnurFbrORMbw3+1f0VFRYSGhl5Xgp3FYsFgMFBcXExDQwNms5ldu3YxZcoUZsyYQXR0NJWVlezatYujR49iMBjIyclh8eLFzJw5Ex8fn4m+BYFAcI1xfdlMBAKBgkqlwtnZmWXLlnHXXXeRnp6Om5vbZ9YeHTp0iMrKShwOx1kBBdcqQ0NDVFVVUVFRQV9fHyMjIxQVFfHMM8+wc+dO2traePPNN/noo48oKyvDx8eH+++/n6VLl5KQkDDRzRcIBNcgwsdOILjOkaM19Xo9X/3qVyktLaW7u/uiz+Ps7Mw3v/lNHnvsMaKjo68L82JxcTFPPvkkb7/99hgztZOTE35+fvj4+NDY2Ijdbmf27Nl885vf5Ktf/eq4iZ8FAoHgciBmFoHgOkelUqFWqwkICGDx4sWfWZNkt9upra1l3759/6+9ew+uur7zP/48l5x7Tu4nCbknBBIuJgQUBWGjAioFpLaoO1unnUp11ul2bO1l9p9qd2fdndFpXevardW61bFb1gLqFikMEZCIEAIJ5EISSMj9xjm5nFvO/fv7g985C3KRWiC392Mmownfc87nfJkcX74/n/fnMysqdi6Xi+7ubqqrqwmFQpf8WSQSYXx8nP7+fhRFobKykk2bNrFmzRoJdUKIm0o+XYQQBINBBgcH8fv9VzxL9nr19vZy9OhRwuHwjA93/f39nD17lp6enquevzsxMQFcOM0jJSUFm802KyqZQojJI80TQsxS0eClKAp2u51t27bxn//5n4yMjHzp5zx37hyKojAxMYHZbJ6RJ1FE79vhw4c5fPjwZdW6zwuHwxw7dgybzcZ9991HQUEBIMevCSFuDqnYCTHL7dmzhxdffJF//ud/ZnR09K96Lr/fz/DwMDt37uT8+fM3aIRTj8/no6qqigMHDlzX9UNDQ2zfvp1HH32UxsZGXC7XzR2gEGLWkmAnxCwUPbN0+/bt/PGPf2Tfvn243e4bMn0aDT3RI8dmmlAoRE1NDd3d3Tidzut6TCQSweVy0d7ezquvvkpNTc2XalARQogvIsFOiFkoHA7j8XjYtWsX+/fvv+rmxF9GIBDg6NGjDA8Px47ZmimiHcTV1dUMDg5ec3+/zwuFQoyPj7Nz507q6+sZGhq6iSMVQsxWEuyEmIXcbjdtbW2cOHHihleOwuEwbW1tnDx58ppHlU1XPp+Pd999l76+vi/1eEVR6OrqmpH3Rggx+aR5QohZKD4+nsWLF/Pee+9x5MgRjhw5wvbt27Hb7X9VV+zFqqqqMJlMlJSU3JDnmwr6+vqora2ls7MTn8933Y/T6/XMnTuX1atX861vfYs5c+bIqRNCiJtCgp0Qs5BGo8FgMFBUVIRarcZms5GWlsaJEyc4c+YM3d3dsa06vqzTp09TXFyM2+3GbDbPiC7Q/v5+Pv30U/x+/xeuR1Sr1ej1ekpKSigvL2f+/PmUlZXFTviYiR3DQojJJ8FOiFkqeqTY3LlzKSws5N5772XXrl3s2bOHgwcP0tvbGzvU/stob2+ntbWVwcFBCgsLp32wC4VCdHd3X9cGzAaDAYvFgs1mY/369TzyyCMUFBQQHx9/i0YrhJit5EgxIcQlQcXn8zE8PMybb77Jf//3f9PT0/OlmyAWLVrE3/3d3/GDH/wAnU53o4Y7KVpaWnj77bf513/912teFxcXx8MPP8z69etZs2YNGRkZsVA73cOtEGLqk2AnhLhEJBIhEAgwODhIR0cHjY2NHDp0iF27dv3FJ1MkJSVRWlrKn//8Z8xmM2q1GkVR8Hq9OJ1OnE4n58+fx+Fw4HQ6cblcuFwuvF4vExMTeL1eXC4XwWCQUCgUmx4Oh8OEQqHY+awajYa4uDi0Wm2sEmkwGNDpdBgMBoxGI2azGYvFgtFoxGQykZKSQkpKClarlaSkJGw2GwaDAa32yhMZv/zlL2MVzc9Tq9UsXLiQsrIyNm7cSGlpKTabjaSkJOLi4iTQCSFuGZmKFUJcQq1WYzAYyM/Px2azkZ6eTkJCAmq1mrNnzzIwMMDg4OB17Xnndrtpb2/n0KFDGAwGQqEQbrf7khA3MjKC1+slEAgQCARiIS76FQwGiUQiRCKR2CkPiqLEfgbErouewapWq/F6vajVajQaTSz4xcXFodFo0Gq1GAwGTCYTZrOZhIQEUlJSMJvNGI1GLBYLVqsVk8mE0WhEr9dTW1tLe3v7Je/PaDRis9lYsGAB5eXllJWVcc8995CcnCxr6IQQk0IqdkKILxSJRBgcHGT79u0cOHCAvXv34vP5rutMWLVaTWVlJXAh6HV1deF0OlEUBZVKhUqlIiUlhcTERJKSkkhOTiY+Ph6z2Ux8fDxJSUno9Xp0Oh0mkwkgFs6MRiNwIdj5/f7YvnLhcDhW9fN4PHg8HsbHxxkbG8PtduNyuRgcHGR0dJRAIBALimazmcTERPLy8iguLiYrK4uMjAzS0tL4yU9+Qnt7O5FIBJVKRVxcHNnZ2VRWVvLMM8+Qm5srna5CiEknwU4I8YUURYmdVuFyuejs7OTll1/ms88+u6yKdSVWq5U777yT+fPnk5+fT05OTmwqdM6cOZd0iUbD3uf/Pfr9xaLfX+lj7OKzcC9+Dxf/u9/vx+VyMTw8TG9vL0NDQ5w/f56uri7a2tro7+/Hbrfj9/tjnbBqtZr8/Hw2bNjAihUrLqnQyZSrEGKyyVSsEOILRQOWx+NhcHCQlpYWnE4noVAIlUr1hVU7r9fLggULuP/++yksLMRsNqPX69Hr9RiNxkkLRdHp2ISEBLKysvD5fPh8PtxuN+Pj4zidTjo7O/npT39Kamoq8fHxWK1WJiYmOHjwIHV1dezcuZPS0lLy8/PJzc2ltLSU1NTUq67VE0KIm0k+eYQQVxUIBPB6vYyPj3P+/Hn6+vro6+ujpaWFYDBIeno6VqsVi8WCz+djbGws1ghxsVAohE6nIykpiXnz5k3Su7lcdP2dwWAgMTHxsj93u92cPXuWHTt2kJmZSUpKCgkJCQwMDDAwMMD4+DhNTU04HA46OjrIyspiYGCAgoICEhMTsVqtpKamEhcXF1v/J4QQN5NMxQohYi7+OFAUhcHBQZqbmzl06BA7duygu7sbv99PQkIC99xzDxUVFSxatIjly5fT2dlJVVUVH374IZ999tll3bP33HMPDz74ID/84Q+B6bH1R3d3N319ffj9fpYuXXrJPnTj4+P09/dTX1/Pnj17qK+vp7OzE6fTydy5c6moqGDFihU89NBD2Gw29Hr9NaeVhRDiRpBgJ4QALgS5cDhMa2srdXV1fPDBB5w8eRKv14vBYKCyspKlS5cyb948SkpKMBqNsU5TnU4Xa2BwOp1UV1fzzjvvcOrUKXp6egCwWCysXLmSP/zhD1it1mlRwQqFQkQiERRFuazqFolECIfDBIPB2Bq88fFxGhoa+OSTT2hqauLMmTOEQiHKysooLy9n1apV3HfffVLBE0LcNBLshJjlgsFgbEqxpqaGjo4O+vr6CIfDJCcnk56eTn5+PvPnzyc7O5vU1FRSUlIua2yICoVC9PX1UV9fT2trK/X19Xz88ce4XC6Ki4t56aWXuPvuuzEYDJPwbm+e6P5/w8PDnDt3jr6+Pnp7e2lubmZ0dJRwOIxOp2PJkiUsXLiQoqIiSkpK0Ol0Ur0TQtwwssZOiFkour3H+Pg4drud7u5uqqqq2LdvH263G51Ox+rVq6msrKSkpIT58+dfd4ODVqslLy+P3NxcBgYGKC0txeVy0dHRgVarpaamhjvuuOOyqcnpLrr/X25uLrm5ubEtV44cOcLBgwdpamqisbGRtrY2br/9dsrLy1EUhTlz5hAfH49er5cqnhDiryYVOyFmoVAohNfr5fXXX+eDDz7g1KlTKIrCI488wpo1a1i5ciXZ2dmXPOYvDWGfX69XU1NDTU0Nu3bt4p133iEtLW1GBbvP+/xHq9vtpre3l9dff519+/bR1dWFVqvlu9/9Lg888AAVFRUzLuwKIW49CXZCzBLRKt2nn37KkSNH+Oijj+jt7WXRokWUl5fzwAMPkJWVFdscOHo81414XSC2WfDIyAg5OTno9fpZdTpD9Bi0kZERzp8/T0dHB3v37mX//v2YzWYKCgr427/9W+69914SEhIk4AkhvhSZihViFvD5fIyPj3PgwAE+++wzurq6UBSFdevWsWTJEhYsWMBtt92GwWC44dOB0YBiMpku2VZktgWX6NYqmZmZJCcnk5KSQlxcHAaDgc7OTkZGRtixYwejo6MsWrSIkpISrFbrrLtPQoi/jlTshJihor/aPp+P4eFhWltb+ad/+if6+vpIS0vj/vvv56mnniI1NXXGNTJMF4qi4HA4+Pjjj6mqquL9998nLy+PNWvW8PDDD1NaWhoL2xLwhBDXQ4KdEDNU9Ff7d7/7Hbt27WLv3r0UFxfz/e9/nxUrVpCbmxurzklomBwXH3E2MTFBS0sLzz33HM3Nzfh8Pn70ox/xta99jZycHPk7EkJcFwl2QsxAgUCAoaEh3njjDfbu3YvJZGLBggU89thjFBYWkpSUJFW6KSbaRdvW1sbx48eprq7m+PHjVFZWsnr1ah566CFprhBCfCFZYyfEDBKt/Jw5c4a6ujr279+PyWTitttu47777uOOO+64YU0R4sbSaDTEx8dTXl6OxWLBYDDQ399Pc3MzoVAIq9XKypUrMZlMcg6tEOKqpGInxAyhKAqKotDR0cFbb73Fzp078Xg8/Nu//RvLly+nsLBwsoco/gKBQICjR4/yL//yL5w5c4aUlBTeeOMN8vLypKlCCHFVEuyEmCF8Ph+Dg4N84xvfYHx8nNzcXH7+85+Tk5NzU7pdxc0VPeKtp6eHjz/+mH//939HURSeffZZ1q9fj81mm+whCiGmIKnnCzEDuN1uWltb+c1vfoPb7eaee+5h3bp1ZGdnS6ibplQqFVqtloyMDFatWoVWq+UXv/gFH374IUNDQzz55JMkJCTI360Q4hLyiSDENBbddPjcuXMcPXqUqqoqiouLY8eBmUwm+Q//NGc0GsnPz2f9+vWUlZXR39/PgQMHqK+vx+/3X3bChRBidpOpWCGmMUVRCAQCPP/88+zdu5dAIMB7771Hbm4uJpPppr3mX/qxEV0PNhXWhV28xci1qFSqKTHeKEVROH/+PC+88AJVVVVYrVa2bdtGZmbmrDrBQwhxbRLshJjGAoEAL730Eh9++CE6nY4XXniBpUuX3tQD5aurq3niiSeYmJi4YjiKhqH4+HhsNhvz589n69atFBUVkZSUdFPG9JcYHR2lq6uL3/zmNzQ1NWG323G73UQiERRFQavVYjab2bdvHxkZGZM93Jhodba9vZ1Dhw7x4x//mKeeeooNGzawYsWKyR6eEGKKkDV2QkxTHo+H3t5e/vznP5Oens6SJUsoLS29qaEOIDs7m61bt9LX18f+/ftpbW2lsLCQTZs2YbVaAfD7/TgcDgYHB6mtrcXv97N582aWLl1Kdnb2TRvbtSiKgtPp5PDhw+zatYuWlhbmzZvHnXfeiclkIhKJMDAwQHd3N0ePHiUcDk/KOK9GpVKh0WiYM2cOZWVlrFu3jurqajIzMyksLCQ9PX1KVRiFEJNDgp0Q09TY2BinTp2ioaGBJ598krVr15KSknLTXzc/P58f/vCHNDY2MjQ0RGdnJ/PmzeO73/0u2dnZRCIRPB4PTU1NHDx4kKamJn7/+99jsVgwm82TFuwABgcHqa6u5g9/+APl5eWsWbOGiooKsrKyCIfDtLa2cuzYMex2+xX3ihsfH8dut6PT6UhNTcVoNN7y92CxWCgoKODhhx/m2Wef5dSpU5SVlZGenn7LxyKEmHpkVbUQ01RLSwuvvfYaixcvZu3atVNmOk6lUmGxWFi+fDmPPvooP/rRj1CpVOzcuZO33357Use2e/duampqUKlU/PSnP2XdunUUFRVhMBgwm81UVFTw1FNPUVNTc8XtRN577z0qKip47LHHqKurm4R3cEFycjJf+9rXWL58eWzfQiGEAAl2QkxLra2tHD9+PFatmzdv3pSZhos2HahUKpKTk6moqECtVjM8PEx3dzcul4tIJDIpYxsYGGBsbAyNRkNxcTFGo/GS8X7+62ome2lydHzf+MY3SEtL48iRI5w8eRKv1zup4xJCTD4JdkJMQ2fOnKGnpwez2Ux5efmUaEq4Eq1Wi8ViQaVSEQwG8fv9k7p2zefzEQgEUKvVmM3mad9NunjxYrKysohEIhLshBCArLETYlqJVoqampoYGBigqKiIoqKiSVnrdT0URSEYDAKgVqvRaDSo1WqCwSCBQIBAIBC7VqfTYTQamZiYIBQKXVLVM5lM6PV6VCoViqIQCoUIBAIEg8HYddENfXU6HVqtNrZGLhQKEQwG8Xq9eDye2HM7nU6CweBVG010Oh16vR6tVovf78fn8+F2u2Ov73Q6cTgcsevNZjMGg+GG38OrUalUFBYWUlRURFpaGp999hmVlZWkpKRMmeqtEOLWk2AnxDRUW1uLw+Hg/vvvn9JVJ7vdzpEjRwiHw2RmZlJQUEB8fDx79+7lzTff5P33349d++ijj/KDH/yAl156ibq6OgYHB3G73QC8+OKLfPOb3yQhIYHx8XEOHTrEnj172LdvH8PDw0QiEYxGIxUVFWzevJnly5dTVlYGwOnTpzlw4ADPPvss4XA4FgSLioquGYAefvhhHn/8cVasWMFbb73FK6+8Qm9vL+FwmNraWjZs2BALhWq1mldffZWtW7fepDt5dfPmzWPZsmW8//77PP3007f89YUQU4sEOyGmkUgkgt1uZ3h4mLi4OMrKyqbcyRKKojAxMcHZs2c5fPgw7777LoqisGbNGjZs2ABARUUFaWlpbNq0if/4j//g1KlT1NfX81//9V+sXbuWr3/96wQCAbq7u/nZz35GJBIhFAoxOjrKz372M1paWvB4PDzxxBNkZ2ej0WhwOp3s3r2bd999lwMHDvDNb36TyspK8vPz2bhxI3PnzuVXv/oVJ0+exOl08tvf/hadTnfJ/fN6vTQ2NvL888/HQqDFYmHz5s2Ul5fzwQcf8MYbb5CXl8fWrVspLS2NPXbhwoW3/F4DZGZmUlJSwtDQEA6HA4/Hg8VimZSxCCEmnwQ7IaaRSCTC6OgoXq+X1NRUsrKyJn3aLRgMMjAwwN69e0lJSUFRFHw+H11dXbS0tGC321mxYgWrV6+mvLwclUpFWloaaWlpZGRk8Mc//hGtVhsLUkuWLCEnJweVSsXAwAB1dXUUFRURDAZpbm5m//79KIrC/PnzqayspLCwEK1Wy9jYGCMjI2zbto1jx46Rnp7O0qVLSUxMJD8/n7y8PHbt2kVHRwd+v5+1a9diNBovqXi6XK7LplPj4uIoLCyksLCQjo4OtFotiYmJ3HnnnVOiEzkhIYH09HR8Ph9Op5OJiQkJdkLMYhLshJhGIpEIDoeDYDCIXq8nNTV10oPdxMQEtbW11NfXA/938oTVaiUjI4OysjK+973vMX/+/Gvus1dUVMRXv/pVFi9eHKuiJSUl8fvf/x6Ajo4Ofve733Hu3DnWrVvHt771LW6//fbY6yUkJPDMM89w5swZ/vd//5c33niDb3/72xiNxivuSTdTmM3mWPPM2NgYbrebtLS0SR6VEGKyzNxPOyFmoGiwC4VC6PX6KbFQPj4+nlWrVvHiiy8yZ86c2M+jW3JoNJrrOg0jNTWVxYsXX/X9jI+P89FHH+H1eikoKODuu+++7BqdTsfChQvp6Ojgk08+obq6Gp1Ox/z58/+6NzmFmc3mWGAeHR2NrUsUQsxOEuyEmEZUKhUGgwGVSkU4HMbv96PT6SZ9THFxcVitVhITE7/082i12qt294bDYQKBAE6nE41Gg9FoxGw2XzYOuBA04+PjURSF0dFRJiYmvvSYpoNQKITf7wdAr9cTFxc3ySMSQkymqbXqWghxTSqVKrb/WjgcxuPxTPpmuTeKWq2+6pSpoihEIpFYR6tarb5qBTC6rQpcCD0z5f5cTSAQiO1fZzQa0ev1kzwiIcRkkmAnxDSiVqtJTU0lLi4Ov99/yT5qM5lWq0Wv12OxWAiHw0xMTFxWiVMUBUVRcLvdOJ1O4MLRWzd6b7nPTxVHXzf6dat5PB5GRkaAC2sSpXFCiNlNgp0Q04hGoyErK4v4+Hg8Hg8NDQ2TdjzXrWa1Wlm3bh1Go5Guri4OHz582TXBYJDTp0/T0tKC0WjkrrvuIisr64aNQavVYjAYLqkEhkIhNm/ezHPPPceePXtu2Gtdr8HBQdra2jCZTKSlpZGQkHDLxyCEmDok2AkxjUTX2OXk5KDX62lsbJw1wS4pKYlNmzaRmZlJZ2cnf/rTn2hra8PpdOJ2uxkeHmbHjh00Nzej1Wp54IEHyMzMvKGncpjNZtLT03E4HIyNjeFwOOjp6aGxsRG/34/Var1hr3W9BgcHaW9vZ968ecTHx8/oDmAhxBeTTwAhppHosVmFhYW4XC5OnTpFIBBAq9Xeso2KJyYmGBwcpK+vD7fbTSQSwePx0NPTQzgcJi4ujszMzKt2tyqKEmuE6O7uxuPxEIlEcLlcdHZ2otPpyMjIwGQyXfK4aMVu586dtLe3U11dTUVFBSUlJWi1WhwOB9u3b8fhcJCfn8+WLVtITk5GURTGx8dxOBw4nU4CgQDhcJiuri6SkpIwm81YrVaGh4cZGRlhaGgIuDDFOTQ0RHd3NzabDZ1OR1JSEvPnz+fgwYN0dHRgNptxOBwoioLNZiMnJ+em3/+L72M4HKavr4+zZ89y2223YbFYptyG1UKIW0wRQkw7H330kfL0008rycnJytmzZxWv13vLXnvfvn2K2WxWVCqVAlzylZycrNx9991KKBS66uMjkYhy/Phx5fHHH7/s8dGvjz766IqPi0QiisPhUHbu3Kk8+eSTSkFBgWI2mxWj0agkJycra9euVV577TXlxIkTsetPnjypvPzyy1d8nTVr1iivvPKK4vP5lM2bNytz5sy57Jrs7Gzlk08+UVwulzIyMqLU1tYqlZWVypw5c5TExEQlPz9feemll5TGxkYlEonczFt/2f3o6+tTnnnmGSUrK0t55513FLvdfsteXwgxNakUZYa3jAkxA9ntdj788EOef/55vv3tb7Nly5ZbdqSV0+nkzJkzV5wCjm5FUlJScs2KndfrjR2BdSXFxcVX3DpF+f9VKpfLxfj4OE6nk2AwCPxfx3C0gSBa8fN6vbhcLrq7uy97vugWLWlpabS3t+PxeGLPFxUXF0dRUREmkwlFUfD7/fT09ODz+YhEImg0GtLT00lISLih075fRFEUfvGLX1BVVYXdbuf999+PNdYIIWYvCXZCTEOhUIhPPvmEl19+Ga/XyzPPPMPf/M3fEB8fP9lDE7dAMBjE6XTy1FNP4fF4yM/P55VXXkGr1U76htVCiMklizGEmIa0Wi35+fls2LCB2tpaGhsb6evrm7QtN8StoygKExMTtLa2cuzYMZKSkli/fr2EOiEEIBU7Iaat6JTk5s2b8Xg8lJWV8ctf/jJ2MoWYmfx+P3V1dTz55JNYLBa2bt3K448/LsFOCAFIxU6IaUutVmM2m/n+979PZmYmDQ0N/PrXv8btdkvVbgaKVmOrqqrYtm0bDoeD73znO6xcuVK2OBFCxEiwE2KaUqlUaDQa7rrrLhYvXozZbGbPnj00NTXNmhMpZotoqGtpaaG6upqGhgZKS0u56667yMnJQaVSSbVOCAFIsBNiWlOr1dhsNrZs2cKDDz7IwYMHefPNN6mrqyMcDsuauxkg+nfo8/l4/fXX+dOf/sTIyAg//vGPycvLu2y/PyHE7Cb1eyFmgEWLFpGSkkJSUhLPPfccvb29NDc38w//8A9SyZnmFEWhoaGBd955h7fffpvHHnuMr3zlK1RWVsrWJkKIy0jzhBAzxMTEBL29vbz22mu0trbi8/nYsmULDz30EGlpaRICpplolW737t18+umnHDhwgIqKCjZu3MiyZcuYM2fOZA9RCDEFyVSsEDOE0Whk7ty5bNq0icLCQhwOBzt27OD48eOxDXXl/+Omh1AohNvtprGxkd27d3PkyBEAHnroIQl1QohrkoqdEDNQZ2cnNTU1fO973yMxMZGvfOUrbN26lZKSktg1MkU7tVz8UWy32zl9+jR///d/j8vlYvny5fzkJz9hyZIlaDSaSRylEGKqk2AnxAwUDAbxeDycPn2aF198kfb2dhRF4Tvf+Q4bNmwgNzdXAsIUoygKwWCQbdu28fHHH1NdXU1aWhpPP/00t99+O3l5eej1egnkQohrkmAnxAwVDodxu93s3r2b48eP09DQgEqlYsWKFSxevJiKigqys7NRq2VFxmQbGRmhr6+PTz/9lKqqKlwuFwaDgQcffJB7772X7OzsW3oOrRBi+pKuWCFmKI1GQ0JCAlu2bGHhwoVUVVXx6quv0t/fT3NzM6FQCK1Wi9VqRa/XS3PFLRaJRAiFQoyPj9Pa2kpdXR1vvfUWbrebZcuWsXHjRrZs2YJGo5EqnRDiuknFTogZLvorHg6HaW9v51e/+hUHDx6koaGBNWvW8Nhjj7Fq1SqKioomeaSzi9vt5uzZs7zwwgvU1tZit9tZtWoV//iP/0hJSQkpKSmxayXYCSGulwQ7IWaJ6PYZQ0NDdHV1ceLECXbu3InX68VisXDXXXexceNGioqKSE9Pn+zhzkjhcJjGxkaqq6upr6+ntrYWg8HA0qVLWbZsGcuXLycrKwuj0SgVVCHElyJTsULMEiqVCqPRSH5+PsnJySQlJeHxeGhqamJ4eJj6+npCoRBFRUUUFhZSVFREZmYmer1eGi2+JEVRCIVCDA0NMTw8TE9PD8eOHaO9vZ2RkRGSk5O58847ueOOO1i8eDEFBQVSnRNC/FWkYifELBX91a+vr+fYsWP8z//8D3V1dSQkJFBUVMQjjzzC6tWrsdlsGAwG4uLiUKvVci7pNVw87R0OhwmFQrhcLg4dOkRNTQ379++ntbWVefPmUV5ezle/+lXWrFkj3a5CiBtGgp0Qs1T0V19RFCKRCOFwmOPHj3Po0CEOHjzIwYMHMRqNFBYWsnz5cr7+9a+zYMECUlJSpJP2KqL3sra2lmPHjnHkyBH27NmD3+8nMzOTZcuW8cQTT1BaWkpKSgoajSZ2LyXYCSFuBAl2QgjgQigZGxvDbrczNDREZ2cnTU1N9Pf3Mzg4iM/nw2azkZGRQXFxMWVlZeTn55Oeno7BYJjs4U+KcDhMMBikubmZjo4Ourq6aG5uprOzE7/fj1arpaCggCVLlpCbm0tWVhaFhYXEx8ej0+kme/hCiBlIgp0Q4jKRSASPx0NdXR2tra2cOnWKEydOEAgE0Ov1ZGRkMG/ePAoLC8nKyiItLQ2r1YrJZMJkMmG1WmfcNh3hcBifz4fX62V8fBy3243H42F0dJSGhgZ6e3sZGhqit7cXtVpNamoqubm53H333SxfvhybzYbZbJ7styGEmOEk2AkhvlA4HKatrY2jR4/S0NDA0aNHqaurIxKJYDabWbRoEUuXLmXu3LkUFxezdOlSTCZTLNxFA97FQW8qhr6Lp6c//0+Px0N/fz+tra0cO3aMpqYmOjo6OHPmDJFIhIKCAoqLi1m1ahX33nsveXl5pKWlTdp7EULMThLshBBfSFGU2LRjKBQiGAwyPDxMa2srbW1tNDQ00NzczPnz53E6nRgMBnJzc8nIyCA3N5fi4mJyc3PJzMyMBZ6p2Gk7NjaGw+Ggq6uLtrY2+vv76e/v5+zZs5w7dw6Px0MkEiErK4u5c+eSl5dHSUkJK1euJCUlBZPJRFxcHDqd7pL1c0IIcatIsBNC/MWie+KNjIzgcDgYHh5meHg49n13dzc+n49gMEgwGMTv96PRaNDr9VitVhISEjCbzVgsFhITE0lISMBkMmE2mzGZTBiNRnQ6HQaDAZPJhFarRavVotfrgQvVPo1GQ1xcHCqVKtaBGg6HgQsVxkAgEHvtQCDAxMQEExMT+Hw+fD4fTqcTp9OJx+PB5XIxMjKCx+PB4/HgdDoJhUJoNBq0Wi06nQ6TyURCQgI2m42srCxSUlJITEwkNTWV7Oxs9Ho9Wq3sICWEmFwS7IQQN4zX62VsbIyGhgY6OzsZGhqir6+P9vZ2RkdH8Xg8BAIB1Go1ZrOZ+Pj4WECyWCyxn5lMJgwGA2azmYSEBHQ6HXFxcZhMJuDCcWkajSbWtHFxkIt+7/V68fv9TExM4PV6cblceDyeWMAbGxtjbGwMt9uN0+nEbrcTiURQqVTo9XpsNhvp6emkp6dTUFBAYWEhOTk55ObmkpycPCWnkoUQQoKdEOKWiFbF2tvb6enpwW63Y7fb6evro6+vj5GREUZGRhgdHcXr9caqbzeSyWTCYrEQHx9PRkYGmZmZpKamkp6eTk5ODunp6dhsNoqKikhMTJyS08VCCHEtEuyEELdEOBwmEokQCARi06YXr9uL/nn0Z36/H7fbjcvlIhgMEggEcLvdwIWu3VAohM/nAy5U8KJVPQCtVovZbI5NoRqNxtgWI2q1GrVaHZvejU63Xvx99DqpygkhphsJdkKIKSe6Zi46lXrx93Ah2EUikdjUq1qtjgW06PfRNW86nS62Xm+mbcEihBCfJ8FOCCGEEGKGkF58IYQQQogZQoKdEEIIIcQMIcFOCCGEEGKGkGAnhBBCCDFDSLATQgghhJghJNgJIYQQQswQEuyEEEIIIWYICXZCCCGEEDOEBDshhBBCiBlCgp0QQgghxAwhwU4IIYQQYoaQYCeEEEIIMUNIsBNCCCGEmCEk2AkhhBBCzBD/Dzmb7qATTwGmAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1500x1000 with 1 Axes>"
       ]
@@ -1593,10 +1601,10 @@
    "id": "43e49972-b137-4cae-9ee7-5c5879ff01ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:39.309994Z",
-     "iopub.status.busy": "2024-10-25T15:09:39.309486Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.622690Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.622044Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.424990Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.424561Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.155719Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.154891Z"
     }
    },
    "outputs": [
@@ -1605,15 +1613,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   0%|          | 0/121 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Evaluating set functions...:   7%|▋         | 8/121 [00:05<01:21,  1.38it/s]"
+      "Evaluating set functions...:   0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     {
@@ -1621,7 +1621,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  10%|▉         | 12/121 [00:11<01:51,  1.02s/it]"
+      "Evaluating set functions...:   7%|▋         | 8/119 [00:05<01:20,  1.37it/s]"
      ]
     },
     {
@@ -1629,7 +1629,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  13%|█▎        | 16/121 [00:17<02:03,  1.17s/it]"
+      "Evaluating set functions...:  10%|█         | 12/119 [00:11<01:49,  1.02s/it]"
      ]
     },
     {
@@ -1637,7 +1637,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  17%|█▋        | 20/121 [00:23<02:07,  1.27s/it]"
+      "Evaluating set functions...:  13%|█▎        | 16/119 [00:17<02:02,  1.19s/it]"
      ]
     },
     {
@@ -1645,7 +1645,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  20%|█▉        | 24/121 [00:28<02:07,  1.32s/it]"
+      "Evaluating set functions...:  17%|█▋        | 20/119 [00:23<02:06,  1.27s/it]"
      ]
     },
     {
@@ -1653,7 +1653,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  23%|██▎       | 28/121 [00:34<02:06,  1.35s/it]"
+      "Evaluating set functions...:  20%|██        | 24/119 [00:29<02:07,  1.34s/it]"
      ]
     },
     {
@@ -1661,7 +1661,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  26%|██▋       | 32/121 [00:40<02:03,  1.39s/it]"
+      "Evaluating set functions...:  24%|██▎       | 28/119 [00:34<02:05,  1.38s/it]"
      ]
     },
     {
@@ -1669,7 +1669,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  30%|██▉       | 36/121 [00:46<01:59,  1.41s/it]"
+      "Evaluating set functions...:  27%|██▋       | 32/119 [00:40<02:02,  1.41s/it]"
      ]
     },
     {
@@ -1677,7 +1677,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  33%|███▎      | 40/121 [00:51<01:55,  1.42s/it]"
+      "Evaluating set functions...:  30%|███       | 36/119 [00:46<01:57,  1.42s/it]"
      ]
     },
     {
@@ -1685,7 +1685,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  36%|███▋      | 44/121 [00:57<01:49,  1.43s/it]"
+      "Evaluating set functions...:  34%|███▎      | 40/119 [00:52<01:53,  1.44s/it]"
      ]
     },
     {
@@ -1693,7 +1693,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  40%|███▉      | 48/121 [01:03<01:44,  1.43s/it]"
+      "Evaluating set functions...:  37%|███▋      | 44/119 [00:58<01:48,  1.45s/it]"
      ]
     },
     {
@@ -1701,7 +1701,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  43%|████▎     | 52/121 [01:09<01:38,  1.43s/it]"
+      "Evaluating set functions...:  40%|████      | 48/119 [01:04<01:43,  1.45s/it]"
      ]
     },
     {
@@ -1709,7 +1709,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  46%|████▋     | 56/121 [01:14<01:33,  1.43s/it]"
+      "Evaluating set functions...:  44%|████▎     | 52/119 [01:10<01:37,  1.45s/it]"
      ]
     },
     {
@@ -1717,7 +1717,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  50%|████▉     | 60/121 [01:20<01:27,  1.43s/it]"
+      "Evaluating set functions...:  47%|████▋     | 56/119 [01:15<01:31,  1.46s/it]"
      ]
     },
     {
@@ -1725,7 +1725,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  53%|█████▎    | 64/121 [01:26<01:21,  1.43s/it]"
+      "Evaluating set functions...:  50%|█████     | 60/119 [01:21<01:25,  1.46s/it]"
      ]
     },
     {
@@ -1733,7 +1733,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  56%|█████▌    | 68/121 [01:32<01:15,  1.43s/it]"
+      "Evaluating set functions...:  54%|█████▍    | 64/119 [01:27<01:20,  1.46s/it]"
      ]
     },
     {
@@ -1741,7 +1741,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  60%|█████▉    | 72/121 [01:37<01:10,  1.43s/it]"
+      "Evaluating set functions...:  57%|█████▋    | 68/119 [01:33<01:14,  1.46s/it]"
      ]
     },
     {
@@ -1749,7 +1749,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  63%|██████▎   | 76/121 [01:43<01:04,  1.43s/it]"
+      "Evaluating set functions...:  61%|██████    | 72/119 [01:39<01:08,  1.46s/it]"
      ]
     },
     {
@@ -1757,7 +1757,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  66%|██████▌   | 80/121 [01:49<00:58,  1.43s/it]"
+      "Evaluating set functions...:  64%|██████▍   | 76/119 [01:45<01:02,  1.45s/it]"
      ]
     },
     {
@@ -1765,7 +1765,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  69%|██████▉   | 84/121 [01:54<00:52,  1.43s/it]"
+      "Evaluating set functions...:  67%|██████▋   | 80/119 [01:50<00:57,  1.46s/it]"
      ]
     },
     {
@@ -1773,7 +1773,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  73%|███████▎  | 88/121 [02:00<00:47,  1.43s/it]"
+      "Evaluating set functions...:  71%|███████   | 84/119 [01:56<00:51,  1.46s/it]"
      ]
     },
     {
@@ -1781,7 +1781,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  76%|███████▌  | 92/121 [02:06<00:41,  1.43s/it]"
+      "Evaluating set functions...:  74%|███████▍  | 88/119 [02:02<00:45,  1.46s/it]"
      ]
     },
     {
@@ -1789,7 +1789,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  79%|███████▉  | 96/121 [02:12<00:35,  1.43s/it]"
+      "Evaluating set functions...:  77%|███████▋  | 92/119 [02:08<00:39,  1.46s/it]"
      ]
     },
     {
@@ -1797,7 +1797,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  83%|████████▎ | 100/121 [02:17<00:29,  1.43s/it]"
+      "Evaluating set functions...:  81%|████████  | 96/119 [02:14<00:33,  1.46s/it]"
      ]
     },
     {
@@ -1805,7 +1805,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  86%|████████▌ | 104/121 [02:23<00:24,  1.44s/it]"
+      "Evaluating set functions...:  84%|████████▍ | 100/119 [02:20<00:27,  1.45s/it]"
      ]
     },
     {
@@ -1813,7 +1813,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  89%|████████▉ | 108/121 [02:29<00:18,  1.44s/it]"
+      "Evaluating set functions...:  87%|████████▋ | 104/119 [02:25<00:21,  1.45s/it]"
      ]
     },
     {
@@ -1821,7 +1821,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  93%|█████████▎| 112/121 [02:35<00:13,  1.45s/it]"
+      "Evaluating set functions...:  91%|█████████ | 108/119 [02:31<00:15,  1.45s/it]"
      ]
     },
     {
@@ -1829,7 +1829,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  96%|█████████▌| 116/121 [02:41<00:07,  1.45s/it]"
+      "Evaluating set functions...:  94%|█████████▍| 112/119 [02:37<00:10,  1.45s/it]"
      ]
     },
     {
@@ -1837,7 +1837,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  99%|█████████▉| 120/121 [02:46<00:01,  1.45s/it]"
+      "Evaluating set functions...:  97%|█████████▋| 116/119 [02:43<00:04,  1.45s/it]"
      ]
     },
     {
@@ -1845,7 +1845,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 121/121 [02:46<00:00,  1.38s/it]"
+      "Evaluating set functions...: 100%|██████████| 119/119 [02:43<00:00,  1.37s/it]"
      ]
     },
     {
@@ -1866,16 +1866,16 @@
    "id": "29424e87-5b18-49bc-9690-f09cd111bcd7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.624891Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.624701Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.759501Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.758875Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.158576Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.158113Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.300498Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.299096Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1904,17 +1904,17 @@
    "id": "a3853007-fb0c-4a1d-bcca-f7bdd4dcef13",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.762670Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.761635Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.948646Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.948076Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.305710Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.304673Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.508823Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.508084Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x7fe1be8d5af0>"
+       "<matplotlib.collections.LineCollection at 0x7f43edba2e20>"
       ]
      },
      "execution_count": 15,
@@ -1969,10 +1969,10 @@
    "id": "c9be4254-9ac2-4abc-a3f3-906925dac53d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.950811Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.950605Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.969445Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.968946Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.511683Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.511434Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.533488Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.532745Z"
     }
    },
    "outputs": [
@@ -2010,10 +2010,10 @@
    "id": "16215ca0-00c9-411c-b293-675a4cb595c2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.971359Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.971006Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.985718Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.985216Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.535691Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.535471Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.553818Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.553131Z"
     }
    },
    "outputs": [
@@ -2052,10 +2052,10 @@
    "id": "00c6b5bf-e6b7-456f-84f6-91e2cbda62d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.987616Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.987237Z",
-     "iopub.status.idle": "2024-10-25T15:12:44.791572Z",
-     "shell.execute_reply": "2024-10-25T15:12:44.790935Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.556092Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.555864Z",
+     "iopub.status.idle": "2024-10-25T15:14:09.052806Z",
+     "shell.execute_reply": "2024-10-25T15:14:09.052128Z"
     }
    },
    "outputs": [
@@ -2064,7 +2064,15 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   0%|          | 0/116 [00:00<?, ?it/s]"
+      "Evaluating set functions...:   0%|          | 0/121 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "Evaluating set functions...:   7%|▋         | 8/121 [00:00<00:01, 61.75it/s]"
      ]
     },
     {
@@ -2072,7 +2080,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   7%|▋         | 8/116 [00:00<00:01, 65.35it/s]"
+      "Evaluating set functions...:  13%|█▎        | 16/121 [00:00<00:01, 60.13it/s]"
      ]
     },
     {
@@ -2080,7 +2088,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  14%|█▍        | 16/116 [00:00<00:01, 67.75it/s]"
+      "Evaluating set functions...:  20%|█▉        | 24/121 [00:00<00:02, 39.21it/s]"
      ]
     },
     {
@@ -2088,7 +2096,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  21%|██        | 24/116 [00:00<00:01, 49.35it/s]"
+      "Evaluating set functions...:  26%|██▋       | 32/121 [00:00<00:02, 35.01it/s]"
      ]
     },
     {
@@ -2096,7 +2104,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  28%|██▊       | 32/116 [00:00<00:02, 41.36it/s]"
+      "Evaluating set functions...:  33%|███▎      | 40/121 [00:01<00:02, 34.06it/s]"
      ]
     },
     {
@@ -2104,7 +2112,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  34%|███▍      | 40/116 [00:00<00:01, 39.08it/s]"
+      "Evaluating set functions...:  40%|███▉      | 48/121 [00:01<00:02, 33.08it/s]"
      ]
     },
     {
@@ -2112,7 +2120,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  41%|████▏     | 48/116 [00:01<00:01, 38.75it/s]"
+      "Evaluating set functions...:  46%|████▋     | 56/121 [00:01<00:02, 32.05it/s]"
      ]
     },
     {
@@ -2120,7 +2128,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  48%|████▊     | 56/116 [00:01<00:01, 37.44it/s]"
+      "Evaluating set functions...:  53%|█████▎    | 64/121 [00:01<00:01, 31.34it/s]"
      ]
     },
     {
@@ -2128,7 +2136,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  55%|█████▌    | 64/116 [00:01<00:01, 36.73it/s]"
+      "Evaluating set functions...:  60%|█████▉    | 72/121 [00:02<00:01, 31.18it/s]"
      ]
     },
     {
@@ -2136,7 +2144,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  62%|██████▏   | 72/116 [00:01<00:01, 36.13it/s]"
+      "Evaluating set functions...:  66%|██████▌   | 80/121 [00:02<00:01, 30.83it/s]"
      ]
     },
     {
@@ -2144,7 +2152,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  69%|██████▉   | 80/116 [00:02<00:01, 35.87it/s]"
+      "Evaluating set functions...:  73%|███████▎  | 88/121 [00:02<00:01, 30.46it/s]"
      ]
     },
     {
@@ -2152,7 +2160,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  76%|███████▌  | 88/116 [00:02<00:00, 35.72it/s]"
+      "Evaluating set functions...:  79%|███████▉  | 96/121 [00:02<00:00, 29.80it/s]"
      ]
     },
     {
@@ -2160,7 +2168,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  83%|████████▎ | 96/116 [00:02<00:00, 36.00it/s]"
+      "Evaluating set functions...:  86%|████████▌ | 104/121 [00:03<00:00, 30.25it/s]"
      ]
     },
     {
@@ -2168,7 +2176,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  90%|████████▉ | 104/116 [00:02<00:00, 36.10it/s]"
+      "Evaluating set functions...:  93%|█████████▎| 112/121 [00:03<00:00, 30.56it/s]"
      ]
     },
     {
@@ -2176,7 +2184,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  97%|█████████▋| 112/116 [00:02<00:00, 36.24it/s]"
+      "Evaluating set functions...:  99%|█████████▉| 120/121 [00:03<00:00, 30.49it/s]"
      ]
     },
     {
@@ -2184,7 +2192,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 116/116 [00:02<00:00, 39.68it/s]"
+      "Evaluating set functions...: 100%|██████████| 121/121 [00:03<00:00, 32.56it/s]"
      ]
     },
     {
@@ -2196,7 +2204,7 @@
     },
     {
      "data": {
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z58/XkiVLnPHhsrjZ7NCMGTPUrVu3HH0MjkkAbsDtJwAWkqtTurNTr149vfzyy876cFl42NIfAABwmzm8o3B2Ll26pEmTJql06dLO+HAAAAAOc3imJjAwMNOtIMMwlJycrIIFC+qbb75xajgAAICccrjUTJw4MdP7Xl5eKl68uB5++GEFBgY6KxcAAIBDHC41Xbt2dUUOAACAPMnVQuHz589r2rRpOnDggCSpWrVq6t69O+cwAQAA0zi8UHjDhg0qX768Jk2apPPnz+v8+fOaNGmSKlSooA0bNrgiIwAAwC05vE9NjRo1FBYWpi+++ELe3t6Sru0i/Nprr2nz5s2KiopySVBnYJ8a4AbsUwPAQhyeqYmOjtagQYPshUaSvL29NXDgQEVHRzs1HAAAQE45XGrq1KljX0vzTwcOHFDNmjWdEgoAAMBROVooHBkZaf91v3791L9/f0VHR6tevXqSpC1btujzzz/XBx984JqUAAAAt5CjNTXXT+a+1UttNpvLTul2BtbUADdgTQ0AC8nRTE1MTIyrcwAAAORJjkpNuXLlXJ0DAAAgT3JUapYuXarmzZsrf/78Wrp06b++tnXr1k4JBgAA4Igcr6mJj49XiRIl5OV18wemWFMDeBjW1ACwkBzN1GRkZGT7awAAAHfh0D41V65cUZMmTXTkyBFX5QEAAMgVh0pN/vz5M+1ZAwAA4C4c3lG4c+fOmjZtmiuyAAAA5FqO1tT809WrVzV9+nStWrVKDzzwgAoVKpTp+vjx450WDgAAIKccLjX79u1TnTp1JEmHDx92eiAAAIDccLjUrF271hU5AAAA8sThNTU9evRQcnJylvELFy6oR48eTgkFAADgKIdLzcyZM3Xp0qUs45cuXdKsWbOcEgoAAMBROb79lJSUJMMwZBiGkpOT5evra7+Wnp6un3/+WSVKlHBJSAAAgFvJcakpWrSobDabbDab7r333izXbTabRo4c6dRwAAAAOZXjUrN27VoZhqHGjRvr+++/V1BQkP1agQIFVK5cOQUHB7skJAAAwK3kuNQ0bNhQkhQTE6OyZcvKZrNleU1sbKzKli3rvHQAAAA55PBC4dDQUP31119Zxs+ePasKFSo4JRQAAICjHC41hmFkO56SkpJp8TAAAMDtlOPbTwMHDpR0bUHw8OHDVbBgQfu19PR0bd26VbVq1XJ6QAAAgJzIcanZvXu3pGszNVFRUSpQoID9WoECBVSzZk29+eabzk8IAACQAw49/SRJ3bt316effip/f3+XhQIAAHCUw2c/zZgxwxU5AAAA8sThUiNJO3bs0IIFCxQbG6u0tLRM1xYtWuSUYAAAAI5w+OmnefPm6ZFHHtGBAwe0ePFiXblyRb///rvWrFmjgIAAV2QEAAC4JYdLzZgxYzRhwgQtW7ZMBQoU0KeffqqDBw+qffv2bLwHAABM43CpOXr0qFq2bCnp2lNPFy5ckM1m04ABA/Tll186PSAAAEBOOFxqAgMDlZycLEkqXbq09u3bJ0lKSEjQxYsXnZsOAAAghxxeKNygQQOtXLlSNWrU0H/+8x/1799fa9as0cqVK9WkSRNXZAQAALglh0vN5MmTdfnyZUnS22+/rfz582vz5s169tlnNWzYMKcHBAAAyAmbcbPDnCwoKSlJAQEBSkxMZPNAQJL8O5idIHeS5pmdAIAbcnhNDQAAgDui1AAAAEug1AAAAEug1AAAAEtwuNTMmDGD/WgAAIDbcbjUDBkyRHfffbd69uypzZs3uyITAACAwxwuNSdPntTMmTP1999/q1GjRqpSpYrGjRun+Ph4V+QDAADIEYdLTb58+dS2bVstWbJEcXFx6tWrl7799luVLVtWrVu31pIlS5SRkeGKrAAAADeVp4XCJUuW1KOPPqqwsDB5eXkpKipKXbt2VcWKFbVu3TonRQQAALi1XJWa06dP6+OPP9Z9992nRo0aKSkpST/++KNiYmJ08uRJtW/fXl27dnV2VgAAgJtyuNS0atVKISEhCg8PV69evXTy5EnNnTtXTZs2lSQVKlRIgwYNUlxcnNPDbtiwQa1atVJwcLBsNpt++OEHp/8ZAADAMzl8oGWJEiW0fv16hYWF3fQ1xYsXV0xMTJ6CZefChQuqWbOmevTooXbt2jn94wMAAM/lcKmZNm3aLV9js9lUrly5XAX6N82bN1fz5s2d/nEBAIDny1GpmTRpUo4/YL9+/XIdxtlSU1OVmppqfz8pKcnENAAAwJVyVGomTJiQow9ms9ncqtSMHTtWI0eONDsGAAC4DXJUalyxPuZ2GDp0qAYOHGh/PykpSSEhISYmAgAAruLwmhpP4uPjIx8fH7NjAACA2yBXpebEiRNaunSpYmNjlZaWluna+PHjnRIMAADAEQ6XmtWrV6t169YKDQ3VwYMHVb16dR0/flyGYahOnTquyGiXkpKi6Oho+/sxMTHas2ePgoKCVLZsWZf+2QAAwL05vPne0KFD9eabbyoqKkq+vr76/vvvFRcXp4YNG+o///mPKzLa7dixQ7Vr11bt2rUlSQMHDlTt2rU1fPhwl/65AADA/dkMwzAc+Q1FihTRnj17VLFiRQUGBioiIkL33Xef9u7dq2eeeUbHjx93UdS8S0pKUkBAgBITE+Xv7292HMB8/h3MTpA7SfPMTgDADTk8U1OoUCH7OppSpUrp6NGj9mt///2385IBAAA4wOE1NfXq1VNERISqVq2qFi1aaNCgQYqKitKiRYtUr149V2QEAAC4JYdLzfjx45WSkiJJGjlypFJSUjR//nzdc889PPkEAABM4/CaGk/GmhrgBqypAWAhedp8LyUlRRkZGZnGKAsAAMAMDi8UjomJUcuWLVWoUCEFBAQoMDBQgYGBKlq0qAIDA12REQAA4JYcnqnp3LmzDMPQ9OnTVbJkSdlsNlfkAgAAcIjDpWbv3r3auXOnKleu7Io8AAAAueLw7acHH3xQcXFxrsgCAACQaw7P1Hz99dd69dVXdfLkSVWvXl358+fPdP3+++93WjgAAICccrjU/PXXXzp69Ki6d+9uH7PZbDIMQzabTenp6U4NCAAAkBMOl5oePXqodu3amjt3LguFAQCA23C41Pzxxx9aunSpKlWq5Io8AAAAueLwQuHGjRtr7969rsgCAACQaw7P1LRq1UoDBgxQVFSUatSokWWhcOvWrZ0WDgAAIKccPvvJy+vmkzvuvlCYs5+AG3D2EwALcXim5sazngAAANyBw2tqAAAA3FGuSs369evVqlUrVapUSZUqVVLr1q21ceNGZ2cDAADIMYdLzTfffKOmTZuqYMGC6tevn/r16yc/Pz81adJEc+bMcUVGAACAW3J4oXDVqlX18ssva8CAAZnGx48fr6+++koHDhxwakBnYqEwcAMWCgOwEIdnao4dO6ZWrVplGW/durViYmKcEgoAAMBRDpeakJAQrV69Osv4qlWrFBIS4pRQAAAAjnL4ke5BgwapX79+2rNnjx555BFJ0qZNmxQeHq5PP/3U6QEBAABywuFS07t3b91999365JNPtGDBAknX1tnMnz9fzzzzjNMDAgAA5ITDC4U9GQuFgRuwUBiAhTg8U3NdWlqazpw5k2WH4bJly+Y5FAAAgKMcLjVHjhxRjx49tHnz5kzjhmG4/dlPAADAuhwuNd26dVO+fPn0448/qlSpUrLZbK7IBQAA4BCHS82ePXu0c+dOValSxRV5AAAAcsXhfWqqVaumv//+2xVZAAAAcs3hUjNu3Di99dZbWrdunc6ePaukpKRMbwAAAGZw+JFuL69rPejGtTSesFCYR7qBG/BINwALcXhNzdq1a12RAwAAIE8cLjUNGza86bV9+/blKQwAAEBuObym5kbJycn68ssv9dBDD6lmzZrOyAQAAOCwXJeaDRs2qGvXripVqpQ+/vhjNW7cWFu2bHFmNgAAgBxz6PZTfHy8wsPDNW3aNCUlJal9+/ZKTU3VDz/8oGrVqrkqIwAAwC3leKamVatWqly5siIjIzVx4kT9+eef+uyzz1yZDQAAIMdyPFPzyy+/qF+/furdu7fuueceV2YCAABwWI5naiIiIpScnKwHHnhADz/8sCZPnszOwgAAwG3kuNTUq1dPX331lU6dOqVXXnlF8+bNU3BwsDIyMrRy5UolJye7MicAAMC/cnhH4X86dOiQpk2bptmzZyshIUHNmjXT0qVLnZnPqdhRGLgBOwoDsJA87VNTuXJlffjhhzpx4oTmzp3rrEwAAAAOy9NMjadhpga4ATM1ACwkzzsKAwAAuANKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsASPKzWff/65ypcvL19fXz388MPatm2b2ZEAAIAb8KhSM3/+fA0cOFAjRozQrl27VLNmTT355JM6c+aM2dEAAIDJPKrUjB8/Xr169VL37t1VrVo1TZkyRQULFtT06dPNjgYAAEzmMaUmLS1NO3fuVNOmTe1jXl5eatq0qX777bdsf09qaqqSkpIyvQEAAGvKZ3aAnPr777+Vnp6ukiVLZhovWbKkDh48mO3vGTt2rEaOHHk74nnudvOSY1vOe+rnybb62btT/rvw7xa4I3jMTE1uDB06VImJifa3uLg4syMBAAAX8ZiZmmLFisnb21unT5/ONH769Gndfffd2f4eHx8f+fj43I54AADAZB4zU1OgQAE98MADWr16tX0sIyNDq1evVlhYmInJAACAO/CYmRpJGjhwoLp27aq6devqoYce0sSJE3XhwgV1797d7GgAAMBkHlVqnn/+ef31118aPny44uPjVatWLf36669ZFg8DAIA7j0eVGknq27ev+vbta3YMAADgZjxmTQ0AAMC/odQAAABLoNQAAABLoNQAAABLoNQAAABL8Linn9wWZ7QAAGAqZmoAAIAlUGoAAIAlUGoAAIAlUGoAAIAlUGoAAIAlUGoAAIAlUGoAAIAlsE8NAOtjHyngjsBMDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsARKDQAAsASPKTWjR4/WI488ooIFC6po0aJmxwEAAG7GY0pNWlqa/vOf/6h3795mRwEAAG4on9kBcmrkyJGSpPDwcHODAAAAt+QxpSY3UlNTlZqaan8/KSnJxDQAAMCVPOb2U26MHTtWAQEB9reQkBCzIwEAABcxtdQMGTJENpvtX98OHjyY648/dOhQJSYm2t/i4uKcmB4AALgTU28/DRo0SN26dfvX14SGhub64/v4+MjHxyfXvx8AAHgOU0tN8eLFVbx4cTMjwFFJ88xOAABAtjxmoXBsbKzOnTun2NhYpaena8+ePZKkSpUqqXDhwuaGAwAAprMZhmGYHSInunXrppkzZ2YZX7t2rRo1apSjj5GUlKSAgAAlJibK39/fyQkBAICZPKbUOAOlBgAA67L0I90AAODOQakBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWQKkBAACWkM/sALfT9bM7k5KSTE4CAAAcVaRIEdlstptev6NKTXJysiQpJCTE5CQAAMBRiYmJ8vf3v+l1m3F9+uIOkJGRoT///POWTc+dJCUlKSQkRHFxcf/6F+np+Dythc/TOu6Ez1Hi8/QUzNT8g5eXl8qUKWN2jFzx9/f3yH+AjuLztBY+T+u4Ez5Hic/T07FQGAAAWAKlBgAAWAKlxs35+PhoxIgR8vHxMTuKS/F5Wgufp3XcCZ+jxOdpFXfUQmEAAGBdzNQAAABLoNQAAABLoNQAAABLoNQAAABLoNQAQB4kJCSYHQG50KNHD/vROf904cIF9ejRw4REcAZKjZtIS0vTe++9p5dfflmbNm0yO85tsXHjRnXu3FlhYWE6efKkJGn27NmKiIgwORkcFRcXpxMnTtjf37Ztm9544w19+eWXJqZyvnHjxmn+/Pn299u3b6+77rpLpUuX1t69e01MBkfNnDlTly5dyjJ+6dIlzZo1y4REcAZKjZvo06ePJk+erNOnT+vJJ5/UwoULzY7kUt9//72efPJJ+fn5affu3UpNTZV07bCyMWPGmJwubwIDAxUUFJSjN6vo2LGj1q5dK0mKj49Xs2bNtG3bNr399tsaNWqUyemcZ8qUKfYDcVeuXKmVK1fql19+UfPmzTV48GCT0yEnkpKSlJiYKMMwlJycrKSkJPvb+fPn9fPPP6tEiRJmx3Sq2NhYZbd7i2EYio2NNSGR69xRZz+5sx9++EHfffedGjVqpBUrVujZZ5/Vnj17dO+996pVq1basGGDEhMT1aVLF7OjOsX777+vKVOmqEuXLpo3b559vH79+nr//fdNTJZ3EydOtP/67Nmzev/99/Xkk08qLCxMkvTbb79p+fLleuedd0xK6Hz79u3TQw89JElasGCBqlevrk2bNmnFihV69dVXNXz4cJMTOkd8fLy91Pz4449q3769nnjiCZUvX14PP/ywyemcLyEhQQsXLtTRo0c1ePBgBQUFadeuXSpZsqRKly5tdrxcKVq0qGw2m2w2m+69994s1202m0aOHGlCMtepUKGCTp06laWsnTt3ThUqVFB6erpJyZyPUuMmMjIy7F8knnjiCS1atEgDBgzQqVOnVKtWLQ0ZMkSHDx+2TKk5dOiQGjRokGU8ICDA49codO3a1f7rZ599VqNGjVLfvn3tY/369dPkyZO1atUqDRgwwIyITnflyhX7DqWrVq1S69atJUlVqlTRqVOnzIzmVIGBgYqLi1NISIh+/fVXewE3DMNS3xgkKTIyUk2bNlVAQICOHz+uXr16KSgoSIsWLVJsbKzH3qJZu3atDMNQ48aN9f3332eaMS1QoIDKlSun4OBgExM6n2EY2Z5snZKSIl9fXxMSuQ6lxk3UrVtX69ev1z333CNJatasmfbt22e/fuDAAbOiucTdd9+t6OholS9fPtN4RESEQkNDzQnlAsuXL9e4ceOyjD/11FMaMmSICYlc47777tOUKVPUsmVLrVy5Uu+9954k6c8//9Rdd91lcjrnadeunTp27Kh77rlHZ8+eVfPmzSVJu3fvVqVKlUxO51wDBw5Ut27d9OGHH6pIkSL28RYtWqhjx44mJsubhg0bSpJiYmJUtmzZbL/ZW8XAgQMlXZt9euedd1SwYEH7tfT0dG3dulW1atUyKZ1rsKbGTQwbNsy+WPZO0KtXL/Xv319bt26VzWbTn3/+qW+//VZvvvmmevfubXY8p7nrrru0ZMmSLONLliyx1Df7cePGaerUqWrUqJFeeOEF1axZU5K0dOlS+20pK5gwYYL69u2ratWqaeXKlSpcuLAk6dSpU3rttddMTudc27dv1yuvvJJlvHTp0oqPjzchkXMdOHAg00MZn3/+uWrVqqWOHTvq/PnzJiZznt27d2v37t0yDENRUVH293fv3q2DBw+qZs2aCg8PNzumcxmACTIyMoz333/fKFSokGGz2QybzWb4+voaw4YNMzuaU82YMcPw9vY2nn76aeO9994z3nvvPePpp5828uXLZ8yYMcPseE519epV49y5c5nGYmJijNOnT5uUyPnWr19vXLlyJcv4lStXjPXr15uQyHWKFy9u7Nq1yzAMwyhcuLBx9OhRwzAMY8WKFUaZMmXMjOYU1atXN3766SfDMAwjMjLSKFCggDF06FCjXr16Rrdu3UxO51zdunUzEhMTzY5xW3CgpZvx9vbOdkHX2bNnVaJECcvdt09LS1N0dLRSUlJUrVo1+0++VrJ161ZNmjTJfguxatWq6tevn6UWlk6fPl2PP/64KlSoYHYUl7qT/v986aWXdPbsWS1YsEBBQUGKjIyUt7e32rRpowYNGmRaEO+JChcurH379ql8+fJ69913tW/fPi1cuFC7du1SixYtLDEbdSei1LgZLy8vxcfHZ/mi+eeff6pixYrZ7qvgiRITE5Wenp7lseZz584pX7588vf3NykZcuOee+7RsWPHVLp0aTVs2FANGzZUo0aNLLfOxMvLS6dPn1bx4sUzjR8+fFh169ZVUlKSScmcLzExUc8995x27Nih5ORkBQcHKz4+XmFhYfr5559VqFAhsyPmSVBQkCIiIlStWjU9+uij6tKli15++WUdP35c1apV08WLF82OmCft2rVTeHi4/P391a5du3997aJFi25TKtdjobCbmDRpkqRrC7q+/vrrTDMW6enp2rBhg6pUqWJWPKfr0KGDWrVqlWUdwoIFC7R06VL9/PPPJiVzvoyMDEVHR+vMmTPKyMjIdC27J8A80ZEjR3Ty5EmtW7dOGzZs0Mcff6xXXnlFpUqVUqNGjfTNN9+YHTFPrn9TsNls6tatm/1JL+na/5+RkZF65JFHzIrnEgEBAVq5cqUiIiIUGRmplJQU1alTR02bNjU7mlM8+uijGjhwoOrXr69t27bZN1U8fPiwypQpY3K6vAsICLAvgvb397f0guh/YqbGTVyftv/jjz9UpkwZeXt7268VKFBA5cuX16hRoyxzyyIoKEibNm1S1apVM40fPHhQ9evX19mzZ01K5lxbtmxRx44d9ccff2TZ/Mpms1nqdsV1Fy9e1MaNGzV37lx9++23MgxDV69eNTtWnnTv3l3StV1o27dvLz8/P/u16/9/9urVS8WKFTMrIhwUGxur1157TXFxcerXr5969uwpSRowYIDS09PtP2h6qqVLl6p58+bKnz+/2VFuK0qNm3n88ce1aNEiBQYGmh3FpQoVKqQtW7aoRo0amcajoqL08MMPe/zU73W1atXSvffeq5EjR6pUqVJZfloKCAgwKZlzrVixQuvWrdO6deu0e/duVa1a1X4LqkGDBpb59zxy5Ei9+eabHn/rJSdutRO0VTZUtCpvb2/Fx8erePHiN10LZkWUGpji8ccfV/Xq1fXZZ59lGu/Tp48iIyO1ceNGk5I5V6FChbR3717LrS25kZeXl4oXL65Bgwbp5ZdfVtGiRc2OhDyqXbt2pvevXLmimJgY5cuXTxUrVtSuXbtMSuY86enp+uGHH+yL+O+77z61bt0600y5p7r77rv11VdfqVWrVjddC2ZFlBo3k56ervDwcK1evTrbNRhr1qwxKZlzbdq0SU2bNtWDDz6oJk2aSJJWr16t7du3a8WKFXrsscdMTugcjRs31ltvvaWnnnrK7CguNXHiRG3YsEEbNmyQj4+PfZamUaNG2W5F70nq1Kmj1atXKzAwULVr1/7XtQlW+Eb/b5KSktStWze1bdtWL774otlx8iQ6OlotWrTQyZMnVblyZUnXdjoPCQnRTz/9pIoVK5qcMG/effddjRo1Kkdraax0G5xS42b69u2r8PBwtWzZMtvbFRMmTDApmfPt2bNHH330kfbs2SM/Pz/df//9Gjp0qH1XZStYvHixhg0bpsGDB6tGjRpZ7m/ff//9JiVznaioKK1fv15r1qzRjz/+qBIlSmQ6wdvTjBw5UoMHD1bBggVveSbQiBEjblMq80RFRalVq1Y6fvy42VHypEWLFjIMQ99++639KcyzZ8+qc+fO8vLy0k8//WRywrw7ePCgoqOj1bp1a82YMeOmM6jPPPPM7Q3mQpQaN1OsWDHNmjVLLVq0MDsKnMDLK+um3TabzX4Wi5V+QjIMQ7t379a6deu0du1aRUREKDk5WTVq1NDu3bvNjgcniYiIUKtWrTx+192brevbu3ev6tevr5SUFJOSOd8/i7nV8Ui3mylQoIBl118kJSXZ95+51X4eVtmnJiYmxuwIt0WrVq20adMmJSUlqWbNmmrUqJF69eqlBg0aWHJ9TVpaWra3h8uWLWtSIue78ekfwzB06tQpzZ49237mlSfz8fFRcnJylvGUlBQVKFDAhESuc30G8a+//tKhQ4ckSZUrV7bkGhtmatzMJ598omPHjmny5MmW21fgnyvwvby8sv38rDiDcScYPHiwGjZsqMcee8wyT3Rl5/Dhw+rZs6c2b96cadyK/25v3B36+mLwxo0ba+jQoZkOufREXbp00a5duzRt2jT7+WRbt25Vr1699MADD1jqTKSLFy+qb9++mj17tv3fqLe3t7p06aLPPvvMUjM4lBo307ZtW61du1ZBQUG67777sqzB8OSdH9evX6/69esrX758Wr9+/b++9vpJulYwe/ZsTZkyRTExMfrtt99Urlw5TZw4URUqVLDUvezrLl++LF9fX7NjuMT1f79DhgzJds3b9YM84f4SEhLUtWtXLVu2zP519urVq2rdurXCw8MtVc5feeUVrVq1SpMnT1b9+vUlXbuN2K9fPzVr1kxffPGFyQmdh1LjZq5v8nUzM2bMuE1JXOfq1asaM2aMevToYYmdO//NF198oeHDh+uNN97Q6NGjtW/fPoWGhio8PFwzZ87U2rVrzY7oFBkZGRo9erSmTJmi06dP6/DhwwoNDdU777yj8uXL2zc283SFChXSzp07LbW7950uOjo607lsVrz9X6xYMS1cuFCNGjXKNL527Vq1b99ef/31lznBXOF2nJoJ3Khw4cJGTEyM2TFcrmrVqsbixYsNw8h80nFUVJRx1113mZjMuUaOHGmEhoYa33zzjeHn52f/POfNm2fUq1fP5HTOU7duXWPjxo1mx7gtUlJSjGHDhhlhYWFGxYoVjQoVKmR682SJiYlGenp6lvH09HRLnmbt5+dn7N+/P8v4vn37jIIFC5qQyHVYKOyGrl69qnXr1uno0aPq2LGjihQpoj///FP+/v6WOcW6cePGWr9+vcqXL292FJeKiYnJsomZdG2R4oULF0xI5BqzZs3Sl19+qSZNmujVV1+1j9esWVMHDx40MZlzjRs3Tm+99ZbGjBmT7SP6VlngLl07pXv9+vV68cUXs73V5qkWL16s//73v9qzZ0+WtSSXLl3Sgw8+qI8//litWrUyKaHzhYWFacSIEZo1a5b91vClS5c0cuRIhYWFmZzOuSg1buaPP/7QU089pdjYWKWmpqpZs2YqUqSIxo0bp9TUVE2ZMsXsiE7RvHlzDRkyRFFRUXrggQeybDvfunVrk5I5V4UKFbRnzx6VK1cu0/ivv/6a5dwrT3by5Mlsp+0zMjJ05coVExK5xvXDHK9vGHmdYcGFwr/88ot++ukn+xoMq/jiiy/01ltvZbs4tlChQvrvf/+ryZMnW6rUTJw4UU899ZTKlCljX/e1d+9e+fr6avny5Sancy5KjZvp37+/6tatq7179+quu+6yj7dt21a9evUyMZlzXT+de/z48VmuWembw8CBA9WnTx9dvnxZhmFo27Ztmjt3rsaOHauvv/7a7HhOU61aNW3cuDFLeVu4cGG2M1WeyiproHIiMDDQvimdlezbt0//+9//bnq9QYMGGjZs2G1M5Ho1atTQkSNH9O2339pnTl944QV16tQp0+GsVkCpcTMbN27U5s2bs+yTUL58eZ08edKkVM534/4eVvXSSy/Jz89Pw4YN08WLF9WxY0cFBwfr008/VYcOHcyO5zTDhw9X165ddfLkSWVkZGjRokU6dOiQZs2apR9//NHseE5jpafybuW9997T8OHDNXPmTEs98nv+/Pl/PTX+ypUrHr+x4D9duXJFVapU0Y8//mipH4xvhlLjZjIyMrKdpThx4oTH7wtx3fHjx7Vy5UpduXJFDRs21H333Wd2JJfq1KmTOnXqpIsXLyolJcWSJ+U+88wzWrZsmUaNGqVChQpp+PDhqlOnjpYtW6ZmzZqZHS/Pli5dmu14QECA7r33XpUqVeo2J3K9Tz75REePHlXJkiVVvnz5LOuHPPWcq/Lly2vHjh03fYJtx44dWWYcPVn+/Pl1+fJls2PcNjzS7Waef/55BQQE6Msvv1SRIkUUGRmp4sWL65lnnlHZsmU9/pHutWvX6umnn9alS5ckSfny5dP06dPVuXNnk5O51pkzZ+w7eVapUsWSO3laWXbHXVxns9nUoUMHffXVV5aa0bDqOVdvv/22vvnmG23btk0lS5bMdC0+Pl4PP/ywOnfurNGjR5uU0PnGjBmjw4cP6+uvv1a+fNaey6DUuJkTJ07oySeflGEYOnLkiOrWrasjR46oWLFi2rBhg8f/lP/oo4+qWLFi+uKLL+Tr66thw4Zp8eLF+vPPP82O5hLJycl67bXXNHfuXPstN29vbz3//PP6/PPPLbXB150oMTFRO3fuVJ8+fdS2bVuNGTPG7Ei4heTkZIWFhSk2NladO3e2n9B98OBBffvttwoJCdGWLVssMzMuXVuTuXr1ahUuXFg1atTI8mCGJ2/qeiNKjRu6evWq5s2bp8jISKWkpKhOnTqWWdBVtGhRbd68WdWqVZN0bftuf39/nT59OtPCaKt4/vnntXv3bn322Wf2Ryd/++039e/fX7Vq1dK8efNMTph7QUFBOnz4sIoVK6bAwMB/feT33LlztzHZ7ffrr7/qjTfesNTj69K1XXcXLlyoo0ePavDgwQoKCtKuXbtUsmRJlS5d2ux4uZaYmKihQ4dq/vz59vUzRYsWVYcOHTR69GgFBgaanNC57oRNXa+j1LgZK28xL12bxo+Pj88041SkSBHt3btXoaGhJiZzjUKFCmn58uV69NFHM41v3LhRTz31lEfvVTNz5kx16NBBPj4+Cg8P/9dS07Vr19uY7PY7fvy4qlevbqmTnSMjI9W0aVMFBATo+PHjOnTokEJDQzVs2DDFxsZq1qxZZkfMM8Mw9Pfff8swDBUvXtwye/Fcl5GRoY8++khLly5VWlqaGjdurHfffdcSPyDfjLVvrnmgEiVKqG3bturcubOaNGnyr/fyPdXy5csz3XbJyMjQ6tWrtW/fPvuYVfapueuuu7K9xRQQEODxPw127dpVP/74o1q0aKFu3bqZHcdUx44dU3BwsNkxnGrgwIHq1q2bPvzww0y3Ylq0aKGOHTuamMx5bDabpde3jR49Wu+++66aNm0qPz8/TZo0SX/99ZemT59udjTXMWUfY9zUokWLjOeee87w8/Mz7r77bqN///7G9u3bzY7lNDab7ZZvXl5eZsd0mqlTpxpNmzY1Tp06ZR87deqU8cQTTxhTpkwxMZlzeHt7G8HBwcb//d//GdHR0WbHMcXu3buN2rVrG2+88YbZUZzK39/f/nf6zyM+jh8/bvj4+JgZDTlUqVKlTF9nVq5caRQoUCDbIyKsgttPbio5OVkLFy7U3LlztWbNGoWGhqpz584aPny42dHggNq1ays6OlqpqakqW7asJCk2NlY+Pj665557Mr3WEx+RjYuL04wZMzRz5kwdP35cjz76qF566SU999xzlprivtmaoQsXLujq1atq1qyZFixYYKljEkqUKKHly5erdu3amW4Rr1y5Uj169FBcXJzZEXELPj4+io6OVkhIiH3M19dX0dHRlj1MmFLjAfbv369OnTopMjLSMjvt3ilu9VjsP3nqI7LXrV27VuHh4fr++++VL18+dejQQT179tSDDz5odrQ8mzlzZrbj/v7+qly5sn3hu5W89NJLOnv2rBYsWKCgoCBFRkbK29tbbdq0UYMGDTRx4kSzI+IWvL29FR8fn+kW2/WtQipUqGBiMteh1Lipy5cva+nSpZozZ45+/fVXlSxZUi+88II++OADs6MB/yo5OVnz5s1TeHi4tmzZourVq2vv3r1mx4KDEhMT9dxzz2nHjh1KTk5WcHCw4uPjFRYWpp9//jnLY8FwP15eXmrevLl8fHzsY8uWLVPjxo0z/f1Z6ZFuFgq7meXLl2vOnDn64YcflC9fPj333HNasWKFGjRoYHY05NHly5c1f/58XbhwQc2aNcty+8kqihQpoiZNmuiPP/7QwYMHtX//frMjIRcCAgK0cuVKRUREZNpe4vqhnp5o0qRJOX5tv379XJjk9sjuqUOrb3TKTI2bKViwoJ5++ml16tRJLVq0yLI1OTzDwIEDdeXKFX322WeSpLS0ND300EPav3+/ChYsqKtXr2rFihV65JFHTE7qPJcuXdJ3332n6dOna+PGjapQoYK6d++ubt26efSeJnequLi4TGsxrCCnt1xsNpuOHTvm4jRwBWZq3Mzp06cttZPlnWrFihWZdpf99ttvFRsbqyNHjqhs2bLq0aOHRo8erZ9++snElM6xZcsWTZ8+XQsWLFBaWpratWunVatW6fHHHzc7GvKgfPnyevTRR9W5c2c999xzHr8FgSTFxMSYHQEuZr1NUDzU9W8I1wvNiRMnMp1kffHiRX344YdmxXO60NBQnT17Nst4QkKCJTbhi42NzbR4dMWKFXruuedUrlw52Ww29e/fX7t37zYxoXNUq1ZN9evX165duzR27FidOnVK33zzDYXGAnbs2KGHHnpIo0aNUqlSpdSmTRstXLhQqampZkcDborbT27C29tbp06dsu+06+/vrz179ti/wZ8+fVrBwcGWefopu52FpWufZ9myZT3+C2fRokW1fft2+7qZChUq6J133lGPHj0kXduBtmrVqvaDPT1Vv3791LNnT9WsWdPsKHARwzC0bt06zZkzR99//70yMjLUrl07S2zgduLECS1dulSxsbFKS0vLdG38+PEmpUJecPvJTdzYLa3aNZcuXWr/9Y07C6enp2v16tUqX768Ccmcq2rVqlq2bJkGDhyo33//XbGxsZlmL/74448sJwR7IkcWXnqqdu3a5fi1VnqK5DqbzabHH39cjz/+uHr37q2ePXtq5syZHl9qVq9erdatWys0NFQHDx5U9erVdfz4cRmGoTp16pgdD7lEqcFt1aZNG0nXvlDeuDI/f/78Kl++vD755BMTkjnXW2+9pQ4dOuinn37S77//rhYtWmRapPjzzz/roYceMjEhcuqfxdswDC1evFgBAQGqW7euJGnnzp1KSEhwqPx4khMnTmjOnDmaM2eO9u3bp7CwMH3++edmx8qzoUOH6s0339TIkSNVpEgRff/99ypRooQ6deqkp556yux4yCVKDW6r6+uEKlSooO3bt6tYsWImJ3KNtm3b6ueff9aPP/6oJ554Qq+//nqm6wULFtRrr71mUjo44p8nGP/3v/9V+/btNWXKFHl7e0u6NsP42muvWWo3YUmaOnWq5syZo02bNqlKlSrq1KmTlixZonLlypkdzSkOHDiguXPnSpLy5cunS5cuqXDhwho1apSeeeYZ9e7d2+SEyA3W1LgJLy8vzZw50/5T4QsvvKCJEyfab1EkJCSoe/fulllTA3ii4sWLKyIiQpUrV840fujQIT3yyCPZLn73VCEhIXrhhRfUqVMnS66Zuvvuu7V27VpVrVpV1apV0wcffKDWrVtr7969ql+/vqVOXL+TMFPjRm68HfPKK69kej+7s2c8yaRJk/Tyyy/L19f3lmsxrLDx1Z3q8uXL8vX1NTuGS1y9elUHDx7MUmoOHjyY6WlFK4iNjfX4rzn/pl69eoqIiFDVqlXVokULDRo0SFFRUVq0aJHq1atndjzkEjM1uG0qVKigHTt26K677vrXTbDY+MrzZGRkaPTo0ZoyZYpOnz6tw4cPKzQ0VO+8847Kly+vnj17mh3RKQYOHKhZs2bp//7v/+xrorZu3aoPPvhAL774ouWemNm4caOmTp2qo0ePauHChSpdurRmz56tChUq6NFHHzU7Xp4cO3ZMKSkpuv/++3XhwgUNGjRImzdv1j333KPx48db5jbbHec2nwoOwIJGjhxphIaGGt98843h5+dnHD161DAMw5g3b55Rr149k9M5T3p6ujFu3DgjODjYsNlshs1mM4KDg41x48YZV69eNTueUy1cuNDw8/MzXnrpJcPHx8f+d/rZZ58ZzZs3NzkdkD1magDkWaVKlTR16lQ1adJERYoU0d69e+2PyoaFhen8+fNmR3S6pKQkSbLcAuHrateurQEDBqhLly6Z/k53796t5s2bKz4+3uyITpGWlqYzZ85kuX1YtmxZkxIhL1hTA1Okp6crPDxcq1evzvYLypo1a0xKhtw4efKkKlWqlGU8IyNDV65cMSGR61m1zFx36NChbA/SDQgIUEJCwu0P5GSHDx9Wz549tXnz5kzjhmHIZrPxUIaHotTAFP3791d4eLhatmyp6tWrW2pBYu3atXP8+ezatcvFaW6PatWqaePGjVnWISxcuFC1a9c2KZVz3Il/n9K1p4Oio6OzbIYZERFhiaNMunfvrnz58unHH39UqVKlLPU16E5GqYEp5s2bpwULFqhFixZmR3G66xsMSteeBPrf//6natWqKSwsTNK1AyB///13S+1TM3z4cHXt2lUnT55URkaGFi1apEOHDmnWrFn68ccfzY6XJ//8+7yT9OrVS/3799f06dNls9n0559/6rffftOgQYM0fPhws+Pl2Z49e7Rz505VqVLF7ChwItbUwBTBwcFat26d7r33XrOjuNRLL72kUqVK6b333ss0PmLECMXFxXn8VvP/tHHjRo0aNUp79+5VSkqK6tSpo+HDh+uJJ54wOxpywTAMjRkzRmPHjtXFixclST4+Pho8eLCGDh0qPz8/kxPmzYMPPqgJEyZ4/FNcyIxS42YCAwOznQa12Wzy9fVVpUqV1K1bN3Xv3t2EdM7zySef6NixY5o8ebKlp30DAgK0Y8cO+8GW1x05ckR169ZVYmKiScmQFzt37tSBAwckSffdd5/H32L7N2lpaYqOjlZKSoqqVaumqVOn6qOPPvL4hcJr1qzRsGHDNGbMGNWoUUP58+fPdN3qa6asittPbmb48OEaPXq0mjdvbt8HY9u2bfr111/Vp08fxcTEqHfv3rp69ap69eplctrci4iI0Nq1a/XLL7/ovvvuy/IFxSoHA/r5+WnTpk1ZSs2mTZssu0GdlZ05c0YdOnTQunXrVLRoUUnXdvt+/PHHNW/ePBUvXtzcgE6Qmpqqd999VytXrrTPzLRp00YzZsxQ27Zt5e3trQEDBpgdM8+aNm0qSWrSpEmmcRYKezZKjZuJiIjQ+++/r1dffTXT+NSpU7VixQp9//33uv/++zVp0iSPLjVFixZV27ZtzY7hcm+88YZ69+6tXbt2Zdqsbfr06XrnnXdMTuc8d8oM4+uvv67k5GT9/vvvqlq1qiRp//796tq1q/r162c/S8iTDR8+XFOnTlXTpk21efNm/ec//1H37t21ZcsWffLJJ/rPf/5jP/fKk61du9bsCHAF03bIQbYKFSpkHDlyJMv4kSNHjEKFChmGYRjR0dFGwYIFb3c05NL8+fONRx55xAgMDDQCAwONRx55xJg/f77ZsZxq/Pjxxl133WV07tzZmDRpkjFp0iSjc+fORrFixYzRo0fbN3D78ssvzY6aJ/7+/sa2bduyjG/dutUICAi4/YFcoEKFCsaSJUsMwzCMqKgow2azGd27dzcyMjJMTgbcGjM1biYoKEjLli3LMr27bNkyBQUFSZIuXLigIkWKmBEPudC+fXu1b98+y/i+fftUvXp1ExI5350yw5iRkZHlVqkk5c+f3zJnP504cUIPPPCAJKl69ery8fHRgAEDLLn2LSEhQdOmTcu0PqpHjx72g4XheVgo7Ga++uor9e7dWy1atLDfrti+fbt+/vlnTZkyRT179tQnn3yibdu2af78+SanddzNblMEBATo3nvv1ZtvvqlmzZqZkOz2SE5O1ty5c/X1119r586dlrlvX7hwYe3ZsyfLBnzR0dGqVauWUlJSdPToUfs5O57qmWeeUUJCgubOnavg4GBJ1zYe7NSpkwIDA7V48WKTE+adt7e34uPj7euDihQposjIyH89r80T7dixQ08++aT8/Pwyfa29dOmSVqxYoTp16picELlBqXFDmzZt0uTJk3Xo0CFJUuXKlfX666/rkUceMTlZ3s2cOTPb8YSEBO3cuVPz58/XwoUL1apVq9uczLU2bNigr7/+WosWLVJwcLDatWunZ599Vg8++KDZ0ZyibNmyGjBgQJYZxgkTJmjChAmKjY1VZGSknnjiCY9+aiYuLk6tW7fW77//rpCQEPtY9erVtXTpUpUpU8bkhHnn5eWl5s2by8fHR9K1WeLGjRurUKFCmV7n6Yv5H3vsMVWqVElfffWV8uW7dtPi6tWreumll3Ts2DFt2LDB5ITIDUoN3Mr48eO1cOHCLFuXe6L4+HiFh4dr2rRpSkpKUvv27TVlyhTt3btX1apVMzueU1l9hvGfDMPQqlWrdPDgQUlS1apV7U/SWEFOF3PPmDHDxUlcy8/PT7t3786y+d7+/ftVt25d+9488CyUGjeUkZGh6OjobM9Eyu4sFis5fPiw6tWrp3PnzpkdJU9atWqlDRs2qGXLlurUqZOeeuopeXt7K3/+/JYsNZK1ZxhhPSVLltTs2bOzbA65fPlydenSRadPnzYpGfKChcJuZsuWLerYsaP++OMP3dg374S9E1JTU1WgQAGzY+TZL7/8on79+ql3795Z9qixqvr166t+/fpmx3CJ3377TWfPntXTTz9tH5s1a5ZGjBihCxcuqE2bNvrss8/st2zg/p5//nn17NlTH3/8sb14b9q0SYMHD9YLL7xgcjrkFqXGzbz66quqW7eufvrppzvykLVp06apVq1aZsfIs4iICE2bNk0PPPCAqlatqhdffFEdOnQwO9ZtcfnyZaWlpWUa8/TdWUeNGqVGjRrZS01UVJR69uypbt26qWrVqvroo48UHBysd99919ygyLGPP/5YNptNXbp00dWrVyVde4qtd+/e+uCDD0xOh9zi9pObKVSokPbu3ZvlKRKrGDhwYLbjiYmJ2rVrlw4fPqwNGzbYHyn1dBcuXND8+fM1ffp0bdu2Tenp6Ro/frx69OhhqcfyL168qLfeeksLFizQ2bNns1z39BnGUqVKadmyZapbt64k6e2339b69esVEREhSfruu+80YsQI7d+/38yYyIWLFy/q6NGjkqSKFSuqYMGCJidCXniZHQCZPfzww4qOjjY7hsvs3r0727e///5bzZo10759+yxTaKRrJbVHjx6KiIhQVFSUBg0apA8++EAlSpRQ69atzY7nNIMHD9aaNWv0xRdfyMfHR19//bVGjhyp4OBgzZo1y+x4eXb+/HmVLFnS/v769evVvHlz+/sPPvig4uLizIiGPCpYsKBq1KihGjVqUGgsgJkaN7N48WINGzZMgwcPzvaQtfvvv9+kZHCW9PR0LVu2TNOnT9fSpUvNjuMUZcuW1axZs9SoUSP5+/tr165dqlSpkmbPnq25c+fq559/NjtinpQrV06zZ89WgwYNlJaWpqJFi2rZsmX2c4OioqLUsGFDj1/gbnXt2rVTeHi4/P391a5du399rac/sn6nYk2Nm3n22WclST169LCP2Ww2DlmzEG9vb7Vp00Zt2rQxO4rTnDt3TqGhoZKurZ+5/s390UcfVe/evc2M5hQtWrTQkCFDNG7cOP3www8qWLCgHnvsMfv1yMhIVaxY0cSEyImAgAD7OkV/f/87bs3inYBS42ZiYmLMjgA4LDQ0VDExMSpbtqyqVKmiBQsW6KGHHtKyZcvsp1l7svfee0/t2rVTw4YNVbhwYc2cOTPTU3rTp0/P8mgw3M8/99YJDw83LwhchttPAPJswoQJ8vb2Vr9+/bRq1Sq1atVKhmHoypUrGj9+vPr37292RKdITExU4cKFs5xSfe7cORUuXNgS2xHcKRo3bqxFixZlKd1JSUlq06aN1qxZY04w5Amlxg0sXbpUzZs3V/78+W+5xsJKi0thXX/88Yd27typSpUqsQ4MbsnLy0vx8fEqUaJEpvEzZ86odOnSunLliknJkBeUGjfwz/+5vLxu/kAaa2rgbjIyMvTRRx9p6dKlSktLU5MmTTRixAj5+fmZHQ3IVmRkpCSpVq1aWrNmjYKCguzX0tPT9euvv2rq1Kk6fvy4SQmRF6ypcQP/PArhxmMRAHc2evRovfvuu2ratKn8/Pz06aef6syZM5o+fbrZ0YBs1apVSzabTTabTY0bN85y3c/PT5999pkJyeAMzNQAyLV77rlHb775pl555RVJ0qpVq9SyZUtdunTpX2cdAbNcP4ImNDRU27ZtU/Hixe3XChQooBIlSmRZMwXPQalxQ6tXr9aECRN04MABSddOAX7jjTcsdRIwrMHHx0fR0dEKCQmxj/n6+io6OlplypQxMRmAOxG3n9zM//73P/Xv31/PPfec/YmRLVu2qEWLFpowYYL69OljckLg/7t69ap8fX0zjeXPn59FlvAY+/fvV2xsbJbzyngowzMxU+NmypQpoyFDhqhv376Zxj///HONGTNGJ0+eNCkZkJWXl5eaN2+e6XTqZcuWqXHjxipUqJB9jN1Z4W6OHTumtm3bKioqyr7BqST7hnw8lOGZuOntZhISEvTUU09lGX/iiSeUmJhoQiLg5rp27aoSJUooICDA/ta5c2cFBwdnGgPcTf/+/VWhQgWdOXNGBQsW1O+//64NGzaobt26WrdundnxkEvM1LiZjh07qnbt2ho8eHCm8Y8//lg7duzQvHnzTEoGANZRrFgxrVmzRvfff78CAgK0bds2Va5cWWvWrNGgQYO0e/dusyMiF1hT42aqVaum0aNHa926dQoLC5N0bU3Npk2bNGjQIE2aNMn+2n79+pkVEwA8Wnp6uooUKSLpWsH5888/VblyZZUrV06HDh0yOR1yi5kaN1OhQoUcvc5ms+nYsWMuTgMA1vTYY49p0KBBatOmjTp27Kjz589r2LBh+vLLL7Vz507t27fP7IjIBUoNAOCOs3z5cl24cEHt2rVTdHS0nn76aR0+fFh33XWX5s+fn+3GfHB/lBo3duNqfACA65w7d06BgYF8zfVgPP3khqZNm6bq1avL19dXvr6+ql69ur7++muzYwGAJVy5ckX58uXLcospKCiIQuPhWCjsZoYPH67x48fr9ddfty8U/u233zRgwADFxsZq1KhRJicEAM+WP39+lS1blr1oLIjbT26mePHimjRpkl544YVM43PnztXrr7+uv//+26RkAGAd06ZN06JFizR79uxMJ3XDszFT42auXLmiunXrZhl/4IEHdPXqVRMSAYD1TJ48WdHR0QoODla5cuUy7YAtSbt27TIpGfKCUuNmXnzxRX3xxRcaP358pvEvv/xSnTp1MikVAFhLmzZtzI4AF+D2k5t5/fXXNWvWLIWEhKhevXqSpK1btyo2NlZdunRR/vz57a+9sfgAAHAno9S4mccffzxHr7PZbFqzZo2L0wCAdSUkJGjhwoU6evSoBg8erKCgIO3atUslS5ZU6dKlzY6HXKDUAADuOJGRkWratKkCAgJ0/PhxHTp0SKGhoRo2bJhiY2M1a9YssyMiF9inxo2dOHFCJ06cMDsGAFjOwIED1a1bNx05ckS+vr728RYtWmjDhg0mJkNeUGrcTEZGhkaNGqWAgACVK1dO5cqVU9GiRfXee+8pIyPD7HgAYAnbt2/XK6+8kmW8dOnSio+PNyERnIGnn9zM22+/rWnTpumDDz5Q/fr1JUkRERF69913dfnyZY0ePdrkhADg+Xx8fJSUlJRl/PDhwypevLgJieAMrKlxM8HBwZoyZYpat26daXzJkiV67bXXdPLkSZOSAYB1vPTSSzp79qwWLFigoKAgRUZGytvbW23atFGDBg00ceJEsyMiF7j95GbOnTunKlWqZBmvUqWKzp07Z0IiALCeTz75RCkpKSpRooQuXbqkhg0bqlKlSipSpAgz4h6MmRo38/DDD+vhhx/WpEmTMo2//vrr2r59u7Zs2WJSMgCwnoiICEVGRiolJUV16tRR06ZNzY6EPKDUuJn169erZcuWKlu2bKYDLePi4vTzzz/rscceMzkhAADuidtPbqZhw4Y6fPiw2rZtq4SEBCUkJKhdu3Y6dOgQhQYAnGj16tV6+umnVbFiRVWsWFFPP/20Vq1aZXYs5AEzNQCAO87//vc/9e/fX88995x9VnzLli1auHChJkyYoD59+picELlBqXFDCQkJ2rZtm86cOZNlb5ouXbqYlAoArKNMmTIaMmSI+vbtm2n8888/15gxY3jS1ENRatzMsmXL1KlTJ6WkpMjf3182m81+zWaz8QQUADhB4cKFtWfPHlWqVCnT+JEjR1S7dm2lpKSYlAx5wZoaNzNo0CD16NFDKSkpSkhI0Pnz5+1vFBoAcI7WrVtr8eLFWcaXLFmip59+2oREcAZmatxMoUKFFBUVpdDQULOjAIBlvf/++/r4449Vv379TGtqNm3apEGDBsnf39/+2n79+pkVEw6i1LiZdu3aqUOHDmrfvr3ZUQDAsipUqJCj19lsNh07dszFaeAslBo3sHTpUvuv//rrL40aNUrdu3dXjRo1lD9//kyvvfH4BAAAcA2lxg14eeVsaZPNZlN6erqL0wDAnePvv/+WJBUrVszkJHAGFgq7gYyMjBy9UWgAIO8SEhLUp08fFStWTCVLllTJkiVVrFgx9e3bVwkJCWbHQx4wUwMAuGOcO3dOYWFhOnnypDp16qSqVatKkvbv3685c+YoJCREmzdvVmBgoMlJkRuUGjfx22+/6ezZs5keJZw1a5ZGjBihCxcuqE2bNvrss8/k4+NjYkoA8GxvvPGGVq9erVWrVqlkyZKZrsXHx+uJJ55QkyZNNGHCBJMSIi+4/eQmRo0apd9//93+flRUlHr27KmmTZtqyJAhWrZsmcaOHWtiQgDwfD/88IM+/vjjLIVGku6++259+OGH2e5fA8/ATI2bKFWqlJYtW6a6detKkt5++22tX79eERERkqTvvvtOI0aM0P79+82MCQAezcfHR0ePHlWZMmWyvX7ixAlVqlRJly9fvs3J4AzM1LiJ8+fPZ/rJYf369WrevLn9/QcffFBxcXFmRAMAyyhWrJiOHz9+0+sxMTEKCgq6fYHgVJQaN1GyZEnFxMRIktLS0rRr1y7Vq1fPfj05OTnLnjUAAMc8+eSTevvtt5WWlpblWmpqqt555x099dRTJiSDM+QzOwCuadGihYYMGaJx48bphx9+UMGCBfXYY4/Zr0dGRqpixYomJgQAzzdq1CjVrVtX99xzj/r06aMqVarIMAwdOHBA//vf/5SamqrZs2ebHRO5xJoaN/H333+rXbt2ioiIUOHChTVz5ky1bdvWfr1JkyaqV6+eRo8ebWJKAPB8MTExeu2117RixQpd/xZos9nUrFkzTZ48OcvJ3fAclBo3k5iYqMKFC8vb2zvT+Llz51S4cGEVKFDApGQAYC3nz5/XkSNHJEmVKlViLY0FUGoAAIAlsFAYAABYAqUGAABYAqUGAABYAqUGAABYAqUGAABYAqUGAABYAqUGAABYAqUGAABYwv8DL0VkYmKIQyMAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2227,10 +2235,10 @@
    "id": "e48b4b77-7ee6-4669-be82-05d0d15f4065",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:44.793809Z",
-     "iopub.status.busy": "2024-10-25T15:12:44.793588Z",
-     "iopub.status.idle": "2024-10-25T15:13:20.983011Z",
-     "shell.execute_reply": "2024-10-25T15:13:20.982440Z"
+     "iopub.execute_input": "2024-10-25T15:14:09.055495Z",
+     "iopub.status.busy": "2024-10-25T15:14:09.055017Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.641772Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.641149Z"
     }
    },
    "outputs": [],
@@ -2252,16 +2260,16 @@
    "id": "4c7b083f-ef7f-47e0-b72f-2d0d1b9501e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:20.985305Z",
-     "iopub.status.busy": "2024-10-25T15:13:20.984934Z",
-     "iopub.status.idle": "2024-10-25T15:13:21.098863Z",
-     "shell.execute_reply": "2024-10-25T15:13:21.098260Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.644066Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.643683Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.784663Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.783984Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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1a9bU2p9JRERE+kPjfWreZOvWrdi+fTv27NkjxqcroEuXLujXrx8aN26MvLw8/O9//8Ply5dx5coVWFpaFulzcJ8aotdwnxoikhHRSk18fDzq1q2LzMxMMT7dOz18+BD29vYICwtDq1ativQxLDVEr2GpISIZ0fjyU2GeP3+O5cuXo3LlymJ8uiJJS0sDANja2r7xNdnZ2WpHOqSnp2s9FxEREUlD41JjY2OjtqZGEARkZGTAwsICv//+u6jh3kSpVGL8+PFo3rw5PD093/i6BQsWaHWdDxEREekOjS8/bdy4Ue19AwMD2NnZoUmTJrCxsRE13JuMGjUKf//9NyIiIlClSpU3vq6wmRonJydefiJ6hZefiEhG9G6fmjFjxmDv3r0IDw9/a6EBAFNTU5iamr6nZERERCSlYq2pefr0KdatW4erV68CADw8PDBs2LC3rm8pKUEQMHbsWOzevRvHjh2Di4uL1v4sIiIi0j8an9IdHh4OZ2dnLF++HE+fPsXTp0+xfPlyuLi4IDw8XBsZAQCjR4/G77//jq1bt6Js2bJISUlBSkoKnj9/rrU/k4iIiPSHxmtq6tSpA29vb6xatQqGhoYAXu4i/NVXX+HkyZOIiYnRTtA37GK8YcMGDB06tEifg7d0E72Ga2qISEY0LjXm5ua4ePFigZ18r1+/jvr16+v0zAlLDdFrWGqISEY0vvzUsGFD1Vqa/7p69Srq1asnSigiIiIiTRVpoXB0dLTqbX9/f4wbNw5xcXFo2rQpAOD06dP45ZdfsHDhQu2kJCIiInqHIl1+enUy97teqlAodPqUbl5+InoNLz8RkYwUaaYmISFB2zmIiIiISqRIpaZatWrazkFERERUIkUqNSEhIejatSuMjY0REhLy1tf6+vqKEoyIiIhIE0VeU5OSkgJ7e3sYGLz5himuqSHSM1xTQ0QyUqSZGqVSWejbRERERLpCo31qcnNz0b59e9y4cUNbeYiIiIiKRaNSY2xsrLZnDREREZGu0HhH4UGDBmHdunXayEJERERUbEVaU/NfeXl5WL9+PQ4dOoRGjRrB0tJS7fklS5aIFo6IiIioqDQuNZcvX0bDhg0BALGxsaIHIiIiIioOjUvN0aNHtZGDiIiIqEQ0XlPj5+eHjIyMAuPPnj2Dn5+fKKGIiIiINKVxqdm4cSOeP39eYPz58+fYtGmTKKGIiIiINFXky0/p6ekQBAGCICAjIwNmZmaq5/Lz87Fv3z7Y29trJSQRERHRuxS51JQrVw4KhQIKhQIffPBBgecVCgVmzZolajgiIiKioipyqTl69CgEQUC7du2wc+dO2Nraqp4zMTFBtWrV4OjoqJWQRERERO9S5FLTunVrAEBCQgKqVq0KhUJR4DWJiYmoWrWqeOmIiIiIikjjhcKurq54+PBhgfHHjx/DxcVFlFBEREREmtK41AiCUOh4Zmam2uJhIiIiovepyJefJk6cCODlguAZM2bAwsJC9Vx+fj7OnDmD+vXrix6QiIiIqCiKXGqioqIAvJypiYmJgYmJieo5ExMT1KtXD5MnTxY/IREREVERaHT3EwAMGzYMP//8M6ysrLQWioiIiEhTGp/9tGHDBm3kICIiIioRjUsNAJw/fx47duxAYmIicnJy1J7btWuXKMGIiIiINKHx3U9BQUFo1qwZrl69it27dyM3Nxf//vsvjhw5Amtra21kJCIiInonjUvN/PnzsXTpUoSGhsLExAQ///wzrl27hr59+3LjPSIiIpKMxqXm5s2b6NatG4CXdz09e/YMCoUCEyZMwJo1a0QPSERERFQUGpcaGxsbZGRkAAAqV66My5cvAwBSU1ORlZUlbjoiIiKiItJ4oXCrVq1w8OBB1KlTB5988gnGjRuHI0eO4ODBg2jfvr02MhIRERG9k8alZuXKlXjx4gUA4Ntvv4WxsTFOnjyJjz/+GNOnTxc9IBEREVFRKIQ3HeYkQ+np6bC2tkZaWho3DyQCAKt+UiconvQgqRMQkQ7SeE0NERERkS5iqSEiIiJZYKkhIiIiWWCpISIiIlnQuNRs2LCB+9EQERGRztG41EydOhUVK1bE8OHDcfLkSW1kIiIiItKYxqUmOTkZGzduxKNHj9CmTRvUqlULixYtQkpKijbyERERERWJxqXGyMgIvXr1wp49e5CUlISRI0diy5YtqFq1Knx9fbFnzx4olUptZCUiIiJ6oxItFHZwcECLFi3g7e0NAwMDxMTEYMiQIahevTqOHTsmUkQiIiKidytWqbl//z4WL16M2rVro02bNkhPT8fevXuRkJCA5ORk9O3bF0OGDBE7KxEREdEbaXxMQo8ePbB//3588MEHGDFiBAYPHgxbW1u11zx48AAVK1bUuctQPCaB6DU8JoGIZETjAy3t7e0RFhYGb2/vN77Gzs4OCQkJJQpGREREpAkeaElUmnGmhohkpEgzNcuXLy/yJ/T39y92GCIiIqLiKtJMjYuLS9E+mUKB+Pj4EofSFs7UEL2GMzVEJCNFmqnh+hgiIiLSdTzQkoiIiGRB47ufAODOnTsICQlBYmIicnJy1J5bsmSJKMGIiIiINKFxqTl8+DB8fX3h6uqKa9euwdPTE7du3YIgCGjYsKE2MhIR6ZysHCXcl7488+7qhIqwMOHEN5HUNP5XOG3aNEyePBkxMTEwMzPDzp07kZSUhNatW+OTTz7RRkYiIiKid9K41Fy9ehWDBw8G8PJwy+fPn6NMmTKYPXs2Fi1aJHpAIiIioqLQuNRYWlqq1tFUqlQJN2/eVD336NEj8ZIRERERaUDjNTVNmzZFREQE3N3d4ePjg0mTJiEmJga7du1C06ZNtZGRiIiI6J00LjVLlixBZmYmAGDWrFnIzMzE9u3bUaNGDd75RERERJLRuNS4urqq3ra0tERAQICogYiIiIiKo1j71LySmZkJpVKpNsbjB4iIiEgKGi8UTkhIQLdu3WBpaQlra2vY2NjAxsYG5cqVg42NjTYyEhEREb2TxjM1gwYNgiAIWL9+PRwcHKBQKLSRi4iIiEgjGpeaS5cu4cKFC6hZs6Y28hAREREVi8aXnxo3boykpCRtZCEiIiIqNo1nan777Td8+eWXSE5OhqenJ4yNjdWer1u3rmjhiIiIiIpK41Lz8OFD3Lx5E8OGDVONKRQKCIIAhUKB/Px8UQMSERERFYXGl5/8/PzQoEEDnDp1CvHx8UhISFD7r7b98ssvcHZ2hpmZGZo0aYKzZ89q/c8kIiIi3afxTM3t27cREhICNzc3beR5q+3bt2PixIkICAhAkyZNsGzZMnTu3BnXr1+Hvb39e89DREREukPjmZp27drh0qVL2sjyTkuWLMHIkSMxbNgweHh4ICAgABYWFli/fr0keYg0kZWjRLVFd1Ft0V1k5Sjf/QFERKQRjWdqevTogQkTJiAmJgZ16tQpsFDY19dXtHD/lZOTgwsXLmDatGmqMQMDA3To0AGnTp0q9GOys7ORnZ2tej89PV0r2UiGrPqJ/zmNTYCvF758u9JQIDdH/D8jPUj8z0lEpCc0LjVffvklAGD27NkFntPmQuFHjx4hPz8fDg4OauMODg64du1aoR+zYMECzJo1Syt5CtDGD8H3QdMfgqXl69RGOchRAktTXr59LxAw0XiiVHyloQRp6/9ZllQinaPxd1WlUvnGh67d+TRt2jSkpaWpHtxfh6RkYWKA29844vY3jrDQhUJDRCQzJTrQ8n2qUKECDA0Ncf/+fbXx+/fvo2LFioV+jKmpKUxNTd9HPCIiIpJYsX5dDAsLQ48ePeDm5gY3Nzf4+vri+PHjYmdTY2JigkaNGuHw4cOqMaVSicOHD8Pb21urfzYRERHpPo1Lze+//44OHTrAwsIC/v7+8Pf3h7m5Odq3b4+tW7dqI6PKxIkTsXbtWmzcuBFXr17FqFGj8OzZM7WNAImIiKh00vjy07x58/DDDz9gwoQJqjF/f38sWbIEc+bMwYABA0QN+F+ffvopHj58iBkzZiAlJQX169fHP//8U2DxMBEREZU+Gs/UxMfHo0ePHgXGfX19kZCQIEqotxkzZgxu376N7OxsnDlzBk2aNNH6n0lERES6T+NS4+TkpLau5ZVDhw7ByclJlFBEREREmtL48tOkSZPg7++PixcvolmzZgCAEydOIDAwED///LPoAYmIiIiKQuNSM2rUKFSsWBE//fQTduzYAQBwd3fH9u3b8dFHH4kesDTLMjaB+//f3OvqD1NhoY3NvYiIiGSiWPvU9OrVC7169RI7CxEREVGxFXvzvZycHDx48ABKpfrBfFWrVi1xKCIiIiJNaVxqbty4AT8/P5w8eVJtXBAErZ79RERERPQ2GpeaoUOHwsjICHv37kWlSpWgUCi0kYuIiIhIIxqXmosXL+LChQuoVauWNvIQERERFYvG+9R4eHjg0aNH2shCREREVGwal5pFixbh66+/xrFjx/D48WOkp6erPYiIiIikoPHlpw4dOgAA2rdvrzbOhcJEREQkJY1LzdGjR7WRg4iIiKhENC41rVu3fuNzly9fLlEYvZYeJP7nzFECS1Nevn0vEDDR+GqhXuDOyUREJIYS/5TMyMjAmjVr8OGHH6JevXpiZCIiIiLSWLFLTXh4OIYMGYJKlSph8eLFaNeuHU6fPi1mNiIiIqIi0+jyU0pKCgIDA7Fu3Tqkp6ejb9++yM7Oxp9//gkPDw9tZSQiIiJ6pyLP1PTo0QM1a9ZEdHQ0li1bhrt372LFihXazEZERERUZEWeqfn777/h7++PUaNGoUaNGtrMRERERKSxIs/UREREICMjA40aNUKTJk2wcuVK7ixMREREOqPIpaZp06ZYu3Yt7t27hy+++AJBQUFwdHSEUqnEwYMHkZGRoc2cRERERG+l8d1PlpaW8PPzQ0REBGJiYjBp0iQsXLgQ9vb28PX11UZGIiIioncq0T41NWvWxA8//IA7d+5g27ZtYmUiIiIi0pgoW9QaGhqiZ8+eCAkJEePTEREREWlMnvvuExERUanDUkNERESywFJDREREsqDxKd1ERHolPUg7nzdHCSxNefn2vUDAhL8jEkmN/wqJiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFrhPjQ6zMDHA7W8cpY5BRESkFzhTQ0RERLLAUkNERESywFJDREREssBSQ0RERLLAUkNERESywFJDREREssBSQ0RERLLAfWpIM+lB4n/OHCWwNOXl2/cCARN2bSIi0hx/ehAREZEssNQQERGRLLDUEBERkSyw1BAREZEssNQQERGRLPDuJyKiYrAwMcDtbxyljkFE/8GZGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBb0oNbdu3cLw4cPh4uICc3NzVK9eHTNnzkROTo7U0YiIiEhHGEkdoCiuXbsGpVKJ1atXw83NDZcvX8bIkSPx7NkzLF68WOp4REREpAP0otR06dIFXbp0Ub3v6uqK69evY9WqVSw1REREBEBPLj8VJi0tDba2tlLHICIiIh2hFzM1r4uLi8OKFSveOUuTnZ2N7Oxs1fvp6enajkZEREQSkXSmZurUqVAoFG99XLt2Te1jkpOT0aVLF3zyyScYOXLkWz//ggULYG1trXo4OTlp88shIiIiCSkEQRCk+sMfPnyIx48fv/U1rq6uMDExAQDcvXsXbdq0QdOmTREYGAgDg7d3ssJmapycnJCWlgYrK6uSfwEkiqwcJdyXpgAArk6oCAsTvb0qSkREEpL08pOdnR3s7OyK9Nrk5GS0bdsWjRo1woYNG95ZaADA1NQUpqamJY1JREREekAv1tQkJyejTZs2qFatGhYvXoyHDx+qnqtYsaKEyYiIiEhX6EWpOXjwIOLi4hAXF4cqVaqoPSfh1TMiIiLSIXqxeGHo0KEQBKHQBxERERGgJ6WGiIiI6F1YaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiIhIFhSCIAhSh3hf0tPTYW1tjbS0NFhZWUkdh4iIiETEmRoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFI6kDvE+CIAAA0tPTJU5CREREmipbtiwUCsUbny9VpSYjIwMA4OTkJHESIiIi0lRaWhqsrKze+LxCeDV9UQoolUrcvXv3nU1Pl6Snp8PJyQlJSUlv/YvUd/w65YVfp3yUhq8R4NepLzhT8x8GBgaoUqWK1DGKxcrKSi//B9QUv0554dcpH6XhawT4deo7LhQmIiIiWWCpISIiIllgqdFxpqammDlzJkxNTaWOolX8OuWFX6d8lIavEeDXKRelaqEwERERyRdnaoiIiEgWWGqIiIhIFlhqiIiISBZYaoiIiEgWWGqIiEogNTVV6ghUDH5+fqqjc/7r2bNn8PPzkyARiYGlRkfk5ORgzpw5+Pzzz3HixAmp47wXx48fx6BBg+Dt7Y3k5GQAwObNmxERESFxMtJUUlIS7ty5o3r/7NmzGD9+PNasWSNhKvEtWrQI27dvV73ft29flC9fHpUrV8alS5ckTEaa2rhxI54/f15g/Pnz59i0aZMEiUgMLDU6YvTo0Vi5ciXu37+Pzp07Izg4WOpIWrVz50507twZ5ubmiIqKQnZ2NoCXh5XNnz9f4nQlY2NjA1tb2yI95GLAgAE4evQoACAlJQUdO3bE2bNn8e2332L27NkSpxNPQECA6kDcgwcP4uDBg/j777/RtWtXTJkyReJ0VBTp6elIS0uDIAjIyMhAenq66vH06VPs27cP9vb2UscUVWJiIgrbvUUQBCQmJkqQSHtK1dlPuuzPP//EH3/8gTZt2uDAgQP4+OOPcfHiRXzwwQfo0aMHwsPDkZaWhsGDB0sdVRRz585FQEAABg8ejKCgINV48+bNMXfuXAmTldyyZctUbz9+/Bhz585F586d4e3tDQA4deoU9u/fj++++06ihOK7fPkyPvzwQwDAjh074OnpiRMnTuDAgQP48ssvMWPGDIkTiiMlJUVVavbu3Yu+ffuiU6dOcHZ2RpMmTSROJ77U1FQEBwfj5s2bmDJlCmxtbREZGQkHBwdUrlxZ6njFUq5cOSgUCigUCnzwwQcFnlcoFJg1a5YEybTHxcUF9+7dK1DWnjx5AhcXF+Tn50uUTHwsNTpCqVSqvkl06tQJu3btwoQJE3Dv3j3Ur18fU6dORWxsrGxKzfXr19GqVasC49bW1nq/RmHIkCGqtz/++GPMnj0bY8aMUY35+/tj5cqVOHToECZMmCBFRNHl5uaqdig9dOgQfH19AQC1atXCvXv3pIwmKhsbGyQlJcHJyQn//POPqoALgiCrHwwAEB0djQ4dOsDa2hq3bt3CyJEjYWtri127diExMVFvL9EcPXoUgiCgXbt22Llzp9qMqYmJCapVqwZHR0cJE4pPEIRCT7bOzMyEmZmZBIm0h6VGR3h5eSEsLAw1atQAAHTs2BGXL19WPX/16lWpomlFxYoVERcXB2dnZ7XxiIgIuLq6ShNKC/bv349FixYVGO/SpQumTp0qQSLtqF27NgICAtCtWzccPHgQc+bMAQDcvXsX5cuXlzideHr37o0BAwagRo0aePz4Mbp27QoAiIqKgpubm8TpxDVx4kQMHToUP/zwA8qWLasa9/HxwYABAyRMVjKtW7cGACQkJKBq1aqF/rCXi4kTJwJ4Ofv03XffwcLCQvVcfn4+zpw5g/r160uUTju4pkZHTJ8+XbVYtjQYOXIkxo0bhzNnzkChUODu3bvYsmULJk+ejFGjRkkdTzTly5fHnj17Cozv2bNHVj/sFy1ahNWrV6NNmzbo378/6tWrBwAICQlRXZaSg6VLl2LMmDHw8PDAwYMHUaZMGQDAvXv38NVXX0mcTlznzp3DF198UWC8cuXKSElJkSCRuK5evap2U8Yvv/yC+vXrY8CAAXj69KmEycQTFRWFqKgoCIKAmJgY1ftRUVG4du0a6tWrh8DAQKljiksgkoBSqRTmzp0rWFpaCgqFQlAoFIKZmZkwffp0qaOJasOGDYKhoaHQvXt3Yc6cOcKcOXOE7t27C0ZGRsKGDRukjieqvLw84cmTJ2pjCQkJwv379yVKJL6wsDAhNze3wHhubq4QFhYmQSLtsbOzEyIjIwVBEIQyZcoIN2/eFARBEA4cOCBUqVJFymii8PT0FP766y9BEAQhOjpaMDExEaZNmyY0bdpUGDp0qMTpxDV06FAhLS1N6hjvBQ+01DGGhoaFLuh6/Pgx7O3tZXfdPicnB3FxccjMzISHh4fqN185OXPmDJYvX666hOju7g5/f39ZLSxdv3492rZtCxcXF6mjaFVp+vc5YsQIPH78GDt27ICtrS2io6NhaGiInj17olWrVmoL4vVRmTJlcPnyZTg7O+P777/H5cuXERwcjMjISPj4+MhiNqo0YqnRMQYGBkhJSSnwTfPu3buoXr16ofsq6KO0tDTk5+cXuK35yZMnMDIygpWVlUTJqDhq1KiB+Ph4VK5cGa1bt0br1q3Rpk0b2a0zMTAwwP3792FnZ6c2HhsbCy8vL6Snp0uUTHxpaWno06cPzp8/j4yMDDg6OiIlJQXe3t7Yt28fLC0tpY5YIra2toiIiICHhwdatGiBwYMH4/PPP8etW7fg4eGBrKwsqSOWSO/evREYGAgrKyv07t37ra/dtWvXe0qlfVworCOWL18O4OWCrt9++01txiI/Px/h4eGoVauWVPFE169fP/To0aPAOoQdO3YgJCQE+/btkyiZ+JRKJeLi4vDgwQMolUq15wq7A0wf3bhxA8nJyTh27BjCw8OxePFifPHFF6hUqRLatGmD33//XeqIJfLqh4JCocDQoUNVd3oBL/99RkdHo1mzZlLF0wpra2scPHgQERERiI6ORmZmJho2bIgOHTpIHU0ULVq0wMSJE9G8eXOcPXtWtalibGwsqlSpInG6krO2tlYtgrayspL1guj/4kyNjng1bX/79m1UqVIFhoaGqudMTEzg7OyM2bNny+aSha2tLU6cOAF3d3e18WvXrqF58+Z4/PixRMnEdfr0aQwYMAC3b98usPmVQqGQ1eWKV7KysnD8+HFs27YNW7ZsgSAIyMvLkzpWiQwbNgzAy11o+/btC3Nzc9Vzr/59jhw5EhUqVJAqImkoMTERX331FZKSkuDv74/hw4cDACZMmID8/HzVL5r6KiQkBF27doWxsbHUUd4rlhod07ZtW+zatQs2NjZSR9EqS0tLnD59GnXq1FEbj4mJQZMmTfR+6veV+vXr44MPPsCsWbNQqVKlAr8tWVtbS5RMXAcOHMCxY8dw7NgxREVFwd3dXXUJqlWrVrL5/3nWrFmYPHmy3l96KYp37QQtlw0V5crQ0BApKSmws7N741owOWKpIUm0bdsWnp6eWLFihdr46NGjER0djePHj0uUTFyWlpa4dOmS7NaWvM7AwAB2dnaYNGkSPv/8c5QrV07qSFRCDRo0UHs/NzcXCQkJMDIyQvXq1REZGSlRMvHk5+fjzz//VC3ir127Nnx9fdVmyvVVxYoVsXbtWvTo0eONa8HkiKVGx+Tn5yMwMBCHDx8udA3GkSNHJEomrhMnTqBDhw5o3Lgx2rdvDwA4fPgwzp07hwMHDqBly5YSJxRHu3bt8PXXX6NLly5SR9GqZcuWITw8HOHh4TA1NVXN0rRp06bQrej1ScOGDXH48GHY2NigQYMGb12bIIcf9G+Tnp6OoUOHolevXvjss8+kjlMicXFx8PHxQXJyMmrWrAng5U7nTk5O+Ouvv1C9enWJE5bM999/j9mzZxdpLY2cLoOz1OiYMWPGIDAwEN26dSv0csXSpUslSia+ixcv4scff8TFixdhbm6OunXrYtq0aapdleVg9+7dmD59OqZMmYI6deoUuL5dt25diZJpT0xMDMLCwnDkyBHs3bsX9vb2aid465tZs2ZhypQpsLCweOeZQDNnznxPqaQTExODHj164NatW1JHKREfHx8IgoAtW7ao7sJ8/PgxBg0aBAMDA/z1118SJyy5a9euIS4uDr6+vtiwYcMbZ1A/+uij9xtMi1hqdEyFChWwadMm+Pj4SB2FRGBgUHDTboVCoTqLRU6/IQmCgKioKBw7dgxHjx5FREQEMjIyUKdOHURFRUkdj0QSERGBHj166P2uu29a13fp0iU0b94cmZmZEiUT33+Ludzxlm4dY2JiItv1F+np6ar9Z961n4dc9qlJSEiQOsJ70aNHD5w4cQLp6emoV68e2rRpg5EjR6JVq1ayXF+Tk5NT6OXhqlWrSpRIfK/f/SMIAu7du4fNmzerzrzSZ6ampsjIyCgwnpmZCRMTEwkSac+rGcSHDx/i+vXrAICaNWvKco0NZ2p0zE8//YT4+HisXLlSdvsK/HcFvoGBQaFfnxxnMEqDKVOmoHXr1mjZsqVs7ugqTGxsLIYPH46TJ0+qjcvx/9vXd4d+tRi8Xbt2mDZtmtohl/po8ODBiIyMxLp161Tnk505cwYjR45Eo0aNZHUmUlZWFsaMGYPNmzer/h81NDTE4MGDsWLFClnN4LDU6JhevXrh6NGjsLW1Re3atQuswdDnnR/DwsLQvHlzGBkZISws7K2vfXWSrhxs3rwZAQEBSEhIwKlTp1CtWjUsW7YMLi4usrqW/cqLFy9gZmYmdQytePX/79SpUwtd8/bqIE/SfampqRgyZAhCQ0NV32fz8vLg6+uLwMBAWZXzL774AocOHcLKlSvRvHlzAC8vI/r7+6Njx45YtWqVxAnFw1KjY15t8vUmGzZseE9JtCcvLw/z58+Hn5+fLHbufJtVq1ZhxowZGD9+PObNm4fLly/D1dUVgYGB2LhxI44ePSp1RFEolUrMmzcPAQEBuH//PmJjY+Hq6orvvvsOzs7Oqo3N9J2lpSUuXLggq929S7u4uDi1c9nkePm/QoUKCA4ORps2bdTGjx49ir59++Lhw4fSBNOG93FqJtHrypQpIyQkJEgdQ+vc3d2F3bt3C4KgftJxTEyMUL58eQmTiWvWrFmCq6ur8Pvvvwvm5uaqrzMoKEho2rSpxOnE4+XlJRw/flzqGO9FZmamMH36dMHb21uoXr264OLiovbQZ2lpaUJ+fn6B8fz8fFmeZm1ubi5cuXKlwPjly5cFCwsLCRJpDxcK66C8vDwcO3YMN2/exIABA1C2bFncvXsXVlZWsjnFul27dggLC4Ozs7PUUbQqISGhwCZmwMtFis+ePZMgkXZs2rQJa9asQfv27fHll1+qxuvVq4dr165JmExcixYtwtdff4358+cXeou+XBa4Ay9P6Q4LC8Nnn31W6KU2fbV792588803uHjxYoG1JM+fP0fjxo2xePFi9OjRQ6KE4vP29sbMmTOxadMm1aXh58+fY9asWfD29pY4nbhYanTM7du30aVLFyQmJiI7OxsdO3ZE2bJlsWjRImRnZyMgIEDqiKLo2rUrpk6dipiYGDRq1KjAtvO+vr4SJROXi4sLLl68iGrVqqmN//PPPwXOvdJnycnJhU7bK5VK5ObmSpBIO14d5vhqw8hXBBkuFP7777/x119/qdZgyMWqVavw9ddfF7o41tLSEt988w1Wrlwpq1KzbNkydOnSBVWqVFGt+7p06RLMzMywf/9+idOJi6VGx4wbNw5eXl64dOkSypcvrxrv1asXRo4cKWEycb06nXvJkiUFnpPTD4eJEydi9OjRePHiBQRBwNmzZ7Ft2zYsWLAAv/32m9TxROPh4YHjx48XKG/BwcGFzlTpK7msgSoKGxsb1aZ0cnL58mX8+uuvb3y+VatWmD59+ntMpH116tTBjRs3sGXLFtXMaf/+/TFw4EC1w1nlgKVGxxw/fhwnT54ssE+Cs7MzkpOTJUolvtf395CrESNGwNzcHNOnT0dWVhYGDBgAR0dH/Pzzz+jXr5/U8UQzY8YMDBkyBMnJyVAqldi1axeuX7+OTZs2Ye/evVLHE42c7sp7lzlz5mDGjBnYuHGjrG75ffr06VtPjc/NzdX7jQX/Kzc3F7Vq1cLevXtl9Yvxm7DU6BilUlnoLMWdO3f0fl+IV27duoWDBw8iNzcXrVu3Ru3ataWOpFUDBw7EwIEDkZWVhczMTFmelPvRRx8hNDQUs2fPhqWlJWbMmIGGDRsiNDQUHTt2lDpeiYWEhBQ6bm1tjQ8++ACVKlV6z4m076effsLNmzfh4OAAZ2fnAuuH9PWcK2dnZ5w/f/6Nd7CdP3++wIyjPjM2NsaLFy+kjvHe8JZuHfPpp5/C2toaa9asQdmyZREdHQ07Ozt89NFHqFq1qt7f0n306FF0794dz58/BwAYGRlh/fr1GDRokMTJtOvBgweqnTxr1aoly5085ayw4y5eUSgU6NevH9auXSurGQ25nnP17bff4vfff8fZs2fh4OCg9lxKSgqaNGmCQYMGYd68eRIlFN/8+fMRGxuL3377DUZG8p7LYKnRMXfu3EHnzp0hCAJu3LgBLy8v3LhxAxUqVEB4eLje/5bfokULVKhQAatWrYKZmRmmT5+O3bt34+7du1JH04qMjAx89dVX2LZtm+qSm6GhIT799FP88ssvstrgqzRKS0vDhQsXMHr0aPTq1Qvz58+XOhK9Q0ZGBry9vZGYmIhBgwapTui+du0atmzZAicnJ5w+fVo2M+PAyzWZhw8fRpkyZVCnTp0CN2bo86aur2Op0UF5eXkICgpCdHQ0MjMz0bBhQ9ks6CpXrhxOnjwJDw8PAC+377ayssL9+/fVFkbLxaeffoqoqCisWLFCdevkqVOnMG7cONSvXx9BQUESJyw+W1tbxMbGokKFCrCxsXnrLb9Pnjx5j8nev3/++Qfjx4+X1e3rwMtdd4ODg3Hz5k1MmTIFtra2iIyMhIODAypXrix1vGJLS0vDtGnTsH37dtX6mXLlyqFfv36YN28ebGxsJE4ortKwqesrLDU6Rs5bzAMvp/FTUlLUZpzKli2LS5cuwdXVVcJk2mFpaYn9+/ejRYsWauPHjx9Hly5d9Hqvmo0bN6Jfv34wNTVFYGDgW0vNkCFD3mOy9+/WrVvw9PSU1cnO0dHR6NChA6ytrXHr1i1cv34drq6umD59OhITE7Fp0yapI5aYIAh49OgRBEGAnZ2dbPbieUWpVOLHH39ESEgIcnJy0K5dO3z//fey+AX5TeR9cU0P2dvbo1evXhg0aBDat2//1mv5+mr//v1ql12USiUOHz6My5cvq8bksk9N+fLlC73EZG1trfe/DQ4ZMgR79+6Fj48Phg4dKnUcScXHx8PR0VHqGKKaOHEihg4dih9++EHtUoyPjw8GDBggYTLxKBQKWa9vmzdvHr7//nt06NAB5ubmWL58OR4+fIj169dLHU17JNnHmN5o165dQp8+fQRzc3OhYsWKwrhx44Rz585JHUs0CoXinQ8DAwOpY4pm9erVQocOHYR79+6pxu7duyd06tRJCAgIkDCZOAwNDQVHR0fhf//7nxAXFyd1HElERUUJDRo0EMaPHy91FFFZWVmp/k7/e8THrVu3BFNTUymjURG5ubmpfZ85ePCgYGJiUugREXLBy086KiMjA8HBwdi2bRuOHDkCV1dXDBo0CDNmzJA6GmmgQYMGiIuLQ3Z2NqpWrQoASExMhKmpKWrUqKH2Wn28RTYpKQkbNmzAxo0bcevWLbRo0QIjRoxAnz59ZDXF/aY1Q8+ePUNeXh46duyIHTt2yOqYBHt7e+zfvx8NGjRQu0R88OBB+Pn5ISkpSeqI9A6mpqaIi4uDk5OTaszMzAxxcXGyPUyYpUYPXLlyBQMHDkR0dLRsdtotLd51W+x/6estsq8cPXoUgYGB2LlzJ4yMjNCvXz8MHz4cjRs3ljpaiW3cuLHQcSsrK9SsWVO18F1ORowYgcePH2PHjh2wtbVFdHQ0DA0N0bNnT7Rq1QrLli2TOiK9g6GhIVJSUtQusb3aKsTFxUXCZNrDUqOjXrx4gZCQEGzduhX//PMPHBwc0L9/fyxcuFDqaERvlZGRgaCgIAQGBuL06dPw9PTEpUuXpI5FGkpLS0OfPn1w/vx5ZGRkwNHRESkpKfD29sa+ffsK3BZMusfAwABdu3aFqampaiw0NBTt2rVT+/uT0y3dXCisY/bv34+tW7fizz//hJGREfr06YMDBw6gVatWUkejEnrx4gW2b9+OZ8+eoWPHjgUuP8lF2bJl0b59e9y+fRvXrl3DlStXpI5ExWBtbY2DBw8iIiJCbXuJV4d66qPly5cX+bX+/v5aTPJ+FHbXodw3OuVMjY6xsLBA9+7dMXDgQPj4+BTYmpz0w8SJE5Gbm4sVK1YAAHJycvDhhx/iypUrsLCwQF5eHg4cOIBmzZpJnFQ8z58/xx9//IH169fj+PHjcHFxwbBhwzB06FC93tOktEpKSlJbiyEHRb3kolAoEB8fr+U0pA2cqdEx9+/fl9VOlqXVgQMH1HaX3bJlCxITE3Hjxg1UrVoVfn5+mDdvHv766y8JU4rj9OnTWL9+PXbs2IGcnBz07t0bhw4dQtu2baWORiXg7OyMFi1aYNCgQejTp4/eb0EAAAkJCVJHIC2T3yYoeurVD4RXhebOnTtqJ1lnZWXhhx9+kCqe6FxdXfH48eMC46mpqbLYhC8xMVFt8eiBAwfQp08fVKtWDQqFAuPGjUNUVJSECcXh4eGB5s2bIzIyEgsWLMC9e/fw+++/s9DIwPnz5/Hhhx9i9uzZqFSpEnr27Ing4GBkZ2dLHY3ojXj5SUcYGhri3r17qp12rayscPHiRdUP+Pv378PR0VE2dz8VtrMw8PLrrFq1qt5/4yxXrhzOnTunWjfj4uKC7777Dn5+fgBe7kDr7u6uOthTX/n7+2P48OGoV6+e1FFISwRBwLFjx7B161bs3LkTSqUSvXv3lsUGbnfu3EFISAgSExORk5Oj9tySJUskSkUlwctPOuL1binXrhkSEqJ6+/WdhfPz83H48GE4OztLkExc7u7uCA0NxcSJE/Hvv/8iMTFRbfbi9u3bBU4I1keaLLzUV7179y7ya+V0F8krCoUCbdu2Rdu2bTFq1CgMHz4cGzdu1PtSc/jwYfj6+sLV1RXXrl2Dp6cnbt26BUEQ0LBhQ6njUTGx1NB71bNnTwAvv1G+vjLf2NgYzs7O+OmnnyRIJq6vv/4a/fr1w19//YV///0XPj4+aosU9+3bhw8//FDChFRU/y3egiBg9+7dsLa2hpeXFwDgwoULSE1N1aj86JM7d+5g69at2Lp1Ky5fvgxvb2/88ssvUscqsWnTpmHy5MmYNWsWypYti507d8Le3h4DBw5Ely5dpI5HxcRSQ+/Vq3VCLi4uOHfuHCpUqCBxIu3o1asX9u3bh71796JTp04YO3as2vMWFhb46quvJEpHmvjvCcbffPMN+vbti4CAABgaGgJ4OcP41VdfyWo3YQBYvXo1tm7dihMnTqBWrVoYOHAg9uzZg2rVqkkdTRRXr17Ftm3bAABGRkZ4/vw5ypQpg9mzZ+Ojjz7CqFGjJE5IxcE1NTrCwMAAGzduVP1W2L9/fyxbtkx1iSI1NRXDhg2TzZoaIn1kZ2eHiIgI1KxZU238+vXraNasWaGL3/WVk5MT+vfvj4EDB8pyzVTFihVx9OhRuLu7w8PDAwsXLoSvry8uXbqE5s2by+rE9dKEMzU65PXLMV988YXa+4WdPaNPli9fjs8//xxmZmbvXIshh42vSqsXL17AzMxM6hhakZeXh2vXrhUoNdeuXVO7W1EOEhMT9f57zts0bdoUERERcHd3h4+PDyZNmoSYmBjs2rULTZs2lToeFRNnaui9cXFxwfnz51G+fPm3boLFja/0j1KpxLx58xAQEID79+8jNjYWrq6u+O677+Ds7Izhw4dLHVEUEydOxKZNm/C///1PtSbqzJkzWLhwIT777DPZ3TFz/PhxrF69Gjdv3kRwcDAqV66MzZs3w8XFBS1atJA6XonEx8cjMzMTdevWxbNnzzBp0iScPHkSNWrUwJIlS2Rzma3Uec+nghORDM2aNUtwdXUVfv/9d8Hc3Fy4efOmIAiCEBQUJDRt2lTidOLJz88XFi1aJDg6OgoKhUJQKBSCo6OjsGjRIiEvL0/qeKIKDg4WzM3NhREjRgimpqaqv9MVK1YIXbt2lTgdUeE4U0NEJebm5obVq1ejffv2KFu2LC5duqS6Vdbb2xtPnz6VOqLo0tPTAUB2C4RfadCgASZMmIDBgwer/Z1GRUWha9euSElJkTqiKHJycvDgwYMClw+rVq0qUSIqCa6pIUnk5+cjMDAQhw8fLvQbypEjRyRKRsWRnJwMNze3AuNKpRK5ubkSJNI+uZaZV65fv17oQbrW1tZITU19/4FEFhsbi+HDh+PkyZNq44IgQKFQ8KYMPcVSQ5IYN24cAgMD0a1bN3h6espqQWKDBg2K/PVERkZqOc374eHhgePHjxdYhxAcHIwGDRpIlEocpfHvE3h5d1BcXFyBzTAjIiJkcZTJsGHDYGRkhL1796JSpUqy+h5UmrHUkCSCgoKwY8cO+Pj4SB1FdK82GARe3gn066+/wsPDA97e3gBeHgD577//ymqfmhkzZmDIkCFITk6GUqnErl27cP36dWzatAl79+6VOl6J/PfvszQZOXIkxo0bh/Xr10OhUODu3bs4deoUJk2ahBkzZkgdr8QuXryICxcuoFatWlJHIRFxTQ1JwtHREceOHcMHH3wgdRStGjFiBCpVqoQ5c+aojc+cORNJSUl6v9X8fx0/fhyzZ8/GpUuXkJmZiYYNG2LGjBno1KmT1NGoGARBwPz587FgwQJkZWUBAExNTTFlyhRMmzYN5ubmEicsmcaNG2Pp0qV6fxcXqWOp0TE2NjaFToMqFAqYmZnBzc0NQ4cOxbBhwyRIJ56ffvoJ8fHxWLlypaynfa2trXH+/HnVwZav3LhxA15eXkhLS5MoGZXEhQsXcPXqVQBA7dq19f4S29vk5OQgLi4OmZmZ8PDwwOrVq/Hjjz/q/ULhI0eOYPr06Zg/fz7q1KkDY2NjteflvmZKrnj5ScfMmDED8+bNQ9euXVX7YJw9exb//PMPRo8ejYSEBIwaNQp5eXkYOXKkxGmLLyIiAkePHsXff/+N2rVrF/iGIpeDAc3NzXHixIkCpebEiROy3aBOzh48eIB+/frh2LFjKFeuHICXu323bdsWQUFBsLOzkzagCLKzs/H999/j4MGDqpmZnj17YsOGDejVqxcMDQ0xYcIEqWOWWIcOHQAA7du3VxvnQmH9xlKjYyIiIjB37lx8+eWXauOrV6/GgQMHsHPnTtStWxfLly/X61JTrlw59OrVS+oYWjd+/HiMGjUKkZGRapu1rV+/Ht99953E6cRTWmYYx44di4yMDPz7779wd3cHAFy5cgVDhgyBv7+/6iwhfTZjxgysXr0aHTp0wMmTJ/HJJ59g2LBhOH36NH766Sd88sknqnOv9NnRo0eljkDaINkOOVQoS0tL4caNGwXGb9y4IVhaWgqCIAhxcXGChYXF+45GxbR9+3ahWbNmgo2NjWBjYyM0a9ZM2L59u9SxRLVkyRKhfPnywqBBg4Tly5cLy5cvFwYNGiRUqFBBmDdvnmoDtzVr1kgdtUSsrKyEs2fPFhg/c+aMYG1t/f4DaYGLi4uwZ88eQRAEISYmRlAoFMKwYcMEpVIpcTKid+NMjY6xtbVFaGhogend0NBQ2NraAgCePXuGsmXLShGPiqFv377o27dvgfHLly/D09NTgkTiKy0zjEqlssClUgAwNjaWzdlPd+7cQaNGjQAAnp6eMDU1xYQJE2S59i01NRXr1q1TWx/l5+enOliY9A8XCuuYtWvXYtSoUfDx8VFdrjh37hz27duHgIAADB8+HD/99BPOnj2L7du3S5xWc2+6TGFtbY0PPvgAkydPRseOHSVI9n5kZGRg27Zt+O2333DhwgXZXLcvU6YMLl68WGADvri4ONSvXx+ZmZm4efOm6pwdffXRRx8hNTUV27Ztg6OjI4CXGw8OHDgQNjY22L17t8QJS87Q0BApKSmq9UFly5ZFdHT0W89r00fnz59H586dYW5urva99vnz5zhw4AAaNmwocUIqDpYaHXTixAmsXLkS169fBwDUrFkTY8eORbNmzSROVnIbN24sdDw1NRUXLlzA9u3bERwcjB49erznZNoVHh6O3377Dbt27YKjoyN69+6Njz/+GI0bN5Y6miiqVq2KCRMmFJhhXLp0KZYuXYrExERER0ejU6dOen3XTFJSEnx9ffHvv//CyclJNebp6YmQkBBUqVJF4oQlZ2BggK5du8LU1BTAy1nidu3awdLSUu11+r6Yv2XLlnBzc8PatWthZPTyokVeXh5GjBiB+Ph4hIeHS5yQioOlhnTKkiVLEBwcXGDrcn2UkpKCwMBArFu3Dunp6ejbty8CAgJw6dIleHh4SB1PVHKfYfwvQRBw6NAhXLt2DQDg7u6uupNGDoq6mHvDhg1aTqJd5ubmiIqKKrD53pUrV+Dl5aXam4f0C0uNDlIqlYiLiyv0TKTCzmKRk9jYWDRt2hRPnjyROkqJ9OjRA+Hh4ejWrRsGDhyILl26wNDQEMbGxrIsNYC8ZxhJfhwcHLB58+YCm0Pu378fgwcPxv379yVKRiXBhcI65vTp0xgwYABu376N1/tmadg7ITs7GyYmJlLHKLG///4b/v7+GDVqVIE9auSqefPmaN68udQxtOLUqVN4/PgxunfvrhrbtGkTZs6ciWfPnqFnz55YsWKF6pIN6b5PP/0Uw4cPx+LFi1XF+8SJE5gyZQr69+8vcToqLpYaHfPll1/Cy8sLf/31V6k8ZG3dunWoX7++1DFKLCIiAuvWrUOjRo3g7u6Ozz77DP369ZM61nvx4sUL5OTkqI3p++6ss2fPRps2bVSlJiYmBsOHD8fQoUPh7u6OH3/8EY6Ojvj++++lDUpFtnjxYigUCgwePBh5eXkAXt7FNmrUKCxcuFDidFRcvPykYywtLXHp0qUCd5HIxcSJEwsdT0tLQ2RkJGJjYxEeHq66pVTfPXv2DNu3b8f69etx9uxZ5OfnY8mSJfDz85PVbflZWVn4+uuvsWPHDjx+/LjA8/o+w1ipUiWEhobCy8sLAPDtt98iLCwMERERAIA//vgDM2fOxJUrV6SMScWQlZWFmzdvAgCqV68OCwsLiRNRSRhIHYDUNWnSBHFxcVLH0JqoqKhCH48ePULHjh1x+fJl2RQa4GVJ9fPzQ0REBGJiYjBp0iQsXLgQ9vb28PX1lTqeaKZMmYIjR45g1apVMDU1xW+//YZZs2bB0dERmzZtkjpeiT19+hQODg6q98PCwtC1a1fV+40bN0ZSUpIU0aiELCwsUKdOHdSpU4eFRgY4U6Njdu/ejenTp2PKlCmFHrJWt25diZKRWPLz8xEaGor169cjJCRE6jiiqFq1KjZt2oQ2bdrAysoKkZGRcHNzw+bNm7Ft2zbs27dP6oglUq1aNWzevBmtWrVCTk4OypUrh9DQUNW5QTExMWjdurXeL3CXu969eyMwMBBWVlbo3bv3W1+r77esl1ZcU6NjPv74YwCAn5+fakyhUPCQNRkxNDREz5490bNnT6mjiObJkydwdXUF8HL9zKsf7i1atMCoUaOkjCYKHx8fTJ06FYsWLcKff/4JCwsLtGzZUvV8dHQ0qlevLmFCKgpra2vVOkUrK6tSt2axNGCp0TEJCQlSRyDSmKurKxISElC1alXUqlULO3bswIcffojQ0FDVadb6bM6cOejduzdat26NMmXKYOPGjWp36a1fv77ArcGke/67t05gYKB0QUhrePmJiEps6dKlMDQ0hL+/Pw4dOoQePXpAEATk5uZiyZIlGDdunNQRRZGWloYyZcoUOKX6yZMnKFOmjCy2Iygt2rVrh127dhUo3enp6ejZsyeOHDkiTTAqEZYaHRASEoKuXbvC2Nj4nWss5LS4lOTr9u3buHDhAtzc3LgOjHSSgYEBUlJSYG9vrzb+4MEDVK5cGbm5uRIlo5JgqdEB//3HZWDw5hvSuKaGdI1SqcSPP/6IkJAQ5OTkoH379pg5cybMzc2ljkZUqOjoaABA/fr1ceTIEdja2qqey8/Pxz///IPVq1fj1q1bEiWkkuCaGh3w36MQXj8WgUiXzZs3D99//z06dOgAc3Nz/Pzzz3jw4AHWr18vdTSiQtWvXx8KhQIKhQLt2rUr8Ly5uTlWrFghQTISA2dqiKjYatSogcmTJ+OLL74AABw6dAjdunXD8+fP3zrrSCSVV0fQuLq64uzZs7Czs1M9Z2JiAnt7+wJrpkh/sNTooMOHD2Pp0qW4evUqgJenAI8fP15WJwGTPJiamiIuLg5OTk6qMTMzM8TFxaFKlSoSJiOi0oiXn3TMr7/+inHjxqFPnz6qO0ZOnz4NHx8fLF26FKNHj5Y4IdH/ycvLg5mZmdqYsbExF1mS3rhy5QoSExMLnFfGmzL0E2dqdEyVKlUwdepUjBkzRm38l19+wfz585GcnCxRMqKCDAwM0LVrV7XTqUNDQ9GuXTtYWlqqxrg7K+ma+Ph49OrVCzExMaoNTgGoNuTjTRn6iRe9dUxqaiq6dOlSYLxTp05IS0uTIBHRmw0ZMgT29vawtrZWPQYNGgRHR0e1MSJdM27cOLi4uODBgwewsLDAv//+i/DwcHh5eeHYsWNSx6Ni4kyNjhkwYAAaNGiAKVOmqI0vXrwY58+fR1BQkETJiIjko0KFCjhy5Ajq1q0La2trnD17FjVr1sSRI0cwadIkREVFSR2RioFranSMh4cH5s2bh2PHjsHb2xvAyzU1J06cwKRJk7B8+XLVa/39/aWKSUSk1/Lz81G2bFkALwvO3bt3UbNmTVSrVg3Xr1+XOB0VF2dqdIyLi0uRXqdQKBAfH6/lNERE8tSyZUtMmjQJPXv2xIABA/D06VNMnz4da9aswYULF3D58mWpI1IxsNQQEVGps3//fjx79gy9e/dGXFwcunfvjtjYWJQvXx7bt28vdGM+0n0sNTrs9dX4RESkPU+ePIGNjQ2/5+ox3v2kg9atWwdPT0+YmZnBzMwMnp6e+O2336SORUQkC7m5uTAyMipwicnW1paFRs9xobCOmTFjBpYsWYKxY8eqFgqfOnUKEyZMQGJiImbPni1xQiIi/WZsbIyqVatyLxoZ4uUnHWNnZ4fly5ejf//+auPbtm3D2LFj8ejRI4mSERHJx7p167Br1y5s3rxZ7aRu0m+cqdExubm58PLyKjDeqFEj5OXlSZCIiEh+Vq5cibi4ODg6OqJatWpqO2ADQGRkpETJqCRYanTMZ599hlWrVmHJkiVq42vWrMHAgQMlSkVEJC89e/aUOgJpAS8/6ZixY8di06ZNcHJyQtOmTQEAZ86cQWJiIgYPHgxjY2PVa18vPkRERKUZS42Oadu2bZFep1AocOTIES2nISKSr9TUVAQHB+PmzZuYMmUKbG1tERkZCQcHB1SuXFnqeFQMLDVERFTqREdHo0OHDrC2tsatW7dw/fp1uLq6Yvr06UhMTMSmTZukjkjFwH1qdNidO3dw584dqWMQEcnOxIkTMXToUNy4cQNmZmaqcR8fH4SHh0uYjEqCpUbHKJVKzJ49G9bW1qhWrRqqVauGcuXKYc6cOVAqlVLHIyKShXPnzuGLL74oMF65cmWkpKRIkIjEwLufdMy3336LdevWYeHChWjevDkAICIiAt9//z1evHiBefPmSZyQiEj/mZqaIj09vcB4bGws7OzsJEhEYuCaGh3j6OiIgIAA+Pr6qo3v2bMHX331FZKTkyVKRkQkHyNGjMDjx4+xY8cO2NraIjo6GoaGhujZsydatWqFZcuWSR2RioGXn3TMkydPUKtWrQLjtWrVwpMnTyRIREQkPz/99BMyMzNhb2+P58+fo3Xr1nBzc0PZsmU5I67HOFOjY5o0aYImTZpg+fLlauNjx47FuXPncPr0aYmSERHJT0REBKKjo5GZmYmGDRuiQ4cOUkeiEmCp0TFhYWHo1q0bqlatqnagZVJSEvbt24eWLVtKnJCIiEg38fKTjmndujViY2PRq1cvpKamIjU1Fb1798b169dZaIiIRHT48GF0794d1atXR/Xq1dG9e3ccOnRI6lhUApypISKiUufXX3/FuHHj0KdPH9Ws+OnTpxEcHIylS5di9OjREiek4mCp0UGpqak4e/YsHjx4UGBvmsGDB0uUiohIPqpUqYKpU6dizJgxauO//PIL5s+fzztN9RRLjY4JDQ3FwIEDkZmZCSsrKygUCtVzCoWCd0AREYmgTJkyuHjxItzc3NTGb9y4gQYNGiAzM1OiZFQSXFOjYyZNmgQ/Pz9kZmYiNTUVT58+VT1YaIiIxOHr64vdu3cXGN+zZw+6d+8uQSISA2dqdIylpSViYmLg6uoqdRQiItmaO3cuFi9ejObNm6utqTlx4gQmTZoEKysr1Wv9/f2likkaYqnRMb1790a/fv3Qt29fqaMQEcmWi4tLkV6nUCgQHx+v5TQkFpYaHRASEqJ6++HDh5g9ezaGDRuGOnXqwNjYWO21rx+fQERERC+x1OgAA4OiLW1SKBTIz8/XchoiotLj0aNHAIAKFSpInITEwIXCOkCpVBbpwUJDRFRyqampGD16NCpUqAAHBwc4ODigQoUKGDNmDFJTU6WORyXAmRoiIio1njx5Am9vbyQnJ2PgwIFwd3cHAFy5cgVbt26Fk5MTTp48CRsbG4mTUnGw1OiIU6dO4fHjx2q3Em7atAkzZ87Es2fP0LNnT6xYsQKmpqYSpiQi0m/jx4/H4cOHcejQITg4OKg9l5KSgk6dOqF9+/ZYunSpRAmpJHj5SUfMnj0b//77r+r9mJgYDB8+HB06dMDUqVMRGhqKBQsWSJiQiEj//fnnn1i8eHGBQgMAFStWxA8//FDo/jWkHzhToyMqVaqE0NBQeHl5AQC+/fZbhIWFISIiAgDwxx9/YObMmbhy5YqUMYmI9JqpqSlu3ryJKlWqFPr8nTt34ObmhhcvXrznZCQGztToiKdPn6r95hAWFoauXbuq3m/cuDGSkpKkiEZEJBsVKlTArVu33vh8QkICbG1t318gEhVLjY5wcHBAQkICACAnJweRkZFo2rSp6vmMjIwCe9YQEZFmOnfujG+//RY5OTkFnsvOzsZ3332HLl26SJCMxGAkdQB6ycfHB1OnTsWiRYvw559/wsLCAi1btlQ9Hx0djerVq0uYkIhI/82ePRteXl6oUaMGRo8ejVq1akEQBFy9ehW//vorsrOzsXnzZqljUjFxTY2OePToEXr37o2IiAiUKVMGGzduRK9evVTPt2/fHk2bNsW8efMkTElEpP8SEhLw1Vdf4cCBA3j1I1ChUKBjx45YuXJlgZO7SX+w1OiYtLQ0lClTBoaGhmrjT548QZkyZWBiYiJRMiIieXn69Clu3LgBAHBzc+NaGhlgqSEiIiJZ4EJhIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpIFlhoiIiKSBZYaIiIikgWWGiIiIpKF/weK/vYLg+NC3QAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2312,10 +2320,10 @@
    "id": "d70a7edb-9374-4fce-b8d3-e9ef9f53f328",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:21.102602Z",
-     "iopub.status.busy": "2024-10-25T15:13:21.102073Z",
-     "iopub.status.idle": "2024-10-25T15:13:21.123269Z",
-     "shell.execute_reply": "2024-10-25T15:13:21.122814Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.787408Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.786979Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.808902Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.808340Z"
     }
    },
    "outputs": [
@@ -2355,10 +2363,10 @@
    "id": "7f4a5065-e436-46cd-be5e-e6c1274fc3b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:21.125274Z",
-     "iopub.status.busy": "2024-10-25T15:13:21.125090Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.423624Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.423055Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.811015Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.810637Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.349481Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.348867Z"
     }
    },
    "outputs": [],
@@ -2379,16 +2387,16 @@
    "id": "fc2467b0-eb84-4f72-b895-954f5a8634e6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.425728Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.425518Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.529699Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.529067Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.352032Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.351562Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.457883Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.457373Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2415,10 +2423,10 @@
    "id": "34b11c19-1256-4ae0-9067-2eb374ce5147",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.532188Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.531831Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.551436Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.550860Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.459947Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.459745Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.476870Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.476442Z"
     }
    },
    "outputs": [
@@ -2471,10 +2479,10 @@
    "id": "1c4785f9-5ec1-4340-8837-6d0cdaf4c406",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.553775Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.553444Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.648181Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.647639Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.479022Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.478841Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.574657Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.574020Z"
     }
    },
    "outputs": [],
diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb
index 76571ae1b..6157159dc 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb
@@ -38,10 +38,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:43.516178Z",
-     "iopub.status.busy": "2024-10-25T15:13:43.515760Z",
-     "iopub.status.idle": "2024-10-25T15:13:43.788256Z",
-     "shell.execute_reply": "2024-10-25T15:13:43.787635Z"
+     "iopub.execute_input": "2024-10-25T15:15:14.568864Z",
+     "iopub.status.busy": "2024-10-25T15:15:14.568629Z",
+     "iopub.status.idle": "2024-10-25T15:15:14.839339Z",
+     "shell.execute_reply": "2024-10-25T15:15:14.838576Z"
     }
    },
    "outputs": [
@@ -194,10 +194,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:43.790254Z",
-     "iopub.status.busy": "2024-10-25T15:13:43.790069Z",
-     "iopub.status.idle": "2024-10-25T15:13:54.249879Z",
-     "shell.execute_reply": "2024-10-25T15:13:54.249211Z"
+     "iopub.execute_input": "2024-10-25T15:15:14.841745Z",
+     "iopub.status.busy": "2024-10-25T15:15:14.841241Z",
+     "iopub.status.idle": "2024-10-25T15:15:25.645002Z",
+     "shell.execute_reply": "2024-10-25T15:15:25.644360Z"
     }
    },
    "outputs": [
@@ -244,10 +244,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:54.253062Z",
-     "iopub.status.busy": "2024-10-25T15:13:54.252705Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.227050Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.226322Z"
+     "iopub.execute_input": "2024-10-25T15:15:25.648905Z",
+     "iopub.status.busy": "2024-10-25T15:15:25.648409Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.728132Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.727483Z"
     }
    },
    "outputs": [],
@@ -276,10 +276,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.229825Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.229506Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.477463Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.476809Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.730732Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.730178Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.946082Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.945355Z"
     }
    },
    "outputs": [
@@ -317,10 +317,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.479724Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.479332Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.482946Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.482441Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.948425Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.948064Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.952028Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.951436Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.484679Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.484488Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.690708Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.689999Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.953870Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.953680Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.313545Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.312773Z"
     }
    },
    "outputs": [
@@ -458,7 +458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 85.79it/s]"
+      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 79.64it/s]"
      ]
     },
     {
@@ -466,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 85.79it/s]  "
+      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 79.64it/s]  "
      ]
     },
     {
@@ -474,7 +474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 76.56it/s]"
+      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 71.49it/s]"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.41it/s]"
+      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.03it/s]"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.68it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]"
      ]
     },
     {
@@ -528,7 +528,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:09,  2.64s/it]"
+      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:03,  2.53s/it]"
      ]
     },
     {
@@ -536,7 +536,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 2/50 [00:05<01:59,  2.48s/it]"
+      "Test permutations of given graph:   4%|▍         | 2/50 [00:05<02:02,  2.56s/it]"
      ]
     },
     {
@@ -544,7 +544,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 3/50 [00:07<01:49,  2.32s/it]"
+      "Test permutations of given graph:   6%|▌         | 3/50 [00:07<01:58,  2.52s/it]"
      ]
     },
     {
@@ -552,7 +552,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 4/50 [00:08<01:36,  2.10s/it]"
+      "Test permutations of given graph:   8%|▊         | 4/50 [00:09<01:47,  2.34s/it]"
      ]
     },
     {
@@ -560,7 +560,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 5/50 [00:11<01:35,  2.12s/it]"
+      "Test permutations of given graph:  10%|█         | 5/50 [00:12<01:46,  2.37s/it]"
      ]
     },
     {
@@ -568,7 +568,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 6/50 [00:13<01:37,  2.22s/it]"
+      "Test permutations of given graph:  12%|█▏        | 6/50 [00:14<01:42,  2.33s/it]"
      ]
     },
     {
@@ -576,7 +576,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 7/50 [00:15<01:32,  2.14s/it]"
+      "Test permutations of given graph:  14%|█▍        | 7/50 [00:16<01:37,  2.27s/it]"
      ]
     },
     {
@@ -584,7 +584,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 8/50 [00:17<01:25,  2.03s/it]"
+      "Test permutations of given graph:  16%|█▌        | 8/50 [00:18<01:32,  2.21s/it]"
      ]
     },
     {
@@ -592,7 +592,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 9/50 [00:19<01:22,  2.02s/it]"
+      "Test permutations of given graph:  18%|█▊        | 9/50 [00:20<01:25,  2.10s/it]"
      ]
     },
     {
@@ -600,7 +600,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 10/50 [00:21<01:19,  1.98s/it]"
+      "Test permutations of given graph:  20%|██        | 10/50 [00:22<01:25,  2.14s/it]"
      ]
     },
     {
@@ -608,7 +608,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 11/50 [00:22<01:13,  1.88s/it]"
+      "Test permutations of given graph:  22%|██▏       | 11/50 [00:24<01:21,  2.08s/it]"
      ]
     },
     {
@@ -616,7 +616,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 12/50 [00:24<01:09,  1.83s/it]"
+      "Test permutations of given graph:  24%|██▍       | 12/50 [00:26<01:18,  2.06s/it]"
      ]
     },
     {
@@ -624,7 +624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 13/50 [00:26<01:07,  1.83s/it]"
+      "Test permutations of given graph:  26%|██▌       | 13/50 [00:28<01:12,  1.96s/it]"
      ]
     },
     {
@@ -632,7 +632,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 14/50 [00:28<01:07,  1.86s/it]"
+      "Test permutations of given graph:  28%|██▊       | 14/50 [00:29<01:07,  1.87s/it]"
      ]
     },
     {
@@ -640,7 +640,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 15/50 [00:29<01:02,  1.79s/it]"
+      "Test permutations of given graph:  30%|███       | 15/50 [00:31<01:00,  1.74s/it]"
      ]
     },
     {
@@ -648,7 +648,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 16/50 [00:31<00:57,  1.69s/it]"
+      "Test permutations of given graph:  32%|███▏      | 16/50 [00:33<00:58,  1.72s/it]"
      ]
     },
     {
@@ -656,7 +656,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 17/50 [00:32<00:54,  1.65s/it]"
+      "Test permutations of given graph:  34%|███▍      | 17/50 [00:34<00:57,  1.74s/it]"
      ]
     },
     {
@@ -664,7 +664,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 18/50 [00:34<00:51,  1.61s/it]"
+      "Test permutations of given graph:  36%|███▌      | 18/50 [00:36<00:52,  1.65s/it]"
      ]
     },
     {
@@ -672,7 +672,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 19/50 [00:35<00:41,  1.33s/it]"
+      "Test permutations of given graph:  38%|███▊      | 19/50 [00:37<00:49,  1.59s/it]"
      ]
     },
     {
@@ -680,7 +680,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 20/50 [00:36<00:40,  1.35s/it]"
+      "Test permutations of given graph:  40%|████      | 20/50 [00:39<00:46,  1.56s/it]"
      ]
     },
     {
@@ -688,7 +688,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 21/50 [00:38<00:43,  1.51s/it]"
+      "Test permutations of given graph:  42%|████▏     | 21/50 [00:41<00:46,  1.62s/it]"
      ]
     },
     {
@@ -696,7 +696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 22/50 [00:40<00:43,  1.57s/it]"
+      "Test permutations of given graph:  44%|████▍     | 22/50 [00:42<00:45,  1.63s/it]"
      ]
     },
     {
@@ -704,7 +704,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 23/50 [00:41<00:41,  1.55s/it]"
+      "Test permutations of given graph:  46%|████▌     | 23/50 [00:43<00:40,  1.51s/it]"
      ]
     },
     {
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 24/50 [00:42<00:37,  1.44s/it]"
+      "Test permutations of given graph:  48%|████▊     | 24/50 [00:44<00:31,  1.21s/it]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 25/50 [00:44<00:35,  1.43s/it]"
+      "Test permutations of given graph:  50%|█████     | 25/50 [00:45<00:30,  1.23s/it]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:45<00:32,  1.35s/it]"
+      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:46<00:29,  1.24s/it]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:46<00:28,  1.24s/it]"
+      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:48<00:28,  1.22s/it]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:47<00:28,  1.30s/it]"
+      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:49<00:30,  1.40s/it]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:49<00:27,  1.29s/it]"
+      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:51<00:28,  1.34s/it]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 30/50 [00:50<00:25,  1.28s/it]"
+      "Test permutations of given graph:  60%|██████    | 30/50 [00:52<00:27,  1.36s/it]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:51<00:24,  1.28s/it]"
+      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:53<00:25,  1.32s/it]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:52<00:22,  1.27s/it]"
+      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:55<00:24,  1.37s/it]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:54<00:21,  1.26s/it]"
+      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:56<00:22,  1.32s/it]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:55<00:22,  1.39s/it]"
+      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:57<00:21,  1.36s/it]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 35/50 [00:56<00:19,  1.28s/it]"
+      "Test permutations of given graph:  70%|███████   | 35/50 [00:59<00:19,  1.30s/it]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:57<00:17,  1.25s/it]"
+      "Test permutations of given graph:  72%|███████▏  | 36/50 [01:00<00:17,  1.29s/it]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 37/50 [00:59<00:16,  1.25s/it]"
+      "Test permutations of given graph:  74%|███████▍  | 37/50 [01:01<00:15,  1.19s/it]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:00<00:15,  1.27s/it]"
+      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:02<00:14,  1.21s/it]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:01<00:12,  1.12s/it]"
+      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:03<00:13,  1.20s/it]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 40/50 [01:02<00:10,  1.02s/it]"
+      "Test permutations of given graph:  80%|████████  | 40/50 [01:04<00:11,  1.19s/it]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:03<00:09,  1.10s/it]"
+      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:06<00:10,  1.18s/it]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:04<00:08,  1.05s/it]"
+      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:07<00:09,  1.17s/it]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:05<00:07,  1.05s/it]"
+      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:08<00:07,  1.13s/it]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:06<00:06,  1.06s/it]"
+      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:09<00:06,  1.07s/it]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 45/50 [01:07<00:05,  1.08s/it]"
+      "Test permutations of given graph:  90%|█████████ | 45/50 [01:09<00:04,  1.01it/s]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:08<00:04,  1.11s/it]"
+      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:10<00:03,  1.02it/s]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:09<00:03,  1.08s/it]"
+      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:12<00:03,  1.11s/it]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:10<00:02,  1.03s/it]"
+      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:13<00:02,  1.15s/it]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:11<00:01,  1.03s/it]"
+      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:14<00:01,  1.12s/it]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:12<00:00,  1.03s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.07s/it]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:12<00:00,  1.45s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.51s/it]"
      ]
     },
     {
@@ -940,7 +940,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -966,82 +966,82 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node Customer DB\n",
-      "- The KL divergence between generated and observed distribution is 0.06851018535777569.\n",
+      "- The KL divergence between generated and observed distribution is 0.05633729962188637.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Shipping Cost Service\n",
-      "- The KL divergence between generated and observed distribution is 0.03806638487810255.\n",
+      "- The KL divergence between generated and observed distribution is 0.01345661046898411.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Product DB\n",
-      "- The KL divergence between generated and observed distribution is 0.0739495966691543.\n",
+      "- The KL divergence between generated and observed distribution is 0.04731204756715253.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Order DB\n",
-      "- The KL divergence between generated and observed distribution is 0.05761158379067712.\n",
+      "- The KL divergence between generated and observed distribution is 0.03163058332984496.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Auth Service\n",
-      "- The MSE is 0.01441171705596729.\n",
-      "- The NMSE is 0.5464573760061932.\n",
-      "- The R2 coefficient is 0.7013207387303906.\n",
-      "- The normalized CRPS is 0.3025250310394936.\n",
+      "- The MSE is 0.014408940211606136.\n",
+      "- The NMSE is 0.5467885527285155.\n",
+      "- The R2 coefficient is 0.7008264081937587.\n",
+      "- The normalized CRPS is 0.30319879712127784.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Caching Service\n",
-      "- The MSE is 0.023177407671490263.\n",
-      "- The NMSE is 0.8450133333760175.\n",
-      "- The R2 coefficient is 0.2854163022252113.\n",
-      "- The normalized CRPS is 0.464166240739747.\n",
+      "- The MSE is 0.023142169283019896.\n",
+      "- The NMSE is 0.8424237004319363.\n",
+      "- The R2 coefficient is 0.2902352340841851.\n",
+      "- The normalized CRPS is 0.4626635608354152.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node Order Service\n",
-      "- The MSE is 0.0143980612006466.\n",
-      "- The NMSE is 0.5485423887933651.\n",
-      "- The R2 coefficient is 0.6989222187579888.\n",
-      "- The normalized CRPS is 0.3033900342657954.\n",
+      "- The MSE is 0.014406030212396392.\n",
+      "- The NMSE is 0.5492949148510787.\n",
+      "- The R2 coefficient is 0.6979095952300487.\n",
+      "- The normalized CRPS is 0.30391606590620085.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Product Service\n",
-      "- The MSE is 0.02043925270114256.\n",
-      "- The NMSE is 0.6494394163446002.\n",
-      "- The R2 coefficient is 0.5781324594465047.\n",
-      "- The normalized CRPS is 0.36367607924553963.\n",
+      "- The MSE is 0.020462217678676974.\n",
+      "- The NMSE is 0.6499756128867937.\n",
+      "- The R2 coefficient is 0.5773982772794385.\n",
+      "- The normalized CRPS is 0.3635695389665327.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node API\n",
-      "- The MSE is 0.014485507834794353.\n",
-      "- The NMSE is 0.21002966758855104.\n",
-      "- The R2 coefficient is 0.9558356948931678.\n",
-      "- The normalized CRPS is 0.11595431985006795.\n",
+      "- The MSE is 0.014484769133253233.\n",
+      "- The NMSE is 0.20977705037425515.\n",
+      "- The R2 coefficient is 0.9559816510988256.\n",
+      "- The normalized CRPS is 0.11572746053441199.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node www\n",
-      "- The MSE is 0.011041595331444064.\n",
-      "- The NMSE is 0.13763913442477363.\n",
-      "- The R2 coefficient is 0.9810370890224425.\n",
-      "- The normalized CRPS is 0.07783774485363718.\n",
+      "- The MSE is 0.011040241699128718.\n",
+      "- The NMSE is 0.13762343752718348.\n",
+      "- The R2 coefficient is 0.981050435883116.\n",
+      "- The normalized CRPS is 0.07801098634259836.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node Website\n",
-      "- The MSE is 0.02077984255867196.\n",
-      "- The NMSE is 0.1852622726654006.\n",
-      "- The R2 coefficient is 0.9656512057924049.\n",
-      "- The normalized CRPS is 0.10196150976628031.\n",
+      "- The MSE is 0.02077833539868886.\n",
+      "- The NMSE is 0.1850812826494141.\n",
+      "- The R2 coefficient is 0.9657338637126042.\n",
+      "- The normalized CRPS is 0.10221678960177454.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
       "\n",
-      "--- The model assumption for node www is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node www is not rejected with a p-value of 0.3838477086692199 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Website is not rejected with a p-value of 0.1451092778434615 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Website is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Auth Service is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Auth Service is not rejected with a p-value of 0.4465020720536922 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node API is not rejected with a p-value of 0.742191149095118 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node API is not rejected with a p-value of 0.7556572625812968 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "--- The model assumption for node Product Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n",
@@ -1056,7 +1056,7 @@
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.10675450625416824\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.1063974083835325\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -1065,7 +1065,7 @@
       "+-------------------------------------------------------------------------------------------------------+\n",
       "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |\n",
       "| equivalence class of the given DAG (p-value: 0.00).                                                   |\n",
-      "| The given DAG violates 5/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
+      "| The given DAG violates 4/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
       "| Based on the provided significance level (0.2) and because the DAG is informative,                    |\n",
       "| we do not reject the DAG.                                                                             |\n",
       "+-------------------------------------------------------------------------------------------------------+\n",
@@ -1113,10 +1113,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.693116Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.692666Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.704079Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.703611Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.316127Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.315664Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.329926Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.329237Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1207,10 +1207,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.705942Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.705595Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.754291Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.753730Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.332110Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.331883Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.386998Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.386438Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1258,10 +1258,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.756436Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.756062Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.403122Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.402534Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.389116Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.388740Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.274521Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.273763Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1301,10 +1301,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.405328Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.405129Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.560911Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.560362Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.277280Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.276806Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.695234Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.694487Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1313,7 +1313,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1349,10 +1349,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.562833Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.562644Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.575914Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.575309Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.697653Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.697207Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.711068Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.710652Z"
     }
    },
    "outputs": [
@@ -1503,10 +1503,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.577740Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.577435Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.593281Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.592800Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.713088Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.712895Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.729802Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.729274Z"
     }
    },
    "outputs": [
@@ -1550,16 +1550,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.595127Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.594943Z",
-     "iopub.status.idle": "2024-10-25T15:18:11.683715Z",
-     "shell.execute_reply": "2024-10-25T15:18:11.683089Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.731769Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.731406Z",
+     "iopub.status.idle": "2024-10-25T15:19:55.834645Z",
+     "shell.execute_reply": "2024-10-25T15:19:55.833944Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1607,10 +1607,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:11.685860Z",
-     "iopub.status.busy": "2024-10-25T15:18:11.685492Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.241741Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.241031Z"
+     "iopub.execute_input": "2024-10-25T15:19:55.836917Z",
+     "iopub.status.busy": "2024-10-25T15:19:55.836716Z",
+     "iopub.status.idle": "2024-10-25T15:19:56.379230Z",
+     "shell.execute_reply": "2024-10-25T15:19:56.378498Z"
     }
    },
    "outputs": [],
@@ -1639,16 +1639,16 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:12.244160Z",
-     "iopub.status.busy": "2024-10-25T15:18:12.243785Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.315222Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.314622Z"
+     "iopub.execute_input": "2024-10-25T15:19:56.381826Z",
+     "iopub.status.busy": "2024-10-25T15:19:56.381599Z",
+     "iopub.status.idle": "2024-10-25T15:19:56.442961Z",
+     "shell.execute_reply": "2024-10-25T15:19:56.442320Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ZLyKqFoNOVl+6dAnx8fG6QzW1Wo3evXvD398f8+bNq4mcj8WT1c/Ak9XPDz9b1VLtWbObN29iw4YNnDWrjVhEzw8/W9UiedYsLy8Phw8f1o2Grly5AktLS/j4+MDf39+o4YjIPOhdRNOnT0dcXBwuX74MCwsLdO/eHW+88Qb8/f3Rq1cv1KtXryZzEpEJ07uIkpOTMWrUKPj7+6N3796wsbGpyVxEZEb0LqITJ07UZA4iMmMG/YoHEZExsYiISHYsIiKSHYuIiGTHIiIi2RmtiCZNmoTXXnvNWC9HRGbEoPWIHsfJyQlKJQdYRCSd0Yros88+M9ZLEZGZ4RCGiGQneUQ0Z86cx25XKBSoV68e2rVrh4CAADRp0qTa4YjIPEguouTkZCQlJUGj0aBDhw4AgCtXrkClUqFjx47YsGED5s6di6NHj6JTp05GD0xEpkfyoVlAQAD69++Pmzdv4syZMzhz5gwyMzMxYMAAjB8/HllZWejbty+CgoJqIi8RmSDJC6M5OTkhJibmkdHOxYsXMXDgQGRlZSEpKQkDBw7E7du3jRr2f3FhtGfgwmjPDz9b1SJ5RFRUVIS8vLxHtt+6dQvFxcUAAHt7e1RUVFQ/HRGZBYMOzaZMmYI9e/YgMzMTmZmZ2LNnD6ZOnYpRo0YBABISEtC+fXtjZyUiEyX5ZHV4eDiCgoIwbtw43L9//8GLWFhg0qRJ+OKLLwAAHTt2xObNm42blIhMlsGL55eWluLq1asAgDZt2qBhw4ZGDaYPniN6Bp4jen742aoWyYdm33zzDe7evYuGDRvC09MTnp6espQQEZkOyUUUFBQEBwcHvPXWWzhw4MBz/fkgIjJNkosoOzsbERERUCgUGDt2LFq2bIkZM2bg+PHjNZGPiMyA5CKysLDA8OHDsXPnTuTl5eGLL77A9evX4e/vj7Zt29ZERiIycdX69r2NjQ0GDRqEgoICpKen49dffzVWLiIyIwZ9+/7u3bvYuXMnhg4dCicnJ6xevRqjR4/GxYsXjZ2PiMyA5BHRuHHjsH//ftjY2GDs2LFYvHgxevbsWRPZiMhMSC4ilUqF7777DoMGDYJKpary2IULF+Dh4WG0cERkHiQX0c6dO6vcLykpwa5du7B582acOXOG0/lEJJnBKzT++9//xqRJk9CyZUusWLECr732Gk6ePGnMbERkJiSNiHJycrBt2zZs2bIFxcXFGDt2LNRqNX744QcugkZEBtN7RDRixAh06NABKSkpWL16NW7evIm1a9fWZDYiMhN6j4gOHjyIWbNm4b333oO7u3tNZiIiM6P3iOjo0aMoKSlB165d0aNHD6xbt67GV2AkIvOgdxH5+Pjgq6++QnZ2Nv76178iIiICrVq1glarRUxMDEpKSgwKsH79eri6uqJevXro0aMHEhISDHodIqq7JM+aNWjQAFOmTMHRo0dx/vx5zJ07F0uXLoWDgwNGjhwp6bX++c9/Ys6cOQgNDUVSUhK8vLwwaNCgxy5FS0Smy+CF0f5Io9EgKioKX3/9Nfbt26f383r06AFvb2+sW7cOAKDVauHi4oKZM2fib3/72yP7q9VqqNVq3f2ioiK0bt0aGRkZ+i2M5jRZ72wmIWur3AnMBz9bT9WoUSMoFIon7yBkolarhUqlEnv27KmyfeLEiWLkyJGPfU5oaKgAwBtvvNWxW1FR0VP7oFrfvq+O27dvQ6PRwNHRscp2R0dHpKamPvY5wcHBVX5pVqvV4s6dO2jatOnT21ZGxcXFcHFx0X/URqSnuvTZatSo0VMfl62IDGFtbQ1ra+sq2+zt7eUJI5GtrW2t/7BQ3WQKny2Dv+JRXc2aNYNKpUJubm6V7bm5uWjRooVMqYhIDrIVkZWVFbp27YrY2FjdNq1Wi9jYWC4rQmRmZD00mzNnDiZNmoRu3bqhe/fuWL16NcrKyjB5sunMQFhbWyM0NPSRQ0qi6jKlz5ZRpu+rY926dfj888+Rk5ODl19+GV9++SV69OghZyQies5kLyIiItnOERERPcQiIiLZsYiISHYsIiKSHYuIiGTHIiIyMQ8nwi9evIhbt27JnEY/LCIiE6NQKLBv3z4MHz4cly9fRl24QodFRGQiHhZOQUEBIiIiEBQUBF9f31q7MsUf1alv35sqIQQUCgUuXryI33//HZaWlnBzc0PHjh3ljkZ1iEKhQHx8PObPn4/69evDx8cHwH8/X7UZi6gWUCgU+P777zF79mw4OTlBpVIhPz8fYWFhGDNmjNzxqA7x8vJCbm4uMjIy8Ntvv6F79+61voQAHprVComJifjLX/6CRYsW4dSpU1iyZAnS0tKQlJQkdzSqY+zt7ZGSkoK2bdti2bJlOHv2rNyR9MLvmtUCW7ZsQVRUFH744QfcuHEDffr0wfDhw7F+/XoAQGZmJpydnWVOSbXNw0Ous2fP4vz58wAAd3d3+Pj44M6dO+jatSscHR2xadMmeHp6ypz26TgiksEfp1cLCgqg1WphY2OD33//Hb1798bgwYN1v6L7yy+/YMuWLSgsLJQxMdVGDw/phw4dik2bNuGbb75Bv379sG3bNjRp0gTJycnIzc3F9OnTkZycLHfcp2IRyUChUGDv3r149dVXcfnyZdjb2+PYsWPo1asXhg0bhvDwcCiVD/6v2b17N65evQpLS0uZU1Ntc/bsWbz77rsICQnBkSNHEBYWhvLycly4cAH379+Hvb09kpOTkZKSggULFqCiokLuyE9WjR/iIIm0Wq0QQoiSkhIxffp0sXLlSt1j7777rlAoFGL//v3i1q1b4vbt22LhwoWiefPm4uLFi3JFplpIo9EIIYT4/vvvxbBhw4QQQly/fl24uLiI6dOn6/ZLS0sTQghRWFio+7u24qzZc6RQKHDy5EmMHz8ejo6OeP3113WPbdiwAXl5eZgyZQosLCzg5uaGzMxMREdHo1OnTjKmJjlptVoolUrd/wIPfkdQqVSirKwMd+/exYULFzB06FAMGTJEd0gfFxeHyMhIBAcHo2XLlrCzs5Pzn/FMLKLnrEOHDnB1dUV8fLzuhwPEf046fv/99zh06BBycnLg4OCAl156CU5OTjInJjkplUpcvXoV6enp8Pf3x7/+9S+Eh4fjwIEDaNOmDQoLC+Hv74+AgACEh4frnrdnzx7k5OSgQYMGMqaXQO4hmTkqKCgQfn5+wtXVVVy4cEEI8d/hNtFDn3zyiTh16pQYM2aMsLGxEYsXLxYqlUps3bpVt8+8efOEQqEQ4eHhIj09XWRmZooFCxaIpk2b6j5bdQGLqAY9PCeUmpoqDh8+LE6dOiVyc3OFEEIUFxcLHx8f4e7uLi5duiRnTKqFli5dKhQKhcjIyBBCCOHt7S0UCoWYP3/+I/tOnjxZdOjQQTRs2FD3mUpKSnrekauFRVRDHpbQ7t27hYODg3jxxReFpaWl6N+/v9i2bZsQ4r9l9OKLL4rz58/LGZdqkaKiItGnTx+xatUqIYQQ0dHRok2bNsLLy0s4OzuLQ4cOicrKyirPOX36tNi9e7c4ceKEyM7OliN2tbCIjOjAgQPi5s2buvuJiYnCzs5ObNiwQeTl5YmjR4+KwMBA8corr4jt27cLIR7MaHTu3Fl07dpVqNVquaJTLfPOO++INm3aiLVr14rGjRuLqKgoIYQQAwYMEK1atXqkjMrLy+WKahQsIiMJCgoSdnZ2Ijc3V3e+Z/369cLHx0fcv39ft9/FixfFn//8ZzFs2DBRVFQkhHhQRteuXZMjNtUyD0fS6enpwsvLSyiVSrF06dIq+wwYMEA4OzuL6OhoUVFRIT755BMxePBgodFodM+va1hERpCamio8PDxEdHS0EELoRkVbtmwR7du3F1lZWVX2P3TokFCpVCIlJeW5Z6Xa53ETFYmJicLe3l507txZvPzyy+LKlStVHh8yZIho3ry58PX1Fba2tiIhIeF5xa0RvLLaCGxtbXHz5k1cunQJMTExcHFxwa1bt9C2bVvk5OQgKioKGo1Gt3/btm3RoUOHKtvIfCmVSqSmpmLRokVIT08HALzwwgv48ccf8fXXX6N58+Z444038Ntvv+mec+DAAYSGhmL06NFISEiAt7e3XPGNQ+4mrOse/tfs22+/FUqlUtSvX1989913useXLFkiLCwsxPr160VaWpooLS0VCxYsEK6uriInJ0eu2FSLVFRU6GbF3N3dRVBQkIiMjNQ9fvLkSfHaa6+Jl156qdZfIW0oXtBYTQ+vdm3QoAGEELh37x7u3LmjezwkJARKpRIhISEICwtDs2bNkJ2djYMHD8LR0VGu2FSLWFpa4s0338T48ePh4eGBY8eOYerUqdi9ezf8/f0xdepUfPzxx1i+fDnefPNN7Nq1y+QWzeMyINWk0WigUqmwa9cuKBQK5ObmIigoCKtWrcIHH3yg2y8xMRFZWVm4d+8eevXqhdatW8sXmmqduLg4BAQEIDY2Ft26dUN2djY2bdqEsLAwdO/eHRMnTkRlZSViYmKQm5uLuLg4WFhY1IlFz/TBIjKQ+M/XMsrKyqpcRl9WVoY1a9bgww8/fKSMiJ5m/vz5yM7OxubNm1GvXj2MGzcO586dg7e3N7KzsxEfH49p06Zh0aJFaNWqldxxjYqHZgZ4WEIHDhzAmjVrkJ+fj4YNG+LDDz+Er68vPvjgAyiVSsyZMwcqlQozZ86UOzLVAT169MCqVatgZWWFd955B3FxcYiNjUXnzp3x66+/4pdffsGrr75qciUEgCerDbV//35hY2MjPvroI3Hy5Enh5+cnnJycRGJiohDiwdWxy5cvFwqFQmzcuFHmtFRX9O3bVyiVStGqVStx9uxZueM8NxwRSaTRaHDv3j2sXbsWCxcuREhICAoKCpCRkYGRI0eiW7duAB5M6U+bNg2Wlpbo27evzKmpthP/GWUvXLgQOTk5WLZsGby8vOrEL3AYA68jegqtVgvgwYdE/OdUmkqlgkKhwJ07d/Dmm2/i1q1b6Ny5M/r3748NGzYAACIjI5Gfnw97e3vMnj2b6wnRMz0sm65du0Kr1eLMmTNVtps6FtETPFyI6sqVK5g1axZef/11rFy5EgBgY2MDa2trrFmzBj4+PggICNAtSJWfn4/Nmzfjxx9/BGA+HyQyDkdHR4SGhuKLL75AQkKC3HGeGxbRYzwsoXPnzsHX1xeZmZmwtrZGcHAwli5dCgAIDAzEvn370LRpU2zcuBFWVlYAgFWrVuHatWvw8/OT859AdZi/vz+8vb1N86T0E/Ac0f94WEIpKSno2bMngoKC8Omnn0Kr1aJZs2bIyckBAIwcORJJSUk4duwY3n77bXTq1AmpqanYu3cv4uLi8MILL8j8L6G6ysnJCQcPHkS9evXkjvLc8Dqix8jIyMArr7wCf39/fPfdd7rt48aNQ2pqKsrLy9GlSxd07twZLVq0wI4dO6BUKuHm5ob58+fznBCRRBwRPYZGo4GbmxvUajWOHTuG3r17Y+nSpYiKikJwcDBatGiBFStW4NKlS4iIiMC0adN0z1OpVDKnJ6p7OCJ6grS0NMyaNQtWVlZwcHDAvn37sGPHDgwcOBAAkJ6eDjc3N6xbtw7Tp08HALOZaiUyNp6sfgJ3d3esWbMG5eXl2LlzJxYsWICBAwdCCIHKykpYWFjA09MTDg4OuuewhIgMwyJ6ivbt22Pjxo3o06cPYmNjceTIESgUClhaWiI8PBzFxcXo0aOH3DGJ6jwemunh4WGaEAJhYWGIiYlBaGgojh8/ji5dusgdj6jOYxHpKS0tDXPmzEFCQgIKCgpw4sQJdO3aVe5YRCaBh2Z6cnd3x4oVK+Dj44Pk5GSWEJERcUQkUWVlJSwtLeWOQWRSWEREJDsemhGR7FhERCQ7FhERyY5FRESyYxERkexYREQkOxYREcmORUREsmMREZHs/h8lOMqiyCfm/AAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 300x200 with 1 Axes>"
       ]
@@ -1689,10 +1689,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:12.317811Z",
-     "iopub.status.busy": "2024-10-25T15:18:12.317445Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.333491Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.332926Z"
+     "iopub.execute_input": "2024-10-25T15:19:56.445516Z",
+     "iopub.status.busy": "2024-10-25T15:19:56.445234Z",
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@@ -1780,10 +1780,10 @@
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diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb
index f218f3293..e899f5159 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb
@@ -27,10 +27,10 @@
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@@ -46,10 +46,10 @@
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@@ -143,10 +143,10 @@
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@@ -250,10 +250,10 @@
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@@ -280,10 +280,10 @@
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@@ -330,10 +330,10 @@
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@@ -388,10 +388,10 @@
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@@ -433,10 +433,10 @@
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@@ -468,10 +468,10 @@
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@@ -553,10 +553,10 @@
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@@ -619,10 +619,10 @@
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@@ -641,10 +641,10 @@
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@@ -676,10 +676,10 @@
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@@ -689,15 +689,8 @@
      "text": [
       "\r",
       "Evaluating set functions...:   0%|          | 0/32 [00:00<?, ?it/s]\r",
-      "Evaluating set functions...:  62%|██████▎   | 20/32 [00:00<00:00, 194.80it/s]\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 186.61it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 216.30it/s]\r",
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 196.90it/s]"
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     {
@@ -706,22 +699,22 @@
      "text": [
       "\r",
       "Evaluating set functions...:   0%|          | 0/32 [00:00<?, ?it/s]\r",
-      "Evaluating set functions...:   0%|          | 0/32 [00:00<?, ?it/s]\r",
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-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 254.36it/s]/github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
-      "  warnings.warn(\n"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 202.05it/s]\r",
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 185.71it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "\r",
+      "Evaluating set functions...:   0%|          | 0/32 [00:00<?, ?it/s]\r",
+      "Evaluating set functions...:   0%|          | 0/32 [00:00<?, ?it/s]\n",
+      "\r",
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 208.74it/s]\r",
+      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:00<00:00, 249.06it/s]\r",
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 234.38it/s]\r",
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 178.93it/s]"
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@@ -731,8 +724,8 @@
       "\n",
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-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 280.21it/s]\r",
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+      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:00<00:00, 278.97it/s]\r",
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 264.99it/s]"
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@@ -755,16 +748,16 @@
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     {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -795,10 +788,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:19:55.863266Z",
-     "iopub.status.busy": "2024-10-25T15:19:55.863051Z",
-     "iopub.status.idle": "2024-10-25T15:19:55.875697Z",
-     "shell.execute_reply": "2024-10-25T15:19:55.875126Z"
+     "iopub.execute_input": "2024-10-25T15:21:51.336014Z",
+     "iopub.status.busy": "2024-10-25T15:21:51.335617Z",
+     "iopub.status.idle": "2024-10-25T15:21:51.349033Z",
+     "shell.execute_reply": "2024-10-25T15:21:51.348321Z"
     },
     "jupyter": {
      "outputs_hidden": false
diff --git a/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb
index a0c3a7708..d648a013a 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb
@@ -16,10 +16,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:00.675813Z",
-     "iopub.status.busy": "2024-10-25T15:20:00.675438Z",
-     "iopub.status.idle": "2024-10-25T15:20:00.691627Z",
-     "shell.execute_reply": "2024-10-25T15:20:00.691004Z"
+     "iopub.execute_input": "2024-10-25T15:21:56.384962Z",
+     "iopub.status.busy": "2024-10-25T15:21:56.384768Z",
+     "iopub.status.idle": "2024-10-25T15:21:56.401963Z",
+     "shell.execute_reply": "2024-10-25T15:21:56.401431Z"
     }
    },
    "outputs": [],
@@ -33,10 +33,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:00.693619Z",
-     "iopub.status.busy": "2024-10-25T15:20:00.693281Z",
-     "iopub.status.idle": "2024-10-25T15:20:02.109181Z",
-     "shell.execute_reply": "2024-10-25T15:20:02.108495Z"
+     "iopub.execute_input": "2024-10-25T15:21:56.404073Z",
+     "iopub.status.busy": "2024-10-25T15:21:56.403886Z",
+     "iopub.status.idle": "2024-10-25T15:21:58.051991Z",
+     "shell.execute_reply": "2024-10-25T15:21:58.051335Z"
     }
    },
    "outputs": [],
@@ -69,10 +69,10 @@
    "execution_count": 3,
    "metadata": {
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    "outputs": [
@@ -128,10 +128,10 @@
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    "metadata": {
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@@ -243,10 +243,10 @@
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@@ -264,10 +264,10 @@
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    "outputs": [
@@ -291,10 +291,10 @@
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@@ -336,10 +336,10 @@
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@@ -353,10 +353,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:02.944054Z"
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    "outputs": [
@@ -396,10 +396,10 @@
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@@ -420,10 +420,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:03.022245Z"
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    "outputs": [
@@ -456,10 +456,10 @@
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@@ -563,10 +563,10 @@
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@@ -584,10 +584,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:03.241210Z"
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    "outputs": [
@@ -611,10 +611,10 @@
    "execution_count": 15,
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-     "shell.execute_reply": "2024-10-25T15:20:03.285373Z"
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    "outputs": [
@@ -639,10 +639,10 @@
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@@ -662,10 +662,10 @@
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    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb b/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb
index 226575e8d..33ffb5282 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb
@@ -18,10 +18,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:07.976591Z"
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     }
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    "outputs": [],
@@ -55,10 +55,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:08.075757Z"
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@@ -136,10 +136,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-10-25T15:20:08.161392Z"
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@@ -218,10 +218,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:08.240229Z"
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     }
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    "outputs": [
@@ -302,10 +302,10 @@
    "execution_count": 5,
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-     "shell.execute_reply": "2024-10-25T15:20:08.328046Z"
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@@ -386,10 +386,10 @@
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    "outputs": [
@@ -470,10 +470,10 @@
    "execution_count": 7,
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+     "shell.execute_reply": "2024-10-25T15:22:05.474259Z"
     }
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    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb
index 12338d6e9..64104150c 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb
@@ -22,10 +22,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:10.662672Z"
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    "outputs": [],
@@ -48,10 +48,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:13.327130Z"
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@@ -80,10 +80,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:13.333025Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.604465Z",
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@@ -107,10 +107,10 @@
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-     "shell.execute_reply": "2024-10-25T15:20:13.373732Z"
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     "scrolled": true
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@@ -138,10 +138,10 @@
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-     "iopub.status.busy": "2024-10-25T15:20:13.375787Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.388547Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.388040Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.655733Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.655294Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.669279Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.668648Z"
     }
    },
    "outputs": [
@@ -290,10 +290,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.390388Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.389972Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.402061Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.401437Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.671470Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.671045Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.684068Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.683422Z"
     },
     "scrolled": true
    },
@@ -339,106 +339,106 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>True</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.000</td>\n",
        "      <td>0.0000</td>\n",
-       "      <td>8061.485</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>7458.1050</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.385363</td>\n",
-       "      <td>2.594954</td>\n",
+       "      <td>0.353143</td>\n",
+       "      <td>2.831718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>True</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>11.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5424.485</td>\n",
-       "      <td>5463.8030</td>\n",
-       "      <td>6788.463</td>\n",
+       "      <td>9594.3080</td>\n",
+       "      <td>16341.16000</td>\n",
+       "      <td>16900.3000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.292719</td>\n",
-       "      <td>3.416244</td>\n",
+       "      <td>0.715871</td>\n",
+       "      <td>1.396900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>True</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>2611.2180</td>\n",
-       "      <td>2484.549</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>506.40760</td>\n",
+       "      <td>604.1987</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.588747</td>\n",
-       "      <td>1.698522</td>\n",
+       "      <td>0.618675</td>\n",
+       "      <td>1.616359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>False</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5214.306</td>\n",
-       "      <td>474.5018</td>\n",
-       "      <td>4812.576</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>445.1704</td>\n",
+       "      <td>74.34345</td>\n",
+       "      <td>6210.6700</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.430120</td>\n",
-       "      <td>2.324932</td>\n",
+       "      <td>0.268051</td>\n",
+       "      <td>3.730636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>True</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2636.353</td>\n",
-       "      <td>2937.2640</td>\n",
-       "      <td>7535.942</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.388545</td>\n",
-       "      <td>2.573708</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>10747.3500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.540418</td>\n",
+       "      <td>1.850419</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   treat   age  educ  black  hisp  married  nodegr      re74       re75  \\\n",
-       "0   True  17.0   8.0    1.0   0.0      0.0     1.0     0.000     0.0000   \n",
-       "1   True  19.0  11.0    0.0   1.0      1.0     1.0  5424.485  5463.8030   \n",
-       "2   True  31.0  12.0    0.0   0.0      0.0     0.0     0.000  2611.2180   \n",
-       "3  False  27.0  13.0    0.0   0.0      0.0     0.0  5214.306   474.5018   \n",
-       "4   True  19.0   8.0    1.0   0.0      0.0     1.0  2636.353  2937.2640   \n",
+       "   treat   age  educ  black  hisp  married  nodegr       re74         re75  \\\n",
+       "0   True  19.0  11.0    1.0   0.0      0.0     1.0     0.0000      0.00000   \n",
+       "1  False  29.0   9.0    0.0   1.0      0.0     1.0  9594.3080  16341.16000   \n",
+       "2  False  22.0   9.0    1.0   0.0      0.0     1.0     0.0000    506.40760   \n",
+       "3   True  17.0   9.0    0.0   1.0      0.0     1.0   445.1704     74.34345   \n",
+       "4   True  26.0  12.0    1.0   0.0      0.0     0.0     0.0000      0.00000   \n",
        "\n",
-       "       re78  u74  u75  propensity_score    weight  \n",
-       "0  8061.485  1.0  1.0          0.385363  2.594954  \n",
-       "1  6788.463  0.0  0.0          0.292719  3.416244  \n",
-       "2  2484.549  1.0  0.0          0.588747  1.698522  \n",
-       "3  4812.576  0.0  0.0          0.430120  2.324932  \n",
-       "4  7535.942  0.0  0.0          0.388545  2.573708  "
+       "         re78  u74  u75  propensity_score    weight  \n",
+       "0   7458.1050  1.0  1.0          0.353143  2.831718  \n",
+       "1  16900.3000  0.0  0.0          0.715871  1.396900  \n",
+       "2    604.1987  1.0  0.0          0.618675  1.616359  \n",
+       "3   6210.6700  0.0  0.0          0.268051  3.730636  \n",
+       "4  10747.3500  1.0  1.0          0.540418  1.850419  "
       ]
      },
      "execution_count": 6,
@@ -464,10 +464,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.403961Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.403521Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.456088Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.455589Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.686337Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.685971Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.745203Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.744548Z"
     }
    },
    "outputs": [
@@ -502,21 +502,21 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.457994Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.457669Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.474035Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.473541Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.747482Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.747053Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.766960Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.766398Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMYAAAAQCAYAAABN/ABvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAABJ0AAASdAHeZh94AAAIaklEQVR4nO2ae7BXVRXHP+BVUHwGKT1VLApNvZihlCI3lAqEoLScBlJngJxkABUfUbT42hhQQYL2AHTAiKkxQxJB4iEjISQziqMOmCQPxRIFuooCEVz6Y+0th8M5v/v7nd+P/+535jf7d/ZZe6+199lrr8ferQ4ePEgLWtCCw1GXfJB0DXAFUA9cCJwEzDGzQXkdSOoLjATOBdoD/waeBSab2eoM+s3AmTndbTOzjin69sBAoC9wPvAxYB/wIjATmGlmTc2MMxeSBgGzw+NQM3sg9X4icDHQGegA7AG2APOA+81sR4r+hiBXKTSZ2THVyBVoWgFDwu88oBWwHngAmJ6el0rpE+0+DtwNfJVD33geIDP7TzNjLXcsFa+9SnkkaHsBw4HuwGnADnw9TTGzhQCtU21+FBrUA2+UIcxE4HHgImARMAV4Dvg68HQQNgvvAMr4/SKD9lpgBnAJ8AxwL/Bn4HP4B304fPCKIekTwP3AeyXIbgHaAUvw8c0B9gPjgBdCH0k8T/bYBDwZaJ6ogVwAvwemA2cBf8Dn4wTgN8CsGtAj6Rx8o7sRWAP8EtiIb4arw8ZVi7FUtPYK8kDSz4Cl+Gb3GDAJWAB8GOgZ6epS7W4BtgL/xLV3eQkGHYHRwDbgAjN7K/GuAV8Ed+MfI41GMxvX3CACXgH6AwuSO5qkMfiH+ibwDVxZykZQppn4bjE3jCULJ5vZ3oz29wBjgB8A34/1ZvY8rhxZPKMFnV6tXJIGAt8BNgHdzGx7qD8On4vBkuaZ2dwi9An8GjgdGGFm9yX4T8bXyz3ATdWMJaDstVeUh6ShwO3AQ8AwM9uXen9s/H+YxTCz5Wa2wczKCTzODO2fSSpF7AfYhWthVTCzJ81sftrMm9mbwG/DY88CXY8AvozvhO+X4H+EUgQ8HMpPl8NM0vnApfhuuKBauXD3EmBSXORB3n3A2PA4vAr6aC16A5uBX6X4W5BvsKR2VY6l0rVXMQ9JbXAlfo0MpQgy/C/+T7tSlWAD7ut3k9QhJUQP3EdcmtO2jaRBksZIGimpQVJJnzsHcSD7K2kkqQswAfcpVxTgC9AvlC+UST8slA+a2YEayBVjsY0Z72Ld5cEiFKEHaAjl4oyNaRfwNO6KXZrusEZzXBIV8rgK36jnAk2S+kq6M6y/7mnitCtVNsxsp6Q7gcnAOknzcHN2Du76LAG+l9O8I4cCpYhNkm40s6fK4S+pDvhueFxUrtyh3Wx85xhTQbvRwInAKbh/ehmuFBPKaHs8MAg4gPv1tZAr7vpnZ7zrFMq68P/lAvQAnwnlKzkybMAtSmdgWawsOseVoACPL4RyL7AWj1GT/a0ArjGzt6E6i4GZ3Yv793XAUOAuPFh+HZiVdrECZgK9cOVoh2eapuEB4ROSLiyT/QR8cAvN7K8ViP1joCtwg5ntqaDdaNx9GIUrxSKgd5zIZvAt4FRgkZm9XiO5ojt2q6QPxcrgJytBd1pBevBNADxZkoVYf2qqvugcV4JKeZweytuBg8DluFdzAbAY6AH8KRIXthgAku4AfgpMxbMCbwKfBcYDcyTVm9kdyTZmplQ3LwE3SXoPuA3P9gykBCSNCLQvA4MrkPcSfHeZlJVKLoWYRpZ0BvBFXDHXSrrazJ5rpnl0o6bVUK4/4mP/Cm6x/4LvhlcCH8F30k8CTQXpC6GaOT7KPKIR2A/0N7PN4fnFkJj4B3CFpO5mtrqwxZDUE5gIPGZmt5rZRjPbHRbJQDzIvE1SpxLdJBED6R7N8B2Op03XAQ1mtrNMeeuA3+FuwdhmyHNhZtvM7FHchWgf+izF9zxckbYCC2slV4hT+uFW+m3g+vDbEPjtCqRvFaEPiBbhFLIR6xurGUslqIJHYyjXJpQCADPbDUSvoxtUZzGuDuURaTUz2y1pDa4gXckO+NKILklehgNJo/A8+ktArxxXLQ8n4r4wwF4pbbgAmCFpBh7MjSrVmZltkbQOqJfUIZnpSaG5oLuwXCGLMjH8PoCktni2bLuZbSpKj++iJORLI2bkYgxS0znOQVEecSyNOf3Gg8rjoTrFaBPKvJRsrD8iLZaDmNnIVKIQ6E/AzwiuKrEQ8/Bf4MGcdxfhCrwSn8ByzfNHQ5mXZWqLuy8HSvA+GnJdBxyHH+JVQx83vd6SWqfOkU4CvgTsBv4eqo/GWNIoymMZHlucmx5LQAzGN0F1ivE3PO89TNI0M/vgtFLS1/BJ2wusStR3AV4zs8PyzZLOwmMUyDgQlDQWPyx8Fg94S7pPIf9+LPBqzE2HAG1IDv04fEIfSl4lkNQZv6byToq+NfATPKBbVeJaxLV4MPt4XtBdRK7E+5PN7N1UXT3wc3wHnFANvZm9Kmkx7jbeDNyXbIpb92nxe1YzlnJRlEew8PPxjOlI3POI7XrjsVcjIcOZvis1ABgQHmPeu7ukWeH/djOLJ4uP4OcUVwLrJT2KB99dcDerFXBX6i7Rt/G4YwV+32gXnt7tC7TFffDDroVIuh5XigO4Mo7IMJ+bzWxW4nkZfgB5Nn44VRR9gPGSVuI7yQ7gDPxktlMY79AS7aMblXvSXSWWSNqDu5a78Lnvi9/n6mdm/6qSHvxUfxUwVX7HaD1+PacBd6F+WIuBVLj2iuJmXHEmy+/4rcXXyAB8fQ2Jm2DaYtTjAVkSnTiU595COHI3syZJfQKz6/B44gRgJ77Ap5rZ4lRfy/HceFfcorTDtXQlnpOenXHyGfPux+Cp0iw8Rc5dnyqxFPgUnp7tiqcl38cXxGx8jJnWK1jHy8gJumuER/C5H4T7xm/gSjjezLbWgD5ajYs5dImwD36JcAplXiIsE/WUufaKwsy2Svo8nurtjyd63gXm43OwJtK2arl23oIWHIn/A7B6C4RcyaiIAAAAAElFTkSuQmCC",
       "text/latex": [
-       "$\\displaystyle 2090.93439377732$"
+       "$\\displaystyle 1852.43749904146$"
       ],
       "text/plain": [
-       "2090.934393777318"
+       "1852.4374990414572"
       ]
      },
      "execution_count": 8,
@@ -540,21 +540,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.475734Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.475538Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.493528Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.492932Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.769128Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.768736Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.790005Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.789340Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMQAAAAQCAYAAABJCdBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAABJ0AAASdAHeZh94AAAFd0lEQVR4nO2aW6gVVRzGf+dkWNkNtDwYZWoZdlWLLhimGEWdLmoFPVTWQxdK6pSHIik+/4WkEabmg1GkpL5IoQ9lmqGoZRhoZSZhpsdI8pSaIpqYlx7WOjXOmdl7r7Vn76fzwWaYWeu/vu//rb1mzVozDSdOnKALXeiCQ7fkiZndD9wCDAauAc4CFkh6KB1oZj2BMUAzcBVwAXAE+AGYA8yRdDwjrg3om6OnXVJTRkzFusrBzJqB54DLgZ7A78B6YJqkrzPqTwWuAwYCvYC/gR3AYmCWpD2p+sG+hOYXyRHbX6HagvyKiakil7I8jamYV4DxPvmdWQkn8ADwHnADsA6YDnwMXAm8Dyw0s4ac2P2AZfzeyqkfoisX3pBPgKHAUmAGsAG4F/jKzLI6+XmgB7Dc118AHAUmARvN7MJU/RhfQvOL4Yjtr1BtoX7FxMTmUpanW0bAb8BW3F1hZYnEtwD3AJ8mR6OZTQS+Ae4DxnqhaeyTNKlE21mJVKorE2bWBLQC7cDVkv5IlI0EVgCvAfNToWdLOpzR3mRgIvAy8HSiKMaX0PxiOGL7K1RbqF8xMbG5lOU5aUBIWpmo1CnTVN0VOdd3mdlsYDIwIkNUMEJ0lUBfoBFYlxwMHe2b2QHgvAzuTgZ6LMSZeGmqfrAvoflFckT1V4S2IL9iYqrIpSxPeoYoCv/449Gc8u7+8eQi4CCwEVgt6ViN9AD8jHvOvN7Mekna3VFgZsNxz8aLA9q72x83BsSU86UIxHDUQ1eMX/Xy+D+ewgeEmXUDHvGnS3OqNQHzUte2m9ljklYVrQlA0l4zewmYBmw2s8XAHmAAbvpdDjyZF29mrcCZwDm4hdnNuI6aUgl/hb5UhRiOWumK8ateHpfiqcUMMQW3uFkiaVlG+RxgDfAjcADoj1u0PQF8ZmY3Sfq+BrqQNN3vcn0APJ4o2grMTT9KpdAK9E6cLwUelfRnhfTlfCkCMRy10hXjV708zuVJ7zJVBTN7FpgA/AQ8nFVHkklaIald0iFJmyQ9hbtzn45b8dcEZvYi8BEwFzcz9ACuBbYBC8zszbxYSU2SGnCz21jcQP7WzIZWwFvWl2oRw1FLXTF+1cvjUjyFzRBmNh63lbUZGCVpb2ATs3EJDS9KUxJmNgKYCiyS9EKiaIOZjcHtXEwws9mStuW1I6kdWGRmG3zMh7i7Uh5vtb6URQxHPXRBuF8xMbG5ZPEUMkOYWQvwDrAJGClpV0QzHdNijyI0ZeAuf+y0bSjpEG67rhEYUkljknbgOuAKM+uVVacgX0oihqMeutKoxK+YmCJySfJUPSD8QvVt4DsvqNRzeCnc6I+5d+cq0d0fO22tpq4fCWizjz922h0r0JdcxHDUQ1cJ5PoVE1NwLn0g9elGKMzsVdzLrPXAbeWmKjMbBPwq6WDq+sXALH+afjEWo2sAcCrwi6SObbg1+MW7mb0raWei/h3AMOAwsDZxfSDuc5L9qfYbgdeB84G1kv5KlQf5EoMYjlrrivGrXh5XytOQ/LjPzEYDo/1pE3A77o69xl/bLanV1x2HW5wew01ZJxF5tEmam2h/Em6dsBr3DckB3OK2GTgNWAKMkXTSXTpEl6/fhnsR109SWyLxZcCtnncRsAsYhHucagBaJM1ItNMCvAF8CWzHbdP2xr2x7e/jR0nanIiJ8SU0vxiO4JhQbZF+xcTE5F8RT3qGGAyMS13r73/g/sQdHdPPH08BWjIEAazywjuwErgM95w+DLde2OdFzgPmScr6/DZEVyYkHTezO4FngAdxH4edAezFDcSZkj5PhX0BXILbpx4CnIt7kbjF652ZcWeK8WUwYfnFcMTEhGqL8ateHlfE09D1+XcXuvA//gU1HN1Qlf6MRQAAAABJRU5ErkJggg==",
       "text/latex": [
-       "$\\displaystyle 1150.04804650325$"
+       "$\\displaystyle 1251.83212213323$"
       ],
       "text/plain": [
-       "1150.0480465032542"
+       "1251.8321221332321"
       ]
      },
      "execution_count": 9,
@@ -587,10 +587,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.495494Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.495163Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.501434Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.500860Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.792425Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.791976Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.799189Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.798685Z"
     }
    },
    "outputs": [
@@ -598,12 +598,12 @@
      "data": {
       "text/plain": [
        "count      445.000000\n",
-       "mean      5122.485177\n",
-       "std       6370.769334\n",
+       "mean      5203.569643\n",
+       "std       6687.889826\n",
        "min          0.000000\n",
        "25%          0.000000\n",
-       "50%       3786.628000\n",
-       "75%       7565.273000\n",
+       "50%       3515.929000\n",
+       "75%       7952.540000\n",
        "max      60307.930000\n",
        "Name: re78, dtype: float64"
       ]
@@ -622,10 +622,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.503193Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.503027Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.508818Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.508320Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.801248Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.800806Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.807841Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.807208Z"
     }
    },
    "outputs": [
@@ -664,10 +664,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.510619Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.510253Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.514883Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.514405Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.809835Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.809481Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.815070Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.814470Z"
     }
    },
    "outputs": [],
@@ -680,10 +680,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.516638Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.516272Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.756215Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.755501Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.817059Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.816714Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.103505Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.102956Z"
     }
    },
    "outputs": [
@@ -699,7 +699,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -719,10 +719,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.758261Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.758069Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.941689Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.941065Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.105726Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.105267Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.279425Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.278591Z"
     }
    },
    "outputs": [
@@ -738,7 +738,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -765,10 +765,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.944221Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.943854Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.982783Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.982238Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.282051Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.281516Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.466557Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.465945Z"
     }
    },
    "outputs": [],
@@ -786,10 +786,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.984678Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.984461Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.997109Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.996623Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.469038Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.468641Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.481827Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.481228Z"
     }
    },
    "outputs": [
@@ -834,106 +834,106 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>True</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>10.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>8546.715</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.520700</td>\n",
-       "      <td>1.920493</td>\n",
+       "      <td>0.374159</td>\n",
+       "      <td>2.672663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>True</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>10.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>3881.284</td>\n",
+       "      <td>9265.788</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.286244</td>\n",
-       "      <td>3.493526</td>\n",
+       "      <td>0.375730</td>\n",
+       "      <td>2.661485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>True</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>3058.531</td>\n",
-       "      <td>1294.409</td>\n",
+       "      <td>10804.320</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.465132</td>\n",
-       "      <td>2.149926</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.563628</td>\n",
+       "      <td>1.774220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>True</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>6083.994</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>8881.665</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7458.105</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.378167</td>\n",
-       "      <td>2.644335</td>\n",
+       "      <td>0.353143</td>\n",
+       "      <td>2.831718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>True</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>12.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6269.341</td>\n",
-       "      <td>3039.960</td>\n",
-       "      <td>8484.239</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1284.079</td>\n",
+       "      <td>60307.930</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.535418</td>\n",
-       "      <td>1.867699</td>\n",
+       "      <td>0.367047</td>\n",
+       "      <td>2.724449</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   treat   age  educ  black  hisp  married  nodegr      re74      re75  \\\n",
-       "0   True  45.0   5.0    1.0   0.0      1.0     1.0     0.000     0.000   \n",
-       "1   True  23.0   8.0    0.0   1.0      0.0     1.0     0.000     0.000   \n",
-       "2   True  42.0   9.0    1.0   0.0      1.0     1.0     0.000  3058.531   \n",
-       "3   True  20.0   9.0    1.0   0.0      0.0     1.0  6083.994     0.000   \n",
-       "4   True  23.0  12.0    1.0   0.0      0.0     0.0  6269.341  3039.960   \n",
+       "   treat   age  educ  black  hisp  married  nodegr  re74      re75       re78  \\\n",
+       "0   True  25.0  10.0    1.0   0.0      0.0     1.0   0.0     0.000      0.000   \n",
+       "1   True  26.0  10.0    1.0   0.0      0.0     1.0   0.0     0.000   9265.788   \n",
+       "2   True  40.0  12.0    1.0   0.0      0.0     0.0   0.0     0.000  10804.320   \n",
+       "3   True  19.0  11.0    1.0   0.0      0.0     1.0   0.0     0.000   7458.105   \n",
+       "4   True  28.0  11.0    1.0   0.0      0.0     1.0   0.0  1284.079  60307.930   \n",
        "\n",
-       "       re78  u74  u75  propensity_score    weight  \n",
-       "0  8546.715  1.0  1.0          0.520700  1.920493  \n",
-       "1  3881.284  1.0  1.0          0.286244  3.493526  \n",
-       "2  1294.409  1.0  0.0          0.465132  2.149926  \n",
-       "3  8881.665  0.0  1.0          0.378167  2.644335  \n",
-       "4  8484.239  0.0  0.0          0.535418  1.867699  "
+       "   u74  u75  propensity_score    weight  \n",
+       "0  1.0  1.0          0.374159  2.672663  \n",
+       "1  1.0  1.0          0.375730  2.661485  \n",
+       "2  1.0  1.0          0.563628  1.774220  \n",
+       "3  1.0  1.0          0.353143  2.831718  \n",
+       "4  1.0  0.0          0.367047  2.724449  "
       ]
      },
      "execution_count": 16,
@@ -964,10 +964,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.998855Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.998670Z",
-     "iopub.status.idle": "2024-10-25T15:20:14.002382Z",
-     "shell.execute_reply": "2024-10-25T15:20:14.001896Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.484032Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.483636Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.487338Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.486795Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb
index f4a91b57b..85d3551f5 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb
@@ -18,10 +18,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:16.668178Z",
-     "iopub.status.busy": "2024-10-25T15:20:16.668001Z",
-     "iopub.status.idle": "2024-10-25T15:20:16.674020Z",
-     "shell.execute_reply": "2024-10-25T15:20:16.673422Z"
+     "iopub.execute_input": "2024-10-25T15:22:14.364374Z",
+     "iopub.status.busy": "2024-10-25T15:22:14.364159Z",
+     "iopub.status.idle": "2024-10-25T15:22:14.370401Z",
+     "shell.execute_reply": "2024-10-25T15:22:14.369911Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:16.675665Z",
-     "iopub.status.busy": "2024-10-25T15:20:16.675444Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.080346Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.079672Z"
+     "iopub.execute_input": "2024-10-25T15:22:14.372118Z",
+     "iopub.status.busy": "2024-10-25T15:22:14.371929Z",
+     "iopub.status.idle": "2024-10-25T15:22:15.967732Z",
+     "shell.execute_reply": "2024-10-25T15:22:15.967061Z"
     }
    },
    "outputs": [],
@@ -65,10 +65,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.082757Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.082425Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.091124Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.090593Z"
+     "iopub.execute_input": "2024-10-25T15:22:15.970563Z",
+     "iopub.status.busy": "2024-10-25T15:22:15.970165Z",
+     "iopub.status.idle": "2024-10-25T15:22:15.979563Z",
+     "shell.execute_reply": "2024-10-25T15:22:15.978919Z"
     }
    },
    "outputs": [
@@ -107,37 +107,37 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
+       "      <td>9</td>\n",
        "      <td>1</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>4</td>\n",
+       "      <td>7</td>\n",
        "      <td>2</td>\n",
        "      <td>20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>9</td>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>6</td>\n",
+       "      <td>8</td>\n",
        "      <td>4</td>\n",
        "      <td>40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>5</td>\n",
+       "      <td>3</td>\n",
        "      <td>5</td>\n",
        "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>60</td>\n",
        "    </tr>\n",
@@ -155,7 +155,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>8</td>\n",
+       "      <td>5</td>\n",
        "      <td>9</td>\n",
        "      <td>90</td>\n",
        "    </tr>\n",
@@ -166,15 +166,15 @@
       "text/plain": [
        "   Z  X   Y\n",
        "0  1  0   0\n",
-       "1  7  1  10\n",
-       "2  4  2  20\n",
-       "3  9  3  30\n",
-       "4  6  4  40\n",
-       "5  5  5  50\n",
-       "6  3  6  60\n",
+       "1  9  1  10\n",
+       "2  7  2  20\n",
+       "3  6  3  30\n",
+       "4  8  4  40\n",
+       "5  3  5  50\n",
+       "6  4  6  60\n",
        "7  2  7  70\n",
        "8  0  8  80\n",
-       "9  8  9  90"
+       "9  5  9  90"
       ]
      },
      "execution_count": 3,
@@ -202,10 +202,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.092957Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.092618Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.197730Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.197172Z"
+     "iopub.execute_input": "2024-10-25T15:22:15.981447Z",
+     "iopub.status.busy": "2024-10-25T15:22:15.981246Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.084396Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.083700Z"
     }
    },
    "outputs": [
@@ -255,10 +255,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.200013Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.199600Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.308258Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.307646Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.086689Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.086424Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.183660Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.183089Z"
     }
    },
    "outputs": [
@@ -309,10 +309,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.310580Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.310346Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.419639Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.418975Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.185886Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.185670Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.274783Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.274116Z"
     }
    },
    "outputs": [
@@ -356,10 +356,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.421820Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.421609Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.533029Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.532430Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.276944Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.276731Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.368549Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.367853Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb b/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
index 472b38f4a..9cc8912f8 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
@@ -90,10 +90,10 @@
    "id": "8c441b37",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:20.213085Z",
-     "iopub.status.busy": "2024-10-25T15:20:20.212907Z",
-     "iopub.status.idle": "2024-10-25T15:20:21.851987Z",
-     "shell.execute_reply": "2024-10-25T15:20:21.851375Z"
+     "iopub.execute_input": "2024-10-25T15:22:18.460057Z",
+     "iopub.status.busy": "2024-10-25T15:22:18.459620Z",
+     "iopub.status.idle": "2024-10-25T15:22:20.263656Z",
+     "shell.execute_reply": "2024-10-25T15:22:20.263038Z"
     },
     "scrolled": true
    },
@@ -117,10 +117,10 @@
    "id": "23c5c320",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:21.854430Z",
-     "iopub.status.busy": "2024-10-25T15:20:21.853980Z",
-     "iopub.status.idle": "2024-10-25T15:20:27.855126Z",
-     "shell.execute_reply": "2024-10-25T15:20:27.854469Z"
+     "iopub.execute_input": "2024-10-25T15:22:20.266473Z",
+     "iopub.status.busy": "2024-10-25T15:22:20.266038Z",
+     "iopub.status.idle": "2024-10-25T15:22:28.637708Z",
+     "shell.execute_reply": "2024-10-25T15:22:28.636983Z"
     }
    },
    "outputs": [
@@ -151,7 +151,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d2bbd56e61840999f012ea910a5d5de",
+       "model_id": "cebd2d4393354d96a237e787ab998e9c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -197,7 +197,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "023e2053e3cb451f8a2b29a57d7a1485",
+       "model_id": "4ec92bee67c54d3a86efe327e6f756d5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -237,7 +237,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d60e503bbfd41c1a2b88a275f3c7b98",
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@@ -277,7 +277,7 @@
     {
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-       "model_id": "049f0a5cc0994afcb5ec20507935a6f6",
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        "version_major": 2,
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@@ -327,10 +327,10 @@
    "id": "64dd7f3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:27.857675Z",
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-     "iopub.status.idle": "2024-10-25T15:20:27.861226Z",
-     "shell.execute_reply": "2024-10-25T15:20:27.860650Z"
+     "iopub.execute_input": "2024-10-25T15:22:28.640388Z",
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+     "shell.execute_reply": "2024-10-25T15:22:28.643765Z"
     }
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@@ -353,10 +353,10 @@
    "id": "a75b74fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:27.863020Z",
-     "iopub.status.busy": "2024-10-25T15:20:27.862692Z",
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-     "shell.execute_reply": "2024-10-25T15:20:28.335798Z"
+     "iopub.execute_input": "2024-10-25T15:22:28.646151Z",
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@@ -390,10 +390,10 @@
    "id": "5201cd08",
    "metadata": {
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-     "iopub.execute_input": "2024-10-25T15:20:28.341193Z",
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-     "shell.execute_reply": "2024-10-25T15:20:29.100626Z"
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@@ -416,10 +416,10 @@
    "id": "afbd74ef",
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-     "iopub.execute_input": "2024-10-25T15:20:29.104776Z",
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-     "shell.execute_reply": "2024-10-25T15:20:29.109509Z"
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@@ -450,10 +450,10 @@
    "id": "5b977c7f",
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-     "shell.execute_reply": "2024-10-25T15:20:29.115711Z"
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@@ -475,10 +475,10 @@
    "id": "9a663fe7",
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-     "shell.execute_reply": "2024-10-25T15:20:29.125064Z"
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@@ -507,10 +507,10 @@
    "id": "51fadf20",
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-     "iopub.execute_input": "2024-10-25T15:20:29.127343Z",
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-     "shell.execute_reply": "2024-10-25T15:20:29.130125Z"
+     "iopub.execute_input": "2024-10-25T15:22:30.310431Z",
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@@ -524,10 +524,10 @@
    "id": "633da7eb",
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-     "iopub.execute_input": "2024-10-25T15:20:29.132311Z",
-     "iopub.status.busy": "2024-10-25T15:20:29.131974Z",
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-     "shell.execute_reply": "2024-10-25T15:20:29.134621Z"
+     "iopub.execute_input": "2024-10-25T15:22:30.316594Z",
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     }
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@@ -551,10 +551,10 @@
    "id": "27f43e54",
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     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:29.136803Z",
-     "iopub.status.busy": "2024-10-25T15:20:29.136498Z",
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-     "shell.execute_reply": "2024-10-25T15:20:52.086444Z"
+     "iopub.execute_input": "2024-10-25T15:22:30.321343Z",
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+     "shell.execute_reply": "2024-10-25T15:22:55.377173Z"
     }
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@@ -613,7 +613,7 @@
     {
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-       "model_id": "cd28ac582c6a4e5e89822d02d978001c",
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@@ -627,7 +627,7 @@
     {
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@@ -641,7 +641,7 @@
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@@ -655,7 +655,7 @@
     {
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@@ -669,7 +669,7 @@
     {
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@@ -683,7 +683,7 @@
     {
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@@ -697,7 +697,7 @@
     {
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-       "model_id": "d100f440f07548d1817c5974a669d7c6",
+       "model_id": "68ee50dedbab457c90b06b8a4bc628f9",
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@@ -741,10 +741,10 @@
    "id": "4f382191",
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-     "iopub.execute_input": "2024-10-25T15:20:52.089055Z",
-     "iopub.status.busy": "2024-10-25T15:20:52.088752Z",
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-     "shell.execute_reply": "2024-10-25T15:20:52.827756Z"
+     "iopub.execute_input": "2024-10-25T15:22:55.379862Z",
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+     "shell.execute_reply": "2024-10-25T15:22:56.135406Z"
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@@ -766,7 +766,7 @@
     {
      "data": {
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-       "model_id": "fb0dc35237d94c2caf46f1f502655af5",
+       "model_id": "d574b56a13024f448bcc68a3a5b0afa9",
        "version_major": 2,
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@@ -784,8 +784,8 @@
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "       Test metric             DataLoader 0\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_acc            0.19179999828338623\n",
-      "        test_loss           1.6588785648345947\n",
+      "        test_acc            0.21080000698566437\n",
+      "        test_loss           1.6715691089630127\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
      ]
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@@ -811,10 +811,10 @@
    "id": "08881e8d",
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-     "iopub.execute_input": "2024-10-25T15:20:52.830275Z",
-     "iopub.status.busy": "2024-10-25T15:20:52.830088Z",
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-     "shell.execute_reply": "2024-10-25T15:20:52.833393Z"
+     "iopub.execute_input": "2024-10-25T15:22:56.138138Z",
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+     "shell.execute_reply": "2024-10-25T15:22:56.141194Z"
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@@ -828,10 +828,10 @@
    "id": "48c87949",
    "metadata": {
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-     "iopub.execute_input": "2024-10-25T15:20:52.835465Z",
-     "iopub.status.busy": "2024-10-25T15:20:52.835288Z",
-     "iopub.status.idle": "2024-10-25T15:20:52.838245Z",
-     "shell.execute_reply": "2024-10-25T15:20:52.837768Z"
+     "iopub.execute_input": "2024-10-25T15:22:56.143532Z",
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@@ -846,10 +846,10 @@
    "id": "2a9b1e8a",
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-     "shell.execute_reply": "2024-10-25T15:21:24.347298Z"
+     "iopub.execute_input": "2024-10-25T15:22:56.147791Z",
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+     "shell.execute_reply": "2024-10-25T15:23:29.876510Z"
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@@ -899,7 +899,7 @@
     {
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-       "model_id": "5850b4eee2884b9a89d312bcf56e86d7",
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@@ -913,7 +913,7 @@
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@@ -927,7 +927,7 @@
     {
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@@ -941,7 +941,7 @@
     {
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@@ -955,7 +955,7 @@
     {
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@@ -969,7 +969,7 @@
     {
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@@ -983,7 +983,7 @@
     {
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-       "model_id": "e10522f49b71433e8fbb08d93f17fb2a",
+       "model_id": "c51679989c61482c872e51ae8de7029f",
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@@ -1014,10 +1014,10 @@
    "id": "cf1640b9",
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-     "iopub.status.busy": "2024-10-25T15:21:24.349447Z",
-     "iopub.status.idle": "2024-10-25T15:21:25.081881Z",
-     "shell.execute_reply": "2024-10-25T15:21:25.081345Z"
+     "iopub.execute_input": "2024-10-25T15:23:29.878786Z",
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+     "shell.execute_reply": "2024-10-25T15:23:30.613151Z"
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    "outputs": [
@@ -1038,7 +1038,7 @@
     {
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-       "model_id": "d0948cf35dcf4f1f8fe97b73270a606b",
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@@ -1056,8 +1056,8 @@
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "       Test metric             DataLoader 0\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_acc            0.6676999926567078\n",
-      "        test_loss           0.6822428703308105\n",
+      "        test_acc            0.6514999866485596\n",
+      "        test_loss            0.687086284160614\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
      ]
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@@ -1099,10 +1099,10 @@
    "id": "8c5cd482",
    "metadata": {
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-     "iopub.execute_input": "2024-10-25T15:21:25.083746Z",
-     "iopub.status.busy": "2024-10-25T15:21:25.083538Z",
-     "iopub.status.idle": "2024-10-25T15:21:35.180999Z",
-     "shell.execute_reply": "2024-10-25T15:21:35.180368Z"
+     "iopub.execute_input": "2024-10-25T15:23:30.615978Z",
+     "iopub.status.busy": "2024-10-25T15:23:30.615505Z",
+     "iopub.status.idle": "2024-10-25T15:23:40.883995Z",
+     "shell.execute_reply": "2024-10-25T15:23:40.883334Z"
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    "outputs": [],
@@ -1119,10 +1119,10 @@
    "id": "4b28707a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:35.183270Z",
-     "iopub.status.busy": "2024-10-25T15:21:35.182898Z",
-     "iopub.status.idle": "2024-10-25T15:21:35.185994Z",
-     "shell.execute_reply": "2024-10-25T15:21:35.185526Z"
+     "iopub.execute_input": "2024-10-25T15:23:40.886631Z",
+     "iopub.status.busy": "2024-10-25T15:23:40.886142Z",
+     "iopub.status.idle": "2024-10-25T15:23:40.889535Z",
+     "shell.execute_reply": "2024-10-25T15:23:40.889033Z"
     }
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    "outputs": [],
@@ -1144,10 +1144,10 @@
    "id": "17b446be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:35.187909Z",
-     "iopub.status.busy": "2024-10-25T15:21:35.187469Z",
-     "iopub.status.idle": "2024-10-25T15:21:45.390016Z",
-     "shell.execute_reply": "2024-10-25T15:21:45.389397Z"
+     "iopub.execute_input": "2024-10-25T15:23:40.891495Z",
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+     "shell.execute_reply": "2024-10-25T15:23:51.149807Z"
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@@ -1164,10 +1164,10 @@
    "id": "9448cc87",
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-     "iopub.execute_input": "2024-10-25T15:21:45.392371Z",
-     "iopub.status.busy": "2024-10-25T15:21:45.391971Z",
-     "iopub.status.idle": "2024-10-25T15:21:45.395515Z",
-     "shell.execute_reply": "2024-10-25T15:21:45.395028Z"
+     "iopub.execute_input": "2024-10-25T15:23:51.153151Z",
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+     "iopub.status.idle": "2024-10-25T15:23:51.155948Z",
+     "shell.execute_reply": "2024-10-25T15:23:51.155493Z"
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@@ -1227,7 +1227,7 @@
   "widgets": {
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     "state": {
-     "0094554be37a4275bf6bf454b968201f": {
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@@ -8388,30 +8387,31 @@
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diff --git a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
index cb5cafd84..ea49720f0 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
@@ -57,10 +57,10 @@
    "id": "bbbab4ea",
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-     "shell.execute_reply": "2024-10-25T15:21:51.235746Z"
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     }
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@@ -75,10 +75,10 @@
    "id": "ba6f68b9",
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-     "shell.execute_reply": "2024-10-25T15:21:52.657315Z"
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@@ -106,10 +106,10 @@
    "id": "41c60ca7",
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-     "shell.execute_reply": "2024-10-25T15:21:52.754509Z"
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@@ -183,10 +183,10 @@
    "id": "75882711",
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-     "shell.execute_reply": "2024-10-25T15:21:52.861124Z"
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@@ -223,10 +223,10 @@
    "id": "636b6a25",
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-     "shell.execute_reply": "2024-10-25T15:21:52.902082Z"
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@@ -451,10 +451,10 @@
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@@ -498,10 +498,10 @@
    "id": "4eaec5dc",
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@@ -516,9 +516,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -543,10 +543,10 @@
    "id": "56889b39",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2024-10-25T15:21:54.717191Z"
+     "iopub.execute_input": "2024-10-25T15:23:59.254776Z",
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+     "iopub.status.idle": "2024-10-25T15:24:01.015521Z",
+     "shell.execute_reply": "2024-10-25T15:24:01.014896Z"
     }
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    "outputs": [
@@ -577,17 +577,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W6+W1+W5+W3+W4 | \n",
+      "b: y~v0+W3+W1+W5+W2+W4+W6 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.722958585421619\n",
-      "Effect estimates: [[11.72295859]]\n",
+      "Mean value: 11.71497437833765\n",
+      "Effect estimates: [[11.71497438]]\n",
       "\n"
      ]
     }
@@ -631,16 +631,16 @@
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-     "shell.execute_reply": "2024-10-25T15:21:55.741924Z"
+     "iopub.execute_input": "2024-10-25T15:24:01.017733Z",
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+     "shell.execute_reply": "2024-10-25T15:24:02.334574Z"
     }
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    "outputs": [
     {
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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -654,7 +654,7 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.722958585421619\n",
+      "Original Effect Estimate : 11.71497437833765\n",
       "Robustness Value : 0.45\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.41000000000000003\n",
@@ -694,10 +694,10 @@
    "id": "52b2e904",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:55.744623Z",
-     "iopub.status.busy": "2024-10-25T15:21:55.744247Z",
-     "iopub.status.idle": "2024-10-25T15:21:55.789430Z",
-     "shell.execute_reply": "2024-10-25T15:21:55.788952Z"
+     "iopub.execute_input": "2024-10-25T15:24:02.337549Z",
+     "iopub.status.busy": "2024-10-25T15:24:02.337030Z",
+     "iopub.status.idle": "2024-10-25T15:24:02.388262Z",
+     "shell.execute_reply": "2024-10-25T15:24:02.387571Z"
     }
    },
    "outputs": [
@@ -754,16 +754,16 @@
    "id": "85eefe08",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:55.791427Z",
-     "iopub.status.busy": "2024-10-25T15:21:55.791077Z",
-     "iopub.status.idle": "2024-10-25T15:22:10.228231Z",
-     "shell.execute_reply": "2024-10-25T15:22:10.227470Z"
+     "iopub.execute_input": "2024-10-25T15:24:02.390594Z",
+     "iopub.status.busy": "2024-10-25T15:24:02.390102Z",
+     "iopub.status.idle": "2024-10-25T15:24:18.999707Z",
+     "shell.execute_reply": "2024-10-25T15:24:18.998961Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -808,16 +808,16 @@
    "id": "df23a49b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:10.230520Z",
-     "iopub.status.busy": "2024-10-25T15:22:10.230258Z",
-     "iopub.status.idle": "2024-10-25T15:22:23.591281Z",
-     "shell.execute_reply": "2024-10-25T15:22:23.590610Z"
+     "iopub.execute_input": "2024-10-25T15:24:19.001952Z",
+     "iopub.status.busy": "2024-10-25T15:24:19.001622Z",
+     "iopub.status.idle": "2024-10-25T15:24:34.949014Z",
+     "shell.execute_reply": "2024-10-25T15:24:34.948429Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -827,7 +827,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -855,10 +855,10 @@
    "id": "934eef00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:23.593400Z",
-     "iopub.status.busy": "2024-10-25T15:22:23.593058Z",
-     "iopub.status.idle": "2024-10-25T15:22:23.638177Z",
-     "shell.execute_reply": "2024-10-25T15:22:23.637536Z"
+     "iopub.execute_input": "2024-10-25T15:24:34.951155Z",
+     "iopub.status.busy": "2024-10-25T15:24:34.950748Z",
+     "iopub.status.idle": "2024-10-25T15:24:34.995967Z",
+     "shell.execute_reply": "2024-10-25T15:24:34.995468Z"
     }
    },
    "outputs": [
@@ -896,14 +896,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.048408</td>\n",
-       "      <td>0.053452</td>\n",
-       "      <td>11.722959</td>\n",
-       "      <td>1.038205</td>\n",
-       "      <td>10.684754</td>\n",
-       "      <td>12.761164</td>\n",
-       "      <td>9.535324</td>\n",
-       "      <td>13.883763</td>\n",
+       "      <td>0.048655</td>\n",
+       "      <td>0.052962</td>\n",
+       "      <td>11.714974</td>\n",
+       "      <td>1.036116</td>\n",
+       "      <td>10.678858</td>\n",
+       "      <td>12.751091</td>\n",
+       "      <td>9.530065</td>\n",
+       "      <td>13.873046</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -911,10 +911,10 @@
       ],
       "text/plain": [
        "     r2tu_w   r2yu_tw  short estimate      bias  lower_ate_bound  \\\n",
-       "0  0.048408  0.053452       11.722959  1.038205        10.684754   \n",
+       "0  0.048655  0.052962       11.714974  1.036116        10.678858   \n",
        "\n",
        "   upper_ate_bound  lower_confidence_bound  upper_confidence_bound  \n",
-       "0        12.761164                9.535324               13.883763  "
+       "0        12.751091                9.530065               13.873046  "
       ]
      },
      "execution_count": 13,
@@ -941,10 +941,10 @@
    "id": "7cff3837",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:23.640125Z",
-     "iopub.status.busy": "2024-10-25T15:22:23.639792Z",
-     "iopub.status.idle": "2024-10-25T15:22:24.227690Z",
-     "shell.execute_reply": "2024-10-25T15:22:24.227009Z"
+     "iopub.execute_input": "2024-10-25T15:24:34.997944Z",
+     "iopub.status.busy": "2024-10-25T15:24:34.997579Z",
+     "iopub.status.idle": "2024-10-25T15:24:35.590885Z",
+     "shell.execute_reply": "2024-10-25T15:24:35.590288Z"
     }
    },
    "outputs": [
@@ -975,17 +975,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W6+W1+W5+W3+W4 | \n",
+      "b: y~v0+W3+W1+W5+W2+W4+W6 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.831637730148332\n",
-      "Effect estimates: [[11.83163773]]\n",
+      "Mean value: 11.816420420308324\n",
+      "Effect estimates: [[11.81642042]]\n",
       "\n"
      ]
     }
@@ -1017,17 +1017,17 @@
    "id": "946e1237",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:24.229804Z",
-     "iopub.status.busy": "2024-10-25T15:22:24.229412Z",
-     "iopub.status.idle": "2024-10-25T15:22:39.334625Z",
-     "shell.execute_reply": "2024-10-25T15:22:39.333974Z"
+     "iopub.execute_input": "2024-10-25T15:24:35.593069Z",
+     "iopub.status.busy": "2024-10-25T15:24:35.592669Z",
+     "iopub.status.idle": "2024-10-25T15:24:50.873580Z",
+     "shell.execute_reply": "2024-10-25T15:24:50.872836Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -1041,7 +1041,7 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.831637730148332\n",
+      "Original Effect Estimate : 11.816420420308324\n",
       "Robustness Value : 0.66\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.64\n",
diff --git a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb
index eb9eef341..0b4635837 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb
@@ -34,10 +34,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:44.304292Z",
-     "iopub.status.busy": "2024-10-25T15:22:44.304112Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.714260Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.713585Z"
+     "iopub.execute_input": "2024-10-25T15:24:56.020002Z",
+     "iopub.status.busy": "2024-10-25T15:24:56.019819Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.581981Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.581342Z"
     }
    },
    "outputs": [],
@@ -82,10 +82,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.716665Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.716348Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.732777Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.732175Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.584834Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.584117Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.601515Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.600944Z"
     }
    },
    "outputs": [],
@@ -105,10 +105,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.734790Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.734383Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.889457Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.888848Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.603726Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.603335Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.766716Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.766064Z"
     }
    },
    "outputs": [
@@ -277,10 +277,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.891860Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.891450Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.035468Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.034805Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.769145Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.768750Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.934497Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.933837Z"
     }
    },
    "outputs": [
@@ -444,10 +444,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.037549Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.037356Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.052458Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.051872Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.936753Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.936529Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.957833Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.957198Z"
     }
    },
    "outputs": [
@@ -461,9 +461,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -494,10 +494,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.054282Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.054097Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.070377Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.069781Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.960243Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.960027Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.980802Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.980155Z"
     }
    },
    "outputs": [
@@ -514,16 +514,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W3+W5+W6+W2+W0+W1\n",
+      "b: y~v0+W1+W0+W2+W3+W5+W6\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.697677486880934\n",
+      "Mean value: 10.69767748688092\n",
       "\n"
      ]
     }
@@ -557,10 +557,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.072158Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.071973Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.365118Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.364439Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.983260Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.983019Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.320714Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.319956Z"
     },
     "scrolled": false
    },
@@ -599,24 +599,24 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.367164Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.366853Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.371057Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.370447Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.323070Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.322657Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.326633Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.326132Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'estimate': 10.697677486880934,\n",
-       " 'standard_error': 0.5938735661282953,\n",
+       "{'estimate': 10.69767748688092,\n",
+       " 'standard_error': 0.5938735661282948,\n",
        " 'degree of freedom': 492,\n",
-       " 't_statistic': 18.01339223872763,\n",
-       " 'r2yt_w': 0.39741498372666834,\n",
-       " 'partial_f2': 0.6595168698094569,\n",
-       " 'robustness_value': 0.546744557218101,\n",
-       " 'robustness_value_alpha': 0.5076289101030929}"
+       " 't_statistic': 18.013392238727622,\n",
+       " 'r2yt_w': 0.39741498372666817,\n",
+       " 'partial_f2': 0.6595168698094563,\n",
+       " 'robustness_value': 0.5467445572181009,\n",
+       " 'robustness_value_alpha': 0.5076289101030926}"
       ]
      },
      "execution_count": 8,
@@ -633,10 +633,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.372919Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.372544Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.380119Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.379496Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.328455Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.328086Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.335819Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.335341Z"
     }
    },
    "outputs": [
@@ -757,10 +757,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.381929Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.381601Z",
-     "iopub.status.idle": "2024-10-25T15:22:54.704772Z",
-     "shell.execute_reply": "2024-10-25T15:22:54.704198Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.337636Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.337261Z",
+     "iopub.status.idle": "2024-10-25T15:25:06.676899Z",
+     "shell.execute_reply": "2024-10-25T15:25:06.676220Z"
     }
    },
    "outputs": [
@@ -792,10 +792,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:54.706827Z",
-     "iopub.status.busy": "2024-10-25T15:22:54.706418Z",
-     "iopub.status.idle": "2024-10-25T15:22:54.709805Z",
-     "shell.execute_reply": "2024-10-25T15:22:54.709295Z"
+     "iopub.execute_input": "2024-10-25T15:25:06.679079Z",
+     "iopub.status.busy": "2024-10-25T15:25:06.678662Z",
+     "iopub.status.idle": "2024-10-25T15:25:06.682013Z",
+     "shell.execute_reply": "2024-10-25T15:25:06.681525Z"
     },
     "scrolled": true
    },
@@ -807,15 +807,15 @@
       "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n",
       "\n",
       "Unadjusted Estimates of Treatment ['v0'] :\n",
-      "Coefficient Estimate : 10.697677486880934\n",
+      "Coefficient Estimate : 10.69767748688092\n",
       "Degree of Freedom : 492\n",
-      "Standard Error : 0.5938735661282953\n",
-      "t-value : 18.01339223872763\n",
-      "F^2 value : 0.6595168698094569\n",
+      "Standard Error : 0.5938735661282948\n",
+      "t-value : 18.013392238727622\n",
+      "F^2 value : 0.6595168698094563\n",
       "\n",
       "Sensitivity Statistics : \n",
-      "Partial R2 of treatment with outcome : 0.39741498372666834\n",
-      "Robustness Value : 0.546744557218101\n",
+      "Partial R2 of treatment with outcome : 0.39741498372666817\n",
+      "Robustness Value : 0.5467445572181009\n",
       "\n",
       "Interpretation of results :\n",
       "Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n",
@@ -850,10 +850,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:54.711393Z",
-     "iopub.status.busy": "2024-10-25T15:22:54.711214Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.117137Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.116532Z"
+     "iopub.execute_input": "2024-10-25T15:25:06.684000Z",
+     "iopub.status.busy": "2024-10-25T15:25:06.683634Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.142967Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.142380Z"
     }
    },
    "outputs": [
@@ -890,21 +890,21 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.119102Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.118903Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.123075Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.122449Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.145169Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.144756Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.149016Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.148522Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'converted_estimate': 2.457447065735426,\n",
-       " 'converted_lower_ci': 2.2288567270043167,\n",
-       " 'converted_upper_ci': 2.709481505797999,\n",
-       " 'evalue_estimate': 4.34995836428987,\n",
-       " 'evalue_lower_ci': 3.8838327336304186,\n",
+       "{'converted_estimate': 2.457447065735423,\n",
+       " 'converted_lower_ci': 2.2288567270043145,\n",
+       " 'converted_upper_ci': 2.7094815057979953,\n",
+       " 'evalue_estimate': 4.349958364289864,\n",
+       " 'evalue_lower_ci': 3.883832733630414,\n",
        " 'evalue_upper_ci': None}"
       ]
      },
@@ -922,10 +922,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.124832Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.124523Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.131441Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.130847Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.150820Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.150459Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.157934Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.157294Z"
     }
    },
    "outputs": [
@@ -1044,10 +1044,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.133292Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.132945Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.136206Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.135714Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.159949Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.159557Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.162963Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.162383Z"
     }
    },
    "outputs": [
@@ -1058,15 +1058,15 @@
       "Sensitivity Analysis to Unobserved Confounding using the E-value\n",
       "\n",
       "Unadjusted Estimates of Treatment: v0\n",
-      "Estimate (converted to risk ratio scale): 2.457447065735426\n",
-      "Lower 95% CI (converted to risk ratio scale): 2.2288567270043167\n",
-      "Upper 95% CI (converted to risk ratio scale): 2.709481505797999\n",
+      "Estimate (converted to risk ratio scale): 2.457447065735423\n",
+      "Lower 95% CI (converted to risk ratio scale): 2.2288567270043145\n",
+      "Upper 95% CI (converted to risk ratio scale): 2.7094815057979953\n",
       "\n",
       "Sensitivity Statistics: \n",
-      "E-value for point estimate: 4.34995836428987\n",
-      "E-value for lower 95% CI: 3.8838327336304186\n",
+      "E-value for point estimate: 4.349958364289864\n",
+      "E-value for lower 95% CI: 3.883832733630414\n",
       "E-value for upper 95% CI: None\n",
-      "Largest Observed Covariate E-value: 1.6811401167656743 (W1)\n",
+      "Largest Observed Covariate E-value: 1.6811401167656792 (W1)\n",
       "\n",
       "Interpretation of results:\n",
       "Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.\n",
@@ -1092,10 +1092,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.137947Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.137619Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.153227Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.152622Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.164755Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.164424Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.180759Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.180110Z"
     }
    },
    "outputs": [],
@@ -1115,10 +1115,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.155225Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.154896Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.281156Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.280501Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.182782Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.182483Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.330841Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.330199Z"
     }
    },
    "outputs": [
@@ -1279,10 +1279,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.283245Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.282929Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.306568Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.305920Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.333226Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.332788Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.364721Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.364099Z"
     }
    },
    "outputs": [
@@ -1296,9 +1296,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W4,W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6,W4])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -1306,7 +1306,13 @@
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
-      "No such variable(s) found!\n",
+      "No such variable(s) found!\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n"
      ]
     }
@@ -1321,10 +1327,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.308434Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.308246Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.324287Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.323676Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.367125Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.366599Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.386712Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.386138Z"
     }
    },
    "outputs": [
@@ -1341,16 +1347,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W4,W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6,W4])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W3+W5+W6+W2+W0+W1\n",
+      "b: y~v0+W1+W0+W2+W3+W5+W6+W4\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.08192437558829\n",
+      "Mean value: 10.081924375588311\n",
       "\n"
      ]
     }
@@ -1365,10 +1371,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.326084Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.325902Z",
-     "iopub.status.idle": "2024-10-25T15:22:59.696935Z",
-     "shell.execute_reply": "2024-10-25T15:22:59.696261Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.388841Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.388607Z",
+     "iopub.status.idle": "2024-10-25T15:25:11.761178Z",
+     "shell.execute_reply": "2024-10-25T15:25:11.760460Z"
     }
    },
    "outputs": [
@@ -1406,10 +1412,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:59.699066Z",
-     "iopub.status.busy": "2024-10-25T15:22:59.698684Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.064278Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.063654Z"
+     "iopub.execute_input": "2024-10-25T15:25:11.763292Z",
+     "iopub.status.busy": "2024-10-25T15:25:11.763099Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.164415Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.163717Z"
     }
    },
    "outputs": [
@@ -1433,10 +1439,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.066342Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.065969Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.068971Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.068438Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.166769Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.166329Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.170110Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.169585Z"
     },
     "scrolled": true
    },
@@ -1448,11 +1454,11 @@
       "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n",
       "\n",
       "Unadjusted Estimates of Treatment ['v0'] :\n",
-      "Coefficient Estimate : 10.08192437558829\n",
+      "Coefficient Estimate : 10.081924375588311\n",
       "Degree of Freedom : 491\n",
-      "Standard Error : 0.10446229543424747\n",
-      "t-value : 96.51256784735537\n",
-      "F^2 value : 18.97082637981746\n",
+      "Standard Error : 0.10446229543424763\n",
+      "t-value : 96.51256784735543\n",
+      "F^2 value : 18.970826379817485\n",
       "\n",
       "Sensitivity Statistics : \n",
       "Partial R2 of treatment with outcome : 0.9499269594066172\n",
@@ -1477,10 +1483,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.070816Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.070443Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.074448Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.073853Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.172027Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.171646Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.176112Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.175591Z"
     },
     "scrolled": true
    },
@@ -1488,12 +1494,12 @@
     {
      "data": {
       "text/plain": [
-       "{'estimate': 10.08192437558829,\n",
-       " 'standard_error': 0.10446229543424747,\n",
+       "{'estimate': 10.081924375588311,\n",
+       " 'standard_error': 0.10446229543424763,\n",
        " 'degree of freedom': 491,\n",
-       " 't_statistic': 96.51256784735537,\n",
+       " 't_statistic': 96.51256784735543,\n",
        " 'r2yt_w': 0.9499269594066172,\n",
-       " 'partial_f2': 18.97082637981746,\n",
+       " 'partial_f2': 18.970826379817485,\n",
        " 'robustness_value': 0.9522057801012398,\n",
        " 'robustness_value_alpha': 0.950386691319526}"
       ]
@@ -1512,10 +1518,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.076256Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.075929Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.083866Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.083244Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.178071Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.177692Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.186536Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.186027Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb b/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
index 8f62b6ea7..01e3da649 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:06.615600Z",
-     "iopub.status.busy": "2024-10-25T15:23:06.615154Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.049138Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.048510Z"
+     "iopub.execute_input": "2024-10-25T15:25:19.104975Z",
+     "iopub.status.busy": "2024-10-25T15:25:19.104546Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.672525Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.671834Z"
     }
    },
    "outputs": [],
@@ -40,10 +40,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.051703Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.051121Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.056626Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.056003Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.675298Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.674756Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.680705Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.680030Z"
     }
    },
    "outputs": [],
@@ -67,10 +67,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.058728Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.058239Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.171519Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.170957Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.682879Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.682506Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.799389Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.798817Z"
     }
    },
    "outputs": [
@@ -116,10 +116,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.173759Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.173379Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.293392Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.292837Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.802130Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.801724Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.923916Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.923294Z"
     }
    },
    "outputs": [
@@ -159,10 +159,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.295510Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.295295Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.300229Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.299666Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.926479Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.926084Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.930803Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.930278Z"
     }
    },
    "outputs": [],
@@ -175,10 +175,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.302269Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.302060Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.403901Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.403366Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.932862Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.932459Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.035193Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.034525Z"
     }
    },
    "outputs": [
@@ -205,10 +205,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.406129Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.405775Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.419013Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.418476Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.037677Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.037229Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.050881Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.050287Z"
     }
    },
    "outputs": [
@@ -238,8 +238,8 @@
        "      <th>V7_-3</th>\n",
        "      <th>V7_-5</th>\n",
        "      <th>V4_-3</th>\n",
-       "      <th>V4_-7</th>\n",
        "      <th>V4_-9</th>\n",
+       "      <th>V4_-7</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -298,7 +298,7 @@
        "</div>"
       ],
       "text/plain": [
-       "   V6_0  V5_-1  V7_-3  V7_-5  V4_-3  V4_-7  V4_-9\n",
+       "   V6_0  V5_-1  V7_-3  V7_-5  V4_-3  V4_-9  V4_-7\n",
        "0     6      0      0      0      0      0      0\n",
        "1     7      5      0      0      0      0      0\n",
        "2     8      6      0      0      0      0      0\n",
@@ -330,17 +330,17 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.421046Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.420829Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.426887Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.426295Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.053270Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.052848Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.057319Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.056778Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['V5_-1', 'V7_-3', 'V7_-5', 'V4_-3', 'V4_-7', 'V4_-9']"
+       "['V5_-1', 'V7_-3', 'V7_-5', 'V4_-3', 'V4_-9', 'V4_-7']"
       ]
      },
      "execution_count": 8,
@@ -361,10 +361,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.428915Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.428704Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.528196Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.527542Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.059391Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.058978Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.160118Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.159398Z"
     }
    },
    "outputs": [
@@ -386,23 +386,23 @@
       "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-7,U) = P(V6_0|V5_-1,V4_-7)\n",
       "\n",
       "## Realized estimand\n",
-      "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-3+V5_-1*V7_-5\n",
+      "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-5+V5_-1*V7_-3\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: -0.12612138021763197\n",
+      "Mean value: -0.12612138021763286\n",
       "p-value: [0.75401158]\n",
       "### Conditional Estimates\n",
-      "__categorical__V7_-3  __categorical__V7_-5\n",
-      "(-0.001, 0.6]         (-0.001, 5.4]           0.111915\n",
-      "(0.6, 5.4]            (7.8, 9.0]             -0.257861\n",
-      "                      (9.0, 10.0]            -0.314176\n",
-      "(5.4, 7.8]            (-0.001, 5.4]           0.073537\n",
-      "                      (7.8, 9.0]             -0.285273\n",
-      "(7.8, 9.0]            (-0.001, 5.4]          -0.034356\n",
-      "                      (5.4, 7.8]             -0.216503\n",
+      "__categorical__V7_-5  __categorical__V7_-3\n",
+      "(-0.001, 5.4]         (-0.001, 0.6]           0.111915\n",
+      "                      (5.4, 7.8]              0.073537\n",
+      "                      (7.8, 9.0]             -0.034356\n",
+      "(5.4, 7.8]            (7.8, 9.0]             -0.216503\n",
+      "(7.8, 9.0]            (0.6, 5.4]             -0.257861\n",
+      "                      (5.4, 7.8]             -0.285273\n",
       "                      (7.8, 9.0]             -0.296238\n",
-      "(9.0, 10.0]           (7.8, 9.0]             -0.261853\n",
+      "                      (9.0, 10.0]            -0.261853\n",
+      "(9.0, 10.0]           (0.6, 5.4]             -0.314176\n",
       "dtype: float64\n"
      ]
     },
@@ -451,10 +451,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.530382Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.529989Z",
-     "iopub.status.idle": "2024-10-25T15:23:11.141113Z",
-     "shell.execute_reply": "2024-10-25T15:23:11.140321Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.162171Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.161832Z",
+     "iopub.status.idle": "2024-10-25T15:25:23.782498Z",
+     "shell.execute_reply": "2024-10-25T15:25:23.781639Z"
     }
    },
    "outputs": [
@@ -462,17 +462,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting tigramite\r\n",
-      "  Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n",
-      "\u001b[?25l"
+      "Collecting tigramite\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
+      "  Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n",
+      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
       "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n",
       "Requirement already satisfied: scipy>=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)\r\n",
       "Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)\r\n"
@@ -511,10 +510,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:11.143682Z",
-     "iopub.status.busy": "2024-10-25T15:23:11.143451Z",
-     "iopub.status.idle": "2024-10-25T15:23:11.151852Z",
-     "shell.execute_reply": "2024-10-25T15:23:11.151366Z"
+     "iopub.execute_input": "2024-10-25T15:25:23.785227Z",
+     "iopub.status.busy": "2024-10-25T15:25:23.784755Z",
+     "iopub.status.idle": "2024-10-25T15:25:23.794030Z",
+     "shell.execute_reply": "2024-10-25T15:25:23.793516Z"
     }
    },
    "outputs": [],
@@ -535,10 +534,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:11.153659Z",
-     "iopub.status.busy": "2024-10-25T15:23:11.153313Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.065057Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.064420Z"
+     "iopub.execute_input": "2024-10-25T15:25:23.795869Z",
+     "iopub.status.busy": "2024-10-25T15:25:23.795583Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.743728Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.743118Z"
     }
    },
    "outputs": [
@@ -563,10 +562,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.067122Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.066772Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.072173Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.071586Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.745850Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.745398Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.751621Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.750975Z"
     }
    },
    "outputs": [],
@@ -586,10 +585,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.074164Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.073812Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.255141Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.254548Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.753621Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.753178Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.940289Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.939600Z"
     }
    },
    "outputs": [
@@ -620,10 +619,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.257137Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.256786Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.642767Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.642085Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.942687Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.942139Z",
+     "iopub.status.idle": "2024-10-25T15:25:25.333636Z",
+     "shell.execute_reply": "2024-10-25T15:25:25.332948Z"
     }
    },
    "outputs": [
@@ -761,7 +760,13 @@
       "\n",
       "Testing condition sets of dimension 0:\n",
       "\n",
-      "    Link (V1 -1) -?> V2 (1/21):\n",
+      "    Link (V1 -1) -?> V2 (1/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.12317 / val = -0.591\n",
       "    Non-significance detected.\n",
       "\n",
@@ -797,13 +802,7 @@
       "    Subset 0: () gives pval = 0.25844 / val =  0.454\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V4 -1) -?> V2 (10/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V4 -1) -?> V2 (10/21):\n",
       "    Subset 0: () gives pval = 0.08165 / val = -0.649\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1961,7 +1960,13 @@
       "    Subset 0: () gives pval = 0.25198 / val =  0.460\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V2 -2) -?> V3 (5/21):\n",
+      "    Link (V2 -2) -?> V3 (5/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.81987 / val = -0.097\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2846,7 +2851,13 @@
       "    Subset 0: () gives pval = 0.30492 / val = -0.416\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V3 -2) -?> V1 (8/21):\n",
+      "    Link (V3 -2) -?> V1 (8/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.90322 / val = -0.052\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2854,13 +2865,7 @@
       "    Subset 0: () gives pval = 0.59372 / val =  0.224\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V4 -1) -?> V1 (10/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V4 -1) -?> V1 (10/21):\n",
       "    Subset 0: () gives pval = 0.08508 / val = -0.644\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2925,13 +2930,7 @@
       "\n",
       "Testing condition sets of dimension 0:\n",
       "\n",
-      "    Link (V1 -1) -?> V2 (1/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V1 -1) -?> V2 (1/21):\n",
       "    Subset 0: () gives pval = 0.12317 / val = -0.591\n",
       "    Non-significance detected.\n",
       "\n",
@@ -6103,10 +6102,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.644964Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.644773Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.722100Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.721456Z"
+     "iopub.execute_input": "2024-10-25T15:25:25.336365Z",
+     "iopub.status.busy": "2024-10-25T15:25:25.335891Z",
+     "iopub.status.idle": "2024-10-25T15:25:25.417540Z",
+     "shell.execute_reply": "2024-10-25T15:25:25.416441Z"
     }
    },
    "outputs": [
diff --git a/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb b/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
index 62c095e8c..6f4eedbde 100644
--- a/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
+++ b/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
@@ -104,10 +104,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:14.980114Z",
-     "iopub.status.busy": "2024-10-25T15:23:14.979691Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.683261Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.682624Z"
+     "iopub.execute_input": "2024-10-25T15:25:27.774601Z",
+     "iopub.status.busy": "2024-10-25T15:25:27.774405Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.577042Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.576355Z"
     }
    },
    "outputs": [],
@@ -152,10 +152,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.686484Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.686067Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.690617Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.690073Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.580486Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.580173Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.584440Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.583924Z"
     }
    },
    "outputs": [],
@@ -181,10 +181,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.693229Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.692781Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.865195Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.864583Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.586695Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.586496Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.746928Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.746340Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.867535Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.867123Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.021231Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.020584Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.749384Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.749031Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.909850Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.909274Z"
     }
    },
    "outputs": [
@@ -278,10 +278,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.023408Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.023191Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.275964Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.275323Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.912508Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.912241Z",
+     "iopub.status.idle": "2024-10-25T15:25:30.077554Z",
+     "shell.execute_reply": "2024-10-25T15:25:30.076836Z"
     }
    },
    "outputs": [
@@ -295,9 +295,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -305,9 +305,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -338,10 +338,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.278175Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.277779Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.782907Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.782327Z"
+     "iopub.execute_input": "2024-10-25T15:25:30.080135Z",
+     "iopub.status.busy": "2024-10-25T15:25:30.079495Z",
+     "iopub.status.idle": "2024-10-25T15:25:30.677136Z",
+     "shell.execute_reply": "2024-10-25T15:25:30.676491Z"
     }
    },
    "outputs": [
@@ -358,16 +358,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W2+W4+W0\n",
+      "b: y~v0+W1+W0+W2+W3+W4\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.167002772144512\n",
+      "Mean value: 9.895558883688192\n",
       "\n"
      ]
     }
@@ -385,10 +385,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.784797Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.784608Z",
-     "iopub.status.idle": "2024-10-25T15:23:22.810653Z",
-     "shell.execute_reply": "2024-10-25T15:23:22.809699Z"
+     "iopub.execute_input": "2024-10-25T15:25:30.679266Z",
+     "iopub.status.busy": "2024-10-25T15:25:30.678894Z",
+     "iopub.status.idle": "2024-10-25T15:25:35.848903Z",
+     "shell.execute_reply": "2024-10-25T15:25:35.847729Z"
     }
    },
    "outputs": [
@@ -405,17 +405,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W2+W4+W0 | \n",
+      "b: y~v0+W1+W0+W2+W3+W4 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.898014646310529\n",
-      "Effect estimates: [[9.89801465]]\n",
+      "Mean value: 10.019941851969312\n",
+      "Effect estimates: [[10.01994185]]\n",
       "\n"
      ]
     }
@@ -450,10 +450,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:22.813421Z",
-     "iopub.status.busy": "2024-10-25T15:23:22.813176Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.174799Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.174119Z"
+     "iopub.execute_input": "2024-10-25T15:25:35.851755Z",
+     "iopub.status.busy": "2024-10-25T15:25:35.851511Z",
+     "iopub.status.idle": "2024-10-25T15:26:10.974933Z",
+     "shell.execute_reply": "2024-10-25T15:26:10.974258Z"
     }
    },
    "outputs": [
@@ -462,9 +462,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:10.167002772144512\n",
-      "New effect:-2.5223045497420395e-05\n",
-      "p value:1.0\n",
+      "Estimated effect:9.895558883688192\n",
+      "New effect:-0.001012646866716932\n",
+      "p value:0.96\n",
       "\n"
      ]
     }
@@ -503,10 +503,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.176926Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.176548Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.180143Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.179539Z"
+     "iopub.execute_input": "2024-10-25T15:26:10.976982Z",
+     "iopub.status.busy": "2024-10-25T15:26:10.976788Z",
+     "iopub.status.idle": "2024-10-25T15:26:10.980247Z",
+     "shell.execute_reply": "2024-10-25T15:26:10.979791Z"
     }
    },
    "outputs": [],
@@ -530,10 +530,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.182127Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.181795Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.506902Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.506267Z"
+     "iopub.execute_input": "2024-10-25T15:26:10.982076Z",
+     "iopub.status.busy": "2024-10-25T15:26:10.981732Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.339143Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.338414Z"
     }
    },
    "outputs": [
@@ -542,16 +542,16 @@
      "output_type": "stream",
      "text": [
       "   Treatment    Outcome        w0         s        w1\n",
-      "0  20.030841  39.674690  3.737967  2.488108 -0.248427\n",
-      "1  25.694528  50.855084 -4.386344  0.104081  0.221216\n",
-      "2  13.591056  26.956307  2.681680  9.584895  0.419222\n",
-      "3  16.291246  32.617104  3.116600  3.939894  0.538060\n",
-      "4  15.858877  32.098983  3.163008  3.914872 -0.095866\n"
+      "0  20.489473  40.330239  3.768185  3.481471 -0.176429\n",
+      "1   8.367748  16.237554  1.425685  1.395533  0.375315\n",
+      "2   5.651532  11.051624 -0.251996  0.937963 -1.389341\n",
+      "3   7.079860  13.399747  0.995682  4.733297 -0.657510\n",
+      "4   7.781453  15.734203 -1.433446  8.602047 -0.114190\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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goADi91pzP/zwA/bt24d169a5LPJv7+ixbds2x3Vf27Zt3V7XZWRkePwejRkzBuPGjXOsLTs7G2azGbt27cLx48cBAGVlZbjxxhuxbds2NGvWDDfccIMjG6NNmzbIyspCXV0dfv31V5w9exYAsGPHDtx+++1Yt24doqLcJ/7X1tZiyJAh+Pzzz50+36FDB7Rv3x4AkJ+fj6NHjzr+fs0112DOnDm4/vrrPf7b7FatWoUBAwbAarXCZDKhe/fuaNWqFU6fPo2tW7c6Mhs2bNiAO+64Az/99FOjNV944YUYMGAASktLnTJR3HVY6dmzp9TaNFMT0airqxNNmjRxREduueUW6WPT09Mdx91+++1OXzt48KBT1GXs2LFSc44ZM8bpuEOHDvnyz/F/xNxmE6J3b7k7A126MMuBiIiIjMNmE6JVK+/nMD17+vUchhkOgbNwW7HoN3GJ6PDSPMdHv4lLxMJtxcFemqGpyXBITk4WAMTll1/udIdeCCFqampEYWGh4/GhQ4dE8+bNHce2a9dOfPnll8JisTgdV1paKv7xj38Ik8nkGDty5Ei363n33XdFr169xNtvvy3y8/Ndjjly5IgYMWKEiImJccz5/PPPe/x+1L/j/uCDD3ocW1/9DIe0tDRhMplEamqqmDVrlrBarY5xNptNzJgxwynz4cknnxRDhw4VAER2drZYtmyZ09wVFRXi8ccfd/o/+Pzzzz2u56WXXnIa/+CDD4o9e/Y0Grds2TKnTJUWLVqI4mLXr5mGGQ72/9cnnnii0TFHjhwRt9xyi9P4L7/80u16XWVP6M2X33GqV3DTTTc5/hHdu3eXOqa0tNTpB/+JJ55oNKZLly6Or1977bVS815zzTWOY7Kysnz6dwgRgDew6mplu4RMwCEqSogQfXMiIiKiMFRVJXcO06qVcs7jJww4BMbCbcWiY71Ag/2j4+8fDDq4pybgYL/mqZZ47QwcONBxzAUXXOA2td5uxowZjvGxsbHi8OHDLsedOXPG63Pbff311445ExISxKlTp9yO1SPgAEA0adJE5OXluR0/atQop3+nyWQS3bt3d7s2m80mLr/8cscx11xzjdu5161b53T9On36dI9rP336tFPQ4amnnnI5rmFQAICYMmWK23ktFovTvJ6uk40WcFBVNBIA/t//+3+Ov+/evRv79+/3esyiRYscqTgAcPnllzcaM3jwYMffV6xYgYMHD3qc8+DBg45+tw2PNwyzGcjNlRvL/tVERERkNN7OT0wmYPVq5ZyHQladTWDs3J0QLr5m/9zYuTtRZ3M1gtSIjY3FrFmzYPby2tmyZQtycnIAADExMfjmm288dggEgMcffxzXXnstAKCmpgYzZsxwOa5h4X5P7rnnHsc13NmzZ7Fo0SLpY9UaPnw4evXq5fbrQ4cOdfy9pqYGQgjMmDEDqampLsebTCY89dRTjsfr1693W4xx8uTJjuvXe++9F08++aTHtaakpDh9nz/99FOcOXPG4zEA0L9/f/zjH/9w+/W4uDinAqLr1q3zWEDSSFQHHO666y7HD7kQAuPHj/c4vqamBpMnT3Y8btKkCW5yUavg4YcfRvTvfZ5tNpvXeceNGwebzQYAiI6ObtQJwzB69QJGjvQ+Lj2dQQciIiIyDnsOg7cxXvZhk/FtKCzFkTL3nUYEgCNl1dhQKFnXg7y66aabHHUAPKnfyeGmm27CBRdcIDX/g/U6zCxZssTn9bnyhz/8wfH3DRs26DKnJ48//rjHr2dkZCAzM9PxuFu3brjyyis9HtOvXz/H36uqqlBYWNhoTGlpKebOnet4/Le//U1qvVdeeSU6deoEQOmmuG7dOq/H1A+AuHPVVVc5/l5VVYV9+/ZJrSfYVBWNBJSCHaNHj8azzz4LAPjkk0/Qo0cPPP/8843GWq1WPPLII/j1118dn3vqqafQokWLRmO7d++OBx98EJ988gkA4OOPP8Zll12GRx99tNHYGTNmYObMmY7HDz30UKPCk4YhBOAtAti9uzKGdweIiIjIKGRbdtfUAC4KyFHoOH5Grq2p7Djy7v/+7/+kxtXP6L7hhhuk57/oooscf9+8eTOEEE5tGBs6ceIEFi9ejF9//RXFxcUoLy9v1D6yfjHLw4cPS69FjU6dOqF169Zex7Vu3RqHDh0C4BwQcadNmzZOj0+5KO6/atUqx43t9PR09O7dW2bJAJTvuz2IsWnTJtx4440ex/fv39/rnO3atXN6fPr0aen1BJPqgAMAPPnkk1i4cCEWLFgAAHjhhRfw3//+F/fffz+6du2K2tpabNmyBR999BHy8/Mdx11yySUeMxemTJmClStXYu/evQCAxx57DHPnzsU999yDtm3boqioCF9//TXmzZvnOCYrK8spg8JwrFbARaVZJ0ePAi1bBmY9RERERDKSkoDzzgP27HE/pls3ZRyFtJZJcl1GZMeRd126dPE6RgiB7du3Ox5/8sknmD9/vtT8VVVVjr9brVaUl5cjJSWl0bgDBw7g73//O3744QfU1tZKzQ34/6JXJtgAAE2bNvXpmPrjASUToaGtW7c6/m61WjFw4ECptQBKhw67EydOeB0vs+aEhASnx67WbESaAg7R0dH47rvvcMcdd2Dx4sUAgNWrV2P16tVuj7niiivw/fffu2yhYte8eXMsXLgQAwYMcESG5syZgzlz5rgc36lTJyxcuNDrPqagiosD2rdX2mO6k5mpjCMiIiIyCqsVaNCCvJGjR5VxzNIMaX07paNNSjyOllW7rONgAtA6RWmRSfpITk72OqasrMwpCLBlyxbVz1dWVtYo4LBx40bceOONqoIHDbMf9Ban4tpIzTHCxbax+q1HKyoqVNerKCsr8zrGWw0PV1yt2YhU13Cwa9q0KRYtWoQPP/wQWVlZbsdlZmbinXfewbJly9CqVSuv83bt2hVbt27FsGHD3L4QU1JSMGzYMGzdutXjcxuC1Qp4KYCJffuUcURERERGERMDeLuoiI/nTZMwEB1lwuhB2QCU4EJ99sejB2UjOor1xvQSFeX9cuzs2bO6PZ99i0D9uQcPHuwINsTGxuK+++7D7NmzsW3bNpSWlqK6uhpC6W4IIQRGjx6t23qMTK/ve8PveaTRlOFgZzKZ8Nhjj+Gxxx7DL7/8gh07duDIkSMAgBYtWuCSSy5Bjx49PO4XciUxMRHTpk1zbLHYv38/SkpK0KxZM3Ts2BFXX321qmhQUNgzHMrK3BdeqqkBYmMDuy4iIiIiT7wU8AagZDhUV7OGQxgY2KMN3r+vN8bO3elUQLJ1SjxGD8rGwB5tPBxN/tCw28K3337r1DFQi1mzZjnqMMTGxmLx4sVOxQldkem6EA7qf9+zs7OxY8eO4C0mhOkScKivV69eHtuWqBEfH48BAwboOmfAWa3AsWOeqzxbLMCCBcCttwZuXURERETu2GzA66/LjWXRyLAxsEcb3JDdGhsKS3H8TDVaJinbKJjZEBwJCQlITExERUUFAODYsWO6zW1vtQkobR+9BRsAOIozhrv6dRWOHz8exJWENs1bKkiS2Qxs3Ai8847ncWPHem89RURERBQIFRVK5oI3UVFAYqL/10MBEx1lwh+6NMPtF2fgD12aMdgQZJdffrnj7zJtFmUdqFefpW/fvl7HCyGwdu1aqbnrbxcJlXoD9dX/np88edKpCYKRNdymE+zvPQMOgdSuHfDee57HbNoE5OYGZj1EREREniQny22psNmA8nL/r4coQt10002Ov//4449OBQ21qJFte/u7nJwcFBUVSY1NrBeErN8tI1T06dMHzZo1czyeOXNmEFcjL7FB8DfY33sGHAJp0SLPLaXsRo1ilgMREREFnxDAJ594H2cyKVkOROQXQ4YMQXq60h3k7NmzePrpp3WZt23bto6///TTTx7HVlZW4vnnn5eeu02bc/U+9shcAxlMTEyM07/3nXfeQV5eXhBXJKf+9x0I/vee7wyBIoQSSJBx6BC7VRAREVHwWSzA7y3KPUpPB5KS/L8eogiVlJSEiRMnOh5/8803uOeee3Dq1Cmvx27atAkPPPAAvvrqq0Zfu/baax1///777zFv3jyXc5SUlODWW2/Fb7/9Jr3mSy65xPH3rVu3qm4rGUzDhg1D165dASiZAjfeeKPb71F9ZWVl+OCDD3DjjTf6e4mNtGnTxinoMHXqVKe2qoGme9FIcsNqBX6vAOuRyQRs2MA+1kRERBR8QshlXZaUsEsFkZ898cQT+OWXXzBjxgwAStBh/vz5+NOf/oQrr7wSGRkZMJvNKCsrw8GDB7FlyxYsXrwY+/fvB+AcXLB7/PHHMWXKFFRUVMBms+H222/H/fffj0GDBqFVq1Y4deoUVq1ahU8++QQlJSVITk7GLbfcgq+//trreq+99lpkZGSgqKgIQggMHDgQ3bt3R8eOHRFXr43usGHDXK7NCJKSkvDjjz+if//+OHXqFEpKSjBo0CD06dMHt99+O3r27Im0tDRUV1ejpKQEO3bswM8//4wVK1bAarWiQ4cOQVn3Aw88gClTpgAAvvjiCyxcuBA9e/ZEUr3AcI8ePTBhwgS/r4UBh0Axm4ElS4ALL/Q8TghlvyQRERFRsPmScWm1MuBA5Gfvv/8+MjMz8eqrr8Jms6GiogIzZ85UXV+gZcuW+Oyzz/CnP/0JtbW1sNls+Oyzz/DZZ581GpuQkIDZs2fj559/lpo7JiYGn332GW6//XacPXsWALBr1y7s2rXLadwdd9yhau2B0r17d2zYsAF33HGHozXmxo0bsXHjxiCvzL1Ro0Zh6dKl2LRpEwCl6OWyZcucxpw+fToga+GWikBq317JYPBGZgwRERGRv5nNcrUZUlOB+Hi/L4co0plMJowcORLbtm3DX/7yFzRt2tTj+LS0NPzxj3/Ef/7zH/z5z392OWbw4MFYsmQJevTo4fLr0dHRuPHGG5GXl+dUvFLGddddh+3bt2PEiBH4wx/+gObNmyM2NtanOYwgKysLeXl5+OCDD9CtWzePY00mEy6++GK8+uqrWLJkSYBW6CwxMRFr1qzBzJkzceutt6JDhw5o2rQpTEG4zjSJYPfJMIDy8nKkpKSgrKwMyf7MLhACyM4Gdu/2PG7hQmDgQP+tg4iIiEiGxQK0aAGcOeN+TGqq0mWrSxe/LiVg52s6qK6uRmFhITp16oR4BmLIj6xWK37++WcUFBTg5MmTqKmpQWJiIjIyMtCtWzd07969UZtEd4QQyMvLw6ZNm1BSUoKkpCS0adMGV1xxBVq3bu3nf0loOXToENavX4/jx4/j9OnTMJvNSEtLQ1ZWFi688EJHgc9w5cvvOG6pCCSrFSgt9T5u1ChgwABmOhAREVFwxcUBzZt7Dji8+67fgw1E5FpcXByuvPJKXHnllZrnMplMuOSSS5yKPZJrmZmZyMzMDPYyQgK3VASS2QysXu09NXHfPnapICIiouBbtMh7l4pp09jOm4iIXGLAIdD27gVsNs9jqqqUOwpEREREwSLb0nvvXt4oISIilxhwCCQhgJde8j6uuhr4vZIrERERUVBYrcChQ97HxcXxRgkREbnEgEMgWa1AQYHc2KNH/bsWIiIiIk/MZmDVKu/jjh1TbpYQERE1wIBDIMXFAS1beh8XHw+wCAkREREFm0xlepMJqKnx/1qIiCjksEtFIFkscqmJKSlMTSQiIqLgS0xUMh0slsZfi4sDZs4Ezj8fMHibSiIiCg5mOASSyaT0qvaGqYlERERkBJMmuQ42AMpW0QMHgD59ArsmIiIKGQw4BJLZDPz0k9xYpiYSERFRMNlswMSJnsdMnOi9+xYREUUsBhwCrXt37/UZMjOBpKTArIeIiIjIlYoKpVW3J9XVyjgiIiIXGHAINIsFKC72PKa42H36IhEREVEgJCcDU6d6HvPWW6zfQEREbjHgEAxCaPs6ERERkb8JAUyf7nnMe+/xvIWIiNxiwIGIiIiIGrNYgL17PY8pKGBWJhERucWAgxGx+BIREREZAbMyiYhIAwYcgiFK4tvOoAMREREFk8kExMR4H8egAxERucGAQ6CZTEBqqvdxb7zh96UQERERuRUTI9em+803/b8WIiIKSQw4BJrZDGzc6H3clCnMciAiIqLgOXlSbtzEiTxnISIilxhwCIZffvE+hn2tiYiIKJhkbpAAgNXKcxYiInKJAYdAE0K5E+BN8+ZAUpL/10NERETUkBDA2LHexyUnA+vWKX8SERE1wIBDoFmtwMGD3sedOKFkORAREREFmtUK7N/vfVx5OXDhhX5fDhERhSYGHALNbAaWLpUba7X6dy1ERERErpjNwPLlcmNlCksSEVFEYsAhGPbt8z7GZFLe7ImIiIiC4YcfvI+JjwcSE/2/FiIiCkkMOASa7J5IIdjXmoiIiILDZgMmTPA+jkWuiYjIAwYcAs1qBfbulRu7eLF/10JERETkypkzcls7s7JY5JqIiNxiwCHQ4uKAdu3kxr7wArMciIiIKPDi4uTGnTjBmlNERORWTLAXEHGsVuDwYbmxhYWAxaLsjyQiIiIKlCVL5Ma1by8fnCAKY9u3b8f8+fOxePFiHDx4EMePH0d1dTVatGiBVq1aoV+/frjppptw3XXXIZ7n9hRBmOEQaHFxypuz7FiTyb/rISIiIqpPCGD8eLmxzHCgCLd161bcfPPNuPDCCzF8+HAsXboU+fn5KCsrg8ViweHDh7F582b861//wq233oquXbti1qxZsNlswV46Pv30U5hMJphMJnTs2DHYy6EwxYBDoFmtwLFjcmPr6oDYWP+uh4iIiKg+qxU4cMD7OLMZ+PlndtWiiDV9+nT07t0bCxcudPp8mzZt0KdPH1x11VU4//zzEVvvfP7w4cN45JFHcOONN6KsrCzQSyYKOAYcAs1sBjZtAr75xvvYmhqlaBMRERFRoJjNwLJl3sdZrUBqqt+XQ2REo0aNwtNPP426ujoAQHR0NJ555hls3boVxcXF2LBhA1asWIHdu3fj+PHj+OSTT5CZmek4funSpbjiiitw4sSJYP0TiAKCAYdgyMwEmjTxPq5ZM9ZvICIiosDLzvZe5LpZM3aooIj07bff4rXXXnM8btGiBdavX493330XF154YaPxqampePjhh7Fr1y7cddddjs9v374dDzzwAASLxFMYY8AhGOx7Iz3VZ8jOBn75hWmKREREFHgWC1Bc7HnMyZNAdXVg1kNkEIcPH8Zjjz3meJyUlITly5fj0ksv9XpsQkICZs+ejZtvvtnxuZycHEybNs0vayUyAgYcgsFqBQ4e9NzysrQUaNkycGsiIiIiqk/mrqtsNwuiMPHmm2+ivLzc8XjKlCm44IILpI+PiYnBzJkzkVpvO9LkyZNR7SZ4N2bMGEdhx6uvvlrqOVasWOE4xuTiBufVV18Nk8mEhx9+2PG5AwcOOB1T/+Ohhx7y+HxWqxX//ve/8eCDD6J79+5o1qwZYmNjkZKSgp49e+L+++/Hp59+Kl2zYt26dXj++edx8cUXo0WLFoiLi0Pr1q3Rt29fjBw5Ejt37tT0fTh27BgmT56Myy67DK1atUJcXBzatWuHP//5z9i4caPLubZt24Ynn3wS3bp1Q9OmTZGQkIDu3bvjxRdfxNGjR6XWU19tbS2++eYbPPDAA+jWrRvS09NhNpuRkZGB6667Dm+88QZKS0t9nteQBImysjIBQJSVlQXuSQ8eFGLzZiHeeUcI5S3d+ePzzwO3FiIiIqL6cnJcn580/OjTRwibLSBLCsr5mkpVVVVi586doqqqKthLIR2dOnVKJCYmCgACgOjSpYuwqfz5HzdunGMeAOLDDz90OW706NGOMVdddZXU3MuXL3eau6GrrrrK6evePh588EG3z/XVV1+JzMxMqXmaNGkiiouL3c514sQJcfvtt3udJzo6Wjz++ONeX1+uvg9z5swR6enpbuc2mUxixowZjjlsNpsYMWKEiIqKcntMSkqKWLVqlZf/lXNycnJE165dvf47U1NTxcyZM6XnDSRffsfF6BCzIDUyM5W9kX/6k+uvjx0L3Hcf22ISERFRYAkBjBwpN/bgQSVzk1tAKQLk5uaioqLC8XjIkCEuMwhkPPLIIxg7dqyj6OQPP/zgtFXDn/r27Yv4+HgUFRVh+/btAID4+HhcddVVLse7qksBAH//+9/x5ptvOn0uPj7eccf+7Nmz2Lt3L06ePAkAqKqqgsVicTnX4cOHcd1112HPnj2Oz0VFReGCCy5A8+bNceTIEezevRsAUFdXhw8//BA7d+7EggULkCRZS2bJkiW48847UVdXh5iYGFx44YVITU3FoUOHUFBQAAAQQmDo0KFo06YNBg0ahGeffRb/+te/ACjbZ7KzsxEfH4/du3fj2O+dB8vKynDLLbdg165daNu2rcc1vP/++3j22Wcd/+8AkJ6ejvPOOw9NmjTB4cOHkZ+fDwA4ffo0hgwZgiNHjmCk7O9kI/J//MP4ghYxX7jQ812DhQsDux4iIiKi6mohEhPlMhz27AnYspjhEASLFwvRvbvyJ4mnn37a6Q70L7/8omm+nj17OuZKTk4WdXV1jcb4I8PBbtasWY4xHTp08GntU6dOdXqOdu3aic8++0ycPXu20djffvtNjB8/XrRt21YUFhY2+npdXZ248sorneZ76KGHGmVD5Ofni5tuuslp3AMPPOB2jQ2/D82aNRMmk0kMHz5cnDp1ymns6tWrRatWrRxjL7jgAvHVV185Mg0++eQTYbVaHeNtNpv44IMPRHR0tOOYRx55xOP3bMGCBcJkMjnGX3755WLlypWNsmT27Nkjbr31Vqesi0WLFnmcO9B8+R3HgIMI0huYzSZEVpbnN/EuXQKWpkhEREQkhFDOPZo3lws4nD4dsGUx4BBgNpuyZQaB3TpjZH369HFcBMbHx4uamhpN8w0ZMsTpgnjnzp2Nxhgx4LBnzx4RExPjOPbCCy8UR48e9XqcxWIRFoul0ec/+ugjp/X+7W9/cztHXV2duOuuu5zGL1u2zOXYht8HAOKDDz5wO/eSJUucxsbFxYkmTZqIvLw8t8eMGDHCMT4hIcFlwEUIIc6cOSNatGjhGPvnP/9Z1NbWup3XZrOJv/zlL04BECPx5Xcci0YGi8UC7N3reczevco4IiIiokCprlY6UHiTlsb23eEsNxewF9DbuFF5HOGOHz/u+Hu7du0QE6Ntd3qHDh3czm9kU6ZMQW1tLQCgSZMm+P7779GqVSuvx8XFxSEuLq7R5+t36cjOzsakSZPczhEVFYWPPvoIzZo1c3zu7bffllr3tddeiyeeeMLt16+77jp07drV8dhqtWLEiBHo1auX22Oeeuopx9/Pnj2LLVu2uBz38ccf48SJEwCATp06YebMmYiOjnY7r8lkwvTp0x3FRXfs2IFly5a5HW9kDDgEi/3egDc2m//XQkRERGRntcqNmzKFtRvClRDAK68A9gui6Gjlscy5axir3zWgfpcJtRrOEQpdCezdFezuu+8+nHfeearny8/Pd9SRAIBhw4Z5DeSkpaXhkUcecTxeuHCh2y4f9T3++ONex/Tr18+nY9q1a4d27do5HrvroPHpp586/v7MM88gXiJYm5ycjDvvvNPxeEmIdgViwCFYZN/MFy707zqIiIiI6ouPBxITvY+bMSPiL0DDlj27wV7Yrq6OWQ6A00WtWYdgW8OLzqqqKs1z+lteXp5T4cw/uSuAL2ndunVOjwcNGiR13O233+74e01Njdt2lvX94Q9/8DqmdevWjr936tRJKnOjTZs2jr+fOnWq0ddPnz6Nbdu2OR7fcMMNXue0u+iiixx/37Rpk/RxRsIuFcHiIp3IpeHDgcGD2a2CiIiIAiMuDujYEah319GloiJ2qAhH9bMb6lXSd2Q53HhjxJ6XpqWlObY9lJWVaZ6v4Rzp6ema5/S3hnfw+/Tpo2k+e0cGAGjevLnXLg92PXv2dHpcUFCAK6+80uMx9YMJ7jRt2tSn8Q2PqaysbPT1bdu2wVYva/2vf/0rYmNjpeYuKipy/N2+JSPUMOAQLLK1GfbvV8ZyjyQREREFgtUK/N7uza2oKGDVKgYbwlH92g311c9yGDAg8OsygPT0dEfAoaSkRPN8DecIhYBD/W0fZrMZycnJmuarnxHQokUL6eOSkpJgNpsdbTZdZRY05Kp+hJ7jAaWtZkMN/5/V1mLQI8gVDNxSESzvvSc3Li0tYqPIREREFARmM+BtT7bN5r34NYUee3ZDlJtLhKioiK7l0KVLF8ffjxw5ornIY/0CgyaTCZ07d9Y0XyDova3EUu8mrK8X+PWfX6aGQ7CcPXtWl3lsIVrbjwGHYLDZgIkT5cauXcu7B0RERBQ4tbXAmjXex40cGbEXnmHLagUOHnRftNxmAw4dkq9FFmYapuw3rD/gC5vN5rQnv3v37mjevLnq+eqrq78VRmdpaWmOv585c0bzRXD9wplnzpyRPk4I4VRLQo8inv7ScG3Hjx+HEMLnj/379wdl/VpxS0UwVFTIb6nIzweysvy7HiIiIiK7o0flxh08yBoO4cZsVrZNeNor3rJlxP6fX3vttU6Pv/zyS6fihb5YuHAhTtZrP3vNNde4HFf/rn9NTY3U3DLbC9SqXyBRCIH8/Hycf/75qudr2bKl4++HDh2C1WqVynQoLCx0CnbUn8doGtaCOHbsmE/bR0IdMxyCITkZWL5cbqvEs8/y7gEREREFTkYG0KGD5zGtWwObN0fshWdYy8wEevd2/1GvBWCk6dOnD3r16uV4/OOPP6KwsFDVXNOmTXN6/MQTT7gcV79GgmzbzPodETyJqrd1xlXtAVcuv/xyp8fLly+XOs6dSy65xPH3mpoa6U4Ma9eudXp86aWXalqHP/Xs2RMJCQmOx1oyY0IRAw7B0rev+/1x9dmLRhIREREFgtUKnD7teYzFotzpJoow//jHPxx/r6mpweOPP+7zHF9//TUWL17seDxw4EBceOGFLsd2qBf8KygocNkFoaEffvhBah2J9drfyrbkbNmypVPQ5YMPPpAOVrhy2WWXoUmTJo7HX3zxhdRxn332mePvHTp0QMeOHVWvwd9iY2Nx/fXXOx5/8sknQVxN4DHgECwrVzq3GnKHRSOJiIgokOLiAG/7oTMz5Vt8E4WRu+++GzfccIPj8ZIlS/Dss89KH7969WqnIEViYmKjbIf66mcA1NbW4r///a/H+b/88kvpDIf62yNOnDiB094Cjb97/vnnHX//9ddf8eabb0od50pCQgL+/Oc/Ox7PnDkTO3bs8HjMwoULsWTJEsdjd9khRvL3v//d8ff169fj/fffD+JqAosBh2CwVwCWCSS89RbTFYmIiChwLBalPoMnEVw4kCJbVFQUvvzyS6eL9ffeew+DBw9GUVGR2+NsNhvee+89DBw40KnY4fvvv4/zPHSFadu2rdM2hpdfftnt8yxYsABDhw6V/rf07NkTsbGxjsdvvPGGVLbCvffeiz59+jgev/TSS5gyZYrHYpVVVVX417/+5bKzxz/+8Q9HlkNNTQ1uueUW5Ofnu5xn3bp1uPfeex2PW7du7dO/OVj69++Pe+65x/H42WefxZQpU1BbW+vxuJqaGsydOxfXXHMNDhw44O9l+gWLRgaDvQKwTPrRtGnAX/7CLAciIiIKjGXLvJ+jMMOBIljLli2xYsUK3Hzzzdj7e3vYH374ATk5Obj55psxYMAAZGZmokmTJjh69Cg2b96M77//3qneQ3R0NN5//33cd999Xp9vxIgRGDRoEAClsGKvXr0wbNgwXHbZZYiNjUVhYSH++9//Yt68eQCAhx56CJ9++qnXeRMSEjB48GB88803AICJEyfi008/xQUXXICmTZs6xl177bUYNmyY43FMTAy+/fZb9O3bFydOnIAQAsOHD8cnn3yCe++9F5dccgnS0tJw9uxZFBQUYM2aNZg3bx7OnDmDW265pdE6zjvvPEydOhVPPvkkAODAgQO46KKL8PDDD+P6669Hs2bNcPToUcybNw9fffWVI7ARHR2NTz/91KlzhpHNnDkTe/bsQV5eHurq6jB8+HBMnz4dd999Ny677DK0aNECQgicPn0ae/bswebNm5Gbm+vIPNGydSWoBImysjIBQJSVlQXuSQ8cEKJbNyGUt3T3H/HxQlRXB25dREREFLlsNiHatvV+fpKaGvDzk6Ccr6lUVVUldu7cKaqqqoK9FPKj48ePizvuuEMA8OmjU6dOIicnx6fneuaZZ6TmHjNmjFi+fLnT5zwpKioSnTt39jjngw8+6PLYvXv3iu7du/v0by8sLHS7lrfffluYTCapeeLj48UPP/zg8d/my/fBbvTo0Y7xV111ldQxV111leOY0aNHexx75swZcdddd/n8M+PtexdovvyO45aKYGnVCpBpWVNdDcQwEYWIiIgCoLoaKC72Pi4jgxkOFPFatGiBH374AWvWrMGdd97pVISxIZPJhN69e2PatGnYvXs3BgwY4NNzvfvuu3j77beR6qa+ynnnnYc5c+Zg9OjRPs3btm1bbNmyBW+//TZuuOEGZGRkOBVx9KRz587Iy8vD1KlT0b59e49js7KyMGbMmEYtIut77rnnsHHjRtx4441OHTTqi4uLw5/+9Cds374dd9xxh9Q6jSQxMRHff/89Fi5ciKuvvhrR0dEex3fs2BFPPvkkVq9ebejCmJ6YhAjV3Az9lJeXIyUlBWVlZU6tZ/xuyxagXpVXt44fByKoVysREREFSXk5kJLifVzz5sDhwwGtMxW08zUVqqurUVhYiE6dOiE+Pj7Yy6EAsVqt+Pnnn3Hw4EEcP34cFosFzZs3R6tWrdC3b1+0atVK83NYLBasXLkSv/32GyoqKtCqVSv06NEDffv21eFfoM3OnTuRl5eHEydO4OzZs0hKSkL79u1x8cUXo1OnTj7NVVJSgpUrV6K4uBjl5eVIS0tD+/btcdVVV3kM7ISasrIyrFmzBocPH0ZJSQlMJhNSUlLQsWNHZGdnO3UpMRJffscx4IAgvoEJAWRlAfv2uR/TpQuQn88aDkRERBQY69cDAwYowQd3/v534PXXA7cmMOBARGQUvvyO45aKYLJagZMnPY85eZJVoImIiChwTp3yHGwAgH/9C7DZArMeIiIKWQw4BFNcHOBl3w6iorhHkoiIiAJDCODll72Pq64G6rX2IyIicoUBh2CqqvJeOPLUKWUcERERkb9ZrZ63egJAkybAypWAwbc1EBFR8DHgEEyydwZ4B4GIiIgCIS4O8FZzoGVLoH//wKyHiIhCGgMOwZSc7H1LRXQ07yAQERFRYFRXK92xPDlwALBYArMeIiIKaQw4BJPJBKSleR6TlsYOFURERBQYixfLjWOTMyIiksCAQzCZzcBbb3kec9VVAe1xTURERBFKCGDMGLmx7KBFREQSGHAIJiGAd95ROlG485//AHV1gVsTERERRSarFSgs9D4uKoo3Q4iISAoDDsFktQIHD3rvYz1+fGDWQ0RERJErLg7IzPQ+rlkzbvckIiIpDDgEk9kMbNgAtGjhedzEid6DEkRERERaWK3eC0YCwJo1zHAgIiIpDDgEW4sWwIkTnsfU1ADz5gVmPURERBSZzGZg40bglls8j/v228Csh4iIQh4DDsFWUyM3bswYVoQmIiIi/2rbFliwwPMYZl5KEzx3I6Iw5MvvNgYcgi0xUW4fZHExK0ITERGRf/3vf95vcFRXAxUVAVlOqIr6vSC4jYEZIgpD9t9tUZ6aH/yOAYdgKyuTy1xYsoT7JYmIiMh/hABGjPA+7q23gORk/68nhEVHRwMAamQzWYmIQoj9d5v9d50nDDgEm+wbUcuW/l0HERERRTaLBSgo8D7u88+5zdOL6OhoxMfH4+zZs8FeChGR7ioqKhAfH8+AQ0hISQGaNvU8JiFBGUdEREQUbIcOcZunhMTERFRUVLCOAxGFFSEEzp49i8TERKnxDDgEW1wckJXleUyXLso4IiIiIn/ydnEcFcW2mJKSkpJgs9lwwls3MiKiEHLixAnYbDYkJSVJjWfAIdisVuDYMc9jeCeBiCiyLVkCZGcrfxL5i8yd+KQkIDPT/2sJA/Hx8WjZsiVKSkpQWlrKTAciCmlCCJSWlqKkpAQtW7ZEfHy81HExfl4XeWM2Axs2ADfdBOzc6XpMcjIzHCg4liwBhg0D3nkHuP76YK+GKDIJAbz8MrBrl/LnddfJdTci8tXUqd7HdOzIcxIfpKeno6amBseOHUNpaSlSUlLQpEkTREdHw8TXMREZnBACdXV1qKqqQllZGWpqapCWlob09HTpOUyC4VaUl5cjJSUFZWVlSA5G1eVFi4CBAz2PWbjQ+xgiNdwFFYQALrsM2LgR6NMH+PlnXuQQBULD12TD94iJE+U6CRD5wmZTWnVXVXke17IlcPBgULZUBP18TSUhBKqrq3H69GmUl5ezVSYRhZyoqCgkJycjNTUV8fHxPgVMGXBAkN/AhAD69gU2bfI8LisL2LOHF3ykL09BhYYXOTk5wIABwVknUaRYvBi45Ralg9F55ylZDZdd5vwe0aQJUFGh7KUn0kt5uVyB6g8/BB57zP/rcSFUAw712Ww21NXVoa6uLthLISKSEh0djejoaESpPO9gwAFBfgOzWID27YHjxz2PM5mAykpAcq8MkRR3QQV7ICIvD6irA6Kjgd69meVA5E9CAOefD+Tnn/vc+PHAK680HjthAjByZODWRuGvrg6Ikdhp26sXsHlzUN4LwiHgQEQUaXh7JNjMZmD1au/jGBcivQmhXMjY++dGRyuPhQByc5WsB/sdmLo65XFubvDWSxTuFi1yDjYASsDBlddeU1LgifRSWio37rffWMiaiIikMeBgBG3byo3jySXpafJk10GFRYucAxF29oDE4sVAhw7KByvmE2lj7z6xeLFSt6Ehdxd2VVXAkCH+XRtFlubNgdatvY+zWIDYWP+vh4iIwgK3VMAAKXqzZwP33ut93NGjQKtW/l8PhT+bTWltVlnZ+GsZGUBRkftj63/90kuVLivcZkHknkxh1ubNgZMnfZ+7trZxcJBIjepqpT6IjNJSIC3Nv+txIejna0RE5DNmOASbEMCUKXJjWb+B9DJpkutgA+A52NDw65s2cZsFkScNW1oKcS6r4dFHlWADoC7YACi1HIj04Mv9p9pa/62DiIjCCgMOwWa1er/As2Pfa5Jlv6Cxb3mo/zg313UROrVGjVLSwe3zN3xuokhmr4cCnNuy9PTTSgBi1izt80+Z0ni7HV+DpEZNjdy4b74BWrTw71qIiChscEsFDJCit2MH0KOH93GnTgGpqX5fDoW4hq0u168H+vVTHl96qfJztHevvs953nlK29ZLL1Ueb9rUuM0mUaSxvxY3b1aCAiaT0gVA9sJO1ty5wK23Oj+nq1a3RJ4IodTmOXTI/ZjMTODAgaD9TAX9fI2IiHzGDAcj6NJFrp/6qlX+XwuFvoZ3VCdNOvd40yb9gw2AEmywz79p07nn5nYLimT216I9A0EI/YMNUVHAuHHn0uEbvv75GiRZFov3jMviYmUcERGRJAYcjELmbsGYMWyPSZ4J4VzpPioKmDgxOGup32aTKNIsXgzcdpv/n8dmUwILHTooz9mww8ywYXwNkjxvPytmMzNmiIjIJww4GIHJJLdV4uBB9r4mzxYtOpdtACgXI+6KQ/qbvc0m77BSpBFCqdMQyN/Xhw4BDz3k3OoWUH4fLFoUuHVQ6Fq+3HvAQQi2xCQiIp8w4GAEZjPw8MPex3XrpowlckUI4Lnngr0KZ1FRzHKgyLNoEZCfH/jnLS52/fnnnuNrkDwTAhg50vu4qiqgosL/6yEiorDBgIMR2GzAv/7lfdzq1WxFRe41zG4wAnu6N7McKBIsWQJ07w48+2ywV+KMWQ7kjdXqPmBVX0wMkJjo//UQEVHYYMDBCCoqlLsGMo4e9e9aKDQZMbvBzmRilgOFPyGAl18Gdu8GCgqCvZrG7FkObJlJrpjNSg0Qb2prgTNn/L8eIiIKGww4GEFSEnD++d7HtWoFZGT4fz0UeiwWYN++YK/CNSGU/eWsP0LhrH53CCMqLASqq5WgyK5dyp8MAlJ9//mP9zFJSUB8vP/XQkREYYMBByOwWoFTp7yPO3GC7ajItZUrjbndxmQC4uKUGiW9evGuKoUnIZQsHpn2xsFSUwNMncqWmeSazSbX0ahTJ+V3OhERkSQDnx1FELMZ2LDB+5u4zaZUkSaqz8gXO0IoAbWpU3lXlcKXPbvBZgv2Sjx77bVzvyfYtpbqmz9fCUp5c/Qos9WIiMgnBrxCiVBpaXJv4iNH8gSRnFmtSstUI1/s2DNzeFeVwo2RA34NVVWd+z3BtrVkJwQwZoz3cdHRwJo17JZFREQ+CYEzpAiRlAQ0b+59XHEx7y6Qs1WrlJ+fP/wh2Cvxjm0yKdyESnaDK8xyIEA5pzh82Ps4sxnIzPT/eoiIKKww4GAU1dXAyZPex61axbsLpFiyRCkkesMNSlX8deuCvSLv2CaTwok9u8FkCvZK1GGWAwHntnVGR3seV1MDxMYGZk1ERBQ2GHAwCpm9kwDQsqV/10GhQQhg+HDg+PFgr8R3zHKgcGG1Avn5of2zzNcjAUBKihKA8qSmhi0xiYjIZww4GEViolyrqdWr/b8WMr7cXGDz5mCvQh17lsPkycFeCZE2cXFA+/bBXoU2Nhvb1pLcuYXJxAxLIiLyGQMORlFRIXfC9+ijvBMV6YRQioeGugkTQnPfO9GSJUB2thI027o12KtRp2VLYPp0JXC5cSMvJCOZbNFIIdgpi4iIfMaAg1EkJytv5N4qnR89qtR7oMgVytkN9VVWAgsWBHsVRL4RQmnvumuXEjQLVcePA598AvTqBbRrF+zVUDD58p7CTllEROQjBhyM5LLLlPaY3ixZ4v+1kDGFS3aD3Ysv8uSVQou9KwWgBM1C2aZNLBgZ6Xxt63rkCLffEBGRTxhwMBKzGXjwQe/jxo/nRVqkCpfsBrs9e4BFi4K9CiI59oszb9X8Q8moUXw/iWRWK3DwoNz2tvbtlW4W3H5DREQ+YMDBSGw2YNo07+MOHuQdhkgUbtkNdsOG8YKHQoM9u8FbNf9QsmkT0KEDM+cildms/EyvWeN9bFER0KKF/9dERERhhQEHIykrkzuRXbKEdxgi0cKFwC+/BHsV+svPZ5YDGV84ZjfYHToEjBjBwF+kyswETp70Po5FfomISAUGHIyktlZuXMuW/l0HGcuSJUD37sATT4TvCR+zHMjowjG7oT7Wc4hcvnSp4O9pIiLyEQMORtKsmdzds7w8/6+FjMFeEX/3buDw4WCvxn/27QMslmCvgsg1XwvrhSp2IIhM9joOsmOJiIh8EOZnTyFm/ny5u2cvv8yTwkhRvyJ+OKurA956K9irIHLNl8J6oWzzZmY5RCKzGZgyxfu4qChu5yQiIp8x4GAUsimNgHI3mHcZwp/9rmqkGD8+/C/oKDTZC+vdemuwV+J/7FoReYQAXnvN+7hwz/AhIiK/4LuHUVitwN69cmMzMoC4OP+uh4IvUrIb7KqqlCwfIiNq2zYyfj7ZtSLyWCxAYaH3cU2bAiaT/9dDRERhhQEHo4iLU07yZN7MmeEQ/iItu8Fu7FjeXSVjGjIkcn422bUisshmlnXowJsdRETkMwYcjMJqBY4dkzvBs1iAmBj/r4mCJ9KyG+w2bwYmTw72Koic1dUBn38e7FUE1qZNfC1GigUL5MYdOcKbHURE5DMGHIzCvkd4wADvY4UA/vc/vy+JgiRSsxvsJkxgLQcyjtdfV34/R+Ldfr4Ww58QwEsvyY197TUWjSQiIp8x4GAkbdvKVwifNCkyT4AjgdUKHDgQvOf/xz+A0aOD9/yVlcrPN1Gw2WzKNh+Z7kHhiK/F8GexAPv3y439+GOedxARkc8YcDCS0lL5N/PDh5naGK7MZuDNN4Pz3FFRwLJlwNy5wXl+u4kTeWeVgm/SJOWi24iSkwPzPHwtkt3BgzzvICIin/k14PCXv/wFJpPJ6WO/bCT9d0VFRZgyZQr69++PjIwMmM1mZGRkoH///pgyZQqKior8s/hgaN4c6NzZ+7hmzYA1a5jaGK6EUFKZg8FmUwrGHT4cnOe3q6xULnSIgsVmM97P4LvvKnVONm8G3ngjMM/JLIfwZjIBKSlyY5ct43kHERH5zCSEf/Lj5syZg9tvv73R5wsLC9GxY0epOT744AP87W9/w9mzZ92OSUxMxJtvvoknnnhC7VJRXl6OlJQUlJWVITlQd41csViUKtDHjnke9/HHSsV0Ck/V1UBSElBb67/naNoUmD5dKT6ang60anXuay1bKkGPEycaH2e1ArfcomTj+FvTpsCZM+z9ToG3ZAlw773AyZP+fZ7WrYEpU4AePZTX3AMPALt2uc50i4oCLrkE+Pln5fFllymFHQOR4s7XYnibNQt45BHPY5KSlPeEIAccDHO+RkRE0vzS6uDUqVMYOnSopjnGjRuH0Q32kXft2hVt27bF4cOHsXfvXgBARUUFhg4dihMnTmDUqFGanjPozGblZLJzZ88prKNGKScH7IcdnkwmIDXVPxc7ycnAokVAu3bKhyeZma4/v2WL62DE2rXAs89qXqKD/c7qyJH6zUnkjRDA8OH+DzbY/elPyu9+iwUoKXEfQLBnH9lT2g8eDNx++spKYP58YNCgwDwfBY4QSvDZm06d2BKTiIhU8UuGw4MPPojPf28hduONNyK3XiFEmQyHH3/8EXfccYfjcXZ2Nr744gv07t3b8blNmzbhgQcewK5du5yOu+2223xer6Ei5nPmAC4yQxqprASaNPH/eig4DhwAJDOBpKWnAxs2AF266DsvoJy0XnopkJen77y8s0qBtmgRMHCg/+Y3mYDu3ZU2m61aOQf+Dh1yHcyza9ny3Hj72KNHgYICoLwcKCsDPvxQ+bveLr1U+f3BQHd4sViU4LKnnztACYIfPcoMByIi8pnuAYcFCxbglltuAQDccsst+OMf/4iHH37Y8XVvAYeamhpkZ2ejoKAAANCuXTts3boVaWlpjcaWlpaiZ8+ejjoOXbt2xc6dOxET41vihmHewIQAevUCfv3V+9iSEuUCksLPkiXKXU89ty2MHAkMHeo9q0Eti0W5GPLHhc7cucCtt+o/L1FDQgB9+ig1EvypdWulM4DeF2/V1UBion+6aiQnA8ePB/2Ck/zg73/3Xqj4oouAX34JesDJMOdrREQkTdfbhmVlZXj88ccBAElJSXj//fd9nmP27NmOYAMATJ061WWwAQDS09MxdepUx+P8/HzMnj3b5+c0DKsVKC6WG/vWW/5dCwWHPZ1b7xoJCxcCGRn6zllfXJz7LRhavfiickd4yRL/zE9kl5vr32CDyQRkZyuZAqF24d6kCVPqw5HNBvzrX97HHTnCDhVERKSKrgGHF154wZFtMHnyZGSquAD59ttvHX9v27Yt7rzzTo/jBw8ejDZt2jgef/fddz4/p2GYzcrJ7gsveB87dSpblYUjf13wFBb692TRavXfnvc9e4Ddu4GXX2YPePIfIYBhw/z/HKWlSjaQP6xc6Z/sBkApZpyT45+5KXgqKoCqKu/jxo8PvSAZEREZgm4Bh0WLFuGTTz4BAFxxxRV48sknfZ6jqqoKixcvdjweOHCg1+0RMTExGFhvv21ubi6qq6t9fm7DaNcOWL7c+7jKSuVEgcKHEP4pkBgVBbRv79+7k2az/1sIbtyoBGSI/GHRIiW45Q9duigdJTZvVn6O/XHhJgTwyiv+rXdy//0M+oWbxES5n5mPPuL/PRERqaLLmUl5eTkee+wxAIDZbMbHH38Mk4p9frt27YLFYnE87t+/v9Rx9cdVV1c7FZIMObLbKlq1UtpUUfiYPNk/2Q02m1IXxJ8X60IAH3zg3/29UVHKBRVPekkPS5Yo2xuWLFF+pp57zn/PVVKitL7s3dt/dVSsVqVzhT8z30pK/B9YpMCaO1fuZ+bAAW6pICIiVXQJOPz973/HoUOHAACvvvoqzj//fFXz7Nixw+lx165dpY5rOG7nzp2qnt8QZO8UHzumFOqj8GCzARMm+G9+f1+s2y92/BkMsNmY5UD6EELZorNrl/JndTXwe6tl3UVFAR06+L/+gdmsvD42b1Y+5s8H/vlPpdOLnsaN43a+cCGEUiNHxooV3FJBRESqaA44LF26FB9++CEA4KKLLsI//vEP1XPt37/f6XH79u2ljuvQoYPT48LCQtVrCDohgHfflRtbb/sJhbghQ5RtMv5isylt9Px1h6rhxY7sz7CvoqOZ5UDa5eYqP6+A8ufUqf6rfRCIDCO7zEwli6J3b+Dmm5V6QLt36/t6tFqVYAaFPotFyVzw5vzzlcK9REREKmgKOFRUVODRRx8FAERHR+Pjjz/2uSVlfeUNWuqlpqZKHZeSkuL0+MyZMx7HWywWlJeXO30YhtUqdwIAAM88wwuvcFBXB3z2mf7zZmef2zfuz73jdvaLnV69gM8/98/2iro6ZjmQNvZaB9HRyuOoKOC11/z7nMHcDtSuHfDpp/rO+eKLfO8JByYT0OD8yaWSEm6nICIi1TQFHF566SVHVsLzzz+PSy+9VNNiKhoUQWzSpInUcQ3HeQs4TJo0CSkpKY4PNd00/MZsVlqm2U+GPSkq4raKcPDaa/45ed+5U+kcYb/j6a+94w35e3sFazmQFvbsBntGg80mV6VfC39nGHnij843+flKkU0KbWazEpT25uqruZ2CiIhUUx1wWLFiBd5//30AQJcuXTBu3DjNi6mpqXF6LJst0XBcw3kaGjFiBMrKyhwf9voThtGlC1Cv1adb/izQR4GRmwuMGeOfuU2m4FyYN9xesXatvj+rwbx4o9DWMLvBn9asOfcaCESGkSv2f68/3isefZRBv3Dw6qvex3z/vf+2HBERUdhTtf+hsrISQ4YMgfj9ZOOjjz6SzkbwJCEhwelxdXU1mkoUvGrYBrPhPA2ZzWaYjRytt1iAw4e9jzObGXQIZUIAjzziv5N2Ic5dmAf65z0zU/kAlJ/nxETAS+aRtHfeAe68k3fcyHf1azf4k8mkdKVITvb/c3li36Lnj98xRUXApElK0U0KTXV1wBdfyI0tLQVatPDveoiIKCypynAYPnw49u3bBwB49NFHcc011+iymMTERKfHlZJF9BqOSwr1dpGydxKaNPF/5XPyn5wc5aRdL9HRSjZBsO+qNhQXB3TsqN9806YBGRn6zUeRwX63P0qX5kyuxcUpF3A//xz8YAOgvPb/+U//zT96NDtWhLKSErlxnToBzZv7dy1ERBS2fD7z2rlzJ9577z0AQJs2bfDGG2/otpgWDaLnR44ckTqu4bjmof7GeOqU3LiTJ5V2bhR6hACGDdN3zro6oLz8XM2GQNZt8MRqBY4f12++vXtZu4R8Z68t4s8LZKsVSE0F+vTx33P4QgglI8hfQZbaWv+28yX/kg2KVVZyCxsREanm81nI8ePHHVspjhw5grS0NJhMJrcfDz/8sNPxnTp1cnytYReKbt26OT0+INmtoeG47qHevqldO6BvX+/jTCbAS70KMqhFi4CCAn3nNGoxRbNZueMbH6/fnLyrSr5qWFtk82Zg6FB9n8NkAsaNM85rMBBBlvHj+XoMVcuWyY17443gZ8oREVHI8mNuqe8uuOACp8d5eXlSxzUcl52drduagsLe+s+b888HQn37SCQSAhg1Sv95jVxMMS1N36wEvS8UKTLYW7f27g1cfDEwc6a+89evm2IEDYMs8+cDzz+v73PU1gITJ+o7J/mfEErhTxnTphkniEZERCHH56KRsbGxaNasmfR4i8Xi1O4yLS0NUb+nd6Y06P+cmZmJLl26YO/evQCAlStXSj1H/XFZWVloZ4Q0ci3mzJF7cz9xIjgFAUkbvdrUmc1K0baWLYH0dKBVK+XvRvx5SE4Gpk7V72Lniy+AWbMC022AwtO8efpliJlMwA8/KAENo70G6xdwFUKuK4Gvxo1Tikf6sz4G6au6GpDctorDh3muQUREqvkccOjfvz9OnjwpPf7TTz912laRl5eHjh4KyA0ePNhRF2LFihU4ePAg2rdv73b8wYMHnQIOgwcPll6bIQkhf7fommt4AhBq7IXr9JCVBfz1r6HRqUQI4Pc2uroZMwb4z3+UPerXX6/v3BTehABefFG/+aKigAED9N025A9Wq1wHJF/V1CjvW/7I3CL/8CXjbOlSnmsQEZFqhrsd8fDDDyP697uWNpsN48eP9zh+3LhxsP2+fzQ6OrpRzYiQY99zK4O9sUOP1Qrk5+sz18mTxknd9mbRImDPHn3nnDAB2LVLubPKdF/yhd41VNLTQyPwZ99isWkToHetowkTWMshlMj+vJpM5zJkiIiIVDBcwKF79+548MEHHY8//vhjfPzxxy7HzpgxAzPr7cF96KGHGhWeDDn2E8IuXeTGv/aaf9dD+oqLAxpsJVJt+fLQuOvkr5oVdhs3KttUiGQsXgzcdpv2efr0ARYsULZH5eWFxmsRUC4eT55UgnV6sliUGhEUGmJj5catXGmMFq9ERBSyTEL499Zgwy0VhYWFHrdUAMDJkyfRr18/Ry0HALjttttwzz33oG3btigqKsLXX3+NefPmOb6elZWFdevWqWqJWV5ejpSUFJSVlSHZCG+sFotyUnjihPexTZoAFRXcOxsqcnKAm27SPk+LFsCxY6FxV9ViATp0UNbrDyYTcOmlSieMUPh+UPAIofysSBYk9sjeJSjU6ogIoXRB2rRJ/7kvuUQJAPJ1aHz//S9w112ex5hMwOnThgo4GO58jYiIvPK5hkMgNG/eHAsXLsSAAQNQWFgIAJgzZw7mzJnjcnynTp2wcOFCVcEGQzKblYJml13mfWxVlRJw4Buv8QkBDBumz1wnTugXvPA3e9ZOwwCaEMB99wG7d2ubX4hzWQ4DBmibi8Jbbq4+wQZA+bn78Ucg1OoGWa36t+S127ePxQVDgRDA5Mnex731Fs8tiIhIM8PeFu/atSu2bt2KYcOGuY1ip6SkYNiwYdi6dSuysrICvEI/69sXuOMOz2MuuQTYsIEnBKFi0SL96jcAwMiRoVO7oH47QvtHjx7AqVP6zG8yKcU4Q+X7QYFnL9iqZzbY5Mmh9zMXFwe0b++frLh27ZT5ydisVuDAAe/j3nsv9H6+iYjIcPy+pUIP1dXVWLlyJfbv34+SkhI0a9YMHTt2xNVXXw2zDndSDJmiZ7MBTZt6riRtNgOVldxOEQqEAM47T987i61bA/v3h/bdxEOHgKNHlQCbHnJymOVAri1aBAwcqO+cofga9Mf3oT6+BkPDb78B3mpeRUcrGZQG6r5iyPM1IiLyyJBbKhqKj4/HgEg7gTlzxnvbKotFGadXEULyH72q4jdtCnz1lZIx0LJlaF3ouJKZqWTp6OWVV4Abb+QecnJWP7tBSyeF2Fhg5kygeXOgVavQew3q9X3wZORIvgZDwddfex9j/PtRREQUAkIi4BCRzGblhM3bGz7TV41PCKV1o1bp6cCWLeHVokwIYOJE/eY7eJB7yKmx3FylzodWNTVAWlpo1E5xxd522Z/tKzdvZj0Vo7PZgDfe8D6OAQciItIBc/GNymQCEhO9j1uyxP9rIW2sVmDPHu3zlJYqd1bDidUKHD6s33yrVjHYQM7sd/X1uONuMgHjxoXuhZi9gOvmza4/XnxRn+cJpfoykaiiQik47U16OjNViIhIMwYcjCouTu7icswYntgZXWyscmdUK3sbvnBiNivt+ebPB6ZNAx57TL4/vCvffqvf2ig82O/q6/F7Ugil7ojVqn2uYHFVwLV3b6BXL2DlSn2eY+/e0P4ehbvkZGDtWs/nGC+8oGTUMYBLREQaMeBgVBaLcmLrze7dPLEzukmTtP8fxcYC69aFZ0eSzEzg5puVlqHTpmkLqrz2mn/TxSn0mM1KnZDzz9c+V/fuylzheBGWm6sE//TQvj23+xnd6dPAyZPuvz53LpCREbDlEBFR+GLAwahMJqBJE+/jrFZtd4TJv2w2YPx47fPU1SkXO+FOawZHVZWSLUFU365dSlV+rY4eVQpFhhu9W4aGehZIuBMCeO45z2Py85Vix0RERBox4GBUcXFyxQFra4Hycv+vh9SR6TbiSWYm8MUXwPr14Znd0FByMjB1qrY5uM2I6hMCGDVK+zwmU/jeude7mGRmZnh+n8KFxQLs2+d9HGtxEBGRDhhwMCqrFThxQm7sqlX+XQupFx+vtLJU69AhoEULoE8f/dZkZEIoARYtfvuNd1fpHL22CggBHDsWnj9b9YtJrl2rPdNh61bl+07GZDLJBYQOHw7Pn3ciIgooBhyMymwG1qyRGzt2LO9CGNVPP2nLcIiKUlKdI+X/12oFiou1zWGzcZsRKfTIboiOVi7CN29WLsrDsX4DcK6YZFmZPpkOo0ZFzu+tUBMbq2RHevPmm+H7805ERAHDgIORtWsnN+7gQd6FMCIhgKeeUuovqGWzRdZ+aLNZCdJoUVWlFOokslqVvehapKae6+Qg+zs5VOm1/QQACgoi5/dWqDlzRu7/5q23GDQiIiLNGHAwMtkCehMm8C6EES1apJx0a5GdHb5V8d3Ro7jf+PFKkc0lS7TPRaErLg7o0EHbHOvXR87rz2pV0uj1YDazjoNRyf6/FBczaERERJox4GBkCQly4958k3chjEYIpeCWVqWl4VkV3x0hlC1CWlksSsvYl1/mayOSWa1K3QW1TKbwz2qoz17LYd06IClJ21zHjgE5Ofqsi/QlG0RYujRygm1EROQ3DDgYWUmJ3LiCAm11Akh/ublAXp62OSIxu0HPO6yAcvHE4nWRy2wG3nhD/fFCRF7AKjMT6NcP2LFDCTykp6uf6777Iu/7FwrWrZMb97//+XUZREQUGRhwMDLZO0w8oTMWvfZBl5REVnYDcO4Oqx6V8u2GDeNrJFLpkTGjtU1rqLIHHt5+W/0cpaXAwoW6LYl04Ev23Wuv6dcqlYiIIhYDDkYmW8MBAFas8NsyyEd6tOEzmYDVqyMru8EuMxPo1Uv5Huhhzx6lngZFnkmTgL17tc0xcWLkXnQJoS3gAAD338+An5FYrfK1haqqgIoK/66HiIjCHgMORpacDAwZIjeWe9WNQa/shkjbO97QypXauns09NxzfH1EGpsNGD1a+zzV1ZF70aXHFidmORhLXBwQHy83dulS5TyEiIhIAwYcjMxmA778Um7s4cOsJm0EVqvSplQrm0256I5EQgCvvKLflgqAWQ6RaN48oLZW2xwdOyr73SP1osu+xal1a23zPPooA35GUV0NHD/ufVy3bsA11/h/PUREFPYYcDCyigr5YpBr10Zm+r3RmM3An/+sfR6TSbnojsSTdHvQRu80dmY5RA4hgKee0j5PVRVw0UXa5wllLVsCJ09qm+PIEeVCl4JP9sZEaSlvYhARkS4YcDCypCTgvPPkxkZy+r2R2GzA++9rn0cI4NChyDzhs99V3bz53MeUKdrnLSxkN5dIkZMDFBVpmyM1FVizhoHclSu1Z4oASno+BV9cnNy4yZP5s09ERLpgwMHIrFbg6FG5sXrudyf1Jk3SflGbna0Undy4MXJP+DIzgd69lY9evYB//1v7nDU1kbtNJZIIobRj1CI6Wnn9demiz5pClZ7bm8aOZYaRESxZIjfuvff4/0VERLpgwMHI4uKUCy8Zkyf7dy3knc0GjB+vfZ7SUqBHD2at2FmtwIED2ueJ5G0qkWThQuU1pEVdnfbuFuFAz+1Nu3dHZsaWkfjSJraoiP9fRESkCwYcjMxqlSvuBCgp55Haus0o5s3Tnt3w7ruRndngSlwc0L699nkieZtKpBBCvrOPJ1FRDE4Bztub3npL21zV1UBsrD7rInWsVrlAWkwMtxMREZFuGHAwMrNZvhhkTU3ktm4zAiGAv/9d2xzR0cDnnwMZGfqsKVz4EnjzJD4e+PlnnkSHs+pq+W1onthsDE7ZZWYq25q++krJElKrthaYOFG/dZHvZLMm6+qUDi1EREQ6YMDB6Dp3VoIJMhIS/LsWcs9iAfbt0zZHXZ1yNzE3V581hQuzGdiwATj/fG3zVFcDv/6qz5rImPR47TRrprTCZKbROfatFVozPiZMYCZeMFmtQHGx93FCAD/+6P/1EBFRRGDAwejmzpU/QeMJQvCYTModdK2Yyu1aq1bAqVPa5xk3jt/bcKVHK8zERCULpl8/1lCpr/7Wiu++Uz+PxaIU1qXgMJuBN96QGztxIn9XEhGRLhhwMDIhgDFj5MfzBCF44uL0CTgwlds1s1np3DF/PnDLLern2bSJGSThKidH7u6tJ126KFll1Ji9c8ydd2qbZ/x4ZjkEixDA9OlyY1k0koiIdMKAg5FZrb71kt+7lycIwZKTA5w8qf749HQljXvzZqZyu5OZCQwcCCxfrn4OdqoIT0IAzz6rfZ5jx/g71ButnXiY5RA8ViuQn+95TFSUUjuK70NERKQTBhyMzGz27eIqI0O5006BJQTwzDPqjm3aVNkK8+uvShp3795M5fakokKpxaCWEKyTEY70qKHy1FO8yPLGZpNPyfeEWQ7BERcHNGnieYzNBpSV8X2IiIh0w4CD0WVlAampcmNPnuTduWBYtEj9xU6nTsCgQTy5k5WcrLRra91a/RzMcgg/Npv2/89PPwXattVlOWGrogKoqtI+j8UCnDmjfR7yjWwXl5Ej+fuRiIh0w4CD0ZnNwPr1cmNXreLduUDTkt0AACUlDBL56uKLtW1fEYJ1MsLNm29qn6OyEliwQPs84Sw5Wdn69c9/asumi4/Xp+YN+WbJErlxrN9AREQ6YsAhFOzcKTeuRQv/roMaW7RIqZ2hRrNmSrtHBol8YzIpFz5qZWby+x5ObDZgyhTt80RHs4uJjMsuA55/HujWTf0cdXVAbKx+ayLvZItQp6dzaxEREemKAQejE0LpPiHjvff8uxZyJgTw8svqjy8pAZo31289kcJsVloXym41aujwYQbnwsmCBUp2glZ1dazvISs3F9i6Vf3xNTXcUhFoViuwa5f3caWlfF8iIiJdMeBgdFYrcPCg3FgW4gosq1V7oTrZFFdylpUFrF6t7lghgGXL9F0PBYcQSlaCXqKiWN/DGyGU71GUxtMH2a2CpI/YWPltEtxOQUREOmLAwejMZmDpUrmxViswf75/10PnxMUB7dtrm4Mp3OplZQEJCeqOffRRft/DQW6ukpWgF5uN9T28sQfBtQa3hw3jazCQzpxRsni8SUxkfQ0iItIVAw6hwJcOBmPH8iQuUKxW5eJEC17cqBcXB3TurO7YI0e0tdek4LPfadciKUkpgrh587kP7l/3zGxWvkebNwObNgHZ2eqyHfbsUQJ/FBiyRT6bN2d7bSIi0hUDDqHAl5O5zZu5BzlQ4uLUBXeiooBZs5S955s28eJGLasVOH5c/fHczhLafNlu5k7nzkoRxN69z32wRa13mZnK9+rkSaWosdpsh1mz5O66k3b//KfcuEOHlLalREREOmHAIRQkJwNr18rddeAe5MBZuBAoK/P9OJsNaNMGuOkmXtxoYTYr3Sa6dlV3/OjRfJ2EMrNZyU7Q4tgxZhippUctByGACRP0WxO5ZrPx+0xEREHDgEOoOHlS7sSYe5ADQwhgyBD1x48axYtdPbRqpS7oAwC//cbXSah77DH1xy5fzu0TWuhVy2HSJBY79rczZ+SzFhISlNbDREREOmHAIRQIoXSgkDkJePJJnkQHQk4OcPSo+uMZFNKH2axkmqhRWQmsXKnveihwamvlC+q6UlHBDCMtGtZy6NZN3TwWixJ0IP+Jj5dvI9yxI2s4EBGRrhhwCAX2O0kyd8Q/+QRo29b/a4pkWrIb0tOVTiKs3aCf4mL1xz71FDNNQtV332k7nh1itLPXcujRQ1sAli2d/ctsBt58U27skSMMhhMRka4YcAgF9jtJMhW9LRZg3jz/rymSVVcrJ2VqnDoFXHst76zqRQilM4tae/cCixbptx4KDD06VDDLSD9xcUrwQS1mOfiXEMA778iNXbqUwXAiItIVAw6hIiNDqegtg3fu/GvxYvXHNmvG/bF6ys1VskW0GDmSr5dQY7EAhYXqjjWZlCK83HqmH60dYwBg4kRmOfhLbi6wdavc2Fat/LsWIiKKOAw4hIrycvn2Ybxz5z9CAC+8oP74lSt5kaMXParkA0BeHlvJhpoVK9RfnAqhFBpllpF+7Fl469YBsbHq5qiuVupqkL58+T3ZpAmQkuL/NRERUURhwCEcTZ7Mi1p/0XJndf58IDtb3/VEMr2q5APsGhJKhACGDdM2x3PP8f9bb5mZQN++6r6vLVoowYrkZP3XFel8+T2ZmMiCkUREpDsGHEKFL3dx//Uvnkz7i5Y7q1dcoetSIl79Kvnz5wN/+Yv6uTZtYpZDqLBYgIICbXMUFsq3CSR58+Yp3UN8dfIk0LOn/ush5+wTb06cUDowERER6YgBh1CRnAy89prcWG6p8A8td1bj45W7R6Qve5X8m24Cdu7UNhezHEKH1m00NTVsiao3IYAxY9Qfy/oN/pOZCQwfLjd2xAj+HiQiIl0x4BBgdTaBdXtL8OOWIqzbW4I6m+QbuxDAzJlyY3NzuaXCHywWYN8+dcdWVyt34ck/rFagqEjbHAUFDNSFgrfekq9n405UlLKvnRdW+rFatbWonTxZv7WQs9pa+QAb22ISEZHOTELwjKu8vBwpKSkoKytDsh/3kOZsP4Kxc3fiSFm143NtUuIxelA2BvZo4/ng6mqgaVO5E+TaWiA6WuNqqZHqaqWollqXXgps2MAuFf7yxRfAAw+oP/6ii4BffuH/j5HZbEBSElBZqX2u1q2B/fsZnNXToUNKWv433wCvv+7bsdHRyoWu1uwVauybb4B77vE+rmlTpZtFly5S09bZBDYUluL4mWq0TIpH307piI7y7+/PQJ2vERGRfmKCvYBIkbP9CJ78Mg8NwwVHy6rx5Jd5eP++3t6DDrKxof/9D7jrLjXLJE+0pvzat7rwAkd/QgDTpmmbw35nj/8/xjV/vrZgQ/fuSmDKZAJatuT/td4yM89tc7r2WmDgQPlj6+qUbYOvvOK/9UUiIYA33pAb+9570sGGBVuPYNSP21F69lw2hPQNFCIiiii8lRAAdTaBsXN3Ngo2AID4/WPs3J3et1fI3vl56SWmCvvD0KHqj1uwQClMyAsc/7BagcOHtc0xaRL/f4xMCGDcOG1znDoF9OihXBCzLaZ/nTjh+zHjx7OWg96sVmDvXrmxkgWnJy3Yiae+ynMKNgDAkd9voORsP6JmpUREFKYYcAiADYWlTtsoXDlSVo0NhaXuB5hMQGqq3BOyArv+6uqUO6NqbNyo3OnjBY7/2CuxaykEOGkSA3VGlpurBO20eOMNBpUCQQjg7bd9P66mRul0QfqJiwPatpUbK1FwesHWYsz4yX1raOkbKEREFDEYcAiAo+Wegw1S48xmZX95s2beJ4qO5j50vT38sPpji4pYhCsQMjOV1qNqLyj37WOgzqiEULqIaBEVBbzzDoNKgaAl4+jFF/l/pCdfMhxycjz+/qyzCYz6cbvXabzeQCEioojCgEMAnDwjF3DwOq5lS6CkxPtEtbVADMtz6EZLdkN8PLB6Ne+qBkpFhfrgjs0GrFih63JIJ3pkN9hsbBkcKPaMo82bgbFjfTu2oABYtMg/64pEsbHyP/Pdunn88obCUpSerZGa6rjkeQ8REYU/BhwCoKRC7q6p13Emk1yXBCHkAhMkZ/x49ceaTECnTvqthTxLTpYvkObKsGG8u2o0QmgrJJicDKxbp1z8btzI4F+gZGYCvXoBn37q+7F8Hepn7lz572WN52DCkdNV0k/bMileeiwREYU3BhwC4NdDZfqMi4sDWrXyPlHTpkBKitRzkhc2m+/t3eqzWJS77hQYQgCzZ6s/Pj+fd1eNxmoFDh5Uf/x77wH9+rFQZDBYLEpNIV+xDpE+hACGDJEfn5Dg8ct5B09JTdM0Lhp9O6XLPy8REYU1BhwC4GCpXBs3r+MsFrl9sR06KMEJ0q6iAqiSv6vjZNYsYP165Q4rBYbWi1OAd1eNxmzWlrUybRr/P4PFZAISE30/rrZWWwFYUlRV+ZbtePKkxy8XnzorNU3nZk0QHcU6UkREpGDAIQAqrbX6jDOZ5DIXCgq4T1mrJUuA7GylTZgaycnAvfcCffrouy7yzL53vGtX9XMUFPDuqpGo7Xhgx7oNwRMXJ1fo2JVRoxgo0ionx7fxXrZsbjl0Wmqaskq+3oiI6BwGHAKgSZzct7myps7zALMZyMsD0r2kKtbUsGikFkIAL78M7NoFjB6tbo6OHZllEiytWgFlctuYXBKCFzpGkpur/N5TIzoaWLOGdRuCxWJRAj5qbNqk/N+TOkIorX5lLVjgMRuvziZQWuXlHOV3JyvlbrIQEVFkYMAhADqke94XaWepFaiyenlDb9ECKJVoN+VrZXA6JzdXuUsOeC2i5dbRo7yrGixms3Kx8tFH6ud480391kPqaW2HmZamFC+k4DCZgNRUdcdGRSnFQhn8U8dqBQ4ckB9/9dUev+xbm0v+nxER0TkMOATAxe3TpMeOnrPN8wDZi9jJk5WCh+Qbe0X8KA0vjYQEYO1a3lUNpsxM4P77gebN1R0/fjxfP0agtR3m66/zdRhMZjPw889y3ZUastmUwC+zHNQxm4Hly+XHezm3OFou3+aySWy0/PMSEVHYY8AhAK7IaiE99sdfij0PiI+Xq+NQUwOcOSP9vPQ7e3aDlovNTp2Azp31WxOpYzYr7RDV3GGtqQHmzdN9SeQDrdkNADB9Ou+QB1uXLsqHGsxy0CY7Gzj/fLmx69d7/HLxabni1wDQMplBPiIiOocBhwDo10W+aJalTqDO5uHkyl4Uz9sd+MREJThB8vTIbgBYpM5IsrKAqVPVHTtmDC90gslqVQp4asHXYvBZrcDx4+qOtdn4f6iF1QocOSI31kuRzrm/yNfiaJbAcw8iIjqHAYcAiI4yQbJuJABg/V4vbaz27fN+B75FCxYt9JUe2Q0AC0YaiRDKXW41du/mhU4wxcUB7durOzYhAfjxR2U7BrdUBJc9SK7m/7JZM2DDBv4fqhUXJ5/hlZ/v8fddYYl8e+grs1RuZSMiorDEgEOA3NErQ3rs2n0eemHL3oUvLAQWLZJ+zohn/76adOgdfuwYL1SNwmoFDh5Ud6zFAsTG6rsekme1KsVXfRUbC+zcCdx2G9Cunf7rIt+1aqWu1WxJifo6LORbl5DMTI+Bcm9NtOprlcIMByIiOocBhwAZecsF0mOLT3m4k2C/gJK5Cz9sGFPCZdm/r1q+X3FxSs2AjRt5R84o7B0r/vc/3/9PbDbgkUf8siySYDYDS5f6flxNDbBli+7LIQ3sxSPVZH6p3RJFyutH9j3t+HG3gfI6m4AveX+tU1QUCSUiorDFgEOAfL9Zfv9jyyQPF0b2E7doiSrQ+/apu6sUiexpv5s3A+PGqZ+nb1/eVTWazEzlbnfbtr4f+9lnQJ0Pt/ZIX2prOIwbx2Cr0Wzbpi7za8IEdoxRQwhgyBC5sV46K63f52WbZwN9O6X7NJ6IiMIbAw4BcqBUvsLzriNeukukpspdBNXVAStWSD9vxMvMBHr1Ar74Qt3xViswf76+ayJ9+JJa3JCWABSpJwTw5JPqjj18mNuajEQIYOxYdcdWVwMLFui7nkhQXa1s75OxZYvHTiKrfjsh/bStEmMRHaXD1kQiIgobDDgESIf0ptJjNx4o9d6pQibDAeC2Cl9ZLEpmiFpjx/L7bUQmk7r2mAAwZQrvsAZDdbW6Gg5JScCaNdzWZCRaaqkAwNCh/L3qq+pquXExMUqw3YPVez3UlWogPk7y3ISIiCIGAw4Bcv8fOkqPraqxYUNhqfsBJpOSAilj715uq/DFypXaUuh5Z9WYzGZlu4xsT/r6LBZg4kT910TuLVni9SLIrY4dgc6ddV0OaWQ2K0EgtUG/oiIgJ0fXJYW92lr5cTExHocIIR9wTYhnoV0iInLGgEOAxMVEISVePvJ/tMxD4ci4OKBDB7mJeGdWnhBKL3ItJk/mnVWjatVK/baKiRP5WgoUIYDhw5UOBWow6GdMWVnA1q1K4O+tt3w//tFHmeXgixYt5LcH/vijxy/Hxcifu7RlhwoiImqAAYcASmsqX6G79KyHE2arVX5vJgAsXy4/NpJZreqL1AHKNpfp03lSbFSxser/b6qquI88UHJzlYtStVJT1XVDIP+z18n59799P7a4WH6bACkWLpQbN3Gix9+NpyrksyQHZLeWHktERJGBAYcASk+QPwlOMHtIcTSbgZ9+kn/il1/mRbCMuDjlLrhadXVKp4vcXP3WRPqpqNB2wcLOB/6nR5bR4cPcRmZkublKq1o12CJTXl0d8NVXcmMPHfLYEnP/Kfnfm+3SJbd7EhFRxGDAIYDOb5MsPXb2zwc8D2gqX4QSBw4wxViG1Qrs369tjqgo4JVXeGFqRMnJStcWk8oK6h5OykknWi5G7VJT1f8fk38Jofx+VPv/M348tzbJeu01uXF//auSUeSuJeZe+a1NJrAlJhERNcaAQwBltUiUHrvnuJfWmM2byz9xZiZTjGXExGi/oLTZeGFqZFVV6oNB69ezPoc/CQGMHKn++JUrlQunvDz+PxmVvVuF2tegxQJMmqTvmsKRzQZMmCA3dvVqICPD7ZfX7ZPvUHFph1S2xCQiokYYcAggXzpV1NR6OSHzJWX44EFeAMt47TXfT4SjooC1a5ULHfvHxo284DEi+93VKJW/9j75RN/1kDMttRu6dQOuvBLo3Rto107fdZF+zGZg1SptGSjMcvCuogKoqZEb6+X8oLZO/j1x2LXnSY8lIqLI4bkXEukqLiYKMSbAWywBALwO8eXCOCODGQ7e5OYCo0f7flxUlFIELZ6VuQ3PfndV7cXKxInKz4jagAW5pzW74dgx5f+XgT7jKyjQtuXMYgHOnAFSUvRbU7jxZcvlmjUeXze/HS2Tnuryrj5kXhIRUcTgmXOAtUiUu/CvFYC11sOFkezdCwDYsYOFDD0RAnjpJXXHpqRwv3ioMJuV7JONG9UFDWprlQsd0p/VCuzapf54s5lB1VAgBDBsmLY5EhIY4PVm7Fj5sR62UwDAziPl0lPV2Vi7iIiIGmPAIcCifLjQmbWm0P0Xk5OVNoyyWMjQvdxcYMsWdcf+85+8qxpKMjOBoiL1WQ68qPUPLS1LAeDoUbZMDAUWi/bCvLGxfB16YrPJd/MwmbzevKi0yv+u/GLdfumxREQUORhwCLAmsfLf8kXbj7r/osmkVGOXxToOrmltwzd2LAM5oUQIdVtn7BYt0m8tdM6CBUpBTy18yfqi4PD1fcuV06eBhQv1WE14qqiQfy29/rpy88KDmGj5c5YDpZXSY4mIKHIw4BBgLZLl74YfPHXW/RfNZqUae4cO3icymZRK1LwT35jWNnyHDvlWwJOCy2pV7oarNWQIA0x6EwIYN07bHEuWeL1wIgOwv2/ZC+xu2gS0bu37PIMGAYsX67++cJCcrBTWlPHVVx5/n9XZBE5XygfyOqT7UDuCiIgiBgMOAdYiQf6iP9pbaYBWrYDiYrnJWLm9Ma3ZDQCQlsYaDqHEXsfhqafUHV9ayrurerMX81QrOhro31+/9ZB/ZWYq3UR69wZOnFAXALTZlNcwg3+NCSG/paK42GPm49qCk94LWNfjSycuIiKKHAw4BJjJhxoOKU1iPQ+IiZFLIxaCJ2auaM1uWLNGuVvHzJHQkpEBfPqp+uOffZavJz2ZzcA996g/Pj2dQb9QpLUzSUEBtzi5snChEhiVsW6dx/evbzbKBwKT46MRF8NTSiIiaozvDgHWLr2J9Nji0xbPVZ//9z/5J2b9Bmd6VEu/4AJmjoSiigptBQb37eOFjp5sNuDDD9Udu3Ilg36hympVtqRpMWoUg3/1+fq+tnq1xy+vL5QMXABI9naDhIiIIhYDDgF2eRf5PtUV1jpscPeGLwQwfLj8E8fyZMCJxQLs3av++CZN2JotVCUnA2vXAl9+CYwZo66I3bBhvNDRi9qCkVlZwJVXMugXqsxmpY7D9Onq59i8mS2f61u0yLf3tWnTPP4es9TWSU/Vq12q/PMSEVFEYcAhwPp1boYoH7J/D5W6KRzpa3uxnBz5sZFg5UqgTv5kqpGOHdmaLZRddhnwl78oQTs1wbj8fOVCmbQRQun0okZFBTO3Ql1mJjB0qLagEbMcFGpqEnkI2NTZBCqr5d8jL8xI9e25iYgoYjDgEGDRUSZc0aWZ9PhvNrpJOfW1vdikSTwps9O6dxgAdu3inbVwYDYr2Q4pKb4f++qrfE1ppbaOyptvKsU/uZUi9C1aBBw+rP74TZv4uxhQt0UlKgp45RWXv8c2FJbCl5C8Lx24iIgosjDgEATT77tUeuyZ6lrXXzCblbv0sgoKeDfQLjdXubOjhcnk9kSNQkxWlpJa7Ku8PF7oaKEl8Dd9ulL8k0KbEMBzz2mbw8NFc0Qxm5VCxunp8sfYbEqQwsW5wdEy37Y5tU6Rr09FRESRhQGHINhWVCY9tkmsh/+i7Gzg9dflJmrThlsAAOWk9Nln9ZnHzYkahRghgJdfVncs07nV0xL4O3BA2VZGoc1iUYqwauHhojniZGUB69fLdW1JT1e6VLjJFDpaLl9YNwpA304+BDqIiCiiMOAQBL7cOSip8PCmL4R8dXdmOCgWLVL232vRtauSxsuU7vBQXa30o1eD6dzqqNlvXl9aGlthhgNftwY2lJbG38UNtW+vtMz2JDYW2LAB6NfPbf2Mn/KPSz9l99YJiPalOBUREUUUBhyC4GSF/J25o2c8BAl8KRxZU+P9JCTc6ZG+CygBi5MnWR0/XGgJxDGdWx2rVQmCqjVlCi8ww4HZrGxNWrdOXeDh1Ckl04+/i8+JiVHe7z2pqVEKH3tQeKJS+inbpDWVHktERJGHAYcgOF3l5WSgnlobYK21uf6iyeTbNolx4+THhiOtrTDtWL8hvMTHK21O1bDZlLurzHLwTVyccidWrenT+foLF5mZyp32rVuVbAVfahAA8tsKI8UPP8iN+/FHj182+fD6ap3M+g1EROQeAw5BYIJvqYez1rjZ4+rrSfuUKcoFUqTS2grTjvUbwktsrLaLVwagfJebq1xgqlVUxNdfuMnMBE6cAEpLfTtu3LjIfl+rz5csvtde8/g7q8Iif2OkU/ME6bFERBR5GHAIgj/40BYTABZtP+r6C1arbydnFgswb55Pzx02tO4Zt/v2W6XQHfcMh48FC5Q6DmoxAOUbra/F9HRg9Wq+/sKN2i1vNhswcaL+6wlFOTny9Wg8/M6qswmcscoHUO//Q0fpsUREFHkYcAiCfp2bISFO/lt/3F0dB7NZSUH94gv5Jx83LjLvxObmKt8rLaKjgUGDgN69uWc4XAihvCa0FCCcNo0BKF9YrcCePeqOve8+4NdfgS5d9F0TBZ+WjhVjxjDLQQhg2DC5sSYTsGKF299ZGwrlb2TEx0QhLoankkRE5B7fJYIgOsqEuy/NlB7fIslDnYbMTCApSf7JI/FOrBBKynuUxh93VsYPP1YrcPCgtiDc5MlARoZ+awp3cXHqa2Z8+y3Qtq2+6yFjMJl8ey+rr64ucrP37BYtki/EumKFUmzTjeNn5DO+Ls5MkR5LRESRiQGHIGnnQ1Xn9Kax7r8oBDB6tPwTL1sWeXdi7ReVWu6AJSUp/c0j7XsX7sxmJTth5Ur1waQjR7RtyYg0OTnAsWPqjrVagfnz9V0PGYPZDKxapf7455+PzOw9wPdtShdd5PHLLZPipadq5cNYIiKKTAw4BEl6ovyFa42nQodWq/yeTSAyU5HtF5Xvvqvu+Ph4YMeOyPzeRYLMTOD//g9Yu9Zrqzi33nxT1yWFLV/Svt0ZOzZyLyzDXXY20K2bumMLC5VtGZHIapXvwGQyeQ2cZ7dJln7qjHS2xCQiIs8YcAiS1snydwU2FJ52/0WzWblQSkzUvqhw1q4d8Pnn6o6trgaa+Vbok0JQr15ARYW6Y197jXvIZfiS9u0OO1SEL6sVKClRd6wQylaBSBQXpwROZUgE656bnSf91P2zmkuPJSKiyMSAQ5D07STfa7y6TqDO5uEkIStL6ZwgQ4+2kKFo4UIgT/4kqhG1F6IUOuzBOzUiuQOMLK3dKdasYYeYcGc2K//PauvtPPJIZGa/WK3AUTfdrFTwpWhkv84MxhMRkWcMOARJdJTJc22GBryeALRqJTfR5MnSzxk2hFCqmKsNtqSkKB8U/tq3V1+47qmnIvNiR5bVChw+rP74khJ2iIkEmZna6qnk5Oi7nlBg3zYYHS03ftkyj1+uqpHL1oqNUs5liIiIPGHAIYj+NuB86bHFpyo9D4jz0MmivilTIi/1OzdXORlT49FHge3beUc1UsTFAZ06qTu2qIjFIz2xXxQNHaru+Eht6RtpTCYgNVX98c8+G5k/J198IR9UHzHC7feoyloH2e9e6xS+LxIRkXcMOARRp+bydRc27POyr7WmRu6uUE1NZG0PsLfElL3z09CWLWx5GEnsHU3UeuMN/dYSjjIy1NdSicSWvpHIbFa2zsTEqDt+716lVkgksdmAiRPlxx875va1NGH+DulpLmyXKv+cREQUsRhwCKK+ndIhm424ZPdxzwOSkuS7KMRHUBsre3aD2u0UvMiJLHFx2lL2WTzSs/nzgUov2Vqu/PADsGkTM40iRWoqUFur/vhhwyIry6GiAqiqkhtrNgMbNrh9Lf16qEz6aWPV1togIqKIwneLIIqOMkkHHMqqajwPsFqVi2MZ990nNy7Uac1uAJQ71rzIiRxai69Zrb7daYwkQgAvvqju2GuuYe2GSBIfD6SlqT9+797IapGZnAzceKPcWItF25aVetqxJSYREUlgwCHIksxyaaO1Ap47VcTKF6DEd99FRrcKrdkNADB1amTdKYt0ZjOwbp36wpEAMGECsxxcWbQIyM/3/bhmzSIrK4uU1+GWLUB2trrjbTZg5Updl2RodXXK+52M5GSPv9/+0EW+g9blXdgSk4iIvGPAIcguzpS/i7N+r4c6DgsW+HZHZ8IE+bGhyJ7doDXls7iYWyoiTVaWEmhSy2JhlkNDQgDPPOP7cfHxwM8/M8soEu3aBezcqf74UaMiJ1h84oT82PJyj9svfvK2ffN3sVFsiUlERHIYcAiyjLQm0mNX5h9z/QUhlAruvrQSC/duFfbif1r/jZMn82In0ggBTJ+ubQ5mOThbtEhJc/dVhw5A5876r4eMzR4w1iKS6u/42kbUTVFNa60Nv52Qq7HSLCGOLTGJiEgKAw5B1iszVXrsgq1u9pbbL659uZtTVaUUcAtX9sJY58u3HnVp+vSwvktWZxNYt7cEP24pwrq9JZ637UQKq1XJbNHCYgHmzdNnPaFObXYDAJSVRcRFo/11+EPeYcxctQ8//NL49RhRr1Wt3WJMJo+FEcOOr1uOJk1y+b42c7V8UNBSF8Y/f0REpCuVfadIL23T5IsuHT5djTqbaHxXwWwGVq8GLr1UOUGXYTIB48cDt97q+92RUNGqlXwhTXfsd8nC8MQ1Z/sRjJ27E0fKqh2fa50cj3v6ZKLWJmATNqQ1NaN5khmtk+NxSYc0bD5wCsfPVKNlUjz6dkoPzztc9mDVFVdo+/kZNw4YNCh8X1+yLBZ12Q3t2gFr14bla68+V69Du6T4GEy640JERQGjftyO0rPnige3SYnH6EHZGNijTSCXGxhms1J/58QJpYhrQQGwbRvw8cdyxwsBfPop8Oqrfl2mYaxa5dt4N+9r/9l8WHqKhDjeryIiIjkmIcL49q2k8vJypKSkoKysDMnJyQF97jqbQJeXF0iP//qxfvhDFzf7JmfNAh55RP7JW7cG9u8P3xN6m01pc6i2aKTZDOzZA7Rvr++6DGDB1iN46qs8n46JMgH1b6ommqPx6BWd8ex1XcMv8JCTA9x0k7Y5wv31JWvBAuCWW3w/zmRSWmiGWcHIOpvA+r0lWLv3JDbuP4UN+0s1zTf9z71wc8+2Oq3OwH74ARg8WH58dLRyUR3urRuFUIpAnj3rfWxKivK7rV07l11fLhydgzMWuffLQT3b4N0/9/Z1tZoF83yNiIjUYYZDkEVHmRAXBVglt3sfLW98FwyActLx/vvyT5yRoVTjD+eLoYkTtXWoqKnRrX2Ykfz4SxH++s0Wn49rmMFdYanD20vz8eGqfZh690Xhc6dVCOC557TNESF3570SAnj0UfXHhlkdjJztRzD8v9twutJLm2MfPP3VL/gXTLi5Z5i8/lwRQtkG4Iu6OmDuXOD22/2zJqOorJQLNgDApk1KUVw3aurkX293X5IpPZaIiCJbmIf+Q0OzRPmWlifPuOlE4eue1yNHgBYt5MeHGptNWyeOf/wDWL9eaSEWRoZ8+jOe+2YL9ExrqrTWYeiXeRg7Z3t47C23WIB9+7TNcfgwsGOHPusJZdXVyu8atSoq9FtLkNTZBNYUnMTQLzZh6Jd5ugYbAEAAeOqrPCzYWhy+dR7U1nR48smwrsEDAHjiCblxJpPLrIb6LLXy36vLu7IlJhERyWGGgwHERkcDkDsJPVHhpp2V2Qy88QbwwANyT5qeHt57y+fP961NaEPLlysdKsKAtdaGL9btx79WFDjtAdfbrLUHMGvtAaQnxOLOizNwfXbr0KzzYDIpmS0nT2qb57nngN27w/t15k2Nhp+3Jk2UFPAQVWcTeG9ZAWb8tBeVVg2ZVpKe+uoXRJt+Qf1afknxMfhj7wzceEGb0Hwt2pnNwP33A2++6dtxR47osz3KqOrqgH//W26sl8CLtdYmHYhOiDWF7s8SEREFHAMOBhDtwwXJtkNuikIKAUybJv+kc+eGb7q3EMCLL2qb4+DBkCwWWWcTWL+v5Pe7mzas+O0Edh09E9A1lJ6twcw1+zFzzf7QLGxnNgN5eUrBOosFuPJKdVtz9uxR2s8NHKj/GkNFYiIQEwPU1vp+bEqKUoMlBPlj64SMho0DzlTXOgKBrZLiMPb2HqH1WrSz2YD33lN37MiRymswHAN/P/6o21Sfrd0vPTalaWi+LomIKDi4pcIAfOlUceCUmx7ZVquSxi1r+nT5saFm0SIgP1/98dHRStePEAs25Gw/gt7jc/GXj3/Ge8sL8P7KfQEPNjR0pKwaQ7/Mw9uL94RWendmJtC7t/KRlqZ+nkcfDf+UbneWLAE6dFAXbACU7gTVbmrWGFjO9iN+2Tqh1bEzVgz9Mg852zVscQmWigr1Pwv794dna1UhlDpFslq08Bh0mbNF/vwh0cx7VUREJI8BBwN4/IrO0mNLK9ycOJnNwLJl8k/6xRfaCioalR4F/9LTlQvOEGK/yCmrUnlx52dvL83HJRMWh97FjtkMbN4MNFe5X7moKCQvmjUTAhgxwrcgaEMmk7YtGQFmr9Xwwrdbgr0Uj1749tfQCv4BSi2d9euBl1/2/dh27UI2U8YjqxU4cEB+vIcitnU2gW3F8sHp7m3Cq7YRERH5FwMOBnDF+fLFG6trhfuTxexsYMUK+Sf+73/lx4YKrQX/pk5V0ulDKLvBWmvDCyq6TgTa6cqa0LzD2qKFtnoOS5fqt5ZQkZurVMRXIzZWafH7888hU7R1wdZiXDpByS6qlG05FCSV1jqs/u1EsJfhu8suA/r18/24bdvCph6PE7NZeZ3Jyshw+6UNhb61Zv1j79AKyBMRUXAx4GAA0VEmdG3eRHr8e8sK3H+xVy/5J/7HP8Iv3XvlSvUp3ICS+eHhxMwo7NXox83dge6vLERljbEvcup78bsQu8OqNWAwdmz4vc48EQIYNUr98SaTUvy2Tx/91uRHkxbsxFNf/YJTlcbMLnLlwc82hl7gTwhg3Dh1x06YEHYtVgEAF1wgP3bxYrdfOn7GtywsdqggIiJfMOBgEHf60NN65qq97i/YkpKUCvsy9u9X6h2ECyGAV17RVhzsl198u2sUBDnbj+CKKctw70fr8cma/Y0KxRndWUsdrn1zGWau2gdrrcEvAuwXOVp+pjZtMvzPlK60ZDcASqr4/Pn6rceP5m0pxoyfCoO9DFWGfpmHF7/dYvzXoJ3a1pgAUFkJTJqk73qMQLYrFQCMGeM28Nk8UT6jr12KmR0qiIjIJww4GERFtXw9hXJLnfsUyOpq4PRp+SceNix87r7m5gIbN2r/94wcadjvSc72I3jyyzwcKQvtugAHSqsxfv4udHtlISYt2Bns5bhnv8jR+vMwapRhf6Z0ZQ/6aWXgrBB7dtHYOTvwzOxfgr0cTf6TV4TzRy3Ea/MN/Bq0M5uVQNbcueqOnzgxvLIc6uqAb76RH19Y6LZ45kYftlTcmN1a/jmJiIjAgINhFJdV+TTebQqkxeLbE+/d6/sxRqRHdoPd3r2GrGpurbXhxe9+le6VHgpsApjxU6Fxgw5msxLE2rQJ6NZN/TybNoXnPvKG7EE/rQ4fNuRrsH520Swf2ggamQDw0apC3PX+GuNvdcrMVIq4qhFuWQ6vvebb+ORkl8Uz62wCn/rws7z5oG/1HoiIiDQFHCwWC5YtW4ZXX30Vt956Kzp37oykpCTExcWhRYsW6NWrF4YOHYrc3FwIlXerioqKMGXKFPTv3x8ZGRkwm83IyMhA//79MWXKFBQVFWn5JxhG21T5Gg4A0DIp3vUXwrHXuAy97kSbTEorP4NVNV+w9Qh6jsnBWUsYdhaBcsFj2NTuzEygRw+lTaMW48eH1x3WhvTKbsjKUoIWBivcGi7ZRe5sPnAavcctMnZtB5sNeP119ceHS5aDzQZMmeLbMUVFLm8ubCgsxekq+W4w+0vctOYmIiJyQ1XA4dixY7j33nvRokULXHfddRg/fjzmz5+PwsJCVFRUoKamBidPnsSWLVswY8YMDBgwABdeeCF+/vlnn57ngw8+wPnnn4/hw4dj7dq1KC4uhtVqRXFxMdauXYvhw4ejW7dumDFjhpp/hqH0z/KtCNPFmamuvxAfD6SlyU+Unh4eQQqzWalqrzVQIATw66+G2nOvFKXLQ3Wtwe8+amATwMOzNhj3DmtcnG+vK1eqqkKmNoEqVqtSF0arM2eAli21z6OjOpvA8P9uC6vsIlfKquuM3UmmokJbm9nKyvB4DVZUKP8WX6SluXyv97VgZHQUE2OJiMg3qt45Dh06hNmzZ+PMGee+zW3atEGfPn1w7bXXIjs7G1H13ph27NiBK664Av+VbMU4btw4PPnkkzh79qzjc127dsVVV12FLl26OD5XUVGBoUOHYsKECWr+KYbRr3Mzn8bPWuOm9aPZ7FtV/csvN9ydRNW+/FKfNOyoKOVOrQH2kIdyUTpfrdlbgvNGLcCCrQa82LFYgEOHtM1hMilZDgb4ufILs1n7tpGxY5XtJwb7nfTesnycrpS/CxzqRv6w3ZgZR8nJwNq1SjehxER1c4wbF/qvweRk4OGH5cenpwPr17t8XbnNlnTjInc3O4iIiNzQHKru168fPvjgAxQWFqK4uBgbNmzA0qVLsWPHDhQVFeGZZ56B6feoem1tLe6991789ttvHuf88ccfMXr0aMfj7OxsbN68GXv27MGKFStQUFCAjRs3onv37o4xr7zyCubMmaP1nxM00VEmJJnl/ztmrvJwEVrv++LVnDlK8alQZ7Opb5nmaq5Dh4K+h3zB1uKQL0rnqzob8NRXecYrYqe13SqgXORs3Gio7BldCaF9j/yPPxqqLW2dTWBNwUl8sHJvsJcSUCVnrbho7CLM22LALYuXXQbcdZdyl1+NgweD/rtdM5sN+OwzubEmE5CXB9S7UVPfJR18y9y6+cI2Po0nIiJSFXCIiorC7bffjs2bN2PdunV44okn0LFjx0bjWrdujXfffRfTpk1zfM5qtWLkyJFu566pqcHf/vY3x+N27dph9erV6N27t9O4Sy+9FKtXr0ZGvZPTv/3tb6jVelEQRK2S5es4lFTWuE8/N5mU9piyxo+XH2tUZ86oO4ls3Vq5o7p5s/NHkPeQ52w/gqe+iqxgQ30frSrE0//eZIwtFvbaBHqkEkdHGyZ7RncWC1BQoG2OoiLDXAzmbD+C/pOX4i8f/4yqGgPe7fezqhobnpm9BY99rkMRUL35ksXX0KpVhsug8dnp0/K1KITwWGhz3d6TPj11u7SmPo0nIiJSdQbdu3dv/O9//2sUBHDn2WefRd++fR2P58+fj0o3+w9nz56NgnonrVOnTkWam73T6enpmDp1quNxfn4+Zs+eLbUmI2qV7NtJ0Pq9Ja6/EBcHuAgAuTVhQugX0lqzRt1xJ04AF1wA9O7t/NGunb7r80GdTeCFb38N2vNHA2ibbEawK3vM33YMl4xfHPz95PaCpHq8RurqwjfLYflybcc/8IBhtlPkbD+CoV/m4Wh5GHTw0WjxzuPGyjgSQtl6o1Zysn5rCZa33/Zt/KRJboOcH61ysz3ThegoE/p2SvftuYmIKOIFrPrP7bff7vh7dXU19rspLvbtt986/t62bVvceeedHucdPHgw2rQ5l+L33XffaVtoEPX0cW/kun1u7kxYrb7tN6+rA+bN8+m5DUUI4L771B1bV6ekyxvIT7uOo9IauG0usVHA4F5t8dbdF+Hrx/phz8Sbsfbl61Ew8Wb89bosJMRFO41PbRKLXu0Cc9J+uqom+EXs7K0x7dkvK1Zom89ANUJ0IwQwYoS2Ob7/HmjbVp/1aFBnExj+n23BXgYAIDbahC4tEjDipm7YPmYA/ti7XVACgR8bqYuM1QocOKD++A8+0G8twWCzAf/8p2/HeNhGUlohH1S7p087REcFOxRNREShJiZQT5Se7hwVLy8vbzSmqqoKixcvdjweOHAgYmI8LzEmJgYDBw7ErFmzAAC5ubmorq5GfLxvhZCM4IouLfD+Cvm7DQXH3exhjYtT7tCfPi3/5GPGAIMGhWbHioULgVOn1B8/ahRw442G+LdPWrAzIEUi+3ZMQ59O6bi8c3P069LM5UlkdJQJf73hfDx73XnYUFiK42eq0TIpHn07pSM6yoTx83Zg5ur9fl8rAIz47zbckN06eCe7mZnKBwAsWqRtrvo1QgxwN18XublKdxctqquVfflBugNdZxPYUFiKz9YW+tQmUA+pTWLxcP9OePLqLth84FSj15rdm3dfhCl/7IlpS/bgw1X7UB2grR4CwD++/xVv39MrIM/nkdmsZNNceKG6oN348cCrr+qzRSoYfO3UYTIp3y83v2sOn5bvdnFrT+PUVyEiotARsIBDw4yGli7anu3atQuWen2i+/fvLzV3//79HQGH6upq7Nq1C716GeDEyEf9ujRDTBQgeyNp+e7jqLOJxhdhVitw/LhvT/7LL8pFw4ABvh0XbHrcWd271xAXf4EINrRONmPMbRdgYA/5wl/RUSb8oUvjLiqv3HoBokwmfOSpgKlOTlXWYNriPXhhwPl+fy6P7PUctHjnHeDOO4P+86YbIQAPdXm8MpmATz5RtjYFKdiQs/0Ixs7diSNlGlouqvT89V3xzLVdHb/HXb3W6ouOMuGFG8/Hc9crgcCjZVVYU3AS3+f5t8Dj/7YU40x1DWY+1Nf7YH+74AIlM+2aa3wvemyzKcWS77jDL0vzu+RkYPVq4Ior5LZ5rV0LZGe7/JK11obyarkTDhPA7RRERKRKQEL8Qgh8//33jsdt2rRBp06dGo3bsWOH0+OuXbtKzd9w3M6dBtpv6oPoKBPu7dteerylTriu42BPAXdTldqtUEzzzs0Ftm7VNkf79kpWSBBVWev8Hmx4/vrzsGb4dT4FG7wZeUs2pv+5N9IT/P/9e2d5ASbMDfJr217PQYt33jFUJwbNFi5UApZqJSUB994L9Omj35p8kLP9CJ78Mi/gwYY2KfH44L7eeO7681Rl7tgDgXf2boc3774Y0//c2+/bLZbuPoFB7/7k52eR1L8/4CUD0q0hQ0Lvva6+Eyfka8p07uz2S5+t3S/9lPGxUdxOQUREqgQk4PDVV19h795zbcX+8pe/OFpl1tcwC6J9e7mL7w4dOjg9Liz0/x1Xf+nYLMGn8W7rOLRqBZS4KSrpTqi1CxNC2Q6h1dGjQf1352w/guxXc/w2/7kLm65+OWG8uWcbbBx5Pf796GVo2qDeg94+XlOIRz8LYtV8s1m5u5iu4U5fQYES5FqyRL91BYsQwPPPayuo2bFj0AJ+dTaBsXN3wt+Xnue1TMSucQPx9WP9MO2ei/H1Y/2w+qVrdQ3+3dyzDf71Z/9n9m0rOoPxc7b7/Xm8WrBA6YyiRmmpEigLRUIAL7wgP95D1tC6fSekp0k0+/d3OxERhS+/BxwOHz6M5557zvE4NTUVI9ykwDes65Camir1HCkpKU6Pz5w543G8xWJBeXm504dRpCf6lmbt9iaNvY6DLyZODK00bz3uNiclKR0ugvTvXrC1GEO/zPPbBc/z13fV/cLGlegoE/pnNcfUuy/y6/MAwJJdx/Ha/B3eB/pLVhawZQvw7rvq5zh8WNkKFMp3WQGlnsWePdrmOHYsaAG/DYWlfs1siALw3j0XI/eFq9AkLhp/6NIMt1+cgT+4qZui1c092+KD+3qjTYp/axjNXHsAP2w+7Nfn8EhrpwoAuOUWYMoUfdYTSBaLsg1Qhpc6FTuKPZ8r1dcqKYTODYiIyFD8GnCorKzE4MGDUVLvTvuMGTMaFZC0q6hwLoLYpEkTqedpOM5bwGHSpElISUlxfGTai8EZQOtk304UE9zddcjNBbb7eBfqnXdC6wJo1SolYKBF584eU079pc4m8PbiPXjqKw2p6B4kx0drStdWa2CPNgG54Plo1X5UBbCTRyPt2gGffaZtjk2bQrs9phBAvWCyKtHRSsZIkAJ+x8/4L9hwc49WyJ94M269OLDbZwb2aIPVL12Lrx/rh7fuvshv252e/+5XPPZ5kLKNrFYlU0irsWNDryW0L12V0tI8FkOu8KE4av+sFvLPS0REVI/fAg61tbW45557sHHjuROSp59+GnfffbfbY2pqnN/8vHWocDeu4TwNjRgxAmVlZY6PQ760kPQzX4syrcp3saVCbRG3X38NnQsgIYCXX5a/0+NOEO6u5mw/gksmLMbbS/P9Mv9dvTPwy6sD/J7V4I7TBc+fLsbgXv5pdXjBqznBa5eZm6sEDLQaNSq0gnz16ZHdkJ5+rvtHANXZBNbtLcGeo/J3eH0x7NosTL/v0qDtea9f32HinT38Vtth8c7jeOrLzaizBfhnOC5O2ZaktbNQVRUwaZI+awoE+/uerHXr3AbzrLU2nPWhy0nzpNDr/EVERMbgl4CDzWbD/fffj7lz5zo+d/fdd2PatGkej0tIcK5fUC3Z+qnhuIbzNGQ2m5GcnOz0YRTRUSYkx8v/txSePNv4k2q3GkRFhU7hyNxcpTCmGgkJwI8/Aps3K3ME8O5qzvYjGPplHk5X+qftXlrTWLz+x4uCXtzLccHTKwNT/9QLl3VK0/05bACGfpkX+KCDvVOFHm31QjXLQWt2Q0yM8trLywt4dkPO9iO4Ysoy3PvRevxrhcaApQupTWPx3PXn6T6vWgN7tMH7fsw6WrD9KC4cvRALthb7ZX6XrFYlWKzHe9XEiaGT5WC1Art3y4930Q3MzpeCkQDQPDG4hZWJiCh06R5wsNlseOihhzB79mzH5+666y78+9//RnS056JDiYmJTo8rK+X6Qzccl6Q1zT7IHrtCPsVfCBcnSmazcjH94Ye+PbHNBhw6ZPzCkVpaE370kXLCdtttQO/evte50MBaa8PLP/iv2JoJwKTBFwY92ODKF0P6+W3uF77dEtg7rPaAnl4XKaES5KtPa3ZDbS3QtWtAX39AYDpSTDbga9CedfTKLd39Mn9ljcBTX/2CSQsC1EXG3olpzRrtc1VWhk6WQ2ysb+/P69e7/dKcX31ro9o6RW6LKxERUUO6BhxsNhuGDBmCL774wvG5O++8E7Nnz5baHtGihfMewSNH5O5cNhzXvHlzqeOMql26fKeK42dqXF9sZWYCDzzgW42DZs2ADRuMXzhSS3bD3XcH/CIHUC50eo3PRelZ/wRz2qTE4/37egdtG4U3cTFReOL/GrfC1UOl1YZ3/LQ9xSX7xc6mTUC3btrn27gxtLIc9KjdAOhzsegDf3eksHeDMeprMDrKhIf6d0LrZP/9fp/xUyEWbA1QxlFmJtCjhz5zhUqWQ3m5EqyT5WbLVp1NYGexfLHstCYxPm/3JCIistMt4GCz2fDoo4/i008/dXzujjvuwDfffCNdi6Fbg5P3AwcOSB3XcFz37v65ixMovtxJEFAqrbsUF6e0nJNVUuJ7oclA05LdEBcHNMiiCQT7NoqzFv8UOQxUJwqtRtycjSf+rxP8cfP33WX5gc1ysF/snDqlfS6TKbSyHCwWYN8+7fM891xA/83+6kjx8OUd/NLm0h+io0wYc9sFfn2OUf/bFrjXYnKyUkRR6/amUMlyqPPxPcRNxuKGwlLU+fBfNLh3O8Nl7RARUejQJeBgDzbMmjXL8bk77rgD3377LWJjY6XnueAC5xOhvLw8qeMajsvOzpZ+TiPy9U5C0Sk3W0+sVuD4cd+e/NFHjX3hoyW7wWpVercHUJ1NYMwc/7RwTG0SE5ROFFqMuDkbu8ffhFdu6Y4H/tAB3VvrEwCyCeCP7wf2jrkj02HdOqBBa16fCBEaW5kAYMkS4OKLlTooWu3frwQvAsQfHSme+L9OGH1bD7+1ufQHeyeZ1Kby782+KK2scR8E94fLLlO6MWgVClkOd90lP3buXCULy0XGoq+vheuzW/s0noiIqD7NAQe9gg0AkJmZiS5dujger5Rs/1R/XFZWFtoFIWVeT9FRJiSZ5U9eF253U6zLfkH02GPyT15cDEgW6ww4LdkNdmPHBvyu6tFy/S+qbu3ZBptfudHwd1RdiYuJwpArO2Pc7T0wb9j/IUanPKtfDpXh0c826DOZrMxMoF8/YPJk9XPExAD//KfxtzLZK+T/9pvSIUAyc82t1FTtXQZ80DxRv+9vFIDpf+6NETeHZnB7YI822DzqBvx7yGV45pouyNC5oKQ/2402Yjb71irSnepqoEFrbkOprfXt32m1ut0+2DxB/rVggu83QYiIiOrTdKrvKthw5513qgo22A0ePNjx9xUrVuCgl24LBw8edAo41D8+lF3asZn02F8Pnnb/xYwMoN42FylLl/o2PlCsVkBym41bAb6TfLSsStf54qJNmP7nXnjvz71D5o6qJ9FRJrx998W6zbdk1wnM2+JbMTTNhPC9QGt9tbVKwMHImUWAc3bRtm2+7SW3a94c+OILJdPol18CGmT56Tcfs73cSI6PRv7Em3Fzz9AL9tUXHWVC/67N8bcB3bD879foutXp5BlLYLc4ZWVpyzIymYC331a2aBjVgw/6Nn7iRLe/U6ot8q/dtinxYfFeQ0REwaM64CCEwGOPPeYUbBg8eDC++eYb1cEGAHj44Ycd3SxsNhvGjx/vcfy4ceNg+z0NMjo6Gg8//LDq5zaSWy5sKz32ZGWt+5O78nKgxscWjAHOApBmNvt+0lVffLxSqC6AFzlrCkp0nW/WQ31xc0/5n41QcOvFGbi+ewvvAyUN/2Fr4LtWFGtsCZiXZ+zCkfbsIi+dhrw6dQr44x+Bm24KaPHWnO1HMGNVoS5zPXtt6GxhkhUXE4XHrtSvqOv4+btwxZRlgWtZazYDy5erP14I4N13jfm+Byi1G776yrdjDh92G1z/5zL5DjPt09mdgoiItFEVcBBC4IknnsAnn3zi+Nwf//hHzcEGQCn4+GC9i8qPP/4YH3/8scuxM2bMwMyZMx2PH3rooUaFJ0NVRlpTn8av3+vmwvann3x/8oMHjbmf3GYD3nlH2/Gd/NMpwZU6m0DuzqO6zdcmJR79ushnvoSSjx/si+u7u+8Z74sKiw2frikMXNDBbFa6u2RkaJvHTUV5Q7BnN/hatK6hlJSAbqMAlNfhc7O36DKXCcCDl3fUZS6jGXFzNm7VMWvjSFk1nvwyL3BBh+xsbVkO+flKu1cj8nLjxaXvvnMbXN9//Kz0NInx/qn1QUREkUPVJtzvvvsOH330keOxyWTCqVOncOutt0rP8eKLL+KGG25w+bUpU6Zg5cqV2Lt3LwDgsccew9y5c3HPPfegbdu2KCoqwtdff4158+Y5jsnKysJkLfuoDcbXPZOrC06if9cG7UCFAMaN8/3JH3zQmPvJFyzQVmTOagXmzQNuu02/NXmwobAU5dUq0s7dGD0oO+zurNb38YN98OMvRXjumy2a5xo/fxc+Xl2I0YOyA1PnomVL4NgxbXNs2qRc2A8YoM+a9FI/u0FrwGHx4oD/bnn2q82w1OpTDPDx/+uEOL2KjhjQDdmtME/HtpYCwIj/bsMN2a39/7vLbFZeQ5dcomT2qXHbbcD8+YCbc5OgsNmACRN8OyY+Hujb1+2Xq3x4PfTpwPoNRESkjaqAQ2Wlc1cEIQSW+rjv/5577nH7tebNm2PhwoUYMGAACguVNNg5c+Zgzpw5Lsd36tQJCxcuRPPmzV1+PRRFR5mQEGvC2Rq5O555B11UBbdalZoFvnrvPaUIntZWY3oSAhgzRvs848YBgwYF5C7r/G361BKIi47CO/deHJIFIn11e68MrPjtOH7YonGLAs7dYX3/vt7+/96ZTEDTpuovdOyeeQbYsyfgWQAeaekM01Bmpj7zSFqwtRgLtmsMBP3uif/rFLJFImW1TNK3eCQAnKqswXvL8vHc9efpPncjWVlKLYZHHlF3fE0N8PTTSmFUo7wGy8t9D/Rt2OA2sFdnE/Alj6p7WwPXtSAiopBgoCtKZ127dsXWrVsxbNgwJLsp5JSSkoJhw4Zh69atyMrKCvAK/S8+Vn6/9JZDpxt/0mwGVq/2/cSpqsp4PcmtVmDXLu3zFBUFZLtIzvYj+HK9imCPC5Pu7BERwQa7KX+8SLe5BICxc3f6f3tFXBzQsaP2eQoKjJXWbc9u0CP4GBUFNAvclqA6m8Dfv9+qeZ4YE7Br3MCwDzYASmZdm5R46H2p/eFP+wKzxUkI4P33tc1htK0VarZZpaa6/dLq/BM+TVVaacDtlUREFFJUnUU+9NBDEEJo+njooYe8Pk9iYiKmTZuGY8eOIScnBx988AFee+01fPDBB8jJycGxY8cwbdo0JCYmqvlnGF5ykzjpsZZa4fqELjNT3Z0ao/Ukj41VHygwmZQ7tJs3K3/6OaW7zibw1Jd5us3X1sd6HqEuLiYKQ67Qr9bGkbJqbCh0kQGkJ6sVOKpTvY5nnzVOLQerVanposfvAptN2dIUIG/n/oazVo1bQAC895feaBKnsVhmiIiOMmH0ICWwomfQ4ay1Du8ty9dxRjfsP69aDRtmnNegmmKYHrI9Jy7wLXDvj6wXIiKKLIbNcKgvPj4eAwYMwBNPPIGXX34ZTzzxBAYMGACzEesM6OhyHwsEri042fiTJhOQlub7k1dWKntZjWLePHVt+ADlxDErC+jdOyCV8ftOWAS9QjWtk80R2QP9lVuz0TNDv1Tej1ft1W0ul+zZRHpkAuzbp61WiZ7sBTGzdbq7H6AOOEM+3YB3V2j/P7/z4rYRlV0EAAN7tMH79/VG6xR9LzRnrNzr/ywH++tQzXtefUbJchBCXbahm85UdTaBPccqpKeJjTZF5PsPERHpKyQCDpFq1K0X+DT+P3mHG3/SbFZa7rVU0QHAKO0xhQBeeEHbHKtW6bMWLwa9uwolldrvqtqNue2CsC4U6cmcZ6/Edd30qcuydPcJTFqwU5e53FKbTdSQzQasWKF9Hr3s+v/t3XlcVOX+B/DPzLApCiiZDIoK4oaISuaSZmZa5HUp9ZZ562dlXnNJb91fN23Tysrstmhldq2rlbm0eDPTi2u55C4aIuYKpYKpoAIiDMyc3x/zmwlwBuac5xlm4fN+vXi9HDznma/COXPO93yf73MEyJT0f1cLU5qGvr8Nm35RVzbujMzpPb4kJdGI7c/0x7JxPTH59tZSxiwus+C9TbVQ5RAfD/z8M/Cf/4iN4w2rxqxfb22EqUZwMOCk6nNPVr6q/g2Dk4x19vOHiIjkYcLBi9ULMqgqaz110cmTi8xM4Px59QHs32+94PG0deuAk4JPK2fMcPvF46qDZ3HorGDTwP8XGmzAgtpodujlPnm4B+bdJ+em76OtWTBJWq3AoS1bxFdxsJk61fM3O0DlFSpEBAVZq5T27XPrlKbv0s4g/YycY3C8n69IURODXoderSPx5MB2iAqTU+3w7qbjWJsu3hC2RjExQEqKWALQ059/imJNeqhVVgYUOb4WOF9YomqoN0bUzYQbERHJVXevpnzEbW1cL2c8/nvR9SWrigI895z2ADz9lEdRrJ37Rbn5yaqp3IKpyw9KG2/WPZ3qfLLBZmhyc8wfnSxlrD/N3SJlnOvYbsxldbY/dsw7SrptK1SIJlLKy4Fbb3XrlCazRcGUL3+WMlZdWJHCVQa9DjOHyvu/mLj0AFIz5C296ZTFIvbZpdNZj2lPff6ZTNYmsmr9+CPgpNF2REigy8OEBOjqdMKNiIjk4aeJl3uoh+vN866VWa5vjqd1aUyb06drZVUHp2RUNyxYYH1a5aYnq6kZuUh4MVXqmLKeKPqLQUlGPDmgjfA4xy8UY/XPbnjCamtWJ/PmxBuSfTJWqGjQwFr94eQmSJY73tosPIYxLBjHZt3NZEMVKYlGLHgwGQ2CNa2kfZ1p36S7v59DquA5WVE8+/kXFFTtahMO6fXAzTc7/euPt59yeaiEqIbq3puIiMgJJhy83IGzl1Vtf66gSslkcDCwY4e2i/3GjYF33nH7qg4ObdwItGwJDB6sfYwOHawN78aPd9uT1dSMXDy+JA3lki6edQCM4SFs1OXA5P5t0Ki+60/onHnuP4fk3+wEB1srAXbsEJ9+YFNLS7g69d//Wvu/iK5QsXUr0KePnJic+GbfaWTnqSsXd2TETc35VNeJlEQj0l4YKOUYvHytHLtO5kmIyglFsa60pFXDhtbpP7WwqpFTpaXqV9wICXFaZWW2KNh2wvX/85RO0erem4iIyAleWXk9dSXaFwsddLePj1ffeAoA8vOBt9+u/aesigJMn2692BIp5f7lF+vKFG5itiiY+Z28RoS2n/SMIQls1OWAQa/Dq/ckCo9TUFLunmUyY2KAggJ5fRx27bLe7GzcaF0lYuNGOeO6QlGAmTOt/5aYGCBA45PtiAh5K1w48eqaTPz963QpY/WKk9Ok1F8FBejx+vBOUsbacux3KeM4ZDIBZxw0UXZVYSGwdi1w5521e9xVpTbZpyjWJaQd2HVKXYLn4d7yliYmIqK6jQkHL9dL5dKY5wuvOf4LLatUANZERW03zpo9W1uCpCpFsSZN3GRPVv71FSUCosJD8CEbRVZrUFI0xvYRvxCetcYNK1bI7uPQvj2wYQPw7LPWlSKefbb2kn+23g2Ataxcy5K0ERFubxL5+tpMLNyWJWWsiPqB6KnyfFsX2aZXGAR/zf+1LRs7T+a5Z2qFreJo/37gxRe1jTFzpvW4GzLEehzWti0a+s1cu2ZNlDga7pjrjaMDdGClDxERScNPFC/XM07dBfDOk05usENCtM+hfu652rvRsViAWbPExwkJAVauBJo0ER/LiYVbNTT0ciCiXiC+eKwHtj/Tn8kGF7wwOAEDOoj9XA/nFGDW6sOSIvp/svs4lJQAEyb8ceO/d2/tJP9EV6YICAAWLwYOHQJay1lS0RFTuQX/2ion2QAAs4d3YmWRi1ISjfjs0R7C4zywcBf6vLHZPU0kY2KArl2BNWu07W+rLigpsTYurs1KP5Fmzy+/7DDWlftdr/iIbBCk7b2JiIgcYMLByxn0OiTHhLu8/e8FTiocgoOtT3uclFtWqzaXB3v9daC4WHycNm2Ae+4RH8eJ19dmYvPRi8Lj6ADMHtEJveNv4M2OCh+P6Y4/JWqs2rGN8VO23CX6Kj5V/fZbOU0ST578o2mjwVA7XfNFV6YoLwfuvdetK1IAwKc7siDjfyI8JIBL0GrQs3UkjOHizW1zr5RgwpI09yQdRKdW2NT2qjEmk7W6QgsHjS7NFgUXr5a5PES3lo20vTcREZEDTDj4gCcHtHN52wtFZc5LVOPitM/Fro0qB4tFrNFXRYcOuS1JYiq34CMJT1aNnEIhZHRP8akVf1txUG5Jd0yMtW/IsGFAerqcBpK2J61ms/urHBTFujqGqPr1xceogYw+HD1jGyPtxTt5DGpg0OswY0iCyi5Dzr20OtN9zVz37BEfa8gQ4I03aqefSmAgUOZ6gsCuRQvrv7XKNKYdJ9Qlx1tEhqp/byIiIieYcPABt7RR18jsvo92OP6LoiJr52staqPKQVZ1g42blhWc/o14g7qpd8RzCoWgi0Uaf5crMJkVTFmWJiEaByIi5DWQtHF3lcP69XL6p3z9tfgYNdirsgleVfUC9fhiXE9WFglISTTiwweThVeuUGCtdHBbM1e1qz04Ul5u7QdRG/1UXntNW8IhJ8fhNMJv0tRVeTQO5ZQKIiKShwkHH2DQ61Av0PWL4v2/XsY1k4MbnbAw67J9776rLRA33cBj40brEpavvCJ33N9+k76soNmi4JsDZ4XGGNunJZ4c2I43OoJubChezg0Aaw6dkzu1wmbXLvljurPKQVZ1AwC8+aZbb8hWHTyLy6ViyZx37u/CY1CClEQjdj87QEqTwfOF8prw2imKNZktg+3zxJ2VRuvXW5OKWoSHO2xaezrvqqphbpB0biUiIgKYcPAZTcPUXQC86qwLf48ewPjx2oI4cUL6DTwUxfq06JdftFdfOBIRYU2uSO6QP2L+dqH92zQJxQuDxZd2JKB7bGMpc8gB4IllB+SWc4s2XqyOXu+eKgeTyXqMy5CW5pYbMrNFwdyNxzF1+UGhcZ4c0IbVRRIFBejxl+4xwuNEBItVSjgkq49DRe6qNFIU4JlntO0bEQHs3u3wMy9LZcIhSuX1BhERUXWYcPARdyZEqdr+4OnL8oOIiACCJJdaVlx+T4apU63LgrmhQ35RSTkOnikQGmPN1L6SoiHbHHIZzArwzoajUsYCIN54sToWi8PGcMKCgqxzwPWSPhYkV0SlZuSi9+xNeGfjMaFxGtUPxOT+bSRFRTZ3dhRP4Ez/z88SIqkiONhacSOTuyqN1q8HDh5Uv9/HHzv9zDOVW5BfrG5Z2+6xjdXHQERE5AQTDj7itnbqOvJXe5lva0Kn1unT1iqEjRvlNM5yx1Pgn34CUlKkd8hPzchFl5fFLi7H943l2uaSpSQaMX90MmRUxr//w0k5VQ6232tZN+5VOWkMJ2z9emujS63nh6okTmlKzcjFhCVpOFcgXgX16j1c/tIdusc2Fp77n1NgwqqDYlPWrqMowNy5cscE5Fc5iCyF2by508+8T3dkqxqqVaMQHh9ERCQV7358RM+4SFXbNwiq5oKhqEhbEGazdbnJSZPkNM5yx1NgNzz5Tc3IxeNL0lAucDM6KDEK0wfJeRpPlQ1KMuL9B7pKGWvyUgkNJE0m6822rBv3qn77DTh8WO6YigJMmSJvPIlTmswWBS+tzpSyBOa4W2MxKIlTKdzBoNdh1jDx6WJTlx+Uu0SmO6ZUANqrHJwl7NevtzZn1qKaVaR2nVK3QsXonq20xUBEROQEEw4+wqDXqXqKm513zflfhocDDRtqC+TMGeua5IBYSans6oagIGDVKmuHfYlPfs0WBTO/c9IPw0U6AO+NTpYTEDk0KCka80cnCy/R99+Mc3h9rdjP274U37591hsLB03chE2dKnf+eGkpcPy4+Djjx0uf0rQnKx+5V8SbCfZrcwOe+xOTfu40KMmIcbeKL1crdYnMisdjY8lTBXQ6dVUOtp5FVRP2ItUNQLXVRD+fvqRqqId7i//8iIiIKmLCwYe0beL62va/F5qcX7AFB1ufkE6cKBaQSEmp7OoGvR4YPFj6VIo9Wfk4VyB2szPljjYsUa0Fg5KM+GC0eKXDR1uzYCoXrE6IiQESE4G8PPes1nDsGDB7trzxNm+WE+emTdKnNMlauWB8v3gp41D1nvtTAsb2aSk0hvQlMmNirMm/fMnLbiqK61V1GzcCLVv+0bOoYsJepLoBAH74wWGi3WxRcPGq6/0bDDpw2h8REUnHTxYfcluHpqq2r/aCrXlza8mzCK0lpRs2APfcI/fJb2mp9qki1fivYGlv/SADptzBBnW1ZVBSNObd11l4nMHztooHY3uyun+/9elqhw7iY1Y0Y4acXiobNliTdTJkZ8tdbQZylj+NCgtmI7xa9MLgRIzt00pojH98I7mBZFmZe6qN5sypuapOUYDp063JCRtbwt5isVY7aNWgARDvOJk2d4O6BquhQbwkJCIi+fjp4kP6xqtrHFntk0GTCcjJEYwI6qscbBdeJSVyn/xu2ACEhckbD8Da9Bx8tvNXoTHevq8zqxtq2dDk5ujSPFxojGPnr+KaSUL1TUwMkJxsrXaQ/XS1rMy1XioV54xXnT+uKNaeLLKOxYgI6Td1FyRMp5g5tCOPw1r2wuCOGJSobnWlik7nX8N3aRIbSIaFATt3Ak89JW9MAJg1q+bjZ/16a9KxIlvCfu1asZ4srVs7XD3KbFHw0baTqoZqWM8Ny5ISEVGdx4SDD+nZOlLVD6xxvWo6hgcHWzvdi/ZQsF00tWzp2pNW0dJRZ8rKpA63Nj0HE5ceEBpj/uiuSElkgzpP+GZib+F+Di+sSpcSC4A/qh1kL88HVF9lVHHO+PTp1q+KSYp16+T0brB5802pPVRSM3Ix5cuDQmPMH53M49BD3hudDJGH5lO/PCivlwMAdO8ObJVQvVTRsWPW48gZRbEuEeuIwQDMnClWFfT77w6ndOzJykdpubr/u790F5sKQ0RE5AgTDj7EoNfhni6uXzjP23S0+g1+/lleD4XTp603M9U96XHncoHPPy/tKe3a9FzhZMOk2+IwKClaSjyknkGvwweCjTq/3i+hAqii5s2BFSvkjglU37jO1isFsD5htT1l3bvXepMkc2UKAHj/fWnHodmiYNo3h4TGmD+6K1el8CCDXod3R2nvq6IAeHdjDZ9japhMwK9iVWsODR1qrbJzxFF1g43ZLJaA/+tfrceygySflt4nj/WV0+iViIioIiYcfMzskV1c3nbvb1ewNt3JTZOiABMmyAnKZt8+509aKzbMcsdygWfPSlkOMzUjFxMlLI14S5smwmOQmEFJRtzRXuzn8OIqsRveSmzLZcqmKI6rHKpbCUans94kyaxuAKw3T1pXrqni/c0ncPma9sqlefd1YdLPCwxKihbq5/DBDyflrlixYwdQr56c8WzKyhxPTaquukGGTz8Foh3/jldb4ejAjQ2D2TCSiIjcgp8uPiYoQI+wkACXt5+y7IDji7WSEjk9HCpy1s/BUcMsGUJDrXNy9+93+pRHDbNFwZMrDkoJ7WKR3MZ5pM1jt4o9sfts52947NO9coIJDrYm5dassT6ZlKlqlUPFBJ+jKiZFkT4NCYC1eknryjUVmC0K5m5U1/CuohaNQjA0uZlQDCTPC4M7omWktpt8iwKh34XrtG4NtHFDI9/jx6+fWlFddYMMpaVAYaHDvzqce0XVULe0jpQRERER0XWYcPBBahpxlSvAvE0OLtbccbPhbNUKd110NW8O9OhhbconYRm+eZuO4VqZnOoLGZ31SVz32MaIChP7WWw8ch6vrsmUE1BMDHD33UCaeBVNJbYqh9mz3Zfgc4XF4voygdV4d8NRiByJrw8XX6mE5BoiUG0yb/MJpAquGGRnMln7HrjDE09Yp1a0bGn9mjLFPStj2ERGAiGOz28r9qk7/kcky11SmoiIyIYJBx/U6oZQVdt/vPXU9VUOYWHArl3A449LjAzXVzm4s6T0xAkp0ygA6xPVBVvUdfR2xhgewiX4vIRBr8PMoQnC4yzclgVTuaSpQO6aRw5YO+anprr3qaoj0dHW95RQbZSakYv3ftB+LEbUD0RPPq31Or3ibhDaf9IXaXKmVtgauO7bB9yobuWnGp04AUycaJ069dtv1oaSMldjqmjHDuDgQYfHmtmiIOtisarhbokX+/kQERE5w4SDD8pRuUzc1TIL9mQ5WJKve3frDYLMJzBVqxxmz3bfzY/ZLL7Kxv/bdTJPdUdvR3QAZgxJ4BJ8XiQl0YgFDybDIPgjeeiTXXICCg4G3n5bzlhVFRdbn7LWtvPngY4dhauNzBYFT335s1Aos4d34vHnhXq2jkSQwEFoVoBJX0j6LImJsS5V6w4nTrhn3Kq6dHF6rO04cVHVUG2a1OcxQ0REbsOEgw9q2bi+6n0cdqy2dbB3xxOYe+6xzmedMUP+2BV9842UYXaeUneB5khkaBA+fJBL8HmjlEQjPnu0h9AYu7MuyalyUBRg7lzxcZw5KadSR5VGjaQkLp9Yuh/FJu0r5yzg8ee1DHodJvYT66mSevg8Zq2WNL0pOBjYvVvOWJ5Q4vzBw4o92aqGGn4Tp1MQEZH7MOHggx7q1Ur1Ptf1FLB1sHfX/NKSEiAlxT29Iir65z+lJEx+OpEntH/j0EDsnH4Hb3a8WM/WkcL9HD7Zfko8EHc3kqstCQl/TKNISxNu2vrqmsNYm6F9bv3gxCgef17uiTvaon6g2GXHxz9lyeupckBs+WOPKi93+ldbVH6eRYVJXrWDiIioAiYcfFBQgB5JzcJU7XM272rlb9iW6HPX/NLaUt1SnC56dU0mDpy+LDTGa/d24pJiXk5GP4ePfhSsHnD3Mnm1KTMTuHhRStPWtem5WLgtW2iM+7u3ENqf3M+g1+Ht+7sIj7NwW5bzJZ9dpSjA668Lx1LrjEbgu++AJo6X/DVbFBSWqKsSYpNjIiJyJ94h+ahn7u6gavt/rDxUueGWrXHW/v3Wm/a4OMkR1hLBZfi+P5iDhduyhEL42x3xfLLqI1ISjejWMlzz/pdLyvH9QYEbHZOp9uZ41wZJS2D+71cHhcYICdSz6Z2PsPVUCQsW67/z5JcHxZpI2pLuvkZRgDvvdPrXavs3ALA2HyIiInITJhx8VHKLRqq2twDXN46MibE+nUxMdLqWt9cTWIZvbXoOJi8XK6ltEByAJ+5oKzQG1a4esWI3ppOXH9C+RF9QENCihXuXyqtNEpbAnLIsDcWCy9G+/efObHrnQ1ISjTgw4y40aRCoeYzScgXbj13QHkRwMLB9O9DYx1YUmjOn2ulLT3+lvunqxaJSkYiIiIiqxYSDj1q6W/2yeueuXHP8F8HB1gaPviIkBNi501qdoXEZvtSMXExcKj5/d86IJN7o+BgZT8InLNG4RJ/JBPz+u29PZdLpgG+/lbIE5qtrDmPNoXNC4YzvG4tBSdFCY1DtM+h1+GvfeKExHv1sn/bkHwDExwNvvikUQ62bNcvp+eP7gzk4V6g+ecApFURE5E4Bng6AtPk1X90a2wBwsaiaJ5EJCdYb+Wo6X3sNi8W6pKdeW77MbFEw7ZtDwmEMTjJiUBKnUviannGRiKgfiMvF2huaKgD+viINb92frC7hZJvKdOECcO4ccOmS9fvnzgH/+7+a46lVkZHWhrCCTSJl9G1474GuGNKZyQZfNeaWVnjtv0c059/MFgWPL0nTvjqJogAffKDtzT3l2DHrA4KUlErfNlsUTP9W/eeaMTwE3WN9rMqDiIh8CiscfJSWpTG/+/ms878MCrI+7fEFJpPQFJBdJ/Nw+ZrY6hnhIQbMHdVVaAzyDINeh9nDOwmP8+3P59B79ib1T1htU5kGDQL+8hfr16RJvjHNolEjYNcu4WSD2aLg+VUZQmPc2yWKyQYfFxSgx19vjRUeZ3rVHkWuMpmAM2eE37/WPf/8dVUOe7LyUVjifOUKZ2YMSWCVHhERuRUTDj5Ky9KYh84WwFTuZK60rdTbFzRqZK3G0MBsUfDuxl+EQ3hjJOeM+zJb47rGoUFC45wrKMWEJWliZd0A8MMPvjHNYts2oHVr4WH2ZOUj/6pY74c3RjLh5w+mD0rA+L5iSYdLxWXYdUrD0sbBwdamyWvWAEuWAIsWAWHqVoDyiLNnr+udcr5QfXXi/NEaK0OIiIhUYMLBRwUF6PFYb/UXaf/edsrxXwQHAz/9pHmaQq3avVvTE9bUjFzcNGsD9v56RfNbG3TQXr5LXiUl0Yhd0+9Ao3raG9cB1ukVmp+wAtZEw3PPCcVQa2LFn0YDwL+2ii0v2jO2MZeh9SPTByVg4m1iiazn/qNxmlxMzB/VRg8/bE1AGMRW0HCr995z2DtFbR+GhKhQTgkkIqJawSs2H/b8kAQ0rq+uDceCLdVc6J86Ze2P4O1+UV+hkJqRi8eXpAnN2weAcbfGMdngR4IC9Hh9hPj0ikvFZXh/83FtO5tMQI7AUpu1acIE4SFM5Rb8cFRgdQEAn43tIRwHeZfebcSauWbnFeO7tGqmDbqqRQtrFZ23+uwzoFmz677dJSZC1TBDujSXFBAREVH1mHDwcT9NG6Bq+8sl5Y6nVSiKdV6ot9PpgFdeUVV+brYoeGl1ppS3v7VtEynjkPdISTRi/uhk4XE+2npKW5WDrZHkO+8Ix+B2n38OmM1CQ/zja/XL9lV0e7smrG7wQz3jIhEWLFZZMOXLg1ibLpi8Cw4G0tKsx6Q3Vvz9+qvDpWif/0+6qmEMvtAzhoiI/IIXfpqSGvWCDAgxqLtwWLw96/pvmkzAb79JisqNFAU4fdrhBZcze7LykXtFfPWNRvUD0TMuUngc8j6DkowYkSzWgLDYZMaukxrmkQNA8+bAF18IvX+tUBRg1SrNu69Nz8G3B8VuCP/aV7yHBHkfg16HOX/uLDzOxKUHxHuqxMRYmyh7Y8XfDz9cN53CbFHwH5XH1ZnLTpbJJiIikowJBz/QK17dTfDyfQ4SC7bmWd9+K9yBXiqDAdixA9i//48vB/NXq6OlmZYjrw/vxEaRfuz14eI3O5/vcpDMc4WvJPwAYPp0TQ0uUzNyMXHpAaG35hJ+/k1WtdHM7w5r76li4y0VAJ07AyNGAC+9BOzZY13Cuoodxy9C7T9Xy0pXREREWqhrAEBeqVmEuguH3/KvwWxRrr95jomxPmmNiwOOHJEYoYDGja1LCAokQbIvFguHwW7e/i8oQI/BSUZ8n6796Wjq4fNYm56rvhmbbVrFgAHAcY29IGrLqVNAaamqlWLMFgUzvzss9LY6cAm/umBQkhGt1tVDdp72J/DnCkqxJysfvVoLVKTt2qV9X5kKC4Gvvqo2AfLOBvV9jbSsdEVERKQFKxz8QHILdQ2uyi0K9mTlO/5LkwnI01gWLtt771nn0gokG8wWBR8JdsSfcntrdvOuI+aO6ip8Upy4VOMymU2bAgUFgu9eCxo1Uv30d09WPs4VlGp+S70O+JCrw9QZD3RvITzGuSsCUwa8qadRdrY1weeE2aIg7Yy680aPVo3YB4WIiGoNP3H8gDGinup9zl5y8tS/4rrkd98tGJmgxYsdduNWY+ryAyg2aW9yZ9ABUwe2E4qBfIdBr8P/9GopPM40Lctk2qoc9u0DOnQQjkGaGTMqT2nSkAT836/EplIMTopmsqEOeaR3nPAY248LrISyfr31OPQGNST3nD48qMaoHuLnOCIiIlcx4eAHusc2Rr1AdT/KxT+dcv6XMTFASgqwZYtgZILOnlXVHLIqU7lFqDweAP755y4s4a5j7pJwY3u5uAzzNh1Tv2NMDJCY6D1VRjodsHYt0LWrdWpTcrJ12pUKqw6cxdnL2qsbAODPN3EJv7okKECP8X1jhcb45kCOtkojb6puAIDQ0GqTDloqOaLCXJ8ORUREJIoJBz9g0OvQt4265RozcouqfwJbVARc82AX63feUd0csqpnvj4oFEJEvQDcmyxWYUG+p3tsYxjDxS/I5246oe2Gp2ID14YNheMQomFVmIrMFgVPfnlQKITQYANuib9BaAzyPdMHJQgnHWasylBfaeRtDVxbtQKCgpz+9Qc/nFA1XOPQIDZeJSKiWsWEg59o07SB6n22Hjnv/C/DwoCdO4G33gLqqZ+yIWzJEqHpFNZlwsSqG2YM6Si0P/kmg16HGUOu7wSvheZu+TExwLBhQEaGw670taJdO2viQyDxN3fjMdXd86t668+dWWVUR00flICMmXdp3v/3QhPe36yyCast4ff990BsLKD38GVSerp1iocD10xmnLhwVdVws4Yl8ngiIqJaxYSDn+gVp/4J4AurM6rfoEcP4KmngKNHgUWLgPBwjdFpIDidYsT8n4RDiAr3QKKFvEJKohELHkwWvjC3dcvXrGlTz02vOHoUuHhR9RQKG7NFwbzN6p6+VvW3O9qwd0Md1yAkAIM7af8deGfjcfWVRjExQEAAkJUFWCya31sKvR544QWHS9GO+2yvqqHCQwxsgExERLWOCQc/0bN1JBoEqVvl9MzlEteevsbEAFFRwJUrGqNToU0b4aeqYxfvxcEzYrFG1A9k2Wkdl5JoxGePdhce51xBifadg4OBPXuAJuqmTEmh0zm90XHFnz8US/qFBurxxB1thMYg/zD3ga4IErhaeXLFAXWVRopi/d33dHUDYE14OJjWZLYo2H5CXTIytkmozMiIiIhc4gWfpiSDQa/DnJFJqvebsjSt5o1qs4lWYaG1aZ7Gp6ovrT6ETb9UM1XERY/cEsuyU0LPuEjhBmsXrggkHABrsuGCQMd9rQT6N3yz7zTSTosl/UZ1b8FjkABYP9/mjU7WvP+1MgVTl6lYKcXWx8HT1Q2RkdapjQ4S8LtOqq98ClX5UIKIiEgGJhz8yKAkI1o3qa9qnzUZ57A2Paf6jUwm4MwZgchcEBBgvagSqGx4fW0mFv0k3uwron4gJvePFx6HfJ9Br8PMoWI9FF5L/UVb80gbgalFmjVq5PRGpybjPtuLv3+dLhzCgIQo4THIf6QkGjG8i/bpAN8fyq35s87GtkTt/v3W46BRI83vq0n79tZKv4MHgZ49HSbgn/v2kOph45qo7/VEREQkigkHPzNzcKLqfZ779lD15aYVL76++kogumqUlwOpqZorG0zlFvxra5aUUGYP78Qnq2Rn6+cQIPA78fiSNO1Jh127NL+vKno9sGOH9ThPT3d6o1OdV9dkYkOmeIWRMTyEU5roOre2ayq0/8SlKqZWxMRYl4Ht2dM6ralxLf4+/v57tZV+10xmZOcVqx722UEeakBLRER1GhMOfuaWNuqbR14qLq+5sZ3t4mvECGv3eneYNUtTCavZouDl1Ych2AwfEfUDseDBZDapo+ukJBrx6SNi/RyeWnEApnKVv9+1OZfcYgEKCqzHuYbEn6ncgo+3iSf9dABmDElg0o+uIzq9CQBGaOktEh9vrTbYvx9Ys8a6itJTTwnH4lRERLVLYc74Tn11Q7OIENQLMggERUREpA0TDn7GoNfhhtBA1fudu3LNtQ1NJiBfoOt+dUpLrT0cVEjNyEWfNzZjyW6xqRQ3t4zA/ucHMtlATvVsHYlQgc51xWUKery2QV2lQ23OJa+mG74rnl15SDjppwPwIZN+5ET32MaICtM25c7m4OkruGYyq9/RlnQfNAgYPRrYtg0wuOkG/swZ6+ehE9+mnVU95FMD2opEREREpBkTDn5oeLL6p5PbjrvYlC442Fp2HRGh+j1qFBkJhLj+BCs1IxcTlqQhV7QpH4C/DWjHJ6pULYNeh7F94oTGuFRcjglqplfYpjP9+KPQ+7rESTd8V5gtClYeEO/zcmjmXUw2kFPWniodhccZ/sF2sQHWr7cel2YNiQtXRERYV4lxwFRugUlD/jG6kbr+TkRERLIw4eCHbmt7o+p9Vh7IwetrM13bOD7eerElknRo1cpamrp//x9fBw+63KDObFHw0upM4SeqAFAvUIeerSMljET+rvWN4k3XFAAvrc5UN5e8Rw/3PU0Fqu2G74q5G45CzaqDjgxMuBENQthFn6qXkmjEmF4thMY48nsRHvt0j7ad1UxzuuEGYMIE4PHHgfBwx9u8917lz8H9+4G0NKfH4YMfq+/p0jDYwJ4oRETkMUw4+KGerSMRZFD/tP6jrVlYm+7ik9f4eGtjubffVv0+AIArV4A77rCWqNq+VMwb35OVL6WyAQAevy2e1Q3kkhsbis8hB4DcKyVY/FOW60kHnU5+p3ydDkhIqLEbfk1M5RbM++GkUCgDOjTBwv+5WWgMqjtSEqOFx9h45AJeXeNikr0iNdOcLl4Ehg2zfl1xsEyswQB89hnQtatLn4Wmcgv2ZF9SHfJrw5P4GUdERB7DhIMfMuh1GNJZ2wXZC6syXL8Jat4cWLZMfUM7nQ5o0aLaplg12ZB5TvO+FTWqH4jJ/dtIGYv8n4w55DavrDmCPm9sdm16RXCw9aln1SehtgZ2S5YAbdqoOxYVBcjMtN4UaVwdJjUjF4kv/lfTvjbv3NcZH48Ra8hJdUv32MYwhosn/xZuy1Lfz6Hiqk379lmTdk6mP0CvB55/HpgyxfHfm83Wsdavd+mtp339s7pYAXSJCdd8PUBERCQDEw5+qk+8+tUqACDvqqnmFStsbPNY1Ta0UxTrsl8a5ooD/z9ffL/4fHEdgNe5BCapIGsOuU3ulRLXl8y0Na2r+DVoEPCXv1hLt48fV38sCjSKTM3IxeNL0jTNJ7fp3qoR7tXQc4bqNoNehxlD5CzxmPBiqvola23HYmIikJfn/PixWIATJ6zHpjMuHoOpGblYeTBHXZwAvpnQW/U+REREMnHCrJ+KCq+ned/zhS5MVag4j9XRTY5OB3ToYC0XdfT058YbNc0VB4Cpy9JwuaRc0742UWHBmDm0IxvUkWopiUYseDAZ01YewuXiMiljTlt5CAMTorQlv2o6FqtTsVGkiuPRbFEwZflBde/lwAPdxebiU92VkmjElP7xmLf5hNA4CoDHl6RpWxLZVu1wwUnTZUUBHnkEuHzZ+RguHINmi4JpK9UvhZnULIwJdSIi8jgmHPyUrfT7XIHzpbWcuaGBCzceNc1jVRTr8pmJiZoTC46M+2wvNmSeFxqjQ1QDfD+lLy/ESLOURCMGJkRh+9ELGPPpXuHxLheX4f3NJzB1gIbpPa7MKY+MBL7/3vE0Jg3JvynL0mAqF1+qUyQxSjR1QFss3HYK18rEfxc1J/1iYqxfjqxbBxyqJlHw3nvALbfUeAy+v/m4puTmkM7NVO9DREQkm05RNC667kcKCgoQHh6OK1euICwszNPhSGMreVarVeMQ/PiPO2re8PRp5092AOtFlMa54Y58fzAHk5cfEB7n2Ky7ERTA2UQkx+trM/HR1izhcSLqB2L/8wO1JcJq8Vg0lVvQ9nmxvg2Atcrop2l3MPFHQrR+zjnyxdge6N1G23TE6yiKdXWZ/fsdJwP1euCmm4Ddu533gIC1uiFxRqqmpIo/ftb56/UaEZE/Y4WDH0tJNOKmmDDsP12gar/s/BIUlZTXvERddU92JDNbFEz7T7rwOLe3a+J3F2DkWdMHJWBPVj4OnHbQhV6Fy8Vl2HUyT9sNTy0ei8+uFD8OAWDm0I5MNpCwlEQj5o9OxsSl4kmH575Nx49P95cQFWquPHJxOtOuk3makg2tb6jPzzoiIvIK/DTycz1aa3tac/e7WyRHIub9zSdQVKqym7gDf+3bWkI0RJU9NaCdlHHGfb5PfQO7WrQ2PQdfp50VGiMkUK9tvjyRE4OSjJh3XxfhcbLzrmHikv2ur9RUnYqrWTj72ru3xulM209UU7lUjZlDEzXtR0REJBsTDn6ud+smmvY7fbkE32voiO0OqRm5eGfjMeFxIkOD0D22sYSIiCrTG+Q8qS82mV1ftaIWmS0K3l5/FBOXik9penVYIpMNJN3Q5GYY0EHb511FazPO4aZXNsg5Bh2tLFPxy4VpTot3aJuudYvGlaqIiIhkY8LBz/VsHQmDxn0nLz/g8Rsfs0XBZAmlsgDwyrBElnCTW1wsUt+ctTovrc6U85RVgtSMXHSauU54NQCb6Eb1pYxDVNXHY7pjQIcbhce5fK3MKxJ/j326F9fK1J8HhnXSuOINERGRGzDh4OcMeh0m9dc+jWD6ykMevfEZ+eF2SGiGjz91isKgJD5VJfe4sWGI1PFyr5RgT1a+1DG1sDXkKzaJT2cCAGN4CKuMyK0+HnMzJtwWK2WsaR78/LtmMmPjEW0rMo28mcvNEhGR92DCoQ6YOqAdtD7suFRchvc2iU9nUMtUbsFTyw/ggMqGl47UD9Jj3gPJEqIicqx7bGMYw+UmHTZmnpM6nlpmi4KZ3x2WNp4OwIwhCXzySm73v3d1gIxZTrblaj3htbWZmvfNLzZJjISIiEgMEw51gEGvw+Tb4zXv/+6mE1ibXnulpa+vzUT7F/6LlZJ6SLx9Xxfe5JBbGfQ6zBiSAJm/ZZ/8lA2TjPIejfZk5eNcgZypIsbwEHzIRpFUSwx6Hd6TlGReuO2kR47DNenaP/9kV1wRERGJYMKhjpg6oK3QD3vi0tqZz/rqmsP4aGsWZFWxPtGvNW9yqFakJBrx4YPJaFQ/UNqY7Z7/b60m+2zMFgXTv/lZylgjkqOx/Zn+PA6pVg1KMqJXbKTwOEWlZvR8fWOt9nN47NO9yC8u17QvmyMTEZG3YcKhjjDodZg6oI3QGM98k+7W+ayrf87Bwm3Z0sYLNOjwtzvlLFdI5IqURCN2PzsADYK1tmqtTIE12fe6QHm1WqkZuUickYrs/GvCY4UGGTBnJCuMyDM+Hdtdyjj5V61NJNcKVB24avXPOZp7NwBsjkxERN6HCYc6pNUNoUL7X7lWjl0n8yRF8wezRcE7G47hiWXiS+5VNPf+rrzwoloXFKDHP//cWeqYH23NcuvNjtmiYOfJPLyy+jAeX5KGa2VySsjfuq8zj0HymKAAPfq3k7c85MSlB9xacXTNZBb6HBzbpxWbIxMRkddhwqEOkTGvc8epixIi+UNqRi5uemU95m46LnXc8X1jeeFFHpOSaMSCB5PRtGGQtDEnLz3glgqj1Ixc9J69CQ8s3IVPfsqWNu780ezZQJ43rq/2/kWOTFyahrkbj0s/Fl9fm4kOL6Zq3v+O9k3wwuCOEiMiIiKSgwmHOkRGJ/1Pf8qS1kDLtuTe5Wva5qo6EqAH5o/uiumDEqSNSaRFSqIRO6YPwJMD2koZzwKg16sbsHDrSfznwFnsPJknfNOzNj0Hjy9Jk9Yc0mZ4VyMTfuQV3LGCzDsbj6HbrA3Sqh1eX5uJj7Zmad6/SYNAfPKwnOkjREREsukURfHMItNepKCgAOHh4bhy5QrCwsI8HY5b2W7yRfVv3wTjbm2N7rGNNZVMm8ot6PHaBlzS2BjLkfAQA9JevIsl3OR1Xl59GP+WWD1gExUWjJlDO2qqJFibnouJS8XPBY4cm3U3ggKYzybvkJqRiwlL0uCOi5072jfBYy5+FpotCvZk5eN8YQlubBiC7rGNYSq3CFU2AMDEfq3xj5T2QmP4irp0vUZE5C+YcEDd+wBLzcjFUysOoljCPG1jeAhmDEmo9oan4kXWDaHB2J2Vh39tO4USSfPEbT5/tDtubdtE6phEMuw8mYcHFu5y2/jzR3fFoKRol7eXlXh05E+dovDBX25yy9hEWqVm5OKl1ZnIvVLilvFr+ix09P4R9QNxtaQcZYKVSl881gO94+X1qvBmde16jYjIHzDhgLr5AfbNvtP4+9fp0sYb27sVBiRE4aaWjbD/10v2JziXrpbilTVH3HaRZxMSqMfhl1JY3UBeyWxR0Hv2JulTF2x0AOY90BVDOkc7fIpq0OtgKrfg853ZyM4rxsq007hqkpvwA4CgAB2OvHw3j0PySmaLgn9vz8Kra49IH9v2G//hg5V7l5gtCt7bdBzvSu5TZNMgOAA/z7izzhxzdfF6jYjI1wV4OgDyjMvXyqSO98lP2fjkp2zodIAnUlh/6d6izlxwke8x6HWYObSj26oKFABPLDuAxTuycPZSCc4V/JHgM4aHILFZGDYdOQ83rmoLAJg3iivDkPcy6HV4tE8sPvjxBC4Xy/0MtB1a//vVz6gXYMDlkjJkXbiKf209KaWa0Jk5I5J4zBERkVdjwqGOatwg2C3jeqpeZkBClGfemMhFKYlGzB/dFROXyl3+taL9v16+7nu5V0rcXmEEAE8OaMtVKcjrGfQ6zB7eyW3Jv6JSM8Ys3uuWsaviakxEROQL2NWrjooKk9u125OM4daycSJvNygpGvNHJ3s6DOmiwoIxub/c5QeJ3MWW/PNlicYGXI2JiIh8AhMOdZQ7lgrzlBlDElhSSj5jUJLRb5IOuv//mjm0I49B8imDkqIxuV9rT4eh2ZjecZ4OgYiIyCVMONRRBr0OM4YkwJdvESLqB2JBlQZdRL5gUJIR74/q4ukwhEWFh1zXJI/IVzx5ZzsE+miirHmj+p4OgYiIyCVMONRhKYlGfPhgsk9OrxiZ3Az7nx/IGx3yWYO7NMP4vrGeDkOTPvGRWDauJ7Y/05/HIPksg16Hh3q19HQYqkWGBnEaIRER+QwmHOq4lEQjfprWH8kx4Z4ORZVb2zZhCTf5vOmDEjB/dDKCA3zrVHx7uxvRq3Ukj0HyeQN9sOHwK8MSeewREZHP8K2rXHILg16Hp1M6eDoMVW5s6HtVGUSODEoy4t9jbvZ0GKo81KuVp0MgksLX+hlxZQoiIvI1TDgQAN+56NKBq1KQ/+nZOtJnpjYNTjIiyMcqMoicqdjPyJtrBgL0wPzRXbkyBRER+RxeNRKAPy66vJntYpCrUpC/Meh1mDnUu48/AKgfZMDcUb69nCBRVfZ+Rl6cdD80MwWDkqI9HQYREZFqTDiQXUqiEQseTEb9IO/8tWBHfPJntuMvNMjg6VCcevu+zkz2kV9KSTRi+zP9sWxcT0y+3buWyxx3ayvU8+LzAhERUXUCPB0AeZeURCMGJkThzwt2IO23yx6NZdytsejfvinOF5bgxobWaRS82SF/Zjv+dpy4iBmrDuFU3jVPhwTAOo1pxpAEJvvIrxn0OvRqHYnzhSWeDsWuW8sIPPenjp4Og4iISDMmHOg6Br0OKyf2xivfH8Yn27M98v7vjerKxlhUJxn0Otzatgk2P90fYxfvwaZfLngsltvbNcFf+7Zmso/qFG9pShwWYsCK8bd4OgwiIiIhTDiQUy8M7oibWjTC9JXpuFJirpX3bFTPgH0v3MWbGyIAnzzc3SNJh+AAHQ6+eBfLuKlOsjVRPnelBIoH45gzklOYiIjI93nnZH3yGoOSopH24l0YXEvVBrufu5MXWEQVfPJwd/Rre0OtvufcUV2ZbKA6q2ITZU99Gr0/qgunMBERkV9gwoFqZNDr8P7oZIzvG+vW9xnfN5bL7RE5MP62+Fp5n3qBeixgY1YipytX1EY+fNytsRjcpZn734iIiKgW6BRF8WTFoFcoKChAeHg4rly5grCwME+H49VM5RZM+yYd/zlwVlqpqQ7AX/vGcn1xIifMFgV93tjs1hLvJ/q3xt8GtGOFEVEFZouCPVn59ubFl66aMGlpmluOQ73OmmzgZ6FzvF4jIvI9TDiAH2BamMot6PrKelwt1dbbIVAP9IqLxG3tbsRDvVqxsoGoBqkZuZiwJA0ApN7sRIUFY+bQjqxqIHJRakYuXlqdidwrclazuK3tDejbpgk/C13A6zUiIt/DhAP4AaaV7QZI7S/QkwPaYnL/eD5JJVJJxo3OmJ4t0SKyPhqHBiEqvB5XoCDSoGrlw00tG2H+DyfwwY8nUGZ27VORy82qx+s1IiLfw4QD+AEmwtENUOPQINwS1xjbT+Th8rUy+/d5cUUkruKNTvbFYry78ZhLST8ef0TuZ7YoeG/TcSzcfqpSBWDj0EAM6xyN5o2Y7BPB6zUiIt/DhAP4ASaq6pMe20WUs+8TkTzVVT1EhgZhWJdoDEyI4vFHVIv4+ecevF4jIvI9TDiAH2BE5NtsNzfnCkqQX1TKJ6hE5Jd4vUZE5HsCPB0AERGJMeh16NU60tNhEBERERFVwnbIRERERERERCQdEw5EREREREREJB0TDkREREREREQkHRMORERERERERCQdEw5EREREREREJB0TDkREREREREQkHRMORERERERERCQdEw5EREREREREJB0TDkREREREREQkHRMORERERERERCQdEw5EREREREREJB0TDkREREREREQkHRMORERERERERCRdgKcD8AaKogAACgoKPBwJERERETliu06zXbcREZH3Y8IBQGFhIQAgJibGw5EQERERUXUKCwsRHh7u6TCIiMgFOoVpYlgsFuTk5KBhw4bQ6XRuf7+CggLExMTg9OnTCAsLc/v7ke/j7wypxd8ZUou/M6RWbf/OKIqCwsJCREdHQ6/nrGAiIl/ACgcAer0ezZs3r/X3DQsL40UdqcLfGVKLvzOkFn9nSK3a/J1hZQMRkW9hepiIiIiIiIiIpGPCgYiIiIiIiIikY8LBA4KDgzFjxgwEBwd7OhTyEfydIbX4O0Nq8XeG1OLvDBER1YRNI4mIiIiIiIhIOlY4EBEREREREZF0TDgQERERERERkXRMOBARERERERGRdEw4EBEREREREZF0TDjU4OzZs3jjjTfQu3dvNGvWDMHBwWjWrBl69+6NN954A2fPnnV7DIqiYO3atXjooYfQvn17hIWFISwsDO3bt8dDDz2EtWvXgr0/Pa+0tBSbN2/Giy++iMGDByMuLg4NGzZEUFAQmjRpgq5du+Lxxx/H+vXr3fbzevjhh6HT6VR/LViwwC3xkHPZ2dmaflZRUVFui4nnGu+l5Xel4lerVq2kxcLzjGcVFhZi69atePfdd/E///M/6NixIwICAqT+rL3h2gfgOYmIyC8o5NSHH36ohIaGKgCcfjVo0EBZsGCB22L49ddfldtvv73aGAAo/fv3V3799Ve3xUHOnTt3Thk1apTSsGHDGn9Otq+OHTsqu3btkh7LmDFjXI6h4teHH34oPRaqXlZWlqafVdOmTd0SD8813k3L70rFr5tuuklaLDzPeE7btm0VnU5X7f9zy5Ythd7DG659FIXnJCIifxHgKAlBwMsvv4wZM2ZU+l6bNm0QHR2NM2fO4OTJkwCAoqIiPP7447hw4QKef/55qTHk5OSgT58+OH36tP17DRs2REJCAhRFwZEjR1BYWAgA2Lx5M2699Vbs2rULRqNRahxUvdOnT2P58uXXfd9oNKJ58+Zo2LAhzp07h19++QUWiwUAcPjwYfTp0wcrVqzA8OHD3RJXdHQ0OnXq5NK2LVq0cEsM5Lq+ffuiXr16NW7XuHFj6e/Nc433u+uuu1Rtn52djaNHj9pfP/jgg7JDAsDzTG07duyYW8f3hmsfgOckIiK/4uGEh1f69ttvK2XPExISlP3791faZu/evUqHDh0qbbdq1SppMZjNZqVbt272sXU6nfLSSy8pRUVF9m0KCwuVGTNmVHra0a1bN8VsNkuLg2q2d+9e+/9/z549lQULFihZWVnXbZebm6tMnjy50s8rKChI+eWXX6TFUvHJ45gxY6SNS/JVrXBw9DtTG3iu8U9Dhw6tdJ65ePGitLF5nvEc2/97aGiocssttyhPPPGEsmjRIiUlJUW4wsEbrn0UheckIiJ/w4RDFSaTSYmPj7d/gDVv3lzJz893uG1eXp7SrFkz+7Zt2rRRysrKpMTx73//u9IH+pw5c5xuO3v27ErbLl68WEoM5Jr9+/crw4YNu+7CzJl58+ZV+nmNGDFCWiy8EfAd3pJw4LnG/+Tm5ioBAQH2n9N9990ndXyeZzxnyZIlSmZm5nU31hV/JloSDt5y7aMoPCcREfkbJhyq+Oyzzyp9eH355ZfVbr9ixYpK23/++edS4oiLi7OPmZiYWG3W3mw2K4mJifbtW7duLSUGcp/u3bvbf14hISHK1atXpYzLGwHf4S0JB55r/M8bb7xR6Xdr3bp1Usfnecb7iCYcvOXaR1F4TiIi8jdcpaKKL7/80v7n6Oho3HvvvdVuP3z48EpzBr/66ivhGPbv349Tp07ZX0+cOBF6vfMflV6vx4QJE+yvT548iQMHDgjHQe4zbNgw+59LSkqQnZ3tuWCozuK5xj/9+9//tv+5RYsWGDBggAejIV/gDdc+AM9JRET+iAmHCq5du4YNGzbYX6ekpCAgoPq+mgEBAUhJSbG/Xr9+PUpKSoTi+O677yq9Hjx4cI37VN1m1apVQjGQe1Vt/FdQUOChSKgu47nG/2zfvr1Ss8hHHnmk2hs2Im+59gF4TiIi8ke8CqngyJEjKC0ttb/u3bu3S/tV3K6kpARHjhwRiqNidj4mJgYxMTE17tOiRQs0b97c4RjkfapWNNx4442eCYTqNJ5r/E/F6gadTodHHnnEg9GQL/CWax+A5yQiIn/EhEMFhw8frvS6TZs2Lu1XdbvMzExpcbgaQ9VtRWMg91EUBV9//bX9tdFoRGxsrPT32bt3L+6++240a9YMwcHBCAsLQ2xsLIYOHYp//vOf+P3336W/J2n3zDPPICkpCREREQgKCkLTpk2RnJyMyZMnY926dVAURfp78lzjXwoLCyuVxg8YMAAtW7Z063vyPOP7vOXap2osPCcREfkHJhwqqPrU2dU1w6te0GVlZQnF8euvv6qOoWocojGQ+yxdutS+ljkA/OUvf4FOp5P+PpmZmUhNTUVOTg5MJhMKCwuRnZ2N1atX4+mnn0bLli3x1FNPVXqyRZ7z5Zdf4tChQ7hy5QrKyspw/vx5HDhwAB988AFSUlLQqVMn7Ny5U+p78lzjX1asWIGrV6/aX48dO9bt78nzjO/zlmsfgOckIiJ/xIRDBVXn0UdERLi0X3h4eKXXhYWFmmO4evUqzGaz6hiqxmE2m3Ht2jXNcZB7nDlzBlOnTrW/joiIwPTp093yXgEBAWjfvj369u2Lfv36oWPHjjAYDPa/Ly0txTvvvINevXrh0qVLbomBXBcZGYnu3bvjjjvuQI8ePXDDDTdU+vvDhw+jb9+++OSTT6S8H881/qfidIrGjRvjnnvucft78jzj+7zh2gfgOYmIyF8x4VBBUVFRpdf16tVzab+q24l86GqNQXYcJF9xcTGGDx+OvLw8+/c++uij6xpIiggLC8P48eOxfv16FBUV4ciRI9iyZQt++OEHZGRk4NKlS5g/fz6aNm1q3+fAgQO45557YLFYpMVBNdPpdOjWrRs++OADnDp1ChcvXsTu3buxceNG7Nq1CxcuXMC+ffswcuRI+z7l5eUYP3481q1bJ/z+PNf4lyNHjlSqgHnwwQcRHBzslvfieca/eMO1j0gc7oiFiIjkYcKhgrKyskqva+rS7Gy7quPURgyy4yC5ysvLMWrUKOzdu9f+vUmTJuG+++6T+j7z5s3DggULMHDgQIc3Gw0bNsSECRPw888/IyEhwf79rVu34rPPPpMaC1WvZcuW2Lt3LyZOnOi0h8dNN92Er776CvPmzbN/z2w2Y/LkycLHN881/qVidQPg3ukUPM/4F2+49hGJwx2xEBGRPEw4VBAaGlrptatLPFXdruo4tRGD7DhIHovFgoceegirV6+2f+++++7D3LlzPRZT06ZNsWrVKgQFBdm/99Zbb3ksHqreE088gUcffdT++sSJE9ctH6cWzzX+o6ysrNKNfLdu3ZCUlOTBiKx4nvEN3nDtIxKHO2IhIiJ5mHCooEGDBpVeFxcXu7Rf1e0aNmxY6zHIjoPksFgsePjhh7F8+XL790aMGIEvvvii0jxnT4iPj8f9999vf52RkYEzZ854MCKqznPPPVfp9X//+1+h8Xiu8R/ff/89zp8/b3/92GOPeTCaynie8X7ecO0jEoc7YiEiInmYcKigSZMmlV7n5ua6tF/V7ao2e1MjMDCwUvMjV2Ooum1ERITHb2jrOovFgrFjx+Lzzz+3f+/ee+/F8uXLVZWKulP//v0rvf7ll188FAnVJC4urlIndtGfFc81/qPidIr69evjgQce8GA01+N5xrt5w7UPwHMSEZG/YsKhgvbt21d6XXF5pupU3a5Dhw7S4nA1hqrbisZAYiwWCx577DEsXrzY/r177rkHK1as8JpkAwAYjcZKry9evOihSMgVFX9eMn5WPNf4vpycnErVLiNHjkRYWJgHI7oezzPezVuufarGwnMSEZF/YMKhgo4dO1Z6nZaW5tJ+Vber2CRLNI7Dhw/DZDLVuE9paSkOHz4sLQbSzpZsWLRokf1799xzD7788ksEBgZ6MLLrVS1DVdMVnGpfxZ+XjJ8VzzW+79NPP620lKA7m0VqxfOMd/OWa5+qsfCcRETkH5hwqCAmJgatW7e2v96yZYtL+1XcLj4+Hs2bNxeKo1+/fvY/l5SUYPfu3TXus3v3bpSWltpf33777UIxkDa+lGwArPOpK4qKivJQJFST0tJSnDhxwv5axs+K5xrfV/Fc06ZNG/Tt29eD0TjG84x385ZrH4DnJCIif8SEQxXDhw+3//nHH3/Eb7/9Vu32v/32W6UP3Yr7azVkyJBKN6euLCNWcZugoCAMHjxYOA5Sx1Gy4d577/XaZIPZbMayZcvsr0NDQ9G1a1cPRkTVWblyZaUnxX369BEek+ca37Z161YcP37c/rriSibegucZ3+AN1z4Az0lERP6ICYcqHnnkEXuzIYvFgldeeaXa7V9++WVYLBYAgMFgwCOPPCIcQ0REBEaOHGl//cUXX1R6slnV8ePHsXTpUvvrkSNHVmq8RO6nKArGjRtXKdkwfPhwrFixwiuTDQDw5ptv4siRI/bXQ4cOrbR8HXmP33//HdOmTbO/1uv1Ui7wea7xbRWbRRoMBowZM8aD0TjG84xv8IZrH4DnJCIiv6TQdR599FEFgP1r4cKFDrdbsGBBpe3Gjh3rdMysrKxK244ZM6baGE6cOKEEBgbat09OTlbOnz9/3Xbnzp1TunTpYt8uKChIOXnypKp/L4mxWCzKuHHjKv18R44cqZSVlQmPreb3ZtKkScrnn3+ulJSUVDumyWRSZs6cqeh0Ovu4gYGByvHjx4XjJdfs2LFDGT9+vPLLL7/UuG16erqSkJBQ6ffg4Ycfdro9zzV1w5UrV5T69evbfx5DhgzRPBbPM75vzJgx9v/nli1bahrDHdc+isJzEhFRXec97fK9yBtvvIEtW7bg5MmTAIBx48Zh9erVGDVqFKKjo3H27FksW7YM33//vX2f+Ph4zJ49W1oMrVu3xpw5c/Dkk08CsDZn6ty5MyZOnIibb74ZiqJg7969mD9/Ps6dO2ffb86cOYiLi5MWB9Xsq6++wsKFC+2vdTodLl26pKqs8+9//zsGDhwoFEdGRgY++OADTJw4ESkpKbjpppvQrl07REREQK/X4/z589izZw+WLVuGM2fOVIr3448/Rnx8vND7k+tKS0vx0Ucf4aOPPkLnzp3Rv39/JCUlISoqCg0bNkRRURFOnDiBdevWYc2aNfYniQDQtWtXzJ07V1osPNf4puXLl1eaYlNbzSJ5nvGsWbNmYdasWdd9v6yszP7nX3/9FSEhIddt89BDD1X6rKrKG659AJ6TiIj8jocTHl7r2LFjSmxsbKWsvLOv2NjYGp/aqM3w2zz77LOVnhA5+9LpdMrzzz8v4V9Oai1atMil35PqvhYtWuRwbDW/N7fddpvq942IiFCWL1/unv8YcuqHH37Q9HsyZMgQ5cKFC9WOzXNN3dCjRw/7z6Rp06ZCFVU8z/iOGTNmaP6cceVcIPvaR1F4TiIiquvYw8GJNm3aID09HVOmTHG6pnl4eDimTJmC9PR0tz21efXVV7FhwwZ069bN6TbdunXDxo0ba5xzSf5t5MiR6NGjh0s9I6KiojB9+nRkZmbi/vvvr4XoqKJWrVrh/vvvh9ForHFbvV6PgQMHYtWqVfjuu+9www03uCUmnmt8x+HDhyt17x8zZgwCAmqnYJHnGf/mLdc+AM9JRET+QqcoiuLpILxdSUkJtmzZguzsbOTl5SEyMhKtWrVCv379EBwcXGtxHD9+HHv37kVubi4AwGg04uabb0abNm1qLQbyfiaTCYcPH8bJkyeRm5uLoqIiWCwWhIeHo0mTJkhOTubvjBfJyclBZmYmfvvtN+Tn5+PatWuoV68eIiIiEB8fj27duqFBgwa1GhPPNVQTnmf8n7dc+wA8JxER+TImHIiIiIiIiIhIOk6pICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAi8hL9+vWDTqeDTqfDzJkzPR0OEREREZEQJhyIiFyUnZ1tTwjI/srOzvb0P4+IiIiISComHIiIiIiIiIhIugBPB0BE5Cvq1auHu+66q8bt9uzZg0uXLgEAQkJCcNttt7k0NhERERGRP9EpiqJ4OggiIn/Sr18/bNmyBQDQsmVLTpcgIiIiojqJUyqIiIiIiIiISDomHIiIiIiIiIhIOiYciIi8hKvLYrZq1cq+3eLFiwEAZrMZX3/9NYYNG4a4uDiEhIQgIiICt956KxYuXAiz2XzdOIWFhXjrrbfQp08fNGrUCEFBQYiOjsaIESOwadMmTf+GAwcOYPr06ejevTuio6MRHByMyMhIJCUlYerUqdi7d6+mcYmIiIjI97BpJBGRj8vJycHo0aPtfSNsSktLsX37dmzfvh0rVqzA6tWr7c0pt27dilGjRiE3N7fSPrm5uVi5ciVWrlyJp556Cm+99ZZLMZw/fx6TJk3C119/fd3f5efnIz8/H4cOHcK8efPwwAMPYOHChQgNDdX4LyYiIiIiX8CEAxGRDysqKsKdd96Jw4cPAwBiY2PRsmVLFBcX4+DBgzCZTACATZs24aGHHsLXX3+Nbdu24a677kJJSQl0Oh06duyIG2+8EefPn0dGRoZ97LfffhtxcXGYNGlStTEcPXoUKSkplZpjBgYGIiEhAZGRkSgoKMChQ4dQWloKAFi2bBmOHj2KH3/8EQ0bNpT8P0JERERE3oJTKoiIfNjMmTNx+PBh3HLLLUhLS8OpU6fwww8/YPfu3cjJycGwYcPs237zzTdYt24d7r//fpSUlODRRx/F2bNncejQIWzatAmHDh3CoUOH0LZtW/s+zz77LIqKipy+f2FhIYYMGWJPNkREROCDDz7ApUuXcPDgQWzatAl79+5Ffn4+5syZg6CgIABAWloaJkyY4J7/FCIiIiLyCkw4EBH5sLy8PNx2223YvHkzunbtWunvIiMj8eWXXyIuLs7+vaFDhyI3NxfTpk3DJ598AqPRWGmfxMREfPvttzAYDACAgoICrFy50un7/+Mf/8Dx48cBAEajEfv378fEiROvmy5Rv359PP3001i1ahX0eutHzxdffMGeDkRERER+jAkHIiIfZjAYsGjRIgQHBzv8+6CgIIwdO9b+2mQyoW3btnjllVecjtmhQwfcfvvt9tfbt293uN25c+ewaNEi++vFixdXSm44kpKSgocfftj++r333qt2eyIiIiLyXUw4EBH5sIEDByI2NrbabXr27Fnp9aOPPoqAgOpb+FTcJzMz0+E2y5cvt/dl6NSpE+68805XQsaYMWPsf9a6GgYREREReT8mHIiIfFivXr1q3CYqKkr1PhWnWly6dMnhNhVXxRg4cGCNY9p07tzZ/uecnBzk5OS4vC8RERER+Q6uUkFE5MOqJhMcqV+/vtA+xcXFDrdJT0+3/3nNmjX2lTLUunDhAqKjozXtS0RERETeiwkHIiIfZlv1wZ37KIri8Pt5eXn2Px89ehRHjx5VHQsAXLlyRdN+REREROTdOKWCiIg0uXr1qpRxLBaLlHGIiIiIyLsw4UBERJpERETY/zxnzhwoiqLpq1+/fh77NxARERGR+zDhQEREmlTsBfH77797MBIiIiIi8kZMOBARkSa33HKL/c87d+70YCRERERE5I2YcCAiIk3uvvtu+5937tyJI0eOeDAaIiIiIvI2TDgQEZEmQ4cORbt27QBYV7IYP348ysrKPBwVEREREXkLJhyIiEgTvV6Pd955BzqdDgCwbds2pKSk4OzZszXue+TIEUyePBlvvvmmu8MkIiIiIg8J8HQARETku+6++2689tprmD59OgBg8+bNiIuLw4gRI3D77bejZcuWqF+/PgoKCpCTk4ODBw9i8+bN9ukXM2bM8GT4RERERORGTDgQEZGQadOmoWnTppg4cSJKSkpgMpmwbNkyLFu2zNOhEREREZEHcUoFEREJe+SRR3D06FFMmjQJ4eHh1W7boEED/OlPf8Knn36Kp59+upYiJCIiIqLaplMURfF0EERE5D/MZjPS0tKQmZmJvLw8XLt2DaGhoYiKikL79u3RsWNHBAYGejpMIiIiInIzJhyIiIiIiIiISDpOqSAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLpmHAgIiIiIiIiIumYcCAiIiIiIiIi6ZhwICIiIiIiIiLp/g8MTi9WwYc80wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -586,10 +586,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.508995Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.508628Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.613929Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.613293Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.341441Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.340994Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.452544Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.451769Z"
     }
    },
    "outputs": [
@@ -626,10 +626,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.616156Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.615858Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.622232Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.621533Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.454870Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.454644Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.461446Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.460759Z"
     }
    },
    "outputs": [
@@ -643,9 +643,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -683,10 +683,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.624102Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.623918Z",
-     "iopub.status.idle": "2024-10-25T15:23:58.011850Z",
-     "shell.execute_reply": "2024-10-25T15:23:58.011273Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.463503Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.463291Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.871330Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.870650Z"
     },
     "scrolled": true
    },
@@ -704,23 +704,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w1+w0\n",
+      "b: Outcome~Treatment+w0+w1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.99899396995031\n",
+      "Mean value: 1.9993089624430813\n",
       "\n",
-      "Causal Estimate is 1.99899396995031\n"
+      "Causal Estimate is 1.9993089624430813\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -755,10 +755,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:58.014049Z",
-     "iopub.status.busy": "2024-10-25T15:23:58.013514Z",
-     "iopub.status.idle": "2024-10-25T15:23:59.848366Z",
-     "shell.execute_reply": "2024-10-25T15:23:59.847444Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.873554Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.873357Z",
+     "iopub.status.idle": "2024-10-25T15:26:13.727890Z",
+     "shell.execute_reply": "2024-10-25T15:26:13.726973Z"
     }
    },
    "outputs": [
@@ -775,17 +775,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w1+w0 | \n",
+      "b: Outcome~Treatment+w0+w1 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.144759400960652\n",
-      "Effect estimates: [[1.1447594]]\n",
+      "Mean value: 1.0590662846792145\n",
+      "Effect estimates: [[1.05906628]]\n",
       "\n"
      ]
     }
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:59.851369Z",
-     "iopub.status.busy": "2024-10-25T15:23:59.850934Z",
-     "iopub.status.idle": "2024-10-25T15:28:21.399206Z",
-     "shell.execute_reply": "2024-10-25T15:28:21.398608Z"
+     "iopub.execute_input": "2024-10-25T15:26:13.731947Z",
+     "iopub.status.busy": "2024-10-25T15:26:13.731446Z",
+     "iopub.status.idle": "2024-10-25T15:30:38.881137Z",
+     "shell.execute_reply": "2024-10-25T15:30:38.880352Z"
     }
    },
    "outputs": [
@@ -837,9 +837,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:1.144759400960652\n",
-      "New effect:1.1582026466388928\n",
-      "p value:0.6599999999999999\n",
+      "Estimated effect:1.0590662846792145\n",
+      "New effect:1.0511514897778662\n",
+      "p value:0.72\n",
       "\n"
      ]
     }
@@ -854,10 +854,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:28:21.401540Z",
-     "iopub.status.busy": "2024-10-25T15:28:21.401308Z",
-     "iopub.status.idle": "2024-10-25T15:28:58.526771Z",
-     "shell.execute_reply": "2024-10-25T15:28:58.525903Z"
+     "iopub.execute_input": "2024-10-25T15:30:38.884047Z",
+     "iopub.status.busy": "2024-10-25T15:30:38.883504Z",
+     "iopub.status.idle": "2024-10-25T15:31:16.368542Z",
+     "shell.execute_reply": "2024-10-25T15:31:16.367590Z"
     }
    },
    "outputs": [
@@ -866,9 +866,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:1.144759400960652\n",
-      "New effect:-0.00012178524613168886\n",
-      "p value:0.44451697404875146\n",
+      "Estimated effect:1.0590662846792145\n",
+      "New effect:0.00011819241215427258\n",
+      "p value:0.38070788967795144\n",
       "\n"
      ]
     }
diff --git a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png
index 82afe890b01b2b55b579122b392eb510e1917c28..b0856a270e5240c14e8fd88b2712fa267639e2a7 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_17_0.png b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_17_0.png
index 901dbca17dd5461cbb52f83dac88c42215ca28f3..ef5e81bc7c942eed68e2fed7a0f6730ccaef8b34 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_19_0.png b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_19_0.png
index d11fb13d064cc8eda8f99d925460b567b7492294..033223164790f42c08909e8307d14947e1657bbc 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_counterfactual_fairness_dowhy_19_1.png b/main/.doctrees/nbsphinx/example_notebooks_counterfactual_fairness_dowhy_19_1.png
index 997bf07bf0d2c3b045e2a878f51847690da28116..a0160518b374d5db257e944b6fd72f1ff16eb940 100644
GIT binary patch
delta 2048
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_do_sampler_demo_13_0.png b/main/.doctrees/nbsphinx/example_notebooks_do_sampler_demo_13_0.png
index 0783a1633f7f0026bfe7f37e6eaad86703a5d11e..0db108769e791e8337eec01e078cb08a97ed99ea 100644
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index a75478ebcdfd1340569923694e1b4ece914bb16a..955ae2e550ae5b8b62230be95377d53d8d199a22 100644
GIT binary patch
delta 2046
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_dowhy_causal_api_10_0.png b/main/.doctrees/nbsphinx/example_notebooks_dowhy_causal_api_10_0.png
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_dowhy_causal_api_4_1.png b/main/.doctrees/nbsphinx/example_notebooks_dowhy_causal_api_4_1.png
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_dowhy_confounder_example_4_0.png b/main/.doctrees/nbsphinx/example_notebooks_dowhy_confounder_example_4_0.png
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_gcm_supply_chain_dist_change_29_0.png b/main/.doctrees/nbsphinx/example_notebooks_gcm_supply_chain_dist_change_29_0.png
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_18_0.png b/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_18_0.png
index 156a2058567be3bcedbc7db44b254a9762ecef15..e15d7b8042a845cbcbd37c551c9105890d5b5942 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_25_1.png b/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_25_1.png
index f1fca7c8c12db617aae6f86d83c33ad340926021..2b972f8e1f693507bda59f42d3ee72d547a4da28 100644
GIT binary patch
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diff --git a/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_26_1.png b/main/.doctrees/nbsphinx/example_notebooks_lalonde_pandas_api_26_1.png
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diff --git a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html
index bd7ce253c..40d519108 100644
--- a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html	
+++ b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html	
@@ -1291,7 +1291,7 @@ <h3>Calculating Expected Counts<a class="headerlink" href="#Calculating-Expected
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 588.5505$</div></div>
+$\displaystyle 588.8275$</div></div>
 </div>
 <p>We now consider the scenario when there were no booking changes and recalculate the expected count.</p>
 <div class="nbinput docutils container">
@@ -1315,7 +1315,7 @@ <h3>Calculating Expected Counts<a class="headerlink" href="#Calculating-Expected
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 572.8318$</div></div>
+$\displaystyle 572.7207$</div></div>
 </div>
 <p>In the 2nd case, we take the scenario when there were booking changes(&gt;0) and recalculate the expected count.</p>
 <div class="nbinput docutils container">
@@ -1339,7 +1339,7 @@ <h3>Calculating Expected Counts<a class="headerlink" href="#Calculating-Expected
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 665.8939$</div></div>
+$\displaystyle 665.7087$</div></div>
 </div>
 <p>There is definitely some change happening when the number of booking changes are non-zero. So it gives us a hint that <em>Booking Changes</em> may be affecting room cancellation.</p>
 <p>But is <em>Booking Changes</em> the only confounding variable? What if there were some unobserved confounders, regarding which we have no information(feature) present in our dataset. Would we still be able to make the same claims as before?</p>
@@ -1468,17 +1468,17 @@ <h3>Step-2. Identify the Causal Effect<a class="headerlink" href="#Step-2.-Ident
 Estimand name: backdoor
 Estimand expression:
             d                                                                  ↪
-──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪
+──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪
 d[different_room_assigned]                                                     ↪
 
 ↪                                                                              ↪
-↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪
+↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪
 ↪                                                                              ↪
 
 ↪
-↪ arking_spaces,booking_changes])
+↪ _list,is_repeated_guest,hotel])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)
+Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1520,20 +1520,20 @@ <h3>Step-3. Estimate the identified estimand<a class="headerlink" href="#Step-3.
 Estimand name: backdoor
 Estimand expression:
             d                                                                  ↪
-──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪
+──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪
 d[different_room_assigned]                                                     ↪
 
 ↪                                                                              ↪
-↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪
+↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪
 ↪                                                                              ↪
 
 ↪
-↪ arking_spaces,booking_changes])
+↪ _list,is_repeated_guest,hotel])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)
+Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)
 
 ## Realized estimand
-b: is_canceled~different_room_assigned+hotel+lead_time+days_in_waiting_list+total_of_special_requests+is_repeated_guest+guests+total_stay+required_car_parking_spaces+booking_changes
+b: is_canceled~different_room_assigned+total_of_special_requests+lead_time+booking_changes+guests+required_car_parking_spaces+total_stay+days_in_waiting_list+is_repeated_guest+hotel
 Target units: ate
 
 ## Estimate
@@ -1610,7 +1610,7 @@ <h4>Method-2<a class="headerlink" href="#Method-2" title="Permalink to this head
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
 Estimated effect:-0.2622285649999514
-New effect:0.05476307825276116
+New effect:0.05477212242057929
 p value:0.0
 
 </pre></div></div>
@@ -1636,8 +1636,8 @@ <h4>Method-3<a class="headerlink" href="#Method-3" title="Permalink to this head
 <div class="highlight"><pre>
 Refute: Use a subset of data
 Estimated effect:-0.2622285649999514
-New effect:-0.26207674572027684
-p value:0.8400000000000001
+New effect:-0.2622173443627082
+p value:0.92
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb
index bc24d1bd8..d64c6ce45 100644
--- a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb	
+++ b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb	
@@ -33,10 +33,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:02.230079Z",
-     "iopub.status.busy": "2024-10-25T14:27:02.229894Z",
-     "iopub.status.idle": "2024-10-25T14:27:04.402470Z",
-     "shell.execute_reply": "2024-10-25T14:27:04.401788Z"
+     "iopub.execute_input": "2024-10-25T14:27:53.724769Z",
+     "iopub.status.busy": "2024-10-25T14:27:53.724366Z",
+     "iopub.status.idle": "2024-10-25T14:27:55.916639Z",
+     "shell.execute_reply": "2024-10-25T14:27:55.915964Z"
     }
    },
    "outputs": [],
@@ -62,10 +62,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:04.404988Z",
-     "iopub.status.busy": "2024-10-25T14:27:04.404638Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.250015Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.249372Z"
+     "iopub.execute_input": "2024-10-25T14:27:55.919296Z",
+     "iopub.status.busy": "2024-10-25T14:27:55.918760Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.190465Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.189803Z"
     }
    },
    "outputs": [
@@ -300,10 +300,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.280116Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.279501Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.284147Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.283572Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.220678Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.220223Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.224807Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.224191Z"
     }
    },
    "outputs": [
@@ -350,10 +350,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.286109Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.285755Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.307632Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.306998Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.226723Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.226394Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.248688Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.248099Z"
     }
    },
    "outputs": [
@@ -404,10 +404,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.309647Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.309298Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.441718Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.441005Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.250826Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.250445Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.383867Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.383165Z"
     }
    },
    "outputs": [],
@@ -423,10 +423,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.444102Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.443907Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.467593Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.466896Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.386351Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.385935Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.411099Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.410466Z"
     }
    },
    "outputs": [],
@@ -441,10 +441,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.469952Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.469596Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.620438Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.619916Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.413428Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.413166Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.569158Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.568528Z"
     }
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    "outputs": [
@@ -781,10 +781,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.622468Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.622119Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.710762Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.710107Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.571185Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.570888Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.660158Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.659526Z"
     }
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    "outputs": [
@@ -966,10 +966,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:05.712763Z",
-     "iopub.status.busy": "2024-10-25T14:27:05.712407Z",
-     "iopub.status.idle": "2024-10-25T14:27:05.719476Z",
-     "shell.execute_reply": "2024-10-25T14:27:05.718818Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.662169Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.661835Z",
+     "iopub.status.idle": "2024-10-25T14:27:56.669639Z",
+     "shell.execute_reply": "2024-10-25T14:27:56.668965Z"
     }
    },
    "outputs": [],
@@ -991,21 +991,21 @@
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-     "iopub.status.busy": "2024-10-25T14:27:05.721379Z",
-     "iopub.status.idle": "2024-10-25T14:27:28.574024Z",
-     "shell.execute_reply": "2024-10-25T14:27:28.573385Z"
+     "iopub.execute_input": "2024-10-25T14:27:56.671964Z",
+     "iopub.status.busy": "2024-10-25T14:27:56.671447Z",
+     "iopub.status.idle": "2024-10-25T14:28:19.311987Z",
+     "shell.execute_reply": "2024-10-25T14:28:19.311389Z"
     }
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    "outputs": [
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      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 588.5505$"
+       "$\\displaystyle 588.8275$"
       ],
       "text/plain": [
-       "588.5505"
+       "588.8275"
       ]
      },
      "execution_count": 10,
@@ -1035,21 +1035,21 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:27:28.576288Z",
-     "iopub.status.busy": "2024-10-25T14:27:28.575850Z",
-     "iopub.status.idle": "2024-10-25T14:28:45.128033Z",
-     "shell.execute_reply": "2024-10-25T14:28:45.127467Z"
+     "iopub.execute_input": "2024-10-25T14:28:19.314140Z",
+     "iopub.status.busy": "2024-10-25T14:28:19.313711Z",
+     "iopub.status.idle": "2024-10-25T14:29:36.043385Z",
+     "shell.execute_reply": "2024-10-25T14:29:36.042777Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 572.8318$"
+       "$\\displaystyle 572.7207$"
       ],
       "text/plain": [
-       "572.8318"
+       "572.7207"
       ]
      },
      "execution_count": 11,
@@ -1080,21 +1080,21 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:28:45.129925Z",
-     "iopub.status.busy": "2024-10-25T14:28:45.129711Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.613277Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.612611Z"
+     "iopub.execute_input": "2024-10-25T14:29:36.045426Z",
+     "iopub.status.busy": "2024-10-25T14:29:36.045173Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.421339Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.420713Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 665.8939$"
+       "$\\displaystyle 665.7087$"
       ],
       "text/plain": [
-       "665.8939"
+       "665.7087"
       ]
      },
      "execution_count": 12,
@@ -1152,10 +1152,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.615677Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.615201Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.621512Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.621053Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.423578Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.423122Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.429351Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.428817Z"
     }
    },
    "outputs": [],
@@ -1214,10 +1214,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.623490Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.623060Z",
-     "iopub.status.idle": "2024-10-25T14:29:10.938842Z",
-     "shell.execute_reply": "2024-10-25T14:29:10.938178Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.431270Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.430903Z",
+     "iopub.status.idle": "2024-10-25T14:30:01.747821Z",
+     "shell.execute_reply": "2024-10-25T14:30:01.747158Z"
     }
    },
    "outputs": [
@@ -1275,10 +1275,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:10.941138Z",
-     "iopub.status.busy": "2024-10-25T14:29:10.940909Z",
-     "iopub.status.idle": "2024-10-25T14:29:13.065607Z",
-     "shell.execute_reply": "2024-10-25T14:29:13.065061Z"
+     "iopub.execute_input": "2024-10-25T14:30:01.750542Z",
+     "iopub.status.busy": "2024-10-25T14:30:01.750104Z",
+     "iopub.status.idle": "2024-10-25T14:30:03.877966Z",
+     "shell.execute_reply": "2024-10-25T14:30:03.877325Z"
     }
    },
    "outputs": [
@@ -1292,17 +1292,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "            d                                                                  ↪\n",
-      "──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪\n",
+      "──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪\n",
       "d[different_room_assigned]                                                     ↪\n",
       "\n",
       "↪                                                                              ↪\n",
-      "↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪\n",
+      "↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪\n",
       "↪                                                                              ↪\n",
       "\n",
       "↪                                \n",
-      "↪ arking_spaces,booking_changes])\n",
+      "↪ _list,is_repeated_guest,hotel])\n",
       "↪                                \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -1333,10 +1333,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:13.067762Z",
-     "iopub.status.busy": "2024-10-25T14:29:13.067301Z",
-     "iopub.status.idle": "2024-10-25T14:29:13.662343Z",
-     "shell.execute_reply": "2024-10-25T14:29:13.661462Z"
+     "iopub.execute_input": "2024-10-25T14:30:03.879921Z",
+     "iopub.status.busy": "2024-10-25T14:30:03.879726Z",
+     "iopub.status.idle": "2024-10-25T14:30:04.429652Z",
+     "shell.execute_reply": "2024-10-25T14:30:04.428779Z"
     }
    },
    "outputs": [
@@ -1353,20 +1353,20 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "            d                                                                  ↪\n",
-      "──────────────────────────(E[is_canceled|hotel,lead_time,days_in_waiting_list, ↪\n",
+      "──────────────────────────(E[is_canceled|total_of_special_requests,lead_time,b ↪\n",
       "d[different_room_assigned]                                                     ↪\n",
       "\n",
       "↪                                                                              ↪\n",
-      "↪ total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_p ↪\n",
+      "↪ ooking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting ↪\n",
       "↪                                                                              ↪\n",
       "\n",
       "↪                                \n",
-      "↪ arking_spaces,booking_changes])\n",
+      "↪ _list,is_repeated_guest,hotel])\n",
       "↪                                \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes,U) = P(is_canceled|different_room_assigned,hotel,lead_time,days_in_waiting_list,total_of_special_requests,is_repeated_guest,guests,total_stay,required_car_parking_spaces,booking_changes)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel,U) = P(is_canceled|different_room_assigned,total_of_special_requests,lead_time,booking_changes,guests,required_car_parking_spaces,total_stay,days_in_waiting_list,is_repeated_guest,hotel)\n",
       "\n",
       "## Realized estimand\n",
-      "b: is_canceled~different_room_assigned+hotel+lead_time+days_in_waiting_list+total_of_special_requests+is_repeated_guest+guests+total_stay+required_car_parking_spaces+booking_changes\n",
+      "b: is_canceled~different_room_assigned+total_of_special_requests+lead_time+booking_changes+guests+required_car_parking_spaces+total_stay+days_in_waiting_list+is_repeated_guest+hotel\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -1435,10 +1435,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:13.664814Z",
-     "iopub.status.busy": "2024-10-25T14:29:13.664603Z",
-     "iopub.status.idle": "2024-10-25T14:29:23.806800Z",
-     "shell.execute_reply": "2024-10-25T14:29:23.806156Z"
+     "iopub.execute_input": "2024-10-25T14:30:04.432276Z",
+     "iopub.status.busy": "2024-10-25T14:30:04.432062Z",
+     "iopub.status.idle": "2024-10-25T14:30:14.546038Z",
+     "shell.execute_reply": "2024-10-25T14:30:14.545398Z"
     }
    },
    "outputs": [
@@ -1473,10 +1473,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:23.808758Z",
-     "iopub.status.busy": "2024-10-25T14:29:23.808549Z",
-     "iopub.status.idle": "2024-10-25T14:29:34.025025Z",
-     "shell.execute_reply": "2024-10-25T14:29:34.024457Z"
+     "iopub.execute_input": "2024-10-25T14:30:14.548407Z",
+     "iopub.status.busy": "2024-10-25T14:30:14.547975Z",
+     "iopub.status.idle": "2024-10-25T14:30:25.173143Z",
+     "shell.execute_reply": "2024-10-25T14:30:25.172558Z"
     }
    },
    "outputs": [
@@ -1486,7 +1486,7 @@
      "text": [
       "Refute: Use a Placebo Treatment\n",
       "Estimated effect:-0.2622285649999514\n",
-      "New effect:0.05476307825276116\n",
+      "New effect:0.05477212242057929\n",
       "p value:0.0\n",
       "\n"
      ]
@@ -1511,10 +1511,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:34.027214Z",
-     "iopub.status.busy": "2024-10-25T14:29:34.026763Z",
-     "iopub.status.idle": "2024-10-25T14:29:43.366837Z",
-     "shell.execute_reply": "2024-10-25T14:29:43.366281Z"
+     "iopub.execute_input": "2024-10-25T14:30:25.175030Z",
+     "iopub.status.busy": "2024-10-25T14:30:25.174848Z",
+     "iopub.status.idle": "2024-10-25T14:30:34.558139Z",
+     "shell.execute_reply": "2024-10-25T14:30:34.557490Z"
     }
    },
    "outputs": [
@@ -1524,8 +1524,8 @@
      "text": [
       "Refute: Use a subset of data\n",
       "Estimated effect:-0.2622285649999514\n",
-      "New effect:-0.26207674572027684\n",
-      "p value:0.8400000000000001\n",
+      "New effect:-0.2622173443627082\n",
+      "p value:0.92\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/counterfactual_fairness_dowhy.html b/main/example_notebooks/counterfactual_fairness_dowhy.html
index 00c1d95fd..83b33b021 100644
--- a/main/example_notebooks/counterfactual_fairness_dowhy.html
+++ b/main/example_notebooks/counterfactual_fairness_dowhy.html
@@ -875,41 +875,41 @@ <h1>1. Load and Clean the Dataset<a class="headerlink" href="#1.-Load-and-Clean-
       <th>0</th>
       <td>0.0</td>
       <td>1.0</td>
-      <td>2.8</td>
-      <td>37.0</td>
-      <td>0.30</td>
+      <td>3.5</td>
+      <td>34.0</td>
+      <td>0.19</td>
     </tr>
     <tr>
       <th>1</th>
       <td>0.0</td>
       <td>0.0</td>
-      <td>3.3</td>
-      <td>32.5</td>
-      <td>0.40</td>
+      <td>2.7</td>
+      <td>31.0</td>
+      <td>-0.10</td>
     </tr>
     <tr>
       <th>2</th>
       <td>0.0</td>
-      <td>1.0</td>
-      <td>3.7</td>
-      <td>33.0</td>
-      <td>1.83</td>
+      <td>0.0</td>
+      <td>4.0</td>
+      <td>47.0</td>
+      <td>1.12</td>
     </tr>
     <tr>
       <th>3</th>
       <td>0.0</td>
-      <td>0.0</td>
-      <td>2.7</td>
-      <td>35.0</td>
-      <td>-0.03</td>
+      <td>1.0</td>
+      <td>3.4</td>
+      <td>48.0</td>
+      <td>0.27</td>
     </tr>
     <tr>
       <th>4</th>
       <td>0.0</td>
       <td>1.0</td>
-      <td>3.0</td>
-      <td>44.0</td>
-      <td>1.48</td>
+      <td>3.3</td>
+      <td>41.0</td>
+      <td>-0.38</td>
     </tr>
   </tbody>
 </table>
@@ -1008,7 +1008,7 @@ <h2>3.1 Univariate Analysis<a class="headerlink" href="#3.1-Univariate-Analysis"
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 125.98it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 216.76it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1017,7 +1017,7 @@ <h2>3.1 Univariate Analysis<a class="headerlink" href="#3.1-Univariate-Analysis"
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle -0.479681464045815$</div></div>
+$\displaystyle -0.484024452360675$</div></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">df_obs</span></code> contains the predicted values for each individual given the observed value of their protected attribute in the real world.</p>
 <div class="nbinput docutils container">
@@ -1064,46 +1064,46 @@ <h2>3.1 Univariate Analysis<a class="headerlink" href="#3.1-Univariate-Analysis"
       <th>0</th>
       <td>0.0</td>
       <td>1.0</td>
-      <td>2.8</td>
-      <td>37.0</td>
-      <td>0.30</td>
-      <td>0.019617</td>
+      <td>3.5</td>
+      <td>34.0</td>
+      <td>0.19</td>
+      <td>0.076392</td>
     </tr>
     <tr>
       <th>1</th>
       <td>0.0</td>
       <td>0.0</td>
-      <td>3.3</td>
-      <td>32.5</td>
-      <td>0.40</td>
-      <td>-0.020426</td>
+      <td>2.7</td>
+      <td>31.0</td>
+      <td>-0.10</td>
+      <td>-0.266839</td>
     </tr>
     <tr>
       <th>2</th>
       <td>0.0</td>
-      <td>1.0</td>
-      <td>3.7</td>
-      <td>33.0</td>
-      <td>1.83</td>
-      <td>0.110852</td>
+      <td>0.0</td>
+      <td>4.0</td>
+      <td>47.0</td>
+      <td>1.12</td>
+      <td>0.746310</td>
     </tr>
     <tr>
       <th>3</th>
       <td>0.0</td>
-      <td>0.0</td>
-      <td>2.7</td>
-      <td>35.0</td>
-      <td>-0.03</td>
-      <td>-0.087888</td>
+      <td>1.0</td>
+      <td>3.4</td>
+      <td>48.0</td>
+      <td>0.27</td>
+      <td>0.621925</td>
     </tr>
     <tr>
       <th>4</th>
       <td>0.0</td>
       <td>1.0</td>
-      <td>3.0</td>
-      <td>44.0</td>
-      <td>1.48</td>
-      <td>0.354123</td>
+      <td>3.3</td>
+      <td>41.0</td>
+      <td>-0.38</td>
+      <td>0.307828</td>
     </tr>
   </tbody>
 </table>
@@ -1154,46 +1154,46 @@ <h2>3.1 Univariate Analysis<a class="headerlink" href="#3.1-Univariate-Analysis"
       <th>0</th>
       <td>1.0</td>
       <td>1.0</td>
-      <td>2.390805</td>
-      <td>28.614502</td>
-      <td>-0.501187</td>
-      <td>0.096867</td>
+      <td>3.132895</td>
+      <td>26.030219</td>
+      <td>-0.791247</td>
+      <td>0.112971</td>
     </tr>
     <tr>
       <th>1</th>
       <td>1.0</td>
       <td>0.0</td>
-      <td>2.890805</td>
-      <td>25.046875</td>
-      <td>-0.554893</td>
-      <td>0.099256</td>
+      <td>2.332895</td>
+      <td>23.030219</td>
+      <td>-0.859527</td>
+      <td>0.024230</td>
     </tr>
     <tr>
       <th>2</th>
       <td>1.0</td>
-      <td>1.0</td>
-      <td>3.290805</td>
-      <td>24.614502</td>
-      <td>0.762635</td>
-      <td>0.121437</td>
+      <td>0.0</td>
+      <td>3.632895</td>
+      <td>39.030219</td>
+      <td>-0.410656</td>
+      <td>0.255002</td>
     </tr>
     <tr>
       <th>3</th>
       <td>1.0</td>
-      <td>0.0</td>
-      <td>2.290805</td>
-      <td>27.546875</td>
-      <td>-0.788582</td>
-      <td>0.081481</td>
+      <td>1.0</td>
+      <td>3.032895</td>
+      <td>40.030219</td>
+      <td>-1.120428</td>
+      <td>0.213736</td>
     </tr>
     <tr>
       <th>4</th>
       <td>1.0</td>
       <td>1.0</td>
-      <td>2.590805</td>
-      <td>35.614502</td>
-      <td>0.447159</td>
-      <td>0.168353</td>
+      <td>2.932895</td>
+      <td>33.030219</td>
+      <td>-1.533649</td>
+      <td>0.151092</td>
     </tr>
   </tbody>
 </table>
@@ -1255,7 +1255,7 @@ <h2>3.2 Intersectional Analysis<a class="headerlink" href="#3.2-Intersectional-A
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 378.38it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 300.00it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1264,7 +1264,7 @@ <h2>3.2 Intersectional Analysis<a class="headerlink" href="#3.2-Intersectional-A
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle -0.489383465264695$</div></div>
+$\displaystyle -0.469321951712711$</div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
@@ -1348,7 +1348,7 @@ <h1>Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unf
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 357.97it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00&lt;00:00, 334.07it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1357,7 +1357,7 @@ <h1>Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unf
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle -1.11022302462516 \cdot 10^{-16}$</div></div>
+$\displaystyle 1.11022302462516 \cdot 10^{-15}$</div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
diff --git a/main/example_notebooks/counterfactual_fairness_dowhy.ipynb b/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
index 1e1c7e2f7..cf1029f1b 100644
--- a/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
+++ b/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
@@ -6,10 +6,10 @@
    "id": "ef2e40dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:46.944617Z",
-     "iopub.status.busy": "2024-10-25T14:29:46.944409Z",
-     "iopub.status.idle": "2024-10-25T14:29:46.947584Z",
-     "shell.execute_reply": "2024-10-25T14:29:46.947116Z"
+     "iopub.execute_input": "2024-10-25T14:30:38.147108Z",
+     "iopub.status.busy": "2024-10-25T14:30:38.146926Z",
+     "iopub.status.idle": "2024-10-25T14:30:38.150156Z",
+     "shell.execute_reply": "2024-10-25T14:30:38.149691Z"
     }
    },
    "outputs": [],
@@ -63,10 +63,10 @@
    "id": "46026b9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:46.949450Z",
-     "iopub.status.busy": "2024-10-25T14:29:46.949136Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.493396Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.492798Z"
+     "iopub.execute_input": "2024-10-25T14:30:38.152010Z",
+     "iopub.status.busy": "2024-10-25T14:30:38.151838Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.714423Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.713788Z"
     }
    },
    "outputs": [],
@@ -296,10 +296,10 @@
    "id": "37212ae1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.495655Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.495355Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.544883Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.544247Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.717189Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.716548Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.768199Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.767583Z"
     }
    },
    "outputs": [
@@ -336,41 +336,41 @@
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>0.30</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>0.40</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>-0.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1.83</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>-0.03</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1.48</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>-0.38</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -378,11 +378,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade\n",
-       "0   0.0     1.0  2.8  37.0       0.30\n",
-       "1   0.0     0.0  3.3  32.5       0.40\n",
-       "2   0.0     1.0  3.7  33.0       1.83\n",
-       "3   0.0     0.0  2.7  35.0      -0.03\n",
-       "4   0.0     1.0  3.0  44.0       1.48"
+       "0   0.0     1.0  3.5  34.0       0.19\n",
+       "1   0.0     0.0  2.7  31.0      -0.10\n",
+       "2   0.0     0.0  4.0  47.0       1.12\n",
+       "3   0.0     1.0  3.4  48.0       0.27\n",
+       "4   0.0     1.0  3.3  41.0      -0.38"
       ]
      },
      "execution_count": 3,
@@ -466,10 +466,10 @@
    "id": "2280c2df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.546794Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.546609Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.549844Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.549368Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.770157Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.769963Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.773096Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.772631Z"
     }
    },
    "outputs": [],
@@ -506,10 +506,10 @@
    "id": "d70d0187",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.551520Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.551344Z",
-     "iopub.status.idle": "2024-10-25T14:29:48.553938Z",
-     "shell.execute_reply": "2024-10-25T14:29:48.553482Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.774953Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.774604Z",
+     "iopub.status.idle": "2024-10-25T14:30:39.777377Z",
+     "shell.execute_reply": "2024-10-25T14:30:39.776800Z"
     }
    },
    "outputs": [],
@@ -536,10 +536,10 @@
    "id": "6fdaa7be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:29:48.555548Z",
-     "iopub.status.busy": "2024-10-25T14:29:48.555374Z",
-     "iopub.status.idle": "2024-10-25T14:30:45.998017Z",
-     "shell.execute_reply": "2024-10-25T14:30:45.997472Z"
+     "iopub.execute_input": "2024-10-25T14:30:39.779130Z",
+     "iopub.status.busy": "2024-10-25T14:30:39.778851Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.652975Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.652290Z"
     }
    },
    "outputs": [
@@ -596,7 +596,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 125.98it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 216.76it/s]"
      ]
     },
     {
@@ -608,12 +608,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -0.479681464045815$"
+       "$\\displaystyle -0.484024452360675$"
       ],
       "text/plain": [
-       "-0.47968146404581513"
+       "-0.4840244523606755"
       ]
      },
      "execution_count": 6,
@@ -651,10 +651,10 @@
    "id": "79c2a233",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.000962Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.000472Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.012865Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.012322Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.655697Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.655463Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.667905Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.667352Z"
     }
    },
    "outputs": [
@@ -692,46 +692,46 @@
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.019617</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.076392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>-0.020426</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>-0.10</td>\n",
+       "      <td>-0.266839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1.83</td>\n",
-       "      <td>0.110852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>0.746310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>-0.03</td>\n",
-       "      <td>-0.087888</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.621925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1.48</td>\n",
-       "      <td>0.354123</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>-0.38</td>\n",
+       "      <td>0.307828</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -739,11 +739,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade     preds\n",
-       "0   0.0     1.0  2.8  37.0       0.30  0.019617\n",
-       "1   0.0     0.0  3.3  32.5       0.40 -0.020426\n",
-       "2   0.0     1.0  3.7  33.0       1.83  0.110852\n",
-       "3   0.0     0.0  2.7  35.0      -0.03 -0.087888\n",
-       "4   0.0     1.0  3.0  44.0       1.48  0.354123"
+       "0   0.0     1.0  3.5  34.0       0.19  0.076392\n",
+       "1   0.0     0.0  2.7  31.0      -0.10 -0.266839\n",
+       "2   0.0     0.0  4.0  47.0       1.12  0.746310\n",
+       "3   0.0     1.0  3.4  48.0       0.27  0.621925\n",
+       "4   0.0     1.0  3.3  41.0      -0.38  0.307828"
       ]
      },
      "execution_count": 7,
@@ -769,10 +769,10 @@
    "id": "56d88825",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.015893Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.014874Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.025958Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.025448Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.670921Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.670015Z",
+     "iopub.status.idle": "2024-10-25T14:31:34.681645Z",
+     "shell.execute_reply": "2024-10-25T14:31:34.681052Z"
     }
    },
    "outputs": [
@@ -810,46 +810,46 @@
        "      <th>0</th>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.390805</td>\n",
-       "      <td>28.614502</td>\n",
-       "      <td>-0.501187</td>\n",
-       "      <td>0.096867</td>\n",
+       "      <td>3.132895</td>\n",
+       "      <td>26.030219</td>\n",
+       "      <td>-0.791247</td>\n",
+       "      <td>0.112971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.890805</td>\n",
-       "      <td>25.046875</td>\n",
-       "      <td>-0.554893</td>\n",
-       "      <td>0.099256</td>\n",
+       "      <td>2.332895</td>\n",
+       "      <td>23.030219</td>\n",
+       "      <td>-0.859527</td>\n",
+       "      <td>0.024230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.290805</td>\n",
-       "      <td>24.614502</td>\n",
-       "      <td>0.762635</td>\n",
-       "      <td>0.121437</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.632895</td>\n",
+       "      <td>39.030219</td>\n",
+       "      <td>-0.410656</td>\n",
+       "      <td>0.255002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.290805</td>\n",
-       "      <td>27.546875</td>\n",
-       "      <td>-0.788582</td>\n",
-       "      <td>0.081481</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.032895</td>\n",
+       "      <td>40.030219</td>\n",
+       "      <td>-1.120428</td>\n",
+       "      <td>0.213736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.590805</td>\n",
-       "      <td>35.614502</td>\n",
-       "      <td>0.447159</td>\n",
-       "      <td>0.168353</td>\n",
+       "      <td>2.932895</td>\n",
+       "      <td>33.030219</td>\n",
+       "      <td>-1.533649</td>\n",
+       "      <td>0.151092</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -857,11 +857,11 @@
       ],
       "text/plain": [
        "   Race  Gender       GPA       LSAT  avg_grade  preds_cf\n",
-       "0   1.0     1.0  2.390805  28.614502  -0.501187  0.096867\n",
-       "1   1.0     0.0  2.890805  25.046875  -0.554893  0.099256\n",
-       "2   1.0     1.0  3.290805  24.614502   0.762635  0.121437\n",
-       "3   1.0     0.0  2.290805  27.546875  -0.788582  0.081481\n",
-       "4   1.0     1.0  2.590805  35.614502   0.447159  0.168353"
+       "0   1.0     1.0  3.132895  26.030219  -0.791247  0.112971\n",
+       "1   1.0     0.0  2.332895  23.030219  -0.859527  0.024230\n",
+       "2   1.0     0.0  3.632895  39.030219  -0.410656  0.255002\n",
+       "3   1.0     1.0  3.032895  40.030219  -1.120428  0.213736\n",
+       "4   1.0     1.0  2.932895  33.030219  -1.533649  0.151092"
       ]
      },
      "execution_count": 8,
@@ -879,16 +879,16 @@
    "id": "a1176177",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.027938Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.027500Z",
-     "iopub.status.idle": "2024-10-25T14:30:46.375239Z",
-     "shell.execute_reply": "2024-10-25T14:30:46.374574Z"
+     "iopub.execute_input": "2024-10-25T14:31:34.683558Z",
+     "iopub.status.busy": "2024-10-25T14:31:34.683368Z",
+     "iopub.status.idle": "2024-10-25T14:31:35.007955Z",
+     "shell.execute_reply": "2024-10-25T14:31:35.007259Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -928,10 +928,10 @@
    "id": "725bf447",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:30:46.377196Z",
-     "iopub.status.busy": "2024-10-25T14:30:46.376904Z",
-     "iopub.status.idle": "2024-10-25T14:31:38.758403Z",
-     "shell.execute_reply": "2024-10-25T14:31:38.757313Z"
+     "iopub.execute_input": "2024-10-25T14:31:35.010094Z",
+     "iopub.status.busy": "2024-10-25T14:31:35.009713Z",
+     "iopub.status.idle": "2024-10-25T14:32:27.107893Z",
+     "shell.execute_reply": "2024-10-25T14:32:27.106827Z"
     }
    },
    "outputs": [
@@ -988,7 +988,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 378.38it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 300.00it/s]"
      ]
     },
     {
@@ -1000,12 +1000,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -0.489383465264695$"
+       "$\\displaystyle -0.469321951712711$"
       ],
       "text/plain": [
-       "-0.48938346526469506"
+       "-0.46932195171271096"
       ]
      },
      "execution_count": 10,
@@ -1037,16 +1037,16 @@
    "id": "ad7671e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:31:38.761035Z",
-     "iopub.status.busy": "2024-10-25T14:31:38.760474Z",
-     "iopub.status.idle": "2024-10-25T14:31:39.177782Z",
-     "shell.execute_reply": "2024-10-25T14:31:39.177212Z"
+     "iopub.execute_input": "2024-10-25T14:32:27.110200Z",
+     "iopub.status.busy": "2024-10-25T14:32:27.109984Z",
+     "iopub.status.idle": "2024-10-25T14:32:27.473342Z",
+     "shell.execute_reply": "2024-10-25T14:32:27.472700Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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3wCu7tLW11a4kbNu2Dffv31cp69SpE54+fSr9nkFaqfMbGBhIMX3M0dER//zzj8oQu5cvX1YZ1Sg1HoVCobIdb9++LY2OpGn5vbyPde3aFffv38eaNWvUpr17904aZer58+dq01MP7FNvV3r27JnKdF1dXVSuXBlCCCQmJmYYg6enp8or9QqGj48PihUrhpUrV0p1hRBYvXo1ypQpozLS2sOHD/HPP/9Iy2nWrBl27dql9ipVqhRq166NXbt2wdvbG8B/V9w+Hi527969ePPmDWrUqJFh7MCH4YIfPnyochsVEVF28IoFEeWIo6MjNm/ejG7duqFSpUoqv7x98uRJbNu2TfptgmrVqqFPnz746aef8PLlS7i7u+PMmTPYsGED2rdvj6ZNm0rtDhw4EEOHDkWnTp3QokULXL58GQcPHlQ5y59TtWrVwqpVqzBr1iw4OTmhdOnSaNasGcaNG4e9e/eibdu26Nu3L2rVqoU3b97g6tWr2L59O27fvp3r5bZt2xYzZsxAv3794ObmhqtXr2LTpk0oV66cSr3evXvjl19+wZgxY3DmzBk0btwYb968weHDh/HVV1/Bx8cH+vr6qFy5MrZu3QpnZ2eUKFECVapUQZUqVdC/f38sXrwYXl5eGDBgAGJiYrB69Wq4uLhID9kDH5K3xYsXo1WrVujZsydiYmKwYsUKODk5qTyboCn5vbyP9erVC7/99huGDh2K4OBgNGzYEMnJyfjnn3/w22+/4eDBg6hduzZmzJiBY8eOoU2bNrCzs0NMTAxWrlyJsmXLSkO+tmzZEpaWlmjYsCEsLCwQHh6OH374AW3atMn2AAZplS1bFqNGjcKCBQuQmJiIOnXqYPfu3Th+/Dg2bdqkcqvXpEmTsGHDBkRHR8Pe3h62trZqzxUBwKhRo2BhYYH27dtLZd7e3nBxccGMGTNw584d1K9fHzdv3sQPP/wAKysrDBgwQKr766+/YseOHWjSpAmMjIxw+PBh/Pbbbxg4cGCWz3IQEakpsPGoiOiTduPGDTFo0CBhb28vdHV1hbGxsWjYsKFYvny5NBSmEEIkJiYKf39/4eDgIIoVKyZsbGzEpEmTVOoI8WHoywkTJoiSJUsKAwMD4eXlJW7evJnhkKRnz55VmT84OFgAEMHBwVLZo0ePRJs2bYSxsbEAoDI86+vXr8WkSZOEk5OT0NXVFSVLlhRubm5i4cKF4v3790KI/4abXbBgQbrbABkMN/vNN98IKysroa+vLxo2bChOnTqV7vCwb9++FZMnT5a2jaWlpejcubOIioqS6pw8eVLUqlVL6Orqqg09++uvv4py5coJXV1dUb16dXHw4MF0h5v9+eefRfny5YVSqRQVK1YUAQEBYtq0aeLjr4CcDDf78XprYnkZ9a0QH4abTW/o1/TW9/3792LevHnCxcVFKJVKYWZmJmrVqiX8/f1FbGysEEKIoKAg4ePjI6ytrYWurq6wtrYWPXr0UBmC+McffxRNmjQR5ubmQqlUCkdHRzFu3DipjdxITk4Wc+bMEXZ2dkJXV1e4uLiIX3/9Nd31AiCio6MzbS+j4WafP38uRo8eLZydnYVSqRQlS5YU3bt3F7du3VKpFxoaKpo0aSLMzMyEnp6eqFatmli9erXKUM5ERNmlECIbTzoSERERERFlgs9YEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICKNmj9/PipWrIiUlJRczd+3b1/Y29trNqg07O3t0bZt2zxr/1Nw+/ZtKBQKrF+/PsfzHjhwAEZGRnjy5InmA5NJ7r6Xnr59+8LIyChbdRUKBaZPn66xZRc206dPh0KhyNW83bt3R9euXTUcEREVNkwsiEhjXr16hXnz5mHChAnQ0vrv34tCoVB5GRoaonLlypg1axbevn1bgBHLt379erX1S31NnDixoMPTuFatWsHJyQlz584t6FBUfLzvJScnw8TEBD4+Pmp1lyxZAoVCgT59+qhNmzp1KhQKBW7cuCE7ppMnT2L69Ol4+fKl7LayI3W/GzhwYLrTJ0+eLNV5+vRpvsSUasKECdixYwcuX76cr8slovylU9ABEFHRsW7dOiQlJaFHjx5q01q0aIHevXsDAOLi4nD8+HH4+fnh8uXL2LZtW36HqnEzZsyAg4ODSlmVKlUKKJq8NWTIEIwdOxb+/v4wNjYu6HAAqO972traqF+/Pk6ePKlWNyQkBDo6OggJCUl3WunSpeHs7JzjGN69ewcdnf++Vk+ePAl/f3/07dsXpqamOW4vN/T09LBjxw6sXLkSurq6KtO2bNkCPT09xMfH50ssadWoUQO1a9fGokWL8Msvv+T78okof/CKBRFpTEBAANq1awc9PT21ac7Ozvjiiy/wxRdfYOjQodi0aRM6d+6MnTt3FsiBjqa1bt1aWr/UV/Xq1Qs6rDzRqVMnJCQkyEoIo6KiNHq1Kr19r1GjRnj69CnCw8NV6oaEhKBr166IiorCo0ePpPKkpCSEhoaiYcOGuYpBT09PJbEoCK1atcKrV6/w559/qpSfPHkS0dHRaNOmTQFFBnTt2hU7d+5EXFxcgcVARHmLiQURaUR0dDSuXLkCT0/PbM9jaWkJhUKR5cHYwoUL4ebmBnNzc+jr66NWrVrYvn17unV//fVX1K1bFwYGBjAzM0OTJk3w119/Zdr+hg0boKOjg3HjxmU79pz6888/0bhxYxgaGsLY2Bht2rTB9evXVeqk3s9/9+5dtG3bFkZGRihTpgxWrFgBALh69SqaNWsGQ0ND2NnZYfPmzSrzP3/+HGPHjoWrqyuMjIxgYmKC1q1bZ/v2k3/++QedO3dGiRIloKenh9q1a2Pv3r1q9UqXLo2qVatiz549udwawMaNG2FlZYWhQ4fi7NmzuW4HyHjfa9SoEQCoXJm4desWHj16hOHDh0NPT09l2qVLl/DmzRtpvrTu37+P9u3bw8jICKVKlcLYsWORnJysUiftMxbTp0+X9icHBwfpFqTbt29L9X/99VfUqlUL+vr6KFGiBLp374579+7J2hZlypRBkyZN1PaNTZs2wdXVNd2raMePH0eXLl1ga2sLpVIJGxsbjB49Gu/evcvWMrO7Hi1atMCbN29w6NCh3K0cERV6TCyISCNSbzmpWbNmutPj4+Px9OlTPH36FHfu3MHmzZuxYcMG9OzZM8vEYtmyZahRowZmzJiBOXPmQEdHB126dMH+/ftV6vn7+6NXr14oVqwYZsyYAX9/f9jY2ODIkSMZtv3TTz+hX79+mDhxIhYsWJDDtf5PbGystH6pr1QbN25EmzZtYGRkhHnz5sHPzw9hYWFo1KiRyoEmACQnJ6N169awsbHB/PnzYW9vj+HDh2P9+vVo1aoVateujXnz5sHY2Bi9e/dGdHS0NO+tW7ewe/dutG3bFosXL8a4ceNw9epVuLu748GDB5nGf/36ddSvXx/h4eGYOHEiFi1aBENDQ7Rv3x67du1Sq1+rVq10bzPKrp49e8LX1xdbt25F3bp1UbVqVSxbtgzPnj3LcVsZ7Xv169eHjo4OTpw4IZWFhITA0NAQderUQe3atVUSi9S/P04skpOT4eXlBXNzcyxcuBDu7u5YtGgRfvrppwxj6tixo3Rb1pIlS7Bx40Zs3LgRpUqVAgDMnj0bvXv3Rvny5bF48WKMGjUKQUFBaNKkiexnMnr27Il9+/ZJVwaSkpKwbds29OzZM93627Ztw9u3b/Hll19i+fLl8PLywvLly6VbFzOTk/WoXLky9PX1070FjYiKCEFEpAFTpkwRAMTr16/VpgFI99W+fXsRHx+vUrdPnz7Czs5Opezt27cq79+/fy+qVKkimjVrJpVFRkYKLS0t0aFDB5GcnKxSPyUlRfrbzs5OtGnTRgghxLJly4RCoRAzZ87M1ToLIURAQECG6yeEEK9fvxampqZi0KBBKvM9evRIFC9eXKW8T58+AoCYM2eOVPbixQuhr68vFAqFCAwMlMr/+ecfAUBMmzZNKouPj1db9+joaKFUKsWMGTNUygCIgIAAqax58+bC1dVVpT9SUlKEm5ubKF++vNp6z5kzRwAQjx8/zuaWSt+7d+/Epk2bRPPmzYVCoRBKpVJ069ZN/PXXX2rrkpHM9r06deoIR0dH6f2QIUNE06ZNhRBCjB8/XtSpU0ea1rlzZ2FgYCASExOlstQ+Sbv9hBCiRo0aolatWiplH/fHggULBAARHR2tUu/27dtCW1tbzJ49W6X86tWrQkdHR608uwCIYcOGiefPnwtdXV2xceNGIYQQ+/fvFwqFQty+fVtMmzZNABBPnjyR5vv48yWEEHPnzhUKhULcuXNHKkudV856ODs7i9atW+dq/Yio8OMVCyLSiGfPnkFHRyfDoTl9fHxw6NAhHDp0CHv27MGkSZNw4MAB9OzZE0KITNvW19eX/n7x4gViY2PRuHFjXLhwQSrfvXs3UlJSMHXqVJURqQCkO0Tm/PnzMXLkSMybNw9TpkzJyaqma8WKFdL6pb4A4NChQ3j58iV69OihcjVDW1sb9erVQ3BwsFpbaUf1MTU1RYUKFWBoaKgyXGeFChVgamqKW7duSWVKpVJa9+TkZDx79gxGRkaoUKGCyrb62PPnz3HkyBF07doVr1+/lmJ89uwZvLy8EBkZifv376vMY2ZmBgCyRxfS09NDz549cfjwYURHR2PSpEkIDQ1Fy5YtUa5cuWyNPpXZvteoUSOVZylCQkLg5uYGAGjYsCEuXrwoPesREhKCevXqpXsFbejQoSrvGzdurLLtc2Lnzp1ISUlB165dVfYJS0tLlC9fPt19IifMzMzQqlUrbNmyBQCwefNmuLm5wc7OLt36aT9fb968wdOnT+Hm5gYhBC5evKjR9TAzM8v3EamIKP9wVCgiyhdly5ZVuQe+Xbt2MDc3x9ixY/H777/D29s7w3l///13zJo1C5cuXUJCQoJUnjZhiIqKgpaWFipXrpxlLH///Tf279+PCRMmaOy5irp166J27dpq5ZGRkQCAZs2apTufiYmJyns9PT3pdplUxYsXR9myZdUSpOLFi+PFixfS+5SUFCxbtgwrV65EdHS0yjMA5ubmGcZ+8+ZNCCHg5+cHPz+/dOvExMSgTJky0vvUZDCz3zV49+4dYmNjVcosLS0zrG9nZ4dp06Zh6NChGDRoEPbt24d58+Zh0qRJGc6TlUaNGmHJkiUICQlB8+bNcf36dcyfPx8A4ObmhqSkJJw5cwZ2dnZ4+PBhukO1ptcnZmZmKts+JyIjIyGEQPny5dOdXqxYsVy1m1bPnj3Rq1cv3L17F7t375bWOT13797F1KlTsXfvXrV1+rj/0srNegghcv1bGERU+DGxICKNMDc3R1JSEl6/fp3tIUibN28OADh27FiGicXx48fRrl07NGnSBCtXroSVlRWKFSuGgIAAtQdUs8vFxQUvX77Exo0bMWTIELVhYjUp9cfaNm7cmO5B9cdnx7W1tdNtJ6PytFd75syZAz8/P/Tv3x8zZ85EiRIloKWlhVGjRmX6o3Gp08aOHQsvL6906zg5Oam8Tz0ALVmyZIbtbt26Ff369csw3rSSkpLwxx9/ICAgAPv374cQAu3bt8egQYMybD9VZvte6vMSJ06cgIGBAQCgQYMGUuzly5fHiRMnpIeN03twO6Ntn1spKSlQKBT4888/0207uz/Il5l27dpBqVSiT58+SEhIyPDH6ZKTk9GiRQs8f/4cEyZMQMWKFWFoaIj79++jb9++We43OV2PFy9eZJiIENGnj4kFEWlExYoVAXwYoadq1arZmicpKQkAMh1+cseOHdDT08PBgwehVCql8oCAAJV6jo6OSElJQVhYWJbDvJYsWRLbt29Ho0aN0Lx5c5w4cQLW1tbZijmnHB0dAXwYSSknI2blxvbt29G0aVP8/PPPKuUvX77MNAEoV64cgA9nmLMbY3R0NEqWLKl2Jj8tLy+vLEcACgsLQ0BAADZu3IjHjx/D2dkZM2fORN++fWFhYZGtWDLb90qXLi0lD6k/zJj2NyXc3NwQEhKCf//9F9ra2lLSoQkZnZl3dHSEEAIODg65+r2M7NDX10f79u3x66+/onXr1hn2/9WrV3Hjxg1s2LBB5WHt7IzclNP1SEpKwr1799CuXbvsrwgRfVL4jAURaUTqAdm5c+eyPc++ffsAANWqVcuwjra2NhQKhcptPbdv38bu3btV6rVv3x5aWlqYMWOG2lnW9M6Sly1bFocPH8a7d+/QokWLXI1GlB1eXl4wMTHBnDlzkJiYqDb9yZMnGluWtra22rpu27ZN7fmIj5UuXRoeHh748ccf8fDhw2zFeP78+SwPwq2srODp6anySnX06FHUr18fLi4uWLFiBVq2bIm///4bERERmDBhQraTCiDrfa9Ro0a4dOkS/vrrL+n5ilRubm44deoUjh8/jqpVq2r0B/8MDQ0BQG10pI4dO0JbWxv+/v5q/SWE0Ni+OHbsWEybNi3D29uA/67GpI1DCIFly5Zl2X5O1yMsLAzx8fFqfUBERQevWBCRRpQrVw5VqlTB4cOH0b9/f7XpN27cwK+//goAePv2LU6fPo0NGzbAyckJvXr1yrDdNm3aYPHixWjVqhV69uyJmJgYrFixAk5OTrhy5YpUz8nJCZMnT8bMmTPRuHFjdOzYEUqlEmfPnoW1tXW6DwE7OTnhr7/+goeHB7y8vHDkyBHpmYe+fftiw4YNiI6Ohr29fa63i4mJCVatWoVevXqhZs2a6N69O0qVKoW7d+9i//79aNiwIX744Ydct59W27ZtMWPGDPTr1w9ubm64evUqNm3aJF2RyMyKFSvQqFEjuLq6YtCgQShXrhweP36MU6dO4d9//1X5LYyYmBhcuXIFw4YNy3Wsf//9NxITE7Fy5Ur07NkTxYsXz3VbWe17jRo1QkBAAM6ePasWs5ubG2JjYxEbG4uvv/461zGkp1atWgCAyZMno3v37ihWrBi8vb3h6OiIWbNmYdKkSbh9+zbat28PY2NjREdHY9euXRg8eDDGjh0L4EMC1rRpU0ybNk36jYzsqlatWqZJO/Dhao+joyPGjh2L+/fvw8TEBDt27MjW8yM5WQ/gw1UQAwMDtGjRIkfrQUSfkPwdhIqIirLFixcLIyMjteEr8dEwrNra2qJs2bJi8ODBasOVpjfc7M8//yzKly8vlEqlqFixoggICFAb+jLVunXrRI0aNYRSqRRmZmbC3d1dHDp0SJqedrjZVKGhocLY2Fg0adJEir1Tp05CX19fvHjxItN1Th1u9uzZs5nWCw4OFl5eXqJ48eJCT09PODo6ir59+4pz586prLuhoaHavO7u7sLFxUWt/ON1iY+PF998842wsrIS+vr6omHDhuLUqVPC3d1duLu7S/XSG25WCCGioqJE7969haWlpShWrJgoU6aMaNu2rdi+fbtKvVWrVgkDAwPx6tWrTNc5M3FxcbmeNz0Z7XtCCBERESHtezdu3FCZlpKSIkxNTQUAsXXrVrV5M+qT9PY/fDTcrBBCzJw5U5QpU0ZoaWmpDT27Y8cO0ahRI2FoaCgMDQ1FxYoVxbBhw0RERIRUZ9++fQKAWL16dZbbAP8/3Gxm0htuNiwsTHh6egojIyNRsmRJMWjQIHH58mW1fSSjz1x21kMIIerVqye++OKLLNeDiD5dCiGyGOeRiCibYmNjUa5cOcyfPx8DBgwo6HBksbCwQO/evWX9aF5RVaNGDXh4eGDJkiUFHYqkKO17aY0fPx5btmzBzZs3VZ4x+tRcunQJNWvWxIULF7J8BoqIPl1MLIhIo+bNm4eAgACEhYWp/Z7Ep+L69eto0KABbt26lelDz5+jAwcOoHPnzrh16xZKly5d0OGoKAr73sfq1KmDQYMGYfDgwQUdiizdu3dHSkoKfvvtt4IOhYjyEBMLIiIiIiKSrWic0iEiIiIiogLFxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItkK3Q/kpaSk4MGDBzA2NoZCoSjocIiIiIiIPltCCLx+/RrW1tZZjrhX6BKLBw8ewMbGpqDDICIiIiKi/3fv3j2ULVs20zqFLrEwNjYG8CF4ExOTAo6GiIiIiOjz9erVK9jY2EjH6JkpdIlF6u1PJiYmTCyIiIiIiAqB7DyiwIe3iYiIiIhINiYWREREREQkGxMLIiIiIiKSrdA9Y5FdycnJSExMLOgwiIjoM6erq5vlEIxERJ+DTy6xEELg0aNHePnyZUGHQkREBC0tLTg4OEBXV7egQyEiKlCfXGKRmlSULl0aBgYG/BE9IiIqMKk/6vrw4UPY2tryO4mIPmufVGKRnJwsJRXm5uYFHQ4RERFKlSqFBw8eICkpCcWKFSvocIiICswndVNo6jMVBgYGBRwJERHRB6m3QCUnJxdwJEREBeuTSixS8VIzEREVFvxOIiL64JNMLIiIiIiIqHBhYlEI2dvbY+nSpQUdhsZ86uuTH/EHBQWhUqVKn8WtFLdv34ZCocClS5c01qZCocDu3bs11l5ByIvt8imbPn06qlevnu36T58+RenSpfHvv//mXVBERJSpHCcWx44dg7e3N6ytrbP8Mh86dCgUCkW+HFR6e+fvKzfu3buH/v37w9raGrq6urCzs8PIkSPx7NkzzW6MT8zbt28xadIkODo6Qk9PD6VKlYK7uzv27NlT0KHlm/Hjx2PKlCnQ1tYGAKxfvx4KhQIKhQJaWlqwsrJCt27dcPfu3QKONHN9+/aV4lYoFDA3N0erVq1w5cqVgg4tW4KDg9G2bVuUKlUKenp6cHR0RLdu3XDs2LGCDi3PHD16VKXPUl9Tpkwp6NBypGTJkujduzemTZtW0KEQEX22cpxYvHnzBtWqVcOKFSsyrbdr1y6cPn0a1tbWuQ6uKLl16xZq166NyMhIbNmyBTdv3sTq1asRFBSEBg0a4Pnz5wUWW3JyMlJSUgps+UOHDsXOnTuxfPly/PPPPzhw4AA6d+782SRcJ06cQFRUFDp16qRSbmJigocPH+L+/fvYsWMHIiIi0KVLlwKKMvtatWqFhw8f4uHDhwgKCoKOjg7atm1b0GFlaeXKlWjevDnMzc2xdetWREREYNeuXXBzc8Po0aMLOrxsef/+fa7njYiIkPrt4cOHmDhxogYjyx/9+vXDpk2bCvT/KRHR5yzHiUXr1q0xa9YsdOjQIcM69+/fx9dff41NmzZx6L3/N2zYMOjq6uKvv/6Cu7s7bG1t0bp1axw+fBj379/H5MmTVeq/fv0aPXr0gKGhIcqUKaOSyAkhMH36dNja2kKpVMLa2hojRoyQpickJGDs2LEoU6YMDA0NUa9ePRw9elSavn79epiammLv3r2oXLkylEol1q5dCz09PbUfHhw5ciSaNWsmvT9x4gQaN24MfX192NjYYMSIEXjz5o00PSYmBt7e3tDX14eDgwM2bdqU5bbZu3cvvv32W/zvf/+Dvb09atWqha+//hr9+/eX6mzcuBG1a9eGsbExLC0t0bNnT8TExEjTU8+6Hjx4EDVq1IC+vj6aNWuGmJgY/Pnnn6hUqRJMTEzQs2dPvH37VprPw8MDw4cPx/Dhw1G8eHGULFkSfn5+EEJkGO/Lly8xcOBAlCpVCiYmJmjWrBkuX74sTb98+TKaNm0KY2NjmJiYoFatWjh37lyG7QUGBqJFixbQ09NTKVcoFLC0tISVlRXc3NwwYMAAnDlzBq9evZLqTJgwAc7OzjAwMEC5cuXg5+en9ov0+/btQ506daCnp4eSJUuqfHaz2ldyQ6lUwtLSEpaWlqhevTomTpyIe/fu4cmTJ+nWT05OxoABA+Dg4AB9fX1UqFABy5YtU6u3bt06uLi4QKlUwsrKCsOHD88whmnTpsHKyirbV0ru3r2LUaNGYdSoUdiwYQOaNWsGOzs7VK1aFSNHjlTrv6w+B/b29pgzZw769+8PY2Nj2Nra4qefflJp48yZM6hRowb09PRQu3ZtXLx4US2ua9euoXXr1jAyMoKFhQV69eqFp0+fStNT999Ro0ahZMmS8PLyytb6pqd06dJSv1laWsLIyAjAhyutXbt2hampKUqUKAEfHx/cvn1bmq9v375o37495syZAwsLC5iammLGjBlISkrCuHHjUKJECZQtWxYBAQEqy8vOvvuxtWvXolKlStDT00PFihWxcuVKlekuLi6wtrbGrl27cr0diIgo9zT+jEVKSgp69eqFcePGwcXFJcv6CQkJePXqlcqrqHn+/DkOHjyIr776Cvr6+irTLC0t4evri61bt6oczC5YsADVqlXDxYsXMXHiRIwcORKHDh0CAOzYsQNLlizBjz/+iMjISOzevRuurq7SvMOHD8epU6cQGBiIK1euoEuXLmjVqhUiIyOlOm/fvsW8efOwdu1aXL9+Hb6+vjA1NcWOHTukOsnJydi6dSt8fX0BAFFRUWjVqhU6deqEK1euYOvWrThx4oTKAV7fvn1x7949BAcHY/v27Vi5cqVKApAeS0tL/PHHH3j9+nWGdRITEzFz5kxcvnwZu3fvxu3bt9G3b1+1etOnT8cPP/yAkydPSgdES5cuxebNm7F//3789ddfWL58uco8GzZsgI6ODs6cOYNly5Zh8eLFWLt2bYaxdOnSRUpYzp8/j5o1a6J58+bSWVJfX1+ULVsWZ8+exfnz5zFx4sRME+zjx4+jdu3amW6jmJgY7Nq1C9ra2tLtUgBgbGyM9evXIywsDMuWLcOaNWuwZMkSafr+/fvRoUMH/O9//8PFixcRFBSEunXrStOz2lfu3r0LIyOjTF9z5szJMO64uDj8+uuvcHJyyvC3Z1JSUlC2bFls27YNYWFhmDp1Kr799lv89ttvUp1Vq1Zh2LBhGDx4MK5evYq9e/fCyclJrS0hBL7++mv88ssvOH78OKpWrZrpdk21Y8cOJCYmYvz48elOTzvqT3Y+BwCwaNEiKWH46quv8OWXXyIiIkLaLm3btkXlypVx/vx5TJ8+HWPHjlWZ/+XLl2jWrBlq1KiBc+fO4cCBA3j8+DG6du2qUm/Dhg3Q1dVFSEgIVq9eDQBSMpLRKzv/m4EPnzsvLy8YGxvj+PHjCAkJgZGREVq1aqVydeTIkSN48OABjh07hsWLF2PatGlo27YtzMzMEBoaiqFDh2LIkCEqzz9kte9+bNOmTZg6dSpmz56N8PBwzJkzB35+ftiwYYNKvbp16+L48ePZWj8iItIwIQMAsWvXLpWyOXPmiBYtWoiUlBQhhBB2dnZiyZIlGbYxbdo0AUDtFRsbq1b33bt3IiwsTLx7905tWtu2+fvKidOnT6e7rVItXrxYABCPHz+WtlmrVq1U6nTr1k20bt1aCCHEokWLhLOzs3j//r1aW3fu3BHa2tri/v37KuXNmzcXkyZNEkIIERAQIACIS5cuqdQZOXKkaNasmfT+4MGDQqlUihcvXgghhBgwYIAYPHiwyjzHjx8XWlpa4t27dyIiIkIAEGfOnJGmh4eHCwCZ7gN///23KFu2rChWrJioXbu2GDVqlDhx4kSG9YUQ4uzZswKAeP36tRBCiODgYAFAHD58WKozd+5cAUBERUVJZUOGDBFeXl7Se3d3d1GpUiVpfxVCiAkTJohKlSpJ79Puw8ePHxcmJiYiPj5eJR5HR0fx448/CiGEMDY2FuvXr880/rSKFy8ufvnlF5Wy1D4yNDQUBgYG0udixIgRmba1YMECUatWLel9gwYNhK+vb7p1s7OvJCYmisjIyExfz549k+bt06eP0NbWFoaGhsLQ0FAAEFZWVuL8+fNSnejoaAFAXLx4McP1GDZsmOjUqZP03traWkyePDnD+gDEtm3bRM+ePUWlSpXEv//+m2Hd9AwdOlSYmJiolG3fvl1aD0NDQ3HlyhUhRNafAyE+7DNffPGFND0lJUWULl1arFq1SgghxI8//ijMzc1V/petWrVKZbvMnDlTtGzZUmU59+7dEwBERESEEOLD/lujRg219fn3338z7bPbt29LdVM/O2nX1dDQUDx9+lRs3LhRVKhQQeXzkZCQIPT19cXBgweFEB/63M7OTiQnJ0t1KlSoIBo3biy9T0pKEoaGhmLLli3pbn8h1PfdadOmiWrVqknvHR0dxebNm1XmmTlzpmjQoIFK2ejRo4WHh0eGy8kLmX03EVEuBLf970UFLjY2NsNj849p9Je3z58/j2XLluHChQvZHtd70qRJGDNmjPT+1atXsLGx0WRYhYbI5PaajzVo0EDtfepD8F26dMHSpUtRrlw5tGrVCv/73//g7e0NHR0dXL16FcnJyXB2dlaZPyEhQeWMsa6urtrZXF9fX9SvXx8PHjyAtbU1Nm3ahDZt2sDU1BTAh1t8rly5onJ7kxACKSkpiI6Oxo0bN6Cjo4NatWpJ0ytWrCjNn5EmTZrg1q1bOH36NE6ePImgoCAsW7YM/v7+8PPzAwDprO7ly5fx4sUL6ZmQu3fvonLlylJbadfJwsJCus0ibdmZM2dUll+/fn2V/bVBgwZYtGgRkpOTVa4OpG6DuLg4tbPv7969Q1RUFABgzJgxGDhwIDZu3AhPT0906dIFjo6OGa7/u3fv1G6DAj6c0b1w4QISExPx559/YtOmTZg9e7ZKna1bt+L7779HVFQU4uLikJSUBBMTE2n6pUuXMGjQoHSXm519RUdHJ90rA5lp2rQpVq1aBQB48eIFVq5cidatW+PMmTOws7NLd54VK1Zg3bp1uHv3Lt69e4f3799LIwLFxMTgwYMHaN68eabLHT16NJRKJU6fPo2SJUvmKGZA/bcIvLy8cOnSJdy/fx8eHh7SiF1ZfQ4qVaoEQHVfTL2tLfXqXXh4OKpWrarS7x9/5i9fvozg4GDplqS0oqKipH5L+3lLVaZMmRytO/DhypmxsbH03szMDJcvX8bNmzdVygEgPj5e2t+BD7cgaWn9dwHcwsICVapUkd5ra2vD3Nxc5eplVvtuWm/evEFUVBQGDBigsj8nJSWhePHiKnX19fVVbnckIqL8o9HE4vjx44iJiYGtra1UlpycjG+++QZLly5VuS83lVKphFKp1GQYhY6TkxMUCgXCw8PTfTYlPDwcZmZmKFWqVLbas7GxQUREBA4fPoxDhw7hq6++woIFC/D3338jLi4O2traOH/+vNpBcdoDFH19fbUDqTp16sDR0RGBgYH48ssvsWvXLqxfv16aHhcXhyFDhqg8z5HK1tYWN27cyFb86SlWrBgaN26Mxo0bY8KECZg1axZmzJiBCRMmSLdjeHl5YdOmTShVqhTu3r0LLy8vtYdV095ypFAo1G5BUigUsh5Uj4uLg5WVVbrPIaQmUNOnT0fPnj2xf/9+/Pnnn5g2bRoCAwMzfC6pZMmSePHihVq5lpaWdFBfqVIlREVF4csvv8TGjRsBAKdOnYKvry/8/f3h5eWF4sWLIzAwEIsWLZLa+PjWu4/XJat95ePELT3ffvstvv32W+m9oaGhSjKydu1aFC9eHGvWrMGsWbPU5g8MDMTYsWOxaNEiNGjQAMbGxliwYAFCQ0OzXIe0WrRogS1btuDgwYPS7XvZVb58ecTGxuLRo0ewtLQE8GEbODk5QUdH9d9kVp+DVHL3vbi4OHh7e2PevHlq06ysrKS/DQ0N1aa3bt0609uB7OzscP36dZUyBwcHtZMAcXFxqFWrVrrPSqX9f5Xeuma2/tnZdz+OAwDWrFmDevXqqUz7eN99/vx5tv+XEhGRZmk0sejVqxc8PT1Vyry8vNCrVy/069dPk4v6pJibm6NFixZYuXIlRo8erXKg9OjRI2zatAm9e/dWOdA/ffq0ShunT5+WzoQCHw62vL294e3tjWHDhqFixYq4evUqatSogeTkZMTExKBx48Y5jtXX1xebNm1C2bJloaWlhTZt2kjTatasibCwsAzPYFesWBFJSUk4f/486tSpA+DDSDMfPxCeHZUrV0ZSUhLi4+MRGRmJZ8+e4bvvvpOuZmX2MHROpR7Apjp9+jTKly+vdsACfNgGjx49go6ODuzt7TNs09nZGc7Ozhg9ejR69OiBgICADBOLGjVqICwsLMs4J06cCEdHR4wePRo1a9bEyZMnYWdnp/Lg/507d1TmqVq1KoKCgtL9/GVnX7G2ts7ydxVKlCiR6fTUIXPfvXuX7vSQkBC4ubnhq6++ksrSng03NjaGvb09goKC0LRp0wyX065dO3h7e6Nnz57Q1tZG9+7dM40rrc6dO2PixImYN29epvf5A1l/DrKjUqVK2LhxI+Lj46WrFh9/5mvWrIkdO3bA3t5eLbnJytq1azPc3oB6IpCRmjVrYuvWrShdunSGVxNyIzv7bloWFhawtrbGrVu3skwar127Bg8PD02FSkREOZDjxCIuLg43b96U3kdHR+PSpUsoUaIEbG1t1W4RKVasGCwtLVGhQgX50X7CfvjhB7i5ucHLywuzZs2Cg4MDrl+/jnHjxqFMmTJqt7iEhIRg/vz5aN++PQ4dOoRt27Zh//79AD6M6pScnIx69erBwMAAv/76K/T19WFnZwdzc3P4+vqid+/eWLRoEWrUqIEnT54gKCgIVatWVUkU0uPr64vp06dj9uzZ6Ny5s8rVpAkTJqB+/foYPnw4Bg4cCENDQ4SFheHQoUP44YcfUKFCBbRq1QpDhgzBqlWroKOjg1GjRmV5xtnDwwM9evRA7dq1YW5ujrCwMHz77bdo2rQpTExMYGtrC11dXSxfvhxDhw7FtWvXMHPmzFz2hLq7d+9izJgxGDJkCC5cuIDly5dneObU09MTDRo0QPv27TF//nw4OzvjwYMH0kPSLi4uGDduHDp37gwHBwf8+++/OHv2rNpQsml5eXmpPYCaHhsbG3To0AFTp07F77//jvLly+Pu3bsIDAxEnTp1sH//frXRcKZNm4bmzZvD0dER3bt3R1JSEv744w9pRJ6s9pXc3AqVkJCAR48eAfhwK9QPP/wgnX1PT/ny5fHLL7/g4MGDcHBwwMaNG3H27Fk4ODhIdaZPn46hQ4eidOnSaN26NV6/fo2QkBB8/fXXKm116NABGzduRK9evaCjo4POnTtnK2ZbW1ssWrQII0eOxPPnz9G3b184ODjg+fPn+PXXXwH8d2Y8q89BdvTs2ROTJ0/GoEGDMGnSJNy+fRsLFy5UqTNs2DCsWbMGPXr0wPjx41GiRAncvHkTgYGBWLt2bbqJb6rc3AqVHl9fXyxYsAA+Pj6YMWMGypYtizt37mDnzp0YP348ypYtm6t2s7Pvfszf3x8jRoxA8eLF0apVKyQkJODcuXN48eKFdDvt27dvcf78+UwHFCAioryT41Ghzp07hxo1aqBGjRoAPtxPXqNGDUydOlXjwRUl5cuXx7lz51CuXDl07doVjo6OGDx4MJo2bYpTp06pnfX95ptvpG09a9YsLF68WBpK0tTUFGvWrEHDhg1RtWpVHD58GPv27ZOSuoCAAPTu3RvffPMNKlSogPbt2+Ps2bMqt2lkxMnJCXXr1sWVK1fUzgxWrVoVf//9N27cuIHGjRtL/Z72t0oCAgJgbW0Nd3d3dOzYEYMHD0bp0qUzXWbqgXXLli1RqVIlfP311/Dy8pJGBSpVqhTWr1+Pbdu2oXLlyvjuu+/UDsLk6N27N969e4e6deti2LBhGDlyJAYPHpxuXYVCgT/++ANNmjRBv3794OzsjO7du+POnTuwsLCAtrY2nj17ht69e8PZ2Rldu3ZF69at4e/vn+HyfX19cf36dWnEoMyMHj0a+/fvx5kzZ9CuXTuMHj0aw4cPR/Xq1XHy5EnpmZRUHh4e2LZtG/bu3Yvq1aujWbNmKs+YyNlXMnLgwAFYWVnBysoK9erVw9mzZ7Ft27YMzyIPGTIEHTt2RLdu3VCvXj08e/ZM5eoFAPTp0wdLly7FypUr4eLigrZt26qMcpZW586dsWHDBvTq1Qs7d+4E8CExyewKEwB8/fXX+Ouvv/DkyRN07twZ5cuXx//+9z9ER0fjwIED0shr2fkcZMXIyAj79u2TrjJOnjxZ7ZYna2trhISEIDk5GS1btoSrqytGjRoFU1NTlecZ8pKBgQGOHTsGW1tbdOzYEZUqVcKAAQMQHx8v6wpGdvbdjw0cOBBr165FQEAAXF1d4e7ujvXr16skoHv27IGtrW2urtYSEZF8CpGTJ4rzwatXr1C8eHHExsaqfXHFx8cjOjoaDg4O6T7sSpRTHh4eqF69er78Onxmxo0bh1evXuHHH38s0DiKqj59+kChUKg8M0RFT/369TFixAj07NkzX5fL7yYiDTua5gq3x76Ci4MAZH5s/rH8Oe1FRJmaPHky7OzsCvQX0IsqIQSOHj2q0dvnqPB5+vQpOnbsiB49ehR0KEREny2NPrxNRLljamqqMrISaY5Cocj0wWAqGkqWLJnhDxwSEVH+YGJBn7X0ho0lIiIiopzjrVBERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWJBGhISEwNXVFcWKFUP79u0LOpxM3b59GwqFApcuXSroUHIlv+L38/PD4MGD83QZhdXRo0ehUCjw8uXLDOtMnz4d1atXz7eYCpq9vX2B/0K9JuS0354+fYrSpUvj33//zbugiIiKiKKTWBz1zt9XLjx69Ahff/01ypUrB6VSCRsbG3h7eyMoKEjDGyNrCoUCu3fv1lh7Y8aMQfXq1REdHY3169drpE1Nx5gT0dHR6NmzJ6ytraGnp4eyZcvCx8cH//zzT4HEk98ePXqEZcuWYfLkyVJZ3759oVAooFAoUKxYMTg4OGD8+PGIj48vwEgz1717d7Rq1Uql7MCBA1AoFJg+fbpK+fTp02Fra5vttseOHavy2e3bt2+eJtU3b95E//79YWtrC6VSiTJlyqB58+bYtGkTkpKS8my5BS11nzt9+rRKeUJCAszNzaFQKPL092hKliyJ3r17Y9q0aXm2DCKioqLoJBaF3O3bt1GrVi0cOXIECxYswNWrV3HgwAE0bdoUw4YNK+jwci0xMREAEBUVhWbNmqFs2bIwNTUt2KBkSkxMRIsWLRAbG4udO3ciIiICW7duhaura6ZnsIuStWvXws3NDXZ2dirlrVq1wsOHD3Hr1i0sWbIEP/74Y6E+4GratClCQkJUDryDg4NhY2OjdjAaHByMpk2bZrttIyMjmJubayrUTJ05cwY1a9ZEeHg4VqxYgWvXruHo0aMYOHAgVq1ahevXr+dLHHK8f/8+1/Pa2NggICBApWzXrl0wMjKSG1a29OvXD5s2bcLz58/zZXlERJ8qJhb55KuvvoJCocCZM2fQqVMnODs7w8XFBWPGjFE5E3f37l34+PjAyMgIJiYm6Nq1Kx4/fixNT++s6KhRo+Dh4SG99/DwwIgRIzB+/HiUKFEClpaWKmdn7e3tAQAdOnSAQqGQ3gPAnj17ULNmTejp6aFcuXLw9/dXOShTKBRYtWoV2rVrB0NDQwwaNAgKhQLPnj1D//79oVAosH79eiQnJ2PAgAFwcHCAvr4+KlSogGXLlqltl3Xr1sHFxQVKpRJWVlYYPnx4pjFmZ/0PHDiARo0awdTUFObm5mjbti2ioqIy6Bl1169fR1RUFFauXIn69evDzs4ODRs2xKxZs1C/fn2p3oQJE+Ds7AwDAwOUK1cOfn5+UqIF/HfLxbp162BrawsjIyN89dVXSE5Oxvz582FpaYnSpUtj9uzZKstP3catW7eGvr4+ypUrh+3bt2ca87Vr19C6dWsYGRnBwsICvXr1wtOnT6Xp27dvh6urK/T19WFubg5PT0+8efMmw/YCAwPh7a1+ZU6pVMLS0hI2NjZo3749PD09cejQIWn6s2fP0KNHD5QpUwYGBgZwdXXFli1bVNpISUnB/Pnz4eTkBKVSCVtbW5VtcO/ePXTt2hWmpqYoUaIEfHx8cPv27UzXPyNNmzZFXFwczp07J5UdPXoUEydORGhoqHS1JT4+HqGhoWqJxfnz51G7dm0YGBjAzc0NERER0rS0t9RMnz4dGzZswJ49e6Qz7KmJi9z1EUKgb9++cHZ2RkhICLy9vVG+fHmUL18ePXr0wIkTJ1C1alWpflbLS/0MLVy4EFZWVjA3N8ewYcNU9t2YmBh4e3tDX18fDg4O2LRpk1pcL1++xMCBA1GqVCmYmJigWbNmuHz5str2Wbt2LRwcHKCnp5ftdf5Ynz59EBgYiHfv3kll69atQ58+fdTqZvW5TM/atWtRqVIl6OnpoWLFili5cqXKdBcXF1hbW2PXrl25Xgcios8BE4t88Pz5cxw4cADDhg2DoaGh2vTUM/wpKSnw8fHB8+fP8ffff+PQoUO4desWunXrluNlbtiwAYaGhggNDcX8+fMxY8YM6QDw7NmzAICAgAA8fPhQen/8+HH07t0bI0eORFhYGH788UesX79e7cB3+vTp6NChA65evQp/f388fPgQJiYmWLp0KR4+fIhu3bohJSUFZcuWxbZt2xAWFoapU6fi22+/xW+//Sa1s2rVKgwbNgyDBw/G1atXsXfvXjg5OWUaY3a8efMGY8aMwblz5xAUFAQtLS106NABKSkp2Zq/VKlS0NLSwvbt25GcnJxhPWNjY6xfvx5hYWFYtmwZ1qxZgyVLlqjUiYqKwp9//okDBw5gy5Yt+Pnnn9GmTRv8+++/+PvvvzFv3jxMmTIFoaGhKvP5+fmhU6dOuHz5Mnx9fdG9e3eEh4enG8fLly/RrFkz1KhRA+fOncOBAwfw+PFjdO3aFQDw8OFD9OjRA/3790d4eDiOHj2Kjh07QgiRbnvPnz9HWFgYateunel2unbtGk6ePAldXV2pLD4+HrVq1cL+/ftx7do1DB48GL169cKZM2ekOpMmTcJ3330HPz8/hIWFYfPmzbCwsADw4WqRl5cXjI2Ncfz4cYSEhMDIyAitWrWSznhv2rQJRkZGmb6OHz8OAHB2doa1tTWCg4MBAK9fv8aFCxfQpUsX2Nvb49SpUwCAkydPIiEhQS2xmDx5MhYtWoRz585BR0cH/fv3T3dbjB07Fl27dpWu6Dx8+BBubm7ZWp+sXLp0CeHh4Rg7diy0tNL/l61QKLK9/YAPV2eioqIQHByMDRs2YP369Sq3MPbt2xf37t1DcHAwtm/fjpUrVyImJkZlmV26dEFMTAz+/PNPnD9/HjVr1kTz5s1VzurfvHkTO3bswM6dO6VngubMmZNl/929e1dlWbVq1YK9vT127NgB4MMJmGPHjqFXr15q2yI7n8u0Nm3ahKlTp2L27NkIDw/HnDlz4Ofnhw0bNqjUq1u3rrRfERFRBkQhExsbKwCI2NhYtWnv3r0TYWFh4t27d+ozBrfN31cOhIaGCgBi586dmdb766+/hLa2trh7965Udv36dQFAnDlzRgghRJ8+fYSPj4/KfCNHjhTu7u7Se3d3d9GoUSOVOnXq1BETJkyQ3gMQu3btUqnTvHlzMWfOHJWyjRs3CisrK5X5Ro0apRZ78eLFRUBAQKbrN2zYMNGpUyfpvbW1tZg8eXKG9dOLMTvr/7EnT54IAOLq1atCCCGio6MFAHHx4sUM5/nhhx+EgYGBMDY2Fk2bNhUzZswQUVFRGdYXQogFCxaIWrVqSe+nTZsmDAwMxKtXr6QyLy8vYW9vL5KTk6WyChUqiLlz50rvAYihQ4eqtF2vXj3x5Zdfphv/zJkzRcuWLVXq37t3TwAQERER4vz58wKAuH37dqbxp7p48aIAoLIfCvFh22trawtDQ0OhVCoFAKGlpSW2b9+eaXtt2rQR33zzjRBCiFevXgmlUinWrFmTbt2NGzeKChUqiJSUFKksISFB6Ovri4MHD0ptREZGZvp6+/atNL+vr6+0ffbv3y8qV64shBBi8ODBYurUqUIIIfz8/ISDg4M0T3BwsAAgDh8+LJXt379fAJD+/0ybNk1Uq1ZNZft8vG9mZ32yEhgYKACICxcuSGWPHz8WhoaG0mvFihXZXl6fPn2EnZ2dSEpKkup06dJFdOvWTQghREREhMr/HCGECA8PFwDEkiVLhBBCHD9+XJiYmIj4+HiVWB0dHcWPP/4obZ9ixYqJmJgYlTrPnj3Lsv8SExOl+qn/B5YuXSqaNm0qhBDC399fdOjQQbx48UIAEMHBwRluv/Q+l2n7zdHRUWzevFllnpkzZ4oGDRqolI0ePVp4eHiku4xMv5uIKOdyebxFeSOzY/OP6eRnEvO5EhmcGf5YeHg4bGxsYGNjI5VVrlwZpqamCA8PR506dbK9zLS3RgCAlZWV2hnHj12+fBkhISEqVyiSk5MRHx+Pt2/fwsDAAACyPJOdasWKFVi3bh3u3r2Ld+/e4f3799KtIzExMXjw4AGaN2+e7XXKrsjISEydOhWhoaF4+vSpdKXi7t27qFKlSrbaGDZsGHr37o2jR4/i9OnT2LZtG+bMmYO9e/eiRYsWAICtW7fi+++/R1RUFOLi4pCUlAQTExOVduzt7WFsbCy9t7CwgLa2tsqZZwsLC7W+adCggdr7jEaBunz5MoKDg9O93zwqKgotW7ZE8+bN4erqCi8vL7Rs2RKdO3eGmZlZuu2l3m6S3q0rTZs2xapVq/DmzRssWbIEOjo66NSpkzQ9OTkZc+bMwW+//Yb79+/j/fv3SEhIkPad8PBwJCQkZNjvly9fxs2bN1W2GfDhSkjq7WzGxsZq0zPj4eGBUaNGITExEUePHpVum3N3d8ePP/4I4MPtUek9X5H2c2RlZQXgw76b3Ye8s7M+uWFubi7tDx4eHtLViOwuz8XFBdra2tJ7KysrXL16FcCHPtLR0UGtWrWk6RUrVlR5dury5cuIi4tTe8bk3bt3Ksuxs7NDqVKlVOqUKFECJUqUyPE6f/HFF5g4cSJu3bqF9evX4/vvv0+3XnY+l6nevHmDqKgoDBgwAIMGDZLKk5KSULx4cZW6+vr6ePv2bY7jJiL6nDCxyAfly5eHQqHQyIhCWlpaaolKevcPFytWTOW9QqHI8laguLg4+Pv7o2PHjmrT0h5kpnc718cCAwMxduxYLFq0CA0aNICxsTEWLFgg3fKjr6+fZRvpyc76e3t7w87ODmvWrIG1tTVSUlJQpUqVHD88amxsDG9vb3h7e2PWrFnw8vLCrFmz0KJFC5w6dQq+vr7w9/eHl5cXihcvjsDAQCxatEiljfT6ITd9k5m4uDh4e3tj3rx5atOsrKygra2NQ4cO4eTJk/jrr7+wfPlyTJ48GaGhoXBwcFCbp2TJkgCAFy9eqB0UGhoaSrerrVu3DtWqVcPPP/+MAQMGAAAWLFiAZcuWYenSpXB1dYWhoSFGjRolbfus+j0uLg61atVK957+1Fg2bdqEIUOGZNrOn3/+icaNGwP4kAy9efMGZ8+eRXBwMMaNGwfgQ2LRv39/PH/+HKGhoem2mbavUm83yklfZWd9slK+fHkAQEREBGrUqAEA0NbWlvpBR+e/f+PZXZ7cfTAuLg5WVlbpjsaUNgFJ73/FnDlzMGfOnEzbDwsLU0veUp+XGjBgAOLj49G6dWu8fv1apU52P5dp1wMA1qxZg3r16qlMS5t4AR9uEcxunxERfa6YWOSDEiVKwMvLCytWrMCIESPUvmxfvnwJU1NTVKpUCffu3cO9e/ekqxZhYWF4+fIlKleuDODDwcG1a9dU5r906ZLagUJWihUrpvb8QM2aNRERESEdsMgREhICNzc3fPXVV1JZ2jOZxsbGsLe3R1BQUIYj8aQXY1br/+zZM0RERGDNmjXSgeWJEydkr49CoUDFihVx8uRJAB/uybezs1MZjvXOnTuyl5Pq9OnT6N27t8r71IPKj9WsWRM7duyAvb29ykHmx/E3bNgQDRs2xNSpU2FnZ4ddu3ZhzJgxanUdHR1hYmKCsLAwODs7ZxijlpYWvv32W4wZMwY9e/aEvr4+QkJC4OPjgy+++ALAh4PwGzduSPtv+fLloa+vj6CgIAwcODDdddm6dStKly6d4Vnmdu3aqR0EfqxMmTIq62NjY4O9e/fi0qVLcHd3l+qUKVMGixYtwvv373M0IlR6dHV10/1MZbU+WalRowYqVqyIhQsXomvXrhk+Z6Gp5VWsWBFJSUk4f/68dJU0IiJCZUS0mjVr4tGjR9DR0VEZ/CE7hg4dKj3/kxFra+t0y/v374///e9/mDBhgtqBP5Dzz6WFhQWsra1x69Yt+Pr6ZhrTtWvXVAaJICIidXx4O5+sWLECycnJqFu3Lnbs2IHIyEiEh4fj+++/l2578fT0hKurK3x9fXHhwgWcOXMGvXv3hru7u3T7UbNmzXDu3Dn88ssviIyMxLRp09QOtLMj9aD+0aNHePHiBQBg6tSp+OWXX+Dv74/r168jPDwcgYGBmDJlSo7bL1++PM6dO4eDBw/ixo0b8PPzU3sAe/r06Vi0aBG+//57REZG4sKFC1i+fHmmMWa1/mZmZjA3N8dPP/2Emzdv4siRI+kePGfm0qVL8PHxwfbt2xEWFoabN2/i559/xrp16+Dj4yOt3927dxEYGIioqCh8//33Gh0xZtu2bVi3bh1u3LiBadOm4cyZM9KIWR8bNmwYnj9/jh49euDs2bOIiorCwYMH0a9fPyQnJyM0NBRz5szBuXPncPfuXezcuRNPnjxBpUqV0m1PS0sLnp6e2UrIunTpAm1tbaxYsQLAh+2SenUkPDwcQ4YMURnVTE9PDxMmTMD48ePxyy+/ICoqCqdPn8bPP/8MAPD19UXJkiXh4+OD48ePIzo6GkePHsWIESOkHygzNjaGk5NTpq+Pr4w0bdoUK1euhJOTk/SgOPDhqsXy5culh7zlsLe3x5UrVxAREYGnT58iMTExW+uTFYVCgYCAAERERKBhw4bYu3cvIiMjERYWhtWrV+PJkyfSQbYmllehQgW0atUKQ4YMQWhoKM6fP4+BAweqbFNPT080aNAA7du3x19//YXbt2/j5MmTmDx5ssoIXOkpUaJElv2XUYLcqlUrPHnyBDNmzEh3em4+l/7+/pg7dy6+//573LhxA1evXkVAQAAWL14s1Xn79i3Onz+Pli1bZtoWEdHnjolFPilXrhwuXLiApk2b4ptvvkGVKlXQokULBAUFYdWqVQA+HEDs2bMHZmZmaNKkCTw9PVGuXDls3bpVasfLywt+fn4YP3486tSpg9evX6uc2c6uRYsW4dChQ7CxsZHOhHt5eeH333/HX3/9hTp16qB+/fpYsmSJ2m8ZZMeQIUPQsWNHdOvWDfXq1cOzZ89Url4AH4aQXLp0KVauXAkXFxe0bdsWkZGRWcaY2fpraWkhMDAQ58+fR5UqVTB69GgsWLAgR7GXLVsW9vb28Pf3R7169VCzZk0sW7YM/v7+0pnQdu3aYfTo0Rg+fDiqV6+OkydPws/PL8fbKSP+/v4IDAxE1apV8csvv2DLli3SWf+PWVtbIyQkBMnJyWjZsiVcXV0xatQomJqaQktLCyYmJjh27Bj+97//wdnZGVOmTMGiRYvQunXrDJc/cOBABAYGZnl7jI6ODoYPH4758+fjzZs3mDJlCmrWrAkvLy94eHjA0tJSbXhgPz8/fPPNN5g6dSoqVaqEbt26Sc+YGBgY4NixY7C1tUXHjh1RqVIl6daX3J6BBz4kFq9fv1Y74+zu7o7Xr1/LvloBAIMGDUKFChVQu3ZtlCpVCiEhIdlan9Rf+c5sCNr69evj/PnzqFChAoYNG4bKlSvDzc0NW7ZswZIlS/Dll18C0Nz2CwgIgLW1Ndzd3dGxY0cMHjwYpUuXlqYrFAr88ccfaNKkCfr16wdnZ2d0794dd+7cUUncNE2hUKBkyZIqI5GllZvP5cCBA7F27VoEBATA1dUV7u7uWL9+vcptgnv27IGtra10FZSIiNKnENl9sjifvHr1CsWLF0dsbKzaF2F8fDyio6Nlj4lOVJgpFArs2rUrT3/FOStCCNSrVw+jR49Gjx49CiyOz0FAQADmzJmDsLCwHN/SSPmjfv36GDFiBHr27JnudH43EWnY0TS/o+Sxr+DiIACZH5t/jFcsiEiNQqHATz/9pPLjiJQ3/vjjD8yZM4dJRSH19OlTdOzYkQk2EVE28OFtIkpX9erVpeGBKe9s27atoEOgTJQsWRLjx48v6DCIiD4JTCyICplCdnciERERUbbwVigiIiIiIpKNiQUREREREcn2SSYWcn6lmIiISJN4+yIR0Qef1DMWurq60NLSwoMHD1CqVCno6upCoVAUdFhERPSZEkLgyZMnUCgUHNmLiD57n1RioaWlBQcHBzx8+BAPHjwo6HCIiIigUChQtmxZ6RfQiYg+V59UYgF8uGpha2uLpKQkJCcnF3Q4RET0mStWrBiTCiIifIKJBQDpkjMvOxMRERERFQ6f5MPbRERERERUuDCxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhky3FicezYMXh7e8Pa2hoKhQK7d++WpiUmJmLChAlwdXWFoaEhrK2t0bt3bzx48ECTMRMRERERUSGT48TizZs3qFatGlasWKE27e3bt7hw4QL8/Pxw4cIF7Ny5ExEREWjXrp1GgiUiIiIiosJJJ6cztG7dGq1bt053WvHixXHo0CGVsh9++AF169bF3bt3YWtrm7soiYiIiIioUMvzZyxiY2OhUChgamqa14siIiIiIqICkuMrFjkRHx+PCRMmoEePHjAxMUm3TkJCAhISEqT3r169ysuQiIiIiIgoD+RZYpGYmIiuXbtCCIFVq1ZlWG/u3Lnw9/fPqzCIiOgj3t45n2ffPs3HQURERUue3AqVmlTcuXMHhw4dyvBqBQBMmjQJsbGx0uvevXt5ERIREREREeUhjV+xSE0qIiMjERwcDHNz80zrK5VKKJVKTYdBRERERET5KMeJRVxcHG7evCm9j46OxqVLl1CiRAlYWVmhc+fOuHDhAn7//XckJyfj0aNHAIASJUpAV1dXc5ETEREREVGhkePE4ty5c2jatKn0fsyYMQCAPn36YPr06di7dy8AoHr16irzBQcHw8PDI/eREhERERFRoZXjxMLDwwNCiAynZzaNiIiIiIiKpjz/HQsiIiIiIir6mFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINp2CDoCIPk/e3rmbb98+zcZB2fMp9FduYiyq+9On0F9EVPTwigUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZcpxYHDt2DN7e3rC2toZCocDu3btVpgshMHXqVFhZWUFfXx+enp6IjIzUVLxERERERFQI5TixePPmDapVq4YVK1akO33+/Pn4/vvvsXr1aoSGhsLQ0BBeXl6Ij4+XHSwRERERERVOOjmdoXXr1mjdunW604QQWLp0KaZMmQIfHx8AwC+//AILCwvs3r0b3bt3lxctEREREREVShp9xiI6OhqPHj2Cp6enVFa8eHHUq1cPp06dSneehIQEvHr1SuVFRERERESflhxfscjMo0ePAAAWFhYq5RYWFtK0j82dOxf+/v6aDIOIiD5h3t4FHUHhwu1Bn7SjaXZgj32aa0sT7ZHGFfioUJMmTUJsbKz0unfvXkGHREREREREOaTRxMLS0hIA8PjxY5Xyx48fS9M+plQqYWJiovIiIiIiIqJPi0YTCwcHB1haWiIoKEgqe/XqFUJDQ9GgQQNNLoqIiIiIiAqRHD9jERcXh5s3b0rvo6OjcenSJZQoUQK2trYYNWoUZs2ahfLly8PBwQF+fn6wtrZG+/btNRk3EREREREVIjlOLM6dO4emTZtK78eMGQMA6NOnD9avX4/x48fjzZs3GDx4MF6+fIlGjRrhwIED0NPT01zURERERERUqOQ4sfDw8IAQIsPpCoUCM2bMwIwZM2QFRkREREREn44CHxWKiIiIiIg+fUwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDadgg6AiPKGt3fO59m3T/NxEBFRATma5ovAIxf/4I9+9EWSmzayG8/Hy6JPEq9YEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIikk3jiUVycjL8/Pzg4OAAfX19ODo6YubMmRBCaHpRRERERERUSOhousF58+Zh1apV2LBhA1xcXHDu3Dn069cPxYsXx4gRIzS9OCIiIiIiKgQ0nlicPHkSPj4+aNOmDQDA3t4eW7ZswZkzZzS9KCIiIiIiKiQ0fiuUm5sbgoKCcOPGDQDA5cuXceLECbRu3VrTiyIiIiIiokJC41csJk6ciFevXqFixYrQ1tZGcnIyZs+eDV9f33TrJyQkICEhQXr/6tUrTYdERERERER5TOOJxW+//YZNmzZh8+bNcHFxwaVLlzBq1ChYW1ujT58+avXnzp0Lf39/TYdBRCSbt3fO59m3T/NxZCQ38ZFmFPZ9gyjXjmayc6ed5sEdmtRp/FaocePGYeLEiejevTtcXV3Rq1cvjB49GnPnzk23/qRJkxAbGyu97t27p+mQiIiIiIgoj2n8isXbt2+hpaWar2hrayMlJSXd+kqlEkqlUtNhEBERERFRPtJ4YuHt7Y3Zs2fD1tYWLi4uuHjxIhYvXoz+/ftrelFERERERFRIaDyxWL58Ofz8/PDVV18hJiYG1tbWGDJkCKZOnarpRRERERERUSGh8cTC2NgYS5cuxdKlSzXdNBERERERFVIaf3ibiIiIiIg+P0wsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJJtOQQdARFSUeHvnbr59+zQbB1F+4T7/iTiaSUd55KIz0rYnd/7ckhsDaRyvWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpItTxKL+/fv44svvoC5uTn09fXh6uqKc+fO5cWiiIiIiIioENDRdIMvXrxAw4YN0bRpU/z5558oVaoUIiMjYWZmpulFERERERFRIaHxxGLevHmwsbFBQECAVObg4KDpxRARERERUSGi8Vuh9u7di9q1a6NLly4oXbo0atSogTVr1mRYPyEhAa9evVJ5ERERERHRp0XjVyxu3bqFVatWYcyYMfj2229x9uxZjBgxArq6uujTp49a/blz58Lf31/TYRBRLnh7526+ffs0GwcVHbndp6joy82+8Vn/rzmaZoN5aHhDHJX5Qc3L2OiTovErFikpKahZsybmzJmDGjVqYPDgwRg0aBBWr16dbv1JkyYhNjZWet27d0/TIRERERERUR7TeGJhZWWFypUrq5RVqlQJd+/eTbe+UqmEiYmJyouIiIiIiD4tGk8sGjZsiIiICJWyGzduwM7OTtOLIiIiIiKiQkLjicXo0aNx+vRpzJkzBzdv3sTmzZvx008/YdiwYZpeFBERERERFRIaTyzq1KmDXbt2YcuWLahSpQpmzpyJpUuXwtfXV9OLIiIiIiKiQkLjo0IBQNu2bdG2bdu8aJqIiIiIiAohjV+xICIiIiKizw8TCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpJNp6ADICIiwNu7oCP4POXndv8U+vhTiPGzcjSDDsmoHAA89uVNLNmVWWyFrb3sbqvczPOZ4hULIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsuV5YvHdd99BoVBg1KhReb0oIiIiIiIqIHmaWJw9exY//vgjqlatmpeLISIiIiKiApZniUVcXBx8fX2xZs0amJmZ5dViiIiIiIioEMizxGLYsGFo06YNPD0982oRRERERERUSOjkRaOBgYG4cOECzp49m2XdhIQEJCQkSO9fvXqVFyEREREREVEe0nhice/ePYwcORKHDh2Cnp5elvXnzp0Lf39/TYdBRPnI27ugIyCiz0Fu/9fs26fZODKTmxhV4jvKf6i5kna7eexLv5zynMZvhTp//jxiYmJQs2ZN6OjoQEdHB3///Te+//576OjoIDk5WaX+pEmTEBsbK73u3bun6ZCIiIiIiCiPafyKRfPmzXH16lWVsn79+qFixYqYMGECtLW1VaYplUoolUpNh0FERERERPlI44mFsbExqlSpolJmaGgIc3NztXIiIiIiIioa+MvbREREREQkW56MCvWxo0eP5sdiiIiIiIiogPCKBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLLpFHQARAXN2zvn8+zbp/k4MpKb+Ioybg8iKmrS/l/za5L9+erWSafwaOb/JM+czX77WS6rKEm73Tzy8Uu+iOEVCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLJpPLGYO3cu6tSpA2NjY5QuXRrt27dHRESEphdDRERERESFiMYTi7///hvDhg3D6dOncejQISQmJqJly5Z48+aNphdFRERERESFhI6mGzxw4IDK+/Xr16N06dI4f/48mjRpounFERERERFRIaDxxOJjsbGxAIASJUqkOz0hIQEJCQnS+1evXuV1SEREREREpGF5mlikpKRg1KhRaNiwIapUqZJunblz58Lf3z8vw8g1b+/czbdvn2bj0LSiul6fgtxueyIiyr3c/O9V+847mqYRj+x9Ifo1+W+emcc+7S/RM2dzPk/dOgW0rKMF8GX78TKzs4/kZp5CLk9HhRo2bBiuXbuGwMDADOtMmjQJsbGx0uvevXt5GRIREREREeWBPLtiMXz4cPz+++84duwYypYtm2E9pVIJpVKZV2EQEREREVE+0HhiIYTA119/jV27duHo0aNwcHDQ9CKIiIiIiKiQ0XhiMWzYMGzevBl79uyBsbExHj16BAAoXrw49PX1Nb04IiIiIiIqBDT+jMWqVasQGxsLDw8PWFlZSa+tW7dqelFERERERFRI5MmtUERERERE9HnJ01GhiIiIiIjo88DEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGTTKegAqGjz9i7oCIiIiDLm1+S/L6qZx/b9N+FoLr7AjnrDr0nWy0lLZZk5dOZsrmctcnK1Lc5m0McZlQOYuQjYl50uS7v/eHyY4eNjoo/3lZmL1JvJ1rIKEV6xICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJFueJRYrVqyAvb099PT0UK9ePZw5cyavFkVERERERAUsTxKLrVu3YsyYMZg2bRouXLiAatWqwcvLCzExMXmxOCIiIiIiKmB5klgsXrwYgwYNQr9+/VC5cmWsXr0aBgYGWLduXV4sjoiIiIiICpjGE4v379/j/Pnz8PT0/G8hWlrw9PTEqVOnNL04IiIiIiIqBHQ03eDTp0+RnJwMCwsLlXILCwv8888/avUTEhKQkJAgvY+NjQUAvHr1StOh5VhiYu7mKwShZyo/1yu3yyrsctvHRXV7EBF9quLi//vHnJj43z/3V28y+Yed0ZfAm0TExeds+WmX+XE8n7pXb3I3X063YX5KTHyVfvdnY3/5+Bjg477+eF9IM2uBSj0mF0JkWVfjiUVOzZ07F/7+/mrlNjY2BRCNZhQvXtAR5I2iul65wW1BRFQ0HDyY9t1//9yLH1Srmm49+VTbOpjpcqngFc/FMUD6M6j3tXq9wnS88fr1axTPIiCNJxYlS5aEtrY2Hj9+rFL++PFjWFpaqtWfNGkSxowZI71PSUnB8+fPYW5uDoVCIZW/evUKNjY2uHfvHkxMTDQdNuUC+6RwYX8UPuyTwod9UriwPwof9knhUhj6QwiB169fw9raOsu6Gk8sdHV1UatWLQQFBaF9+/YAPiQLQUFBGD58uFp9pVIJpVKpUmZqapph+yYmJtzRCxn2SeHC/ih82CeFD/ukcGF/FD7sk8KloPsjqysVqfLkVqgxY8agT58+qF27NurWrYulS5fizZs36NevX14sjoiIiIiIClieJBbdunXDkydPMHXqVDx69AjVq1fHgQMH1B7oJiIiIiKioiHPHt4ePnx4urc+5ZZSqcS0adPUbpuigsM+KVzYH4UP+6TwYZ8ULuyPwod9Urh8av2hENkZO4qIiIiIiCgTefLL20RERERE9HlhYkFERERERLIxsSAiIiIiItkKdWIxe/ZsuLm5wcDAINPftkhLCIGpU6fCysoK+vr68PT0RGRkZN4G+pl4/vw5fH19YWJiAlNTUwwYMABxcXGZzvPo0SP06tULlpaWMDQ0RM2aNbFjx458irjoy02fAMCpU6fQrFkzGBoawsTEBE2aNMG7d+/yIeKiL7d9Anz4/9W6dWsoFArs3r07bwP9TOS0P54/f46vv/4aFSpUgL6+PmxtbTFixAjExsbmY9RFy4oVK2Bvbw89PT3Uq1cPZ86cybT+tm3bULFiRejp6cHV1RV//PFHPkX6+chJn6xZswaNGzeGmZkZzMzM4OnpmWUfUs7k9DOSKjAwEAqFQvrduMKgUCcW79+/R5cuXfDll19me5758+fj+++/x+rVqxEaGgpDQ0N4eXkhPj4+DyP9PPj6+uL69es4dOgQfv/9dxw7dgyDBw/OdJ7evXsjIiICe/fuxdWrV9GxY0d07doVFy9ezKeoi7bc9MmpU6fQqlUrtGzZEmfOnMHZs2cxfPhwaGkV6n8Hn4zc9EmqpUuXQqFQ5HGEn5ec9seDBw/w4MEDLFy4ENeuXcP69etx4MABDBgwIB+jLjq2bt2KMWPGYNq0abhw4QKqVasGLy8vxMTEpFv/5MmT6NGjBwYMGICLFy+iffv2aN++Pa5du5bPkRddOe2To0ePokePHggODsapU6dgY2ODli1b4v79+/kcedGU0/5Idfv2bYwdOxaNGzfOp0izSXwCAgICRPHixbOsl5KSIiwtLcWCBQukspcvXwqlUim2bNmShxEWfWFhYQKAOHv2rFT2559/CoVCIe7fv5/hfIaGhuKXX35RKStRooRYs2ZNnsX6uchtn9SrV09MmTIlP0L87OS2T4QQ4uLFi6JMmTLi4cOHAoDYtWtXHkdb9Mnpj7R+++03oaurKxITE/MizCKtbt26YtiwYdL75ORkYW1tLebOnZtu/a5du4o2bdqolNWrV08MGTIkT+P8nOS0Tz6WlJQkjI2NxYYNG/IqxM9KbvojKSlJuLm5ibVr14o+ffoIHx+ffIg0e4rUKcro6Gg8evQInp6eUlnx4sVRr149nDp1qgAj+/SdOnUKpqamqF27tlTm6ekJLS0thIaGZjifm5sbtm7diufPnyMlJQWBgYGIj4+Hh4dHPkRdtOWmT2JiYhAaGorSpUvDzc0NFhYWcHd3x4kTJ/Ir7CItt5+Tt2/fomfPnlixYgUsLS3zI9TPQm7742OxsbEwMTGBjk6e/fRTkfT+/XucP39e5TtZS0sLnp6eGX4nnzp1SqU+AHh5efE7XENy0ycfe/v2LRITE1GiRIm8CvOzkdv+mDFjBkqXLl0or6QWqcTi0aNHAKD2C98WFhbSNMqdR48eoXTp0iplOjo6KFGiRKbb9rfffkNiYiLMzc2hVCoxZMgQ7Nq1C05OTnkdcpGXmz65desWAGD69OkYNGgQDhw4gJo1a6J58+Z8FkkDcvs5GT16NNzc3ODj45PXIX5WctsfaT19+hQzZ87M9u1s9J+nT58iOTk5R9/Jjx494nd4HspNn3xswoQJsLa2VksAKedy0x8nTpzAzz//jDVr1uRHiDmW74nFxIkToVAoMn39888/+R3WZyuv+8PPzw8vX77E4cOHce7cOYwZMwZdu3bF1atXNbgWRUte9klKSgoAYMiQIejXrx9q1KiBJUuWoEKFCli3bp0mV6NIycs+2bt3L44cOYKlS5dqNugiLL++R169eoU2bdqgcuXKmD59uvzAiT5x3333HQIDA7Fr1y7o6ekVdDifndevX6NXr15Ys2YNSpYsWdDhpCvfr+t+88036Nu3b6Z1ypUrl6u2U28hePz4MaysrKTyx48fo3r16rlqs6jLbn9YWlqqPUiUlJSE58+fZ3jrRlRUFH744Qdcu3YNLi4uAIBq1arh+PHjWLFiBVavXq2RdShq8rJPUj8XlStXVimvVKkS7t69m/ugi7i87JMjR44gKipKbeS7Tp06oXHjxjh69KiMyIumvOyPVK9fv0arVq1gbGyMXbt2oVixYnLD/uyULFkS2traePz4sUr548ePM9z+lpaWOapPOZObPkm1cOFCfPfddzh8+DCqVq2al2F+NnLaH1FRUbh9+za8vb2lstQThjo6OoiIiICjo2PeBp2FfE8sSpUqhVKlSuVJ2w4ODrC0tERQUJCUSLx69QqhoaE5Glnqc5Ld/mjQoAFevnyJ8+fPo1atWgA+HBClpKSgXr166c7z9u1bAFAbbUhbW1v6IJC6vOwTe3t7WFtbIyIiQqX8xo0baN26tfzgi6i87JOJEydi4MCBKmWurq5YsmSJypcH/Scv+wP48L3h5eUFpVKJvXv38sxsLunq6qJWrVoICgqShsNMSUlBUFAQhg8fnu48DRo0QFBQEEaNGiWVHTp0CA0aNMiHiIu+3PQJ8GHEzdmzZ+PgwYMqzyyRPDntj4oVK6rd8TFlyhS8fv0ay5Ytg42NTX6EnbmCfno8M3fu3BEXL14U/v7+wsjISFy8eFFcvHhRvH79WqpToUIFsXPnTun9d999J0xNTcWePXvElStXhI+Pj3BwcBDv3r0riFUoUlq1aiVq1KghQkNDxYkTJ0T58uVFjx49pOn//vuvqFChgggNDRVCCPH+/Xvh5OQkGjduLEJDQ8XNmzfFwoULhUKhEPv37y+o1ShSctonQgixZMkSYWJiIrZt2yYiIyPFlClThJ6enrh582ZBrEKRk5s++Rg4KpTG5LQ/YmNjRb169YSrq6u4efOmePjwofRKSkoqqNX4ZAUGBgqlUinWr18vwsLCxODBg4Wpqal49OiREEKIXr16iYkTJ0r1Q0JChI6Ojli4cKEIDw8X06ZNE8WKFRNXr14tqFUocnLaJ999953Q1dUV27dvV/k8pD0Wo9zLaX98rLCNClWoE4s+ffoIAGqv4OBgqQ4AERAQIL1PSUkRfn5+wsLCQiiVStG8eXMRERGR/8EXQc+ePRM9evQQRkZGwsTERPTr10/lH0t0dLRa/9y4cUN07NhRlC5dWhgYGIiqVauqDT9LuZebPhFCiLlz54qyZcsKAwMD0aBBA3H8+PF8jrzoym2fpMXEQnNy2h/BwcHpfu8AENHR0QWzEp+45cuXC1tbW6Grqyvq1q0rTp8+LU1zd3cXffr0Uan/22+/CWdnZ6GrqytcXFx4IioP5KRP7Ozs0v08TJs2Lf8DL6Jy+hlJq7AlFgohhMjHCyRERERERFQEFanhZomIiIiIqGAwsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREnzQPDw+MGjWqoMMgIvrsMbEgIiIiIiLZmFgQEVGBS0xMLOgQiIhIJiYWRERFzIEDB9CoUSOYmprC3Nwcbdu2RVRUFADAzc0NEyZMUKn/5MkTFCtWDMeOHQMAPHz4EG3atIG+vj4cHBywefNm2NvbY+nSpdla/j///INGjRpBT08PlStXxuHDh6FQKLB7924AwO3bt6FQKLB161a4u7tDT08PmzZtwrNnz9CjRw+UKVMGBgYGcHV1xZYtW1TafvPmDXr37g0jIyNYWVlh0aJFastPSEjA2LFjUaZMGRgaGqJevXo4evRozjYiERHlGBMLIqIi5s2bNxgzZgzOnTuHoKAgaGlpoUOHDkhJSYGvry8CAwMhhJDqb926FdbW1mjcuDEAoHfv3njw4AGOHj2KHTt24KeffkJMTEy2lp2cnIz27dvDwMAAoaGh+OmnnzB58uR0606cOBEjR45EeHg4vLy8EB8fj1q1amH//v24du0aBg8ejF69euHMmTPSPOPGjcPff/+NPXv24K+//sLRo0dx4cIFlXaHDx+OU6dOITAwEFeuXEGXLl3QqlUrREZG5nRTEhFRTggiIirSnjx5IgCIq1evipiYGKGjoyOOHTsmTW/QoIGYMGGCEEKI8PBwAUCcPXtWmh4ZGSkAiCVLlmS5rD///FPo6OiIhw8fSmWHDh0SAMSuXbuEEEJER0cLAGLp0qVZttemTRvxzTffCCGEeP36tdDV1RW//fabNP3Zs2dCX19fjBw5UgghxJ07d4S2tra4f/++SjvNmzcXkyZNynJ5RESUezoFmtUQEZHGRUZGYurUqQgNDcXTp0+RkpICALh79y6qVKmCli1bYtOmTWjcuDGio6Nx6tQp/PjjjwCAiIgI6OjooGbNmlJ7Tk5OMDMzy9ayIyIiYGNjA0tLS6msbt266datXbu2yvvk5GTMmTMHv/32G+7fv4/3798jISEBBgYGAICoqCi8f/8e9erVk+YpUaIEKlSoIL2/evUqkpOT4ezsrNJ2QkICzM3Ns7UORESUO0wsiIiKGG9vb9jZ2WHNmjWwtrZGSkoKqlSpgvfv3wMAfH19MWLECCxfvhybN2+Gq6srXF1d8z1OQ0NDlfcLFizAsmXLsHTpUri6usLQ0BCjRo2S4s6OuLg4aGtr4/z589DW1laZZmRkpJG4iYgofXzGgoioCHn27BkiIiIwZcoUNG/eHJUqVcKLFy9U6vj4+CA+Ph4HDhzA5s2b4evrK02rUKECkpKScPHiRans5s2bam1kpEKFCrh37x4eP34slZ09ezZb84aEhMDHxwdffPEFqlWrhnLlyuHGjRvSdEdHRxQrVgyhoaFS2YsXL1Tq1KhRA8nJyYiJiYGTk5PKK+1VFCIi0jwmFkRERYiZmRnMzc3x008/4ebNmzhy5AjGjBmjUsfQ0BDt27eHn58fwsPD0aNHD2laxYoV4enpicGDB+PMmTO4ePEiBg8eDH19fSgUiiyX36JFCzg6OqJPnz64cuUKQkJCMGXKFADIcv7y5cvj0KFDOHnyJMLDwzFkyBCVBMXIyAgDBgzAuHHjcOTIEVy7dg19+/aFltZ/X2XOzs7w9fVF7969sXPnTkRHR+PMmTOYO3cu9u/fn61tSEREucPEgoioCNHS0kJgYCDOnz+PKlWqYPTo0ViwYIFaPV9fX1y+fBmNGzeGra2tyrRffvkFFhYWaNKkCTp06IBBgwbB2NgYenp6WS5fW1sbu3fvRlxcHOrUqYOBAwdKo0JlNf+UKVNQs2ZNeHl5wcPDA5aWlmjfvr1KnQULFqBx48bw9vaGp6cnGjVqhFq1aqnUCQgIQO/evfHNN9+gQoUKaN++Pc6ePau2nkREpFkKIdKMOUhERPSRf//9FzY2Njh8+DCaN2+e4/lDQkLQqFEj3Lx5E46OjnkQIRERFQZMLIiISMWRI0cQFxcHV1dXPHz4EOPHj8f9+/dx48YNFCtWLMv5d+3aBSMjI5QvXx43b97EyJEjYWZmhhMnTuRD9EREVFB4KxQREalITEzEt99+CxcXF3To0AGlSpXC0aNHUaxYMWzatAlGRkbpvlxcXAAAr1+/xrBhw1CxYkX07dsXderUwZ49ewp4rYiIKK/xigUREWXb69evVR6oTqtYsWKws7PL54iIiKiwYGJBRERERESy8VYoIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCTb/wFY/jE2fA79XAAAAABJRU5ErkJggg==",
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5nqeurq7GlYS1a9dqnOS1bdsWjx8/ln7PILWU8VN+xCytZXB2dsa///4r62L3/Pnzsl6NUtqjUqlk6/HGjRtS70ja9rHn96EOHTrg7t27WLJkicawN2/eSL1MPX36VGP4h70lPXnyRDZcX18fZcuWhRAC7969S7cNXl5eslfKFYyWLVuiQIECWLhwoVRXCIFffvkFRYsWlfW0dv/+ffz777/SfBo2bIiNGzdqvAoXLoxq1aph48aN8PX1BQCUKlUKFStWxObNm2U9d+3atQu3b99G48aN01+BeN9d8P3796VnoYiIsopXLIgoW5ydnbFq1Sp07NgRZcqUkf3y9pEjR7B27VrptwkqVqyI7t2749dff8Xz58/h4eGBEydOYMWKFWjVqhUaNGggTbdPnz4YMGAA2rZti8aNG+P8+fPYuXOn7Fv+7KpatSoWLVqEqVOnwsXFBUWKFEHDhg0xevRobNmyBc2bN0ePHj1QtWpVvHr1ChcvXsS6detw48aNHM+3efPmmDx5Mnr27Al3d3dcvHgRISEhKFGihKxet27d8Pvvv2PEiBE4ceIE6tWrh1evXmHPnj0YOHAgWrZsCUNDQ5QtWxZr1qyBq6srChYsiPLly6N8+fLo1asX5syZA29vb/Tu3RsxMTH45ZdfUK5cOekhe+B9eJszZw58fHzQpUsXxMTEYMGCBXBxcZE9m6AtH3t+H+ratSv+/PNPDBgwAPv27UOdOnWQlJSEf//9F3/++Sd27tyJatWqYfLkyTh48CCaNWsGBwcHxMTEYOHChShWrJjU5WuTJk1gY2ODOnXqwNraGhEREfj555/RrFmzLHdgkFqxYsUwbNgwzJo1C+/evUP16tWxadMmHDp0CCEhIbJbvQIDA7FixQpER0fD0dER9vb2Gs8VAcCwYcNgbW2NVq1aycrnzp2Lxo0bo27duujfvz9iY2MxZ84cuLq64ptvvpHq/fHHH1i/fj3q168PExMT7NmzB3/++Sf69OmT6bMcREQa8q5DKiL6lF25ckX07dtXODo6Cn19fWFqairq1KkjfvrpJ6krTCGEePfunQgKChJOTk6iQIEConjx4iIwMFBWR4j3XV8GBASIQoUKCSMjI+Ht7S2uXbuWbpekJ0+elI2/b98+AUDs27dPKnvw4IFo1qyZMDU1FQBk3bO+fPlSBAYGChcXF6Gvry8KFSok3N3dxY8//ijevn0rhPivu9lZs2aluQ6QTnezI0eOFLa2tsLQ0FDUqVNHHD16NM3uYV+/fi3GjRsnrRsbGxvRrl07ERUVJdU5cuSIqFq1qtDX19foevaPP/4QJUqUEPr6+qJSpUpi586daXY3+9tvv4mSJUsKtVotSpcuLYKDg8XEiRPFhx8B2elu9sPl1sb80tu2Qrzvbjatrl/TWt63b9+KGTNmiHLlygm1Wi0sLS1F1apVRVBQkIiNjRVCCLF3717RsmVLYWdnJ/T19YWdnZ3o3LmzrAvixYsXi/r16wsrKyuhVquFs7OzGD16tDSNnEhKShLTpk0TDg4OQl9fX5QrV0788ccfaS4XABEdHZ3h9NLqbjbF7t27Ra1atYSBgYEoWLCg6Nq1q7h//76szvHjx0X9+vWFpaWlMDAwEBUrVhS//PKLrCtnIqKsUgmRhScdiYiIiIiIMsBnLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAirZo5cyZKly6N5OTkHI3fo0cPODo6ardRqTg6OqJ58+a5Nv1PwY0bN6BSqbB8+fJsj7tjxw6YmJjg0aNH2m+YQkr3vbT06NEDJiYmWaqrUqkwadIkrc07v5k0aRJUKlWOxu3UqRM6dOig5RYRUX7DYEFEWvPixQvMmDEDAQEB0NH578+LSqWSvYyNjVG2bFlMnToVr1+/zsMWK7d8+XKN5Ut5jR07Nq+bp3U+Pj5wcXHB9OnT87opMh/ue0lJSTAzM0PLli016s6dOxcqlQrdu3fXGDZhwgSoVCpcuXJFcZuOHDmCSZMm4fnz54qnlRUp+12fPn3SHD5u3DipzuPHjz9Km1IEBARg/fr1OH/+/EedLxF9XHp53QAi+nwsW7YMiYmJ6Ny5s8awxo0bo1u3bgCAuLg4HDp0COPHj8f58+exdu3aj91UrZs8eTKcnJxkZeXLl8+j1uSu/v37Y9SoUQgKCoKpqWleNweA5r6nq6uLWrVq4ciRIxp1w8LCoKenh7CwsDSHFSlSBK6urtluw5s3b6Cn99/H6pEjRxAUFIQePXrAwsIi29PLCQMDA6xfvx4LFy6Evr6+bNjq1athYGCA+Pj4j9KW1CpXroxq1aph9uzZ+P333z/6/Ino4+AVCyLSmuDgYLRo0QIGBgYaw1xdXfH111/j66+/xoABAxASEoJ27dphw4YNeXKio21NmzaVli/lValSpbxuVq5o27YtEhISFAXCqKgorV6tSmvfq1u3Lh4/foyIiAhZ3bCwMHTo0AFRUVF48OCBVJ6YmIjjx4+jTp06OWqDgYGBLFjkBR8fH7x48QLbt2+XlR85cgTR0dFo1qxZHrUM6NChAzZs2IC4uLg8awMR5S4GCyLSiujoaFy4cAFeXl5ZHsfGxgYqlSrTk7Eff/wR7u7usLKygqGhIapWrYp169alWfePP/5AjRo1YGRkBEtLS9SvXx+7du3KcPorVqyAnp4eRo8eneW2Z9f27dtRr149GBsbw9TUFM2aNcPly5dldVLu57916xaaN28OExMTFC1aFAsWLAAAXLx4EQ0bNoSxsTEcHBywatUq2fhPnz7FqFGj4ObmBhMTE5iZmaFp06ZZvv3k33//Rbt27VCwYEEYGBigWrVq2LJli0a9IkWKoEKFCti8eXMO1wawcuVK2NraYsCAATh58mSOpwOkv+/VrVsXAGRXJq5fv44HDx5g0KBBMDAwkA07d+4cXr16JY2X2t27d9GqVSuYmJigcOHCGDVqFJKSkmR1Uj9jMWnSJGl/cnJykm5BunHjhlT/jz/+QNWqVWFoaIiCBQuiU6dOuH37tqJ1UbRoUdSvX19j3wgJCYGbm1uaV9EOHTqE9u3bw97eHmq1GsWLF8fw4cPx5s2bLM0zq8vRuHFjvHr1Crt3787ZwhFRvsdgQURakXLLSZUqVdIcHh8fj8ePH+Px48e4efMmVq1ahRUrVqBLly6ZBov58+ejcuXKmDx5MqZNmwY9PT20b98e27Ztk9ULCgpC165dUaBAAUyePBlBQUEoXrw4/vnnn3Sn/euvv6Jnz54YO3YsZs2alc2l/k9sbKy0fCmvFCtXrkSzZs1gYmKCGTNmYPz48QgPD0fdunVlJ5oAkJSUhKZNm6J48eKYOXMmHB0dMWjQICxfvhw+Pj6oVq0aZsyYAVNTU3Tr1g3R0dHSuNevX8emTZvQvHlzzJkzB6NHj8bFixfh4eGBe/fuZdj+y5cvo1atWoiIiMDYsWMxe/ZsGBsbo1WrVti4caNG/apVq6Z5m1FWdenSBX5+flizZg1q1KiBChUqYP78+Xjy5Em2p5XevlerVi3o6enh8OHDUllYWBiMjY1RvXp1VKtWTRYsUv7/YbBISkqCt7c3rKys8OOPP8LDwwOzZ8/Gr7/+mm6b2rRpI92WNXfuXKxcuRIrV65E4cKFAQDff/89unXrhpIlS2LOnDkYNmwY9u7di/r16yt+JqNLly7YunWrdGUgMTERa9euRZcuXdKsv3btWrx+/RrffPMNfvrpJ3h7e+Onn36Sbl3MSHaWo2zZsjA0NEzzFjQi+kwIIiIt+O677wQA8fLlS41hANJ8tWrVSsTHx8vqdu/eXTg4OMjKXr9+LXv/9u1bUb58edGwYUOp7OrVq0JHR0e0bt1aJCUlyeonJydL/3dwcBDNmjUTQggxf/58oVKpxJQpU3K0zEIIERwcnO7yCSHEy5cvhYWFhejbt69svAcPHghzc3NZeffu3QUAMW3aNKns2bNnwtDQUKhUKhEaGiqV//vvvwKAmDhxolQWHx+vsezR0dFCrVaLyZMny8oAiODgYKmsUaNGws3NTbY9kpOThbu7uyhZsqTGck+bNk0AEA8fPszimkrbmzdvREhIiGjUqJFQqVRCrVaLjh07il27dmksS3oy2veqV68unJ2dpff9+/cXDRo0EEIIMWbMGFG9enVpWLt27YSRkZF49+6dVJayTVKvPyGEqFy5sqhataqs7MPtMWvWLAFAREdHy+rduHFD6Orqiu+//15WfvHiRaGnp6dRnlUAhL+/v3j69KnQ19cXK1euFEIIsW3bNqFSqcSNGzfExIkTBQDx6NEjabwPjy8hhJg+fbpQqVTi5s2bUlnKuEqWw9XVVTRt2jRHy0dE+R+vWBCRVjx58gR6enrpds3ZsmVL7N69G7t378bmzZsRGBiIHTt2oEuXLhBCZDhtQ0ND6f/Pnj1DbGws6tWrhzNnzkjlmzZtQnJyMiZMmCDrkQpAml1kzpw5E0OHDsWMGTPw3XffZWdR07RgwQJp+VJeALB79248f/4cnTt3ll3N0NXVRc2aNbFv3z6NaaXu1cfCwgKlSpWCsbGxrLvOUqVKwcLCAtevX5fK1Gq1tOxJSUl48uQJTExMUKpUKdm6+tDTp0/xzz//oEOHDnj58qXUxidPnsDb2xtXr17F3bt3ZeNYWloCgOLehQwMDNClSxfs2bMH0dHRCAwMxPHjx9GkSROUKFEiS71PZbTv1a1bV/YsRVhYGNzd3QEAderUwdmzZ6VnPcLCwlCzZs00r6ANGDBA9r5evXqydZ8dGzZsQHJyMjp06CDbJ2xsbFCyZMk094nssLS0hI+PD1avXg0AWLVqFdzd3eHg4JBm/dTH16tXr/D48WO4u7tDCIGzZ89qdTksLS0/eo9URPTxsFcoIvooihUrJrsHvkWLFrCyssKoUaPw119/wdfXN91x//rrL0ydOhXnzp1DQkKCVJ46MERFRUFHRwdly5bNtC0HDhzAtm3bEBAQoLXnKmrUqIFq1applF+9ehUA0LBhwzTHMzMzk703MDCQbpdJYW5ujmLFimkEJHNzczx79kx6n5ycjPnz52PhwoWIjo6WPQNgZWWVbtuvXbsGIQTGjx+P8ePHp1knJiYGRYsWld6nhMGMftfgzZs3iI2NlZXZ2NikW9/BwQETJ07EgAED0LdvX2zduhUzZsxAYGBguuNkpm7dupg7dy7CwsLQqFEjXL58GTNnzgQAuLu7IzExESdOnICDgwPu37+fZletaW0TS0tL2brPjqtXr0IIgZIlS6Y5vECBAjmabmpdunRB165dcevWLWzatEla5rTcunULEyZMwJYtWzSW6cPtl1pOlkMIkePfwiCi/I/Bgoi0wsrKComJiXj58mWWuyBt1KgRAODgwYPpBotDhw6hRYsWqF+/PhYuXAhbW1sUKFAAwcHBGg+oZlW5cuXw/PlzrFy5Ev3799foJlabUn6sbeXKlWmeVH/47biurm6a00mvPPXVnmnTpmH8+PHo1asXpkyZgoIFC0JHRwfDhg3L8EfjUoaNGjUK3t7eadZxcXGRvU85AS1UqFC6012zZg169uyZbntTS0xMxN9//43g4GBs27YNQgi0atUKffv2TXf6KTLa91Kelzh8+DCMjIwAALVr15baXrJkSRw+fFh62DitB7fTW/c5lZycDJVKhe3bt6c57az+IF9GWrRoAbVaje7duyMhISHdH6dLSkpC48aN8fTpUwQEBKB06dIwNjbG3bt30aNHj0z3m+wux7Nnz9INIkT06WOwICKtKF26NID3PfRUqFAhS+MkJiYCQIbdT65fvx4GBgbYuXMn1Gq1VB4cHCyr5+zsjOTkZISHh2fazWuhQoWwbt061K1bF40aNcLhw4dhZ2eXpTZnl7OzM4D3PSllp8esnFi3bh0aNGiA3377TVb+/PnzDANAiRIlALz/hjmrbYyOjkahQoU0vslPzdvbO9MegMLDwxEcHIyVK1fi4cOHcHV1xZQpU9CjRw9YW1tnqS0Z7XtFihSRwkPKDzOm/k0Jd3d3hIWF4c6dO9DV1ZVChzak9828s7MzhBBwcnLK0e9lZIWhoSFatWqFP/74A02bNk13+1+8eBFXrlzBihUrZA9rZ6XnpuwuR2JiIm7fvo0WLVpkfUGI6JPCZyyISCtSTshOnTqV5XG2bt0KAKhYsWK6dXR1daFSqWS39dy4cQObNm2S1WvVqhV0dHQwefJkjW9Z0/qWvFixYtizZw/evHmDxo0b56g3oqzw9vaGmZkZpk2bhnfv3mkMf/Tokdbmpaurq7Gsa9eu1Xg+4kNFihSBp6cnFi9ejPv372epjadPn870JNzW1hZeXl6yV4r9+/ejVq1aKFeuHBYsWIAmTZrgwIEDiIyMREBAQJZDBZD5vle3bl2cO3cOu3btkp6vSOHu7o6jR4/i0KFDqFChglZ/8M/Y2BgANHpHatOmDXR1dREUFKSxvYQQWtsXR40ahYkTJ6Z7exvw39WY1O0QQmD+/PmZTj+7yxEeHo74+HiNbUBEnw9esSAirShRogTKly+PPXv2oFevXhrDr1y5gj/++AMA8Pr1axw7dgwrVqyAi4sLunbtmu50mzVrhjlz5sDHxwddunRBTEwMFixYABcXF1y4cEGq5+LignHjxmHKlCmoV68e2rRpA7VajZMnT8LOzi7Nh4BdXFywa9cueHp6wtvbG//884/0zEOPHj2wYsUKREdHw9HRMcfrxczMDIsWLULXrl1RpUoVdOrUCYULF8atW7ewbds21KlTBz///HOOp59a8+bNMXnyZPTs2RPu7u64ePEiQkJCpCsSGVmwYAHq1q0LNzc39O3bFyVKlMDDhw9x9OhR3LlzR/ZbGDExMbhw4QL8/f1z3NYDBw7g3bt3WLhwIbp06QJzc/McTyuzfa9u3boIDg7GyZMnNdrs7u6O2NhYxMbGYvDgwTluQ1qqVq0KABg3bhw6deqEAgUKwNfXF87Ozpg6dSoCAwNx48YNtGrVCqampoiOjsbGjRvRr18/jBo1CsD7ANagQQNMnDhR+o2MrKpYsWKGoR14f7XH2dkZo0aNwt27d2FmZob169dn6fmR7CwH8P4qiJGRERo3bpyt5SCiT8jH7YSKiD5nc+bMESYmJhrdV+KDblh1dXVFsWLFRL9+/TS6K02ru9nffvtNlCxZUqjValG6dGkRHBys0fVlimXLlonKlSsLtVotLC0thYeHh9i9e7c0PHV3symOHz8uTE1NRf369aW2t23bVhgaGopnz55luMwp3c2ePHkyw3r79u0T3t7ewtzcXBgYGAhnZ2fRo0cPcerUKdmyGxsba4zr4eEhypUrp1H+4bLEx8eLkSNHCltbW2FoaCjq1Kkjjh49Kjw8PISHh4dUL63uZoUQIioqSnTr1k3Y2NiIAgUKiKJFi4rmzZuLdevWyeotWrRIGBkZiRcvXmS4zBmJi4vL8bhpSW/fE0KIyMhIad+7cuWKbFhycrKwsLAQAMSaNWs0xk1vm6S1/+GD7maFEGLKlCmiaNGiQkdHR6Pr2fXr14u6desKY2NjYWxsLEqXLi38/f1FZGSkVGfr1q0CgPjll18yXQf4/+5mM5JWd7Ph4eHCy8tLmJiYiEKFCom+ffuK8+fPa+wj6R1zWVkOIYSoWbOm+PrrrzNdDiL6dKmEyKSfRyKiLIqNjUWJEiUwc+ZM9O7dO6+bo4i1tTW6deum6EfzPleVK1eGp6cn5s6dm9dNkXxO+15qY8aMwerVq3Ht2jXZM0afmnPnzqFKlSo4c+ZMps9AEdGni8GCiLRqxowZCA4ORnh4uMbvSXwqLl++jNq1a+P69esZPvT8JdqxYwfatWuH69evo0iRInndHJnPYd/7UPXq1dG3b1/069cvr5uiSKdOnZCcnIw///wzr5tCRLmIwYKIiIiIiBT7PL7SISIiIiKiPMVgQUREREREijFYEBERERGRYgwWRERERESkWL77gbzk5GTcu3cPpqamUKlUed0cIiIiIqIvlhACL1++hJ2dXaY97uW7YHHv3j0UL148r5tBRERERET/7/bt2yhWrFiGdfJdsDA1NQXwvvFmZmZ53BoiIiIioi/XixcvULx4cekcPSP5Llik3P5kZmbGYEFERERElA9k5REFPrxNRERERESKMVgQEREREZFiDBZERERERKRYvnvGIquSkpLw7t27vG4GERF94fT19TPtgpGI6EvwyQULIQQePHiA58+f53VTiIiIoKOjAycnJ+jr6+d1U4iI8tQnFyxSQkWRIkVgZGTEH9EjIqI8k/Kjrvfv34e9vT0/k4joi/ZJBYukpCQpVFhZWeV1c4iIiFC4cGHcu3cPiYmJKFCgQF43h4goz3xSN4WmPFNhZGSUxy0hIiJ6L+UWqKSkpDxuCRFR3vqkgkUKXmomIqL8gp9JRETvfZLBgoiIiIiI8hcGi3zI0dER8+bNy+tmaM2nvjwfo/179+5FmTJlvohbKW7cuAGVSoVz585pbZoqlQqbNm3S2vTyQm6sl0/ZpEmTUKlSpSzXf/z4MYoUKYI7d+7kXqOIiChD2Q4WBw8ehK+vL+zs7DL9MB8wYABUKtVHOan09f24r5y4ffs2evXqBTs7O+jr68PBwQFDhw7FkydPtLsyPjGvX79GYGAgnJ2dYWBggMKFC8PDwwObN2/O66Z9NGPGjMF3330HXV1dAMDy5cuhUqmgUqmgo6MDW1tbdOzYEbdu3crjlmasR48eUrtVKhWsrKzg4+ODCxcu5HXTsmTfvn1o3rw5ChcuDAMDAzg7O6Njx444ePBgXjct1+zfv1+2zVJe3333XV43LVsKFSqEbt26YeLEiXndFCKiL1a2g8WrV69QsWJFLFiwIMN6GzduxLFjx2BnZ5fjxn1Orl+/jmrVquHq1atYvXo1rl27hl9++QV79+5F7dq18fTp0zxrW1JSEpKTk/Ns/gMGDMCGDRvw008/4d9//8WOHTvQrl27LyZwHT58GFFRUWjbtq2s3MzMDPfv38fdu3exfv16REZGon379nnUyqzz8fHB/fv3cf/+fezduxd6enpo3rx5XjcrUwsXLkSjRo1gZWWFNWvWIDIyEhs3boS7uzuGDx+e183Lkrdv3+Z43MjISGm73b9/H2PHjtViyz6Onj17IiQkJE//nhIRfcmyHSyaNm2KqVOnonXr1unWuXv3LgYPHoyQkBB2vff//P39oa+vj127dsHDwwP29vZo2rQp9uzZg7t372LcuHGy+i9fvkTnzp1hbGyMokWLyoKcEAKTJk2Cvb091Go17OzsMGTIEGl4QkICRo0ahaJFi8LY2Bg1a9bE/v37peHLly+HhYUFtmzZgrJly0KtVmPp0qUwMDDQ+OHBoUOHomHDhtL7w4cPo169ejA0NETx4sUxZMgQvHr1ShoeExMDX19fGBoawsnJCSEhIZmumy1btuDbb7/FV199BUdHR1StWhWDBw9Gr169pDorV65EtWrVYGpqChsbG3Tp0gUxMTHS8JRvXXfu3InKlSvD0NAQDRs2RExMDLZv344yZcrAzMwMXbp0wevXr6XxPD09MWjQIAwaNAjm5uYoVKgQxo8fDyFEuu19/vw5+vTpg8KFC8PMzAwNGzbE+fPnpeHnz59HgwYNYGpqCjMzM1StWhWnTp1Kd3qhoaFo3LgxDAwMZOUqlQo2NjawtbWFu7s7evfujRMnTuDFixdSnYCAALi6usLIyAglSpTA+PHjNX6RfuvWrahevToMDAxQqFAh2bGb2b6SE2q1GjY2NrCxsUGlSpUwduxY3L59G48ePUqzflJSEnr37g0nJycYGhqiVKlSmD9/vka9ZcuWoVy5clCr1bC1tcWgQYPSbcPEiRNha2ub5Sslt27dwrBhwzBs2DCsWLECDRs2hIODAypUqIChQ4dqbL/MjgNHR0dMmzYNvXr1gqmpKezt7fHrr7/KpnHixAlUrlwZBgYGqFatGs6ePavRrkuXLqFp06YwMTGBtbU1unbtisePH0vDU/bfYcOGoVChQvD29s7S8qalSJEi0nazsbGBiYkJgPdXWjt06AALCwsULFgQLVu2xI0bN6TxevTogVatWmHatGmwtraGhYUFJk+ejMTERIwePRoFCxZEsWLFEBwcLJtfVvbdDy1duhRlypSBgYEBSpcujYULF8qGlytXDnZ2dti4cWOO1wMREeWc1p+xSE5ORteuXTF69GiUK1cu0/oJCQl48eKF7PW5efr0KXbu3ImBAwfC0NBQNszGxgZ+fn5Ys2aN7GR21qxZqFixIs6ePYuxY8di6NCh2L17NwBg/fr1mDt3LhYvXoyrV69i06ZNcHNzk8YdNGgQjh49itDQUFy4cAHt27eHj48Prl69KtV5/fo1ZsyYgaVLl+Ly5cvw8/ODhYUF1q9fL9VJSkrCmjVr4OfnBwCIioqCj48P2rZtiwsXLmDNmjU4fPiw7ASvR48euH37Nvbt24d169Zh4cKFsgCQFhsbG/z99994+fJlunXevXuHKVOm4Pz589i0aRNu3LiBHj16aNSbNGkSfv75Zxw5ckQ6IZo3bx5WrVqFbdu2YdeuXfjpp59k46xYsQJ6eno4ceIE5s+fjzlz5mDp0qXptqV9+/ZSYDl9+jSqVKmCRo0aSd+S+vn5oVixYjh58iROnz6NsWPHZhiwDx06hGrVqmW4jmJiYrBx40bo6upKt0sBgKmpKZYvX47w8HDMnz8fS5Yswdy5c6Xh27ZtQ+vWrfHVV1/h7Nmz2Lt3L2rUqCENz2xfuXXrFkxMTDJ8TZs2Ld12x8XF4Y8//oCLi0u6vz2TnJyMYsWKYe3atQgPD8eECRPw7bff4s8//5TqLFq0CP7+/ujXrx8uXryILVu2wMXFRWNaQggMHjwYv//+Ow4dOoQKFSpkuF5TrF+/Hu/evcOYMWPSHJ6615+sHAcAMHv2bCkwDBw4EN988w0iIyOl9dK8eXOULVsWp0+fxqRJkzBq1CjZ+M+fP0fDhg1RuXJlnDp1Cjt27MDDhw/RoUMHWb0VK1ZAX18fYWFh+OWXXwBACiPpvbLytxl4f9x5e3vD1NQUhw4dQlhYGExMTODj4yO7OvLPP//g3r17OHjwIObMmYOJEyeiefPmsLS0xPHjxzFgwAD0799f9vxDZvvuh0JCQjBhwgR8//33iIiIwLRp0zB+/HisWLFCVq9GjRo4dOhQlpaPiIi0TCgAQGzcuFFWNm3aNNG4cWORnJwshBDCwcFBzJ07N91pTJw4UQDQeMXGxmrUffPmjQgPDxdv3rzRGNa8+cd9ZcexY8fSXFcp5syZIwCIhw8fSuvMx8dHVqdjx46iadOmQgghZs+eLVxdXcXbt281pnXz5k2hq6sr7t69Kytv1KiRCAwMFEIIERwcLACIc+fOyeoMHTpUNGzYUHq/c+dOoVarxbNnz4QQQvTu3Vv069dPNs6hQ4eEjo6OePPmjYiMjBQAxIkTJ6ThERERAkCG+8CBAwdEsWLFRIECBUS1atXEsGHDxOHDh9OtL4QQJ0+eFADEy5cvhRBC7Nu3TwAQe/bskepMnz5dABBRUVFSWf/+/YW3t7f03sPDQ5QpU0baX4UQIiAgQJQpU0Z6n3ofPnTokDAzMxPx8fGy9jg7O4vFixcLIYQwNTUVy5cvz7D9qZmbm4vff/9dVpayjYyNjYWRkZF0XAwZMiTDac2aNUtUrVpVel+7dm3h5+eXZt2s7Cvv3r0TV69ezfD15MkTadzu3bsLXV1dYWxsLIyNjQUAYWtrK06fPi3ViY6OFgDE2bNn010Of39/0bZtW+m9nZ2dGDduXLr1AYi1a9eKLl26iDJlyog7d+6kWzctAwYMEGZmZrKydevWScthbGwsLly4IITI/DgQ4v0+8/XXX0vDk5OTRZEiRcSiRYuEEEIsXrxYWFlZyf6WLVq0SLZepkyZIpo0aSKbz+3btwUAERkZKYR4v/9WrlxZY3nu3LmT4Ta7ceOGVDfl2Em9rMbGxuLx48di5cqVolSpUrLjIyEhQRgaGoqdO3cKId5vcwcHB5GUlCTVKVWqlKhXr570PjExURgbG4vVq1enuf6F0Nx3J06cKCpWrCi9d3Z2FqtWrZKNM2XKFFG7dm1Z2fDhw4Wnp2e688kNGX02Ecnsa/7fS0kdoo8oNjY23XPzD2n1l7dPnz6N+fPn48yZM1nu1zswMBAjRoyQ3r948QLFixfXZrPyDZHB7TUfql27tsb7lIfg27dvj3nz5qFEiRLw8fHBV199BV9fX+jp6eHixYtISkqCq6urbPyEhATZN8b6+voa3+b6+fmhVq1auHfvHuzs7BASEoJmzZrBwsICwPtbfC5cuCC7vUkIgeTkZERHR+PKlSvQ09ND1apVpeGlS5eWxk9P/fr1cf36dRw7dgxHjhzB3r17MX/+fAQFBWH8+PEAIH2re/78eTx79kx6JuTWrVsoW7asNK3Uy2RtbS3dZpG67MSJE7L516pVS7a/1q5dG7Nnz0ZSUpLs6kDKOoiLi9P49v3NmzeIiooCAIwYMQJ9+vTBypUr4eXlhfbt28PZ2Tnd5X/z5o3GbVDA+290z5w5g3fv3mH79u0ICQnB999/L6uzZs0a/O9//0NUVBTi4uKQmJgIMzMzafi5c+fQt2/fNOeblX1FT08vzSsDGWnQoAEWLVoEAHj27BkWLlyIpk2b4sSJE3BwcEhznAULFmDZsmW4desW3rx5g7dv30o9AsXExODevXto1KhRhvMdPnw41Go1jh07hkKFCmWrzYDmbxF4e3vj3LlzuHv3Ljw9PaUeuzI7DsqUKQNAvi+m3NaWcvUuIiICFSpUkG33D4/58+fPY9++fdItSalFRUVJ2y318ZaiaNGi2Vp24P2VM1NTU+m9paUlzp8/j2vXrsnKASA+Pl7a34H3tyDp6Px3Adza2hrly5eX3uvq6sLKykp29TKzfTe1V69eISoqCr1795btz4mJiTA3N5fVNTQ0lN3uSEREH49Wg8WhQ4cQExMDe3t7qSwpKQkjR47EvHnzZPflplCr1VCr1dpsRr7j4uIClUqFiIiINJ9NiYiIgKWlJQoXLpyl6RUvXhyRkZHYs2cPdu/ejYEDB2LWrFk4cOAA4uLioKuri9OnT2ucFKc+QTE0NNQ4kapevTqcnZ0RGhqKb775Bhs3bsTy5cul4XFxcejfv7/seY4U9vb2uHLlSpban5YCBQqgXr16qFevHgICAjB16lRMnjwZAQEB0u0Y3t7eCAkJQeHChXHr1i14e3trPKya+pYjlUqlcQuSSqVS9KB6XFwcbG1t03wOISVATZo0CV26dMG2bduwfft2TJw4EaGhoek+l1SoUCE8e/ZMo1xHR0c6qS9TpgyioqLwzTffYOXKlQCAo0ePws/PD0FBQfD29oa5uTlCQ0Mxe/ZsaRof3nr34bJktq98GNzS8u233+Lbb7+V3hsbG8vCyNKlS2Fubo4lS5Zg6tSpGuOHhoZi1KhRmD17NmrXrg1TU1PMmjULx48fz3QZUmvcuDFWr16NnTt3SrfvZVXJkiURGxuLBw8ewMbGBsD7deDi4gI9PfmfycyOgxRK9724uDj4+vpixowZGsNsbW2l/xsbG2sMb9q0aYa3Azk4OODy5cuyMicnJ40vAeLi4lC1atU0n5VK/fcqrWXNaPmzsu9+2A4AWLJkCWrWrCkb9uG++/Tp0yz/LSUiIu3SarDo2rUrvLy8ZGXe3t7o2rUrevbsqc1ZfVKsrKzQuHFjLFy4EMOHD5edKD148AAhISHo1q2b7ET/2LFjsmkcO3ZM+iYUeH+y5evrC19fX/j7+6N06dK4ePEiKleujKSkJMTExKBevXrZbqufnx9CQkJQrFgx6OjooFmzZtKwKlWqIDw8PN1vsEuXLo3ExEScPn0a1atXB/C+p5kPHwjPirJlyyIxMRHx8fG4evUqnjx5gh9++EG6mpXRw9DZlXICm+LYsWMoWbKkxgkL8H4dPHjwAHp6enB0dEx3mq6urnB1dcXw4cPRuXNnBAcHpxssKleujPDw8EzbOXbsWDg7O2P48OGoUqUKjhw5AgcHB9mD/zdv3pSNU6FCBezduzfN4y8r+4qdnV2mv6tQsGDBDIendJn75s2bNIeHhYXB3d0dAwcOlMpSfxtuamoKR0dH7N27Fw0aNEh3Pi1atICvry+6dOkCXV1ddOrUKcN2pdauXTuMHTsWM2bMyPA+fyDz4yArypQpg5UrVyI+Pl66avHhMV+lShWsX78ejo6OGuEmM0uXLk13fQOaQSA9VapUwZo1a1CkSJF0rybkRFb23dSsra1hZ2eH69evZxoaL126BE9PT201lYiIsiHbwSIuLg7Xrl2T3kdHR+PcuXMoWLAg7O3tNW4RKVCgAGxsbFCqVCnlrf2E/fzzz3B3d4e3tzemTp0KJycnXL58GaNHj0bRokU1bnEJCwvDzJkz0apVK+zevRtr167Ftm3bALzv1SkpKQk1a9aEkZER/vjjDxgaGsLBwQFWVlbw8/NDt27dMHv2bFSuXBmPHj3C3r17UaFCBVlQSIufnx8mTZqE77//Hu3atZNdTQoICECtWrUwaNAg9OnTB8bGxggPD8fu3bvx888/o1SpUvDx8UH//v2xaNEi6OnpYdiwYZl+4+zp6YnOnTujWrVqsLKyQnh4OL799ls0aNAAZmZmsLe3h76+Pn766ScMGDAAly5dwpQpU3K4JTTdunULI0aMQP/+/XHmzBn89NNP6X5z6uXlhdq1a6NVq1aYOXMmXF1dce/ePekh6XLlymH06NFo164dnJyccOfOHZw8eVKjK9nUvL29NR5ATUvx4sXRunVrTJgwAX/99RdKliyJW7duITQ0FNWrV8e2bds0esOZOHEiGjVqBGdnZ3Tq1AmJiYn4+++/pR55MttXcnIrVEJCAh48eADg/a1QP//8s/Tte1pKliyJ33//HTt37oSTkxNWrlyJkydPwsnJSaozadIkDBgwAEWKFEHTpk3x8uVLhIWFYfDgwbJptW7dGitXrkTXrl2hp6eHdu3aZanN9vb2mD17NoYOHYqnT5+iR48ecHJywtOnT/HHH38A+O+b8cyOg6zo0qULxo0bh759+yIwMBA3btzAjz/+KKvj7++PJUuWoHPnzhgzZgwKFiyIa9euITQ0FEuXLk0z+KbIya1QafHz88OsWbPQsmVLTJ48GcWKFcPNmzexYcMGjBkzBsWKFcvRdLOy734oKCgIQ4YMgbm5OXx8fJCQkIBTp07h2bNn0u20r1+/xunTpzPsUICIiHJPtnuFOnXqFCpXrozKlSsDeH8/eeXKlTFhwgStN+5zUrJkSZw6dQolSpRAhw4d4OzsjH79+qFBgwY4evSoxre+I0eOlNb11KlTMWfOHKkrSQsLCyxZsgR16tRBhQoVsGfPHmzdulUKdcHBwejWrRtGjhyJUqVKoVWrVjh58qTsNo30uLi4oEaNGrhw4YLGN4MVKlTAgQMHcOXKFdSrV0/a7ql/qyQ4OBh2dnbw8PBAmzZt0K9fPxQpUiTDeaacWDdp0gRlypTB4MGD4e3tLfUKVLhwYSxfvhxr165F2bJl8cMPP2ichCnRrVs3vHnzBjVq1IC/vz+GDh2Kfv36pVlXpVLh77//Rv369dGzZ0+4urqiU6dOuHnzJqytraGrq4snT56gW7ducHV1RYcOHdC0aVMEBQWlO38/Pz9cvnxZ6jEoI8OHD8e2bdtw4sQJtGjRAsOHD8egQYNQqVIlHDlyRHomJYWnpyfWrl2LLVu2oFKlSmjYsKHsGRMl+0p6duzYAVtbW9ja2qJmzZo4efIk1q5dm+63yP3790ebNm3QsWNH1KxZE0+ePJFdvQCA7t27Y968eVi4cCHKlSuH5s2by3o5S61du3ZYsWIFunbtig0bNgB4H0wyusIEAIMHD8auXbvw6NEjtGvXDiVLlsRXX32F6Oho7NixQ+p5LSvHQWZMTEywdetW6SrjuHHjNG55srOzQ1hYGJKSktCkSRO4ublh2LBhsLCwkD3PkJuMjIxw8OBB2Nvbo02bNihTpgx69+6N+Ph4RVcwsrLvfqhPnz5YunQpgoOD4ebmBg8PDyxfvlwWQDdv3gx7e/scXa0lIiLlVCI7TxR/BC9evIC5uTliY2M1Prji4+MRHR0NJyenNB92JcouT09PVKpU6aP8OnxGRo8ejRcvXmDx4sV52o7PVffu3aFSqWTPDNHnp1atWhgyZAi6dOnyUefLzybKsv2prtx6bs15HaKPKKNz8w99nK+9iChD48aNg4ODQ57+AvrnSgiB/fv3a/X2Ocp/Hj9+jDZt2qBz58553RQioi+WVh/eJqKcsbCwkPWsRNqjUqkyfDCYPg+FChVK9wcOiYjo42CwoC9aWt3GEhEREVH28VYoIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCCtCAsLg5ubGwoUKIBWrVrldXMydOPGDahUKpw7dy6vm5IjH6v948ePR79+/XJ1HvnV/v37oVKp8Pz583TrTJo0CZUqVfpobcprjo6Oef4L9dqQ3e32+PFjFClSBHfu3Mm9RhERfSY+n2Cx3/fjvnLgwYMHGDx4MEqUKAG1Wo3ixYvD19cXe/fu1fLKyJxKpcKmTZu0Nr0RI0agUqVKiI6OxvLly7UyTW23MTuio6PRpUsX2NnZwcDAAMWKFUPLli3x77//5kl7PrYHDx5g/vz5GDdunFTWo0cPqFQqqFQqFChQAE5OThgzZgzi4+PzsKUZ69SpE3x8fGRlO3bsgEqlwqRJk2TlkyZNgr29fZanPWrUKNmx26NHj1wN1deuXUOvXr1gb28PtVqNokWLolGjRggJCUFiYmKuzTevpexzx44dk5UnJCTAysoKKpUqV3+PplChQujWrRsmTpyYa/MgIvpcfD7BIp+7ceMGqlatin/++QezZs3CxYsXsWPHDjRo0AD+/v553bwce/fuHQAgKioKDRs2RLFixWBhYZG3jVLo3bt3aNy4MWJjY7FhwwZERkZizZo1cHNzy/Ab7M/J0qVL4e7uDgcHB1m5j48P7t+/j+vXr2Pu3LlYvHhxvj7hatCgAcLCwmQn3vv27UPx4sU1Tkb37duHBg0aZHnaJiYmsLKy0lZTM3TixAlUqVIFERERWLBgAS5duoT9+/ejT58+WLRoES5fvvxR2qHE27dvczxu8eLFERwcLCvbuHEjTExMlDYrS3r27ImQkBA8ffr0o8yPiOhTxWDxkQwcOBAqlQonTpxA27Zt4erqinLlymHEiBGyb+Ju3bqFli1bwsTEBGZmZujQoQMePnwoDU/rW9Fhw4bB09NTeu/p6YkhQ4ZgzJgxKFiwIGxsbGTfzjo6OgIAWrduDZVKJb0HgM2bN6NKlSowMDBAiRIlEBQUJDspU6lUWLRoEVq0aAFjY2P07dsXKpUKT548Qa9evaBSqbB8+XIkJSWhd+/ecHJygqGhIUqVKoX58+drrJdly5ahXLlyUKvVsLW1xaBBgzJsY1aWf8eOHahbty4sLCxgZWWF5s2bIyoqKp0to+ny5cuIiorCwoULUatWLTg4OKBOnTqYOnUqatWqJdULCAiAq6srjIyMUKJECYwfP14KWsB/t1wsW7YM9vb2MDExwcCBA5GUlISZM2fCxsYGRYoUwffffy+bf8o6btq0KQwNDVGiRAmsW7cuwzZfunQJTZs2hYmJCaytrdG1a1c8fvxYGr5u3Tq4ubnB0NAQVlZW8PLywqtXr9KdXmhoKHx9Na/MqdVq2NjYoHjx4mjVqhW8vLywe/duafiTJ0/QuXNnFC1aFEZGRnBzc8Pq1atl00hOTsbMmTPh4uICtVoNe3t72Tq4ffs2OnToAAsLCxQsWBAtW7bEjRs3Mlz+9DRo0ABxcXE4deqUVLZ//36MHTsWx48fl662xMfH4/jx4xrB4vTp06hWrRqMjIzg7u6OyMhIaVjqW2omTZqEFStWYPPmzdI37CnBRenyCCHQo0cPuLq6IiwsDL6+vihZsiRKliyJzp074/Dhw6hQoYJUP7P5pRxDP/74I2xtbWFlZQV/f3/ZvhsTEwNfX18YGhrCyckJISEhGu16/vw5+vTpg8KFC8PMzAwNGzbE+fPnNdbP0qVL4eTkBAMDgywv84e6d++O0NBQvHnzRipbtmwZunfvrlE3s+MyLUuXLkWZMmVgYGCA0qVLY+HChbLh5cqVg52dHTZu3JjjZSAi+hIwWHwET58+xY4dO+Dv7w9jY2ON4Snf8CcnJ6Nly5Z4+vQpDhw4gN27d+P69evo2LFjtue5YsUKGBsb4/jx45g5cyYmT54snQCePHkSABAcHIz79+9L7w8dOoRu3bph6NChCA8Px+LFi7F8+XKNE99JkyahdevWuHjxIoKCgnD//n2YmZlh3rx5uH//Pjp27Ijk5GQUK1YMa9euRXh4OCZMmIBvv/0Wf/75pzSdRYsWwd/fH/369cPFixexZcsWuLi4ZNjGrHj16hVGjBiBU6dOYe/evdDR0UHr1q2RnJycpfELFy4MHR0drFu3DklJSenWMzU1xfLlyxEeHo758+djyZIlmDt3rqxOVFQUtm/fjh07dmD16tX47bff0KxZM9y5cwcHDhzAjBkz8N133+H48eOy8caPH4+2bdvi/Pnz8PPzQ6dOnRAREZFmO54/f46GDRuicuXKOHXqFHbs2IGHDx+iQ4cOAID79++jc+fO6NWrFyIiIrB//360adMGQog0p/f06VOEh4ejWrVqGa6nS5cu4ciRI9DX15fK4uPjUbVqVWzbtg2XLl1Cv3790LVrV5w4cUKqExgYiB9++AHjx49HeHg4Vq1aBWtrawDvrxZ5e3vD1NQUhw4dQlhYGExMTODj4yN94x0SEgITE5MMX4cOHQIAuLq6ws7ODvv27QMAvHz5EmfOnEH79u3h6OiIo0ePAgCOHDmChIQEjWAxbtw4zJ49G6dOnYKenh569eqV5roYNWoUOnToIF3RuX//Ptzd3bO0PJk5d+4cIiIiMGrUKOjopP0nW6VSZXn9Ae+vzkRFRWHfvn1YsWIFli9fLruFsUePHrh9+zb27duHdevWYeHChYiJiZHNs3379oiJicH27dtx+vRpVKlSBY0aNZJ9q3/t2jWsX78eGzZskJ4JmjZtWqbb79atW7J5Va1aFY6Ojli/fj2A91/AHDx4EF27dtVYF1k5LlMLCQnBhAkT8P333yMiIgLTpk3D+PHjsWLFClm9GjVqSPsVERGlQ+QzsbGxAoCIjY3VGPbmzRsRHh4u3rx5oznivuYf95UNx48fFwDEhg0bMqy3a9cuoaurK27duiWVXb58WQAQJ06cEEII0b17d9GyZUvZeEOHDhUeHh7Sew8PD1G3bl1ZnerVq4uAgADpPQCxceNGWZ1GjRqJadOmycpWrlwpbG1tZeMNGzZMo+3m5uYiODg4w+Xz9/cXbdu2ld7b2dmJcePGpVs/rTZmZfk/9OjRIwFAXLx4UQghRHR0tAAgzp49m+44P//8szAyMhKmpqaiQYMGYvLkySIqKird+kIIMWvWLFG1alXp/cSJE4WRkZF48eKFVObt7S0cHR1FUlKSVFaqVCkxffp06T0AMWDAANm0a9asKb755ps02z9lyhTRpEkTWf3bt28LACIyMlKcPn1aABA3btzIsP0pzp49KwDI9kMh3q97XV1dYWxsLNRqtQAgdHR0xLp16zKcXrNmzcTIkSOFEEK8ePFCqNVqsWTJkjTrrly5UpQqVUokJydLZQkJCcLQ0FDs3LlTmsbVq1czfL1+/Voa38/PT1o/27ZtE2XLlhVCCNGvXz8xYcIEIYQQ48ePF05OTtI4+/btEwDEnj17pLJt27YJANLfn4kTJ4qKFSvK1s+H+2ZWliczoaGhAoA4c+aMVPbw4UNhbGwsvRYsWJDl+XXv3l04ODiIxMREqU779u1Fx44dhRBCREZGyv7mCCFERESEACDmzp0rhBDi0KFDwszMTMTHx8va6uzsLBYvXiytnwIFCoiYmBhZnSdPnmS6/d69eyfVT/k7MG/ePNGgQQMhhBBBQUGidevW4tmzZwKA2LdvX7rrL63jMvV2c3Z2FqtWrZKNM2XKFFG7dm1Z2fDhw4Wnp2ea88jws4kotaycR+TwXIMot2R0bv4hvY8ZYr5UIp1vhj8UERGB4sWLo3jx4lJZ2bJlYWFhgYiICFSvXj3L80x9awQA2Nraanzj+KHz588jLCxMdoUiKSkJ8fHxeP36NYyMjAAg02+yUyxYsADLli3DrVu38ObNG7x9+1a6dSQmJgb37t1Do0aNsrxMWXX16lVMmDABx48fx+PHj6UrFbdu3UL58uWzNA1/f39069YN+/fvx7Fjx7B27VpMmzYNW7ZsQePGjQEAa9aswf/+9z9ERUUhLi4OiYmJMDMzk03H0dERpqam0ntra2vo6urKvnm2trbW2Da1a9fWeJ9eL1Dnz5/Hvn370rzfPCoqCk2aNEGjRo3g5uYGb29vNGnSBO3atYOlpWWa00u53SStW1caNGiARYsW4dWrV5g7dy709PTQtm1baXhSUhKmTZuGP//8E3fv3sXbt2+RkJAg7TsRERFISEhId7ufP38e165dk60z4P2VkJTb2UxNTTWGZ8TT0xPDhg3Du3fvsH//fum2OQ8PDyxevBjA+9uj0nq+IvVxZGtrC+D9vpvVh7yzsjw5YWVlJe0Pnp6e0tWIrM6vXLly0NXVld7b2tri4sWLAN5vIz09PVStWlUaXrp0admzU+fPn0dcXJzGMyZv3ryRzcfBwQGFCxeW1SlYsCAKFiyY7WX++uuvMXbsWFy/fh3Lly/H//73vzTrZeW4TPHq1StERUWhd+/e6Nu3r1SemJgIc3NzWV1DQ0O8fv062+0mIvqSMFh8BCVLloRKpdJKj0I6OjoaQSWt+4cLFCgge69SqTK9FSguLg5BQUFo06aNxrDUJ5lp3c71odDQUIwaNQqzZ89G7dq1YWpqilmzZkm3/BgaGmY6jbRkZfl9fX3h4OCAJUuWwM7ODsnJyShfvny2Hx41NTWFr68vfH19MXXqVHh7e2Pq1Klo3Lgxjh49Cj8/PwQFBcHb2xvm5uYIDQ3F7NmzZdNIazvkZNtkJC4uDr6+vpgxY4bGMFtbW+jq6mL37t04cuQIdu3ahZ9++gnjxo3D8ePH4eTkpDFOoUKFAADPnj3TOCk0NjaWbldbtmwZKlasiN9++w29e/cGAMyaNQvz58/HvHnz4ObmBmNjYwwbNkxa95lt97i4OFStWjXNe/pT2hISEoL+/ftnOJ3t27ejXr16AN6HoVevXuHkyZPYt28fRo8eDeB9sOjVqxeePn2K48ePpznN1Nsq5Xaj7GyrrCxPZkqWLAkAiIyMROXKlQEAurq60nbQ0/vvz3hW56d0H4yLi4OtrW2avTGlDiBp/a2YNm0apk2bluH0w8PDNcJbyvNSvXv3Rnx8PJo2bYqXL1/K6mT1uEy9HACwZMkS1KxZUzYsdfAC3t8imNVtRkT0pWKw+AgKFiwIb29vLFiwAEOGDNH4sH3+/DksLCxQpkwZ3L59G7dv35auWoSHh+P58+coW7YsgPcnB5cuXZKNf+7cOY0ThcwUKFBA4/mBKlWqIDIyUjphUSIsLAzu7u4YOHCgVJb6m0xTU1M4Ojpi79696fbEk1YbM1v+J0+eIDIyEkuWLJFOLA8fPqx4eVQqFUqXLo0jR44AeH9PvoODg6w71ps3byqeT4pjx46hW7dusvcpJ5UfqlKlCtavXw9HR0fZSeaH7a9Tpw7q1KmDCRMmwMHBARs3bsSIESM06jo7O8PMzAzh4eFwdXVNt406Ojr49ttvMWLECHTp0gWGhoYICwtDy5Yt8fXXXwN4fxJ+5coVaf8tWbIkDA0NsXfvXvTp0yfNZVmzZg2KFCmS7rfMLVq00DgJ/FDRokVly1O8eHFs2bIF586dg4eHh1SnaNGimD17Nt6+fZutHqHSoq+vn+YxldnyZKZy5cooXbo0fvzxR3To0CHd5yy0Nb/SpUsjMTERp0+flq6SRkZGynpEq1KlCh48eAA9PT1Z5w9ZMWDAAOn5n/TY2dmlWd6rVy989dVXCAgI0DjxB7J/XFpbW8POzg7Xr1+Hn59fhm26dOmSrJMIIiLSxGDxkSxYsAB16tRBjRo1MHnyZFSoUAGJiYnYvXs3Fi1ahIiICHh5ecHNzQ1+fn6YN28eEhMTMXDgQHh4eEi3HzVs2BCzZs3C77//jtq1a+OPP/7ApUuX0j3pTE/KSX2dOnWgVqthaWmJCRMmoHnz5rC3t0e7du2go6OD8+fP49KlS5g6dWq2pl+yZEn8/vvv2LlzJ5ycnLBy5UqcPHlS9g35pEmTMGDAABQpUkT69jEsLAyDBw9Ot42ZLb+lpSWsrKzw66+/wtbWFrdu3cLYsWOz1fZz585h4sSJ6Nq1K8qWLQt9fX0cOHAAy5YtQ0BAgLR8t27dQmhoKKpXr45t27ZptceYtWvXolq1aqhbty5CQkJw4sQJ/Pbbb2nW9ff3x5IlS9C5c2epJ7Br164hNDQUS5culR5ib9KkCYoUKYLjx4/j0aNHKFOmTJrT09HRgZeXFw4fPpzp7zK0b98eo0ePxoIFCzBq1CiULFkS69atw5EjR2BpaYk5c+bg4cOHUrAwMDBAQEAAxowZA319fdSpUwePHj3C5cuX0bt3b/j5+WHWrFlo2bIlJk+ejGLFiuHmzZvYsGEDxowZg2LFimX7Vijg/VWLhQsXwsXFRXpQHHh/1eKnn36SHvL+UFQUkHJ+nnJ+Gh0NvHsHPHkCJCQAV6++LzcxccSZMzsRGRkJKysrmJubZ2l5MqNSqRAcHIzGjRujTp06CAwMRJkyZfDu3TscPHgQjx49kk6ytTG/UqVKwcfHB/3798eiRYugp6eHYcOGya42eXl5oXbt2mjVqhVmzpwJV1dX3Lt3D9u2bUPr1q0zvF0yp7dCAe+7O3706FG6oSknx2VQUBCGDBkCc3Nz+Pj4ICEhAadOncKzZ8+k4P369WucPn060ystmUmjo7Us2bo1f8+LiCgFe4X6SEqUKIEzZ86gQYMGGDlyJMqXL4/GjRtj7969WLRoEYD3JxCbN2+GpaUl6tevDy8vL5QoUQJr1qyRpuPt7Y3x48djzJgxqF69Ol6+fCn7ZjurZs+ejd27d6N48eLSSbm3tzf++usv7Nq1C9WrV0etWrUwd+5cjd8yyIr+/fujTZs26NixI2rWrIknT57Irl4A77uQnDdvHhYuXIhy5cqhefPmuJpylpZBGzNafh0dHYSGhuL06dMoX748hg8fjlmzZmWr7cWKFYOjoyOCgoJQs2ZNVKlSBfPnz0dQUJD0TWiLFi0wfPhwDBo0CJUqVcKRI0cwfvz4bK+n9AQFBSE0NBQVKlTA77//jtWrV0sn5x+ys7NDWFgYkpKS0KRJE7i5uWHYsGGwsLCAjo4OzMzMcPDgQXz11VdwdXXFd999h9mzZ6Np06bpzr9Pnz4IDQ3N9PYYPT09DBo0CDNnzsSrV6/w3XffoUqVKvD29oanpydsbGw0wsn48eMxcuRITJgwAWXKlEHHjh2lZ0yMjIxw8OBB2Nvbo02bNihTpox060tOv4EH3geLly9fanzj7OHhgZcvXyq+WgEAHTv2hZNTKVSrVg2FCxdGWFhYlpYn5Ve+M+qCtlatWjh9+jRKlSoFf39/lC1bFu7u7li9ejXmzp2Lb775BoD21l9wcDDs7Ozg4eGBNm3aoF+/fihSpIg0XKVS4e+//0b9+vXRs2dPuLq6olOnTrh586YsuGmbSqVCoUKFZD2RpZaT47JPnz5YunQpgoOD4ebmBg8PDyxfvlz2JcjmzZthb28vXQUlIqK0qURWnyz+SF68eAFzc3PExsZqfBDGx8cjOjpacZ/oRPmZSqXCxo0bc/VXnDMjhEDNmjUxfPhwdO7cOc/akddS5dws+/9HIrIsODgY06ZNQ3h4eLZvaaSPo1atWhgyZAi6dOmS5vCsfjbxigVhf6oN45nOys5KHaKPKKNz8w/xigURaVCpVPj1119lP45IuePvv//GtGnTGCryqcePH6NNmzZfdMAmIsoqPmNBRGmqVKmS1D0w5Z61a9fmdRMoA4UKFcKYMWPyuhlERJ8EBguifCaf3Z1IRERElCW8FYqIiIiIiBRjsCAiIiIiIsU+yWCh5FeKiYiItIm3LxIRvfdJPWOhr68PHR0d3Lt3D4ULF4a+vj5UKlVeN4uIPlMf/JB2lsTHa78dlH8JIfDo0SOoVCr27EVEX7xPKljo6OjAyckJ9+/fx7179/K6OUT0mfv/3+3LFn55/eVRqVQoVqyY9AvoRERfqk8qWADvr1rY29sjMTERSTn5OpGIKItmzsz+OIsWab8dlL8VKFCAoYKICJ9gsAAgXXLmZWciyk2PH2d/nAx+eJmIiOiz9kk+vE1ERERERPkLgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYtkOFgcPHoSvry/s7OygUqmwadMmadi7d+8QEBAANzc3GBsbw87ODt26dcO9e/e02WYiIiIiIspnsh0sXr16hYoVK2LBggUaw16/fo0zZ85g/PjxOHPmDDZs2IDIyEi0aNFCK40lIiIiIqL8SS+7IzRt2hRNmzZNc5i5uTl2794tK/v5559Ro0YN3Lp1C/b29jlrJRERERER5Wu5/oxFbGwsVCoVLCwscntWRERERESUR7J9xSI74uPjERAQgM6dO8PMzCzNOgkJCUhISJDev3jxIjebREREREREuSDXgsW7d+/QoUMHCCGwaNGidOtNnz4dQUFBudUMIqLPlq9vzsbbulW77SD67O1PdbB58gAiSk+u3AqVEipu3ryJ3bt3p3u1AgACAwMRGxsrvW7fvp0bTSIiIiIiolyk9SsWKaHi6tWr2LdvH6ysrDKsr1aroVartd0MIiIiIiL6iLIdLOLi4nDt2jXpfXR0NM6dO4eCBQvC1tYW7dq1w5kzZ/DXX38hKSkJDx48AAAULFgQ+vr62ms5ERERERHlG9kOFqdOnUKDBg2k9yNGjAAAdO/eHZMmTcKWLVsAAJUqVZKNt2/fPnh6eua8pURERERElG9lO1h4enpCCJHu8IyGERERERHR5ynXf8eCiIiIiIg+fwwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESmml9cNICLKDl/f7I+zdav22/Ep4zr8T07WBfD5rg9SYH+qncmTOwh9mXjFgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlIs28Hi4MGD8PX1hZ2dHVQqFTZt2iQbLoTAhAkTYGtrC0NDQ3h5eeHq1avaai8REREREeVD2Q4Wr169QsWKFbFgwYI0h8+cORP/+9//8Msvv+D48eMwNjaGt7c34uPjFTeWiIiIiIjyJ73sjtC0aVM0bdo0zWFCCMybNw/fffcdWrZsCQD4/fffYW1tjU2bNqFTp07KWktERERERPmSVp+xiI6OxoMHD+Dl5SWVmZubo2bNmjh69Gia4yQkJODFixeyFxERERERfVqyfcUiIw8ePAAAWFtby8qtra2lYR+aPn06goKCtNkMIiLSMl/fjzevrVs/3ryIFNmfzoGRutwznR06K3WIPjF53itUYGAgYmNjpdft27fzuklERERERJRNWg0WNjY2AICHDx/Kyh8+fCgN+5BarYaZmZnsRUREREREnxatBgsnJyfY2Nhg7969UtmLFy9w/Phx1K5dW5uzIiIiIiKifCTbz1jExcXh2rVr0vvo6GicO3cOBQsWhL29PYYNG4apU6eiZMmScHJywvjx42FnZ4dWrVpps91ERERERJSPZDtYnDp1Cg0aNJDejxgxAgDQvXt3LF++HGPGjMGrV6/Qr18/PH/+HHXr1sWOHTtgYGCgvVYTEREREVG+ku1g4enpCSFEusNVKhUmT56MyZMnK2oYERERERF9OvK8VygiIiIiIvr0MVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWJ6ed0AIvr0+fpmf5ytW7XfDiKibNuf6g+Y52f6h+lLWEbKF3jFgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlJM68EiKSkJ48ePh5OTEwwNDeHs7IwpU6ZACKHtWRERERERUT6hp+0JzpgxA4sWLcKKFStQrlw5nDp1Cj179oS5uTmGDBmi7dkREREREVE+oPVgceTIEbRs2RLNmjUDADg6OmL16tU4ceKEtmdFRERERET5hNZvhXJ3d8fevXtx5coVAMD58+dx+PBhNG3aVNuzIiIiIiKifELrVyzGjh2LFy9eoHTp0tDV1UVSUhK+//57+Pn5pVk/ISEBCQkJ0vsXL15ou0lERERERJTLtB4s/vzzT4SEhGDVqlUoV64czp07h2HDhsHOzg7du3fXqD99+nQEBQVpuxlEREREn7b9vmmXe279uO0gyiKt3wo1evRojB07Fp06dYKbmxu6du2K4cOHY/r06WnWDwwMRGxsrPS6ffu2tptERERERES5TOtXLF6/fg0dHXle0dXVRXJycpr11Wo11Gq1tptBREREREQfkdaDha+vL77//nvY29ujXLlyOHv2LObMmYNevXppe1ZERERERJRPaD1Y/PTTTxg/fjwGDhyImJgY2NnZoX///pgwYYK2Z0VERERERPmE1oOFqakp5s2bh3nz5ml70kRERERElE9p/eFtIiIiIiL68jBYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKSYXl43gIjoc+Lrm9ct+HJx3VOa9v//juG5Net1s1qfiGR4xYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSLFeCxd27d/H111/DysoKhoaGcHNzw6lTp3JjVkRERERElA/oaXuCz549Q506ddCgQQNs374dhQsXxtWrV2FpaantWRERERERUT6h9WAxY8YMFC9eHMHBwVKZk5OTtmdDRERERET5iNZvhdqyZQuqVauG9u3bo0iRIqhcuTKWLFmSbv2EhAS8ePFC9iIiIiIiok+L1q9YXL9+HYsWLcKIESPw7bff4uTJkxgyZAj09fXRvXt3jfrTp09HUFCQtptBRESfKF/fvG4BUQb2Z3MHzW59bU3Tc6v250uUCa1fsUhOTkaVKlUwbdo0VK5cGf369UPfvn3xyy+/pFk/MDAQsbGx0uv27dvabhIREREREeUyrQcLW1tblC1bVlZWpkwZ3Lp1K836arUaZmZmshcREREREX1atB4s6tSpg8jISFnZlStX4ODgoO1ZERERERFRPqH1YDF8+HAcO3YM06ZNw7Vr17Bq1Sr8+uuv8Pf31/asiIiIiIgon9B6sKhevTo2btyI1atXo3z58pgyZQrmzZsHPz8/bc+KiIiIiIjyCa33CgUAzZs3R/PmzXNj0kRERERElA9p/YoFERERERF9eRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxvbxuABF9mXx9P8950ZeB+28+tD/VivLcmv3hn5v93HHo4+MVCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEixXA8WP/zwA1QqFYYNG5bbsyIiIiIiojySq8Hi5MmTWLx4MSpUqJCbsyEiIiIiojyWa8EiLi4Ofn5+WLJkCSwtLXNrNkRERERElA/kWrDw9/dHs2bN4OXllVuzICIiIiKifEIvNyYaGhqKM2fO4OTJk5nWTUhIQEJCgvT+xYsXudEkIiIiIiLKRVoPFrdv38bQoUOxe/duGBgYZFp/+vTpCAoK0nYziL54vr553QIi+tR8zL8bW7dmf5yM2je+/n//nzJbXl6jevbnhf0fb2WkXq7Uy5GZnCxXeutwfP3/Bkw5qLlxcrK96Muj9VuhTp8+jZiYGFSpUgV6enrQ09PDgQMH8L///Q96enpISkqS1Q8MDERsbKz0un37trabREREREREuUzrVywaNWqEixcvysp69uyJ0qVLIyAgALq6urJharUaarVa280gIiIiIqKPSOvBwtTUFOXLl5eVGRsbw8rKSqOciIiIiIg+D/zlbSIiIiIiUixXeoX60P79+z/GbIiIiIiIKI/wigURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYnp53QCiL4mvb/bH2bpV++0gIvocjK+f9h/VD8tPnPygwskc/DHOohrVc23SGjSWS0tSr78pB1N9CO1PY7158kOK/sMrFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiDBZERERERKQYgwURERERESnGYEFERERERIoxWBARERERkWIMFkREREREpBiDBRERERERKcZgQUREREREijFYEBERERGRYgwWRERERESkGIMFEREREREpxmBBRERERESKMVgQEREREZFiWg8W06dPR/Xq1WFqaooiRYqgVatWiIyM1PZsiIiIiIgoH9F6sDhw4AD8/f1x7Ngx7N69G+/evUOTJk3w6tUrbc+KiIiIiIjyCT1tT3DHjh2y98uXL0eRIkVw+vRp1K9fX9uzIyIiIiKifEDrweJDsbGxAICCBQumOTwhIQEJCQnS+xcvXuR2k4iIiIiISMtyNVgkJydj2LBhqFOnDsqXL59mnenTpyMoKCg3m5Fjvr45G2/rVu22g3JXTrbzx9zGOd0PiYhy0/j6//1xmnIw7T+KGdXh37b/pF5P+Y3Utv1ZqLxfvhwnTr7/N739Iz08j/p05WqvUP7+/rh06RJCQ0PTrRMYGIjY2Fjpdfv27dxsEhERERER5YJcu2IxaNAg/PXXXzh48CCKFSuWbj21Wg21Wp1bzSAiIiIioo9A68FCCIHBgwdj48aN2L9/P5ycnLQ9CyIiIiIiyme0Hiz8/f2xatUqbN68Gaampnjw4AEAwNzcHIaGhtqeHRERERER5QNaf8Zi0aJFiI2NhaenJ2xtbaXXmjVrtD0rIiIiIiLKJ3LlVigiIiIiIvqy5GqvUERERERE9GVgsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixfTyugH0nq9vzsbbujV/z+tjyuly5fd5ERF9icbX/+8P7ZSDW9Msz49OnMzrFmQsp+svveWaMjv1tHPeltTb+HOVk3OH/H7u9SFesSAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUY7AgIiIiIiLFGCyIiIiIiEgxBgsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJSjMGCiIiIiIgUy7VgsWDBAjg6OsLAwAA1a9bEiRMncmtWRERERESUx3IlWKxZswYjRozAxIkTcebMGVSsWBHe3t6IiYnJjdkREREREVEey5VgMWfOHPTt2xc9e/ZE2bJl8csvv8DIyAjLli3LjdkREREREVEe03qwePv2LU6fPg0vL6//ZqKjAy8vLxw9elTbsyMiIiIionxAT9sTfPz4MZKSkmBtbS0rt7a2xr///qtRPyEhAQkJCdL72NhYAMCLFy+03bRse/cuZ+PlpOmf67w+ppwuFxERZV9c/H9/dN+9S/sDIit1sjv91OWU97KybbK7f+T3842cysl5Sn5YFynn5EKITOtqPVhk1/Tp0xEUFKRRXrx48TxojXaYm3NeRET0edu5M/W7tD8gslInu9OXl1Pey8q2yd7+wfON/+SndfHy5UuYZ9IgrQeLQoUKQVdXFw8fPpSVP3z4EDY2Nhr1AwMDMWLECOl9cnIynj59CisrK6hUKm0375P14sULFC9eHLdv34aZmVleN4dS4bbJv7ht8idul/yL2yb/4rbJvz73bSOEwMuXL2FnZ5dpXa0HC319fVStWhV79+5Fq1atALwPC3v37sWgQYM06qvVaqjValmZhYWFtpv12TAzM/ssd9rPAbdN/sVtkz9xu+Rf3Db5F7dN/vU5b5vMrlSkyJVboUaMGIHu3bujWrVqqFGjBubNm4dXr16hZ8+euTE7IiIiIiLKY7kSLDp27IhHjx5hwoQJePDgASpVqoQdO3ZoPNBNRERERESfh1x7eHvQoEFp3vpEOaNWqzFx4kSN28Yo73Hb5F/cNvkTt0v+xW2Tf3Hb5F/cNv9Riaz0HUVERERERJSBXPnlbSIiIiIi+rIwWBARERERkWIMFkREREREpBiDRT7x/fffw93dHUZGRln+HQ8hBCZMmABbW1sYGhrCy8sLV69eldV5+vQp/Pz8YGZmBgsLC/Tu3RtxcXG5sASfr+yuwxs3bkClUqX5Wrt2rVQvreGhoaEfY5E+GznZvz09PTXW+4ABA2R1bt26hWbNmsHIyAhFihTB6NGjkZiYmJuL8tnJ7rZ5+vQpBg8ejFKlSsHQ0BD29vYYMmQIYmNjZfV43GTfggUL4OjoCAMDA9SsWRMnTpzIsP7atWtRunRpGBgYwM3NDX///bdseFY+eyhrsrNtlixZgnr16sHS0hKWlpbw8vLSqN+jRw+N48PHxye3F+Ozk53tsnz5co11bmBgIKvzRR0zgvKFCRMmiDlz5ogRI0YIc3PzLI3zww8/CHNzc7Fp0yZx/vx50aJFC+Hk5CTevHkj1fHx8REVK1YUx44dE4cOHRIuLi6ic+fOubQUn6fsrsPExERx//592SsoKEiYmJiIly9fSvUAiODgYFm91NuOMpeT/dvDw0P07dtXtt5jY2Ol4YmJiaJ8+fLCy8tLnD17Vvz999+iUKFCIjAwMLcX57OS3W1z8eJF0aZNG7FlyxZx7do1sXfvXlGyZEnRtm1bWT0eN9kTGhoq9PX1xbJly8Tly5dF3759hYWFhXj48GGa9cPCwoSurq6YOXOmCA8PF999950oUKCAuHjxolQnK589lLnsbpsuXbqIBQsWiLNnz4qIiAjRo0cPYW5uLu7cuSPV6d69u/Dx8ZEdH0+fPv1Yi/RZyO52CQ4OFmZmZrJ1/uDBA1mdL+mYYbDIZ4KDg7MULJKTk4WNjY2YNWuWVPb8+XOhVqvF6tWrhRBChIeHCwDi5MmTUp3t27cLlUol7t69q/W2f460tQ4rVaokevXqJSsDIDZu3Kitpn5xcrptPDw8xNChQ9Md/vfffwsdHR3ZB8OiRYuEmZmZSEhI0ErbP3faOm7+/PNPoa+vL969eyeV8bjJnho1agh/f3/pfVJSkrCzsxPTp09Ps36HDh1Es2bNZGU1a9YU/fv3F0Jk7bOHsia72+ZDiYmJwtTUVKxYsUIq6969u2jZsqW2m/pFye52yey87Us7Zngr1CcqOjoaDx48gJeXl1Rmbm6OmjVr4ujRowCAo0ePwsLCAtWqVZPqeHl5QUdHB8ePH//obf4UaWMdnj59GufOnUPv3r01hvn7+6NQoUKoUaMGli1bBsHen7NMybYJCQlBoUKFUL58eQQGBuL169ey6bq5ucl+0NPb2xsvXrzA5cuXtb8gnyFt/e2JjY2FmZkZ9PTkP7nE4yZr3r59i9OnT8s+J3R0dODl5SV9Tnzo6NGjsvrA+/0/pX5WPnsocznZNh96/fo13r17h4IFC8rK9+/fjyJFiqBUqVL45ptv8OTJE622/XOW0+0SFxcHBwcHFC9eHC1btpR9Vnxpx0yu/UAe5a4HDx4AgMavmVtbW0vDHjx4gCJFisiG6+npoWDBglIdypg21uFvv/2GMmXKwN3dXVY+efJkNGzYEEZGRti1axcGDhyIuLg4DBkyRGvt/5zldNt06dIFDg4OsLOzw4ULFxAQEIDIyEhs2LBBmm5ax1XKMMqcNo6bx48fY8qUKejXr5+snMdN1j1+/BhJSUlp7s///vtvmuOkt/+n/lxJKUuvDmUuJ9vmQwEBAbCzs5OdsPr4+KBNmzZwcnJCVFQUvv32WzRt2hRHjx6Frq6uVpfhc5ST7VKqVCksW7YMFSpUQGxsLH788Ue4u7vj8uXLKFas2Bd3zDBY5KKxY8dixowZGdaJiIhA6dKlP1KLKEVWt41Sb968wapVqzB+/HiNYanLKleujFevXmHWrFlf/AlSbm+b1Ceqbm5usLW1RaNGjRAVFQVnZ+ccT/dL8LGOmxcvXqBZs2YoW7YsJk2aJBvG44YI+OGHHxAaGor9+/fLHhTu1KmT9H83NzdUqFABzs7O2L9/Pxo1apQXTf3s1a5dG7Vr15beu7u7o0yZMli8eDGmTJmShy3LGwwWuWjkyJHo0aNHhnVKlCiRo2nb2NgAAB4+fAhbW1up/OHDh6hUqZJUJyYmRjZeYmIinj59Ko3/pcrqtlG6DtetW4fXr1+jW7dumdatWbMmpkyZgoSEBKjV6kzrf64+1rZJUbNmTQDAtWvX4OzsDBsbG40eQB4+fAgAPG4+wrZ5+fIlfHx8YGpqio0bN6JAgQIZ1udxk75ChQpBV1dX2n9TPHz4MN3tYGNjk2H9rHz2UOZysm1S/Pjjj/jhhx+wZ88eVKhQIcO6JUqUQKFChXDt2jUGiyxQsl1SFChQAJUrV8a1a9cAfIHHTF4/5EFy2X14+8cff5TKYmNj03x4+9SpU1KdnTt38uHtbFC6Dj08PDR6tUnP1KlThaWlZY7b+qXR1v59+PBhAUCcP39eCPHfw9upewBZvHixMDMzE/Hx8dpbgM9YTrdNbGysqFWrlvDw8BCvXr3K0rx43GSsRo0aYtCgQdL7pKQkUbRo0Qwf3m7evLmsrHbt2hoPb2f02UNZk91tI4QQM2bMEGZmZuLo0aNZmsft27eFSqUSmzdvVtzeL0VOtktqiYmJolSpUmL48OFCiC/vmGGwyCdu3rwpzp49K3VLevbsWXH27FlZ96SlSpUSGzZskN7/8MMPwsLCQmzevFlcuHBBtGzZMs3uZitXriyOHz8uDh8+LEqWLMnuZrMps3V4584dUapUKXH8+HHZeFevXhUqlUps375dY5pbtmwRS5YsERcvXhRXr14VCxcuFEZGRmLChAm5vjyfk+xum2vXronJkyeLU6dOiejoaLF582ZRokQJUb9+fWmclO5mmzRpIs6dOyd27NghChcuzO5msym72yY2NlbUrFlTuLm5iWvXrsm6bkxMTBRC8LjJidDQUKFWq8Xy5ctFeHi46Nevn7CwsJB6PevatasYO3asVD8sLEzo6emJH3/8UURERIiJEyem2d1sZp89lLnsbpsffvhB6Ovri3Xr1smOj5TzhJcvX4pRo0aJo0ePiujoaLFnzx5RpUoVUbJkSX4pkg3Z3S5BQUFi586dIioqSpw+fVp06tRJGBgYiMuXL0t1vqRjhsEin+jevbsAoPHat2+fVAf/3397iuTkZDF+/HhhbW0t1Gq1aNSokYiMjJRN98mTJ6Jz587CxMREmJmZiZ49e8rCCmUus3UYHR2tsa2EECIwMFAUL15cJCUlaUxz+/btolKlSsLExEQYGxuLihUril9++SXNupS+7G6bW7duifr164uCBQsKtVotXFxcxOjRo2W/YyGEEDdu3BBNmzYVhoaGolChQmLkyJGyLk8pc9ndNvv27UvzbyAAER0dLYTgcZNTP/30k7C3txf6+vqiRo0a4tixY9IwDw8P0b17d1n9P//8U7i6ugp9fX1Rrlw5sW3bNtnwrHz2UNZkZ9s4ODikeXxMnDhRCCHE69evRZMmTUThwoVFgQIFhIODg+jbt6/GbypQ5rKzXYYNGybVtba2Fl999ZU4c+aMbHpf0jGjEoL99BERERERkTL8HQsiIiIiIlKMwYKIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiL6pHl6emLYsGF53Qwioi8egwURERERESnGYEFERHnu3bt3ed0EIiJSiMGCiOgzs2PHDtStWxcWFhawsrJC8+bNERUVBQBwd3dHQECArP6jR49QoEABHDx4EABw//59NGvWDIaGhnBycsKqVavg6OiIefPmZWn+//77L+rWrQsDAwOULVsWe/bsgUqlwqZNmwAAN27cgEqlwpo1a+Dh4QEDAwOEhITgyZMn6Ny5M4oWLQojIyO4ublh9erVsmm/evUK3bp1g4mJCWxtbTF79myN+SckJGDUqFEoWrQojI2NUbNmTezfvz97K5GIiLKNwYKI6DPz6tUrjBgxAqdOncLevXuho6OD1q1bIzk5GX5+fggNDYUQQqq/Zs0a2NnZoV69egCAbt264d69e9i/fz/Wr1+PX3/9FTExMVmad1JSElq1agUjIyMcP34cv/76K8aNG5dm3bFjx2Lo0KGIiIiAt7c34uPjUbVqVWzbtg2XLl1Cv3790LVrV5w4cUIaZ/To0Thw4AA2b96MXbt2Yf/+/Thz5oxsuoMGDcLRo0cRGhqKCxcuoH379vDx8cHVq1ezuyqJiCg7BBERfdYePXokAIiLFy+KmJgYoaenJw4ePCgNr127tggICBBCCBERESEAiJMnT0rDr169KgCIuXPnZjqv7du3Cz09PXH//n2pbPfu3QKA2LhxoxBCiOjoaAFAzJs3L9PpNWvWTIwcOVIIIcTLly+Fvr6++PPPP6XhT548EYaGhmLo0KFCCCFu3rwpdHV1xd27d2XTadSokQgMDMx0fkRElHN6eZpqiIhI665evYoJEybg+PHjePz4MZKTkwEAt27dQvny5dGkSROEhISgXr16iI6OxtGjR7F48WIAQGRkJPT09FClShVpei4uLrC0tMzSvCMjI1G8eHHY2NhIZTVq1EizbrVq1WTvk5KSMG3aNPz555+4e/cu3r59i4SEBBgZGQEAoqKi8PbtW9SsWVMap2DBgihVqpT0/uLFi0hKSoKrq6ts2gkJCbCyssrSMhARUc4wWBARfWZ8fX3h4OCAJUuWwM7ODsnJyShfvjzevn0LAPDz88OQIUPw008/YdWqVXBzc4Obm9tHb6exsbHs/axZszB//nzMmzcPbm5uMDY2xrBhw6R2Z0VcXBx0dXVx+vRp6OrqyoaZmJhopd1ERJQ2PmNBRPQZefLkCSIjI/Hdd9+hUaNGKFOmDJ49eyar07JlS8THx2PHjh1YtWoV/Pz8pGGlSpVCYmIizp49K5Vdu3ZNYxrpKVWqFG7fvo2HDx9KZSdPnszSuGFhYWjZsiW+/vprVKxYESVKlMCVK1ek4c7OzihQoACOHz8ulT179kxWp3LlykhKSkJMTAxcXFxkr9RXUYiISPsYLIiIPiOWlpawsrLCr7/+imvXruGff/7BiBEjZHWMjY3RqlUrjB8/HhEREejcubM0rHTp0vDy8kK/fv1w4sQJnD17Fv369YOhoSFUKlWm82/cuDGcnZ3RvXt3XLhwAWFhYfjuu+8AINPxS5Ysid27d+PIkSOIiIhA//79ZQHFxMQEvXv3xujRo/HPP//g0qVL6NGjB3R0/vsoc3V1hZ+fH7p164YNGzYgOjoaJ06cwPTp07Ft27YsrUMiIsoZBgsios+Ijo4OQkNDcfr0aZQvXx7Dhw/HrFmzNOr5+fnh/PnzqFevHuzt7WXDfv/9d1hbW6N+/fpo3bo1+vbtC1NTUxgYGGQ6f11dXWzatAlxcXGoXr06+vTpI/UKldn43333HapUqQJvb294enrCxsYGrVq1ktWZNWsW6tWrB19fX3h5eaFu3bqoWrWqrE5wcDC6deuGkSNHolSpUmjVqhVOnjypsZxERKRdKiFS9TlIRET0gTt37qB48eLYs2cPGjVqlO3xw8LCULduXVy7dg3Ozs650EIiIsoPGCyIiEjmn3/+QVxcHNzc3HD//n2MGTMGd+/exZUrV1CgQIFMx9+4cSNMTExQsmRJXLt2DUOHDoWlpSUOHz78EVpPRER5hbdCERGRzLt37/Dtt9+iXLlyaN26NQoXLoz9+/ejQIECCAkJgYmJSZqvcuXKAQBevnwJf39/lC5dGj169ED16tWxefPmPF4qIiLKbbxiQUREWfby5UvZA9WpFShQAA4ODh+5RURElF8wWBARERERkWK8FYqIiIiIiBRjsCAiIiIiIsUYLIiIiIiISDEGCyIiIiIiUozBgoiIiIiIFGOwICIiIiIixRgsiIiIiIhIMQYLIiIiIiJS7P8AgxQMW7aagJwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1114,10 +1114,10 @@
    "id": "c6baef0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:31:39.179712Z",
-     "iopub.status.busy": "2024-10-25T14:31:39.179403Z",
-     "iopub.status.idle": "2024-10-25T14:32:31.417906Z",
-     "shell.execute_reply": "2024-10-25T14:32:31.417327Z"
+     "iopub.execute_input": "2024-10-25T14:32:27.475630Z",
+     "iopub.status.busy": "2024-10-25T14:32:27.475191Z",
+     "iopub.status.idle": "2024-10-25T14:33:22.001405Z",
+     "shell.execute_reply": "2024-10-25T14:33:22.000747Z"
     }
    },
    "outputs": [
@@ -1174,7 +1174,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 357.97it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 334.07it/s]"
      ]
     },
     {
@@ -1186,12 +1186,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -1.11022302462516 \\cdot 10^{-16}$"
+       "$\\displaystyle 1.11022302462516 \\cdot 10^{-15}$"
       ],
       "text/plain": [
-       "-1.1102230246251565e-16"
+       "1.1102230246251565e-15"
       ]
      },
      "execution_count": 12,
@@ -1223,16 +1223,16 @@
    "id": "84b1c218",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:31.420521Z",
-     "iopub.status.busy": "2024-10-25T14:32:31.420061Z",
-     "iopub.status.idle": "2024-10-25T14:32:31.758128Z",
-     "shell.execute_reply": "2024-10-25T14:32:31.757513Z"
+     "iopub.execute_input": "2024-10-25T14:33:22.003960Z",
+     "iopub.status.busy": "2024-10-25T14:33:22.003716Z",
+     "iopub.status.idle": "2024-10-25T14:33:22.416143Z",
+     "shell.execute_reply": "2024-10-25T14:33:22.415532Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
diff --git a/main/example_notebooks/do_sampler_demo.html b/main/example_notebooks/do_sampler_demo.html
index 18e594601..014d548ec 100644
--- a/main/example_notebooks/do_sampler_demo.html
+++ b/main/example_notebooks/do_sampler_demo.html
@@ -653,7 +653,7 @@ <h2>Demo<a class="headerlink" href="#Demo" title="Permalink to this heading">#</
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 1.63621412453131$</div></div>
+$\displaystyle 1.62365401783177$</div></div>
 </div>
 <p>So the naive effect is around 60% high. Now, let’s build a causal model for this data.</p>
 <div class="nbinput nblast docutils container">
@@ -725,7 +725,7 @@ <h2>Demo<a class="headerlink" href="#Demo" title="Permalink to this heading">#</
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 1.1216896229639$</div></div>
+$\displaystyle 1.02180320673952$</div></div>
 </div>
 <p>Now we’re much closer to the true effect, which is around 1.0!</p>
 </section>
diff --git a/main/example_notebooks/do_sampler_demo.ipynb b/main/example_notebooks/do_sampler_demo.ipynb
index 3bbb8561a..87dec46fe 100644
--- a/main/example_notebooks/do_sampler_demo.ipynb
+++ b/main/example_notebooks/do_sampler_demo.ipynb
@@ -64,10 +64,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:36.434267Z",
-     "iopub.status.busy": "2024-10-25T14:32:36.434085Z",
-     "iopub.status.idle": "2024-10-25T14:32:36.439833Z",
-     "shell.execute_reply": "2024-10-25T14:32:36.439376Z"
+     "iopub.execute_input": "2024-10-25T14:33:27.109007Z",
+     "iopub.status.busy": "2024-10-25T14:33:27.108503Z",
+     "iopub.status.idle": "2024-10-25T14:33:27.114326Z",
+     "shell.execute_reply": "2024-10-25T14:33:27.113860Z"
     }
    },
    "outputs": [],
@@ -81,10 +81,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:36.441481Z",
-     "iopub.status.busy": "2024-10-25T14:32:36.441309Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.880757Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.880128Z"
+     "iopub.execute_input": "2024-10-25T14:33:27.116019Z",
+     "iopub.status.busy": "2024-10-25T14:33:27.115721Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.587055Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.586335Z"
     },
     "scrolled": true
    },
@@ -100,10 +100,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.883067Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.882758Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.887826Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.887344Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.589909Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.589283Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.594954Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.594328Z"
     }
    },
    "outputs": [],
@@ -122,21 +122,21 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.889654Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.889309Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.944011Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.943403Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.597136Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.596688Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.654604Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.653965Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 1.63621412453131$"
+       "$\\displaystyle 1.62365401783177$"
       ],
       "text/plain": [
-       "1.6362141245313135"
+       "1.623654017831768"
       ]
      },
      "execution_count": 4,
@@ -160,10 +160,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.946125Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.945756Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.949164Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.948693Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.656823Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.656430Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.660018Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.659521Z"
     }
    },
    "outputs": [],
@@ -193,10 +193,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.950745Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.950575Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.955534Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.954967Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.661934Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.661597Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.666782Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.666273Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.957464Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.957130Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.964356Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.963904Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.668754Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.668404Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.676084Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.675591Z"
     }
    },
    "outputs": [],
@@ -251,10 +251,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.965959Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.965793Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.977352Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.976885Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.678139Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.677776Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.689639Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.689128Z"
     }
    },
    "outputs": [],
@@ -267,21 +267,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:37.979201Z",
-     "iopub.status.busy": "2024-10-25T14:32:37.978787Z",
-     "iopub.status.idle": "2024-10-25T14:32:37.996081Z",
-     "shell.execute_reply": "2024-10-25T14:32:37.995479Z"
+     "iopub.execute_input": "2024-10-25T14:33:28.691537Z",
+     "iopub.status.busy": "2024-10-25T14:33:28.691213Z",
+     "iopub.status.idle": "2024-10-25T14:33:28.709379Z",
+     "shell.execute_reply": "2024-10-25T14:33:28.708862Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALkAAAAQCAYAAABKvt56AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAABJ0AAASdAHeZh94AAAGU0lEQVR4nO3aa8wdVRUG4OertXgFEYQqolKUACJ+eKlBpZQUa5SLrWJCDMgfwUQbKVAQibhcJliqAVog0aAJ9dJoUKSCNBUQghdMm0ANkCrWlFZLBGlLlQhN7cUfe5/2dHp6mXO+H19IV3Kyz8ys2et939mzZu09M7Rt2zb7bb+9lG1s90ZmnoNTMYx347VYEBHnte24TV+ZeQim4wy8C0dgEx7Drbg1IrYOEqPHuVMwAyfjYKyr8eZFxKKG7xm4GMfjEPwTD+P6iPhjj76H8Ln6eyeG8Gd8H7f04tIWV1vNBtC4Ly4t9Z2D9+EYHIoXsRoLcXNErBsE15jGuV+twIbxVC/wLaxNX5/G9/ABLMFc3I4TKujbKqlBYmy3zPwW7lOEvRPX4W68AZMbvnPwK7wHizEPj+AT+ENm9rqhfoxb8Db8pHJ4Fb6D+SOBS3vN+tW4NZeWPOASvBr3KvouwGZ8HY9m5pGD4BrbOPESrMHflAz5QC8S+2ht+vorzsbdjexzFZbiU/ikclEGwpuZF+Jy/AAXRcSmxvGXd/0fj1l4BidGxL+6jp2G+/ENRfDO/un4DJ7ExIhYW/ePq/jPz8yFEfGLfnFVa6tZa4374dIHDzgwIjY2d2bmNbgKX8EX+sW1UyaPiAciYkVEDFyot+krIu6PiLuaj76IeBrfrZuTB4kBmXkArsHf9bgAtc//dW2+VdFoSfcA78TG80p26rbptb2uI37134Sr6+aMAXG11qxPjVtx6YdH3bfLAK92W23fMQiuZiYfjdYRZfMI9PURZVDOxdZaa5+AjVjao75eodStEzPz0G5BM3OSMgdY2DhnfG1X9ojf2XdKZo7rGgRtce3N2mq2O/+2XEaax1m1fXQQXKN6kGfmWHy2bi4egS7fX9uNWKZcgO54v8U5EfEsRMT6zPwyrsfyzFyoTKCOVh799+LzjRidG+GoHvEn1HZs/f+XfnDtydpqthf/tlwG4pGZs/AaHKTU8x9WBvi1g+BqTjxHm12rCLUoIn49Av0dVtvLsQ2nKNn4RNyDSfhZ9wkRMVepVcfiQlypTOL+gfnNMkaZYMGlmfn6zs5ai2aX38GD4NqDtdVsT/5tuQzKYxYCM5UBvhhTe9wUrXCN2kyemV/CZUqGOH+Euu3c1JtxdkSsqtuP1cnMEzg1M0/uPFoz8wp8EzfiZjyNYzEbCzJzOCKu6Irx04r3o0r2/6WS2U7HG5V69S3oro1b4+plbTXbB/+2XAbiERHjK67D8UHlBlyWmWdGxCP94hqVmTwzZyhLSctxWkSsH6GuN9R2WdcFABHxAjqZbGLFMRlzcGdEXBoRKyPihSr4dGXZ8rLMnNDVzxallrwSz+KC+luhXLjnq2v3E6AVrl7WVrN98e+Dy8A8qu8zEXEHpirvJX44CK5Rl8kzcyZuwOOY0qMcGMSeqO2G3Rx/rravrO2Ztd1laTIiXsjMpcpgP0nXJKiuIMypv+2Wma9QVgrWRsSTA+Daydpq1sa/JZeBePSIvTozl2O4OfFvg2tUZfI6ybsBf1Kyy0gOcPiNUisen5m9uHcmSp2LdkBtm8uEGvt3WSrbjZ2LccrLi0Fwbbe2mo2gxr249M1jD/am2m7pF9dAgzwzj87MY3ezwN+2r6uVGuxhJbus3csprS0iVuMupV67uBF/qlLjbbBjleF3tb0oM49o+H8MH1JqwYcaxw5sxs7MYXxbyWY7rRb0gatzrJVm/Wjchks/PDLzmMw8qEeMMfVl0GF4KCKeaxzfZ1xD3R9oZeY0TKub4yuolXZc7LURMavLf5XywuSoZg3Wpq/MvEB5FbsFN+HfTQJYFRHz+43Rdc6blUF5pJJ5lilLUdOULHRuRNxefccodeTpSp13hzLxPE4pZYYwMyLmNWIsUb6/eLyed5zyzciLOCsiHmySa4OrH80G0LgVlz54zFQm8b9XMvw6HK68wZ6g6D0lIpb3i6tZkw8rBXy3TbBj7XG1ssyzL9amr85658uU5aNe9qBdv5VoEwNExJrMfC++pqx1T8J/lAw0OyKWdvluzcyP44vKY3C68n3EeizCjRFxTw+sP6/+5yn151PKdxazI2JNL3JtcFVrq1m/Grfi0geP+/B2ZcnwJLwO/1U+Q/iRonGvSfQ+4xra/6ntfnup2/8BXVhCj9Kz0tAAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 1.1216896229639$"
+       "$\\displaystyle 1.02180320673952$"
       ],
       "text/plain": [
-       "1.1216896229638995"
+       "1.0218032067395173"
       ]
      },
      "execution_count": 9,
diff --git a/main/example_notebooks/dowhy-conditional-treatment-effects.html b/main/example_notebooks/dowhy-conditional-treatment-effects.html
index d5a389ec1..f5ba97b98 100644
--- a/main/example_notebooks/dowhy-conditional-treatment-effects.html
+++ b/main/example_notebooks/dowhy-conditional-treatment-effects.html
@@ -620,19 +620,19 @@ <h1>Conditional Average Treatment Effects (CATE) with DoWhy and EconML<a class="
 <div class="output_area docutils container">
 <div class="highlight"><pre>
          X0        X1   Z0        Z1        W0        W1 W2 W3         v0  \
-0 -0.080632  1.506814  0.0  0.225366 -0.233580  0.169233  2  2  15.317527
-1  0.000844  2.339207  0.0  0.572259 -0.782164  0.032473  2  2  16.253707
-2 -0.965817  0.816462  1.0  0.476658 -1.627418  0.414192  2  3  29.035199
-3  1.846773  1.236049  0.0  0.708715 -0.767937 -0.039566  3  2  24.549901
-4  2.093956  0.654808  0.0  0.882181 -1.590903 -0.137460  2  0  21.239132
+0  0.381310  0.546194  1.0  0.333875 -1.084854 -1.511882  3  2  29.116836
+1 -0.575241 -1.335250  1.0  0.390085  0.235700 -2.045413  0  3  15.339584
+2 -1.503456 -0.155814  1.0  0.021253  1.379312  0.517977  1  0  18.797177
+3  1.233182  0.004915  1.0  0.896209 -0.033976 -0.089706  3  1  36.356471
+4 -1.100251 -0.057382  1.0  0.346384 -0.423885  0.690338  2  2  30.524211
 
             y
-0  257.916920
-1  336.641725
-2  273.191908
-3  601.159182
-4  486.369924
-True causal estimate is 15.528281358714274
+0  379.475032
+1   64.896549
+2  114.841747
+3  478.142873
+4  229.026784
+True causal estimate is 11.247611011059492
 </pre></div></div>
 </div>
 <div class="nbinput nblast docutils container">
@@ -689,9 +689,9 @@ <h1>Conditional Average Treatment Effects (CATE) with DoWhy and EconML<a class="
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ### Estimand : 2
 Estimand name: iv
@@ -739,43 +739,43 @@ <h2>Linear Model<a class="headerlink" href="#Linear-Model" title="Permalink to t
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+v0*X1+v0*X0
+b: y~v0+W0+W2+W1+W3+v0*X0+v0*X1
 Target units:
 
 ## Estimate
-Mean value: 15.528159699566729
+Mean value: 11.24771198163937
 ### Conditional Estimates
-__categorical__X1             __categorical__X0
-(-2.9099999999999997, 0.129]  (-3.393, -0.533]      3.088191
-                              (-0.533, 0.0432]      7.058095
-                              (0.0432, 0.525]       9.181673
-                              (0.525, 1.103]       11.941213
-                              (1.103, 3.796]       15.592924
-(0.129, 0.714]                (-3.393, -0.533]      6.872997
-                              (-0.533, 0.0432]     10.755943
-                              (0.0432, 0.525]      13.311670
-                              (0.525, 1.103]       15.585864
-                              (1.103, 3.796]       19.772407
-(0.714, 1.232]                (-3.393, -0.533]      9.134048
-                              (-0.533, 0.0432]     13.011229
-                              (0.0432, 0.525]      15.566041
-                              (0.525, 1.103]       17.909869
-                              (1.103, 3.796]       21.807051
-(1.232, 1.838]                (-3.393, -0.533]     11.329517
-                              (-0.533, 0.0432]     15.572366
-                              (0.0432, 0.525]      17.904951
-                              (0.525, 1.103]       20.317267
-                              (1.103, 3.796]       24.246892
-(1.838, 5.002]                (-3.393, -0.533]     15.332247
-                              (-0.533, 0.0432]     19.324308
-                              (0.0432, 0.525]      21.504073
-                              (0.525, 1.103]       23.944776
-                              (1.103, 3.796]       28.138810
+__categorical__X0              __categorical__X1
+(-4.4190000000000005, -1.184]  (-3.4819999999999998, -0.27]     2.860144
+                               (-0.27, 0.307]                   5.956420
+                               (0.307, 0.814]                   7.813714
+                               (0.814, 1.419]                   9.770860
+                               (1.419, 4.311]                  12.949686
+(-1.184, -0.582]               (-3.4819999999999998, -0.27]     4.998142
+                               (-0.27, 0.307]                   8.056506
+                               (0.307, 0.814]                   9.879637
+                               (0.814, 1.419]                  11.824167
+                               (1.419, 4.311]                  15.024859
+(-0.582, -0.071]               (-3.4819999999999998, -0.27]     6.351348
+                               (-0.27, 0.307]                   9.377655
+                               (0.307, 0.814]                  11.165485
+                               (0.814, 1.419]                  13.175461
+                               (1.419, 4.311]                  16.254339
+(-0.071, 0.519]                (-3.4819999999999998, -0.27]     7.444772
+                               (-0.27, 0.307]                  10.609427
+                               (0.307, 0.814]                  12.529936
+                               (0.814, 1.419]                  14.460766
+                               (1.419, 4.311]                  17.563421
+(0.519, 3.619]                 (-3.4819999999999998, -0.27]     9.709384
+                               (-0.27, 0.307]                  12.750406
+                               (0.307, 0.814]                  14.564136
+                               (0.814, 1.419]                  16.526277
+                               (1.419, 4.311]                  19.575332
 dtype: float64
 </pre></div></div>
 </div>
@@ -821,23 +821,936 @@ <h2>EconML methods<a class="headerlink" href="#EconML-methods" title="Permalink
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2 | X1,X0
+b: y~v0+W0+W2+W1+W3 | X0,X1
 Target units: Data subset defined by a function
 
 ## Estimate
-Mean value: 21.474097597869555
-Effect estimates: [[23.92786788]
- [22.51303763]
- [17.01246052]
- ...
- [22.90897968]
- [15.56349972]
- [25.6520251 ]]
+Mean value: 15.267669524379059
+Effect estimates: [[12.90664534]
+ [14.38138836]
+ [10.2357385 ]
+ [10.23987427]
+ [13.47109986]
+ [11.19468597]
+ [ 8.71140809]
+ [14.27102838]
+ [14.77774453]
+ [ 9.61839297]
+ [15.59684456]
+ [19.68728306]
+ [13.76608678]
+ [22.10228465]
+ [21.00718484]
+ [10.83986598]
+ [18.26539699]
+ [21.54713752]
+ [20.58758719]
+ [17.66321804]
+ [15.54830641]
+ [13.17608218]
+ [21.30665667]
+ [17.25260255]
+ [16.76473542]
+ [12.14064485]
+ [19.97405448]
+ [19.00251288]
+ [11.35059086]
+ [20.22038945]
+ [20.04743649]
+ [10.11288552]
+ [18.16878219]
+ [14.11827826]
+ [12.3183592 ]
+ [20.50438781]
+ [17.42956696]
+ [15.68772439]
+ [20.37568615]
+ [12.86751839]
+ [16.60542198]
+ [15.13267292]
+ [11.1276136 ]
+ [19.66905016]
+ [ 8.65818075]
+ [14.12847087]
+ [13.1443254 ]
+ [14.21545231]
+ [11.34122461]
+ [17.70535722]
+ [14.32058541]
+ [12.74878696]
+ [11.44668147]
+ [19.16437979]
+ [16.54197279]
+ [18.10513861]
+ [14.51118135]
+ [21.35868267]
+ [ 8.77959824]
+ [17.84519068]
+ [17.21129456]
+ [16.67419526]
+ [17.39785843]
+ [15.76837168]
+ [10.86722358]
+ [14.6715169 ]
+ [14.89835161]
+ [17.04686222]
+ [14.48667435]
+ [16.66594093]
+ [14.56128981]
+ [20.14249386]
+ [10.36170885]
+ [18.39657393]
+ [ 7.47143542]
+ [15.18909267]
+ [17.94803876]
+ [10.2032646 ]
+ [13.43462399]
+ [ 8.17497236]
+ [10.07499358]
+ [12.48877858]
+ [11.82906593]
+ [15.36362058]
+ [13.6090933 ]
+ [17.06079318]
+ [18.26409511]
+ [13.33224459]
+ [11.755378  ]
+ [15.17132044]
+ [13.33230185]
+ [10.45490005]
+ [21.18709346]
+ [15.01311924]
+ [11.72365471]
+ [21.26991562]
+ [13.29190176]
+ [19.92643768]
+ [16.90790474]
+ [17.51032902]
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+ [15.01755365]
+ [15.62720641]
+ [10.56369581]
+ [13.96581874]
+ [17.00095255]
+ [18.62267886]
+ [19.11328605]
+ [18.48449261]
+ [18.8122607 ]
+ [ 9.36405072]
+ [15.36521508]
+ [11.79840485]
+ [13.61227422]
+ [20.2746336 ]
+ [10.42621519]
+ [14.66847901]
+ [16.70452605]
+ [22.17665051]
+ [14.23592962]
+ [ 8.29622405]
+ [11.62441771]
+ [14.73706899]
+ [16.91326237]
+ [16.97793647]
+ [16.46893716]
+ [15.64543299]
+ [11.29510719]
+ [11.10635205]
+ [17.76345427]
+ [13.64205605]
+ [19.07462592]]
 
 </pre></div></div>
 </div>
@@ -854,7 +1767,7 @@ <h2>EconML methods<a class="headerlink" href="#EconML-methods" title="Permalink
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-True causal estimate is 15.528281358714274
+True causal estimate is 11.247611011059492
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -889,23 +1802,23 @@ <h2>EconML methods<a class="headerlink" href="#EconML-methods" title="Permalink
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2 | X1,X0
+b: y~v0+W0+W2+W1+W3 | X0,X1
 Target units:
 
 ## Estimate
-Mean value: 15.471429603219727
-Effect estimates: [[16.07009773]
- [20.03571549]
- [ 8.96384864]
+Mean value: 11.182367944351238
+Effect estimates: [[12.79993158]
+ [ 3.7112351 ]
+ [ 5.76948667]
  ...
- [ 7.14006316]
- [ 6.11592992]
- [17.9611212 ]]
+ [12.36146871]
+ [16.2363937 ]
+ [19.84331551]]
 
 </pre></div></div>
 </div>
@@ -950,28 +1863,28 @@ <h3>CATE Object and Confidence Intervals<a class="headerlink" href="#CATE-Object
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2 | X1,X0
+b: y~v0+W0+W2+W1+W3 | X0,X1
 Target units: ate
 
 ## Estimate
-Mean value: 15.504044662998552
-Effect estimates: [[16.19325628]
- [20.24036528]
- [ 9.09597254]
+Mean value: 11.238487459603933
+Effect estimates: [[12.89507289]
+ [ 3.68601742]
+ [ 5.73421756]
  ...
- [ 6.97654031]
- [ 6.38853545]
- [17.95558472]]
-95.0% confidence interval: [[[16.32550901 20.45287404  9.01676031 ...  6.48860366  6.24417781
-   18.00102197]]
+ [12.45379182]
+ [16.37185436]
+ [20.01769281]]
+95.0% confidence interval: [[[13.01604156  3.28553672  5.52193098 ... 12.55393218 16.58389431
+   20.28215107]]
 
- [[16.65259291 20.99199809  9.42151539 ...  7.02160604  6.88463651
-   18.34895763]]]
+ [[13.26912412  3.79973322  5.80719612 ... 12.80864789 16.96236057
+   20.89301761]]]
 
 </pre></div></div>
 </div>
@@ -1004,16 +1917,16 @@ <h3>Can provide a new inputs as target units and estimate CATE on them.<a class=
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-[[15.94311345]
- [11.71072198]
- [16.1674401 ]
- [14.5076398 ]
- [13.21364746]
- [14.86479933]
- [16.99195916]
- [18.03051279]
- [14.49054386]
- [14.20683584]]
+[[13.05623832]
+ [12.98682823]
+ [14.53693928]
+ [12.44537756]
+ [10.66232785]
+ [13.12070985]
+ [12.92463772]
+ [12.90844965]
+ [13.94570379]
+ [15.15154461]]
 </pre></div></div>
 </div>
 </section>
@@ -1032,7 +1945,7 @@ <h3>Can also retrieve the raw EconML estimator object for any further operations
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;econml.dml.dml.DML object at 0x7f21861582e0&gt;
+&lt;econml.dml.dml.DML object at 0x7f24fcb02250&gt;
 </pre></div></div>
 </div>
 </section>
@@ -1067,17 +1980,17 @@ <h3>Binary treatment, Binary outcome<a class="headerlink" href="#Binary-treatmen
 <div class="output_area docutils container">
 <div class="highlight"><pre>
             X0        X1   Z0        W0        W1        W2        W3  v0  y
-0     1.848937 -0.903341  1.0 -1.357018  0.359030 -0.158734  0.547312   1  1
-1    -0.166756 -1.358425  1.0 -0.545606 -2.206641 -0.613952 -0.342323   0  0
-2    -1.009845 -0.463540  1.0  1.071495 -1.730563 -1.159340  1.685405   1  1
-3     1.721945 -0.219205  0.0 -0.401258 -1.359670  0.215030 -0.403456   0  0
-4    -0.227010 -0.505793  1.0 -0.853896 -0.787990 -1.158951  0.733975   1  1
+0     0.382932  2.345769  0.0 -1.428223  0.526978 -1.831798 -0.937137   0  0
+1     0.323482  0.191478  0.0  1.653139 -0.727859 -0.366949 -1.571791   0  1
+2     0.263324 -0.088392  0.0  1.087412  1.253476  1.720935  0.482786   1  1
+3     0.779199  1.292685  1.0  0.481355  2.038458 -0.642406  1.758478   1  1
+4     0.134317  0.842260  0.0  0.295010  0.232721 -0.160742  0.205964   1  1
 ...        ...       ...  ...       ...       ...       ...       ...  .. ..
-9995  1.216954 -0.117298  1.0  1.190832 -1.800529 -1.404404  1.478945   1  1
-9996  1.670534  0.426617  1.0  1.868842  0.514159 -0.522367 -1.006508   1  1
-9997  0.634803 -0.151029  1.0  0.108213 -1.416494 -0.408316  0.783284   1  1
-9998  1.089458 -0.117143  1.0 -1.723675 -2.177703 -0.913882  0.198161   0  0
-9999  1.615717 -1.471271  1.0  0.224152 -0.409710 -1.051757  0.457010   1  1
+9995  1.866050  0.530130  0.0  1.023069  0.168696 -1.391754 -0.191480   0  0
+9996  0.912797  1.169468  0.0  0.965489 -0.489325 -1.148618  0.095484   0  1
+9997 -0.642280  1.079737  0.0  2.789080 -0.074786 -0.972265  0.125235   1  1
+9998  1.951512  2.407281  0.0  0.464253  0.681882  0.421487 -0.183010   1  1
+9999 -0.505607  3.069471  1.0  2.014816 -0.287527 -1.342139  0.203378   1  1
 
 [10000 rows x 9 columns]
 </pre></div></div>
@@ -1117,25 +2030,25 @@ <h4>Using DRLearner estimator<a class="headerlink" href="#Using-DRLearner-estima
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2])
+─────(E[y|W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2 | X1,X0
+b: y~v0+W0+W2+W1+W3 | X0,X1
 Target units: ate
 
 ## Estimate
-Mean value: 0.5517154689937798
-Effect estimates: [[0.56087692]
- [0.53034022]
- [0.52440591]
+Mean value: 0.3968213931846329
+Effect estimates: [[0.46341228]
+ [0.38036417]
+ [0.36709047]
  ...
- [0.54895095]
- [0.55540547]
- [0.55409699]]
+ [0.3676809 ]
+ [0.54003283]
+ [0.44825981]]
 
-True causal estimate is 0.5364
+True causal estimate is 0.3365
 </pre></div></div>
 </div>
 </section>
@@ -1180,18 +2093,18 @@ <h3>Instrumental Variable Method<a class="headerlink" href="#Instrumental-Variab
 Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2 | X1,X0
+b: y~v0+W0+W2+W1+W3 | X0,X1
 Target units: Data subset defined by a function
 
 ## Estimate
-Mean value: 16.396141098363664
-Effect estimates: [[16.20952269]
- [20.31761618]
- [ 8.99574685]
+Mean value: 12.312450374293359
+Effect estimates: [[12.87600707]
+ [ 4.04658904]
+ [13.00104868]
  ...
- [15.89593262]
- [ 6.86027739]
- [18.00872617]]
+ [12.45626241]
+ [16.20365244]
+ [19.68928866]]
 
 </pre></div></div>
 </div>
@@ -1221,30 +2134,30 @@ <h3>Metalearners<a class="headerlink" href="#Metalearners" title="Permalink to t
 <div class="output_area docutils container">
 <div class="highlight"><pre>
             X0        X1        X2        X3        X4   Z0        Z1  \
-0    -1.928622 -1.460600 -1.060879 -0.428721 -0.585863  0.0  0.833845
-1    -0.354271 -0.970412 -2.236240 -1.434037 -0.052678  1.0  0.014492
-2    -0.074867 -1.492009  0.798875  0.294336 -1.352786  1.0  0.453896
-3    -1.130675  0.104157  0.189918  1.204923 -0.750851  1.0  0.005845
-4    -1.319822  1.815768  0.287191 -0.016356  0.631682  1.0  0.509215
+0    -1.908063  1.816612 -1.782549  0.702879  0.428015  1.0  0.853746
+1    -0.841354  1.831363  0.622043 -2.117324  2.878573  0.0  0.547690
+2     0.148732 -0.151823  0.801329 -0.784210  0.265481  1.0  0.257635
+3    -0.461454  1.294300 -1.239469 -1.050843  2.835228  1.0  0.763303
+4    -0.015284  0.043538 -0.944665 -2.759310  1.061391  0.0  0.181512
 ...        ...       ...       ...       ...       ...  ...       ...
-9995 -0.548747 -1.033007 -1.326244 -1.708462  1.071464  1.0  0.214273
-9996 -1.567399 -1.900533  0.286898 -1.991567  0.838530  1.0  0.057444
-9997 -0.748685 -0.461194 -1.679336  0.511443 -2.390655  1.0  0.503427
-9998 -0.875187 -0.830123 -1.068104 -1.842930 -0.387148  1.0  0.207693
-9999 -0.880693  0.763965  0.942783 -1.150989 -2.767426  1.0  0.271196
+9995 -0.622211  0.630081 -0.504322  0.090627  1.190173  1.0  0.491685
+9996  0.438390  0.571294 -0.981292  1.184738  1.261173  0.0  0.934166
+9997 -0.149968  1.402410  0.989695  0.092680  3.186254  0.0  0.296926
+9998  1.221613 -0.114503 -1.586336 -0.079467  1.314031  1.0  0.029542
+9999 -2.146341  1.379550 -0.623245 -0.703477  1.266934  1.0  0.801060
 
             W0        W1        W2        W3        W4  v0          y
-0     1.538836  0.356235  0.562544 -1.229352 -1.412987   1 -12.553256
-1     0.629364  1.026922 -0.971291 -0.524729  0.799605   1  -2.751671
-2    -0.536827  0.753210  0.798975  0.652538 -0.751729   1   9.283271
-3     1.518184  1.360522  0.413248 -0.392738 -1.794745   1   7.341700
-4     0.143308  0.676154  0.564906 -0.280576 -1.100754   1   8.983174
+0    -0.268623 -0.019217  1.039168  1.864035  1.528477   1  31.875318
+1    -0.640923  1.041966  1.959528  0.571702 -1.069802   1  22.447270
+2    -1.254305  1.170127 -0.166980 -0.295455  1.066763   1  10.654918
+3     0.610376  0.068494  1.902022  2.266399  0.332300   1  35.074787
+4    -2.346280 -0.076409  0.007971 -1.487368 -1.287827   0 -21.300166
 ...        ...       ...       ...       ...       ...  ..        ...
-9995  0.727406 -0.769579  0.287496  0.919897 -0.996558   1   3.489504
-9996  2.535569  1.591533 -0.929597 -0.160212 -0.719416   1  10.491038
-9997 -0.196327 -0.294150 -0.834032  0.684718  0.132787   1  -7.312851
-9998  1.698052 -0.275638 -1.178926 -0.288767 -1.333009   1  -5.539157
-9999  1.114213 -0.004539 -0.946504 -2.163850 -1.660811   1 -12.611732
+9995 -0.161351 -1.566292  1.007896  0.678905 -0.321988   1  10.933773
+9996 -0.962916  0.585787  2.459705 -0.293871  0.312810   1  31.844450
+9997 -0.298513  1.390892  1.936229  0.492862  0.157574   1  41.957955
+9998 -1.055555  0.676774 -0.010229  0.528897  1.068823   1  16.869197
+9999  0.400382 -0.040731  0.135707  0.114662 -0.318792   1  10.570814
 
 [10000 rows x 14 columns]
 </pre></div></div>
@@ -1281,25 +2194,25 @@ <h3>Metalearners<a class="headerlink" href="#Metalearners" title="Permalink to t
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W4,W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4
+b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3
 Target units: ate
 
 ## Estimate
-Mean value: 6.437717655807301
-Effect estimates: [[-4.16181067]
- [ 0.774689  ]
- [11.02032889]
+Mean value: 18.87771266237887
+Effect estimates: [[27.78513921]
+ [23.45800939]
+ [10.86480525]
  ...
- [-5.22952473]
- [-1.44104664]
- [ 1.56876933]]
+ [32.21640236]
+ [17.6237358 ]
+ [15.38137178]]
 
-True causal estimate is 1.87902954705715
+True causal estimate is 10.035889599894295
 </pre></div></div>
 </div>
 </section>
@@ -1337,23 +2250,23 @@ <h2>Avoiding retraining the estimator<a class="headerlink" href="#Avoiding-retra
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W4,W0,W2,W1,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)
 
 ## Realized estimand
-b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4
+b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3
 Target units: Data subset provided as a data frame
 
 ## Estimate
-Mean value: 2.043488559407042
-Effect estimates: [[ 4.54315475]
- [10.77609008]
- [-5.22952473]
- [-1.44104664]
- [ 1.56876933]]
-
-True causal estimate is 1.87902954705715
+Mean value: 22.84531697294303
+Effect estimates: [[21.97693322]
+ [27.02814169]
+ [32.21640236]
+ [17.6237358 ]
+ [15.38137178]]
+
+True causal estimate is 10.035889599894295
 </pre></div></div>
 </div>
 </section>
@@ -1376,9 +2289,9 @@ <h3>Adding a random common cause variable<a class="headerlink" href="#Adding-a-r
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:15.012721377524025
-New effect:15.061049698929667
-p value:0.19999999999999996
+Estimated effect:13.173875684629346
+New effect:13.122022363488286
+p value:0.2
 
 </pre></div></div>
 </div>
@@ -1402,8 +2315,8 @@ <h3>Adding an unobserved common cause variable<a class="headerlink" href="#Addin
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add an Unobserved Common Cause
-Estimated effect:15.012721377524025
-New effect:15.04397103511404
+Estimated effect:13.173875684629346
+New effect:13.117896772844379
 
 </pre></div></div>
 </div>
@@ -1428,9 +2341,9 @@ <h3>Replacing treatment with a random (placebo) variable<a class="headerlink" hr
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:15.012721377524025
-New effect:-0.039633288288616876
-p value:0.3019778331621793
+Estimated effect:13.173875684629346
+New effect:-0.011817586304644403
+p value:0.4059910355354093
 
 </pre></div></div>
 </div>
@@ -1454,9 +2367,9 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:15.012721377524025
-New effect:15.02989599562622
-p value:0.3803137657532839
+Estimated effect:13.173875684629346
+New effect:13.145734147056782
+p value:0.2631763306816288
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb b/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb
index c6522ee9a..5c17fcba5 100644
--- a/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb
+++ b/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:40.102787Z",
-     "iopub.status.busy": "2024-10-25T14:32:40.102299Z",
-     "iopub.status.idle": "2024-10-25T14:32:40.120054Z",
-     "shell.execute_reply": "2024-10-25T14:32:40.119592Z"
+     "iopub.execute_input": "2024-10-25T14:33:30.785394Z",
+     "iopub.status.busy": "2024-10-25T14:33:30.785170Z",
+     "iopub.status.idle": "2024-10-25T14:33:30.803802Z",
+     "shell.execute_reply": "2024-10-25T14:33:30.803173Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:40.122004Z",
-     "iopub.status.busy": "2024-10-25T14:32:40.121577Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.593233Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.592547Z"
+     "iopub.execute_input": "2024-10-25T14:33:30.805886Z",
+     "iopub.status.busy": "2024-10-25T14:33:30.805462Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.324248Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.323511Z"
     }
    },
    "outputs": [],
@@ -64,10 +64,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.595508Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.595216Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.651970Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.651120Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.326932Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.326590Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.383169Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.382591Z"
     }
    },
    "outputs": [
@@ -76,19 +76,19 @@
      "output_type": "stream",
      "text": [
       "         X0        X1   Z0        Z1        W0        W1 W2 W3         v0  \\\n",
-      "0 -0.080632  1.506814  0.0  0.225366 -0.233580  0.169233  2  2  15.317527   \n",
-      "1  0.000844  2.339207  0.0  0.572259 -0.782164  0.032473  2  2  16.253707   \n",
-      "2 -0.965817  0.816462  1.0  0.476658 -1.627418  0.414192  2  3  29.035199   \n",
-      "3  1.846773  1.236049  0.0  0.708715 -0.767937 -0.039566  3  2  24.549901   \n",
-      "4  2.093956  0.654808  0.0  0.882181 -1.590903 -0.137460  2  0  21.239132   \n",
+      "0  0.381310  0.546194  1.0  0.333875 -1.084854 -1.511882  3  2  29.116836   \n",
+      "1 -0.575241 -1.335250  1.0  0.390085  0.235700 -2.045413  0  3  15.339584   \n",
+      "2 -1.503456 -0.155814  1.0  0.021253  1.379312  0.517977  1  0  18.797177   \n",
+      "3  1.233182  0.004915  1.0  0.896209 -0.033976 -0.089706  3  1  36.356471   \n",
+      "4 -1.100251 -0.057382  1.0  0.346384 -0.423885  0.690338  2  2  30.524211   \n",
       "\n",
       "            y  \n",
-      "0  257.916920  \n",
-      "1  336.641725  \n",
-      "2  273.191908  \n",
-      "3  601.159182  \n",
-      "4  486.369924  \n",
-      "True causal estimate is 15.528281358714274\n"
+      "0  379.475032  \n",
+      "1   64.896549  \n",
+      "2  114.841747  \n",
+      "3  478.142873  \n",
+      "4  229.026784  \n",
+      "True causal estimate is 11.247611011059492\n"
      ]
     }
    ],
@@ -110,10 +110,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.654640Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.654106Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.696339Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.695798Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.385824Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.385535Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.429722Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.429107Z"
     }
    },
    "outputs": [],
@@ -128,10 +128,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.699091Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.698637Z",
-     "iopub.status.idle": "2024-10-25T14:32:41.887684Z",
-     "shell.execute_reply": "2024-10-25T14:32:41.887046Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.432404Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.432152Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.624456Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.623809Z"
     }
    },
    "outputs": [
@@ -167,10 +167,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:41.890028Z",
-     "iopub.status.busy": "2024-10-25T14:32:41.889611Z",
-     "iopub.status.idle": "2024-10-25T14:32:42.235167Z",
-     "shell.execute_reply": "2024-10-25T14:32:42.234495Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.626876Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.626447Z",
+     "iopub.status.idle": "2024-10-25T14:33:32.990517Z",
+     "shell.execute_reply": "2024-10-25T14:33:32.989827Z"
     },
     "scrolled": true
    },
@@ -185,9 +185,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -226,10 +226,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:42.237197Z",
-     "iopub.status.busy": "2024-10-25T14:32:42.236904Z",
-     "iopub.status.idle": "2024-10-25T14:32:42.583511Z",
-     "shell.execute_reply": "2024-10-25T14:32:42.582863Z"
+     "iopub.execute_input": "2024-10-25T14:33:32.992731Z",
+     "iopub.status.busy": "2024-10-25T14:33:32.992350Z",
+     "iopub.status.idle": "2024-10-25T14:33:33.339932Z",
+     "shell.execute_reply": "2024-10-25T14:33:33.339388Z"
     }
    },
    "outputs": [
@@ -246,43 +246,43 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+v0*X1+v0*X0\n",
+      "b: y~v0+W0+W2+W1+W3+v0*X0+v0*X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.528159699566729\n",
+      "Mean value: 11.24771198163937\n",
       "### Conditional Estimates\n",
-      "__categorical__X1             __categorical__X0\n",
-      "(-2.9099999999999997, 0.129]  (-3.393, -0.533]      3.088191\n",
-      "                              (-0.533, 0.0432]      7.058095\n",
-      "                              (0.0432, 0.525]       9.181673\n",
-      "                              (0.525, 1.103]       11.941213\n",
-      "                              (1.103, 3.796]       15.592924\n",
-      "(0.129, 0.714]                (-3.393, -0.533]      6.872997\n",
-      "                              (-0.533, 0.0432]     10.755943\n",
-      "                              (0.0432, 0.525]      13.311670\n",
-      "                              (0.525, 1.103]       15.585864\n",
-      "                              (1.103, 3.796]       19.772407\n",
-      "(0.714, 1.232]                (-3.393, -0.533]      9.134048\n",
-      "                              (-0.533, 0.0432]     13.011229\n",
-      "                              (0.0432, 0.525]      15.566041\n",
-      "                              (0.525, 1.103]       17.909869\n",
-      "                              (1.103, 3.796]       21.807051\n",
-      "(1.232, 1.838]                (-3.393, -0.533]     11.329517\n",
-      "                              (-0.533, 0.0432]     15.572366\n",
-      "                              (0.0432, 0.525]      17.904951\n",
-      "                              (0.525, 1.103]       20.317267\n",
-      "                              (1.103, 3.796]       24.246892\n",
-      "(1.838, 5.002]                (-3.393, -0.533]     15.332247\n",
-      "                              (-0.533, 0.0432]     19.324308\n",
-      "                              (0.0432, 0.525]      21.504073\n",
-      "                              (0.525, 1.103]       23.944776\n",
-      "                              (1.103, 3.796]       28.138810\n",
+      "__categorical__X0              __categorical__X1           \n",
+      "(-4.4190000000000005, -1.184]  (-3.4819999999999998, -0.27]     2.860144\n",
+      "                               (-0.27, 0.307]                   5.956420\n",
+      "                               (0.307, 0.814]                   7.813714\n",
+      "                               (0.814, 1.419]                   9.770860\n",
+      "                               (1.419, 4.311]                  12.949686\n",
+      "(-1.184, -0.582]               (-3.4819999999999998, -0.27]     4.998142\n",
+      "                               (-0.27, 0.307]                   8.056506\n",
+      "                               (0.307, 0.814]                   9.879637\n",
+      "                               (0.814, 1.419]                  11.824167\n",
+      "                               (1.419, 4.311]                  15.024859\n",
+      "(-0.582, -0.071]               (-3.4819999999999998, -0.27]     6.351348\n",
+      "                               (-0.27, 0.307]                   9.377655\n",
+      "                               (0.307, 0.814]                  11.165485\n",
+      "                               (0.814, 1.419]                  13.175461\n",
+      "                               (1.419, 4.311]                  16.254339\n",
+      "(-0.071, 0.519]                (-3.4819999999999998, -0.27]     7.444772\n",
+      "                               (-0.27, 0.307]                  10.609427\n",
+      "                               (0.307, 0.814]                  12.529936\n",
+      "                               (0.814, 1.419]                  14.460766\n",
+      "                               (1.419, 4.311]                  17.563421\n",
+      "(0.519, 3.619]                 (-3.4819999999999998, -0.27]     9.709384\n",
+      "                               (-0.27, 0.307]                  12.750406\n",
+      "                               (0.307, 0.814]                  14.564136\n",
+      "                               (0.814, 1.419]                  16.526277\n",
+      "                               (1.419, 4.311]                  19.575332\n",
       "dtype: float64\n"
      ]
     }
@@ -314,10 +314,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:42.585730Z",
-     "iopub.status.busy": "2024-10-25T14:32:42.585292Z",
-     "iopub.status.idle": "2024-10-25T14:32:47.317461Z",
-     "shell.execute_reply": "2024-10-25T14:32:47.316598Z"
+     "iopub.execute_input": "2024-10-25T14:33:33.341903Z",
+     "iopub.status.busy": "2024-10-25T14:33:33.341694Z",
+     "iopub.status.idle": "2024-10-25T14:33:38.265423Z",
+     "shell.execute_reply": "2024-10-25T14:33:38.264343Z"
     }
    },
    "outputs": [
@@ -334,23 +334,936 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: Data subset defined by a function\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 21.474097597869555\n",
-      "Effect estimates: [[23.92786788]\n",
-      " [22.51303763]\n",
-      " [17.01246052]\n",
-      " ...\n",
-      " [22.90897968]\n",
-      " [15.56349972]\n",
-      " [25.6520251 ]]\n",
+      "Mean value: 15.267669524379059\n",
+      "Effect estimates: [[12.90664534]\n",
+      " [14.38138836]\n",
+      " [10.2357385 ]\n",
+      " [10.23987427]\n",
+      " [13.47109986]\n",
+      " [11.19468597]\n",
+      " [ 8.71140809]\n",
+      " [14.27102838]\n",
+      " [14.77774453]\n",
+      " [ 9.61839297]\n",
+      " [15.59684456]\n",
+      " [19.68728306]\n",
+      " [13.76608678]\n",
+      " [22.10228465]\n",
+      " [21.00718484]\n",
+      " [10.83986598]\n",
+      " [18.26539699]\n",
+      " [21.54713752]\n",
+      " [20.58758719]\n",
+      " [17.66321804]\n",
+      " [15.54830641]\n",
+      " [13.17608218]\n",
+      " [21.30665667]\n",
+      " [17.25260255]\n",
+      " [16.76473542]\n",
+      " [12.14064485]\n",
+      " [19.97405448]\n",
+      " [19.00251288]\n",
+      " [11.35059086]\n",
+      " [20.22038945]\n",
+      " [20.04743649]\n",
+      " [10.11288552]\n",
+      " [18.16878219]\n",
+      " [14.11827826]\n",
+      " [12.3183592 ]\n",
+      " [20.50438781]\n",
+      " [17.42956696]\n",
+      " [15.68772439]\n",
+      " [20.37568615]\n",
+      " [12.86751839]\n",
+      " [16.60542198]\n",
+      " [15.13267292]\n",
+      " [11.1276136 ]\n",
+      " [19.66905016]\n",
+      " [ 8.65818075]\n",
+      " [14.12847087]\n",
+      " [13.1443254 ]\n",
+      " [14.21545231]\n",
+      " [11.34122461]\n",
+      " [17.70535722]\n",
+      " [14.32058541]\n",
+      " [12.74878696]\n",
+      " [11.44668147]\n",
+      " [19.16437979]\n",
+      " [16.54197279]\n",
+      " [18.10513861]\n",
+      " [14.51118135]\n",
+      " [21.35868267]\n",
+      " [ 8.77959824]\n",
+      " [17.84519068]\n",
+      " [17.21129456]\n",
+      " [16.67419526]\n",
+      " [17.39785843]\n",
+      " [15.76837168]\n",
+      " [10.86722358]\n",
+      " [14.6715169 ]\n",
+      " [14.89835161]\n",
+      " [17.04686222]\n",
+      " [14.48667435]\n",
+      " [16.66594093]\n",
+      " [14.56128981]\n",
+      " [20.14249386]\n",
+      " [10.36170885]\n",
+      " [18.39657393]\n",
+      " [ 7.47143542]\n",
+      " [15.18909267]\n",
+      " [17.94803876]\n",
+      " [10.2032646 ]\n",
+      " [13.43462399]\n",
+      " [ 8.17497236]\n",
+      " [10.07499358]\n",
+      " [12.48877858]\n",
+      " [11.82906593]\n",
+      " [15.36362058]\n",
+      " [13.6090933 ]\n",
+      " [17.06079318]\n",
+      " [18.26409511]\n",
+      " [13.33224459]\n",
+      " [11.755378  ]\n",
+      " [15.17132044]\n",
+      " [13.33230185]\n",
+      " [10.45490005]\n",
+      " [21.18709346]\n",
+      " [15.01311924]\n",
+      " [11.72365471]\n",
+      " [21.26991562]\n",
+      " [13.29190176]\n",
+      " [19.92643768]\n",
+      " [16.90790474]\n",
+      " [17.51032902]\n",
+      " [18.88750317]\n",
+      " [12.80985805]\n",
+      " [24.75916998]\n",
+      " [14.33463832]\n",
+      " [14.12386723]\n",
+      " [15.37102754]\n",
+      " [10.29608484]\n",
+      " [13.39710456]\n",
+      " [17.31736753]\n",
+      " [11.22318368]\n",
+      " [15.48416813]\n",
+      " [16.23934648]\n",
+      " [13.82774575]\n",
+      " [14.53030707]\n",
+      " [14.55146397]\n",
+      " [12.6354816 ]\n",
+      " [19.28797727]\n",
+      " [17.35094533]\n",
+      " [10.9784859 ]\n",
+      " [13.4492492 ]\n",
+      " [15.40772192]\n",
+      " [15.57198414]\n",
+      " [15.92431296]\n",
+      " [11.87757454]\n",
+      " [16.8856623 ]\n",
+      " [23.47537601]\n",
+      " [18.12208163]\n",
+      " [15.14986711]\n",
+      " [10.56906557]\n",
+      " [14.48131411]\n",
+      " [16.26447362]\n",
+      " [12.91546473]\n",
+      " [11.37852567]\n",
+      " [14.24560624]\n",
+      " [16.51904611]\n",
+      " [14.79927997]\n",
+      " [11.35679738]\n",
+      " [18.60165901]\n",
+      " [15.01743548]\n",
+      " [14.04279446]\n",
+      " [18.10881439]\n",
+      " [16.70144315]\n",
+      " [13.23799299]\n",
+      " [16.72905592]\n",
+      " [17.12408819]\n",
+      " [ 9.3793803 ]\n",
+      " [20.7650646 ]\n",
+      " [15.05016365]\n",
+      " [16.01509607]\n",
+      " [13.69188919]\n",
+      " [15.3320399 ]\n",
+      " [18.71384061]\n",
+      " [19.67131172]\n",
+      " [17.44365811]\n",
+      " [11.9362698 ]\n",
+      " [13.59657777]\n",
+      " [14.63298498]\n",
+      " [12.83723052]\n",
+      " [15.66583142]\n",
+      " [19.85320136]\n",
+      " [12.00137471]\n",
+      " [18.1832713 ]\n",
+      " [15.56393982]\n",
+      " [22.62845798]\n",
+      " [12.98821458]\n",
+      " [11.32235399]\n",
+      " [20.34115035]\n",
+      " [19.67424878]\n",
+      " [11.00334334]\n",
+      " [11.16535237]\n",
+      " [ 8.20358919]\n",
+      " [17.31478377]\n",
+      " [13.67732429]\n",
+      " [17.90738032]\n",
+      " [14.48755595]\n",
+      " [13.0916963 ]\n",
+      " [13.14483303]\n",
+      " [21.08400961]\n",
+      " [11.77325363]\n",
+      " [13.68274944]\n",
+      " [19.83498302]\n",
+      " [ 6.87666018]\n",
+      " [14.02289941]\n",
+      " [15.28890978]\n",
+      " [20.50102975]\n",
+      " [15.62421512]\n",
+      " [ 3.46593099]\n",
+      " [21.61730522]\n",
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+      " [11.98932786]\n",
+      " [19.89704706]\n",
+      " [11.17264618]\n",
+      " [20.13961574]\n",
+      " [19.92136557]\n",
+      " [13.91154672]\n",
+      " [22.19960389]\n",
+      " [15.28329025]\n",
+      " [ 8.20572948]\n",
+      " [15.03259966]\n",
+      " [11.54066153]\n",
+      " [17.47475084]\n",
+      " [12.09493529]\n",
+      " [12.89414985]\n",
+      " [27.4703543 ]\n",
+      " [15.6414925 ]\n",
+      " [13.05638996]\n",
+      " [18.38303655]\n",
+      " [21.27298202]\n",
+      " [13.51419934]\n",
+      " [15.60881231]\n",
+      " [14.22350239]\n",
+      " [15.2084249 ]\n",
+      " [17.33164613]\n",
+      " [10.77311022]\n",
+      " [14.60803611]\n",
+      " [12.61505335]\n",
+      " [ 7.8426579 ]\n",
+      " [15.6586185 ]\n",
+      " [12.25674442]\n",
+      " [14.15349524]\n",
+      " [14.48888026]\n",
+      " [16.03195259]\n",
+      " [16.4059316 ]\n",
+      " [ 9.62081148]\n",
+      " [13.81136501]\n",
+      " [ 9.78588639]\n",
+      " [22.2621919 ]\n",
+      " [17.71046534]\n",
+      " [13.65214743]\n",
+      " [17.15702414]\n",
+      " [18.73547983]\n",
+      " [18.47265599]\n",
+      " [15.69172039]\n",
+      " [11.24702021]\n",
+      " [17.16723421]\n",
+      " [13.01433774]\n",
+      " [10.6410377 ]\n",
+      " [16.16613755]\n",
+      " [22.1500304 ]\n",
+      " [11.5248423 ]\n",
+      " [ 8.94300795]\n",
+      " [14.65291482]\n",
+      " [20.86251932]\n",
+      " [20.06906932]\n",
+      " [13.67132003]\n",
+      " [15.79306143]\n",
+      " [14.86626984]\n",
+      " [12.88594758]\n",
+      " [15.44917132]\n",
+      " [13.88340672]\n",
+      " [14.58176868]\n",
+      " [16.82253049]\n",
+      " [15.5551518 ]\n",
+      " [14.96311331]\n",
+      " [ 9.01451518]\n",
+      " [20.36977421]\n",
+      " [12.97486582]\n",
+      " [14.09306063]\n",
+      " [ 8.08414129]\n",
+      " [13.63466685]\n",
+      " [17.01066197]\n",
+      " [21.06640024]\n",
+      " [13.23935049]\n",
+      " [17.6809231 ]\n",
+      " [17.6758432 ]\n",
+      " [ 9.82627011]\n",
+      " [18.98174477]\n",
+      " [11.63461713]\n",
+      " [14.87028157]\n",
+      " [15.42029064]\n",
+      " [ 7.45766328]\n",
+      " [17.15644357]\n",
+      " [18.42078586]\n",
+      " [17.19992649]\n",
+      " [19.90136524]\n",
+      " [18.12825871]\n",
+      " [17.3767844 ]\n",
+      " [11.9633955 ]\n",
+      " [ 6.85485707]\n",
+      " [19.06364978]\n",
+      " [ 8.44097892]\n",
+      " [12.17154843]\n",
+      " [16.6864876 ]\n",
+      " [ 8.33408186]\n",
+      " [14.00261847]\n",
+      " [14.12077598]\n",
+      " [15.01337669]\n",
+      " [12.16479932]\n",
+      " [14.55316329]\n",
+      " [13.31724044]\n",
+      " [11.5729382 ]\n",
+      " [11.4190672 ]\n",
+      " [14.56249742]\n",
+      " [ 6.33786666]\n",
+      " [13.52862815]\n",
+      " [18.17130753]\n",
+      " [18.50135751]\n",
+      " [13.28209306]\n",
+      " [16.76670271]\n",
+      " [12.98667281]\n",
+      " [15.18229719]\n",
+      " [16.51132785]\n",
+      " [23.23687763]\n",
+      " [18.3374187 ]\n",
+      " [16.02888301]\n",
+      " [23.5549559 ]\n",
+      " [18.16764372]\n",
+      " [15.05194278]\n",
+      " [18.37433976]\n",
+      " [14.35191557]\n",
+      " [18.28278914]\n",
+      " [12.63762715]\n",
+      " [11.53327183]\n",
+      " [15.97709567]\n",
+      " [12.11587264]\n",
+      " [13.67959057]\n",
+      " [10.39692145]\n",
+      " [11.89972404]\n",
+      " [12.81424374]\n",
+      " [19.43859557]\n",
+      " [11.26525835]\n",
+      " [17.08835748]\n",
+      " [17.8959596 ]\n",
+      " [14.12365959]\n",
+      " [20.59560679]\n",
+      " [17.06096365]\n",
+      " [18.07247797]\n",
+      " [24.39201819]\n",
+      " [15.01755365]\n",
+      " [15.62720641]\n",
+      " [10.56369581]\n",
+      " [13.96581874]\n",
+      " [17.00095255]\n",
+      " [18.62267886]\n",
+      " [19.11328605]\n",
+      " [18.48449261]\n",
+      " [18.8122607 ]\n",
+      " [ 9.36405072]\n",
+      " [15.36521508]\n",
+      " [11.79840485]\n",
+      " [13.61227422]\n",
+      " [20.2746336 ]\n",
+      " [10.42621519]\n",
+      " [14.66847901]\n",
+      " [16.70452605]\n",
+      " [22.17665051]\n",
+      " [14.23592962]\n",
+      " [ 8.29622405]\n",
+      " [11.62441771]\n",
+      " [14.73706899]\n",
+      " [16.91326237]\n",
+      " [16.97793647]\n",
+      " [16.46893716]\n",
+      " [15.64543299]\n",
+      " [11.29510719]\n",
+      " [11.10635205]\n",
+      " [17.76345427]\n",
+      " [13.64205605]\n",
+      " [19.07462592]]\n",
       "\n"
      ]
     }
@@ -377,10 +1290,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:47.321461Z",
-     "iopub.status.busy": "2024-10-25T14:32:47.321217Z",
-     "iopub.status.idle": "2024-10-25T14:32:47.399122Z",
-     "shell.execute_reply": "2024-10-25T14:32:47.398507Z"
+     "iopub.execute_input": "2024-10-25T14:33:38.271067Z",
+     "iopub.status.busy": "2024-10-25T14:33:38.270821Z",
+     "iopub.status.idle": "2024-10-25T14:33:38.338128Z",
+     "shell.execute_reply": "2024-10-25T14:33:38.337509Z"
     }
    },
    "outputs": [
@@ -388,7 +1301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "True causal estimate is 15.528281358714274\n"
+      "True causal estimate is 11.247611011059492\n"
      ]
     }
    ],
@@ -401,10 +1314,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:47.401075Z",
-     "iopub.status.busy": "2024-10-25T14:32:47.400739Z",
-     "iopub.status.idle": "2024-10-25T14:32:51.025348Z",
-     "shell.execute_reply": "2024-10-25T14:32:51.024291Z"
+     "iopub.execute_input": "2024-10-25T14:33:38.340024Z",
+     "iopub.status.busy": "2024-10-25T14:33:38.339812Z",
+     "iopub.status.idle": "2024-10-25T14:33:41.999243Z",
+     "shell.execute_reply": "2024-10-25T14:33:41.998216Z"
     }
    },
    "outputs": [
@@ -421,23 +1334,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.471429603219727\n",
-      "Effect estimates: [[16.07009773]\n",
-      " [20.03571549]\n",
-      " [ 8.96384864]\n",
+      "Mean value: 11.182367944351238\n",
+      "Effect estimates: [[12.79993158]\n",
+      " [ 3.7112351 ]\n",
+      " [ 5.76948667]\n",
       " ...\n",
-      " [ 7.14006316]\n",
-      " [ 6.11592992]\n",
-      " [17.9611212 ]]\n",
+      " [12.36146871]\n",
+      " [16.2363937 ]\n",
+      " [19.84331551]]\n",
       "\n"
      ]
     }
@@ -469,10 +1382,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:32:51.028686Z",
-     "iopub.status.busy": "2024-10-25T14:32:51.028450Z",
-     "iopub.status.idle": "2024-10-25T14:35:04.352159Z",
-     "shell.execute_reply": "2024-10-25T14:35:04.351570Z"
+     "iopub.execute_input": "2024-10-25T14:33:42.001875Z",
+     "iopub.status.busy": "2024-10-25T14:33:42.001655Z",
+     "iopub.status.idle": "2024-10-25T14:35:55.150017Z",
+     "shell.execute_reply": "2024-10-25T14:35:55.149406Z"
     }
    },
    "outputs": [
@@ -489,28 +1402,28 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 15.504044662998552\n",
-      "Effect estimates: [[16.19325628]\n",
-      " [20.24036528]\n",
-      " [ 9.09597254]\n",
+      "Mean value: 11.238487459603933\n",
+      "Effect estimates: [[12.89507289]\n",
+      " [ 3.68601742]\n",
+      " [ 5.73421756]\n",
       " ...\n",
-      " [ 6.97654031]\n",
-      " [ 6.38853545]\n",
-      " [17.95558472]]\n",
-      "95.0% confidence interval: [[[16.32550901 20.45287404  9.01676031 ...  6.48860366  6.24417781\n",
-      "   18.00102197]]\n",
+      " [12.45379182]\n",
+      " [16.37185436]\n",
+      " [20.01769281]]\n",
+      "95.0% confidence interval: [[[13.01604156  3.28553672  5.52193098 ... 12.55393218 16.58389431\n",
+      "   20.28215107]]\n",
       "\n",
-      " [[16.65259291 20.99199809  9.42151539 ...  7.02160604  6.88463651\n",
-      "   18.34895763]]]\n",
+      " [[13.26912412  3.79973322  5.80719612 ... 12.80864789 16.96236057\n",
+      "   20.89301761]]]\n",
       "\n"
      ]
     }
@@ -547,10 +1460,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:04.355501Z",
-     "iopub.status.busy": "2024-10-25T14:35:04.354468Z",
-     "iopub.status.idle": "2024-10-25T14:35:08.003677Z",
-     "shell.execute_reply": "2024-10-25T14:35:08.002354Z"
+     "iopub.execute_input": "2024-10-25T14:35:55.153511Z",
+     "iopub.status.busy": "2024-10-25T14:35:55.152703Z",
+     "iopub.status.idle": "2024-10-25T14:35:58.816092Z",
+     "shell.execute_reply": "2024-10-25T14:35:58.814692Z"
     }
    },
    "outputs": [
@@ -558,16 +1471,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[15.94311345]\n",
-      " [11.71072198]\n",
-      " [16.1674401 ]\n",
-      " [14.5076398 ]\n",
-      " [13.21364746]\n",
-      " [14.86479933]\n",
-      " [16.99195916]\n",
-      " [18.03051279]\n",
-      " [14.49054386]\n",
-      " [14.20683584]]\n"
+      "[[13.05623832]\n",
+      " [12.98682823]\n",
+      " [14.53693928]\n",
+      " [12.44537756]\n",
+      " [10.66232785]\n",
+      " [13.12070985]\n",
+      " [12.92463772]\n",
+      " [12.90844965]\n",
+      " [13.94570379]\n",
+      " [15.15154461]]\n"
      ]
     }
    ],
@@ -600,10 +1513,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:08.006967Z",
-     "iopub.status.busy": "2024-10-25T14:35:08.006733Z",
-     "iopub.status.idle": "2024-10-25T14:35:08.105745Z",
-     "shell.execute_reply": "2024-10-25T14:35:08.105237Z"
+     "iopub.execute_input": "2024-10-25T14:35:58.819860Z",
+     "iopub.status.busy": "2024-10-25T14:35:58.819053Z",
+     "iopub.status.idle": "2024-10-25T14:35:58.918933Z",
+     "shell.execute_reply": "2024-10-25T14:35:58.918293Z"
     }
    },
    "outputs": [
@@ -611,7 +1524,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<econml.dml.dml.DML object at 0x7f21861582e0>\n"
+      "<econml.dml.dml.DML object at 0x7f24fcb02250>\n"
      ]
     }
    ],
@@ -639,10 +1552,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:08.107677Z",
-     "iopub.status.busy": "2024-10-25T14:35:08.107380Z",
-     "iopub.status.idle": "2024-10-25T14:35:09.081183Z",
-     "shell.execute_reply": "2024-10-25T14:35:09.080648Z"
+     "iopub.execute_input": "2024-10-25T14:35:58.920922Z",
+     "iopub.status.busy": "2024-10-25T14:35:58.920613Z",
+     "iopub.status.idle": "2024-10-25T14:35:59.899494Z",
+     "shell.execute_reply": "2024-10-25T14:35:59.898801Z"
     }
    },
    "outputs": [
@@ -651,17 +1564,17 @@
      "output_type": "stream",
      "text": [
       "            X0        X1   Z0        W0        W1        W2        W3  v0  y\n",
-      "0     1.848937 -0.903341  1.0 -1.357018  0.359030 -0.158734  0.547312   1  1\n",
-      "1    -0.166756 -1.358425  1.0 -0.545606 -2.206641 -0.613952 -0.342323   0  0\n",
-      "2    -1.009845 -0.463540  1.0  1.071495 -1.730563 -1.159340  1.685405   1  1\n",
-      "3     1.721945 -0.219205  0.0 -0.401258 -1.359670  0.215030 -0.403456   0  0\n",
-      "4    -0.227010 -0.505793  1.0 -0.853896 -0.787990 -1.158951  0.733975   1  1\n",
+      "0     0.382932  2.345769  0.0 -1.428223  0.526978 -1.831798 -0.937137   0  0\n",
+      "1     0.323482  0.191478  0.0  1.653139 -0.727859 -0.366949 -1.571791   0  1\n",
+      "2     0.263324 -0.088392  0.0  1.087412  1.253476  1.720935  0.482786   1  1\n",
+      "3     0.779199  1.292685  1.0  0.481355  2.038458 -0.642406  1.758478   1  1\n",
+      "4     0.134317  0.842260  0.0  0.295010  0.232721 -0.160742  0.205964   1  1\n",
       "...        ...       ...  ...       ...       ...       ...       ...  .. ..\n",
-      "9995  1.216954 -0.117298  1.0  1.190832 -1.800529 -1.404404  1.478945   1  1\n",
-      "9996  1.670534  0.426617  1.0  1.868842  0.514159 -0.522367 -1.006508   1  1\n",
-      "9997  0.634803 -0.151029  1.0  0.108213 -1.416494 -0.408316  0.783284   1  1\n",
-      "9998  1.089458 -0.117143  1.0 -1.723675 -2.177703 -0.913882  0.198161   0  0\n",
-      "9999  1.615717 -1.471271  1.0  0.224152 -0.409710 -1.051757  0.457010   1  1\n",
+      "9995  1.866050  0.530130  0.0  1.023069  0.168696 -1.391754 -0.191480   0  0\n",
+      "9996  0.912797  1.169468  0.0  0.965489 -0.489325 -1.148618  0.095484   0  1\n",
+      "9997 -0.642280  1.079737  0.0  2.789080 -0.074786 -0.972265  0.125235   1  1\n",
+      "9998  1.951512  2.407281  0.0  0.464253  0.681882  0.421487 -0.183010   1  1\n",
+      "9999 -0.505607  3.069471  1.0  2.014816 -0.287527 -1.342139  0.203378   1  1\n",
       "\n",
       "[10000 rows x 9 columns]\n"
      ]
@@ -694,10 +1607,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:09.083263Z",
-     "iopub.status.busy": "2024-10-25T14:35:09.082877Z",
-     "iopub.status.idle": "2024-10-25T14:35:19.067139Z",
-     "shell.execute_reply": "2024-10-25T14:35:19.066537Z"
+     "iopub.execute_input": "2024-10-25T14:35:59.901688Z",
+     "iopub.status.busy": "2024-10-25T14:35:59.901213Z",
+     "iopub.status.idle": "2024-10-25T14:36:10.763795Z",
+     "shell.execute_reply": "2024-10-25T14:36:10.763202Z"
     }
    },
    "outputs": [
@@ -714,25 +1627,25 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                    \n",
-      "─────(E[y|W1,W3,W0,W2])\n",
+      "─────(E[y|W0,W2,W1,W3])\n",
       "d[v₀]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,U) = P(y|v0,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W2,W1,W3,U) = P(y|v0,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 0.5517154689937798\n",
-      "Effect estimates: [[0.56087692]\n",
-      " [0.53034022]\n",
-      " [0.52440591]\n",
+      "Mean value: 0.3968213931846329\n",
+      "Effect estimates: [[0.46341228]\n",
+      " [0.38036417]\n",
+      " [0.36709047]\n",
       " ...\n",
-      " [0.54895095]\n",
-      " [0.55540547]\n",
-      " [0.55409699]]\n",
+      " [0.3676809 ]\n",
+      " [0.54003283]\n",
+      " [0.44825981]]\n",
       "\n",
-      "True causal estimate is 0.5364\n"
+      "True causal estimate is 0.3365\n"
      ]
     }
    ],
@@ -763,10 +1676,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:19.069709Z",
-     "iopub.status.busy": "2024-10-25T14:35:19.069475Z",
-     "iopub.status.idle": "2024-10-25T14:35:52.756931Z",
-     "shell.execute_reply": "2024-10-25T14:35:52.756365Z"
+     "iopub.execute_input": "2024-10-25T14:36:10.766201Z",
+     "iopub.status.busy": "2024-10-25T14:36:10.765992Z",
+     "iopub.status.idle": "2024-10-25T14:36:37.524658Z",
+     "shell.execute_reply": "2024-10-25T14:36:37.523187Z"
     },
     "scrolled": true
    },
@@ -791,18 +1704,18 @@
       "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2 | X1,X0\n",
+      "b: y~v0+W0+W2+W1+W3 | X0,X1\n",
       "Target units: Data subset defined by a function\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 16.396141098363664\n",
-      "Effect estimates: [[16.20952269]\n",
-      " [20.31761618]\n",
-      " [ 8.99574685]\n",
+      "Mean value: 12.312450374293359\n",
+      "Effect estimates: [[12.87600707]\n",
+      " [ 4.04658904]\n",
+      " [13.00104868]\n",
       " ...\n",
-      " [15.89593262]\n",
-      " [ 6.86027739]\n",
-      " [18.00872617]]\n",
+      " [12.45626241]\n",
+      " [16.20365244]\n",
+      " [19.68928866]]\n",
       "\n"
      ]
     }
@@ -832,10 +1745,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:52.759447Z",
-     "iopub.status.busy": "2024-10-25T14:35:52.759229Z",
-     "iopub.status.idle": "2024-10-25T14:35:53.312342Z",
-     "shell.execute_reply": "2024-10-25T14:35:53.311783Z"
+     "iopub.execute_input": "2024-10-25T14:36:37.529354Z",
+     "iopub.status.busy": "2024-10-25T14:36:37.529123Z",
+     "iopub.status.idle": "2024-10-25T14:36:38.088908Z",
+     "shell.execute_reply": "2024-10-25T14:36:38.088380Z"
     }
    },
    "outputs": [
@@ -844,30 +1757,30 @@
      "output_type": "stream",
      "text": [
       "            X0        X1        X2        X3        X4   Z0        Z1  \\\n",
-      "0    -1.928622 -1.460600 -1.060879 -0.428721 -0.585863  0.0  0.833845   \n",
-      "1    -0.354271 -0.970412 -2.236240 -1.434037 -0.052678  1.0  0.014492   \n",
-      "2    -0.074867 -1.492009  0.798875  0.294336 -1.352786  1.0  0.453896   \n",
-      "3    -1.130675  0.104157  0.189918  1.204923 -0.750851  1.0  0.005845   \n",
-      "4    -1.319822  1.815768  0.287191 -0.016356  0.631682  1.0  0.509215   \n",
+      "0    -1.908063  1.816612 -1.782549  0.702879  0.428015  1.0  0.853746   \n",
+      "1    -0.841354  1.831363  0.622043 -2.117324  2.878573  0.0  0.547690   \n",
+      "2     0.148732 -0.151823  0.801329 -0.784210  0.265481  1.0  0.257635   \n",
+      "3    -0.461454  1.294300 -1.239469 -1.050843  2.835228  1.0  0.763303   \n",
+      "4    -0.015284  0.043538 -0.944665 -2.759310  1.061391  0.0  0.181512   \n",
       "...        ...       ...       ...       ...       ...  ...       ...   \n",
-      "9995 -0.548747 -1.033007 -1.326244 -1.708462  1.071464  1.0  0.214273   \n",
-      "9996 -1.567399 -1.900533  0.286898 -1.991567  0.838530  1.0  0.057444   \n",
-      "9997 -0.748685 -0.461194 -1.679336  0.511443 -2.390655  1.0  0.503427   \n",
-      "9998 -0.875187 -0.830123 -1.068104 -1.842930 -0.387148  1.0  0.207693   \n",
-      "9999 -0.880693  0.763965  0.942783 -1.150989 -2.767426  1.0  0.271196   \n",
+      "9995 -0.622211  0.630081 -0.504322  0.090627  1.190173  1.0  0.491685   \n",
+      "9996  0.438390  0.571294 -0.981292  1.184738  1.261173  0.0  0.934166   \n",
+      "9997 -0.149968  1.402410  0.989695  0.092680  3.186254  0.0  0.296926   \n",
+      "9998  1.221613 -0.114503 -1.586336 -0.079467  1.314031  1.0  0.029542   \n",
+      "9999 -2.146341  1.379550 -0.623245 -0.703477  1.266934  1.0  0.801060   \n",
       "\n",
       "            W0        W1        W2        W3        W4  v0          y  \n",
-      "0     1.538836  0.356235  0.562544 -1.229352 -1.412987   1 -12.553256  \n",
-      "1     0.629364  1.026922 -0.971291 -0.524729  0.799605   1  -2.751671  \n",
-      "2    -0.536827  0.753210  0.798975  0.652538 -0.751729   1   9.283271  \n",
-      "3     1.518184  1.360522  0.413248 -0.392738 -1.794745   1   7.341700  \n",
-      "4     0.143308  0.676154  0.564906 -0.280576 -1.100754   1   8.983174  \n",
+      "0    -0.268623 -0.019217  1.039168  1.864035  1.528477   1  31.875318  \n",
+      "1    -0.640923  1.041966  1.959528  0.571702 -1.069802   1  22.447270  \n",
+      "2    -1.254305  1.170127 -0.166980 -0.295455  1.066763   1  10.654918  \n",
+      "3     0.610376  0.068494  1.902022  2.266399  0.332300   1  35.074787  \n",
+      "4    -2.346280 -0.076409  0.007971 -1.487368 -1.287827   0 -21.300166  \n",
       "...        ...       ...       ...       ...       ...  ..        ...  \n",
-      "9995  0.727406 -0.769579  0.287496  0.919897 -0.996558   1   3.489504  \n",
-      "9996  2.535569  1.591533 -0.929597 -0.160212 -0.719416   1  10.491038  \n",
-      "9997 -0.196327 -0.294150 -0.834032  0.684718  0.132787   1  -7.312851  \n",
-      "9998  1.698052 -0.275638 -1.178926 -0.288767 -1.333009   1  -5.539157  \n",
-      "9999  1.114213 -0.004539 -0.946504 -2.163850 -1.660811   1 -12.611732  \n",
+      "9995 -0.161351 -1.566292  1.007896  0.678905 -0.321988   1  10.933773  \n",
+      "9996 -0.962916  0.585787  2.459705 -0.293871  0.312810   1  31.844450  \n",
+      "9997 -0.298513  1.390892  1.936229  0.492862  0.157574   1  41.957955  \n",
+      "9998 -1.055555  0.676774 -0.010229  0.528897  1.068823   1  16.869197  \n",
+      "9999  0.400382 -0.040731  0.135707  0.114662 -0.318792   1  10.570814  \n",
       "\n",
       "[10000 rows x 14 columns]\n"
      ]
@@ -891,10 +1804,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:35:53.314386Z",
-     "iopub.status.busy": "2024-10-25T14:35:53.313991Z",
-     "iopub.status.idle": "2024-10-25T14:36:01.874288Z",
-     "shell.execute_reply": "2024-10-25T14:36:01.873644Z"
+     "iopub.execute_input": "2024-10-25T14:36:38.090829Z",
+     "iopub.status.busy": "2024-10-25T14:36:38.090621Z",
+     "iopub.status.idle": "2024-10-25T14:36:46.681131Z",
+     "shell.execute_reply": "2024-10-25T14:36:46.680528Z"
     }
    },
    "outputs": [
@@ -911,25 +1824,25 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W4,W0,W2,W1,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4\n",
+      "b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 6.437717655807301\n",
-      "Effect estimates: [[-4.16181067]\n",
-      " [ 0.774689  ]\n",
-      " [11.02032889]\n",
+      "Mean value: 18.87771266237887\n",
+      "Effect estimates: [[27.78513921]\n",
+      " [23.45800939]\n",
+      " [10.86480525]\n",
       " ...\n",
-      " [-5.22952473]\n",
-      " [-1.44104664]\n",
-      " [ 1.56876933]]\n",
+      " [32.21640236]\n",
+      " [17.6237358 ]\n",
+      " [15.38137178]]\n",
       "\n",
-      "True causal estimate is 1.87902954705715\n"
+      "True causal estimate is 10.035889599894295\n"
      ]
     }
    ],
@@ -966,10 +1879,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:36:01.876340Z",
-     "iopub.status.busy": "2024-10-25T14:36:01.875964Z",
-     "iopub.status.idle": "2024-10-25T14:36:01.929723Z",
-     "shell.execute_reply": "2024-10-25T14:36:01.929090Z"
+     "iopub.execute_input": "2024-10-25T14:36:46.683234Z",
+     "iopub.status.busy": "2024-10-25T14:36:46.682868Z",
+     "iopub.status.idle": "2024-10-25T14:36:46.734130Z",
+     "shell.execute_reply": "2024-10-25T14:36:46.733551Z"
     }
    },
    "outputs": [
@@ -986,23 +1899,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W4,W0,W2,W1,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W0,W2,W1,W3,U) = P(y|v0,W4,W0,W2,W1,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+X0+X4+X1+X3+X2+W1+W3+W0+W2+W4\n",
+      "b: y~v0+X3+X2+X4+X0+X1+W4+W0+W2+W1+W3\n",
       "Target units: Data subset provided as a data frame\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 2.043488559407042\n",
-      "Effect estimates: [[ 4.54315475]\n",
-      " [10.77609008]\n",
-      " [-5.22952473]\n",
-      " [-1.44104664]\n",
-      " [ 1.56876933]]\n",
+      "Mean value: 22.84531697294303\n",
+      "Effect estimates: [[21.97693322]\n",
+      " [27.02814169]\n",
+      " [32.21640236]\n",
+      " [17.6237358 ]\n",
+      " [15.38137178]]\n",
       "\n",
-      "True causal estimate is 1.87902954705715\n"
+      "True causal estimate is 10.035889599894295\n"
      ]
     }
    ],
@@ -1037,10 +1950,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:36:01.931771Z",
-     "iopub.status.busy": "2024-10-25T14:36:01.931466Z",
-     "iopub.status.idle": "2024-10-25T14:42:08.323291Z",
-     "shell.execute_reply": "2024-10-25T14:42:08.321792Z"
+     "iopub.execute_input": "2024-10-25T14:36:46.736219Z",
+     "iopub.status.busy": "2024-10-25T14:36:46.735838Z",
+     "iopub.status.idle": "2024-10-25T14:42:54.994281Z",
+     "shell.execute_reply": "2024-10-25T14:42:54.993146Z"
     }
    },
    "outputs": [
@@ -1049,9 +1962,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.061049698929667\n",
-      "p value:0.19999999999999996\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.122022363488286\n",
+      "p value:0.2\n",
       "\n"
      ]
     }
@@ -1073,10 +1986,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:08.329668Z",
-     "iopub.status.busy": "2024-10-25T14:42:08.328215Z",
-     "iopub.status.idle": "2024-10-25T14:42:12.021669Z",
-     "shell.execute_reply": "2024-10-25T14:42:12.020587Z"
+     "iopub.execute_input": "2024-10-25T14:42:54.998347Z",
+     "iopub.status.busy": "2024-10-25T14:42:54.998083Z",
+     "iopub.status.idle": "2024-10-25T14:42:58.695637Z",
+     "shell.execute_reply": "2024-10-25T14:42:58.694552Z"
     }
    },
    "outputs": [
@@ -1085,8 +1998,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.04397103511404\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.117896772844379\n",
       "\n"
      ]
     }
@@ -1110,10 +2023,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:12.025077Z",
-     "iopub.status.busy": "2024-10-25T14:42:12.024837Z",
-     "iopub.status.idle": "2024-10-25T14:42:48.792071Z",
-     "shell.execute_reply": "2024-10-25T14:42:48.790906Z"
+     "iopub.execute_input": "2024-10-25T14:42:58.700132Z",
+     "iopub.status.busy": "2024-10-25T14:42:58.699125Z",
+     "iopub.status.idle": "2024-10-25T14:43:35.533982Z",
+     "shell.execute_reply": "2024-10-25T14:43:35.533454Z"
     }
    },
    "outputs": [
@@ -1122,9 +2035,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:-0.039633288288616876\n",
-      "p value:0.3019778331621793\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:-0.011817586304644403\n",
+      "p value:0.4059910355354093\n",
       "\n"
      ]
     }
@@ -1149,10 +2062,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:42:48.796726Z",
-     "iopub.status.busy": "2024-10-25T14:42:48.795358Z",
-     "iopub.status.idle": "2024-10-25T14:43:18.092151Z",
-     "shell.execute_reply": "2024-10-25T14:43:18.090388Z"
+     "iopub.execute_input": "2024-10-25T14:43:35.537328Z",
+     "iopub.status.busy": "2024-10-25T14:43:35.536426Z",
+     "iopub.status.idle": "2024-10-25T14:44:04.666511Z",
+     "shell.execute_reply": "2024-10-25T14:44:04.665911Z"
     }
    },
    "outputs": [
@@ -1161,9 +2074,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:15.012721377524025\n",
-      "New effect:15.02989599562622\n",
-      "p value:0.3803137657532839\n",
+      "Estimated effect:13.173875684629346\n",
+      "New effect:13.145734147056782\n",
+      "p value:0.2631763306816288\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy-simple-iv-example.html b/main/example_notebooks/dowhy-simple-iv-example.html
index a147fcbec..00e0ce4f2 100644
--- a/main/example_notebooks/dowhy-simple-iv-example.html
+++ b/main/example_notebooks/dowhy-simple-iv-example.html
@@ -761,7 +761,7 @@ <h2>Using DoWhy to estimate the causal effect of education on future income<a cl
 Target units: ate
 
 ## Estimate
-Mean value: 3.987497536617294
+Mean value: 4.122408234201723
 p-value: [0, 0.001]
 
 </pre></div></div>
@@ -784,9 +784,9 @@ <h2>Using DoWhy to estimate the causal effect of education on future income<a cl
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:3.987497536617294
-New effect:-0.025021095946670787
-p value:0.86
+Estimated effect:4.122408234201723
+New effect:-0.022474970252860365
+p value:0.92
 
 </pre></div></div>
 </div>
@@ -813,22 +813,22 @@ <h2>Using DoWhy to estimate the causal effect of education on future income<a cl
 <table class="simpletable">
 <caption>IV2SLS Regression Results</caption>
 <tr>
-  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.889</td>
+  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.904</td>
 </tr>
 <tr>
-  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.889</td>
+  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.904</td>
 </tr>
 <tr>
-  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1361.</td>
+  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1876.</td>
 </tr>
 <tr>
-  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>1.30e-188</td>
+  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>2.04e-231</td>
 </tr>
 <tr>
   <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>                     </th>     <td> </td>
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:43:26</td>     <th>                     </th>     <td> </td>
+  <th>Time:</th>                 <td>14:44:13</td>     <th>                     </th>     <td> </td>
 </tr>
 <tr>
   <th>No. Observations:</th>      <td>  1000</td>      <th>                     </th>     <td> </td>
@@ -845,24 +845,24 @@ <h2>Using DoWhy to estimate the causal effect of education on future income<a cl
       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>
 </tr>
 <tr>
-  <th>Intercept</th> <td>    9.6387</td> <td>    0.977</td> <td>    9.865</td> <td> 0.000</td> <td>    7.721</td> <td>   11.556</td>
+  <th>Intercept</th> <td>    8.9729</td> <td>    0.877</td> <td>   10.231</td> <td> 0.000</td> <td>    7.252</td> <td>   10.694</td>
 </tr>
 <tr>
-  <th>education</th> <td>    3.9875</td> <td>    0.108</td> <td>   36.889</td> <td> 0.000</td> <td>    3.775</td> <td>    4.200</td>
+  <th>education</th> <td>    4.1224</td> <td>    0.095</td> <td>   43.308</td> <td> 0.000</td> <td>    3.936</td> <td>    4.309</td>
 </tr>
 </table>
 <table class="simpletable">
 <tr>
-  <th>Omnibus:</th>       <td> 2.539</td> <th>  Durbin-Watson:     </th> <td>   1.959</td>
+  <th>Omnibus:</th>       <td> 1.392</td> <th>  Durbin-Watson:     </th> <td>   1.913</td>
 </tr>
 <tr>
-  <th>Prob(Omnibus):</th> <td> 0.281</td> <th>  Jarque-Bera (JB):  </th> <td>   2.587</td>
+  <th>Prob(Omnibus):</th> <td> 0.499</td> <th>  Jarque-Bera (JB):  </th> <td>   1.460</td>
 </tr>
 <tr>
-  <th>Skew:</th>          <td> 0.121</td> <th>  Prob(JB):          </th> <td>   0.274</td>
+  <th>Skew:</th>          <td>-0.067</td> <th>  Prob(JB):          </th> <td>   0.482</td>
 </tr>
 <tr>
-  <th>Kurtosis:</th>      <td> 2.940</td> <th>  Cond. No.          </th> <td>    24.5</td>
+  <th>Kurtosis:</th>      <td> 2.870</td> <th>  Cond. No.          </th> <td>    25.1</td>
 </tr>
 </table></div>
 </div>
diff --git a/main/example_notebooks/dowhy-simple-iv-example.ipynb b/main/example_notebooks/dowhy-simple-iv-example.ipynb
index 7c77d19bd..9966878e7 100644
--- a/main/example_notebooks/dowhy-simple-iv-example.ipynb
+++ b/main/example_notebooks/dowhy-simple-iv-example.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:21.254404Z",
-     "iopub.status.busy": "2024-10-25T14:43:21.254225Z",
-     "iopub.status.idle": "2024-10-25T14:43:21.269730Z",
-     "shell.execute_reply": "2024-10-25T14:43:21.269238Z"
+     "iopub.execute_input": "2024-10-25T14:44:08.071035Z",
+     "iopub.status.busy": "2024-10-25T14:44:08.070614Z",
+     "iopub.status.idle": "2024-10-25T14:44:08.087961Z",
+     "shell.execute_reply": "2024-10-25T14:44:08.087444Z"
     }
    },
    "outputs": [],
@@ -29,10 +29,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:21.271373Z",
-     "iopub.status.busy": "2024-10-25T14:43:21.271200Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.722132Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.721479Z"
+     "iopub.execute_input": "2024-10-25T14:44:08.090072Z",
+     "iopub.status.busy": "2024-10-25T14:44:08.089711Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.710219Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.709558Z"
     }
    },
    "outputs": [],
@@ -60,10 +60,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.724525Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.724091Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.757499Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.756885Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.713069Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.712479Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.748957Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.748334Z"
     }
    },
    "outputs": [],
@@ -115,10 +115,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.759646Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.759301Z",
-     "iopub.status.idle": "2024-10-25T14:43:22.928671Z",
-     "shell.execute_reply": "2024-10-25T14:43:22.928110Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.751614Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.751198Z",
+     "iopub.status.idle": "2024-10-25T14:44:09.928111Z",
+     "shell.execute_reply": "2024-10-25T14:44:09.927475Z"
     }
    },
    "outputs": [
@@ -170,10 +170,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:22.930905Z",
-     "iopub.status.busy": "2024-10-25T14:43:22.930492Z",
-     "iopub.status.idle": "2024-10-25T14:43:23.187851Z",
-     "shell.execute_reply": "2024-10-25T14:43:23.187235Z"
+     "iopub.execute_input": "2024-10-25T14:44:09.930622Z",
+     "iopub.status.busy": "2024-10-25T14:44:09.930129Z",
+     "iopub.status.idle": "2024-10-25T14:44:10.237344Z",
+     "shell.execute_reply": "2024-10-25T14:44:10.236669Z"
     }
    },
    "outputs": [
@@ -215,10 +215,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:23.189952Z",
-     "iopub.status.busy": "2024-10-25T14:43:23.189536Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.115355Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.114774Z"
+     "iopub.execute_input": "2024-10-25T14:44:10.239523Z",
+     "iopub.status.busy": "2024-10-25T14:44:10.239123Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.199909Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.199359Z"
     }
    },
    "outputs": [
@@ -260,7 +260,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 3.987497536617294\n",
+      "Mean value: 4.122408234201723\n",
       "p-value: [0, 0.001]\n",
       "\n"
      ]
@@ -289,10 +289,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:26.117422Z",
-     "iopub.status.busy": "2024-10-25T14:43:26.117065Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.557525Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.556949Z"
+     "iopub.execute_input": "2024-10-25T14:44:13.201881Z",
+     "iopub.status.busy": "2024-10-25T14:44:13.201532Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.643838Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.643149Z"
     }
    },
    "outputs": [
@@ -301,9 +301,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:3.987497536617294\n",
-      "New effect:-0.025021095946670787\n",
-      "p value:0.86\n",
+      "Estimated effect:4.122408234201723\n",
+      "New effect:-0.022474970252860365\n",
+      "p value:0.92\n",
       "\n"
      ]
     }
@@ -335,10 +335,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:26.559590Z",
-     "iopub.status.busy": "2024-10-25T14:43:26.559200Z",
-     "iopub.status.idle": "2024-10-25T14:43:26.609942Z",
-     "shell.execute_reply": "2024-10-25T14:43:26.609331Z"
+     "iopub.execute_input": "2024-10-25T14:44:13.646000Z",
+     "iopub.status.busy": "2024-10-25T14:44:13.645623Z",
+     "iopub.status.idle": "2024-10-25T14:44:13.696430Z",
+     "shell.execute_reply": "2024-10-25T14:44:13.695781Z"
     }
    },
    "outputs": [
@@ -348,22 +348,22 @@
        "<table class=\"simpletable\">\n",
        "<caption>IV2SLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.889</td> \n",
+       "  <th>Dep. Variable:</th>         <td>income</td>      <th>  R-squared:         </th> <td>   0.904</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.889</td> \n",
+       "  <th>Model:</th>                 <td>IV2SLS</td>      <th>  Adj. R-squared:    </th> <td>   0.904</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1361.</td> \n",
+       "  <th>Method:</th>               <td>Two Stage</td>    <th>  F-statistic:       </th> <td>   1876.</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>1.30e-188</td>\n",
+       "  <th></th>                    <td>Least Squares</td>  <th>  Prob (F-statistic):</th> <td>2.04e-231</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:43:26</td>     <th>                     </th>     <td> </td>    \n",
+       "  <th>Time:</th>                 <td>14:44:13</td>     <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>  1000</td>      <th>                     </th>     <td> </td>    \n",
@@ -380,24 +380,24 @@
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th> <td>    9.6387</td> <td>    0.977</td> <td>    9.865</td> <td> 0.000</td> <td>    7.721</td> <td>   11.556</td>\n",
+       "  <th>Intercept</th> <td>    8.9729</td> <td>    0.877</td> <td>   10.231</td> <td> 0.000</td> <td>    7.252</td> <td>   10.694</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>education</th> <td>    3.9875</td> <td>    0.108</td> <td>   36.889</td> <td> 0.000</td> <td>    3.775</td> <td>    4.200</td>\n",
+       "  <th>education</th> <td>    4.1224</td> <td>    0.095</td> <td>   43.308</td> <td> 0.000</td> <td>    3.936</td> <td>    4.309</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td> 2.539</td> <th>  Durbin-Watson:     </th> <td>   1.959</td>\n",
+       "  <th>Omnibus:</th>       <td> 1.392</td> <th>  Durbin-Watson:     </th> <td>   1.913</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.281</td> <th>  Jarque-Bera (JB):  </th> <td>   2.587</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.499</td> <th>  Jarque-Bera (JB):  </th> <td>   1.460</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td> 0.121</td> <th>  Prob(JB):          </th> <td>   0.274</td>\n",
+       "  <th>Skew:</th>          <td>-0.067</td> <th>  Prob(JB):          </th> <td>   0.482</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 2.940</td> <th>  Cond. No.          </th> <td>    24.5</td>\n",
+       "  <th>Kurtosis:</th>      <td> 2.870</td> <th>  Cond. No.          </th> <td>    25.1</td>\n",
        "</tr>\n",
        "</table>"
       ],
@@ -405,12 +405,12 @@
        "\\begin{center}\n",
        "\\begin{tabular}{lclc}\n",
        "\\toprule\n",
-       "\\textbf{Dep. Variable:}    &      income      & \\textbf{  R-squared:         } &     0.889   \\\\\n",
-       "\\textbf{Model:}            &      IV2SLS      & \\textbf{  Adj. R-squared:    } &     0.889   \\\\\n",
-       "\\textbf{Method:}           &    Two Stage     & \\textbf{  F-statistic:       } &     1361.   \\\\\n",
-       "\\textbf{}                  &  Least Squares   & \\textbf{  Prob (F-statistic):} & 1.30e-188   \\\\\n",
+       "\\textbf{Dep. Variable:}    &      income      & \\textbf{  R-squared:         } &     0.904   \\\\\n",
+       "\\textbf{Model:}            &      IV2SLS      & \\textbf{  Adj. R-squared:    } &     0.904   \\\\\n",
+       "\\textbf{Method:}           &    Two Stage     & \\textbf{  F-statistic:       } &     1876.   \\\\\n",
+       "\\textbf{}                  &  Least Squares   & \\textbf{  Prob (F-statistic):} & 2.04e-231   \\\\\n",
        "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{                     } &             \\\\\n",
-       "\\textbf{Time:}             &     14:43:26     & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Time:}             &     14:44:13     & \\textbf{                     } &             \\\\\n",
        "\\textbf{No. Observations:} &        1000      & \\textbf{                     } &             \\\\\n",
        "\\textbf{Df Residuals:}     &         998      & \\textbf{                     } &             \\\\\n",
        "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
@@ -419,15 +419,15 @@
        "\\begin{tabular}{lcccccc}\n",
        "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{Intercept} &       9.6387  &        0.977     &     9.865  &         0.000        &        7.721    &       11.556     \\\\\n",
-       "\\textbf{education} &       3.9875  &        0.108     &    36.889  &         0.000        &        3.775    &        4.200     \\\\\n",
+       "\\textbf{Intercept} &       8.9729  &        0.877     &    10.231  &         0.000        &        7.252    &       10.694     \\\\\n",
+       "\\textbf{education} &       4.1224  &        0.095     &    43.308  &         0.000        &        3.936    &        4.309     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       &  2.539 & \\textbf{  Durbin-Watson:     } &    1.959  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &  0.281 & \\textbf{  Jarque-Bera (JB):  } &    2.587  \\\\\n",
-       "\\textbf{Skew:}          &  0.121 & \\textbf{  Prob(JB):          } &    0.274  \\\\\n",
-       "\\textbf{Kurtosis:}      &  2.940 & \\textbf{  Cond. No.          } &     24.5  \\\\\n",
+       "\\textbf{Omnibus:}       &  1.392 & \\textbf{  Durbin-Watson:     } &    1.913  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.499 & \\textbf{  Jarque-Bera (JB):  } &    1.460  \\\\\n",
+       "\\textbf{Skew:}          & -0.067 & \\textbf{  Prob(JB):          } &    0.482  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.870 & \\textbf{  Cond. No.          } &     25.1  \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "%\\caption{IV2SLS Regression Results}\n",
@@ -438,25 +438,25 @@
        "\"\"\"\n",
        "                          IV2SLS Regression Results                           \n",
        "==============================================================================\n",
-       "Dep. Variable:                 income   R-squared:                       0.889\n",
-       "Model:                         IV2SLS   Adj. R-squared:                  0.889\n",
-       "Method:                     Two Stage   F-statistic:                     1361.\n",
-       "                        Least Squares   Prob (F-statistic):          1.30e-188\n",
+       "Dep. Variable:                 income   R-squared:                       0.904\n",
+       "Model:                         IV2SLS   Adj. R-squared:                  0.904\n",
+       "Method:                     Two Stage   F-statistic:                     1876.\n",
+       "                        Least Squares   Prob (F-statistic):          2.04e-231\n",
        "Date:                Fri, 25 Oct 2024                                         \n",
-       "Time:                        14:43:26                                         \n",
+       "Time:                        14:44:13                                         \n",
        "No. Observations:                1000                                         \n",
        "Df Residuals:                     998                                         \n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "Intercept      9.6387      0.977      9.865      0.000       7.721      11.556\n",
-       "education      3.9875      0.108     36.889      0.000       3.775       4.200\n",
+       "Intercept      8.9729      0.877     10.231      0.000       7.252      10.694\n",
+       "education      4.1224      0.095     43.308      0.000       3.936       4.309\n",
        "==============================================================================\n",
-       "Omnibus:                        2.539   Durbin-Watson:                   1.959\n",
-       "Prob(Omnibus):                  0.281   Jarque-Bera (JB):                2.587\n",
-       "Skew:                           0.121   Prob(JB):                        0.274\n",
-       "Kurtosis:                       2.940   Cond. No.                         24.5\n",
+       "Omnibus:                        1.392   Durbin-Watson:                   1.913\n",
+       "Prob(Omnibus):                  0.499   Jarque-Bera (JB):                1.460\n",
+       "Skew:                          -0.067   Prob(JB):                        0.482\n",
+       "Kurtosis:                       2.870   Cond. No.                         25.1\n",
        "==============================================================================\n",
        "\"\"\""
       ]
diff --git a/main/example_notebooks/dowhy_causal_api.html b/main/example_notebooks/dowhy_causal_api.html
index 8a3f29677..4a631ab2d 100644
--- a/main/example_notebooks/dowhy_causal_api.html
+++ b/main/example_notebooks/dowhy_causal_api.html
@@ -632,33 +632,33 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
   <tbody>
     <tr>
       <th>0</th>
-      <td>0.301186</td>
+      <td>-3.122581</td>
       <td>False</td>
-      <td>1.042313</td>
+      <td>-7.031578</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.811655</td>
+      <td>-0.929257</td>
       <td>True</td>
-      <td>6.596058</td>
+      <td>3.592709</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.766516</td>
-      <td>True</td>
-      <td>7.338039</td>
+      <td>-1.290274</td>
+      <td>False</td>
+      <td>-3.556782</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.661593</td>
-      <td>True</td>
-      <td>6.659962</td>
+      <td>-0.461701</td>
+      <td>False</td>
+      <td>0.099132</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.343496</td>
-      <td>True</td>
-      <td>11.734157</td>
+      <td>-0.916448</td>
+      <td>False</td>
+      <td>-0.665845</td>
     </tr>
     <tr>
       <th>...</th>
@@ -668,33 +668,33 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
     </tr>
     <tr>
       <th>995</th>
-      <td>1.519674</td>
-      <td>True</td>
-      <td>9.643222</td>
+      <td>-1.115158</td>
+      <td>False</td>
+      <td>-1.502734</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>2.292310</td>
+      <td>-0.206983</td>
       <td>True</td>
-      <td>11.775713</td>
+      <td>5.886683</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>1.160542</td>
-      <td>True</td>
-      <td>7.793988</td>
+      <td>-0.601760</td>
+      <td>False</td>
+      <td>-0.381091</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>1.425328</td>
+      <td>-0.760529</td>
       <td>True</td>
-      <td>7.713643</td>
+      <td>3.084998</td>
     </tr>
     <tr>
       <th>999</th>
-      <td>1.438840</td>
-      <td>True</td>
-      <td>8.366888</td>
+      <td>-3.552914</td>
+      <td>False</td>
+      <td>-6.888076</td>
     </tr>
   </tbody>
 </table>
@@ -818,43 +818,43 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.334809</td>
+      <td>-1.759866</td>
       <td>False</td>
-      <td>2.523580</td>
-      <td>0.070441</td>
-      <td>14.196215</td>
+      <td>-2.350318</td>
+      <td>0.951203</td>
+      <td>1.051300</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-1.356694</td>
+      <td>-3.705130</td>
       <td>False</td>
-      <td>-2.405052</td>
-      <td>0.945274</td>
-      <td>1.057894</td>
+      <td>-7.813499</td>
+      <td>0.998083</td>
+      <td>1.001920</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.751573</td>
+      <td>-0.973429</td>
       <td>False</td>
-      <td>2.686315</td>
-      <td>0.197265</td>
-      <td>5.069321</td>
+      <td>-1.255691</td>
+      <td>0.837799</td>
+      <td>1.193603</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.645999</td>
+      <td>0.218265</td>
       <td>False</td>
-      <td>0.176300</td>
-      <td>0.233166</td>
-      <td>4.288799</td>
+      <td>-0.253118</td>
+      <td>0.408403</td>
+      <td>2.448563</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.560783</td>
+      <td>-0.332033</td>
       <td>False</td>
-      <td>1.851092</td>
-      <td>0.265294</td>
-      <td>3.769405</td>
+      <td>-1.476009</td>
+      <td>0.636166</td>
+      <td>1.571916</td>
     </tr>
     <tr>
       <th>...</th>
@@ -866,43 +866,43 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
     </tr>
     <tr>
       <th>995</th>
-      <td>1.299631</td>
+      <td>0.166965</td>
       <td>False</td>
-      <td>4.398113</td>
-      <td>0.075232</td>
-      <td>13.292265</td>
+      <td>-0.419433</td>
+      <td>0.429489</td>
+      <td>2.328350</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>1.232811</td>
+      <td>-0.992005</td>
       <td>False</td>
-      <td>3.883010</td>
-      <td>0.085162</td>
-      <td>11.742291</td>
+      <td>-3.588534</td>
+      <td>0.842017</td>
+      <td>1.187624</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>1.060851</td>
+      <td>-1.459873</td>
       <td>False</td>
-      <td>3.086283</td>
-      <td>0.116364</td>
-      <td>8.593744</td>
+      <td>-3.009158</td>
+      <td>0.921539</td>
+      <td>1.085141</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>1.754843</td>
+      <td>-1.169292</td>
       <td>False</td>
-      <td>5.241172</td>
-      <td>0.031457</td>
-      <td>31.789811</td>
+      <td>-2.088625</td>
+      <td>0.877902</td>
+      <td>1.139079</td>
     </tr>
     <tr>
       <th>999</th>
-      <td>1.704140</td>
+      <td>-1.273194</td>
       <td>False</td>
-      <td>3.767435</td>
-      <td>0.034726</td>
-      <td>28.796507</td>
+      <td>-5.106035</td>
+      <td>0.895499</td>
+      <td>1.116696</td>
     </tr>
   </tbody>
 </table>
@@ -950,43 +950,43 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.580815</td>
+      <td>0.755504</td>
       <td>True</td>
-      <td>7.397887</td>
-      <td>0.955898</td>
-      <td>1.046136</td>
+      <td>6.365941</td>
+      <td>0.782084</td>
+      <td>1.278635</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.360499</td>
+      <td>0.479045</td>
       <td>True</td>
-      <td>9.336263</td>
-      <td>0.932877</td>
-      <td>1.071952</td>
+      <td>5.289250</td>
+      <td>0.692317</td>
+      <td>1.444425</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.626459</td>
+      <td>-0.380484</td>
       <td>True</td>
-      <td>4.675073</td>
-      <td>0.759713</td>
-      <td>1.316286</td>
+      <td>4.133301</td>
+      <td>0.345114</td>
+      <td>2.897595</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>2.343496</td>
+      <td>-1.065471</td>
       <td>True</td>
-      <td>11.734157</td>
-      <td>0.990191</td>
-      <td>1.009907</td>
+      <td>4.594604</td>
+      <td>0.142170</td>
+      <td>7.033826</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>-0.436224</td>
+      <td>0.479045</td>
       <td>True</td>
-      <td>4.542532</td>
-      <td>0.270430</td>
-      <td>3.697812</td>
+      <td>5.289250</td>
+      <td>0.692317</td>
+      <td>1.444425</td>
     </tr>
     <tr>
       <th>...</th>
@@ -998,43 +998,43 @@ <h1>Demo for the DoWhy causal API<a class="headerlink" href="#Demo-for-the-DoWhy
     </tr>
     <tr>
       <th>995</th>
-      <td>1.936492</td>
+      <td>-1.332082</td>
       <td>True</td>
-      <td>9.103130</td>
-      <td>0.977981</td>
-      <td>1.022515</td>
+      <td>4.402306</td>
+      <td>0.095554</td>
+      <td>10.465275</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>0.479328</td>
+      <td>-0.778026</td>
       <td>True</td>
-      <td>7.944845</td>
-      <td>0.701475</td>
-      <td>1.425568</td>
+      <td>3.073351</td>
+      <td>0.212161</td>
+      <td>4.713404</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>2.136369</td>
+      <td>-2.635462</td>
       <td>True</td>
-      <td>9.453021</td>
-      <td>0.985179</td>
-      <td>1.015044</td>
+      <td>-0.007357</td>
+      <td>0.011558</td>
+      <td>86.520595</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>1.868190</td>
+      <td>-0.625354</td>
       <td>True</td>
-      <td>8.101663</td>
-      <td>0.974811</td>
-      <td>1.025840</td>
+      <td>4.370008</td>
+      <td>0.258435</td>
+      <td>3.869445</td>
     </tr>
     <tr>
       <th>999</th>
-      <td>0.537524</td>
+      <td>-0.161104</td>
       <td>True</td>
-      <td>6.396701</td>
-      <td>0.725461</td>
-      <td>1.378433</td>
+      <td>5.344751</td>
+      <td>0.432882</td>
+      <td>2.310096</td>
     </tr>
   </tbody>
 </table>
@@ -1058,7 +1058,7 @@ <h2>Comparing the estimate to Linear Regression<a class="headerlink" href="#Comp
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 5.22322409908235$</div></div>
+$\displaystyle 5.04346104281236$</div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -1074,7 +1074,7 @@ <h2>Comparing the estimate to Linear Regression<a class="headerlink" href="#Comp
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 0.24237321967821$</div></div>
+$\displaystyle 0.184546201364702$</div></div>
 </div>
 <p>Comparing to the estimate from OLS.</p>
 <div class="nbinput docutils container">
@@ -1095,25 +1095,25 @@ <h2>Comparing the estimate to Linear Regression<a class="headerlink" href="#Comp
 <table class="simpletable">
 <caption>OLS Regression Results</caption>
 <tr>
-  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.983</td>
+  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.928</td>
 </tr>
 <tr>
-  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.983</td>
+  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.928</td>
 </tr>
 <tr>
-  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.872e+04</td>
+  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   6458.</td>
 </tr>
 <tr>
   <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:43:30</td>     <th>  Log-Likelihood:    </th>          <td> -1423.4</td>
+  <th>Time:</th>                 <td>14:44:17</td>     <th>  Log-Likelihood:    </th>          <td> -1393.1</td>
 </tr>
 <tr>
-  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2851.</td>
+  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2790.</td>
 </tr>
 <tr>
-  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2861.</td>
+  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2800.</td>
 </tr>
 <tr>
   <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>
@@ -1127,24 +1127,24 @@ <h2>Comparing the estimate to Linear Regression<a class="headerlink" href="#Comp
    <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>
 </tr>
 <tr>
-  <th>x1</th> <td>    2.6696</td> <td>    0.038</td> <td>   70.632</td> <td> 0.000</td> <td>    2.595</td> <td>    2.744</td>
+  <th>x1</th> <td>    1.7154</td> <td>    0.022</td> <td>   78.441</td> <td> 0.000</td> <td>    1.672</td> <td>    1.758</td>
 </tr>
 <tr>
-  <th>x2</th> <td>    5.0716</td> <td>    0.060</td> <td>   84.656</td> <td> 0.000</td> <td>    4.954</td> <td>    5.189</td>
+  <th>x2</th> <td>    4.9826</td> <td>    0.060</td> <td>   82.359</td> <td> 0.000</td> <td>    4.864</td> <td>    5.101</td>
 </tr>
 </table>
 <table class="simpletable">
 <tr>
-  <th>Omnibus:</th>       <td> 1.123</td> <th>  Durbin-Watson:     </th> <td>   2.013</td>
+  <th>Omnibus:</th>       <td> 0.921</td> <th>  Durbin-Watson:     </th> <td>   2.067</td>
 </tr>
 <tr>
-  <th>Prob(Omnibus):</th> <td> 0.570</td> <th>  Jarque-Bera (JB):  </th> <td>   1.034</td>
+  <th>Prob(Omnibus):</th> <td> 0.631</td> <th>  Jarque-Bera (JB):  </th> <td>   0.805</td>
 </tr>
 <tr>
-  <th>Skew:</th>          <td> 0.075</td> <th>  Prob(JB):          </th> <td>   0.596</td>
+  <th>Skew:</th>          <td> 0.058</td> <th>  Prob(JB):          </th> <td>   0.669</td>
 </tr>
 <tr>
-  <th>Kurtosis:</th>      <td> 3.046</td> <th>  Cond. No.          </th> <td>    3.31</td>
+  <th>Kurtosis:</th>      <td> 3.075</td> <th>  Cond. No.          </th> <td>    2.77</td>
 </tr>
 </table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.</div>
 </div>
diff --git a/main/example_notebooks/dowhy_causal_api.ipynb b/main/example_notebooks/dowhy_causal_api.ipynb
index be6e5a976..73dc771c4 100644
--- a/main/example_notebooks/dowhy_causal_api.ipynb
+++ b/main/example_notebooks/dowhy_causal_api.ipynb
@@ -13,10 +13,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:28.420037Z",
-     "iopub.status.busy": "2024-10-25T14:43:28.419847Z",
-     "iopub.status.idle": "2024-10-25T14:43:29.830914Z",
-     "shell.execute_reply": "2024-10-25T14:43:29.830311Z"
+     "iopub.execute_input": "2024-10-25T14:44:15.712292Z",
+     "iopub.status.busy": "2024-10-25T14:44:15.711873Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.236256Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.235633Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:29.833247Z",
-     "iopub.status.busy": "2024-10-25T14:43:29.832855Z",
-     "iopub.status.idle": "2024-10-25T14:43:29.887255Z",
-     "shell.execute_reply": "2024-10-25T14:43:29.886670Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.238913Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.238393Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.277145Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.276625Z"
     }
    },
    "outputs": [
@@ -72,33 +72,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.301186</td>\n",
+       "      <td>-3.122581</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.042313</td>\n",
+       "      <td>-7.031578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.811655</td>\n",
+       "      <td>-0.929257</td>\n",
        "      <td>True</td>\n",
-       "      <td>6.596058</td>\n",
+       "      <td>3.592709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.766516</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7.338039</td>\n",
+       "      <td>-1.290274</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-3.556782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.661593</td>\n",
-       "      <td>True</td>\n",
-       "      <td>6.659962</td>\n",
+       "      <td>-0.461701</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.099132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.343496</td>\n",
-       "      <td>True</td>\n",
-       "      <td>11.734157</td>\n",
+       "      <td>-0.916448</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.665845</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -108,33 +108,33 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.519674</td>\n",
-       "      <td>True</td>\n",
-       "      <td>9.643222</td>\n",
+       "      <td>-1.115158</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1.502734</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>2.292310</td>\n",
+       "      <td>-0.206983</td>\n",
        "      <td>True</td>\n",
-       "      <td>11.775713</td>\n",
+       "      <td>5.886683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>1.160542</td>\n",
-       "      <td>True</td>\n",
-       "      <td>7.793988</td>\n",
+       "      <td>-0.601760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.381091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.425328</td>\n",
+       "      <td>-0.760529</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.713643</td>\n",
+       "      <td>3.084998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>1.438840</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8.366888</td>\n",
+       "      <td>-3.552914</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-6.888076</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -142,18 +142,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0     v0          y\n",
-       "0    0.301186  False   1.042313\n",
-       "1    0.811655   True   6.596058\n",
-       "2    0.766516   True   7.338039\n",
-       "3    0.661593   True   6.659962\n",
-       "4    2.343496   True  11.734157\n",
-       "..        ...    ...        ...\n",
-       "995  1.519674   True   9.643222\n",
-       "996  2.292310   True  11.775713\n",
-       "997  1.160542   True   7.793988\n",
-       "998  1.425328   True   7.713643\n",
-       "999  1.438840   True   8.366888\n",
+       "           W0     v0         y\n",
+       "0   -3.122581  False -7.031578\n",
+       "1   -0.929257   True  3.592709\n",
+       "2   -1.290274  False -3.556782\n",
+       "3   -0.461701  False  0.099132\n",
+       "4   -0.916448  False -0.665845\n",
+       "..        ...    ...       ...\n",
+       "995 -1.115158  False -1.502734\n",
+       "996 -0.206983   True  5.886683\n",
+       "997 -0.601760  False -0.381091\n",
+       "998 -0.760529   True  3.084998\n",
+       "999 -3.552914  False -6.888076\n",
        "\n",
        "[1000 rows x 3 columns]"
       ]
@@ -184,10 +184,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:29.889220Z",
-     "iopub.status.busy": "2024-10-25T14:43:29.888861Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.053081Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.052362Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.279121Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.278752Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.459567Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.458879Z"
     },
     "scrolled": true
    },
@@ -204,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": 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EOeecE3fddVeMGjVqu8c3NTVFNptt3+rr63dpYACgchUdFvvvv3888sgj8de//jXOPffcmDx5cjz++OPbPb6xsTFaWlrat+bm5l0aGACoXEXfCunVq1eMHDkyIiIOP/zwWLZsWXz3u9+NH/7wh+94fCaTiUwms2tTAgBVYZffx6Ktra3DGgoAYPdV1BWLxsbGmDBhQgwbNiw2btwYCxYsiCVLlsTixYtLNR8AUEWKCosNGzbE5z//+Vi/fn1ks9k4+OCDY/HixfGxj32sVPMBAFWkqLD48Y9/XKo5AIBuwGeFAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyfQs9wC7k+HTF5Z7BLrQszMnlnsEgC5X1BWLpqamOOKII6Jv374xcODAOO200+Kpp54q1WwAQJUpKizuv//+mDp1ajz88MPxxz/+Md5888046aSTYvPmzaWaDwCoIkXdClm0aFGHx/PmzYuBAwfGihUr4rjjjks6GABQfXZpjUVLS0tERLz3ve/d7jH5fD7y+Xz741wutyunBAAqWKdfFdLW1hYXXnhhHHPMMXHggQdu97impqbIZrPtW319fWdPCQBUuE6HxdSpU+PRRx+N22+/fYfHNTY2RktLS/vW3Nzc2VMCABWuU7dCvvrVr8Zvf/vbeOCBB2Lo0KE7PDaTyUQmk+nUcABAdSkqLAqFQpx33nlx1113xZIlS2Lfffct1VwAQBUqKiymTp0aCxYsiF/96lfRt2/fePHFFyMiIpvNRp8+fUoyIABQPYpaYzFnzpxoaWmJE044IfbZZ5/27Y477ijVfABAFSn6VggAwPb4EDIAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkig6LBx54IE455ZQYMmRI1NTUxN13312CsQCAalR0WGzevDkOOeSQuO6660oxDwBQxXoW+4QJEybEhAkTSjELAFDlig6LYuXz+cjn8+2Pc7lcqU8JAJRJyRdvNjU1RTabbd/q6+tLfUoAoExKHhaNjY3R0tLSvjU3N5f6lABAmZT8Vkgmk4lMJlPq0wAAFcD7WAAAyRR9xWLTpk2xevXq9sdr166NRx55JN773vfGsGHDkg4HAFSXosNi+fLlMW7cuPbH06ZNi4iIyZMnx7x585INBgBUn6LD4oQTTohCoVCKWQCAKmeNBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDKdCovrrrsuhg8fHr17946jjjoq/va3v6WeCwCoQkWHxR133BHTpk2Lb3/727Fy5co45JBD4uSTT44NGzaUYj4AoIoUHRbXXnttnH322TFlypQYNWpU3HDDDbHnnnvGT37yk1LMBwBUkZ7FHLx169ZYsWJFNDY2tu/r0aNHjB8/PpYuXfqOz8nn85HP59sft7S0RERELpfrzLxVrS2/pdwj0IV2x//Hd2d+vncvu+PP99v/zYVCYYfHFRUW//73v6O1tTUGDRrUYf+gQYPiySeffMfnNDU1xYwZM7bZX19fX8ypoepkZ5d7AqBUduef740bN0Y2m93u14sKi85obGyMadOmtT9ua2uLV199Nfr37x81NTWlPj1llsvlor6+Ppqbm6Ourq7c4wAJ+fnevRQKhdi4cWMMGTJkh8cVFRbve9/7ora2Nl566aUO+1966aUYPHjwOz4nk8lEJpPpsG/vvfcu5rR0A3V1df7hgW7Kz/fuY0dXKt5W1OLNXr16xeGHHx733ntv+762tra49957Y+zYscVPCAB0K0XfCpk2bVpMnjw5xowZE0ceeWTMnj07Nm/eHFOmTCnFfABAFSk6LD796U/Hyy+/HJdddlm8+OKL8eEPfzgWLVq0zYJOiPjvrbBvf/vb29wOA6qfn2/eSU3h3V43AgCwk3xWCACQjLAAAJIRFgBAMsICAEhGWAAAyQgLAHban//85zjzzDNj7NixsW7duoiI+OlPfxoPPvhgmSejUggLSmbr1q3x1FNPxVtvvVXuUYAEfvGLX8TJJ58cffr0ib///e/tn1zd0tIS3/nOd8o8HZVCWJDcli1b4otf/GLsueeeMXr06HjuueciIuK8886LmTNnlnk6oLOuuuqquOGGG+LGG2+MPfbYo33/McccEytXrizjZFQSYUFyjY2NsWrVqliyZEn07t27ff/48ePjjjvuKONkwK546qmn4rjjjttmfzabjddff73rB6IiCQuSu/vuu+MHP/hBHHvssVFTU9O+f/To0fHMM8+UcTJgVwwePDhWr169zf4HH3wwRowYUYaJqETCguRefvnlGDhw4Db7N2/e3CE0gOpy9tlnxwUXXBB//etfo6amJl544YWYP39+XHzxxXHuueeWezwqRNEfQgbvZsyYMbFw4cI477zzIiLaY+Kmm26KsWPHlnM0YBdMnz492tra4sQTT4wtW7bEcccdF5lMJi6++OL2n3fwIWQk9+CDD8aECRPizDPPjHnz5sWXv/zlePzxx+Ohhx6K+++/Pw4//PByjwjsgq1bt8bq1atj06ZNMWrUqNhrr73KPRIVRFhQEs8880zMnDkzVq1aFZs2bYrDDjssLrnkkjjooIPKPRoAJSQsANgp48aN2+E6qfvuu68Lp6FSWWNBcitXrow99tij/erEr371q5g7d26MGjUqLr/88ujVq1eZJwQ648Mf/nCHx2+++WY88sgj8eijj8bkyZPLMxQVxxULkjviiCNi+vTp8alPfSrWrFkTo0aNitNPPz2WLVsWEydOjNmzZ5d7RCChyy+/PDZt2hTXXHNNuUehAggLkstms7Fy5crYb7/9YtasWXHffffF4sWL4y9/+UucccYZ0dzcXO4RgYRWr14dRx55ZLz66qvlHoUK4H0sSK5QKERbW1tERNxzzz3x8Y9/PCIi6uvr49///nc5RwNKYOnSpR3eZZfdmzUWJDdmzJi46qqrYvz48XH//ffHnDlzIiJi7dq1MWjQoDJPB3TW6aef3uFxoVCI9evXx/Lly+PSSy8t01RUGmFBcrNnz46Ghoa4++6745vf/GaMHDkyIiJ+/vOfx9FHH13m6YDOymazHR736NEj9t9//7jiiivipJNOKtNUVBprLOgyb7zxRtTW1nb4VESgOrS2tsZf/vKXOOigg6Jfv37lHocKJiwA2Cm9e/eOJ554Ivbdd99yj0IFcyuEJPr167fTHzBm5ThUpwMPPDDWrFkjLNghYUES3psCur+rrroqLr744rjyyivj8MMPj/e85z0dvl5XV1emyagkboUAsENXXHFFfO1rX4u+ffu27/u/VygLhULU1NREa2trOcajwggLSuqNN96IrVu3dtjnrxqoLrW1tbF+/fp44okndnjc8ccf30UTUcmEBclt3rw5LrnkkrjzzjvjlVde2ebr/qqB6tKjR4948cUXY+DAgeUehSrgnTdJ7hvf+Ebcd999MWfOnMhkMnHTTTfFjBkzYsiQIXHLLbeUezygE3Z2cTa4YkFyw4YNi1tuuSVOOOGEqKuri5UrV8bIkSPjpz/9adx2223xu9/9rtwjAkXo0aNHZLPZd40Lr/giwqtCKIFXX301RowYERH/XU/x9j82xx57bJx77rnlHA3opBkzZmzzzpvwToQFyY0YMSLWrl0bw4YNiwMOOCDuvPPOOPLII+M3v/lN7L333uUeD+iEM844wxoLdoo1FiSzZs2aaGtriylTpsSqVasiImL69Olx3XXXRe/eveOiiy6Kr3/962WeEiiW9RUUwxoLknn7JWlv/1Xz6U9/Or73ve/FG2+8EStWrIiRI0fGwQcfXOYpgWJ5VQjFEBYk8///8enbt2+sWrWqfb0FAN2fWyEAQDLCgmRqamq2uRfr3izA7sWrQkimUCjEWWedFZlMJiL++3be55xzzjYfVPTLX/6yHOMB0AWEBclMnjy5w+MzzzyzTJMAUC4WbwIAyVhjAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICSG7JkiVx2GGHRSaTiZEjR8a8efPKPRLQRYQFkNTatWtj4sSJMW7cuHjkkUfiwgsvjC996UuxePHico8GdAHvYwEU5Uc/+lFcfvnl8fzzz0ePHv/7t8mpp54a/fv3jwEDBsTChQvj0Ucfbf/aGWecEa+//nosWrSoHCMDXcgVC6AokyZNildeeSX+9Kc/te979dVXY9GiRdHQ0BBLly6N8ePHd3jOySefHEuXLu3qUYEyEBZAUfr16xcTJkyIBQsWtO/7+c9/Hu973/ti3Lhx8eKLL8agQYM6PGfQoEGRy+XiP//5T1ePC3QxYQEUraGhIX7xi19EPp+PiIj58+fHGWec0eHWCLB78q8AULRTTjklCoVCLFy4MJqbm+PPf/5zNDQ0RETE4MGD46WXXupw/EsvvRR1dXXRp0+fcowLdCGfbgoUrXfv3nH66afH/PnzY/Xq1bH//vvHYYcdFhERY8eOjd/97ncdjv/jH/8YY8eOLceoQBdzxQLolIaGhli4cGH85Cc/ab9aERFxzjnnxJo1a+Ib3/hGPPnkk3H99dfHnXfeGRdddFEZpwW6ipebAp3S1tYWQ4cOjfXr18czzzwTI0aMaP/akiVL4qKLLorHH388hg4dGpdeemmcddZZ5RsW6DLCAgBIxq0QACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZP4Hi2ddjqLqEpIAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -227,10 +227,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.055102Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.054905Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.171620Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.170896Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.461790Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.461277Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.604972Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.604336Z"
     }
    },
    "outputs": [
@@ -246,7 +246,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -269,10 +269,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.173708Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.173497Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.195174Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.194575Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.607110Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.606755Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.628217Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.627598Z"
     }
    },
    "outputs": [],
@@ -295,10 +295,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.197054Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.196869Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.206063Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.205471Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.630190Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.629898Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.638836Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.638319Z"
     },
     "scrolled": true
    },
@@ -334,43 +334,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.334809</td>\n",
+       "      <td>-1.759866</td>\n",
        "      <td>False</td>\n",
-       "      <td>2.523580</td>\n",
-       "      <td>0.070441</td>\n",
-       "      <td>14.196215</td>\n",
+       "      <td>-2.350318</td>\n",
+       "      <td>0.951203</td>\n",
+       "      <td>1.051300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.356694</td>\n",
+       "      <td>-3.705130</td>\n",
        "      <td>False</td>\n",
-       "      <td>-2.405052</td>\n",
-       "      <td>0.945274</td>\n",
-       "      <td>1.057894</td>\n",
+       "      <td>-7.813499</td>\n",
+       "      <td>0.998083</td>\n",
+       "      <td>1.001920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.751573</td>\n",
+       "      <td>-0.973429</td>\n",
        "      <td>False</td>\n",
-       "      <td>2.686315</td>\n",
-       "      <td>0.197265</td>\n",
-       "      <td>5.069321</td>\n",
+       "      <td>-1.255691</td>\n",
+       "      <td>0.837799</td>\n",
+       "      <td>1.193603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.645999</td>\n",
+       "      <td>0.218265</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.176300</td>\n",
-       "      <td>0.233166</td>\n",
-       "      <td>4.288799</td>\n",
+       "      <td>-0.253118</td>\n",
+       "      <td>0.408403</td>\n",
+       "      <td>2.448563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.560783</td>\n",
+       "      <td>-0.332033</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.851092</td>\n",
-       "      <td>0.265294</td>\n",
-       "      <td>3.769405</td>\n",
+       "      <td>-1.476009</td>\n",
+       "      <td>0.636166</td>\n",
+       "      <td>1.571916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -382,43 +382,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.299631</td>\n",
+       "      <td>0.166965</td>\n",
        "      <td>False</td>\n",
-       "      <td>4.398113</td>\n",
-       "      <td>0.075232</td>\n",
-       "      <td>13.292265</td>\n",
+       "      <td>-0.419433</td>\n",
+       "      <td>0.429489</td>\n",
+       "      <td>2.328350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>1.232811</td>\n",
+       "      <td>-0.992005</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.883010</td>\n",
-       "      <td>0.085162</td>\n",
-       "      <td>11.742291</td>\n",
+       "      <td>-3.588534</td>\n",
+       "      <td>0.842017</td>\n",
+       "      <td>1.187624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>1.060851</td>\n",
+       "      <td>-1.459873</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.086283</td>\n",
-       "      <td>0.116364</td>\n",
-       "      <td>8.593744</td>\n",
+       "      <td>-3.009158</td>\n",
+       "      <td>0.921539</td>\n",
+       "      <td>1.085141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.754843</td>\n",
+       "      <td>-1.169292</td>\n",
        "      <td>False</td>\n",
-       "      <td>5.241172</td>\n",
-       "      <td>0.031457</td>\n",
-       "      <td>31.789811</td>\n",
+       "      <td>-2.088625</td>\n",
+       "      <td>0.877902</td>\n",
+       "      <td>1.139079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>1.704140</td>\n",
+       "      <td>-1.273194</td>\n",
        "      <td>False</td>\n",
-       "      <td>3.767435</td>\n",
-       "      <td>0.034726</td>\n",
-       "      <td>28.796507</td>\n",
+       "      <td>-5.106035</td>\n",
+       "      <td>0.895499</td>\n",
+       "      <td>1.116696</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -426,18 +426,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0     v0         y  propensity_score     weight\n",
-       "0    1.334809  False  2.523580          0.070441  14.196215\n",
-       "1   -1.356694  False -2.405052          0.945274   1.057894\n",
-       "2    0.751573  False  2.686315          0.197265   5.069321\n",
-       "3    0.645999  False  0.176300          0.233166   4.288799\n",
-       "4    0.560783  False  1.851092          0.265294   3.769405\n",
-       "..        ...    ...       ...               ...        ...\n",
-       "995  1.299631  False  4.398113          0.075232  13.292265\n",
-       "996  1.232811  False  3.883010          0.085162  11.742291\n",
-       "997  1.060851  False  3.086283          0.116364   8.593744\n",
-       "998  1.754843  False  5.241172          0.031457  31.789811\n",
-       "999  1.704140  False  3.767435          0.034726  28.796507\n",
+       "           W0     v0         y  propensity_score    weight\n",
+       "0   -1.759866  False -2.350318          0.951203  1.051300\n",
+       "1   -3.705130  False -7.813499          0.998083  1.001920\n",
+       "2   -0.973429  False -1.255691          0.837799  1.193603\n",
+       "3    0.218265  False -0.253118          0.408403  2.448563\n",
+       "4   -0.332033  False -1.476009          0.636166  1.571916\n",
+       "..        ...    ...       ...               ...       ...\n",
+       "995  0.166965  False -0.419433          0.429489  2.328350\n",
+       "996 -0.992005  False -3.588534          0.842017  1.187624\n",
+       "997 -1.459873  False -3.009158          0.921539  1.085141\n",
+       "998 -1.169292  False -2.088625          0.877902  1.139079\n",
+       "999 -1.273194  False -5.106035          0.895499  1.116696\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
@@ -456,10 +456,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.207751Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.207545Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.216194Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.215722Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.640650Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.640291Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.648663Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.648184Z"
     }
    },
    "outputs": [
@@ -494,43 +494,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.580815</td>\n",
+       "      <td>0.755504</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.397887</td>\n",
-       "      <td>0.955898</td>\n",
-       "      <td>1.046136</td>\n",
+       "      <td>6.365941</td>\n",
+       "      <td>0.782084</td>\n",
+       "      <td>1.278635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.360499</td>\n",
+       "      <td>0.479045</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.336263</td>\n",
-       "      <td>0.932877</td>\n",
-       "      <td>1.071952</td>\n",
+       "      <td>5.289250</td>\n",
+       "      <td>0.692317</td>\n",
+       "      <td>1.444425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.626459</td>\n",
+       "      <td>-0.380484</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.675073</td>\n",
-       "      <td>0.759713</td>\n",
-       "      <td>1.316286</td>\n",
+       "      <td>4.133301</td>\n",
+       "      <td>0.345114</td>\n",
+       "      <td>2.897595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.343496</td>\n",
+       "      <td>-1.065471</td>\n",
        "      <td>True</td>\n",
-       "      <td>11.734157</td>\n",
-       "      <td>0.990191</td>\n",
-       "      <td>1.009907</td>\n",
+       "      <td>4.594604</td>\n",
+       "      <td>0.142170</td>\n",
+       "      <td>7.033826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.436224</td>\n",
+       "      <td>0.479045</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.542532</td>\n",
-       "      <td>0.270430</td>\n",
-       "      <td>3.697812</td>\n",
+       "      <td>5.289250</td>\n",
+       "      <td>0.692317</td>\n",
+       "      <td>1.444425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -542,43 +542,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>1.936492</td>\n",
+       "      <td>-1.332082</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.103130</td>\n",
-       "      <td>0.977981</td>\n",
-       "      <td>1.022515</td>\n",
+       "      <td>4.402306</td>\n",
+       "      <td>0.095554</td>\n",
+       "      <td>10.465275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>0.479328</td>\n",
+       "      <td>-0.778026</td>\n",
        "      <td>True</td>\n",
-       "      <td>7.944845</td>\n",
-       "      <td>0.701475</td>\n",
-       "      <td>1.425568</td>\n",
+       "      <td>3.073351</td>\n",
+       "      <td>0.212161</td>\n",
+       "      <td>4.713404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>2.136369</td>\n",
+       "      <td>-2.635462</td>\n",
        "      <td>True</td>\n",
-       "      <td>9.453021</td>\n",
-       "      <td>0.985179</td>\n",
-       "      <td>1.015044</td>\n",
+       "      <td>-0.007357</td>\n",
+       "      <td>0.011558</td>\n",
+       "      <td>86.520595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>1.868190</td>\n",
+       "      <td>-0.625354</td>\n",
        "      <td>True</td>\n",
-       "      <td>8.101663</td>\n",
-       "      <td>0.974811</td>\n",
-       "      <td>1.025840</td>\n",
+       "      <td>4.370008</td>\n",
+       "      <td>0.258435</td>\n",
+       "      <td>3.869445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>0.537524</td>\n",
+       "      <td>-0.161104</td>\n",
        "      <td>True</td>\n",
-       "      <td>6.396701</td>\n",
-       "      <td>0.725461</td>\n",
-       "      <td>1.378433</td>\n",
+       "      <td>5.344751</td>\n",
+       "      <td>0.432882</td>\n",
+       "      <td>2.310096</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -586,18 +586,18 @@
        "</div>"
       ],
       "text/plain": [
-       "           W0    v0          y  propensity_score    weight\n",
-       "0    1.580815  True   7.397887          0.955898  1.046136\n",
-       "1    1.360499  True   9.336263          0.932877  1.071952\n",
-       "2    0.626459  True   4.675073          0.759713  1.316286\n",
-       "3    2.343496  True  11.734157          0.990191  1.009907\n",
-       "4   -0.436224  True   4.542532          0.270430  3.697812\n",
-       "..        ...   ...        ...               ...       ...\n",
-       "995  1.936492  True   9.103130          0.977981  1.022515\n",
-       "996  0.479328  True   7.944845          0.701475  1.425568\n",
-       "997  2.136369  True   9.453021          0.985179  1.015044\n",
-       "998  1.868190  True   8.101663          0.974811  1.025840\n",
-       "999  0.537524  True   6.396701          0.725461  1.378433\n",
+       "           W0    v0         y  propensity_score     weight\n",
+       "0    0.755504  True  6.365941          0.782084   1.278635\n",
+       "1    0.479045  True  5.289250          0.692317   1.444425\n",
+       "2   -0.380484  True  4.133301          0.345114   2.897595\n",
+       "3   -1.065471  True  4.594604          0.142170   7.033826\n",
+       "4    0.479045  True  5.289250          0.692317   1.444425\n",
+       "..        ...   ...       ...               ...        ...\n",
+       "995 -1.332082  True  4.402306          0.095554  10.465275\n",
+       "996 -0.778026  True  3.073351          0.212161   4.713404\n",
+       "997 -2.635462  True -0.007357          0.011558  86.520595\n",
+       "998 -0.625354  True  4.370008          0.258435   3.869445\n",
+       "999 -0.161104  True  5.344751          0.432882   2.310096\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
@@ -624,21 +624,21 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.217993Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.217645Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.264148Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.263636Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.650402Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.650102Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.697364Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.696725Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 5.22322409908235$"
+       "$\\displaystyle 5.04346104281236$"
       ],
       "text/plain": [
-       "5.223224099082352"
+       "5.043461042812358"
       ]
      },
      "execution_count": 8,
@@ -655,21 +655,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.266121Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.265760Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.282025Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.281395Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.699397Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.699079Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.715106Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.714610Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 0.24237321967821$"
+       "$\\displaystyle 0.184546201364702$"
       ],
       "text/plain": [
-       "0.2423732196782096"
+       "0.18454620136470185"
       ]
      },
      "execution_count": 9,
@@ -693,10 +693,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:43:30.283965Z",
-     "iopub.status.busy": "2024-10-25T14:43:30.283677Z",
-     "iopub.status.idle": "2024-10-25T14:43:30.390023Z",
-     "shell.execute_reply": "2024-10-25T14:43:30.389357Z"
+     "iopub.execute_input": "2024-10-25T14:44:17.717339Z",
+     "iopub.status.busy": "2024-10-25T14:44:17.716772Z",
+     "iopub.status.idle": "2024-10-25T14:44:17.734654Z",
+     "shell.execute_reply": "2024-10-25T14:44:17.734106Z"
     }
    },
    "outputs": [
@@ -706,31 +706,31 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.983</td> \n",
+       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.928</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.983</td> \n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.928</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.872e+04</td>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   6458.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
+       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:43:30</td>     <th>  Log-Likelihood:    </th>          <td> -1423.4</td> \n",
+       "  <th>Time:</th>                 <td>14:44:17</td>     <th>  Log-Likelihood:    </th>          <td> -1393.1</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2851.</td> \n",
+       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th>          <td>   2790.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2861.</td> \n",
+       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th>          <td>   2800.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -738,24 +738,24 @@
        "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>x1</th> <td>    2.6696</td> <td>    0.038</td> <td>   70.632</td> <td> 0.000</td> <td>    2.595</td> <td>    2.744</td>\n",
+       "  <th>x1</th> <td>    1.7154</td> <td>    0.022</td> <td>   78.441</td> <td> 0.000</td> <td>    1.672</td> <td>    1.758</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>x2</th> <td>    5.0716</td> <td>    0.060</td> <td>   84.656</td> <td> 0.000</td> <td>    4.954</td> <td>    5.189</td>\n",
+       "  <th>x2</th> <td>    4.9826</td> <td>    0.060</td> <td>   82.359</td> <td> 0.000</td> <td>    4.864</td> <td>    5.101</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td> 1.123</td> <th>  Durbin-Watson:     </th> <td>   2.013</td>\n",
+       "  <th>Omnibus:</th>       <td> 0.921</td> <th>  Durbin-Watson:     </th> <td>   2.067</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.570</td> <th>  Jarque-Bera (JB):  </th> <td>   1.034</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.631</td> <th>  Jarque-Bera (JB):  </th> <td>   0.805</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td> 0.075</td> <th>  Prob(JB):          </th> <td>   0.596</td>\n",
+       "  <th>Skew:</th>          <td> 0.058</td> <th>  Prob(JB):          </th> <td>   0.669</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 3.046</td> <th>  Cond. No.          </th> <td>    3.31</td>\n",
+       "  <th>Kurtosis:</th>      <td> 3.075</td> <th>  Cond. No.          </th> <td>    2.77</td>\n",
        "</tr>\n",
        "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
@@ -763,13 +763,13 @@
        "\\begin{center}\n",
        "\\begin{tabular}{lclc}\n",
        "\\toprule\n",
-       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared (uncentered):}      &     0.983   \\\\\n",
-       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.983   \\\\\n",
-       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          & 2.872e+04   \\\\\n",
+       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared (uncentered):}      &     0.928   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared (uncentered):} &     0.928   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       }          &     6458.   \\\\\n",
        "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):}          &     0.00    \\\\\n",
-       "\\textbf{Time:}             &     14:43:30     & \\textbf{  Log-Likelihood:    }          &   -1423.4   \\\\\n",
-       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               }          &     2851.   \\\\\n",
-       "\\textbf{Df Residuals:}     &         998      & \\textbf{  BIC:               }          &     2861.   \\\\\n",
+       "\\textbf{Time:}             &     14:44:17     & \\textbf{  Log-Likelihood:    }          &   -1393.1   \\\\\n",
+       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               }          &     2790.   \\\\\n",
+       "\\textbf{Df Residuals:}     &         998      & \\textbf{  BIC:               }          &     2800.   \\\\\n",
        "\\textbf{Df Model:}         &           2      & \\textbf{                     }          &             \\\\\n",
        "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     }          &             \\\\\n",
        "\\bottomrule\n",
@@ -777,15 +777,15 @@
        "\\begin{tabular}{lcccccc}\n",
        "            & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{x1} &       2.6696  &        0.038     &    70.632  &         0.000        &        2.595    &        2.744     \\\\\n",
-       "\\textbf{x2} &       5.0716  &        0.060     &    84.656  &         0.000        &        4.954    &        5.189     \\\\\n",
+       "\\textbf{x1} &       1.7154  &        0.022     &    78.441  &         0.000        &        1.672    &        1.758     \\\\\n",
+       "\\textbf{x2} &       4.9826  &        0.060     &    82.359  &         0.000        &        4.864    &        5.101     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       &  1.123 & \\textbf{  Durbin-Watson:     } &    2.013  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &  0.570 & \\textbf{  Jarque-Bera (JB):  } &    1.034  \\\\\n",
-       "\\textbf{Skew:}          &  0.075 & \\textbf{  Prob(JB):          } &    0.596  \\\\\n",
-       "\\textbf{Kurtosis:}      &  3.046 & \\textbf{  Cond. No.          } &     3.31  \\\\\n",
+       "\\textbf{Omnibus:}       &  0.921 & \\textbf{  Durbin-Watson:     } &    2.067  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.631 & \\textbf{  Jarque-Bera (JB):  } &    0.805  \\\\\n",
+       "\\textbf{Skew:}          &  0.058 & \\textbf{  Prob(JB):          } &    0.669  \\\\\n",
+       "\\textbf{Kurtosis:}      &  3.075 & \\textbf{  Cond. No.          } &     2.77  \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "%\\caption{OLS Regression Results}\n",
@@ -800,25 +800,25 @@
        "\"\"\"\n",
        "                                 OLS Regression Results                                \n",
        "=======================================================================================\n",
-       "Dep. Variable:                      y   R-squared (uncentered):                   0.983\n",
-       "Model:                            OLS   Adj. R-squared (uncentered):              0.983\n",
-       "Method:                 Least Squares   F-statistic:                          2.872e+04\n",
+       "Dep. Variable:                      y   R-squared (uncentered):                   0.928\n",
+       "Model:                            OLS   Adj. R-squared (uncentered):              0.928\n",
+       "Method:                 Least Squares   F-statistic:                              6458.\n",
        "Date:                Fri, 25 Oct 2024   Prob (F-statistic):                        0.00\n",
-       "Time:                        14:43:30   Log-Likelihood:                         -1423.4\n",
-       "No. Observations:                1000   AIC:                                      2851.\n",
-       "Df Residuals:                     998   BIC:                                      2861.\n",
+       "Time:                        14:44:17   Log-Likelihood:                         -1393.1\n",
+       "No. Observations:                1000   AIC:                                      2790.\n",
+       "Df Residuals:                     998   BIC:                                      2800.\n",
        "Df Model:                           2                                                  \n",
        "Covariance Type:            nonrobust                                                  \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "x1             2.6696      0.038     70.632      0.000       2.595       2.744\n",
-       "x2             5.0716      0.060     84.656      0.000       4.954       5.189\n",
+       "x1             1.7154      0.022     78.441      0.000       1.672       1.758\n",
+       "x2             4.9826      0.060     82.359      0.000       4.864       5.101\n",
        "==============================================================================\n",
-       "Omnibus:                        1.123   Durbin-Watson:                   2.013\n",
-       "Prob(Omnibus):                  0.570   Jarque-Bera (JB):                1.034\n",
-       "Skew:                           0.075   Prob(JB):                        0.596\n",
-       "Kurtosis:                       3.046   Cond. No.                         3.31\n",
+       "Omnibus:                        0.921   Durbin-Watson:                   2.067\n",
+       "Prob(Omnibus):                  0.631   Jarque-Bera (JB):                0.805\n",
+       "Skew:                           0.058   Prob(JB):                        0.669\n",
+       "Kurtosis:                       3.075   Cond. No.                         2.77\n",
        "==============================================================================\n",
        "\n",
        "Notes:\n",
diff --git a/main/example_notebooks/dowhy_causal_discovery_example.html b/main/example_notebooks/dowhy_causal_discovery_example.html
index 2b986065f..4d6108567 100644
--- a/main/example_notebooks/dowhy_causal_discovery_example.html
+++ b/main/example_notebooks/dowhy_causal_discovery_example.html
@@ -759,7 +759,7 @@ <h1>Causal Discovery with causal-learn<a class="headerlink" href="#Causal-Discov
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "02642067c392497183ebe2eb9ffe39a3"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "a5b2a54eb3dd4ee7930d3d9593af145d"}</script></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -951,7 +951,7 @@ <h1>Causal Discovery with causal-learn<a class="headerlink" href="#id2" title="P
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "b934016f51ee4104805cafd73303706a"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "5c82882b1d6d42db9f23885456cfcade"}</script></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1071,11 +1071,11 @@ <h2>Estimate effects using Linear Regression<a class="headerlink" href="#Estimat
 Estimand name: frontdoor
 No such variable(s) found!
 
-Causal Estimate is 0.033971892284519356
+Causal Estimate is 0.0339718922845158
 </pre></div></div>
 </div>
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diff --git a/main/example_notebooks/dowhy_causal_discovery_example.ipynb b/main/example_notebooks/dowhy_causal_discovery_example.ipynb
index 9968eea06..34e2daee9 100644
--- a/main/example_notebooks/dowhy_causal_discovery_example.ipynb
+++ b/main/example_notebooks/dowhy_causal_discovery_example.ipynb
@@ -14,10 +14,10 @@
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    "metadata": {
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-     "iopub.status.busy": "2024-10-25T14:43:32.231155Z",
-     "iopub.status.idle": "2024-10-25T14:43:33.696984Z",
-     "shell.execute_reply": "2024-10-25T14:43:33.696334Z"
+     "iopub.execute_input": "2024-10-25T14:44:19.690744Z",
+     "iopub.status.busy": "2024-10-25T14:44:19.690114Z",
+     "iopub.status.idle": "2024-10-25T14:44:21.242462Z",
+     "shell.execute_reply": "2024-10-25T14:44:21.241782Z"
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@@ -47,10 +47,10 @@
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diff --git a/main/example_notebooks/dowhy_confounder_example.html b/main/example_notebooks/dowhy_confounder_example.html
index afb86337a..ea7b5e608 100644
--- a/main/example_notebooks/dowhy_confounder_example.html
+++ b/main/example_notebooks/dowhy_confounder_example.html
@@ -616,11 +616,11 @@ <h2>Let’s create a mystery dataset for which we need to determine whether ther
 <div class="output_area docutils container">
 <div class="highlight"><pre>
    Treatment    Outcome        w0
-0   6.173883  11.125651 -0.338081
-1   1.376022   2.994333 -4.672328
-2   2.204764   4.649972 -3.743316
-3  10.198390  20.007447  3.995013
-4   6.082778  12.156471  0.083552
+0   3.141553   6.185347 -2.846096
+1   2.853058   5.837480 -3.058273
+2   8.414148  16.227897  2.123453
+3   6.970236  13.863212  0.923817
+4   2.492288   5.095843 -3.246356
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -744,7 +744,7 @@ <h3>STEP 3: Estimate the causal effect<a class="headerlink" href="#STEP-3:-Estim
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Causal Estimate is 0.011132662400108018
+Causal Estimate is 1.0019898175815642
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -771,8 +771,8 @@ <h3>Checking if the estimate is correct<a class="headerlink" href="#Checking-if-
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-DoWhy estimate is 0.011132662400108018
-Actual true causal effect was 0
+DoWhy estimate is 1.0019898175815642
+Actual true causal effect was 1
 </pre></div></div>
 </div>
 </section>
@@ -797,9 +797,9 @@ <h3>Adding a random common cause variable<a class="headerlink" href="#Adding-a-r
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:0.011132662400108018
-New effect:0.01112672213326368
-p value:0.84
+Estimated effect:1.0019898175815642
+New effect:1.0019914265231784
+p value:0.98
 
 </pre></div></div>
 </div>
@@ -822,9 +822,9 @@ <h3>Replacing treatment with a random (placebo) variable<a class="headerlink" hr
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:0.011132662400108018
-New effect:-4.757591404255024e-05
-p value:0.82
+Estimated effect:1.0019898175815642
+New effect:-3.169002204966631e-05
+p value:0.96
 
 </pre></div></div>
 </div>
@@ -847,9 +847,9 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:0.011132662400108018
-New effect:0.011014981045932223
-p value:0.98
+Estimated effect:1.0019898175815642
+New effect:1.0016764361269999
+p value:0.84
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_confounder_example.ipynb b/main/example_notebooks/dowhy_confounder_example.ipynb
index 895f2ef69..ffed51bc6 100644
--- a/main/example_notebooks/dowhy_confounder_example.ipynb
+++ b/main/example_notebooks/dowhy_confounder_example.ipynb
@@ -14,10 +14,10 @@
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-     "shell.execute_reply": "2024-10-25T14:48:23.516402Z"
+     "iopub.execute_input": "2024-10-25T14:49:17.350583Z",
+     "iopub.status.busy": "2024-10-25T14:49:17.350234Z",
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@@ -61,10 +61,10 @@
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-     "shell.execute_reply": "2024-10-25T14:48:23.526730Z"
+     "iopub.execute_input": "2024-10-25T14:49:18.889258Z",
+     "iopub.status.busy": "2024-10-25T14:49:18.888899Z",
+     "iopub.status.idle": "2024-10-25T14:49:18.898106Z",
+     "shell.execute_reply": "2024-10-25T14:49:18.897484Z"
     }
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    "outputs": [
@@ -73,11 +73,11 @@
      "output_type": "stream",
      "text": [
       "   Treatment    Outcome        w0\n",
-      "0   6.173883  11.125651 -0.338081\n",
-      "1   1.376022   2.994333 -4.672328\n",
-      "2   2.204764   4.649972 -3.743316\n",
-      "3  10.198390  20.007447  3.995013\n",
-      "4   6.082778  12.156471  0.083552\n"
+      "0   3.141553   6.185347 -2.846096\n",
+      "1   2.853058   5.837480 -3.058273\n",
+      "2   8.414148  16.227897  2.123453\n",
+      "3   6.970236  13.863212  0.923817\n",
+      "4   2.492288   5.095843 -3.246356\n"
      ]
     }
    ],
@@ -96,16 +96,16 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-10-25T14:48:23.894585Z"
+     "iopub.execute_input": "2024-10-25T14:49:18.900151Z",
+     "iopub.status.busy": "2024-10-25T14:49:18.899807Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.279761Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.279007Z"
     }
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    "outputs": [
     {
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",
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3Zo8RQiA7O9v2/MMPP8S6deucmr+6utr2tclkQkVFBSIjIy857tixY/jzn/+M7777DnV1dU7NDaj/odeZYAMAtGrVyqUx9scDUiZCfXv37rV9bTKZMHbsWKfWAkgdOqxKSkqaPd6ZNYeFhTk8b2jNWsSAAxFJhABeegk4cED678iRvEtFRBSIamqA31vZOaVjR2D1aqBDBwYbfNyQxGjERYbiZHlNg3UcdABiI6UWmaSMCCfqnpSXlzsEAXbv3i37fOXl5ZcEHHbs2IHRo0fLCh7Uz35QWkhIiEfGiAbq1ti3Hq2srJRdr6K8vLzZY5qr4dGQhtasRazhQESStDTA2rN3xw7pORERBZ6NG107/vRpoF8/oF66L/meIL0Oc8YnAZCCC/asz+eMT0KQnjcklKLXN/9x7MKFC4qdz7pFwH7uCRMm2IINLVq0wEMPPYQvv/wS+/btQ1lZGWpqaiCk7oYQQmDOnDmKrUfLlPq51/+ZBxpmOBCRlN0we7bj92bPBkaPZpYDEVEgEQL44x9dG9OyJd8r/MjY/nF496FkzFuT41BAMjYyFHPGJ2Fs/7gmRpMa6ndb+Prrr/F///d/isz90Ucf2eowtGjRAhs3bnQoTtgQZ7ou+AP7n3tSUhL279/vvcX4MAYciEjKZsjMdPxeZqb0/TFjvLMmIiLyPKMRcLXyeXw8ICOFmbRrbP84jEqKxfb8Mpw+X4OYcGkbBTMbvCMsLAytW7dG5e+dYE6dOqXY3NZWm4DU9rG5YAMAW3FGf2dfV+H06dNeXIlv45YKokCQng4kJUn/rU8IqVBkQ2bPduzF3tQ8RETk+378EXA1/ffAAW7D80NBeh2u7t4Wt1/eCVd3b8tgg5ddc801tq+dabPorGPHjtm+HjJkSLPHCyHw66+/OjW3/XYRX6k3YM/+Z37mzBnk5uZ6cTXOq79Nx9s/ewYciPxd/WKQ9V90UlOBQ4caHpuZCXTpIgUYhAAmT5bmmTz50nmsGJQgIvJNTQWgm6LTAS+/3Pj7AhG57eabb7Z9vWrVKoeChu6ora116fiUlBQUFRU5dWzr1q1tX9t3y/AVgwcPRtu2bW3PV6xY4cXVOM/+5w54/2fPgAORv6tfDHLJkosBASGa77NeWAjMnAmkpFwMTBw6JAUq6msouMEABBGRb0hNBfLyXB8nhPReYTIpvyYiAgBMnDgR0dFSd5ALFy5g8uTJiszbsWNH29c//fRTk8dWVVXhhRdecHruuLiL9T4ONXZzS8OCg4Md/r5vvPEGsrKyvLgi59j/3AHv/+wZcCDyZ0JId52CgqTnQUHAwoUXAwLV1VLf9OZkZgLjxjl+b/r0S+9m1Q9upKY2nV1BRETaIIT0uu6KAQOA9euBnTul13y2xCRSTXh4OBYtWmR7/tVXX+G+++7D2bNnmx2bmZmJRx55BJ9//vklfzZixAjb1//973+xdu3aBucoLS3Frbfeit9++83pNV9xxRW2r/fu3Su7raQ3TZs2DT179gQgZQqMHj260Z+RvfLycrz33nsYPXq02ku8RFxcnEPQ4fXXX3doq+ppbhWNNBqN2LJlCzZt2oSsrCzk5OSgpKQERqMRkZGRiI+Px9ChQzFhwgSMGjUKOhkVjIuKirBy5UqsXr0aR48exZkzZ9CuXTt07doVt912Gx566CF06tTJnb8Gkf9JT5fSYh9++GIAAADMZqCqSvp6xw7g2Wfln8Oa5TB2rPTcPrhhNkvfe/LJiwENa6tNFqEkItIeOcUi9+2TuhlZg9pEpKpnnnkGu3btwvLlywFIQYd169bh3nvvxXXXXYdOnTrBYDCgvLwcBQUF2L17NzZu3IijR48CcAwuWD399NNYunQpKisrYbFYcPvtt+Phhx/G+PHj0aFDB5w9exY///wzPvzwQ5SWliIiIgK33HILvvjii2bXO2LECHTq1AlFRUUQQmDs2LHo27cvunbtihC7QrPTpk1rcG1aEB4ejlWrVmHYsGE4e/YsSktLMX78eAwePBi33347Bg4ciKioKNTU1KC0tBT79+/Htm3bsGnTJphMJnTp0sUr637kkUewdOlSAMBnn32GDRs2YODAgQgPD7cd079/fyxcuFD9xQgZTp48Ke677z4RHh4uADj16Nevn9i6datL53n33XdFWFhYk/O2bt1avPfee3L+Gjbl5eUCgCgvL3drHiKP2LhRiL59pf829D2LRYjBg4UAhGjVSgi9Xvq6oYdO1/ifOfPo1OnieVNSGj7Gev6gIGldaWmXrp+IiLyrsdfw5h4nT3psib50vVZdXS1ycnJEdXW1t5dCHjR8+HDbZ5QuXbo0epz9Z5kff/zRpXNYLBaxcOFCodfrnf4cZn189NFHDc75zTffiODg4GbHh4WFifXr14s5c+bYvjd8+PAm15uent7s57n663Jlfiv7n/2cOXOcGuPK/4fc3FzRr18/l3/mjf07+PHHHx2Oc5azaz5//ry48sorm1ybsz/bhrjyGidrS0VhYSG+/PLLS3qwxsXFYfDgwRgxYgSSkpIcKmTu378f1157Lb799lunzjF//nw8++yzuHDhgu17PXv2xPDhw9G9e3fb9yorKzFp0iTPRGeIvG3jRmD8eMctCvXrJixZcjGroaqq6Wrj7m5xKCqSzjtzZuOFxqznN5uldU2Zwi0WRERaIoTUlUiObduUXQsRNUmn02HWrFnYt28fHnzwQbRq1arJ46OionD33Xfjm2++wQMPPNDgMRMmTEB6ejr69+/f4J8HBQVh9OjRyMrKcihe6YyRI0ciOzsbM2fOxNVXX4127dqhRYsWLs2hBT169EBWVhbee+899OnTp8ljdTodLr/8cvz1r39FupdqmLVu3RpbtmzBihUrcOutt6JLly5o1aqVrB0H7tL9HilxSWZmJgYPHgwAuOqqq/DYY49hzJgx6Nq1q8NxJ0+exCuvvIK3337b1o4jJCQEe/fuRe/evRudf9WqVbjjjjtsz5OSkvDZZ58hOTnZYQ2PPPIIDhw44DDutttuc/Wvg4qKCkRGRqK8vBwREREujyfyCCGAPn0cO0pYeydbtzUA0h5ao9Gza3OWTucYZEhJ4RYLIiJvMxqBmBigosL1scnJUp0fD1zE+tL1Wk1NDfLz85GYmIjQ0FBvL4f8mMlkwrZt25CXl4czZ86gtrYWrVu3RqdOndCnTx/07dv3kjaJjRFCICsrC5mZmSgtLUV4eDji4uJw7bXXIjY2VuW/iW8pLCzE1q1bcfr0aZw7dw4GgwFRUVHo0aMHBgwYYCvw6a9ceY2TFXDIysrC/Pnz8de//tUhCNCYN998E9Ps7n7edddd+O9//9vgsbW1tUhKSkLe71WS4+PjsXfvXkRFRV1ybFlZGQYOHGhrzdKzZ0/k5OQgONi10hS+9AZGAcZai+GNN4C6OsA+qqzXSxd6ALBr18W6Cb7kyiuB7ds9cqFKRESNEEJqgVxYKG+8h4LHvnS9xoADEfkzV17jZG2pSE5Oxv/+9z+ngg0AMHXqVAwZMsT2fN26daiyFq6r58svv7QFGwCpqmZDwQYAiI6Oxuuvv257npubiy+//NKpNRFpnv1WiYa2LFgs0l2lzEzfDDYA0tq7dGHLTCIibzIa5QcbAGk7BrfIERFRAzzWFvP222+3fV1TU2Orllrf119/bfu6Y8eOuPPOO5ucd8KECQ5tP/7zn/+4t1AirbBvMZmZCeTmenc9aikslAIqQkiBh6QkBiCIiDzphx/cG19YCJhMyqyFiIj8iscCDvX3sVQ0sE+wuroaGzdutD0fO3Zss9sjgoODMdZu/3paWhpqamrcXC2Rl1g/cG/cKL+Aly/KzAQWL3Ysfsm7ZURE6hNCamEsxzvvAOvXS6/hBoOy6yIiIr/gsYBD/YyGmJiYS445cOAAjHbF7oYNG+bU3PbH1dTUOBSSJPIZ9lsopkyRLuACyV//ejGjY8cOKcODiIjUVVMDFBe7Pu7BB4Fnn5VqC8XHK78uIiLyCx4JOAghHIpExsXFITEx8ZLj9u/f7/C8Z8+eTs1f/7icnBwZqyTyMvstFPadKAJF/ToUEydyewURkdrsamG55Lvvmm67TEREBA8FHD7//HMcPnzY9vzBBx9ssAdo/SyIzp07OzV/ly5dHJ7n5+e7vkgibxICePllICjI2yvRjqIibq8gIlKTxQIsWiRvbE0NUFmp7HqIiMjvqB5wOH78OKZPn2573qZNG8ycObPBY+vXdWjTpo1T54iMjHR4fv78+SaPNxqNqKiocHgQeZU1u8FXu02oidsriIjUsW4d0EjXsEZFRgKffQZs3QpovDUlERF5n6oBh6qqKkyYMAGlpaW27y1fvvySApJWlfUi5S1btnTqPPWPay7gsHjxYkRGRtoeCQkJTp2HSFH2BSKZ3dA4vV76+TDLgYhIOUIACxYADWScNqm8HLjrLmDwYHXWRUREfkW1gENdXR3uu+8+7LDuSQcwefJk3HPPPY2Oqa2tdXjeXIeKxo6rP099M2fORHl5ue1R6E7vaSI56heIZHZD4ywWZjkQESnNZAIKCuQFc5u5ziIiIrJSJeBgsVjw8MMPY82aNbbv3XPPPVi2bFmT48LCwhyeO9vesv5x9eepz2AwICIiwuFB5FH1C0S6eocpEE2cCPTtyyKSRERKMBik96FbbnFtXHw8EB6uzpqIiMjvKB5wsFgseOyxx/Dll1/avnfXXXfh3//+N4KaSRlv3bq1w/MqJ/cV1j8unG+EpGVCANOmXfo9alpREXDwIItIEhEppWNHqY6DK4qLAbsW5kRERE1RNOBgsVgwceJEfPbZZ7bv3Xnnnfjyyy+d2h7Rvn17h+fFTvaFrn9cu3btnBpH5BWpqYHZ9lIpO3YAXbow04GIyF0TJ7o+JiyMWXlEROQ0xQIOFosFTz75JD7++GPb9+644w589dVXTtdi6NOnj8PzY8eOOTWu/nF9+/Z1ahyRxwkB2HVtIZkKC4HJk6Wimww8EBG5zmwGPvnE9XFdugAhIcqvh4iI/JIiAQdrsOGjjz6yfe+OO+7A119/jRYtWjg9T79+/RyeZ2VlOTWu/nFJSUlOn5NIddZuFOnpzG5Q0qFDUtHNyZO5xYKIyFULFsgbV1IiFZwkIiJygtsBB6WCDQCQkJCA7t27255v3rzZqXH2x/Xo0QPx8fEunZdINfbdKGbOBKZO9faK/M+hQ8Dixd5eBRGR77BYgL/9zfVxnTsD27dLBSeJiIic4FbAoaFgw5133ikr2GA1YcIE29ebNm1CQUFBk8cXFBQ4BBzsxxN5nX03isxMIC/Pu+vxV7NmST9r+2wSIiJqWGUlUF3t+riCAmD/fuXXQ0REfkt2wEEIgaeeesoh2DBhwgR89dVXsoMNAPD444/bullYLBYsaCblb/78+bBYLACAoKAgPP7447LPTaQoIYCXXwas3Vn0qnShJavHH5eySA4cYCcLIqKmREQATz8tb+zs2Xx9JSIip8n6BCSEwDPPPIMPP/zQ9r27777b7WADIBV8fPTRR23PP/jgA3zwwQcNHrt8+XKsWLHC9vyxxx67pPAkkddYsxvMZun574ExUsmJE1IWCSD93NPSvLseIiKtslgAuxtGLjl+nDUciIjIac61j6jnP//5D95//33bc51Oh7Nnz+LWW291eo4//vGPGDVqVIN/tnTpUmzevBmHDx8GADz11FNYs2YN7rvvPnTs2BFFRUX44osvsHbtWtuYHj16YMmSJXL+OkTK27gRuOMOKauBgQbPCwqSsktGj2b7NiKi+taulRc0iIwEtmxhDQciInKarIBDVVWVw3MhBL7//nuX5rjvvvsa/bN27dphw4YNGDNmDPLz8wEAq1evxurVqxs8PjExERs2bEC7du1cWgORKoQApkwBamq8vZLAZTZfzHIYM8bbqyEi0g4hgEmTXBvTrh3w978DI0YALMxNREQu0Oym8p49e2Lv3r2YNm0aIiIiGjwmMjIS06ZNw969e9GjRw8Pr5CoEWx9qQ3WLAfuNSYiuqimBigudm1MVRVw770MNhARkct0Qmj/arympgabN2/G0aNHUVpairZt26Jr16644YYbYFAgra+iogKRkZEoLy9vNLhB1KT0dGDaNGDZMqlw4c6d3l4RWaWkMMuBiMhq/nxgzhzXxoSGAhcueL34sS9dr9XU1CA/Px+JiYkIDQ319nKIiBTlymucTwQc1OZLb2CkQUIAQ4dKKfy9ejG7QUt0OuDKK4Ft21jLgYjIYgFCQi4WM3aWXg+cPSt1t/AiX7peY8CBiPyZK69xsmo4EJEdazcKQAo26HT+ncY/fz7QrZv0dVgYcPfdrl+8eooQUt94k4lFzoiIXnnF9dfrJ56Qaj5o/AM+ERFpEwMORO4QQqoTEBR08SLOn4MNQUHAmjUXMwaMRiAqCjhzxtsra1xQEPDzz8BNN3l7JURE3mOxAAsWuD5u924pU4yImpSdnY1169Zh48aNKCgowOnTp1FTU4P27dujQ4cOuOqqq3DzzTdj5MiRzHqhgKLZopFEPsGa3aDVO/xKs+/+AEhZA1lZQEYGEB3t3bU15sQJqa6GPweCiIias3YtUFvr+rjCQnktNIkCxN69ezFu3DgMGDAAM2bMwPfff4/c3FyUl5fDaDTi+PHj2LlzJ95++23ceuut6NmzJz766CNYNNA2/eOPP4ZOp4NOp0PXrl29vRzyUww4EMlln93gL958E8jMBJKSGq95oNc7dn9ISACuukoKPGg1Yp+ZeTFIQkQUaIQA/vAHeWOXLuWWNKJGvPPOO0hOTsaGDRscvh8XF4fBgwdj+PDh6N27N1q0aGH7s+PHj+OJJ57A6NGjUV5e7uklE3kcAw5EcvlCdkP79sBnnwErVwLORK4/+QTo1w8oLW08I8BiafiOV1SUtu+CzZoFbNwoBVPS0729GiIizzEagSNH5I195x1miBE1YPbs2Zg8eTLMv18HBgUFYcqUKdi7dy9OnDiB7du3Y9OmTTh48CBOnz6NDz/8EAkJCbbx33//Pa699lqUlJR4669A5BGs4UAkhzW7QesFIs+dk4o66nTO3d0qLJSO3bEDaOoNMCbm0jteERHAr78CeXmO38/LAw4fBg4ckDINvGXnTuD++6VgyuTJwMGD7FxBRIFBp5My0KqrXR97+DAL7xLV8/XXX+OVV16xPW/fvj3Wr1+PKxupd9KmTRs8/vjjuOeee/Doo4/im2++ASDVfXjkkUewfv166HhNQn6KAQciOTZskD7AajnYAABt2kgXmgaD9GF/3z6ptVlDoqOBAQOkYxMSpIerhg6VHvUJAVxxhevzKa20VPrvoUNAaiowdqx310NE5AkhIUB4uLyAQ0KCNJ6IAEhbIp566inb8/DwcPz444/o169fs2PDwsLw5Zdf4vbbb8f69esBACkpKVi2bBmef/55tZZM5FXcUkHkKiGAF16QthZoyfz5UhDE/pGVdfGuVEICMG4c8OCDDT9uvhmIj1dnbWlpwK5d6swt1/Tp2g8YEREpoaYGOH1a3thTp7S9XY7Iw1577TVUVFTYni9dutSpYINVcHAwVqxYgTZt2ti+t2TJEtTU1DR4/Ny5c22FHW+44QanzrFp0ybbmIYyJ2644QbodDo8/vjjtu8dO3bMYYz947HHHmvyfCaTCf/+97/x6KOPom/fvmjbti1atGiByMhIDBw4EA8//DA+/vhjp2tWZGRk4IUXXsDll1+O9u3bIyQkBLGxsRgyZAhmzZqFnJwct34Op06dwpIlSzB06FB06NABISEhiI+PxwMPPIAd1lb39ezbtw/PPvss+vTpg1atWiEsLAx9+/bFH//4R5w8edKp9dirq6vDV199hUceeQR9+vRBdHQ0DAYDOnXqhJEjR+Jvf/sbysrKXJ5XkwSJ8vJyAUCUl5d7eynkCzZsEEL6qKqdR1CQEIMHC2GxePuncymLRVqbXu/9n1P9x+OPC9G3rxAbN3r7p0REpJ6FC+W9RiYmClFQ4O3V2/jS9Vp1dbXIyckR1dXV3l4KKejs2bOidevWAoAAILp37y4sMq+95s+fb5sHgPjXv/7V4HFz5syxHTN8+HCn5v7xxx8d5q5v+PDhDn/e3OPRRx9t9Fyff/65SEhIcGqeli1bihMnTjQ6V0lJibj99tubnScoKEg8/fTTzf5+NfRzWL16tYiOjm50bp1OJ5YvX26bw2KxiJkzZwq9Xt/omMjISPHzzz8383/lopSUFNGzZ89m/55t2rQRK1ascHpeT3LlNY5bKohcIQQwbZq3V3Ep+3aVY8Z4ezWOTCagoEB7GSEA8NFH0n9fegkYOZI1HYjI/1gswKJF8sZWVEg1e4gIAJCWlobKykrb84kTJ8quvfDEE09g3rx5tqKT3333ncNWDTUNGTIEoaGhKCoqQnZ2NgAgNDQUw4cPb/D4AQMGNPj9P//5z3jttdccvhcaGmq7Y3/hwgUcPnwYZ86cAQBUV1fDaDQ2ONfx48cxcuRIHDp0yPY9vV6Pfv36oV27diguLsbBgwcBAGazGf/617+Qk5OD9evXIzw83Km/d3p6Ou68806YzWYEBwdjwIABaNOmDQoLC5H3ew0yIQQmTZqEuLg4jB8/HlOnTsXbb78NQNo+k5SUhNDQUBw8eBCnTp0CAJSXl+OWW27BgQMH0LFjxybX8O6772Lq1Km2/+8AEB0djV69eqFly5Y4fvw4cnNzAQDnzp3DxIkTUVxcjFmzZjn1d9Qk9eMf2udLEXPyMi1mN1gfer12sxwKCoTYuVOIjAwhoqO9/7Nq6JGS4u2fEhGR8uRmNwBCfPiht1fvwJeu1/wmw2HjRmYC2pk8ebLDHehdu3a5Nd/AgQNtc0VERAiz2XzJMWpkOFh99NFHtmO6dOni0tpff/11h3PEx8eLTz75RFy4cOGSY3/77TexYMEC0bFjR5Gfn3/Jn5vNZnHdddc5zPfYY49dkg2Rm5srbr75ZofjHnnkkUbXWP/n0LZtW6HT6cSMGTPE2bNnHY795ZdfRIcOHWzH9uvXT3z++ee2TIMPP/xQmEwm2/EWi0W89957IigoyDbmiSeeaPJntn79eqHT6WzHX3PNNWLz5s2XZMkcOnRI3HrrrbbjdDqdSE1NbXJuT3PlNY4BB+Fbb2DkRRaLED17eufDcOvWQmze3PyH9dhYIWpqvP2Tapo1+GB9ZGYK0aOHd4MNWt6SQkQkl9ksRKtW8l8br7hCU6+LvnS95hcBB+uWSIDvkb8bPHiw7UNgaGioqK2tdWu+iRMnOnwgzsnJueQYLQYcDh06JIKDg21jBwwYIE6ePNnsOKPRKIxG4yXff//99x3W+6c//anROcxms7jrrrscjv/hhx8aPLb+zwGAeO+99xqdOz093eHYkJAQ0bJlS5GVldXomJkzZ9qODwsLazDgIoQQ58+fF+3bt7cd+8ADD4i6urpG57VYLOLBBx90CIBoiSuvcSwaSeSM9HSgQwfg9xQnj+veHbjuOmD37ksLQ9o/duzQfuuyhAQgOfnio39/4Px5767JfksKEZG/qKyUCkbKVVTEgpGBLC1Nem8E+B75u9N2xVfj4+MRHOze7vQuXbo0Or+WLV26FHV1dQCAli1b4r///S86dOjQ7LiQkBCENND1ZtmyZbavk5KSsHjx4kbn0Ov1eP/999G2bVvb9/75z386te4RI0bgmWeeafTPR44ciZ49e9qem0wmzJw5E4MGDWp0zHPPPWf7+sKFC9i9e3eDx33wwQco+b3lfGJiIlasWIGgoKBG59XpdHjnnXdsxUX379+PH374odHjtYwBB6LmCAHMmAH8/iLhFdYq4fU/rNd/qNVlQk0Gg3Qh8+uvQBMvvKoLCgJefln6/01E5A8iIoAtW4B27Vwb17YtkJHhG0FsUocQ0nui9X2Z75EA4NA1wL7LhFz15/CFrgTW7gpWDz30EHr16iV7vtzcXFsdCQCYNm1as4GcqKgoPPHEE7bnGzZsaLTLh72nn3662WOuuuoql8bEx8cj3u76u7EOGh9//LHt6ylTpiA0NLTZtURERODOO++0PU9PT292jBYx4EDUnLQ0KXvAG/7xD9/JXHCHNZASFeW9NTDLgYj80blzwO8F25wSEiK1Mb7qKt8MYpMyrNkN1sJ2fI8EAIcPtQYFrsvqf+isrq52e061ZWVlORTOvPfee92aLyMjw+H5+PHjnRp3++23276ura1ttJ2lvauvvrrZY2JjY21fJyYmOpW5ERcXZ/v67Nmzl/z5uXPnsG/fPtvzUaNGNTun1WWXXWb7OjMz0+lxWsIuFURNEQKwi6B63MqVwPTpgdE9wWAAsrIcM0mEAB55BDh40HNdLmbPBkaPDoyfORH5NyGAmTNdG2MyeTf4S95nn91gV0nfluUQwO+RUVFRtm0P5eXlbs9Xf47o6Gi351Rb/Tv4gwcPdmu+XLvtyu3atWu2y4PVwIEDHZ7n5eXhuuuua3KMfTChMa1atXLp+PpjqqqqLvnzffv2wWJ3Hfv888+jRYsWTs1dVFRk+7rEm9nWbmDAgagpixcDJ0547/w7d2qz1aVaEhKkh1VqKtBIappqsrKADRuAceM8e14iIqWZTMBvv7k+7n//Ax56SPHlkI+wr91gT8stuD0kOjraFnAoLS11e776c/hCwMF+24fBYEBERIRb89lnBLRv397pceHh4TAYDLY2mw1lFtTXUP0IJY8HANHAtqP6/5/l1mJQIsjlDdxSQdQYiwWYM8e7a9DrA3fPpPUOi97DL1MWC/DCC4H5Myci/9KiheMdame98QZfAwNVc++9gXxdAqB79+62r4uLi90u8mhfYFCn06Fbt25uzecJSm8rsQYMANc/4Nuf35kaDt5y4cIFReaxeCrbV2EMOBA1ZtEi4PcKvF5jsQCFhYFZJdxkAgoKPLeVwt6hQ8CSJZ4/LxGRktavl/f+cexYYL7vUPPvvYF8XQJckrJfv/6AKywWi8Oe/L59+6KdqwVeG2GWE2h0UpTdlqvz58+7/SHYvnDmeRe6lgkhHGpJKFHEUy3113b69GkIIVx+HD161Cvrdxe3VBA1xJPZDe+/D7RsCURHS60364uJ8e+CkY2xdq8oKZFauw0f7tnzv/QScMUV0l5VIiJfIwQwd67r43Q64McfA/N9hxzfexsTqNclkNoq2lu5cqVD8UJXbNiwAWfsCrreeOONDR5nf9e/trbWqbmd2V4gl32BRCEEcnNz0bt3b9nzxcTE2L4uLCyEyWRyKtMhPz/fIdhhP4/W1K8FcerUKZe2j/g6BhyIGrJ6tWfurAcFSftknWiNE5Dsazps3Qp8+y3w6queO//jjwPHjwdscSwi8mEmk1Rw11V9+gB9+yq/HvId9espkc3gwYMxaNAg7Nq1CwCwatUq5OfnIzEx0eW5li1b5vD8mWeeafA4+xoJzrbNtO+I0BS93daZhmoPNOSaa65xeP7jjz+6FXC44oorbF/X1tYiMzPzknM05Ndff3V4fuWVV8peg9oGDhyIsLAw29aKjIwM9O/f38ur8hxuqSCqTwjggQc8cy6zGdi82TPn8nVDhkh33aw9wT3hxAkgJcVz5yMiUkpIiLx99mfPBmy6PJEz/vKXv9i+rq2txdNPP+3yHF988QU2btxoez527FgMGDCgwWO7dOli+zovL6/BLgj1fffdd06to3Xr1ravnW3JGRMTg0GDBtmev/fee04HKxoydOhQtGzZ0vb8s88+c2rcJ598Yvu6S5cu6Nq1q+w1qK1Fixa46aabbM8//PBDL67G8xhwIKpv3TrAU32QA7z4kkvq9wT3lHvu4f8fIvI969cDTnwwcdC5M7B9e8CmyxM545577sGoUaNsz9PT0zF16lSnx//yyy8OQYrWrVtfku1gzz4DoK6uDt9++22T869cudLpDAf77RElJSU4d+6cU+NeeOEF29d79uzBa6+95tS4hoSFheEBuxt9K1aswP79+5scs2HDBqSnp9ueN5YdoiV//vOfbV9v3boV7777rhdX41kMOBDZEwK4/37PnS/Aiy85zVsdKwCpfkT79oDdGxsRkaYJATz8sOvjCgqAZi70iQKdXq/HypUrHT6sv/XWW5gwYQKKiooaHWexWPDWW29h7NixDsUO3333XfTq1avRcR07dnTYYvDSSy81ep7169dj0qRJTv9dBg4ciBYtWtie/+1vf3MqW+H+++/H4MGDbc9ffPFFLF26tMlildXV1Xj77bcb7Ozxl7/8xZblUFtbi1tuuQW5ubkNzpORkYH77a7VY2NjXfo7e8uwYcNw33332Z5PnToVS5cuRV0zBepra2uxZs0a3HjjjTh27Jjay1QFazgQ2auqkj5gqqVtW2DtWinV1SqAiy85zZsdKwCgtBR49lmpewXrORCR1m3YIG2NkGP2bGDMGL7WETUhJiYGmzZtwrhx43D48GEA0jaGlJQUjBs3DmPGjEFCQgJatmyJkydPYufOnfjvf/+L/Px82xxBQUF499138dBDDzV7vpkzZ2L8+PEApMKKgwYNwrRp0zB06FC0aNEC+fn5+Pbbb7F27VoAwGOPPYaPP/642XnDwsIwYcIEfPXVVwCARYsW4eOPP0a/fv3QqlUr23EjRozAtGnTbM+Dg4Px9ddfY8iQISgpKYEQAjNmzMCHH36I+++/H1dccQWioqJw4cIF5OXlYcuWLVi7di3Onz+PW2655ZJ19OrVC6+//jqeffZZAMCxY8dw2WWX4fHHH8dNN92Etm3b4uTJk1i7di0+//xzW2AjKCgIH3/8sUPnDC1bsWIFDh06hKysLJjNZsyYMQPvvPMO7rnnHgwdOhTt27eHEALnzp3DoUOHsHPnTqSlpdkyT9zZuuJVgkR5ebkAIMrLy729FPK2m24SQro3pOzjf/8TYudOIQoLvf039F0FBdLP0PrIzBQiKUkInU6d/2cNPRIShNi40ds/CSKixlksQlx2mfzXudhYIWpqvP23aJAvXa9VV1eLnJwcUV1d7e2lkIpOnz4t7rjjDgHApUdiYqJISUlx6VxTpkxxau65c+eKH3/80eF7TSkqKhLdunVrcs5HH320wbGHDx8Wffv2denvnp+f3+ha/vnPfwqdTufUPKGhoeK7775r8u/mys/Bas6cObbjhw8f7tSY4cOH28bMmTOnyWPPnz8v7rrrLpf/zTT3s/M0V17juKWCyColRb20+dBQIDkZiI9XZ/5AkJAg/Qytj/79pcwDT0Z7CwuBmTM9e04iIlekpQF79rg+7u23gZ07pVo5zLojckr79u3x3XffYcuWLbjzzjsdijDWp9PpkJycjGXLluHgwYMYM2aMS+d688038c9//hNt2rRp8M979eqF1atXY46Lbd07duyI3bt345///CdGjRqFTp06ORRxbEq3bt2QlZWF119/HZ07d27y2B49emDu3LmXtIi0N336dOzYsQOjR4926KBhLyQkBPfeey+ys7Nxxx13OLVOLWndujX++9//YsOGDbjhhhsQ1Ewx9K5du+LZZ5/FL7/8ounCmE3RCcEr54qKCkRGRqK8vNyh9QwFgPR0YNo0YNky4N575aegNkWnA668Eti2jSmqSissdOwVXlAA3HWX+lsvUlKklGMiIi0RAhg6VAoauOrcOSAyUvElKcmXrtdqamps7RJD2fo6YJhMJmzbtg0FBQU4ffo0jEYj2rVrhw4dOmDIkCHo0KGD2+cwGo3YvHkzfvvtN1RWVqJDhw7o378/hgwZosDfwD05OTnIyspCSUkJLly4gPDwcHTu3BmXX365y61DS0tLsXnzZpw4cQIVFRWIiopC586dMXz48CYDO76mvLwcW7ZswfHjx1FaWgqdTofIyEh07doVSUlJDl1KtMSV1zgGHOBbb2CkIPsLs7g4oLhYvXPFxgJHj/KukdqMRimL5MwZdc/Tq5fU354BJCLSEqNR6jTRQFG2JoWFSRljGn+P8qXrNQYciMifufIaxy0VFLisbRYBdYMNb77JFFVPMRiAjAxA7cj3oUNAaqq65yAicpXBAPz8s+vj2rZ1LGZMRESkEAYcKDBZ2yw2s2/KbXo98OmnQKdO6p6HLureHXAxbU+WadNYy4GItOfAAdfHHD8uZUcQEREpjAEHCkzW7IYm+gUrwmKR6gyYTOqehy4ymVxPJ5YjN5dZDkSkLUIAzz3n+rjoaG4RIyIiVTDgQIHHE9kNv/4qVftmxW/PMxiAX36RLqDVduutwMaN6p+HiMgZNTXAiRPOH6/TAWvXArt28X2KiIhUEeztBRB5nH3tBjUEBQGDBkmtMMk7evQAdu+WMh0mTJC6V6jBbAZefBG46SbeHSQi73M1u0EIYNgwoJE2e0RERO5ihgMFFiGAWbPUPYfZDGzerO45qHkJCUC/fq7d7ZNj1y4piEVE5E1mM/DJJ66PY7CUiIhUxIADBZYNG6RtDmrS66UtGywo6H2bNwN1deqfZ9Ys/v8mIu+aOFHe69DWrcqvhYiI6HcMOFDgEAKYPl3981gs0pYN3vX2LmutDr0HXuZ27uT/byLyHrNZ6ogkBwOmRESkIgYcKHCkpgJ5eZ45F7McvM9kkmo3WCyeOR/bZBKRt7zyivzXH3ZSIiIiFbFoJAUGIYCpUz13Pvt2mKz87R0Gg5RpUlJy8XtCAOPGqdM289AhKag1dqzycxMRNcZiAZYulTe2a1fgp5/4PkVERKphwIECg5rZDTod0LevlM5qX3wrJoYXcd6WkCA9rGpqgLIy9c43bRrw228swkZEnrN+PVBVJW9sVZX0XkVERKQSBhzI/23cCIwfr978QgA5OcCZM8CYMeqdh9yn00nt386cUWf+/HzAaGRLVCLyDCGAefPkjX3zTeCOOxgYV5ngVjsi8kOuvLaxhgP5r/R0oEsXKYVe7U4FrNngGwwGICtLKvK4di3QqZOy89fVsSUqEXmOySQve69bN2DyZCA+Xvk1EQBA/3vBYoun6ggREXmQ9bVN70RxdgYcyD8JAcycKRUN9ERbRPuaDaRtCQlAcjIQHAwUFSk//+zZDDwRkWeEhDhuG3NWZSXfr1QWFBQEAKitrfXySoiIlGd9bbO+1jWFWyrIP6WlAZmZ6s0fFSXtmw0Jufg91mzwHUJIgQE1ZGZK//64vYaI1GYyAadOuT6OhSJVFxQUhNDQUFy4cAFt2rTx9nKIiBRVWVmJ0NBQpwIOzHAg/6Pmh0kAaNtW6n5w1VXSnXLrg6mpvsPaMlMtzHIgIk8wGIBHH3V93KefKr8WukTr1q1RWVnJOg5E5FeEELhw4QJat27t1PEMOJD/UTO7oU8fYNcuoHt3deYnzzAYpH8j69YBf/iD8vNnZQEbNig/LxGRPYsFePtt18e9/ro0llQVHh4Oi8WCEvv2zEREPq6kpAQWiwXh4eFOHc+AA/kXtbMbysrYQsxfJCQAN98spRYrzWIB5sxhlgMRqUtuS0yjUarjQKoKDQ1FTEwMSktLUVZWxkwHIvJpQgiUlZWhtLQUMTExCHWyKxtrOJB/USu7YdAg4IUXgBtv5L5Xf6Lm1grWciAiNbnTEvNvfwMiIpRdDzUoOjoatbW1OHXqFMrKyhAZGYmWLVsiKCgIOp3O28sjImqSEAJmsxnV1dUoLy9HbW0toqKiEB0d7fQcOsFwKyoqKhAZGYny8nJE8A3YdwkBDBmiTsChZUvpbpATrV/IxxQWAvv2AWfPSs9nzwaOHlVm7p49gd9+A3hRSURKS00Fxo6VN7ZXL+DgQZ97bfLV6zUhBGpqanDu3DlUVFSwVSYR+Ry9Xo+IiAi0adMGoaGhLgVMGXCA776BUT1Go5Qmr9ZeydWrgfHj1ZmbtMFoBDp3Bk6fVm7ODRvkfyggImqIuwH2Fi2AigrAyXRYrfCH6zWLxQKz2Qyz2eztpRAROSUoKAhBQUHQy7zxyoAD/OMNjH6XlwcMHgycO6fsvDodcOWVwLZtPndHiFy0ciXw8MPKzccsByJSmtEIxMbKf69r317K7vKxLYK8XiMi8j3MDyf/0qOHFBRQmhDSxZnJpPzcpB1CAMuWKTtnbi6wZImycxJRYAsJAdq0kTe2d29g506fCzYQEZFvYsCB/M8XXyg3V9u2QEaGdHG2Ywcv0PydWkVH581jCzoiUk5qqvxaM2fPstsSERF5DLtUkH9ZsgSYO9e9Oa68Epg/H+jQQbooi49XZGmkcUIAL78MBAUBSu+tNRqBtWuB225Tdl4iCjxCAFOnyh+/ZAmD50RE5DGs4QDuCfQL6enAlClSDQclPizW1UkfPClwuFPx3RkdOwKRkcAbbwA33aTeeYjIv9XUAGFh8rKmfLweEa/XiIh8D7dUkO8TAnjpJakwn1J3pl95RZl5yDdYsxvUbHt64gRw4ID0b5VxXiKSa9Mm+Vu0WI+IiIg8jAEH8m3p6UCXLlJ9BSUtXco994HEZAIKCjzz/3zHDqlWBBGRq4QApk2TN9Zak4j1iIiIyINYw4F8lxDAzJnS3RqlVVUB69cDt96q/NykPQaDdBFeUnLxeydPSnUX3n1X2XPp9VI2xejRPpnSTERelJoqdb5xVZs20jaK7t0VXxIREVFTWMMB3BPos9Tcc+/j+1xJAUIAQ4dKXSvUeJlMSQHGjFF+XiLyT0JILS3lBBz27weSkpRfk4fxeo2IyPdwSwX5JiGAWbPUnZ/7XAObyQQcO6ZOsCEoSMpyYLyXiJxlNAL5+fLG9u6t7FqIiIicxIAD+aa0NGDnTmXn7NNHupu9c6f04D7XwGYwAFu2ANHRys9tNrOWAxG5ZvNmqYOSHKWlyq6FiIjISdxSAabo+RwhgMGDlQ84tGsHHD/OIAM5KiyU7ioOH67svNy2Q0TOEkIKih865PpYvR64cAEIDVV+XR7G6zUiIt/DDAfyPWpkN7RsCWzdymADXSohAbj+eunfx6uvKjevEEBWFrBhg3JzEpF/Sk2VF2wAgKgoBjWJiMhrGHAg3+JOS7DGfPutdCHH6t3UlKFDpbuESjKbgblzWcuBiBrnzvteUBCD6URE5FUMOJBvMRqBw4eVnXPvXiA+Xtk5yf9YLMpmOFixlgMRNcWdYpEtWkhZWkRERF7CgAP5lk2bpLvCSnr1VenDJFFTKiuBmhp15mbHCiJqjDvFIuvqpKADERGRlzDgQL5DCGD2bOXnraoC1q1Tfl7yLxERQEYGMGOG8nMzy4GIGuLu+15dHXD+vHLrISIichEDDuQ71CgWabVgAe8wU/OGDgUWL5buOOoVfvmcPZv/BonIUVqa1K5Zruhov+hOQUREvosBB/INQkhp50p/yLMqLARMJnXmJv8zdKhU+V1JmZnAkiVAUhKQnq7s3ETke9zNbggNBbZvZ8FIIiLyKp0Q8m+pnT9/Hrt27UJWVhaysrKwc+dO/PbbbzD/vse+S5cuOHr0qEtzHj16FImJiS6vpUOHDjh58qTL4wD2dfYJqanA2LHKzde1q5TV0LYt0KEDEBPDwpHkmrw8YMgQ4OxZ5eZs2RKorgYGDwa2bWMrO6JAZjQCsbHAuXPyxoeESK8nagXqvYDXa0REvidY7sDevXsjNzcXbsQriJxjzW7Q6ZRLOS8vB/7v/3jnh+Tr0QP4+Wegf3/l5qyulv5rrekwZoxycxORbwkJAdq0kR9wMJmk+g2RkUquioiIyCWyAw6HDh1Sch2Nuv7669GyZctmj4uOjvbAasgrTCagoEDZ/e2dOkkXc0Tu6NtXCloZjcrOGxQkBdlGj2aWA1GgMhql9z65WL+BiIg0QHbAwSosLAyXXXYZrrjiCiQnJ+Orr75CSkqKEmsDAHzyySfo2rWrYvORj0lPB6ZNA5YuBf7wB6CsTJl5S0qkQAYzHMgdlZVAba3y85rNzHIgCnSbNslv2fz668ziIyIiTZAdcFi5ciWSk5PRu3dv6O32B27atEmJdRFJGQ0vvQQcOAAsWiSlhbobcPjnP4FevYABA3ghRu6LiAB+/RXYtw946ill52aWA1Hgcrdg5L//DTz/vGLLISIikkt2JaEHH3wQffv2dQg2ECkqLU26ywsAhw4B+fnuz3nuHHDzzSwQScoZOhSYOBFISFB2XvssByIKLCYTcPy4/PHHj7PzEhERaQKjBaRN1kKRQUHKzvvqq/JTVIkaYzQCJ04oP69eL/0esDgvUWAxGKSA43PPuT62Rw9pLLP4iIhIAxhwIG2yZjf83mJVMTU10r57IiXpdEBYmPLzWixAYSHvVBIFok6dgOXLXR9XWSm1eiYiItIAt4tGEinOmt2g1yuTjZCYCMyfL30o7NVL2ndPpKSQEKBrV6mWg1LZCG++CVxzjfTBgXcqiQLP2rWuB91/+QXo0oWvGUREpBmaDzi8+OKLOHDgAAoKClBVVYWoqCh06tQJ11xzDcaPH4/Ro0dDx4Jq/sW+doMSqqtZrZvUZTIBp04pu/Xhk0+AyZNZMJIoEAkBTJ/u+rhTp4Bhw5RfDxERkUya31Lx9ddfY9++fSgvL0dtbS1Onz6NXbt24e2338bYsWMxYMAAZGRkeHuZpBRrdoNSH7L+9jfuZSX1GQzSncXoaOXmzMxkwUiiQJWaChw96toYnQ5YsoQ1X4iISFM0H3Bo27YthgwZgpEjR2Lo0KFo166dw5/v378f119/PVasWOH0nEajERUVFQ4P0giTCSgoUO6C6csvpX2wRGrr0QPYvRvYuRNYt05qZ+mu2bP54YEo0AghZTfJGcfONkREpDGaCzjodDpceeWVePvtt3HkyBGcOXMG27ZtQ3p6OrZu3YqSkhJkZmbi7rvvto2pq6vDM888g9TUVKfOsXjxYkRGRtoeCUq3syP5DAZg+3agd29l5isqYsE98pyEBCA5GRg7Fvj5Z/fnO3iQ/36JAk1qKnDkiLyx7GxDREQao7mAQ5cuXbBjxw4899xzSExMbPCYK664Av/5z3/wxhtv2L5nNpsxZcoU1NbWNnuOmTNnory83PYoLCxUbP2kgH//G/jtN/fmaNUKyMjgdgryjspKqXaIu2prgRYt3J+HiHyD3OwGK3a2ISIijdFcwMEVU6dOxRNPPGF7npeXh9WrVzc7zmAwICIiwuFBGmGxAAsXuj9Pjx7A0KFAfLz7cxG5SqkWmUajtD2DiAKD0eh67QZAeq9bt07a0sVAOxERaYhPBxwAYNasWQ7PN2zY4KWVkNvS06V2XlVV7s+1dy/3sZL3nDmj3Fzz5jE9mihQbNokrx10TQ0wcqS0pYuBdiIi0hCfDzh069YNXbp0sT0/ePCgF1dDsgkBzJwJHD+u3JwsuEfeEhqq3Fw7dzJ4RhQIhJDet+R49VVmNRARkSb5fMABAOLi4mxfn1HyziJ5Tlqa1AZQSWwrSN6ydauy8zF4RuT/TCZg3z55Y996i68RRESkSX4RcKiyS8Fv2bKlF1dCsrhzV6cprNZN3iCE9O8uKEi5ORk8I/J/wcHyiz0eOsRCkUREpEk+H3AwGo3Iy8uzPY+NjfXiakgWNbIbAFbrJu9IS5OKtpnNys2p0zF4RuTv7Ipgu4wdbYiISKOCvb0Ad3377bcOGQ7XXnutF1dDLlM6u6FNG2DDBiAkRHoeE8N9reQ51uwGvV5e4bem5i0okIJn/PdM5H/MZuDTT+WPN5mkdrzsukVERBrj0wGHU6dOYcaMGbbner0eEyZM8OKKyGVKZzf8/DPQv79y8xG5wmSSAgNKBhusXnuNwQYifzVxorxxBoNUMPLqqxlsICIiTdLUloqMjAxMmjQJv/32W7PH7tu3DyNGjEBBQYHte4888gj69u2r5hJJSda7wUrq3FnZ+YhcYTBI2yl27pQemZlAUpIycy9bxi0VRP7IbAY++UTeWKMRePJJYPBgZddERESkENkZDgsXLsTChQsv+X5tba3t62PHjiG0gfZwDz/8MN5///1Lvm80GrF8+XIsX74cl112GUaMGIGBAwciNjYW4eHhqKysRF5eHlJTU7Fu3TpY7O4iDho0CMuWLZP71yFvsN4NVoJOB/TpA4SHKzMfkVwJCdIDAFJTgZwcZebNzeWWCiJ/1MC1lEsqK4FWrZRZCxERkcJkBxzq6upgNBqbPa6hY+yDEo3Zs2cP9uzZ49Raxo8fjw8//BARTCf0LdZU0EcfdX8uIYADB6QtGmPGuD8fkbuUrueg0wGXXw68+SZw003uz0dE3mexAEuXujcHr32IiEjDNLWlomvXrrj33nsRFxfX7LF6vR6jRo3CqlWrsHr1arRr184DKyRFCQG89JJy87ENJmmJ0vUczp0DDh6Ufmc2bpS2aqSnKzM3EXlHZSVQUyN/fLBPl+IiIqIAoBNCm5/OTpw4gZycHBQUFKCsrAzV1dVo2bIl2rRpgx49euDKK69E69atFTlXRUUFIiMjUV5eziwJT0lPBx57DCgqUnbe2Fjg6FGmnZM2FBYCJSWO3/v3v4HXX3dv3l69gEOHpH3b27ZJ2Q9E5Jt+/RUYNkze2PbtpdeZAHnP4/UaEZHv0WxovGPHjujYsaO3l0FqEAKYOVO5YEOfPsDKldKHLrbBJC2xr+cASP/277/f/XkPHZL+u2MHtxER+br33pM/dscOvucREZGmaTbgQH5syRJlW2GeOSO1wuRFF2md0QgcOaLcfEFB0jai0aOZ5UDki8xm4LPP5I9nsUgiItI4TdVwoABgsbhfkdteeDiwdSuDDeQbNm8G6uqUm89svpjlQES+R+77YevWwDffSFsqiIiINIwBB/KsxYuBqipl5oqOBnbtArp3V2Y+IjVZu1YEBSk7rzXLQZvleIioMXI7VLz5ptSVacIE5ddERESkMAYcyHMsFmDRImXm0umArCwGG8h3pKVJ2Qhms7LzMsuByDdVVgLV1a6P++QToFMn5ddDRESkAgYcyHOUzG4QgntXyXdYsxv0Kr3ksiUske/JyJA3rrBQartLRETkAxhwIM9QMrvhxReBNWu4d5V8h8kEFBRIvwdqsFj4IYTIlwgBTJrk+ri775aKLrNuERER+Qh2qSDPWL9eueyGa68Fbr1VmbmIPMFgkLY9lJQ4ft9kAm6+GTh3zr3533wTuOMOfggh8hWpqcDRo66PW7cO+OorxZdDRESkFp0QzMGtqKhAZGQkysvLERER4e3l+B8hgKFDpbsy7v5z0+mAK68Etm1jG0DyD4WFwO7dwG23yZ+jRw/g0CH+ThD5AiGA3r2B3FzXx+r1wNmzQIBeq/B6jYjI93BLBanPmk6uRGxLCKaOk39JSJACaO7Iy5PumBKR9qWmygs2AFKGAz9oExGRD2HAgdRnTSfPzAR69nR/vr/9janj5D8sFuD1192fZ9o0Fo0k0johpN9VuWprlVsLERGRBzDgQJ6RkAD07w+cOOHePDod8MYb/GBF/qOyEqipcX+e/HzAaHR/HiJSjzvZDQAwdy7f/4iIyKcw4ECe06IFUFfn3hzcUkH+Jjxc2s/trro6YPNm9+chInW4m90A8P2PiIh8DgMO5Dnr17t3B3bzZmDnTml7BrdUkL8wmYCyMmXmmjWLdz+JtMpoBA4flj++dWsgI4Pvf0RE5FMYcCDPEAL44x/ljw8NldphJicD8fHKrYvI2wwGYMsWICrK/bl27gTS0tyfh4iUp9NJQQM5EhKAnByge3dl10RERKQyBhzIM1JTpbZ9chmN0l53In/Uo4fUGlOJoqrMciDSppAQoHNneWOLioD27ZVdDxERkQcw4EDqc3ffql4P/PQTW4GRf+vQATh3zv15fvuNe7yJtCgtDcjOlje2TRspQ4KIiMjHMOBA6nO3Knd0NDB4sHLrIdIigwFYutT9eYxGqUArEWmHEFL2kVzLlrF2AxER+SQGHEhd7mY3dO0KZGXxQov8nxDAu++6fxezthZYtEiZNRGRMkwmIC9P/ni2gyYiIh/FgAOpy92q3GVlQEyMcush0iqTCSgoUOZDxYIFgMXi/jxEpIyQEPeCiQUF3CpFREQ+KdjbCyA/9+OP7n3wiY6WLtSI/J3BILV8LSmRfmeGDZP/AcNkAs6fByIjlV0jEclTXS2/RktQEPDLL8z0IyIin8QMB1KPEMDEie7Ncfy4lCVBFAgSEqTWr716uX8389dflVkTEbnvtdfkj42Kkl4biIiIfBADDqSemhqguNi9OViZmwJReLiU3eOOJ57gnm8iLUhLA+bOdX1cu3bAunXArl3MbiAiIp/FgAOpZ+NG98YHBQEZGbzQosBTUyPVL3HHyZNASooy6yEieYSQH/w7dw4YMQKIj1d8WURERJ7CgAOpQwjgqafcm6NVK6aRUmBSqjjclCnMciDylvR0oH17oKhI3vioKGb4ERGRz2PAgdRRUwOcPu3eHN26sWAkBSaDQcrwcdfRo6yBQuQNQgAzZgClpfLnmDuXGX5EROTzGHAgdSxd6v4cp06xDRgFJp1Oql+ixDxE5HlpacDOne7N8fHHzFAiIiKfx4ADKc9iARYskD/+zTelC7UdO3h3hwKTwSAVilu3DvjoI2l7kRxmM7B5s7JrI6KmCQHMmuX+PIWFDLoTEZHPC/b2AsgPrVkjBR3kevJJIDRUufUQ+aKEhIs1TIYMAfr1kzfP7NnA6NHMdiDyFCWyG7ZsATp3ZtCdiIh8HjMcSFlCAM895/4cRHRRUhKwaZO8sXl5vEtK5ClKZTckJbE7BRER+QUGHEhZKSnAiRPuzZGersxaiPxJebm8cQYDi68SeYoS2Q0AoOflGRER+Qe+o5FyhACmTnVvDp1Oqv/ALAeii4QA5s2TN/bUKSkQSETqEkLawuSOoCBg61YgIkKZNREREXkZAw6knNRU4PBh9+YQQioWmZamzJqI/IHJ5N7v1sSJUoo2s4eI1GMyAQUF7s1hNgN9+iizHiIiIg1gwIGUodS+VUBKJX35ZWY5EFmFhABRUfLHFxcDBw4AL73E3ysitRgMQGYmsHYt0KGDvDmiolg0mYiI/AoDDqQMkwnIyVFmLouF7cCI7BmNwPHj7s/D7CEidSUkSNsiTp1ybVxICLBqFbB3LztTEBGRX2FbTFLGpk1ATY28sbGxwIoV0n+tYmJ40UVkpdMBkZFAaal78wQFSdlDbJNJpA4hpNbOrurdGxg/nr+XRETkd5jhQO4TAnj8cfnjjUZg5EggOfnig+3AiC4yGKRCcq1buzeP2cwsByK1pKdLWymKilwfe/Iks/qIiMgvMeBA7ktJkfaIyxUfz7Z9RM3p3h1o3979eaxZDqzlQKQcIYAZM4CSEnnjlyxhVh8REfklBhzIPUoUizx+nHd2iJpjNEq1TdzFLAci5aWlATt3yh+/aBGDgERE5JcYcCD3pKUBu3a5N0dCAjMciJqj0wFt2igzFzvBEClHicD7sWNSUJGIiMjPMOBA8gkBTJvm/jynTjHDgag5BgOQkQFER7s/FzvBECnH3ewGAAgPZ8FIIiLySww4kHypqcChQ+7NMX++1Lece1eJmtejB7B7t/Q707On/A8offsC27fz947IXUIAs2e7P88jj/D3kYiI/BIDDiSPEMD06e7Ps2YN0KmT+/MQBYqEBODMGSA3V/6WiAMHgJwcZddFFIjS0qQAoLuWL5cyj4iIiPwMAw4kj9EIHD7s/jxM6yZyjRBS/QW9my/frOFA5B6lshsA6X2wslKZuYiIiDSEAQeSZ/Nmqdq9XMHBUqX8HTuYRkrkCpMJKChw/27ojh1SKz4iksf6uyhXhw7AZ58BK1cCW7cCERHKrY2IiEgjGHAg1ylxV6euDiguBuLjlVkTUaAwGKRgwc6dUhHJli3lz7VwIdO4ieSy/i7KLeR66hTQrh3w4IPA4MHKro2IiEgjGHAg1y1Zosye1XnzmNJNJEdCApCcDAwZ4l7AoKoKWLxYuXURBZr9+4GyMvnjZ8/m+yAREfk1BhzINRYLsGCBMnMVFbF+A5E71q2T6qm445VXmOVAJMfGjcD48e7N8dtvfB8kIiK/xoADuWb9eqC62v15Nm9m/QYidwghBf/ktsa0qq6Wfq+JyHlCAC++6F4tI0DaXtiihTJrIiIi0iAGHMh5QgDz57v3ASckRCqOdf31rN9A5I60NClop0Q6Nrc3EbkmLQ3Ytcv9eWpqpEwlIiIiP8WAAzlPiQ84tbVA377KrYkoECnVGtMqM1P6/Sai5inZDlOnkzKVGPAjIiI/xYADOUepDzidOgHh4cqsiShQKdUa0x6L1xE5Jy1NmcLJgPQ7V1joU3UczBaBjMOlWLW7CBmHS2G28HWDiIgaF+ztBZCPsGY3uOvkSanIXWio+3MRBSprO76SEqm97MMPA2fPujfn4cPShx7WVSFqnH3w3d2A35w5wG23ATExPvF7Z7YIvPVDHj7ako9z1bW277dp2QKPD0vElBE9EKR3s6YMERH5HWY4UPOsF1juFqcDgKgoZeYhCnTW1phBQe4HG6zzhYS4Pw+RP7MG390NNuh0UrHWQYN8op5RSnYxrli4Ef9IP+QQbACAc9W1+Ef6IVyxcCNSsou9tEIiItIqBhyoeSYTcOyY++nWbdsCGRk+cSeHyCcouZfcx9K6iTxOyeC7D22lSMkuxqSVWThXVdvkceeqajFpZRaDDkRE5IABB2qewQBs2SK/9kKPHtJ+1927ge7dFV0aUUBLSwN27lRmLoOBGQ5ETbHWTnE3+P7mm9LvrQ+0hjZbBOatyXFpzLw1OazrQERENgw4kHO6dweio+WNzcsDzpzxibRRIp+hdKeKkyeBlBRl5iLyRz//LAXeb7tN/hx6PfDppz6zlWJ7fhmKy2tcGlNcXoPt+WUqrYiIiHwNAw7knNRUaVuFXKyAT6QsNTpVPPUUf0+JGiIE8NJLUgB9zRr581gsPrOVAgBOn3ct2ODuOCIi8j8MOFDzhACmTXNvjtxcn7nAIvIJ1k4VO3dKW5b69nV/zqIiZjkQNcS+U5PcoFxiovS76gNbKaxiwuV1lJI7joiI/A/bYlLzUlOlgIE74uO5P5xIaQkJ0sNoBEpLlZnzqaekO7DsJkMksS8W6U4G0IULQP/+PhNsAIAhidFo06pFswUj7bUK0WNIoswtmERE5HeY4UBNUyK7AZBqODDDgUgdBoN053TdOmn7kjvBvaIioIbp0EQ21uwGd4INH3wgZSP5ULDBylRrdun4sf1iEaRnwJKIiCQMOFDTjEbgyBH35ujZ06dSSIl8UkICMG4csGAB8Nxz7s2VlgYkJQHp6cqsjchXKdUK84EHfKJIZH1vfn8IVbWu1YmJa9NSpdUQEZEvYsCB1JebC+S41laLiGSyWIB//Uv++J49gYULgQMHpCJ5LCJJgUyJ7AYAqHV+S4JWpGQX45/f57k8TihYx5aIiHwfAw7UtM2bAbNr6ZSX0OulO0T84KJpZotAxuFSrNpdhIzDpeyj7qsqK93bEpGbK23PAKQPWmlpyqyLyNdYsxvc1aeP1E7Th5gtAjO+3SdrbOkFbskiIqKLWDSSGieEtB/cXfZtwLitQpNSsosxb02OQ7/1uMhQzBmfhLH947y4MnJZRASwZQtwxx3AqVPuzRUUJH3gGj2aRSQp8GzYAGRluT/PyZM+9/639UipS4Ui7f106IzCqyEiIl/GDAdqnMkk9RyXQ6eTsiN27pQerOGgWSnZxXh2ZZZDsAEATpbX4NmVWUjJLvbSyki2yy9XpmuF2cwsBwpMQgBz50q/Ay1auDdXly4+16Up47D814+TFUZmyBERkQ0DDtS4kBCpEJ0cvXsD110HJCdLDx8slhUIzBaBeWty0NClofV789bk8OLR1+h0QGSkMnNZsxy4JYoCibV2A+B+/YU9e3wuaHek5LzssQLA9vwy5RZDREQ+jQEHapzJBBQUyBt79izbYGpEU7UZtueXXZLZYE8AKC6v4cWjrzEYgIwMZbZBMMuBAo21doNeoUsknc6ngnZmi8DmQyVuzXH6POs4EBGRhDUcqHEhIUB0NFBe7vrYX37hFgoNaK42Q3rOSafmceXi0WwR2J5fhtPnaxATHoohidHsye4NnTtLv79KbK2wFn5lLQcKBPbZDUoQwqfqGP2aewYXTO61mogJD1VoNURE5OsYcKDGrV8P5OfLG9upk7JrIZdZazPUv6dWXF6DSSuzkJwQiaxC54JJ7VobkHG4tNkgQnMBDgYjPMhgkLpNJCa6PxcLv1KgsM9usLjxobtlS2DGDGDwYKBDByAmxid+d1KyizH1811uzREbYcCQxGiFVkRERL5OJ4T8HL/z589j165dyMrKQlZWFnbu3InffvsN5t/bKHbp0gVHjx51a4FFRUVYuXIlVq9ejaNHj+LMmTNo164dunbtittuuw0PPfQQOrn54baiogKRkZEoLy9HRESEW3P5DSGAK6+UX6G7vFyqlk8eZ7YIbD1cismfZ+Fctfu933UA2rcOwenKi1tkOoQb8MDQzujaLgwx4aG4oksU3v4xD8u+z210nmeuT8TqPcUOwYjYCAPuH3JxHgYgFFZSIn3QcUffvsBnn0kfmliLhfxdaiowdqz78+h0QFUVEOo7d/pTsosxaaX7XTmu79kOn04cqsCKLsXrNSIi3yM74NC7d2/k5uaiqeHuBhzee+89/OlPf8KFCxcaPaZ169Z47bXX8Mwzz8g+D9/AGuDORVdCAnDsGFOvvSAluxhzV+/HyQqjt5ciC1txquDTT4FHH5U/PiZGquXiA3dnidwiBDB0qJQZpES9hQ0blAleeICpzoKhi9JxVmYrTHvX9WyHzxhwICKi38muiHTo0KEmgw3umj9/Pp599lmHYEPPnj0xfPhwdO/e3fa9yspKTJo0CQsXLlRtLQHHmlIqV3ExYPTND7y+bP3eE5i0Mstngw0AW3Gq4pFHgFWrgNmz5Y3/6ScGGygwmExSsFypa5vp032iUGRKdjGuWqxMsAEAurZtpcg8RETkH2RnOOh+v3sdFhaGyy67DFdccQWSk5Px1VdfISUlBYD8DIdVq1bhjjvusD1PSkrCZ599huTkZNv3MjMz8cgjj+DAgQMO42677TaXz8eIeT3uppS2bQsUFfFDikLs6x5EtwzBwVPnUVBWBSEEWhmCcLrCiCpjHdIOnPb2UhWhAxAbGYpfXhzB7RVKsliAVq1cDwa2aiUFLG66SZ11EWnJZ59JQTolBAcD589reltFY7V+3HFg/li0DAlScMaLeL1GROR7ZBeNXLlyJZKTk9G7d2/o7VpHbdq0ya0F1dbW4k9/+pPteXx8PH755RdERUU5HHfllVfil19+wcCBA1FUVAQA+NOf/oRx48YhOJi1MGVzN7sBADZvZrBBIQ0VYfR31lacK346ggpjLQAdru7eFld1a8sAhDvWrZOXeVRVJRW/27GD26TIf6WnA1OnSgECpURFafp3xmwRmPHtPkWDDVd2aaNasIGIiHyT7C0VDz74IPr27esQbFDCl19+iby8PNvz119//ZJgg1V0dDRef/112/Pc3Fx8+eWXiq4n4CjRDiwhQZm1BLjVWccxaWVWQAUb7C1KOYi3fjyMt37Mw4MfbMMVCzdyq4VcQgDz58sfv3On9NpA5I+EAF56CTh4UMrOU0JSkvR7o+Hg+1s/5OKcQtsorB6+uqui8xERke9TNlqggK+//tr2dceOHXHnnXc2efyECRMQF3exwNx//vMf1dbm99zNbvj734Ht29mdwk1mi8Bd72zBtK/3eHspmnKuqhaTWN9BHpMJsAvkyjJ7tk/sRydymRKB9vrKytzvEKMis0Xgoy1HFZ83Jly720eIiMg7NBVwqK6uxsaNG23Px44d2+z2iODgYIy1qzeQlpaGmprAvCPsNpMJkNtVpHVrYPJkqec4yWK2CPxj42/oOWs9dhac8/ZyNGvemhyYLfzg65KQEKBzZ/fSuzMzmeVA/scaaA9SaBvAO+9ImQ07dmg6u2F7fpkibZPtxUVKrY2JiIjsaSrgcODAARjt9hgPGzbMqXH2x9XU1DgUkiQXGAzAokXyxnbpIn2ooWaZLQJb8s7gtdTf8FrqQWzJPYNVu4rQ5+UNWPZ9HvhZumnF5TXYnl/m7WX4FpMJOHXK/QyFWbOY5UD+xZrdYDYrM19pKZCcDMTHKzOfSk6fV/7GzJzxSayzQ0REl9BUdcX9+/c7PO/Zs6dT4+ofl5OTg0GDBim2roAhBPDqq/LG7t8vXbiNGaPsmvxMSnYxZny7z2Hf7Fs/HvbiinzTJxn5GJIYzYtbZxkM0oeqkhLp+YcfAm+/7fo81loO/D0nf2Cf3aBUwGHRIqkehML1rZR2pORC8we5ICxEj1FJsYrOSURE/kFT74j1W2h27tzZqXFdunRxeJ6fn6/UkgJLaiqQmyt/PPd4NykluxiTVmYpXqQrEKVkn8LAualYlp6LVbuLkHG4lNssmpOQIN15HTQI2LBB/jzMciB/oXR2AwDU1ACVlcrNpwKzReDTjKOKznnBZGHmGRERNUhTGQ4VFRUOz9u0aePUuMjISIfn55tpa2U0Gh22btQ/b0ASApg+3b05Cgqk1G0N71v1NLNFYOuRUvx8qAQf/MJAmJIumMz4R/oh2/OoVi3wyNVd0K19a8SEhzIDojFGI3DsmPzxv/3G33PyfdbsBr0esFiUm7d3byA8XLn5VPDWD7k4q0LgW41tGkRE5Ps0FXCorHdXoGXLlk6Nq39ccwGHxYsXY968ea4tzt+lpgKHDjV/XGPatAF+/ZUfQuw0tH2C1HO2qhbLvr/YiSE2IhRzb0vC2P5xTYwKQDqd9PtaWipvfE0NcPnlwJtvAjfdpOTKiDzHZJKC5EoGGwDg7FlNB+RSsovxj3Q3MhmbwA4VRETUEE1tqaitdfxg1lyHisaOqz9PfTNnzkR5ebntUVhY6NpC/Y0Q0nYIOeLjgXXrgH37gO7dlV2XD+P2Ce87WVHDNpoNMRiAXbuA5cvlja+rAw4elLrScGsF+SprXZPMTKmDizvCw4GPPwbWr5fm02iwwWwRmLt6f/MHyhAbYWCHCiIiapCmAg5hYWEOz51tb1n/uPrz1GcwGBAREeHwCGhpaVIxODmMRmDkSM1X5PYEs0Ug43Apvss6jj/9Z6+3l0O/++PXe1jfob6EBGD8ePfmOHRIyowi8lUJCVIh1YIC9+Z55x3g0UeBm2/W9Hvh9vwynKwwNn+gDHNv68ctbERE1CBNbalo3bq1w/Oqqiq0atWq2XFVVVUOz8M1vn9SU9zdx7ppk2bv5niK2SLw1g+5+GjLUcX7mpP7LpjMePP7XDw/qpe3l6ItShTKmz5dynbQ8YMG+SAhgCefdH+eZcuABx/U/O/ByfJqxeeMbBmMpXcN5NY1IiJqlKYyHNq3b+/wvLjYuVTo+se1a9dOsTX5PXf2scbHA337Kr8mH5KSXYwrFm7EP9JzGWzQsDe+z8XPh0qY6WAvPh744APgqqvkz8EsB/JlNTVAUZH781gLJmvcmUpl1/j8yJ7Ienk0gw1ERNQkTQUc+vTp4/D8mJOV1Osf1zfAPwS7xLqP9bnnXB9bVCRtqQhQ6/eyToOvsAB4+MPt6DcnBXNWZWPFz0fwXdZxttN84gkppdwd06ezlgP5po0b3Z8jMtJnCiafvaBcwOH5kT3x/Khe3EZBRETN0tSWin79+jk8z8rKwq233trsuKysLIfnSUlJiq7L73XqBHz0kevj9JqKV3nU+r0nMPnzXd5eBrmoptaCTzIcA5RxkaGYMz5Au1m42yITuJjlMHasMmsi8gQhgBdecG+OefOkoJ2G6zbYyzh8WpF5DMF6TB3ZU5G5iIjI/2nqE2NCQgK623U62Lx5s1Pj7I/r0aMH4n3kzV8zKiuBahl7O6OjNb9nVQ3r9xbjuc93gfd0/UNxeQ2eDdRuFjodoETR3GnTmOVAviU1FThyxL05/vc/KWDvA15Zl4Ndx5tuGe6sV+8eyMwGIiJymqYCDgAwYcIE29ebNm1CQTPVowsKChwCDvbjyUm//ur6mKQkqbOFD6SRKknKbMhq/kDyKQLAvDU5MNVZkHG4FKt2FwXGdguDAdi2zf2gw5EjAb29inyMEFKQzF0nTvhE7YbF63Pw/s/5isw1MD4Ct1/uG0EWIiLSBs0FHB5//HEEBQUBACwWCxYsWNDk8fPnz4fl94KHQUFBePzxx1Vfo18RAnjgAdfH5eRIjwCSks3MBn9WXF6DqxZ/j/vf34rpX+7G/e9vxbVLf/D/zIcePeQFHYl8VWoqkJvr3hz9+gGZmZoPupvqLPjXT8oEG0b2aYfVU65TZC4iIgocmgs49O3bF48++qjt+QcffIAPPvigwWOXL1+OFStW2J4/9thjlxSepGZs2ACcPStv7Msv+2UatdkikHG4FN/tKsL7Px3BqykHsGTDATz/JWs2+LuyekXVTgbKdovu3d2ryWI2Sy1yibROCGDWLPfn2b8fiI11fx6VfbwlX5Egef+41ljx2FAFZiIiokCjE0LeJ8aFCxdi4cKFl3y/trbWlnEAAIYGov8PP/ww3n///UbnPnPmDK666iocPnzY9r3bbrsN9913Hzp27IiioiJ88cUXWLt2re3Pe/TogYyMDFktMSsqKhAZGYny8nJEKLGf2VcIAbRvD5SWyhsfGwscPar5OzzOMlsE3vohFx9tOcoWl2SjAxAbGYpfXhzhv/uWjUagY0egrEz+HL16AQcPBmRdF/IhRiPQoQNQXu7+XF9+Cdx7r/vzqOjud7cg89g5t+eZNa4vnrq+m/sLclPAXq8REfkw2V0q6urqYHRiz25Dx9TWNv1hrl27dtiwYQPGjBmD/HwpFXD16tVYvXp1g8cnJiZiw4YNsoINAW3RIvnBhtdfB/7v//wm2JCSXYwZ3+5ji0u6hIC03WJ7fhmu7t7W28tRh8EApKcDycny52C3CvIFISFAaKgyAYe//x245x5NB9mKztYoMs+j13RVZB4iIgo8mttSYdWzZ0/s3bsX06ZNazSKHRkZiWnTpmHv3r3o0aOHh1fo4ywWYM4c+ePfe89nqnM3JyW7GM+uzGKwgZr0ya/52JJ7xn8LSl5+OZCY6N4c06f75TYr8iM1NcCpU8rMVVio6aKRZovAyQr3Aw63DohDSLBmLxeJiEjjZG+p8KSamhps3rwZR48eRWlpKdq2bYuuXbvihhtuaHDLhqsCMkVvwQLgr3+VPz44GDh/XrpT5MPMFoFrl/6A4nJl7gJpkV4HyP1srAMQHRaC2bf0RUx4KP74nz04VVHDwpkA4iJDMWd8Esb2j/P2UpRhNErdKtz5ABUUJLXZ9fHXBfJD6elSZ4p77wXmznVvrrZtgbVrgfh46aFRW/LO4MEPtrk9z7L7LtdMZ4qAvF4jIvJxPhFwUFvAvYFZLFJaqdksf4727aW7Oz6+pSLjcCnuf3+rt5ehCmvtgc1/vhE7j51Fes5JrNhy1KXxAPDuQ8m2D9XWbBAAAR90aOjn49PMZimQ6K61a4FbbnF/HiKlCAEMHQrs2CEFxdx570tKAlJSgIQE5dangpTsYjy3MguW5g9t1r8nDsWwntrYshpw12tERH6AOXKBaM0a+RdcM2cCO3cCWVk+H2wAgNPn/TOzwfpheM74JIQE63F197Z4eXw/vPdQMuIiHe8+x0WG4pnrEy/5fmxk6CUfpsf2j8O7DyUjtt6xUa1aoE2rFqr8XbTKGnCZtybHP7ZXyK3nUt+ddwIbNyozF5ES0tKkYAPgXrABAE6cAGJi3F+TilKyizFJoWADgItvKERERDIwwwEBFjF3tzPFoEFSwEHDRbKaY7YIbM8vw+nzNThdYcQr6w94e0my3TowDjHhBvxv9wmHlo5Npfvb//1jwkMxJDEaQXpdo99vSEPHAsD2/DKk55zEd7uLUHbhYk2MuMhQ3HZZHJYr1A9ea7546irfLyhpNEqdZ86dc3+unj2B337z6dcJ8hNCAEOGAJmZysw3YACwZ49m/22bLQLXLP4ep843X9TbWdxSQURE7lAgf5Z8yoYN7t3JLC6W9nj7aHZDSnYx5q7OUaSQlje1NgTh1bsGYtzAjgCAWbckOR0sCNLrGvxw3Nj3XZnj6u5tcXX3tnipkfUM6hyFeWtyHGpmhIUE4YLJzbuOXuYXmTIGA7B3L3DkCHDDDe7NlZvLjhWkDWlpygUbAKCkRNPvgW/9kKdosAEAYsJZk4WIiORjwCGQCCFVkZdj6VLgppukVFKNXmg1x5pm6otmjeuL8upaAAJXd2uHq7q3dQgouBIs8ITG1jO2fxxGJcVeEoxIzS7G7FXZDlkRvqRdmG/+TlwiIQHIyVFmrmnTmOVA3iUEMGuW+/MsWQKMGiV9reH3wJTsYvwj/ZCic8ZFXsxgIyIikoNbKhBAKXo1NUB4OFBX5/rYyy+X6jb46IcHs0Vg4NxUn7yTHhcZil9eHNFoxoK/sG7TOHG2CnPX5uB8jYx/p17SKiQIr99zme8XjxQCGDxY2jalhA0bmOVA3qNUlk1cHFBUpOn3PzU6LumgvaK4AXO9RkTkR1g0MpDodEDLlvLGWrdS+Khf885oPthQ/1JW9/tjzvgkvw82ABezIu66MgF/u3ugT9UpqzKZMWllFlKyi729FPekpSkXbACkLAfGtMkbhACmTlVmruJiKWCvYdvzyxQNNrRp1UJzwQYiIvJNDDgEkpAQ+QGH117TbBppU8wWgYzDpZi7er+3l9KkicO6XtL5oaEuEYHC2g2jfucMrZu7er/vdqwQApg9W9k5rbUciDzNaATy8pSbr1bb273Sc04qOt/b9wfmew8RESmPNRwCSUoKcPq0vLHLlgEPPqjplNL61u4+gZn/2+cTqfk3JcU2WmgxUDVU7+HsBSNe+l82zlVp8+L/ZIURy9IP4Q+je3t7Ka4zmYCCAuXnnT0bGDPGp147yA/8+KMy2TU6HZCeDmg4fT8luxgrthxVZC4dpGD3VRqqCURERL6NAYdAIQTw1FPyxxcWaroyd31PfrId6QdKvL0Mp1iLcmmt8KMWNPQzGdM/DlsPlyLjyBkAOuh1wIdbjqDSqFjXebe88UMeDpyswPuPDPb2UlxjMEjV/Pftk+4MFxQAH3/sXlcbQNr77kOvHeQHhJC287hr4UJg9GiprolGmS0Cc1ZlKzpnoGzjIyIiz2DRSARIEaLqaqBVK3lje/YEvv9eqmDvA578ZAfSD8jM5PAwLRbl8kWmOgsGLUjDBaN26nQ8dV0iZt2S5O1lyGc0Al26AKdOuTfPp58CixcDb7whdbohUltKCnDzze7NodMBVVVAqLa3dS1Lz1WsM0V0WAssunOApt+PAuJ6jYjIz7CGQ6BIT5c/9vx5qRWYD1i7+4Qmgw2RLYMRZghy+F5cANdoUFpIsB5PX9fN28tw8MHP+TDVSVkX1loiq3YXIeNwqW/UeTAYgF9+ASIj3Ztn2jTgwAHgpZdYQJLUp1R2g177l0dKt8F8+dZ+fD8iIiLFcUtFIBACmDdP3tgtW4DOnX0iHdpUZ8Gfv9nj7WU46N8pArPGJdn6mLNGg3qmjOiJf/10RDPdSASAmd/sxah+HTBvTY5DBfm4yFDMGZ+k/Yv7Hj2kvfDJyfLnOHdO+u+OHVIXjDFjFFkaUYNSU6Vipe6KjtZ03RGzRWDemhxF54yN0HY2BxER+SZuqUAApOgZjUCHDkB5uetjz51z/w6nB6RkF+Ol77JRdkEbrTtbtgjCq3cPxPjLOnp7KQFFyfRiNVk/xvhMhsvq1cCrr0oBSLmCgqTAxbZtmv4gRz5MCKB3b2UCDp99Bjz0kPvzqCTjcCnuf3+rYvPFRYbilxdHaD4I7vfXa0REfkj7OYPkvpAQKUvBVXq9T2Q2rN9bjEkrszQTbJh8Y3dkzxvDYIMXTBnRA21atfD2MppljfLOW5PjG9srbr1VKijpDrP5YpYDkRqMRuDIEWXm+uc/Nb0FKHV/saLzsVAkERGphQGHQJCWJlWed5XG70KaLQL/2PgbJn+e5e2lOLi2R3teuHlJkF6HJRMGeHsZThEAistrsD2/zNtLad7589KHOSXMnq3pD3Lko9LTpQLHZoW2VO3cqdng2Pq9xfgk45gic+l0wDsP+EimFRER+SQGHPydENIFvhxhYZoNOqRkF+OKhRux7Ps8aOmjS3RYC1u9BvKOsf3j8N5DyYiLdNyPrNUY0OnzUm0HTReWNBiUK6KXmanZD3LkozZulLJwjh9Xbk6dDnj5Zc0Fx1Kyi/Hc51mKLevt+wdh3EAGG4iISD0sGunvTCYgL0/e2MREaTuGxqRkF+PZlVmaCjRYLby9P7MbNGBs/ziMSop1KNJ59oIRkz/fpbl/NzHhoUjJLtZ2YUmdDoiKAkpLlZlv9mxg9GjNBjTJhwgBTJmiXAaO/bwFBdJ7qEa2FpotAjO+lZGt2Ihl912OcQO59Y+IiNTFDAd/J7d+AwCcPCldbGmItTK31j40AsAz1yfy4k1DgvQ6XN29LW6/vBOu7t4W4wZ2xLsPJWuqEntcpBQIeXZllkOwAQBOltfg2ZVZSMlWdq+2LAaDlGKu1AevAweApCT32vUSAVJXikMqFIpt21YqkqqRYAMAvPl9Ls5V1SoyV4RBh9sv76TIXERERE1hwMHfpaUBe/e6Pq5NG81dbAFSW8n6H8y8rW1YCN55IBkzxyV5eynUjLH947Blxgi8cFNPby8FAFBebcJL/8tuMICmucKSe/cqdxf5wgXg4EHgpZc0l7JOPkQIYPp05eaLjAQyMqTg2u7dQPfuys3tppTsYvzzewW6b/yuwihgqrMoNh8REVFjuKXCnwkh7UGVY8sWTV1sWR0vu+DV84/rH4tRSR1QdsGE6NYGxEaEYkhiNLdR+JAgvQ7Tb+qF3rHhmPHtPsXuGMpRZbKgytT4Rb99Ycmru7f13MIuWYgAFixQfl5r14oxY5Sfm/yfktkNOp1UX6RHD2XmU5DSWymsPss4ionXdVN8XiIiInsMOPgzk0nag+qqPn2Avn2VX4+bUrKL8dfV+726hoev7urdD36kGGudh62HS5Fx5AwAHYL1OkXvIirFWljSa0wm4JgyVfEdWAvzsZ4DuUrp7Aa9HoiPV24+Bb31g3JbKewdK6tSfE4iIqL6GHDwZwYD8OuvrmcqnD2rqUJZgHYKRXr9gx8pKkivw7Ce7TCsZzvb9/rEhWPqv7NQ6+1/bHZiwr1cd8JgkLKehg4FyhRs4ykEsxxIHqVrN0RFaTLoZbYIvL3psCpzd4lupcq8RERE9ljDwd89+aRrx8fGSh8ANBRsMFsE5q7e7/VgA6CBD36kurH943DwlXHoEt3S20uBDlJhSU20Wu3RQ9rXvn27svPq9ZpsP0ga5k6754a0bQtkZWnqfc9q+hdZqtRa0OukjD0iIiK1MeDgz+rqgB9/dG1MSQnQvr0665FpWfohnKxQuOWZizT1wY9UF6TXYcldl3l1DdZ7rXPGJ2mnRkhCAtC1q7JzWiwXsxyInGEyAYWFys1XWgq0a9f8cR62dvcJrN13UpW5n7ouESHBvAQkIiL18d3GX6WnS9kKrtJIWqnZIpBxuBS3vvkz3vghz6tr0eQHP1LdkMRor7bQjI0MxbsPJWNs/zivraFB7dsDq1YBc+YALVooN++0acxyIOcYDMDf/67snBpr0ZqSXYwpX+5SfF69TmrhzK5KRETkKToheIVXUVGByMhIlJeXIyIiwtvLcZ8QwJAhUsVtVyxYADz2mNcLZ6VkF2PemhzNtL+MiwzFnPFJ2vvgR6pLyS7GpJVZHj/v4M5t8O+nr9b2HUghpKDm6dPKzKfXS+0yQ7ltiepJT5cCUm+8Adx0k/Rvb/BgqX2lUq68UtoupJGA+7VLf1D0PfCGXu1wXc/2ePjqrtp+XWmG312vEREFAAYc4IdvYKmpwNixro/TwAWXt4tD6gB0iDDg7/dcjjOVRsSEs+1loEvJLsYfv96DCyazR88brNfhrQcGaTfQVV0NtFK46NyGDfJeu8h/CSEVK92xQwoybNsmbb9R+t9Jhw5SJxYN1HHIOFyK+9/fqth8E4d1xcvj+yk2nzf53fUaEVEA8N0wNzVMCOlOkBx5edLeWC8wWwS25J7Bi9/s82qwAQDm3tYPw3q0w+2Xd8LV3dsy2BDgxvaPw965YzCufwePnrfOIjBpZRZSsos9el6n1Srfpg/Tp3NbBTlKS5OCDYD038WLgVmzlD1H9+6aKpZ84ly1ovPdlCRjeyUREZFCGHDwN+60CouPB0JClF2PE1Kyi3Ht0h/w4IptKK9W4UOMkzS7Z568LkivwzsPXYmnruvq8XPPXb0fZosGP4S3bq38B7RDh6QPlElJmttTT14ghNTBRG93qTJ/vrJbKQBpK09MjLJzymCtXbR8s3J1i2IjDCx2TEREXhXs7QWQgoSQ7hDKVVIiZTh48C6Pt7dQAMATw7piVFIst05Qs2bd0g8D46Mw9Qvli7k15mSFEdvzy3B197YeO6dT1q0DjCp0j5k/X5r3pZeAkSM1saeevMQ+u8FKqX9zPXoAX34p/fuKifF6doNatYvm3taP72tERORVDDj4E7nZDQYD8PXXQHKyRy+6zBaBeWtyvBZsaNOqBZZMGMCMBnLJ+Ms6okWQzqOFTdP2F2sr4CCEVGRWp1N+C4T1A6W1VeaYMcrOT77BPrvBYlF+/vPngf79vR5oANQLvN+dHM/3NyIi8jpuqfAX7mQ3REYC48d7vDvF9vwyr3WimDWuD3bOHsWLMZJlbP84/PLiCPx74lC0CglS/Xwf/XoM6/eeUP08TjOZgIICdest6PXSB07WdAhM1uwGpYMNISHA2rVSFycNBBvUDLwP69lOhVmJiIhcw4CDvzAagSNH5I09fRqo8fwH/405Jz1+TquYiFCmmZJbgvQ6DOvZDq/fcxk88S9p8ue7sH6vRgpIGgzSh8HMTKBvX3XOYbFI51iyRJ35Sbus2Q1qbKeprQWuu87r7Z+t1Ay8x0awxSwREXkfAw7+YvNmoK5O/viNG5VbixPMFoH/7fbeHduYcF6IkTLG9o/Duw8lo02rFqqeRwB47nMNda1ISJBS0otVXs/Cheqk1JN2qZlB07s3EB6u/LwynT6vTrAhOqwFi0USEZEmMODgDxqq5O2qefM8mrq8Pb8MZRc834JTByAuMpQXYqSosf3jsHP2KFzvgRTmuav3Y0vuGazaXYSMw6Xe7WAREgJ07qxuYceqKqlzBQUOgwHYtk2drklnz3qt/XND1Ap+33l5J2bxERGRJrBopD+w3g1y5y7g8eMe7VCh1l2dplgvveaMT+KFGCkuSK/Dncnx+Cn3jKrnOVlhxIMrttmex0WGYs74JO/UIzGZgFOn1A9WLloEzJzpXlCVfMvevcoHBqKigC1bNFG7weqKLlGqzHtTUqwq8xIREbmKV2/+wHo3SO7FeEQE8OuvHr0I88aWhtjIULz7UDILRZJqvLFn+mR5DZ5d6aWtFtZaDhkZQLSKWUNVVVIbTgoMQgCTJik/79//DnTvrvy8MpgtAhmHSzH9S+Vb7DKLj4iItIQZDv5izx55GQ4hIUBWlscvwjyZ4TDlxu4Y1qM9hiRGM7OBVDUkMRpxkaEe7b4iIGXvzFuTg1FJsZ7/N56QID127wZKSqQCttdco/x5FiwAbr1V3e0bpA2LFwMnVKjx8847wGOPef3fUEp2Meau3o+TFUZV5mcWHxERaQkzHPyBEMAf/yhv7HvveTzY8Mq6/Zj+5W6Pna9nh3Bc3b0tL8BIdUF6HeaMT/JI1wp7AkBxeQ2255d5+Mx2EhKA5GTg8svVmX/HDqlVIvk3iwWYP1+duQsLvV6/ISW7GJNWZqkWbHjhpl7M4iMiIk1hwMEfpKYCeXnyxr7zjkeLRb6ybj/e//mox84HsCMFeZa1a4U3tld4ozbKJb7/Xr25X37Zo69X5AWLF0tZMkr79lupjasX6zeYLQIzvt2n2vxRrYIxZUQP1eYnIiKSgwEHXycEMGuW/PGHD3vsjs+aPSc8GmxgRwrylrH947Blxgi8cFNPj5736Jkqj57vEkJId6fVSlnPygI2bFBnbvI+i0XqmKSGa68F4uPVmdtJv+aewbmqWtXmf+WOAczkIyIizWHAwdeZTFLQQK7OndVpPVZPSnYxpn6hfHGsxrAjBXlbkF6H6Tf18mjQ4R/ph7AsPdd7rTKtHXPUykIwm4G5c5nl4K/WrgVqFf5AHhoKfPMN0L69svO6KCW7GM+szFRt/meuT8S4gR1Vm5+IiEguBhx8XUiItHdarlOnVM9wMFsE5q7OUfUc9bEjBWnFlBE9ER2mflDP6h/phzBsyffe7Vqxc6f0+Ne/lM92YC0H/5CeDiQlSf8FpCDSc88pew6dDujdG7jzTmXndZG1bkNVrRutqxsR2kKPdx5IxsxxSYrPTUREpASdELxVVFFRgcjISJSXlyMiIsLby3GN0Si1o6uSkUq9eTPQrZvqaaZT/p2JtftOqXoOq4nDuuKmpFh2pCBNWb+3GM99nuXRc+oA7wfdamqA1q2lzASl6HRSYGP1amDUKOXmJc8RAhg6VAoeDR4stXVOSQHGjVP+XLGxwNGjXqvdYLYIXLFwoypbKeIiDPhlxsiAeq/z6es1IqIAxQwHX9eihXRR76o+fYDrrlM92PDkJ9s9EmyIiwzFew8l4+Xx/diRgjRn3MA4PHN9osfPO29Njve2VwBScCAyUtk5hZBe8x5/3PEOOfmOtDQp2ABI/01NBSZOVP48SUnA9u1eLRS59XCpKsGG0GB9wAUbiIjINwV7ewHkpjVrpEJbrjp7VtpKoeKF2IK1OUg/UKLa/H1jwzHphu6ICQ9lRgNp3sxxSbgsvg1e+HoPjHXKp1bXZ98q8+rubVU/X4MMBunu9ZVXAuXlys5dVCQ9XnoJGDlSvUKVpCwhpG4jQUFS5oteD9x+uzpb+8rKgJgY5ed1QcaRM6rM+/o9l/E9j4iIfAIzHHyZO3tet21TNdiwfu8JrPglX7X5dQBWTbkWt1/eiRkN5DPGDeyInPljMX1kTxiCPPNv1uutMnv0APbulbZ+qYE1HXyLNbvBus3GYlE+2NCxo9QCc8cOr2Y3SJT/Pb91YBwLRBIRkc9gwMGXpaQAJ07IG9uqlbJrsWO2CPz5m72qzQ8AT1+fiJBg/vMl3xOk1+GFUb2Qs+BmPD9S/Q4WMeGhqp+jWe3bS3eb1XLHHcDGjerNT8qwz25Q0+nTQL9+Xm+DCQBKx8KDdMCy+wYpOykREZGK+InNVwkBPPmkvLH/+Y+qLcJ+zTuDC0YFi8TVM6JPe1bkJp8XpNfh+VG9MLafeinfUS2DMSRRpcwCV7z+urrz19QAU6awXabW1c9uUEtdnVQU2cvW7j6BN37IU3TOOwZ1ZEYfERH5FAYcfJU72Q16Zf63my0CGYdLsWp3ETIOl8JsEVi/9wQe+3i7IvM35qnruqs6P5En9YhRr9L62eo6vJpyQLX5nWKxAIsWqX+eQ4ek4oOkTdbsBoXef5qk00nn8mIA6pV1OZjy5S7F51084TLF5yQiIlITi0b6IiGAqVPljdXpgCVLpL7kbhRZS8kuxrw1OSguv7g/vFVIEKpM6t65iosM1cYdWyKFXN29Ld76Udm7oPaW/yTVUvFaVtD69fLa9soxfTpw8CALSGqRyQQUFMgrcuwqIYDCQtULIzfmlXX78f7PRxWf98lh3EpIRES+h+9cvsholPqKy2F/ISZTSnYxnl2Z5RBsAKB6sAEA5oxPYjop+ZWrurVFm1YtVD3H+z/nw+SBzhiXEAKYP99zAQBmOWiXwSC1qOzcWb1z/PWvwM6d0sNLBSPX7j6hSrChW/tWmD2eWwmJiMj3MODgi3Q6ICpK3ti//92tCzGzRWDemhx4OlG1TasWeO+hZIztH+fhMxOpK0ivw5IJA1Q9h0UAn2UcVfUcDbLe1fZkavvEiazloFU5OdK/B7WsWQMMGgQkJ3ulYOT6vcWqbKMwBOuw8YUbFJ+XiIjIE7ilwhcZDNIdnGuvlbIVXPHvfwMvvCD71Nvzyy7JbFBTyxZ6TBreA1NG9GBmA/mtsf3j8N5DyZi7OgcnK9T5/Xr7920b0WEhiI1siSGJ0er/ThkMUoCzpMTx+xaL9PplNCp/zhMnpBo3N9+s/NwknxDAtGnqnuPIEa9to0jJLsZzn2epMvc/7hnE9z8iIvJZDDj4qpUrXQ82AEBRkVsXZKfPey7YAADP39QLzwxnkUjyf2P7x2FUUize+iEX/0jPVXz+sqpaLFh3sYBkXGQo5oxPUj9rKCFBethbu1adYIPVU09Jr4+s5aAdRiNw+LC65+jaFQgJUfccDTBbBGZ8s0+VuSde2xXjBjKzj4iIfBe3VPgii0XaF+2qbt3c3tcaEx4qe6wcFTW1Hj0fkTcF6XWYflMvPDGsq+rnKi6vwbMrs5CSXaz6uRx4oq5DUZHUKpO0Y9Mm9QtGnjzpVn0iuaZ+vhPnqpV/r7qpb3u8fGs/xeclIiLyJAYcfNGiRfLuDlZVATExbp367AXPXszpwDuUFHhGJcV65DwCwLw1OTBbPFjzwFN1Hbp0AdLT1T0HOUcI4Mkn1T1HdDSwZYvHt1M89ekOrM8+pfi8E6/pig8eHaL4vERERJ7GgIOvsViAhQtdG2MwABkZbmc3mC0CC9blyB4vx9Xd23r0fERaMCQxGnGRoR4JtxWX12B7fpkHzvQ7a12HnTuBzEwgTqV08ZISYPJkFpD0tvR0IClJyjpRQ+vWwKpVwJ49QHfPbr9bu7sIG3NOKz5vt3at8PJtzGwgIiL/wICDr5GT3SAEMGSI21W7PV0wMqpVC1zVjQEHCjxBeh3m/N4CzxNBB0/XZkFCgtRJoF+/SwtKKsnaJtP6oZcZD54lBPDSS8DBg+rMr9cDu3YBt93m8a4UZovAn77Zq8rc88b3V2VeIiIib2DAwZdYLFLAwVUmE1BZ6fbpN+acdHsOVyyeMICVuSlgje0fh3cfSkZspPp1U/JLLqh+jgbpdEBkpLrneOgh4NZbgQMHpA+/zHjwnCVLpGwWtYSHX1qQ1EPe/D4XNbXK16Roodfhmp7tFJ+XiIjIWxhw8CXr1gHV1a6P69NHujBzg9ki8L/dJ9yaw1lRrVrgvYeS1a+eT6RxY/vH4ZcXR+DlW/qqep5/fp+L9Xs9XDwSkLZX7NoF/O9/QMuW6pyjtPRiVtiOHdKHYGY7qE/O9j9X6PVe60qxfu8JLPte+U4yADAqKYaBdiIi8isMOPgKIYAFC+SNPXvW7crd2/PLUKZywcgwQxA+e2IIMmePYrCB6HdBeh3ahatfCG/y51lYv9czQUUHCQlSSnzPnp4538KFzHZQWkNbViZOlAoVq8Vikeo2pKWpd44GpGQX47nPd0GtfzkPDu2q0sxERETewYCDrzCZgGPHXB/35ZdSYTY3K3ene2A7xd//7zJc16s97+4Q1eOJdrQCwHOf7/J8m0xA+tC4V5398JewfgjescPjH1b9krVOg30Qx2wGPv5Y/XPr9cDLL3sscGS2CMxQqW4DABiC9biKhZKJiMjPMODgKwwG4JdfXO9dv2GD28W0zBaB73arVGEcQKsWem6hIGqCtWuFJ8xdvd+zbTKFAGbP9tz5rIKCPPph1W+lpV2s02AN4tx8s2fObbEAhYVuZ/A5w2wRePG/e3Cuuk61c0wa3o0BdyIi8jsMOPiShATXAw6ffirdbXKDtJ2i1q05mvL+o4MZbCBqgn3XCrWdrDDi4y35ngs6pKVJWVieZjYzy8FdQkhBm6Ag6bleD4wfD2zcqM755s+X2qnaP9xs9+yMlOxiDFvyPf6bpV7gPVivw7SRvVSbn4iIyFsYcPAlOp3rF1ZCAGVlbp32ZIW6LfPOVLrY5pMoAI3tH4d3HkiGJ26ALlh3ANcu/UH97RXWD6x6L70V6fXAHXeo9wHZnzRUp8Ga3WANalssQK16wWmsXAkMGiS1VLU+VG6HmZJdjGdXZuFkhbrvU3dc3onZDURE5JcYcPAlLVpcrLburG7dgHbutdg6c17dCy1P7E8n8gfjBsbhrfsHeeRcJ8tr8OzKLHWDDiYTUFAgfVD1BosFqKkBpkzh1oqmNFSnwRvBoiNHXH8PdIPZIjBvTY5qBSKtdDpg0YQBKp+FiIjIOxhw8CVr1rh+YX7hgtv7W3ccLXVrfGN0AOIiQzEkMVqV+Yn80biBHfHeQ8lo07KFquexfsiatyZHve0VBoN0h9w+RX7dOqngYESEOudsyKFDQGqq4/cauqMfqBqq0/Dkk9LXngwWRUW5vq3QDdvzy1Bcrm6GHwA8fV0iQoJ5OUZERP4p2NsLICcJATz1lGtjXnxRunPnxv7W9XuLsTHntOzxjbFeMs4Zn8Q0UiIXje0fh3BDCzy4Ypuq5xEAistrsD2/DFerVT0/IUF61HfjjcDYsdJddU+YOlUKPOh0l97RHznSox90NcW+ToPZLP131iwgK8uz62jbFsjIUL1eg73T59UPNjx1XSJmjvNMfRYiIiJv0GRI/ejRo9DpdC4/YmNjvb109aSkACUlro3573+BTp3knzK7GM99nqVKOmlsZCjeZWcKItmu6t4WcZGh8MTH4I0eaIt7iQ4d3K4/45K8vItZDg3d0Q9US5Y41mkwm6VMFE9sQeneXSoounMnsHu39NyD2oWpG9x4677LMesWBhuIiMi/aTLgQPUIATz3nOvj8vNl73c1WwRmfLtP1tjmvHxLX/zy4ggGG4jc4MnOFR9uOap+Acn6rNst3nzTc+e87TbpA/Ydd1ysTRDI7TMtFmDhQu+d//Bh4MwZjxSHrK/aZMbSFHWya1r+3gr61svl3xAgIiLyFT6xpeL6669Hy5Ytmz0uOtpPawEYjcDRo66Pc2O/69YjpThXpU618XbhBm6jIFLA2P5xePehZLz4zV6UV9epeq55a3IwKinWs7+78fFSPQdPqa0FZs50/J59+8wxYzy3Fi1YvBioqvLe+fV6KdgzerRHt7RM/HgHvj+o/FZCqw8eHYxhPdwr5kxEROQrfCLg8Mknn6Br167eXob3yLmzFh8P/Pqr7P2uL3y1S9Y4Z7ArBZFyxvaPQ3hoCzz4gbr1HFSv5dAQkwk4ftxz52uMNcvBwx98vcpiARYt8v4aPBzsGf63H3CstFq1+eMiQ3FVNw/+DhEREXkZt1T4AjlV0o8fB3JyZJ1uwdr9OH3evc4WDWFXCiJ1XNVNquegtuJz6n0Qa5DBAPztb549Z0OsWQ5dugRO14p167yb3WBlzXLwwJaWeWv2qRpsAFgomYiIAg8DDlonBPCHP8gbK+MizVRnwYpfjso7XxPYlYJIPdZ6DjpA1SKSL36zB8vSc7FqdxEyDpeq1y7TSghg2TJ1z+GKwkJpy4W/13MQAvjTn7y9ConFIv3c3Wzv3JxqkxkfbSlQ9RzvPDCItYuIiCjg+MSWioBmNEqFs+QoKJAu0lzYVvHJr0flnasZsZGhmDM+iRdbRCqx1nOYtyYHxeXqtPOrtQD/SD9kex6n9u+1VrZU2MvMlApL1q/14E9SU6UWoZ6WmAjMny+1wOzQ4eL3Y2JUbYe5fm8xnv96t2rzA8A7DyRj3EC+/xERUeBhwEHrfvhB3t20oCDgl19cvkhbs6fI9XM1YvIN3dErNhwx4dI2CmY2EKlrbP84jEqKxfb8MmzJK8FbP8oMVjqpuLwGz67MUq/FrbVThbUl8Jw5wNq1yp+nOcHBQJ1dUc6FC4EXX7zYycJfpKcD06YBp9UrmNik/HygfXuP1WswWwSmfZGFdfvUa/tqCNJh2f3MbCAiosDlZ1dLfkYIYOpUeWOjo4GEBJeGPPXpDuwtqpB3vgZc27M9br+8E67u3pbBBiIPCdLrcHX3tujZIdwj5xOQOliotr0iIUFqi3jZZVJdAW+oq9cBpKpK6uBQX3o6kJSk3ToP9ddn/3zjRmD8eODAAaC01Dvr82AL0pTsYvSfk6JqsAEA/m9wAoMNREQU0Hwi4PDiiy9i4MCBaNOmDUJCQtChQwckJydjypQpSE1NhfDX/bSpqcCRI66N0emk7hRZWS5lN7yyLgcbc5S7qxUd1oLFIYm8yJPdYKwdLFS1apW2aicsWiTVF7ASApg8WfrAPnmyttYKXLo+iwV46SXp+cyZ0vdq1NmK4zT7FqQqSskuxqSVWaiutTR/sJsS24apfg4iIiIt84mAw9dff419+/ahvLwctbW1OH36NHbt2oW3334bY8eOxYABA5CRkeHtZSpLbnaDEEBFhdQW00mmOgve/znf9XM1YeHt/ZnVQORFQxKjERcZqmoRSXup+4vVm1wIqW6CllpS1s9ysK97cOiQ9FxL6q9v0SLpwz0g1aXIzfXe2uyp3JXCbBGYu3q/KnM35OGru3rsXERERFrkEwGHtm3bYsiQIRg5ciSGDh2Kdu3aOfz5/v37cf3112PFihVOzWc0GlFRUeHw0JzUVCAvT97Y2bNduli7fJ6yF8bPXJ+IcQM7KjonEbnG2rkCULdzhdW3WUXqbaswmaQiuFrLGpg9W6rvsHQpMH26459Nny5tU/DUFoumtnMIIdVmsLdwobYCOFYqd6XYnl+GkxVGVeau76nruiIk2Ccus4iIiFSjyXdCnU6HK6+8Em+//TaOHDmCM2fOYNu2bUhPT8fWrVtRUlKCzMxM3H333bYxdXV1eOaZZ5DqxF2lxYsXIzIy0vZIcLHWgeqEkFJd5XLhYu2bncdRpWBa6fMje2LmuCTF5iMi+aydK2Ij1d9eUVFTp962CmvxyJ07pS1jQUHqnEcOs1l6va7f1eHQoYtbGF56Sd1gifU9o7FzpaZemsFgNHo/gBMaevH/q/1jxw7VulKcPu+ZbSOjkmIw65Z+HjkXERGRlumEjxdAePPNNzHN7s5Njx49kJOTgxYtWjQ6xmg0wmi8eIejoqICCQkJKC8vR0REhKrrdYrRKG2JOHNG3vjcXKBHj0b/2GwR2J5fhuNlF/Dit/ug1E3JyNBgZP11NLdSEGmM9Xf+ZEUN/vL1btSq9Kp/Y+/2+OjxIepMbuXu66O3pKQo230hPR2YOFH6etIkxyC1/bmEAHr31s6WifpWr5aKVXpIxuFS3P/+VlXPMfXG7vjjmD6qniNQVVRUIDIyUjvXa0RE1CxNZji4YurUqXjiiSdsz/Py8rB69eomxxgMBkRERDg8NMVgADIygJAQeeM7dWr0j1Kyi3Ht0h9w//tb8edvlAs2AMAT1yYy2ECkQdbOFXcO6oRrerZrfoBMP/5WAlOdyoX4DAapKG79u+Jbtqh7Xnfo9dKWhi5dpEdzWyya63YhhFTosaBAesyf7/jn9tvqGspu0JIFCzyWaWG2CGTkqRuoigwNwvOjeqt6DiIiIl/i8wEHAJg1a5bD8w0bNnhpJQrq1k3+Htba2ga/nZJdjGdXZqG4XPmU0taGYEwZ0VPxeYlIWdf3bK/q/HNWZatXy8HK2irT/tFPw+nrFou0xcIaIJg50/FDtn2AwX57xGOPSQGWV191nC8tTSr0aFW/u0RmpnRMQ7UbtEbFeg1WpjoL/vT1HvSZvQFv/CizNpKTnri2GwPvREREdvwi4NCtWzd06dLF9vzgwYNeXI1CVq1yfYxOB/z8M9BAxobZIjBvTQ7U+hhwz5XxvMgi8gEPX90Vav6qfrGjEFcs2IiUbBW7VjTElzoVWQMCgFRYcvz4i/UXliy52D2iqEj6MD5vnhS0sAYmnAkiTJkCrFunzeyGv/wFWL9e9XoNZovAlM+z0Hv2Bvw36zhq1Q6EAejajm0wiYiI7AV7ewFKiYuLw7FjxwAAZ3xtb299QgBPPun6uOhoYPDgBv9oe36ZKpkNVqOSYlWbm4iUExKsx1PXJWL5T8q2wrV3rroWk1Zm4b2HkjG2f5xq57ER4tIuEVp3221AbKxUdNKaobBjB7B376XHVlVJbSxXr5YCE87IywOeekq59Srphx8UbXNqrVFy+nwNYsJDMSQxGhtzTuIPX+9BlcmsyDmcFROufoFWIiIiX+I3AYeqqirb1y1btvTiShRQXQ2UuVjtPSwM2Lat0TtFalbmjouULvCIyDfMHJeEPYXl2KpWV4nfvfDVLoxKilU/+yk19dIuEVpnbfVZn7GRlo0LFzb+Z405edL1dXlCXp7091cgsyEluxjz1uQ4BNTbtGqBc1UNby1Uiw5ALN8LiYiILuEXWyqMRiPy8i7uy4yN9fG77X/7m+tjwsKkug+NUPOuy5zxSdxOQeRjPp04FGr/1lbXCoz5xyZsyTujXl0HIaQiif7O1WCDlkVFyS+KbKexukTeCDYAfC8kIiJqiF8EHL799luHDIdrr73Wi6txk8UCLF7s+rjTpy8tHGbnii5Rin+40OuAdx7wUMo0ESkqJFiPp69PVP08eSVVePCDbeg3JwVrd59Q/gQmE3D8uPLzknqOH3c7gKJ2XSJXxEaG4l1PbR8iIiLyMT6/peLUqVOYMWOG7bler8eECRO8uCI3rV0r70JMp5O6UzSynWTH0TLFL8zeun8Qxg3kBRaRr5o5LgkA8P7P+Yq2yG1ITa0FU77chY8y8vH1M9codyfYYJBqH5SUNPznBQXAhAkea71ITqitBTZvBsaMkT2F2nWJnPH4NV0xul8shiRGM7OBiIioEZrLcMjIyMCkSZPw22+/NXvsvn37MGLECBTY7YN95JFH0LdvXzWXqB4hLu2n7gyDAfjppwa7U1hlHC51Y2GO2rRqgfceSsa4gR0Vm5OIvGPmuCQcXHAzbrvMM7/PO4+dw8C5qcp2sWioTab1cfPNUkFd0g69Hnj5ZbeCQGrWJXLGOw8Mwpzb+uHq7m0ZbCAiImqC5jIcjEYjli9fjuXLl+Oyyy7DiBEjMHDgQMTGxiI8PByVlZXIy8tDamoq1q1bB4vFYhs7aNAgLFu2zIurd5Pc1OA+fYBhwxr9Y7NFYP0+ZVKZ70ruiFfvvpwXWER+JCRYj/uHdMbqPSpseWjABZPZc10sDAYgKwsYMQI4fFjdc5FzLBagsNCtwpHe7Abxwk09GXAnIiJykuYCDvb27NmDPXv2OHXs+PHj8eGHHyKiibv8mmcwSL3kmyj+2KBTpxq9cEvJLlasNVhrQzCDDUR+akhiNOIiQz2apj7jm72e6WJx4ACDDd7Stq20VbB+kciYGLe6VHjj36tV13ZhHj8nERGRr9JcwKFr166499578dNPP6G4uOmUW71ej5EjR2LKlCm47bbbPLRClX3wgfPHhoQAW7ZIvdztLtysPcnTc05ixZajii1t8YQBDDYQ+akgvQ5zxidh0sosj53zXHUd7lv+K56/qTeuUis1PVC6WGiJfZAhJgaIj1dkWut72+nzNYgJ///27jw+qvre//h7si9kIUFIwhpILIRFQURwQUSRqICKXEULIiIXRdHberVibXGrVaza6lXx54JVlEXrQ8BaRUFBWtTIIpAgsiSsiUACWYAsZM7vj5gxE7LMcmbN6/l4zOMx35nv95wPmeE7Zz7zXaJ0Vb8UvWbiZ5yjfDm6AgCAQGMxDP9dSevgwYPKy8vT3r17VVJSopMnTyo6OlqJiYnKyMjQ4MGD1a5dO7fPU1ZWpoSEBJWWlvp2hITVWpc4OHXK8TbHjkkJCbbiJ1sL9fCyPBWVmf+rz8LpQzWsV7LpxwXgPz7eXKg7F27w+hqLiTHhenJ8f/OnWFRV1SVljx0z97hoWlSUtH271K2bqYf9ZGuhHlme59OFIi2q25Fi7e9Gknz3Eb+5XgMAOMzvRjg0lJaWprS0NjRP8qOPnEs2xMbWXdz97JOthR79ddLXi3QB8LwrB6TqBevZumvRJq+e99iJGt2+YINeummgufPjIyLqvvyScPCO6mopMdHUQ36ytVB3LNjg0y0w69MLc8ZmkWwAAMAJfrdLRZtlGNK99zrXpl0727zYWquhBz7Y4oHAfsEwUqBtGHN2Z02/qIdPzn3nuxv18WYTd7Corq5b5waeFxIiffllizsmOavWaujhZXk+TTZIdSMbXvbGIqcAAAQZvx7h0KZUVUn5+c61+eknqbJSio7W17uLdexEjWdik5SaEKUh6WwtB7QVv7+qrySLXv3KyX7JTYakme9u0PCcDrr6rDSltY/RkPQk139VjoyUcnKkAwekq66SSkpMjRcNWK3SiROmHvL/Vu3wyBRBR916QQ+Nykpx7z0IAEAbRsLBX3z5pVTrwk4Sn30mjRunBV/vMT2khhhGCrQ9v78qSwO7ttf9/9isiionpnuZYM2OI1qz44ikuoTnnLFZrv+63LVr3W3TJunQIenmm6W8PPOCRZ3QUOkPf5Auv1yyuP958cnWQj33+Q4TAnPNSzcN0pUDGNEAAIA7mFLhD9xZRf2++/TJloP619Yic2P6WWJMuOYxjBRos64ckKrv51yuYT4c4VRYWqk7FmzQJ1vdnGrRtavUr59UXGxOYLBXW1s3mmTFCvcPZTX0m8Wb3I/JRf9zaQbJBgAATEDCwR9UV0v797vW9scf9a9n/m5uPD8bMyBV6x8aRbIBaONCQyxaMH2oEqJ9NyjOkPTAP7bo3zuPqNbqxoz+yEjp22+lPn1Miw0NhITUjXJwcpuTWquhdbuKtXTTAa3bVay/fr5dJ2usHgqyZe1jwjXr0jN9cm4AAIKNX2+L6S1+sc1SQYGUnu50M0PSlk4ZGjflOVOGsEpSZJhFz11/trkrxQMIeJ7eCcdRSbERevzqfq7/Al1VVbdzxaFD5gaGOikpdZ9pkZEOVW9qy0uL5LOFIhnV57/84noNAOAU1nDwF9OmudTMIimlolgRtadUHRbudhhxkaFa/4fLFRHG4BcA9rL7peqeSzP1t5W+m1cvSSXHqzXz3Q26dMMZuu2iXs4v6BcZKX33nbRli3T0aN2v8bNnuz7SrK1ITJT+9a+6hTePHpWSkqROnU6v17GjU8mGpra89EWyITYyVM/811kkGwAAMBEJB39w6pS0apVjdSMjpblzpcxMqVMnbdp3VLd/ut+UZIMkXT+4K8kGAM26+9JM/f0/+Tp20ruLSDZl5Q+HtfKHw64tKlm/kKQkffopyYaWJCdLH30kdelSdzNJrdXQI8t9v+WlJA3PTNb8qeexODIAACYj4eAPpkxxvG5VlXTbbVJMjD7ZWqjfbTqs0vgOpoVyWVaKaccCEHxCQyx68roBfjG1ol7Rz4tKvuzKUHjDqFtzICSkblvHtq59e+k3v5ESEmyJbXXsaGqiod7Xu4vtplH4SohFem3KEJINAAB4AAkHX6utld5917k2Tz+tT677b9Mv+JNiIzTEhyvRAwgM2f1SNWN4ul5Zk+/rUCTVDb+3SHpkeZ5GZaU498Wxulrau7flZENCglRa6m6YgaG8XLrvPikqyqOn+XjzQd33j80ePYejpl+Uzsg+AAA8hISDry1b5nQT46mn9EjY+aaH8vjV/fiFB0Craq2Gln3v5haVJjNUt33mt/klGtYr2fGGkZF1WzkePtx8nQ4dpIwMqabG7Tj9Xvv2pi1A3Fit1dC3+SV6Zc0ufbm9hb+3l4RY6pINs6/M8nUoAAAELRIOvmQY0hNPON3McvKk+mz4SoWZ55kWylX9U9hzHIBDvs0v8Yuh8E35PK/IuYSDZL+eQ1OWLQv+ZIPFUrdV6CefOLzgozOa2onCVyad103pHWI1eVgPRjYAAOBhfNL60ooVdSulO8mQdM9/Fjm9z3lzQiU9f+MgU44FIPgdKvf9l8bmvP7vAn2y1cTRF4YhPfqoecfzV4Yh5eXV3UxWvxOFPyQbpl/UQ49f21/TLupJsgEAAC/g09ZX6hcqc4FFUmr5EUXUmrNK/KxLM5lKAcBhHeM8O7/fXY8sz1Ot9fSEbK3V0LpdxVq66YDW7Spuss5pqqvbzg4WISF1n0smJbMl/9qJokdyjH5/VV9fhwEAQJvClApfqV+ozFndumnVn17Wg/85bMpWmLGRoZp1aabbxwHQdgxJT1JqQpSKSiv94otkY02t5dDUkH6HttNsbY2H//xHmjXLrNB9y2qV9u2r+3wyaVqFP02/+dM1/X0dAgAAbQ4jHHwlMlJ66inn21VV6RNLBxWZtBXmM/91FqMbADglNMSiOWPrFtrz197j/ve/t41iaG5If/12mq1OwejaVRo06PTbwIHSW295bJFFr3rhBWn9+rrkiolrOBSVnjTtWO5IjAnXUGfX9gAAAG6zGIaJYycDVFlZmRISElRaWqr4+HjvnNQwpLPOkrZscbxNp0764u2PNHXlT26fPiosRH+deLbze9YDwM/8aSHA5nSKi9Tx6lpVVDU9Bc0iKSUhSmt/N9L55GtVldStm3TokPuB+pLFIg0eLH3zjcvJk/odKA6VV6pjXJTO7pqohz7cog83HlCtH1xlzJs0iM+7IOCT6zUAgFuYUuEr1dXSnj1ONTEOHdL9awplxsCUjX+8XNERoW4fB0Dbld0vVaOyUvRtfolW5BVp/r8LfB3SaX4qr2rxeZe305TqRgL8+9/SeedJJSWuB+lrhuHWVIpPthbq4WV5Kirzv8RTYnSYnrxuAMkGAAB8hISDr0RE1A3Tzc11uIlhsaj0xCkpPMLt02/ad8z5i2sAaCQ0xKJhvZI1rFeyIkItemVNvq9DconLX5YzMqRNm35Z46GoSDp61L7OQw9JBQXuhOdZycnS2rUuJxtuX7DBA0G5738uzWRRZAAAfIyEg6+sWOFUskGSQqxWDd23RWt6nuP26f15WzsAgWn2lVk6q0uiHlq6VSXHa3wdjlMe+nCLosNDXPslvGvXultTqqqke+91L7jmtGsnVVQ0/7zFIvXpI82dKx079svjSUlSp06/lDt2lLp0afYwjadLDElPUmiIRbVWQ/cu+d79f4cH/N/EszXm7M6+DgMAgDaPhIMvGEbdL15OslosuverBVqTPsjtRcr8fVs7AIHpygFpGt0v1e4L6tHj1brv/e91vLrW1+E163hVrW5fsEHzJg2yTRNp/AXbJfW7XBw4IF11lXtTLywWqUcP6bHHpPbtpQceaHkdIMOoO99ll7m8EGRT63SkxEfpxiHdtOqHIr98TWcMTyfZAACAnyDh4AsrVkjffed0sxDDUGr5EUXUnnJrS8zk2AgNSU9yuT0AtKR+mkVDo/ul6Nevfa2vd/v3Wgf3vve94iJzVVT2y9oPSbERenRsXyXHRbqWhKgfAdFw6kW96mrHExGGIeXnSx06SCNGtL5YpRtTJSTZdvdovOZjUVmlnvv8R5eO6WmRYSG6P7uPr8MAAAA/Y5cKeXnVY8OoW2AsJ8epZrUhoRr366dV3C7J7S0xX7ppkK4cwAJaALyr1mpo8GMrdPRk0ztGBJL6X/l7dIhxfxTEvn2/JCIMQ7r5Zmnbtrr7jYWESOecU7ejxP79pycwGmplqkRLqk9ZNfTPK1VyvNql9r60cPpQ1igKUuxSAQCBhxEO3ubC7hSSFGKtVUH7zjoeHevW6WcMTyfZAMAnQkMs+tO1/TXz3Y2+DsVtjX/lT02I0pyxWe6vAVFVJRUXN51skCSr9ZcdJVpaO8JFtVZDL6zcoXlrdqmyxmrqsb2FNYoAAPAfJBy8LTJSWrNG6t3bqWYVYZGqcWN3isToMD1xbX9dOSDN5WMAgLuuHJCmGfuPBexuFs0pKq3UHQs26OVJg9zbgrF+zYfWRi44OU2iuYUfG/pka6HuXeLfa204gjWKAADwHyQcfGHzZqeqG5L2J6aqOtS1l2toepLemT6UrcEA+IW63Sza/7ybReAN2W9K/XiEBz7YorjIcA3tlex6n2vyyIWWFn6snxJypKJKsxYG9sgTi6SUhCjWKAIAwI+QcPA2w5BuvdWpJhZJySdLXV4s8sbzupFsAOBXrhyQqtH9UvT17mJNf+s7nQjwX9XrHTtRo1+//o17UyxMFIgLP7qi/hNuztgsPu8AAPAjIb4OoM05caLlfdMbsUqacOOTGnfzcy7vTMHwUgD+KDTEogsyOujZ68/ydSimKyyt1O0LNuix5blat6tYtVbPrc9cfcqq17/arT8u3arXv9qt6lN1ay/UWg09sjzvtGRDIIsJD9X/XJqplHj7z7WUhCj3p7MAAADTsUuFvLzq8e9/Lz3xhMPVHx9+s14bdr3Lp4uLCtOmP17OLz4A/FpTw/6DSUp8pB4e11ejslJaXEvBkbUWGvrzx3l69at8NcxnWCzSiDPPUFpilN75Zp8n/1le95vLMnXPZWc6/XdCcGCXCgAIPCQc5MUPMKu1bqGvU45tCWeVtCUlU1ff/GzdFaQL/m/iQI05m4UiAfi/+i+Rj3+Uq9zCcl+H4xGJMeE6dqLGVk6KDde1Z3fWZVkpKi6v0h+Xb1XJ8V+ebxcZqtsu7KlZl2ZKku1LdofYSL3zTYE+3vqT1/8NvtIuMkzfzyGB3paRcACAwEPCQV78ADt2TGrf3qkmh6ITdOHMN12aTtE9KVqr7x/pdDsA8LVpb36jlT8c8XUYfiMiLETR4SEqPelYwjoY3XpBD/1xbF9fhwEfIuEAAIGHNRy8KcS5P7dV0sSb/uzy2g3XDOzsUjsA8LXXbzlP5/VwLkEbzKpPWdt0skGSRmWl+DoEAADgJBIO3hQfL02Y4HD1BQOytbtDN5dPtzhnn0cXKgMAT5o4xPX+D8Elle0uAQAISCQcvMlqld5/36GqhqQJeV/UtXFRUVmVvs0vcbk9APhSSkK0r0OAj1l+vrHdJQAAgYmEgzcdO+ZwVYukmFNVGrn7O7dOeag8OFd8BxD8hqQnKTWBbX3bMra7BAAgsJFw8KZZs5yqbki6598LJTfW9ewYx8U6gMAUGmLRnLFZ4nfttufmYd21cPpQrf3dSJINAAAEMBIO3lJbK737rlNNLJLSyg4rota1hcKY8wog0GX3S9XLkwYx0qGNuaJfqob1SmYaBQAAAS7M1wG0GR9+6HSTo5HtNH7S0y7vUsGcVwDBILtfqkZlpejb/BIdKq9U/uHj+uvKHb4OCx5gUd00CpLlAAAEBxIO3mAY0p/+5FSTtwdcrhcvuElF8R2cPl1iTLieHN+fYagAgkZoiEXDeiXbyr1T4/TI8jwVlrJOTbCoT4+TLAcAIHhYDMONBQKCRFlZmRISElRaWqr4+HjzT1BVJXXqJJWWOlTdkHQyNEJZv31fCnF81ovFIt0zMlOzLs3kYg1A0Ku1Gvo2v0Sf5RXpjX8XyKK6/hOBKTUhSnPGZpEsR7M8fr0GADAdIxy8ISJC6trV4YSDRVJkbY1iqyt1PCrG4dO8eONAXTkgzcUgASCw1I96GNYrWUPSkxjxEIAmD+2mwT2S1DGubhoFyXIAAIILCQdvqK6Wdu92qskjl9zmVLLhin6dSDYAaLMar/PQMS5KWanxuvaltdp95ISvw0MTQizSH8b0VUQY61cDABCs+JT3hvDwuqSDgwxJE3JXObUd5k3ndnchMAAIHvUjHq4+u7OG9UpWQky47rnsTF+HhWZMvyidZAMAAEGOT3pvKCuTTjm+taVFUkpFsVPbYYaEMgwVABrrGMd2mr4QFxWqbY9ma+yAFDX+dAqxSDOGp2v2lVk+iQ0AAHgPUyq8wcGFHw1Jjw+/RTs69tCPZ/RwajvMIxVVLgYHAMFrSHqSUhOiVFRayYKSXnT9OV0VHRGqF246R89cb9Xb6wq0p+SEuifFaPKwHoxsAACgjSDh4A3x8dLcudL997dYrVYWvT14nKrDI5w+Bb/iAcDpQkMsmjM2S3cs2ODrUNqUy7JSbPcjwkI07aKePowGAAD4Cj8xeINhSIsWtV7N4vy0CIvqthIbkp7kQmAAEPyy+6Xq5UmDlJpAYtbT+EwCAAANMcLBG6qrpf37W61WExouOZF0qK85Z2wWW4kBQAsa7mJRVFapkooqrd15WF9sP+Lr0PxWRKhUXet4fT6TAABAYyQcvCEyUsrJkS69VMbOnactoCXVrd9gsVpVHRLq8GFTEqI0Z2yWsvulmhYqAASr+l0s6k27qKc+2nRQsxZtZH2HRl66aaBCQiy2qSgN/z6Wn8uJMeE6dqLG9jifSQAAoDESDt7SqZN05EiTyQap7gIu2lqjkbu/06qMIS0eauoFPXR5VoqGpCfxKxIAuGHM2WkKCbFo5rus8VBvdN+OunJAmiTp5UmD9MjyPBWWVtqer08s1I8YOVReqY5xUXwmAQCA05Bw8JaICFm7dpPl2LFmkw6GpHvWvqtVvc5tcWrF5Vkpdr/SAQBcd+WAVM0LOf2LdVt189B02/2GU1GaSizwWQQAAFpCwsFbqqtVtf+AoluoYpGUWn5EEbWnWtwS81A5F8QAYKamvlgfPV6lx/65rU0lIRJjwjW0URKh8VQUAAAAR5Fw8JbISE2/6yWFbstTQmVFk1WORcXpxzN6tJhskNgCEwA8oakv1qP7perb/BJ9llekN/5d4JvAvOjJ8f2ZFgEAAExDwsFLqk9ZtbY6Vup1rlvHYbsxAPCe+iTEsF7JGpKe1Oy0i8SYukRxw0UU/dWorI7asr9URWVVtsdS4iP18Li+LPgIAABMRcLBS95eV2DKcdhuDAB8o6mtNZNiI5SSEG1LBH+bX6LP84r0wcYDOupnyQeLpP8enq7ZV2ap1mqw4CMAAPA4i2EYbX43sLKyMiUkJKi0tFTx8fEeOceFT67U/mPuzQN+4caBGntWmkkRAQA8pfEXel+vBzF5aDf9YUxfRYSF+OT8gBm8cb0GADAXIxy84GR1rdvJhiv7dSLZAAABoqX1IIpKT+pIRbWOnaiWxSKFhoTo+ZU7JNXtVtRYTESoTlTXNnuuEItkGE23tahuG8uHx/VjBAMAAPA6Eg5e8MTHeW61D7NIL9x0jknRAAB8oaXdHvqkxp22PkRqQpTmjM2SJN2xYIMk+6RCffpg+kXp+n9r8mVp5nmm4gEAAF8h4eAFu48cd6v9rEvP5GIRAIJYU9tyNlxX4eVJg05LSKT8nJDI7peqgd3at/g8AACAL5Bw8IJoN+bMxkSE6q6RGSZGAwDwRy2NgGgtIdHa8wAAAL5AwsELMjrG6fMfDrvUdsbwnlwwAgBaTEg48jwAAIC3sVy1F4SGupYwiA4P1V0jM02OBgAAAAAAzyPh4AWJ0REutZs4pCujGwAAAAAAAYmEgxckxbqWcLg8K8XkSAAAAAAA8A4SDl5w7ES1021S4iM1JD3JA9EAAAAAAOB5JBy8wJURDg+P68t0CgAAAABAwCLh4AUpCdFO1f+/iWezbzoAAAAAIKCRcPCCIelJSomPdKjutAt7aMzZnT0cEQAAAAAAnkXCwQtCQyx6eFzfVuuNyuqoP4xpvR4AAAAAAP6OhIOXZPdL1bxJg5QYE37ac9HhIXrhxoF69eZzfRAZAAAAAADmC/N1AK05cOCAFixYoGXLlqmgoEBHjhxRhw4d1KNHD40bN06TJk1S586BMQUhu1+qRmWl6OtdxVq3+4gki4b1StbQnsksEAkAAAAACCoWwzAMXwfRnHnz5ul///d/dfz48WbrtGvXTn/5y180Y8YMl89TVlamhIQElZaWKj4+3uXjAAAAwDO4XgOAwOO3UyoeffRR3XHHHXbJhszMTF188cXq1auX7bGKigrdfvvtevzxx30RJgAAAAAAaIJfJhyWLl2qOXPm2MpZWVlav369fvzxR3355ZfauXOncnJy1KdPH1udP/zhD1q2bJkvwgUAAAAAAI343ZSKmpoaZWVlaefOnZKkLl26aPPmzWrfvv1pdUtKSjRgwAAdOHBAUt0IiLy8PIWFObc0BUP0AAAA/BvXawAQePxuhMOiRYtsyQZJevbZZ5tMNkhSUlKSnn32WVt5x44dWrRokcdjBAAAAAAALfO7hMOSJUts99PS0nTttde2WH/8+PFKTU21ld977z2PxQYAAAAAABzjVwmHkydP6rPPPrOVs7OzW50eERYWpuzsbFt5xYoVqqys9FiMAAAAAACgdX6VcNi2bZuqqqps5QsuuMChdg3rVVZWatu2babHBgAAAAAAHOdXCYfc3Fy7cmZmpkPtGtfLy8szLSYAAAAAAOA857Zz8LCCggK7crdu3Rxq1717d7tyfn5+i/WrqqrsRlKUlZU5FiAAAAAAAHCIX41waPzFPzEx0aF2CQkJduXy8vIW6//5z39WQkKC7da1a1en4gQAAAAAAC3zq4RDRUWFXTk6Otqhdo3rtZZwmD17tkpLS223ffv2ORcoAAAAAABokV9NqaipqbErt7ZDRXP1Gh+nscjISEVGRjoXHAAAAAAAcJhfjXCIjY21Kzu6vWXjeo2PAwAAAAAAvMuvRji0a9fOrnzixAnFxMS02u7EiRN25bi4OKfOaxiGJBaPBAAA8Ff112n1120AAP/nVwmHM844w65cWFioDh06tNqusLDQruxIm4bq13xg8UgAAAD/Vl5eftqC4QAA/+RXCYfevXvblffs2aP+/fu32m7Pnj125T59+jh13rS0NO3bt09xcXGyWCxOtXVFWVmZunbtqn379ik+Pt7j50Pg4z0DZ/GegbN4z8BZ3n7PGIah8vJypaWlefxcAABz+FXCoW/fvnblDRs2aMyYMa2227Bhg105KyvLqfOGhISoS5cuTrUxQ3x8PBd1cArvGTiL9wycxXsGzvLme4aRDQAQWPxq0ciuXbuqV69etvLq1asdatewXkZGhk+SBwAAAAAA4Bd+lXCQpPHjx9vuf/nll9q7d2+L9ffu3WuXcGjYHgAAAAAA+IbfJRymTp2q0NBQSZLVatVjjz3WYv1HH31UVqtVkhQaGqqpU6d6PEZ3RUZGas6cOYqMjPR1KAgQvGfgLN4zcBbvGTiL9wwAoDUWww/3Fpo2bZreeOMNW/nVV1/Vbbfddlq9V155Rbfffrtdu9dee80rMQIAAAAAgOb5ZcLhyJEjGjp0qHbt2mV7bNy4cZo4caLS0tJ04MABLVy4UB999JHt+YyMDK1bt87pLTEBAAAAAID5/DLhIEk7duzQ6NGjlZ+f32rd9PR0rVixQhkZGV6IDAAAAAAAtMbv1nCol5mZqc2bN+vuu+9udqulhIQE3X333dq8eTPJBgAAAAAA/IjfjnBoqLKyUqtXr1ZBQYGKi4uVnJysHj16aMSIESxUBAAAAACAH/LbEQ4NRUVFafTo0ZoxY4YefPBBzZgxQ6NHj/ZasuHAgQN66qmndMEFF6hz586KjIxU586ddcEFF+ipp57SgQMHPB6DYRj6+OOPNXnyZPXu3Vvx8fGKj49X7969NXnyZH388ccKgNxRUKuqqtKqVav0xz/+UWPGjFHPnj0VFxeniIgInXHGGRo4cKBuv/12rVixwmOv1S233CKLxeL0bd68eR6JB80rKChw6bVKSUnxWEz0M/7LlfdKw1uPHj1Mi4V+xrfKy8u1Zs0a/fWvf9XNN9+svn37KiwszNTX2h+ueyT6JAAICgZa9PLLLxuxsbGGpGZv7dq1M+bNm+exGPbs2WNccsklLcYgyRg5cqSxZ88ej8WBphUVFRkTJ0404uLiWn2N6m99+/Y1vv76a9NjmTJlisMxNLy9/PLLpseCluXn57v0WnXq1Mkj8dDP+DdX3isNb+ecc45psdDP+M6ZZ55pWCyWFv/O3bt3d+sc/nDdYxj0SQAQLMKaSkKgzqOPPqo5c+bYPZaZmam0tDTt37/ftotGRUWFbr/9dh0+fFgPPfSQqTEcPHhQF154ofbt22d7LC4uTllZWTIMQ9u2bVN5ebkkadWqVbrooov09ddfKzU11dQ40Lx9+/Zp0aJFpz2empqqLl26KC4uTkVFRfrhhx9ktVolSbm5ubrwwgu1ePFijR8/3iNxpaWlqX///g7V7datm0digOOGDx+u6OjoVuslJSWZfm76Gf83evRop+oXFBRo+/bttvKkSZPMDkkS/Yy3/fjjjx49vj9c90j0SQAQVHyc8PBbH374oV0GPSsry1i/fr1dnZycHKNPnz529ZYuXWpaDLW1tcbgwYNtx7ZYLMYjjzxiVFRU2OqUl5cbc+bMsfvFY/DgwUZtba1pcaBlOTk5tr/90KFDjXnz5hn5+fmn1SssLDTuuusuu9cqIiLC+OGHH0yLpeEvj1OmTDHtuDBf4xEOTb1nvIF+JjiNGzfOrp85cuSIacemn/Gd+r97bGyscf755xuzZs0y5s+fb2RnZ7s9wsEfrnsMgz4JAIINCYcmVFdXGxkZGbYPsS5duhglJSVN1i0uLjY6d+5sq5uZmWnU1NSYEscbb7xh96E+d+7cZus++eSTdnXffPNNU2JA69avX29cffXVp12YNef555+3e62uu+4602Lhi0Dg8JeEA/1M8CksLDTCwsJsr9P1119v6vHpZ3xnwYIFRl5e3mlfrBu+Jq4kHPzluscw6JMAINiQcGjCW2+9ZfcBtmTJkhbrL1682K7+22+/bUocPXv2tB2zX79+LWbua2trjX79+tnq9+rVy5QY4BlDhgyxvVZRUVHG8ePHTTkuXwQCh78kHOhngs9TTz1l99769NNPTT0+/Yz/cTfh4C/XPYZBnwQAwSYgdqnwtiVLltjup6Wl6dprr22x/vjx4+3mDb733ntux7B+/Xrt3r3bVp45c6ZCQpp/uUJCQnTHHXfYyrt27dLGjRvdjgOecfXVV9vuV1ZWqqCgwHfBoM2inwlOb7zxhu1+t27ddNlll/kwGgQCf7jukeiTACAYkXBo5OTJk/rss89s5ezsbIWFtby2ZlhYmLKzs23lFStWqLKy0q04li1bZlceM2ZMq20a11m6dKlbMcBzGi/8V1ZW5qNI0JbRzwSftWvX2i0WOXXq1Ba/sAH+ct0j0ScBQDDiKqSRbdu2qaqqyla+4IILHGrXsF5lZaW2bdvmVhwNM/Rdu3ZV165dW23TrVs3denSpcljwL80HtHQsWNH3wSCNo1+Jvg0HN1gsVg0depUH0aDQOAv1z0SfRIABCMSDo3k5ubalTMzMx1q17heXl6eaXE4GkPjuu7GAM8wDEPvv/++rZyamqr09HTTz5OTk6MrrrhCnTt3VmRkpOLj45Wenq5x48bpL3/5i3766SfTzwnX/e53v9OAAQOUmJioiIgIderUSYMGDdJdd92lTz/9VIZhmH5O+pngUl5ebjc0/rLLLlP37t09ek76mcDnL9c9jWOhTwKA4EDCoZHGvzw7um9444u6/Px8t+LYs2eP0zE0jsPdGOAZ7777rm0vc0n69a9/LYvFYvp58vLy9Mknn+jgwYOqrq5WeXm5CgoKtHz5ct13333q3r27fvvb39r9sgXfWbJkibZs2aLS0lLV1NTo0KFD2rhxo1588UVlZ2erf//+WrdunannpJ8JLosXL9bx48dt5WnTpnn8nPQzgc9frnsk+iQACEYkHBppPJc+MTHRoXYJCQl25fLycpdjOH78uGpra52OoXEctbW1OnnypMtxwHz79+/XPffcYysnJiZq9uzZHjlXWFiYevfureHDh2vEiBHq27evQkNDbc9XVVXpueee07Bhw3T06FGPxADHJScna8iQIbr00kt13nnnqUOHDnbP5+bmavjw4Xr99ddNOR/9TPBpOJ0iKSlJ11xzjcfPST8T+PzhukeiTwKAYEXCoZGKigq7cnR0tEPtGtdz54PX1RjMjgPmOnHihMaPH6/i4mLbY6+88sppC0i6Iz4+XjNmzNCKFStUUVGhbdu2afXq1friiy+0detWHT16VC+99JI6depka7Nx40Zdc801slqtpsWB1lksFg0ePFgvvviidu/erSNHjuibb77R559/rq+//lqHDx/Wd999pwkTJtjanDp1SjNmzNCnn37q9vnpZ4LLtm3b7EbATJo0SZGRkR45F/1McPGH6x534vBELAAA85BwaKSmpsau3NpKzc3Va3wcb8Rgdhwwz6lTpzRx4kTl5OTYHrvzzjt1/fXXm3qe559/XvPmzdOoUaOa/LIRFxenO+64Q99//72ysrJsj69Zs0ZvvfWWqbGgZd27d1dOTo5mzpzZ7Boe55xzjt577z09//zztsdqa2t11113uf1/m34muDQc3SB5djoF/Uxw8YfrHnfi8EQsAADzkHBoJDY21q7s6DZPjes1Po43YjA7DpjDarVq8uTJWr58ue2x66+/Xn/72998FlOnTp20dOlSRURE2B575plnfBYPWjZr1izdeuuttvLOnTtP2z7OWfQzwaOmpsbui/zgwYM1YMAAH0ZUh34mMPjDdY87cXgiFgCAeUg4NNKuXTu78okTJxxq17heXFyc12MwOw64z2q16pZbbtGiRYtsj1133XV655137OY5+0JGRoZuuOEGW3nr1q3av3+/DyNCS37/+9/blf/1r3+5dTz6meDx0Ucf6dChQ7bybbfd5sNo7NHP+D9/uO5xJw5PxAIAMA8Jh0bOOOMMu3JhYaFD7RrXa7zgmzPCw8PtFkByNIbGdRMTE33+pbYts1qtmjZtmt5++23bY9dee60WLVrk1FBRTxo5cqRd+YcffvBRJGhNz5497VZid/e1op8JHg2nU8TExOjGG2/0YTSno5/xb/5w3SPRJwFAsCLh0Ejv3r3tyg23aGpJ43p9+vQxLQ5HY2hc190Y4Dqr1arbbrtNb775pu2xa665RosXL/abZIMkpaam2pWPHDnio0jgiIavlxmvFf1M4Dt48KDdaJcJEyYoPj7ehxGdjn7Gv/nLdU/jWOiTACA4kHBopG/fvnblDRs2ONSucb2GC2W5G0dubq6qq6tbbVNVVaXc3FzTYoBr6pMN8+fPtz12zTXXaMmSJQoPD/dhZKdrPAzVmVXB4X0NXy8zXiv6mcD397//3W4rQU8uFukq+hn/5i/XPY1joU8CgOBAwqGRrl27qlevXrby6tWrHWrXsF5GRoa6dOniVhwjRoyw3a+srNQ333zTaptvvvlGVVVVtvIll1ziVgxwXiAlG6S6+dQNpaSk+CgStKaqqko7d+60lc14rehnAl/DviYzM1PDhw/3YTRNo5/xb/5y3SPRJwFAMCLh0ITx48fb7n/55Zfau3dvi/X37t1r98HbsL2rxo4da/cF1ZGtxBrWiYiI0JgxY9yOA45rKtlw7bXX+m2yoba2VgsXLrSVY2NjNXDgQB9GhJZ88MEHdr8UX3jhhW4fk34msK1Zs0Y7duywlRvuZOIv6GcCgz9c90j0SQAQjEg4NGHq1Km2BYesVqsee+yxFus/+uijslqtkqTQ0FBNnTrV7RgSExM1YcIEW/mdd96x+3WzsR07dujdd9+1lSdMmGC3+BI8yzAMTZ8+3S7ZMH78eC1evNgvkw2S9PTTT2vbtm228rhx4+y2r4P/+Omnn/TAAw/YyiEhIaZc4NPPBLaGi0WGhoZqypQpPoymafQzgcEfrnsk+iQACEoGmnTrrbcakmy3V199tcl68+bNs6s3bdq0Zo+Zn59vV3fKlCktxrBz504jPDzcVn/QoEHGoUOHTqtXVFRknH322bZ6ERERxq5du5z698J1VqvVmD59ut1rO2HCBKOmpsbtYzvznrnzzjuNt99+26isrGzxmNXV1cbDDz9sWCwW23HDw8ONHTt2uB0vHPOf//zHmDFjhvHDDz+0Wnfz5s1GVlaW3fvglltuabY+/UzbUFpaasTExNhej7Fjx7p8LPqZwDdlyhTb37l79+4uHcMT1z2GQZ8EAG2d/yyX72eeeuoprV69Wrt27ZIkTZ8+XcuXL9fEiROVlpamAwcOaOHChfroo49sbTIyMvTkk0+aFkOvXr00d+5c/eY3v5FUt0DTWWedpZkzZ+rcc8+VYRjKycnRSy+9pKKiIlu7uXPnqmfPnqbFgZa99957evXVV21li8Wio0ePOjWs895779WoUaPcimPr1q168cUXNXPmTGVnZ+ucc87Rr371KyUmJiokJESHDh3St99+q4ULF2r//v128b722mvKyMhw6/xwXFVVlV555RW98sorOuusszRy5EgNGDBAKSkpiouLU0VFhXbu3KlPP/1U//znP22/JErSwIED9be//c20WOhnAtOiRYvspth4a7FI+hnfevzxx/X444+f9nhNTY3t/p49exQVFXVancmTJ9t9VjXmD9c9En0SAAQdHyc8/NqPP/5opKen22Xmm7ulp6e3+suNs1n+eg8++KDdr0TN3SwWi/HQQw+Z8C+HM+bPn+/Qe6Sl2/z585s8tjPvmYsvvtjp8yYmJhqLFi3yzB8Gzfriiy9cep+MHTvWOHz4cIvHpp9pG8477zzba9KpUye3RlTRzwSOOXPmuPw540hfYPZ1j2HQJwFAW8caDi3IzMzU5s2bdffddze7r3lCQoLuvvtubd682WO/3PzpT3/SZ599psGDBzdbZ/Dgwfr8889bnXeJ4DVhwgSdd955Dq0ZkZKSotmzZysvL0833HCDF6JDQz169NANN9yg1NTUVuuGhIRo1KhRWrp0qZYtW6YOHTp4JCb6mcCRm5trt3r/lClTFBbmnQGL9DPBzV+ueyT6JAAIFhbDMAxfBxEIKisrtXr1ahUUFKi4uFjJycnq0aOHRowYocjISK/FsWPHDuXk5KiwsFCSlJqaqnPPPVeZmZleiwH+rbq6Wrm5udq1a5cKCwtVUVEhq9WqhIQEnXHGGRo0aBDvFz9y8OBB5eXlae/evSopKdHJkycVHR2txMREZWRkaPDgwWrXrp1XY6KfQWvoZ4Kfv1z3SPRJABDISDgAAAAAAADTMaUCAAAAAACYjoQDAAAAAAAwHQkHAAAAAABgOhIOAAAAAADAdCQcAAAAAACA6Ug4AAAAAAAA05FwAAAAAAAApiPhAAAAAAAATEfCAQAAAAAAmI6EAwAAAAAAMB0JBwAAAAAAYDoSDgDgJ0aMGCGLxSKLxaKHH37Y1+EAAAAAbiHhAAAOKigosCUEzL4VFBT4+p8HAAAAmIqEAwAAAAAAMF2YrwMAgEARHR2t0aNHt1rv22+/1dGjRyVJUVFRuvjiix06NgAAABBMLIZhGL4OAgCCyYgRI7R69WpJUvfu3ZkuAQAAgDaJKRUAAAAAAMB0JBwAAAAAAIDpSDgAgJ9wdFvMHj162Oq9+eabkqTa2lq9//77uvrqq9WzZ09FRUUpMTFRF110kV599VXV1taedpzy8nI988wzuvDCC9W+fXtFREQoLS1N1113nVauXOnSv2Hjxo2aPXu2hgwZorS0NEVGRio5OVkDBgzQPffco5ycHJeOCwAAgMDDopEAEOAOHjyom266ybZuRL2qqiqtXbtWa9eu1eLFi7V8+XLb4pRr1qzRxIkTVVhYaNemsLBQH3zwgT744AP99re/1TPPPONQDIcOHdKdd96p999//7TnSkpKVFJSoi1btuj555/XjTfeqFdffVWxsbEu/osBAAAQCEg4AEAAq6io0OWXX67c3FxJUnp6urp3764TJ05o06ZNqq6uliStXLlSkydP1vvvv6+vvvpKo0ePVmVlpSwWi/r27auOHTvq0KFD2rp1q+3Yzz77rHr27Kk777yzxRi2b9+u7Oxsu8Uxw8PDlZWVpeTkZJWVlWnLli2qqqqSJC1cuFDbt2/Xl19+qbi4OJP/IgAAAPAXTKkAgAD28MMPKzc3V+eff742bNig3bt364svvtA333yjgwcP6uqrr7bV/cc//qFPP/1UN9xwgyorK3XrrbfqwIED2rJli1auXKktW7Zoy5YtOvPMM21tHnzwQVVUVDR7/vLyco0dO9aWbEhMTNSLL76oo0ePatOmTVq5cqVycnJUUlKiuXPnKiIiQpK0YcMG3XHHHZ75owAAAMAvkHAAgABWXFysiy++WKtWrdLAgQPtnktOTtaSJUvUs2dP22Pjxo1TYWGhHnjgAb3++utKTU21a9OvXz99+OGHCg0NlSSVlZXpgw8+aPb8999/v3bs2CFJSk1N1fr16zVz5szTpkvExMTovvvu09KlSxUSUvfR884777CmAwAAQBAj4QAAASw0NFTz589XZGRkk89HRERo2rRptnJ1dbXOPPNMPfbYY80es0+fPrrkkkts5bVr1zZZr6ioSPPnz7eV33zzTbvkRlOys7N1yy232MovvPBCi/UBAAAQuEg4AEAAGzVqlNLT01usM3ToULvyrbfeqrCwlpfwadgmLy+vyTqLFi2yrcvQv39/XX755Y6ErClTptjuu7obBgAAAPwfCQcACGDDhg1rtU5KSorTbRpOtTh69GiTdRruijFq1KhWj1nvrLPOst0/ePCgDh486HBbAAAABA52qQCAANY4mdCUmJgYt9qcOHGiyTqbN2+23f/nP/9p2ynDWYcPH1ZaWppLbQEAAOC/SDgAQACr3/XBk20Mw2jy8eLiYtv97du3a/v27U7HIkmlpaUutQMAAIB/Y0oFAMAlx48fN+U4VqvVlOMAAADAv5BwAAC4JDEx0XZ/7ty5MgzDpduIESN89m8AAACA55BwAAC4pOFaED/99JMPIwEAAIA/IuEAAHDJ+eefb7u/bt06H0YCAAAAf0TCAQDgkiuuuMJ2f926ddq2bZsPowEAAIC/IeEAAHDJuHHj9Ktf/UpS3U4WM2bMUE1NjY+jAgAAgL8g4QAAcElISIiee+45WSwWSdJXX32l7OxsHThwoNW227Zt01133aWnn37a02ECAADAR8J8HQAAIHBdccUVeuKJJzR79mxJ0qpVq9SzZ09dd911uuSSS9S9e3fFxMSorKxMBw8e1KZNm7Rq1Srb9Is5c+b4MnwAAAB4EAkHAIBbHnjgAXXq1EkzZ85UZWWlqqurtXDhQi1cuNDXoQEAAMCHmFIBAHDb1KlTtX37dt15551KSEhosW67du101VVX6e9//7vuu+8+L0UIAAAAb7MYhmH4OggAQPCora3Vhg0blJeXp+LiYp08eVKxsbFKSUlR79691bdvX4WHh/s6TAAAAHgYCQcAAAAAAGA6plQAAAAAAADTkXAAAAAAAACmI+EAAAAAAABMR8IBAAAAAACYjoQDAAAAAAAwHQkHAAAAAABgOhIOAAAAAADAdCQcAAAAAACA6Ug4AAAAAAAA05FwAAAAAAAApiPhAAAAAAAATEfCAQAAAAAAmI6EAwAAAAAAMB0JBwAAAAAAYLr/D6/nWIzDOh48AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -133,10 +133,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:23.897438Z",
-     "iopub.status.busy": "2024-10-25T14:48:23.897091Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.010767Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.010093Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.282289Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.281756Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.416513Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.415788Z"
     }
    },
    "outputs": [
@@ -173,10 +173,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.012794Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.012597Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.017465Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.016882Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.418822Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.418593Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.424665Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.424081Z"
     }
    },
    "outputs": [
@@ -209,10 +209,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.019249Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.019067Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.025696Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.025087Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.426859Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.426645Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.434827Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.434270Z"
     }
    },
    "outputs": [
@@ -262,10 +262,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.027483Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.027300Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.422163Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.421606Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.436912Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.436501Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.863942Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.863214Z"
     }
    },
    "outputs": [
@@ -273,12 +273,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 0.011132662400108018\n"
+      "Causal Estimate is 1.0019898175815642\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -308,10 +308,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.424105Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.423808Z",
-     "iopub.status.idle": "2024-10-25T14:48:24.427313Z",
-     "shell.execute_reply": "2024-10-25T14:48:24.426826Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.866201Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.865876Z",
+     "iopub.status.idle": "2024-10-25T14:49:19.869666Z",
+     "shell.execute_reply": "2024-10-25T14:49:19.869147Z"
     }
    },
    "outputs": [
@@ -319,8 +319,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DoWhy estimate is 0.011132662400108018\n",
-      "Actual true causal effect was 0\n"
+      "DoWhy estimate is 1.0019898175815642\n",
+      "Actual true causal effect was 1\n"
      ]
     }
    ],
@@ -350,10 +350,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:24.428925Z",
-     "iopub.status.busy": "2024-10-25T14:48:24.428747Z",
-     "iopub.status.idle": "2024-10-25T14:48:26.190730Z",
-     "shell.execute_reply": "2024-10-25T14:48:26.190026Z"
+     "iopub.execute_input": "2024-10-25T14:49:19.871540Z",
+     "iopub.status.busy": "2024-10-25T14:49:19.871357Z",
+     "iopub.status.idle": "2024-10-25T14:49:21.772321Z",
+     "shell.execute_reply": "2024-10-25T14:49:21.771054Z"
     }
    },
    "outputs": [
@@ -362,9 +362,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:0.01112672213326368\n",
-      "p value:0.84\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:1.0019914265231784\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
@@ -386,10 +386,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:26.193192Z",
-     "iopub.status.busy": "2024-10-25T14:48:26.192980Z",
-     "iopub.status.idle": "2024-10-25T14:48:28.341409Z",
-     "shell.execute_reply": "2024-10-25T14:48:28.340837Z"
+     "iopub.execute_input": "2024-10-25T14:49:21.774965Z",
+     "iopub.status.busy": "2024-10-25T14:49:21.774747Z",
+     "iopub.status.idle": "2024-10-25T14:49:24.097491Z",
+     "shell.execute_reply": "2024-10-25T14:49:24.096787Z"
     }
    },
    "outputs": [
@@ -398,9 +398,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:-4.757591404255024e-05\n",
-      "p value:0.82\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:-3.169002204966631e-05\n",
+      "p value:0.96\n",
       "\n"
      ]
     }
@@ -423,10 +423,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:28.343876Z",
-     "iopub.status.busy": "2024-10-25T14:48:28.343331Z",
-     "iopub.status.idle": "2024-10-25T14:48:30.349956Z",
-     "shell.execute_reply": "2024-10-25T14:48:30.349370Z"
+     "iopub.execute_input": "2024-10-25T14:49:24.101515Z",
+     "iopub.status.busy": "2024-10-25T14:49:24.100617Z",
+     "iopub.status.idle": "2024-10-25T14:49:26.158253Z",
+     "shell.execute_reply": "2024-10-25T14:49:26.157654Z"
     }
    },
    "outputs": [
@@ -435,9 +435,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:0.011132662400108018\n",
-      "New effect:0.011014981045932223\n",
-      "p value:0.98\n",
+      "Estimated effect:1.0019898175815642\n",
+      "New effect:1.0016764361269999\n",
+      "p value:0.84\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html
index a83c88755..af55637e2 100644
--- a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html
+++ b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html
@@ -680,42 +680,42 @@ <h2>Create the Dataset<a class="headerlink" href="#Create-the-Dataset" title="Pe
     <tr>
       <th>0</th>
       <td>0.0</td>
-      <td>-0.920419</td>
-      <td>1.795900</td>
-      <td>5.340684</td>
-      <td>58.169113</td>
+      <td>0.911649</td>
+      <td>0.078613</td>
+      <td>2.341508</td>
+      <td>24.421081</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.0</td>
-      <td>0.163070</td>
-      <td>0.869024</td>
-      <td>16.179971</td>
-      <td>165.504448</td>
+      <td>0.0</td>
+      <td>0.562554</td>
+      <td>-0.616058</td>
+      <td>-0.305178</td>
+      <td>-5.510270</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.0</td>
-      <td>1.055829</td>
-      <td>0.227073</td>
-      <td>15.099906</td>
-      <td>154.320058</td>
+      <td>0.0</td>
+      <td>-1.098908</td>
+      <td>-0.478822</td>
+      <td>-1.843562</td>
+      <td>-21.410585</td>
     </tr>
     <tr>
       <th>3</th>
       <td>0.0</td>
-      <td>1.116559</td>
-      <td>-1.527957</td>
-      <td>-4.061567</td>
-      <td>-43.885508</td>
+      <td>1.196146</td>
+      <td>-0.922624</td>
+      <td>-0.333307</td>
+      <td>-6.761015</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.0</td>
-      <td>0.380152</td>
-      <td>-1.710498</td>
-      <td>3.028824</td>
-      <td>24.619150</td>
+      <td>0.0</td>
+      <td>1.147047</td>
+      <td>0.834155</td>
+      <td>3.801952</td>
+      <td>42.695680</td>
     </tr>
   </tbody>
 </table>
@@ -777,9 +777,9 @@ <h2>Identify the Estimand<a class="headerlink" href="#Identify-the-Estimand" tit
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W0,W1])
+─────(E[y|W1,W0])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,U) = P(y|v0,W0,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,U) = P(y|v0,W1,W0)
 
 ### Estimand : 2
 Estimand name: iv
@@ -851,7 +851,7 @@ <h2>Estimating the Effect<a class="headerlink" href="#Estimating-the-Effect" tit
 Target units: ate
 
 ## Estimate
-Mean value: 9.995081558025035
+Mean value: 10.001669761926992
 
 </pre></div></div>
 </div>
@@ -880,8 +880,8 @@ <h2>Using a Randomly Generated Outcome<a class="headerlink" href="#Using-a-Rando
 <div class="highlight"><pre>
 Refute: Use a Dummy Outcome
 Estimated effect:0
-New effect:1.6615045299294222e-05
-p value:0.8200000000000001
+New effect:-0.00019410486144059497
+p value:0.8
 
 </pre></div></div>
 </div>
@@ -925,8 +925,8 @@ <h2>Using a Function that Generates the Outcome from the Confounders<a class="he
 <div class="highlight"><pre>
 Refute: Use a Dummy Outcome
 Estimated effect:0
-New effect:-3.1229077590307696e-05
-p value:0.98
+New effect:0.0001040097892780143
+p value:0.8
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
index de9eb78ac..a4a47d5c8 100644
--- a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
+++ b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
@@ -20,10 +20,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:32.827006Z",
-     "iopub.status.busy": "2024-10-25T14:48:32.826598Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.275970Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.275324Z"
+     "iopub.execute_input": "2024-10-25T14:49:28.798605Z",
+     "iopub.status.busy": "2024-10-25T14:49:28.798413Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.322332Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.321572Z"
     }
    },
    "outputs": [],
@@ -70,10 +70,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.278529Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.278026Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.329795Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.329283Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.324892Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.324558Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.377443Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.376750Z"
     }
    },
    "outputs": [
@@ -109,54 +109,54 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.920419</td>\n",
-       "      <td>1.795900</td>\n",
-       "      <td>5.340684</td>\n",
-       "      <td>58.169113</td>\n",
+       "      <td>0.911649</td>\n",
+       "      <td>0.078613</td>\n",
+       "      <td>2.341508</td>\n",
+       "      <td>24.421081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.163070</td>\n",
-       "      <td>0.869024</td>\n",
-       "      <td>16.179971</td>\n",
-       "      <td>165.504448</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.562554</td>\n",
+       "      <td>-0.616058</td>\n",
+       "      <td>-0.305178</td>\n",
+       "      <td>-5.510270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.055829</td>\n",
-       "      <td>0.227073</td>\n",
-       "      <td>15.099906</td>\n",
-       "      <td>154.320058</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.098908</td>\n",
+       "      <td>-0.478822</td>\n",
+       "      <td>-1.843562</td>\n",
+       "      <td>-21.410585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.116559</td>\n",
-       "      <td>-1.527957</td>\n",
-       "      <td>-4.061567</td>\n",
-       "      <td>-43.885508</td>\n",
+       "      <td>1.196146</td>\n",
+       "      <td>-0.922624</td>\n",
+       "      <td>-0.333307</td>\n",
+       "      <td>-6.761015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.380152</td>\n",
-       "      <td>-1.710498</td>\n",
-       "      <td>3.028824</td>\n",
-       "      <td>24.619150</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.147047</td>\n",
+       "      <td>0.834155</td>\n",
+       "      <td>3.801952</td>\n",
+       "      <td>42.695680</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    Z0        W0        W1         v0           y\n",
-       "0  0.0 -0.920419  1.795900   5.340684   58.169113\n",
-       "1  1.0  0.163070  0.869024  16.179971  165.504448\n",
-       "2  1.0  1.055829  0.227073  15.099906  154.320058\n",
-       "3  0.0  1.116559 -1.527957  -4.061567  -43.885508\n",
-       "4  1.0  0.380152 -1.710498   3.028824   24.619150"
+       "    Z0        W0        W1        v0          y\n",
+       "0  0.0  0.911649  0.078613  2.341508  24.421081\n",
+       "1  0.0  0.562554 -0.616058 -0.305178  -5.510270\n",
+       "2  0.0 -1.098908 -0.478822 -1.843562 -21.410585\n",
+       "3  0.0  1.196146 -0.922624 -0.333307  -6.761015\n",
+       "4  0.0  1.147047  0.834155  3.801952  42.695680"
       ]
      },
      "execution_count": 2,
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.332986Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.331978Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.338475Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.338023Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.380258Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.379729Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.386381Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.385443Z"
     }
    },
    "outputs": [],
@@ -217,10 +217,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.341191Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.340745Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.506082Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.505536Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.388609Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.388222Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.551343Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.550862Z"
     }
    },
    "outputs": [
@@ -260,10 +260,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.508215Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.508010Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.649505Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.648956Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.553699Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.553285Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.686690Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.685968Z"
     }
    },
    "outputs": [
@@ -277,9 +277,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d              \n",
-      "─────(E[y|W0,W1])\n",
+      "─────(E[y|W1,W0])\n",
       "d[v₀]            \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,U) = P(y|v0,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,U) = P(y|v0,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -315,10 +315,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.651358Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.651165Z",
-     "iopub.status.idle": "2024-10-25T14:48:34.668176Z",
-     "shell.execute_reply": "2024-10-25T14:48:34.667675Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.688832Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.688605Z",
+     "iopub.status.idle": "2024-10-25T14:49:30.706898Z",
+     "shell.execute_reply": "2024-10-25T14:49:30.706254Z"
     },
     "scrolled": true
    },
@@ -361,7 +361,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.995081558025035\n",
+      "Mean value: 10.001669761926992\n",
       "\n"
      ]
     }
@@ -393,10 +393,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:34.670033Z",
-     "iopub.status.busy": "2024-10-25T14:48:34.669845Z",
-     "iopub.status.idle": "2024-10-25T14:48:36.314914Z",
-     "shell.execute_reply": "2024-10-25T14:48:36.314274Z"
+     "iopub.execute_input": "2024-10-25T14:49:30.709031Z",
+     "iopub.status.busy": "2024-10-25T14:49:30.708826Z",
+     "iopub.status.idle": "2024-10-25T14:49:32.254456Z",
+     "shell.execute_reply": "2024-10-25T14:49:32.253868Z"
     }
    },
    "outputs": [
@@ -406,8 +406,8 @@
      "text": [
       "Refute: Use a Dummy Outcome\n",
       "Estimated effect:0\n",
-      "New effect:1.6615045299294222e-05\n",
-      "p value:0.8200000000000001\n",
+      "New effect:-0.00019410486144059497\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:36.316824Z",
-     "iopub.status.busy": "2024-10-25T14:48:36.316632Z",
-     "iopub.status.idle": "2024-10-25T14:48:36.319758Z",
-     "shell.execute_reply": "2024-10-25T14:48:36.319276Z"
+     "iopub.execute_input": "2024-10-25T14:49:32.256460Z",
+     "iopub.status.busy": "2024-10-25T14:49:32.256226Z",
+     "iopub.status.idle": "2024-10-25T14:49:32.259576Z",
+     "shell.execute_reply": "2024-10-25T14:49:32.259075Z"
     }
    },
    "outputs": [],
@@ -477,10 +477,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:36.321410Z",
-     "iopub.status.busy": "2024-10-25T14:48:36.321220Z",
-     "iopub.status.idle": "2024-10-25T14:48:37.857928Z",
-     "shell.execute_reply": "2024-10-25T14:48:37.857340Z"
+     "iopub.execute_input": "2024-10-25T14:49:32.261348Z",
+     "iopub.status.busy": "2024-10-25T14:49:32.261142Z",
+     "iopub.status.idle": "2024-10-25T14:49:33.736648Z",
+     "shell.execute_reply": "2024-10-25T14:49:33.736046Z"
     }
    },
    "outputs": [
@@ -490,8 +490,8 @@
      "text": [
       "Refute: Use a Dummy Outcome\n",
       "Estimated effect:0\n",
-      "New effect:-3.1229077590307696e-05\n",
-      "p value:0.98\n",
+      "New effect:0.0001040097892780143\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_efficient_backdoor_example.html b/main/example_notebooks/dowhy_efficient_backdoor_example.html
index 289d3d3ac..e7cc0c716 100644
--- a/main/example_notebooks/dowhy_efficient_backdoor_example.html
+++ b/main/example_notebooks/dowhy_efficient_backdoor_example.html
@@ -706,13 +706,13 @@ <h2>The design of an observational study<a class="headerlink" href="#The-design-
 Estimand name: backdoor
 Estimand expression:
     d                                                                          ↪
-──────────(E[injury|neuromusc fatigue,previous injury,team motivation,contact  ↪
+──────────(E[injury|previous injury,contact sport,team motivation,tissue disor ↪
 d[warm-up]                                                                     ↪
 
 ↪
-↪ sport,tissue disorder])
+↪ der,neuromusc fatigue])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder)
+Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue)
 
 ### Estimand : 2
 Estimand name: iv
@@ -757,13 +757,13 @@ <h2>The design of an observational study<a class="headerlink" href="#The-design-
 Estimand name: backdoor
 Estimand expression:
     d                                                                          ↪
-──────────(E[injury|neuromusc fatigue,previous injury,team motivation,tissue d ↪
+──────────(E[injury|previous injury,team motivation,tissue disorder,neuromusc  ↪
 d[warm-up]                                                                     ↪
 
 ↪
-↪ isorder])
+↪ fatigue])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder)
+Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue)
 
 ### Estimand : 2
 Estimand name: iv
@@ -807,9 +807,9 @@ <h2>The design of an observational study<a class="headerlink" href="#The-design-
 Estimand name: backdoor
 Estimand expression:
     d
-──────────(E[injury|previous injury,fitness,team motivation])
+──────────(E[injury|fitness,previous injury,team motivation])
 d[warm-up]
-Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,fitness,team motivation,U) = P(injury|warm-up,previous injury,fitness,team motivation)
+Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,fitness,previous injury,team motivation,U) = P(injury|warm-up,fitness,previous injury,team motivation)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1113,9 +1113,9 @@ <h2>An example with costs<a class="headerlink" href="#An-example-with-costs" tit
 Estimand name: backdoor
 Estimand expression:
  d
-────(E[Y|L,K])
+────(E[Y|K,L])
 d[X]
-Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,L,K,U) = P(Y|X,L,K)
+Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,K,L,U) = P(Y|X,K,L)
 
 ### Estimand : 2
 Estimand name: iv
diff --git a/main/example_notebooks/dowhy_efficient_backdoor_example.ipynb b/main/example_notebooks/dowhy_efficient_backdoor_example.ipynb
index 398a51496..6cf5c1fa4 100644
--- a/main/example_notebooks/dowhy_efficient_backdoor_example.ipynb
+++ b/main/example_notebooks/dowhy_efficient_backdoor_example.ipynb
@@ -60,10 +60,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:40.354271Z",
-     "iopub.status.busy": "2024-10-25T14:48:40.354088Z",
-     "iopub.status.idle": "2024-10-25T14:48:41.825502Z",
-     "shell.execute_reply": "2024-10-25T14:48:41.824954Z"
+     "iopub.execute_input": "2024-10-25T14:49:36.739242Z",
+     "iopub.status.busy": "2024-10-25T14:49:36.739043Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.311834Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.311060Z"
     }
    },
    "outputs": [],
@@ -96,10 +96,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:41.828036Z",
-     "iopub.status.busy": "2024-10-25T14:48:41.827520Z",
-     "iopub.status.idle": "2024-10-25T14:48:41.834343Z",
-     "shell.execute_reply": "2024-10-25T14:48:41.833865Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.314753Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.314145Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.321034Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.320528Z"
     }
    },
    "outputs": [],
@@ -166,10 +166,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:41.836213Z",
-     "iopub.status.busy": "2024-10-25T14:48:41.835891Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.021934Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.021272Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.322903Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.322715Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.515062Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.513731Z"
     }
    },
    "outputs": [
@@ -200,10 +200,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.024108Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.023894Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.027430Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.026864Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.517592Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.517271Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.520547Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.519988Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.029680Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.029277Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.117677Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.117123Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.522657Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.522451Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.609168Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.608621Z"
     }
    },
    "outputs": [
@@ -233,13 +233,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                                          ↪\n",
-      "──────────(E[injury|neuromusc fatigue,previous injury,team motivation,contact  ↪\n",
+      "──────────(E[injury|previous injury,contact sport,team motivation,tissue disor ↪\n",
       "d[warm-up]                                                                     ↪\n",
       "\n",
       "↪                        \n",
-      "↪ sport,tissue disorder])\n",
+      "↪ der,neuromusc fatigue])\n",
       "↪                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,contact sport,tissue disorder)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,contact sport,team motivation,tissue disorder,neuromusc fatigue)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -287,10 +287,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.119502Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.119319Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.190244Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.189744Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.611345Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.610878Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.680986Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.680355Z"
     }
    },
    "outputs": [
@@ -304,13 +304,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                                          ↪\n",
-      "──────────(E[injury|neuromusc fatigue,previous injury,team motivation,tissue d ↪\n",
+      "──────────(E[injury|previous injury,team motivation,tissue disorder,neuromusc  ↪\n",
       "d[warm-up]                                                                     ↪\n",
       "\n",
       "↪          \n",
-      "↪ isorder])\n",
+      "↪ fatigue])\n",
       "↪          \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder,U) = P(injury|warm-up,neuromusc fatigue,previous injury,team motivation,tissue disorder)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue,U) = P(injury|warm-up,previous injury,team motivation,tissue disorder,neuromusc fatigue)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -351,10 +351,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.191967Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.191782Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.330829Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.330248Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.683106Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.682732Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.819223Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.818628Z"
     }
    },
    "outputs": [
@@ -368,9 +368,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                                                        \n",
-      "──────────(E[injury|previous injury,fitness,team motivation])\n",
+      "──────────(E[injury|fitness,previous injury,team motivation])\n",
       "d[warm-up]                                                   \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,fitness,team motivation,U) = P(injury|warm-up,previous injury,fitness,team motivation)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,fitness,previous injury,team motivation,U) = P(injury|warm-up,fitness,previous injury,team motivation)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -433,10 +433,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.332821Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.332623Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.336273Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.335823Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.821388Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.820989Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.824706Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.824214Z"
     }
    },
    "outputs": [],
@@ -471,10 +471,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.338005Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.337687Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.341363Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.340893Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.826594Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.826227Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.830039Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.829523Z"
     }
    },
    "outputs": [
@@ -505,10 +505,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.343068Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.342787Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.469277Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.468680Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.831863Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.831511Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.958211Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.957600Z"
     }
    },
    "outputs": [
@@ -563,10 +563,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.471317Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.470953Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.478299Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.477823Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.960406Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.959936Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.967714Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.967094Z"
     }
    },
    "outputs": [
@@ -635,10 +635,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.480258Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.479919Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.483513Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.482932Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.969778Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.969328Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.972904Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.972332Z"
     }
    },
    "outputs": [],
@@ -662,10 +662,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.485307Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.484898Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.488793Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.488301Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.974698Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.974343Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.978052Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.977594Z"
     }
    },
    "outputs": [
@@ -712,10 +712,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.490658Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.490347Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.495220Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.494655Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.979768Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.979590Z",
+     "iopub.status.idle": "2024-10-25T14:49:38.984458Z",
+     "shell.execute_reply": "2024-10-25T14:49:38.983973Z"
     },
     "pycharm": {
      "name": "#%%\n"
@@ -773,10 +773,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.497010Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.496841Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.696737Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.696052Z"
+     "iopub.execute_input": "2024-10-25T14:49:38.986330Z",
+     "iopub.status.busy": "2024-10-25T14:49:38.985872Z",
+     "iopub.status.idle": "2024-10-25T14:49:39.202167Z",
+     "shell.execute_reply": "2024-10-25T14:49:39.201523Z"
     },
     "pycharm": {
      "name": "#%%\n"
@@ -793,9 +793,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       " d            \n",
-      "────(E[Y|L,K])\n",
+      "────(E[Y|K,L])\n",
       "d[X]          \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,L,K,U) = P(Y|X,L,K)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,K,L,U) = P(Y|X,K,L)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -849,10 +849,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:42.698842Z",
-     "iopub.status.busy": "2024-10-25T14:48:42.698510Z",
-     "iopub.status.idle": "2024-10-25T14:48:42.799244Z",
-     "shell.execute_reply": "2024-10-25T14:48:42.798541Z"
+     "iopub.execute_input": "2024-10-25T14:49:39.204089Z",
+     "iopub.status.busy": "2024-10-25T14:49:39.203898Z",
+     "iopub.status.idle": "2024-10-25T14:49:39.303434Z",
+     "shell.execute_reply": "2024-10-25T14:49:39.302879Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/dowhy_estimation_methods.html b/main/example_notebooks/dowhy_estimation_methods.html
index 19759f4e6..f57b34082 100644
--- a/main/example_notebooks/dowhy_estimation_methods.html
+++ b/main/example_notebooks/dowhy_estimation_methods.html
@@ -648,62 +648,62 @@ <h1>DoWhy: Different estimation methods for causal inference<a class="headerlink
     <tr>
       <th>0</th>
       <td>1.0</td>
-      <td>0.146419</td>
-      <td>0.719678</td>
-      <td>1.474503</td>
-      <td>-0.660766</td>
-      <td>0.254804</td>
-      <td>0.248627</td>
+      <td>0.802889</td>
+      <td>0.192251</td>
+      <td>0.478185</td>
+      <td>1.207485</td>
+      <td>1.327381</td>
+      <td>-1.431369</td>
       <td>True</td>
-      <td>17.465511</td>
+      <td>15.397068</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.0</td>
-      <td>0.798909</td>
-      <td>1.288351</td>
-      <td>-0.142688</td>
-      <td>-0.924025</td>
-      <td>-0.057606</td>
-      <td>1.141256</td>
+      <td>1.0</td>
+      <td>0.384253</td>
+      <td>0.034604</td>
+      <td>0.432514</td>
+      <td>-1.598181</td>
+      <td>-1.038358</td>
+      <td>-0.583790</td>
       <td>True</td>
-      <td>16.122811</td>
+      <td>-1.885605</td>
     </tr>
     <tr>
       <th>2</th>
       <td>0.0</td>
-      <td>0.754634</td>
-      <td>1.463066</td>
-      <td>0.969293</td>
-      <td>-0.656878</td>
-      <td>0.754685</td>
-      <td>0.745172</td>
+      <td>0.963544</td>
+      <td>0.067244</td>
+      <td>-1.716632</td>
+      <td>-1.006641</td>
+      <td>1.038315</td>
+      <td>-0.177907</td>
       <td>True</td>
-      <td>22.201326</td>
+      <td>5.922537</td>
     </tr>
     <tr>
       <th>3</th>
       <td>0.0</td>
-      <td>0.141993</td>
-      <td>-0.216277</td>
-      <td>1.013538</td>
-      <td>0.923354</td>
-      <td>-1.233122</td>
-      <td>-0.472881</td>
+      <td>0.819110</td>
+      <td>-0.634177</td>
+      <td>0.209765</td>
+      <td>-0.045081</td>
+      <td>0.311348</td>
+      <td>-1.310749</td>
       <td>True</td>
-      <td>5.330300</td>
+      <td>3.810727</td>
     </tr>
     <tr>
       <th>4</th>
       <td>0.0</td>
-      <td>0.844711</td>
-      <td>0.899253</td>
-      <td>-1.059884</td>
-      <td>-0.306315</td>
-      <td>1.385281</td>
-      <td>0.708705</td>
+      <td>0.999171</td>
+      <td>2.072248</td>
+      <td>1.195379</td>
+      <td>-0.336769</td>
+      <td>-0.377211</td>
+      <td>1.341917</td>
       <td>True</td>
-      <td>18.422903</td>
+      <td>23.129556</td>
     </tr>
     <tr>
       <th>...</th>
@@ -719,63 +719,63 @@ <h1>DoWhy: Different estimation methods for causal inference<a class="headerlink
     </tr>
     <tr>
       <th>9995</th>
-      <td>1.0</td>
-      <td>0.367345</td>
-      <td>0.798867</td>
-      <td>1.208066</td>
-      <td>-0.793425</td>
-      <td>-1.796504</td>
-      <td>0.140363</td>
+      <td>0.0</td>
+      <td>0.771256</td>
+      <td>0.464651</td>
+      <td>1.593722</td>
+      <td>0.695716</td>
+      <td>-0.538747</td>
+      <td>-2.698634</td>
       <td>True</td>
-      <td>5.591300</td>
+      <td>1.915655</td>
     </tr>
     <tr>
       <th>9996</th>
-      <td>1.0</td>
-      <td>0.223508</td>
-      <td>-0.319477</td>
-      <td>0.129182</td>
-      <td>-1.441393</td>
-      <td>0.052671</td>
-      <td>1.265595</td>
+      <td>0.0</td>
+      <td>0.187240</td>
+      <td>0.350261</td>
+      <td>1.437157</td>
+      <td>2.566900</td>
+      <td>-0.473194</td>
+      <td>-0.907145</td>
       <td>True</td>
-      <td>15.172138</td>
+      <td>16.509397</td>
     </tr>
     <tr>
       <th>9997</th>
       <td>0.0</td>
-      <td>0.306972</td>
-      <td>1.745715</td>
-      <td>0.505480</td>
-      <td>0.092517</td>
-      <td>1.253911</td>
-      <td>0.763002</td>
+      <td>0.801760</td>
+      <td>0.990208</td>
+      <td>2.271466</td>
+      <td>-0.731640</td>
+      <td>-0.067994</td>
+      <td>-1.118754</td>
       <td>True</td>
-      <td>24.799447</td>
+      <td>11.595858</td>
     </tr>
     <tr>
       <th>9998</th>
       <td>0.0</td>
-      <td>0.879923</td>
-      <td>1.333993</td>
-      <td>0.135373</td>
-      <td>-0.938021</td>
-      <td>0.600853</td>
-      <td>2.210361</td>
-      <td>True</td>
-      <td>26.230045</td>
+      <td>0.081604</td>
+      <td>-0.255929</td>
+      <td>0.852812</td>
+      <td>-0.913018</td>
+      <td>-0.753771</td>
+      <td>-1.655911</td>
+      <td>False</td>
+      <td>-13.153870</td>
     </tr>
     <tr>
       <th>9999</th>
       <td>0.0</td>
-      <td>0.295673</td>
-      <td>1.801730</td>
-      <td>1.293909</td>
-      <td>-0.421017</td>
-      <td>1.161574</td>
-      <td>-0.149046</td>
+      <td>0.963800</td>
+      <td>0.065191</td>
+      <td>0.795102</td>
+      <td>0.108932</td>
+      <td>-1.039872</td>
+      <td>-1.137772</td>
       <td>True</td>
-      <td>21.223555</td>
+      <td>1.890986</td>
     </tr>
   </tbody>
 </table>
@@ -853,9 +853,9 @@ <h2>Identifying the causal estimand<a class="headerlink" href="#Identifying-the-
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ### Estimand : 2
 Estimand name: iv
@@ -863,9 +863,9 @@ <h2>Identifying the causal estimand<a class="headerlink" href="#Identifying-the-
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+ ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 
 ### Estimand : 3
 Estimand name: frontdoor
@@ -903,19 +903,19 @@ <h2>Method 1: Regression<a class="headerlink" href="#Method-1:-Regression" title
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+W4
+b: y~v0+W1+W4+W0+W2+W3
 Target units: ate
 
 ## Estimate
-Mean value: 9.999489976408874
+Mean value: 10.000263653094079
 p-value: [0.]
 
-Causal Estimate is 9.999489976408874
+Causal Estimate is 10.000263653094079
 </pre></div></div>
 </div>
 </section>
@@ -949,18 +949,18 @@ <h2>Method 2: Distance Matching<a class="headerlink" href="#Method-2:-Distance-M
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+W4
+b: y~v0+W1+W4+W0+W2+W3
 Target units: att
 
 ## Estimate
-Mean value: 11.103626751304049
+Mean value: 10.721226135478332
 
-Causal Estimate is 11.103626751304049
+Causal Estimate is 10.721226135478332
 </pre></div></div>
 </div>
 </section>
@@ -993,18 +993,18 @@ <h2>Method 3: Propensity Score Stratification<a class="headerlink" href="#Method
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+W4
+b: y~v0+W1+W4+W0+W2+W3
 Target units: att
 
 ## Estimate
-Mean value: 9.995565463987674
+Mean value: 9.968475783307126
 
-Causal Estimate is 9.995565463987674
+Causal Estimate is 9.968475783307126
 </pre></div></div>
 </div>
 </section>
@@ -1037,18 +1037,18 @@ <h2>Method 4: Propensity Score Matching<a class="headerlink" href="#Method-4:-Pr
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+W4
+b: y~v0+W1+W4+W0+W2+W3
 Target units: atc
 
 ## Estimate
-Mean value: 9.725131389529945
+Mean value: 9.909765204233272
 
-Causal Estimate is 9.725131389529945
+Causal Estimate is 9.909765204233272
 </pre></div></div>
 </div>
 </section>
@@ -1082,18 +1082,18 @@ <h2>Method 5: Weighting<a class="headerlink" href="#Method-5:-Weighting" title="
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W0,W2,W4])
+─────(E[y|W1,W4,W0,W2,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)
 
 ## Realized estimand
-b: y~v0+W1+W3+W0+W2+W4
+b: y~v0+W1+W4+W0+W2+W3
 Target units: ate
 
 ## Estimate
-Mean value: 11.220427986837883
+Mean value: 10.461426422877393
 
-Causal Estimate is 11.220427986837883
+Causal Estimate is 10.461426422877393
 </pre></div></div>
 </div>
 </section>
@@ -1127,9 +1127,9 @@ <h2>Method 6: Instrumental Variable<a class="headerlink" href="#Method-6:-Instru
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+ ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 
 ## Realized estimand
 Realized estimand: Wald Estimator
@@ -1142,17 +1142,17 @@ <h2>Method 6: Instrumental Variable<a class="headerlink" href="#Method-6:-Instru
  ⎡ d     ⎤
 E⎢───(v₀)⎥
  ⎣dZ₀    ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 Estimand assumption 3, treatment_effect_homogeneity: Each unit&#39;s treatment [&#39;v0&#39;] is affected in the same way by common causes of [&#39;v0&#39;] and [&#39;y&#39;]
 Estimand assumption 4, outcome_effect_homogeneity: Each unit&#39;s outcome [&#39;y&#39;] is affected in the same way by common causes of [&#39;v0&#39;] and [&#39;y&#39;]
 
 Target units: ate
 
 ## Estimate
-Mean value: 10.178003799779505
+Mean value: 10.897552036840848
 
-Causal Estimate is 10.178003799779505
+Causal Estimate is 10.897552036840848
 </pre></div></div>
 </div>
 </section>
@@ -1189,9 +1189,9 @@ <h2>Method 7: Regression Discontinuity<a class="headerlink" href="#Method-7:-Reg
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+ ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 
 ## Realized estimand
 Realized estimand: Wald Estimator
@@ -1204,17 +1204,17 @@ <h2>Method 7: Regression Discontinuity<a class="headerlink" href="#Method-7:-Reg
  ⎡        d             ⎤
 E⎢──────────────────(v₀)⎥
  ⎣dlocal_rd_variable    ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 Estimand assumption 3, treatment_effect_homogeneity: Each unit&#39;s treatment [&#39;v0&#39;] is affected in the same way by common causes of [&#39;v0&#39;] and [&#39;y&#39;]
 Estimand assumption 4, outcome_effect_homogeneity: Each unit&#39;s outcome [&#39;y&#39;] is affected in the same way by common causes of [&#39;v0&#39;] and [&#39;y&#39;]
 
 Target units: ate
 
 ## Estimate
-Mean value: 9.840139131031695
+Mean value: 82.74381691014266
 
-Causal Estimate is 9.840139131031695
+Causal Estimate is 82.74381691014266
 </pre></div></div>
 </div>
 </section>
diff --git a/main/example_notebooks/dowhy_estimation_methods.ipynb b/main/example_notebooks/dowhy_estimation_methods.ipynb
index 48d0fa484..e24be3a17 100644
--- a/main/example_notebooks/dowhy_estimation_methods.ipynb
+++ b/main/example_notebooks/dowhy_estimation_methods.ipynb
@@ -18,10 +18,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:45.719871Z",
-     "iopub.status.busy": "2024-10-25T14:48:45.719690Z",
-     "iopub.status.idle": "2024-10-25T14:48:45.735581Z",
-     "shell.execute_reply": "2024-10-25T14:48:45.734972Z"
+     "iopub.execute_input": "2024-10-25T14:49:42.435913Z",
+     "iopub.status.busy": "2024-10-25T14:49:42.435733Z",
+     "iopub.status.idle": "2024-10-25T14:49:42.452524Z",
+     "shell.execute_reply": "2024-10-25T14:49:42.451905Z"
     }
    },
    "outputs": [],
@@ -35,10 +35,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:45.737803Z",
-     "iopub.status.busy": "2024-10-25T14:48:45.737356Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.194277Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.193582Z"
+     "iopub.execute_input": "2024-10-25T14:49:42.454383Z",
+     "iopub.status.busy": "2024-10-25T14:49:42.454033Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.052593Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.051894Z"
     }
    },
    "outputs": [],
@@ -66,10 +66,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.196701Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.196365Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.479895Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.479232Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.055162Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.054799Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.342278Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.341401Z"
     }
    },
    "outputs": [
@@ -109,62 +109,62 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.146419</td>\n",
-       "      <td>0.719678</td>\n",
-       "      <td>1.474503</td>\n",
-       "      <td>-0.660766</td>\n",
-       "      <td>0.254804</td>\n",
-       "      <td>0.248627</td>\n",
+       "      <td>0.802889</td>\n",
+       "      <td>0.192251</td>\n",
+       "      <td>0.478185</td>\n",
+       "      <td>1.207485</td>\n",
+       "      <td>1.327381</td>\n",
+       "      <td>-1.431369</td>\n",
        "      <td>True</td>\n",
-       "      <td>17.465511</td>\n",
+       "      <td>15.397068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.798909</td>\n",
-       "      <td>1.288351</td>\n",
-       "      <td>-0.142688</td>\n",
-       "      <td>-0.924025</td>\n",
-       "      <td>-0.057606</td>\n",
-       "      <td>1.141256</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.384253</td>\n",
+       "      <td>0.034604</td>\n",
+       "      <td>0.432514</td>\n",
+       "      <td>-1.598181</td>\n",
+       "      <td>-1.038358</td>\n",
+       "      <td>-0.583790</td>\n",
        "      <td>True</td>\n",
-       "      <td>16.122811</td>\n",
+       "      <td>-1.885605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.754634</td>\n",
-       "      <td>1.463066</td>\n",
-       "      <td>0.969293</td>\n",
-       "      <td>-0.656878</td>\n",
-       "      <td>0.754685</td>\n",
-       "      <td>0.745172</td>\n",
+       "      <td>0.963544</td>\n",
+       "      <td>0.067244</td>\n",
+       "      <td>-1.716632</td>\n",
+       "      <td>-1.006641</td>\n",
+       "      <td>1.038315</td>\n",
+       "      <td>-0.177907</td>\n",
        "      <td>True</td>\n",
-       "      <td>22.201326</td>\n",
+       "      <td>5.922537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.141993</td>\n",
-       "      <td>-0.216277</td>\n",
-       "      <td>1.013538</td>\n",
-       "      <td>0.923354</td>\n",
-       "      <td>-1.233122</td>\n",
-       "      <td>-0.472881</td>\n",
+       "      <td>0.819110</td>\n",
+       "      <td>-0.634177</td>\n",
+       "      <td>0.209765</td>\n",
+       "      <td>-0.045081</td>\n",
+       "      <td>0.311348</td>\n",
+       "      <td>-1.310749</td>\n",
        "      <td>True</td>\n",
-       "      <td>5.330300</td>\n",
+       "      <td>3.810727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.844711</td>\n",
-       "      <td>0.899253</td>\n",
-       "      <td>-1.059884</td>\n",
-       "      <td>-0.306315</td>\n",
-       "      <td>1.385281</td>\n",
-       "      <td>0.708705</td>\n",
+       "      <td>0.999171</td>\n",
+       "      <td>2.072248</td>\n",
+       "      <td>1.195379</td>\n",
+       "      <td>-0.336769</td>\n",
+       "      <td>-0.377211</td>\n",
+       "      <td>1.341917</td>\n",
        "      <td>True</td>\n",
-       "      <td>18.422903</td>\n",
+       "      <td>23.129556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -180,63 +180,63 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9995</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.367345</td>\n",
-       "      <td>0.798867</td>\n",
-       "      <td>1.208066</td>\n",
-       "      <td>-0.793425</td>\n",
-       "      <td>-1.796504</td>\n",
-       "      <td>0.140363</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.771256</td>\n",
+       "      <td>0.464651</td>\n",
+       "      <td>1.593722</td>\n",
+       "      <td>0.695716</td>\n",
+       "      <td>-0.538747</td>\n",
+       "      <td>-2.698634</td>\n",
        "      <td>True</td>\n",
-       "      <td>5.591300</td>\n",
+       "      <td>1.915655</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.223508</td>\n",
-       "      <td>-0.319477</td>\n",
-       "      <td>0.129182</td>\n",
-       "      <td>-1.441393</td>\n",
-       "      <td>0.052671</td>\n",
-       "      <td>1.265595</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.187240</td>\n",
+       "      <td>0.350261</td>\n",
+       "      <td>1.437157</td>\n",
+       "      <td>2.566900</td>\n",
+       "      <td>-0.473194</td>\n",
+       "      <td>-0.907145</td>\n",
        "      <td>True</td>\n",
-       "      <td>15.172138</td>\n",
+       "      <td>16.509397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.306972</td>\n",
-       "      <td>1.745715</td>\n",
-       "      <td>0.505480</td>\n",
-       "      <td>0.092517</td>\n",
-       "      <td>1.253911</td>\n",
-       "      <td>0.763002</td>\n",
+       "      <td>0.801760</td>\n",
+       "      <td>0.990208</td>\n",
+       "      <td>2.271466</td>\n",
+       "      <td>-0.731640</td>\n",
+       "      <td>-0.067994</td>\n",
+       "      <td>-1.118754</td>\n",
        "      <td>True</td>\n",
-       "      <td>24.799447</td>\n",
+       "      <td>11.595858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.879923</td>\n",
-       "      <td>1.333993</td>\n",
-       "      <td>0.135373</td>\n",
-       "      <td>-0.938021</td>\n",
-       "      <td>0.600853</td>\n",
-       "      <td>2.210361</td>\n",
-       "      <td>True</td>\n",
-       "      <td>26.230045</td>\n",
+       "      <td>0.081604</td>\n",
+       "      <td>-0.255929</td>\n",
+       "      <td>0.852812</td>\n",
+       "      <td>-0.913018</td>\n",
+       "      <td>-0.753771</td>\n",
+       "      <td>-1.655911</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-13.153870</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.295673</td>\n",
-       "      <td>1.801730</td>\n",
-       "      <td>1.293909</td>\n",
-       "      <td>-0.421017</td>\n",
-       "      <td>1.161574</td>\n",
-       "      <td>-0.149046</td>\n",
+       "      <td>0.963800</td>\n",
+       "      <td>0.065191</td>\n",
+       "      <td>0.795102</td>\n",
+       "      <td>0.108932</td>\n",
+       "      <td>-1.039872</td>\n",
+       "      <td>-1.137772</td>\n",
        "      <td>True</td>\n",
-       "      <td>21.223555</td>\n",
+       "      <td>1.890986</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -244,31 +244,31 @@
        "</div>"
       ],
       "text/plain": [
-       "       Z0        Z1        W0        W1        W2        W3        W4    v0  \\\n",
-       "0     1.0  0.146419  0.719678  1.474503 -0.660766  0.254804  0.248627  True   \n",
-       "1     0.0  0.798909  1.288351 -0.142688 -0.924025 -0.057606  1.141256  True   \n",
-       "2     0.0  0.754634  1.463066  0.969293 -0.656878  0.754685  0.745172  True   \n",
-       "3     0.0  0.141993 -0.216277  1.013538  0.923354 -1.233122 -0.472881  True   \n",
-       "4     0.0  0.844711  0.899253 -1.059884 -0.306315  1.385281  0.708705  True   \n",
-       "...   ...       ...       ...       ...       ...       ...       ...   ...   \n",
-       "9995  1.0  0.367345  0.798867  1.208066 -0.793425 -1.796504  0.140363  True   \n",
-       "9996  1.0  0.223508 -0.319477  0.129182 -1.441393  0.052671  1.265595  True   \n",
-       "9997  0.0  0.306972  1.745715  0.505480  0.092517  1.253911  0.763002  True   \n",
-       "9998  0.0  0.879923  1.333993  0.135373 -0.938021  0.600853  2.210361  True   \n",
-       "9999  0.0  0.295673  1.801730  1.293909 -0.421017  1.161574 -0.149046  True   \n",
+       "       Z0        Z1        W0        W1        W2        W3        W4     v0  \\\n",
+       "0     1.0  0.802889  0.192251  0.478185  1.207485  1.327381 -1.431369   True   \n",
+       "1     1.0  0.384253  0.034604  0.432514 -1.598181 -1.038358 -0.583790   True   \n",
+       "2     0.0  0.963544  0.067244 -1.716632 -1.006641  1.038315 -0.177907   True   \n",
+       "3     0.0  0.819110 -0.634177  0.209765 -0.045081  0.311348 -1.310749   True   \n",
+       "4     0.0  0.999171  2.072248  1.195379 -0.336769 -0.377211  1.341917   True   \n",
+       "...   ...       ...       ...       ...       ...       ...       ...    ...   \n",
+       "9995  0.0  0.771256  0.464651  1.593722  0.695716 -0.538747 -2.698634   True   \n",
+       "9996  0.0  0.187240  0.350261  1.437157  2.566900 -0.473194 -0.907145   True   \n",
+       "9997  0.0  0.801760  0.990208  2.271466 -0.731640 -0.067994 -1.118754   True   \n",
+       "9998  0.0  0.081604 -0.255929  0.852812 -0.913018 -0.753771 -1.655911  False   \n",
+       "9999  0.0  0.963800  0.065191  0.795102  0.108932 -1.039872 -1.137772   True   \n",
        "\n",
        "              y  \n",
-       "0     17.465511  \n",
-       "1     16.122811  \n",
-       "2     22.201326  \n",
-       "3      5.330300  \n",
-       "4     18.422903  \n",
+       "0     15.397068  \n",
+       "1     -1.885605  \n",
+       "2      5.922537  \n",
+       "3      3.810727  \n",
+       "4     23.129556  \n",
        "...         ...  \n",
-       "9995   5.591300  \n",
-       "9996  15.172138  \n",
-       "9997  24.799447  \n",
-       "9998  26.230045  \n",
-       "9999  21.223555  \n",
+       "9995   1.915655  \n",
+       "9996  16.509397  \n",
+       "9997  11.595858  \n",
+       "9998 -13.153870  \n",
+       "9999   1.890986  \n",
        "\n",
        "[10000 rows x 9 columns]"
       ]
@@ -317,10 +317,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.482529Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.482131Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.525030Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.524450Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.345518Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.344713Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.390702Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.390082Z"
     }
    },
    "outputs": [],
@@ -340,10 +340,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.528466Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.527414Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.706532Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.705955Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.393805Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.393260Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.577814Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.577097Z"
     }
    },
    "outputs": [
@@ -367,10 +367,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.709021Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.708620Z",
-     "iopub.status.idle": "2024-10-25T14:48:47.751585Z",
-     "shell.execute_reply": "2024-10-25T14:48:47.750995Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.580599Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.580113Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.625243Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.624687Z"
     }
    },
    "outputs": [
@@ -402,10 +402,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:47.753943Z",
-     "iopub.status.busy": "2024-10-25T14:48:47.753529Z",
-     "iopub.status.idle": "2024-10-25T14:48:48.016014Z",
-     "shell.execute_reply": "2024-10-25T14:48:48.015375Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.627637Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.627178Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.929684Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.929050Z"
     }
    },
    "outputs": [
@@ -419,9 +419,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -429,9 +429,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -459,10 +459,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:48.018128Z",
-     "iopub.status.busy": "2024-10-25T14:48:48.017760Z",
-     "iopub.status.idle": "2024-10-25T14:48:48.070273Z",
-     "shell.execute_reply": "2024-10-25T14:48:48.069364Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.931940Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.931548Z",
+     "iopub.status.idle": "2024-10-25T14:49:44.986178Z",
+     "shell.execute_reply": "2024-10-25T14:49:44.985585Z"
     }
    },
    "outputs": [
@@ -479,19 +479,19 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.999489976408874\n",
+      "Mean value: 10.000263653094079\n",
       "p-value: [0.]\n",
       "\n",
-      "Causal Estimate is 9.999489976408874\n"
+      "Causal Estimate is 10.000263653094079\n"
      ]
     }
    ],
@@ -517,10 +517,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:48.073301Z",
-     "iopub.status.busy": "2024-10-25T14:48:48.072740Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.286702Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.286129Z"
+     "iopub.execute_input": "2024-10-25T14:49:44.989155Z",
+     "iopub.status.busy": "2024-10-25T14:49:44.988639Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.198041Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.197483Z"
     }
    },
    "outputs": [
@@ -537,18 +537,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.103626751304049\n",
+      "Mean value: 10.721226135478332\n",
       "\n",
-      "Causal Estimate is 11.103626751304049\n"
+      "Causal Estimate is 10.721226135478332\n"
      ]
     }
    ],
@@ -575,10 +575,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.288762Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.288345Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.782963Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.782304Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.200120Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.199746Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.714955Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.714348Z"
     }
    },
    "outputs": [
@@ -595,18 +595,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.995565463987674\n",
+      "Mean value: 9.968475783307126\n",
       "\n",
-      "Causal Estimate is 9.995565463987674\n"
+      "Causal Estimate is 9.968475783307126\n"
      ]
     }
    ],
@@ -632,10 +632,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.784927Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.784738Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.834176Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.833579Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.717043Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.716661Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.770839Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.770181Z"
     }
    },
    "outputs": [
@@ -652,18 +652,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.725131389529945\n",
+      "Mean value: 9.909765204233272\n",
       "\n",
-      "Causal Estimate is 9.725131389529945\n"
+      "Causal Estimate is 9.909765204233272\n"
      ]
     }
    ],
@@ -692,10 +692,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.836216Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.835796Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.884224Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.883685Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.772957Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.772583Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.820036Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.819536Z"
     }
    },
    "outputs": [
@@ -712,18 +712,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W0,W2,W4])\n",
+      "─────(E[y|W1,W4,W0,W2,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W4,U) = P(y|v0,W1,W3,W0,W2,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W4,W0,W2,W3,U) = P(y|v0,W1,W4,W0,W2,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W0+W2+W4\n",
+      "b: y~v0+W1+W4+W0+W2+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.220427986837883\n",
+      "Mean value: 10.461426422877393\n",
       "\n",
-      "Causal Estimate is 11.220427986837883\n"
+      "Causal Estimate is 10.461426422877393\n"
      ]
     }
    ],
@@ -750,10 +750,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.886237Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.885806Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.926831Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.926172Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.821965Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.821600Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.860360Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.859800Z"
     },
     "scrolled": true
    },
@@ -773,9 +773,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "## Realized estimand\n",
       "Realized estimand: Wald Estimator\n",
@@ -788,17 +788,17 @@
       " ⎡ d     ⎤\n",
       "E⎢───(v₀)⎥\n",
       " ⎣dZ₀    ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.178003799779505\n",
+      "Mean value: 10.897552036840848\n",
       "\n",
-      "Causal Estimate is 10.178003799779505\n"
+      "Causal Estimate is 10.897552036840848\n"
      ]
     }
    ],
@@ -823,10 +823,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:50.928691Z",
-     "iopub.status.busy": "2024-10-25T14:48:50.928469Z",
-     "iopub.status.idle": "2024-10-25T14:48:50.967977Z",
-     "shell.execute_reply": "2024-10-25T14:48:50.967315Z"
+     "iopub.execute_input": "2024-10-25T14:49:47.862344Z",
+     "iopub.status.busy": "2024-10-25T14:49:47.861860Z",
+     "iopub.status.idle": "2024-10-25T14:49:47.898139Z",
+     "shell.execute_reply": "2024-10-25T14:49:47.897524Z"
     }
    },
    "outputs": [
@@ -845,9 +845,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "## Realized estimand\n",
       "Realized estimand: Wald Estimator\n",
@@ -860,17 +860,17 @@
       " ⎡        d             ⎤\n",
       "E⎢──────────────────(v₀)⎥\n",
       " ⎣dlocal_rd_variable    ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n",
       "\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.840139131031695\n",
+      "Mean value: 82.74381691014266\n",
       "\n",
-      "Causal Estimate is 9.840139131031695\n"
+      "Causal Estimate is 82.74381691014266\n"
      ]
     }
    ],
diff --git a/main/example_notebooks/dowhy_functional_api.html b/main/example_notebooks/dowhy_functional_api.html
index a56248e60..1d87fa03c 100644
--- a/main/example_notebooks/dowhy_functional_api.html
+++ b/main/example_notebooks/dowhy_functional_api.html
@@ -724,9 +724,9 @@ <h2>Identify Effect - Functional API (Preview)<a class="headerlink" href="#Ident
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W2,W0])
+─────(E[y|W2,W1,W0])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)
 
 ### Estimand : 2
 Estimand name: iv
@@ -788,7 +788,7 @@ <h2>Estimate Effect - Functional API (Preview)<a class="headerlink" href="#Estim
 Estimand type: EstimandType.NONPARAMETRIC_ATE
 
 ## Realized estimand
-b: y~v0+W1+W2+W0
+b: y~v0+W2+W1+W0
 Target units: ate
 
 ## Estimate
@@ -801,7 +801,7 @@ <h2>Estimate Effect - Functional API (Preview)<a class="headerlink" href="#Estim
 Estimand type: EstimandType.NONPARAMETRIC_ATE
 
 ## Realized estimand
-b: y~v0+W1+W2+W0
+b: y~v0+W2+W1+W0
 Target units: ate
 
 ## Estimate
@@ -855,7 +855,7 @@ <h2>Estimate Effect - Functional API (Preview)<a class="headerlink" href="#Estim
 Estimand type: EstimandType.NONPARAMETRIC_ATE
 
 ## Realized estimand
-b: y~v0+W1+W2+W0
+b: y~v0+W2+W1+W0
 Target units: ate
 
 ## Estimate
@@ -961,9 +961,9 @@ <h2>Identify Effect<a class="headerlink" href="#Identify-Effect" title="Permalin
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W2,W0])
+─────(E[y|W2,W1,W0])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)
 
 ### Estimand : 2
 Estimand name: iv
@@ -971,9 +971,9 @@ <h2>Identify Effect<a class="headerlink" href="#Identify-Effect" title="Permalin
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
-Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
+ ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
+Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
 
 ### Estimand : 3
 Estimand name: frontdoor
@@ -1010,12 +1010,12 @@ <h2>Estimate Effect<a class="headerlink" href="#Estimate-Effect" title="Permalin
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W2,W0])
+─────(E[y|W2,W1,W0])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)
 
 ## Realized estimand
-b: y~v0+W1+W2+W0
+b: y~v0+W2+W1+W0
 Target units: ate
 
 ## Estimate
diff --git a/main/example_notebooks/dowhy_functional_api.ipynb b/main/example_notebooks/dowhy_functional_api.ipynb
index b8d082977..ace4c1412 100644
--- a/main/example_notebooks/dowhy_functional_api.ipynb
+++ b/main/example_notebooks/dowhy_functional_api.ipynb
@@ -40,10 +40,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:54.509881Z",
-     "iopub.status.busy": "2024-10-25T14:48:54.509444Z",
-     "iopub.status.idle": "2024-10-25T14:48:55.966892Z",
-     "shell.execute_reply": "2024-10-25T14:48:55.966260Z"
+     "iopub.execute_input": "2024-10-25T14:49:51.461325Z",
+     "iopub.status.busy": "2024-10-25T14:49:51.460897Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.001966Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.001350Z"
     }
    },
    "outputs": [],
@@ -108,10 +108,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:55.969320Z",
-     "iopub.status.busy": "2024-10-25T14:48:55.968829Z",
-     "iopub.status.idle": "2024-10-25T14:48:55.999697Z",
-     "shell.execute_reply": "2024-10-25T14:48:55.999058Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.004473Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.003974Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.035012Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.034410Z"
     }
    },
    "outputs": [
@@ -178,10 +178,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.001638Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.001272Z",
-     "iopub.status.idle": "2024-10-25T14:48:56.020609Z",
-     "shell.execute_reply": "2024-10-25T14:48:56.020014Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.037028Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.036643Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.055967Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.055429Z"
     }
    },
    "outputs": [
@@ -195,9 +195,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -244,10 +244,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.022640Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.022198Z",
-     "iopub.status.idle": "2024-10-25T14:48:56.041005Z",
-     "shell.execute_reply": "2024-10-25T14:48:56.040397Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.057882Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.057521Z",
+     "iopub.status.idle": "2024-10-25T14:49:53.077329Z",
+     "shell.execute_reply": "2024-10-25T14:49:53.076796Z"
     }
    },
    "outputs": [
@@ -261,7 +261,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -274,7 +274,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -320,10 +320,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:56.042869Z",
-     "iopub.status.busy": "2024-10-25T14:48:56.042519Z",
-     "iopub.status.idle": "2024-10-25T14:48:57.414935Z",
-     "shell.execute_reply": "2024-10-25T14:48:57.414385Z"
+     "iopub.execute_input": "2024-10-25T14:49:53.079235Z",
+     "iopub.status.busy": "2024-10-25T14:49:53.078865Z",
+     "iopub.status.idle": "2024-10-25T14:49:54.571285Z",
+     "shell.execute_reply": "2024-10-25T14:49:54.570596Z"
     }
    },
    "outputs": [
@@ -337,7 +337,7 @@
       "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -389,10 +389,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:57.417131Z",
-     "iopub.status.busy": "2024-10-25T14:48:57.416661Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.494315Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.493680Z"
+     "iopub.execute_input": "2024-10-25T14:49:54.573599Z",
+     "iopub.status.busy": "2024-10-25T14:49:54.573181Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.719567Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.718932Z"
     }
    },
    "outputs": [
@@ -474,10 +474,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.496401Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.496104Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.500866Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.500260Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.721711Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.721291Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.726399Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.725799Z"
     }
    },
    "outputs": [],
@@ -498,10 +498,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.502776Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.502425Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.637460Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.636870Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.728198Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.727872Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.871557Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.870894Z"
     }
    },
    "outputs": [
@@ -515,9 +515,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -525,9 +525,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
+      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -556,10 +556,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.639515Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.639079Z",
-     "iopub.status.idle": "2024-10-25T14:48:59.648742Z",
-     "shell.execute_reply": "2024-10-25T14:48:59.648196Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.873814Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.873414Z",
+     "iopub.status.idle": "2024-10-25T14:49:56.882826Z",
+     "shell.execute_reply": "2024-10-25T14:49:56.882316Z"
     }
    },
    "outputs": [
@@ -576,12 +576,12 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                 \n",
-      "─────(E[y|W1,W2,W0])\n",
+      "─────(E[y|W2,W1,W0])\n",
       "d[v₀]               \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,U) = P(y|v0,W1,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W0,U) = P(y|v0,W2,W1,W0)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W2+W0\n",
+      "b: y~v0+W2+W1+W0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -610,10 +610,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:48:59.650708Z",
-     "iopub.status.busy": "2024-10-25T14:48:59.650356Z",
-     "iopub.status.idle": "2024-10-25T14:49:00.826329Z",
-     "shell.execute_reply": "2024-10-25T14:49:00.825689Z"
+     "iopub.execute_input": "2024-10-25T14:49:56.884769Z",
+     "iopub.status.busy": "2024-10-25T14:49:56.884326Z",
+     "iopub.status.idle": "2024-10-25T14:49:58.107599Z",
+     "shell.execute_reply": "2024-10-25T14:49:58.106908Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/dowhy_ihdp_data_example.html b/main/example_notebooks/dowhy_ihdp_data_example.html
index 8b3fc9234..2f6bf0441 100644
--- a/main/example_notebooks/dowhy_ihdp_data_example.html
+++ b/main/example_notebooks/dowhy_ihdp_data_example.html
@@ -823,13 +823,13 @@ <h2>2.Identify<a class="headerlink" href="#2.Identify" title="Permalink to this
 Estimand name: backdoor
 Estimand expression:
      d                                                                         ↪
-────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪
+────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪
 d[treatment]                                                                   ↪
 
 ↪
-↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])
+↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)
+Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)
 
 ### Estimand : 2
 Estimand name: iv
@@ -879,23 +879,23 @@ <h3>3.1 Using Linear Regression<a class="headerlink" href="#3.1-Using-Linear-Reg
 Estimand name: backdoor
 Estimand expression:
      d                                                                         ↪
-────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪
+────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪
 d[treatment]                                                                   ↪
 
 ↪
-↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])
+↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)
+Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)
 
 ## Realized estimand
-b: y_factual~treatment+x15+x11+x22+x18+x17+x19+x24+x3+x5+x25+x8+x6+x20+x12+x2+x10+x16+x21+x4+x1+x13+x7+x9+x23+x14
+b: y_factual~treatment+x20+x5+x24+x1+x13+x23+x12+x2+x9+x11+x21+x25+x3+x7+x6+x10+x16+x4+x14+x8+x15+x22+x18+x17+x19
 Target units: ate
 
 ## Estimate
-Mean value: 3.9286717508727182
+Mean value: 3.9286717508727174
 p-value: [1.58915682e-156]
 
-Causal Estimate is 3.9286717508727182
+Causal Estimate is 3.9286717508727174
 ATE 4.021121012430829
 </pre></div></div>
 </div>
@@ -972,7 +972,7 @@ <h3>3.4 Using Propensity Score Weighting<a class="headerlink" href="#3.4-Using-P
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Causal Estimate is 4.028748218389614
+Causal Estimate is 4.028748218389554
 ATE 4.021121012430829
 </pre></div></div>
 </div>
@@ -996,8 +996,8 @@ <h2>4. Refute<a class="headerlink" href="#4.-Refute" title="Permalink to this he
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:4.028748218389614
-New effect:4.028748218389616
+Estimated effect:4.028748218389554
+New effect:4.028748218389554
 p value:1.0
 
 </pre></div></div>
@@ -1018,9 +1018,9 @@ <h2>4. Refute<a class="headerlink" href="#4.-Refute" title="Permalink to this he
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:4.028748218389614
-New effect:-0.4684785244087347
-p value:0.0
+Estimated effect:4.028748218389554
+New effect:-0.46487915108125505
+p value:0.02
 
 </pre></div></div>
 </div>
@@ -1042,9 +1042,9 @@ <h3>4.3 Data Subset Refuter<a class="headerlink" href="#4.3-Data-Subset-Refuter"
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:4.028748218389614
-New effect:4.033024672491376
-p value:0.9199999999999999
+Estimated effect:4.028748218389554
+New effect:4.0276366446854865
+p value:0.98
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_ihdp_data_example.ipynb b/main/example_notebooks/dowhy_ihdp_data_example.ipynb
index d5d30ec87..ad122b59f 100644
--- a/main/example_notebooks/dowhy_ihdp_data_example.ipynb
+++ b/main/example_notebooks/dowhy_ihdp_data_example.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:03.450361Z",
-     "iopub.status.busy": "2024-10-25T14:49:03.450184Z",
-     "iopub.status.idle": "2024-10-25T14:49:04.894868Z",
-     "shell.execute_reply": "2024-10-25T14:49:04.894240Z"
+     "iopub.execute_input": "2024-10-25T14:50:00.713000Z",
+     "iopub.status.busy": "2024-10-25T14:50:00.712815Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.361743Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.360936Z"
     }
    },
    "outputs": [],
@@ -39,10 +39,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:04.897425Z",
-     "iopub.status.busy": "2024-10-25T14:49:04.896943Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.079459Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.078917Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.364477Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.364081Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.402570Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.401795Z"
     }
    },
    "outputs": [
@@ -268,10 +268,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.081410Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.081049Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.309582Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.309012Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.404748Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.404524Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.663390Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.662764Z"
     }
    },
    "outputs": [
@@ -321,10 +321,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.311987Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.311602Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.330223Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.329633Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.665918Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.665687Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.692454Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.691777Z"
     }
    },
    "outputs": [
@@ -338,13 +338,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪\n",
+      "────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])\n",
+      "↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -382,10 +382,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.332117Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.331932Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.357518Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.356901Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.695013Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.694649Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.724867Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.724282Z"
     }
    },
    "outputs": [
@@ -402,23 +402,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x ↪\n",
+      "────────────(E[y_factual|x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6, ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14])\n",
+      "↪ x10,x16,x4,x14,x8,x15,x22,x18,x17,x19])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14,U) = P(y_factual|treatment,x15,x11,x22,x18,x17,x19,x24,x3,x5,x25,x8,x6,x20,x12,x2,x10,x16,x21,x4,x1,x13,x7,x9,x23,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19,U) = P(y_factual|treatment,x20,x5,x24,x1,x13,x23,x12,x2,x9,x11,x21,x25,x3,x7,x6,x10,x16,x4,x14,x8,x15,x22,x18,x17,x19)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y_factual~treatment+x15+x11+x22+x18+x17+x19+x24+x3+x5+x25+x8+x6+x20+x12+x2+x10+x16+x21+x4+x1+x13+x7+x9+x23+x14\n",
+      "b: y_factual~treatment+x20+x5+x24+x1+x13+x23+x12+x2+x9+x11+x21+x25+x3+x7+x6+x10+x16+x4+x14+x8+x15+x22+x18+x17+x19\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 3.9286717508727182\n",
+      "Mean value: 3.9286717508727174\n",
       "p-value: [1.58915682e-156]\n",
       "\n",
-      "Causal Estimate is 3.9286717508727182\n",
+      "Causal Estimate is 3.9286717508727174\n",
       "ATE 4.021121012430829\n"
      ]
     }
@@ -450,10 +450,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.360162Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.359774Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.385344Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.384813Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.728257Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.727144Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.754870Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.754224Z"
     }
    },
    "outputs": [
@@ -488,10 +488,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.387995Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.387773Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.437381Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.436791Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.757845Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.757329Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.811581Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.810925Z"
     }
    },
    "outputs": [
@@ -526,10 +526,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.440034Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.439795Z",
-     "iopub.status.idle": "2024-10-25T14:49:05.458740Z",
-     "shell.execute_reply": "2024-10-25T14:49:05.458203Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.814930Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.814336Z",
+     "iopub.status.idle": "2024-10-25T14:50:02.833434Z",
+     "shell.execute_reply": "2024-10-25T14:50:02.832406Z"
     }
    },
    "outputs": [
@@ -537,7 +537,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 4.028748218389614\n",
+      "Causal Estimate is 4.028748218389554\n",
       "ATE 4.021121012430829\n"
      ]
     }
@@ -566,10 +566,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:05.461338Z",
-     "iopub.status.busy": "2024-10-25T14:49:05.461125Z",
-     "iopub.status.idle": "2024-10-25T14:49:06.373059Z",
-     "shell.execute_reply": "2024-10-25T14:49:06.372451Z"
+     "iopub.execute_input": "2024-10-25T14:50:02.835767Z",
+     "iopub.status.busy": "2024-10-25T14:50:02.835509Z",
+     "iopub.status.idle": "2024-10-25T14:50:03.806040Z",
+     "shell.execute_reply": "2024-10-25T14:50:03.805349Z"
     }
    },
    "outputs": [
@@ -578,8 +578,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:4.028748218389616\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:4.028748218389554\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -603,10 +603,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:06.375152Z",
-     "iopub.status.busy": "2024-10-25T14:49:06.374711Z",
-     "iopub.status.idle": "2024-10-25T14:49:07.305902Z",
-     "shell.execute_reply": "2024-10-25T14:49:07.305180Z"
+     "iopub.execute_input": "2024-10-25T14:50:03.808487Z",
+     "iopub.status.busy": "2024-10-25T14:50:03.807933Z",
+     "iopub.status.idle": "2024-10-25T14:50:04.795778Z",
+     "shell.execute_reply": "2024-10-25T14:50:04.795062Z"
     }
    },
    "outputs": [
@@ -615,9 +615,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:-0.4684785244087347\n",
-      "p value:0.0\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:-0.46487915108125505\n",
+      "p value:0.02\n",
       "\n"
      ]
     }
@@ -640,10 +640,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:07.307874Z",
-     "iopub.status.busy": "2024-10-25T14:49:07.307686Z",
-     "iopub.status.idle": "2024-10-25T14:49:08.165221Z",
-     "shell.execute_reply": "2024-10-25T14:49:08.164542Z"
+     "iopub.execute_input": "2024-10-25T14:50:04.797982Z",
+     "iopub.status.busy": "2024-10-25T14:50:04.797758Z",
+     "iopub.status.idle": "2024-10-25T14:50:05.691224Z",
+     "shell.execute_reply": "2024-10-25T14:50:05.690648Z"
     }
    },
    "outputs": [
@@ -652,9 +652,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:4.028748218389614\n",
-      "New effect:4.033024672491376\n",
-      "p value:0.9199999999999999\n",
+      "Estimated effect:4.028748218389554\n",
+      "New effect:4.0276366446854865\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_interpreter.html b/main/example_notebooks/dowhy_interpreter.html
index 9d1a49bfe..ae8c04062 100644
--- a/main/example_notebooks/dowhy_interpreter.html
+++ b/main/example_notebooks/dowhy_interpreter.html
@@ -615,7 +615,7 @@ <h1>DoWhy: Interpreters for Causal Estimators<a class="headerlink" href="#DoWhy:
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-8002
+8979
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -656,62 +656,62 @@ <h1>DoWhy: Interpreters for Causal Estimators<a class="headerlink" href="#DoWhy:
     <tr>
       <th>0</th>
       <td>0.0</td>
-      <td>0.993429</td>
-      <td>0.769897</td>
-      <td>-0.847178</td>
-      <td>0.928229</td>
-      <td>0.365930</td>
+      <td>0.477734</td>
+      <td>-1.192526</td>
+      <td>1.272471</td>
+      <td>1.959071</td>
+      <td>0.463038</td>
       <td>3</td>
       <td>True</td>
-      <td>3.352911</td>
+      <td>2.389759</td>
     </tr>
     <tr>
       <th>1</th>
       <td>0.0</td>
-      <td>0.258673</td>
-      <td>1.128014</td>
-      <td>1.153442</td>
-      <td>-0.152917</td>
-      <td>-0.825393</td>
-      <td>1</td>
+      <td>0.718861</td>
+      <td>-1.115188</td>
+      <td>1.201292</td>
+      <td>0.061369</td>
+      <td>0.767281</td>
+      <td>2</td>
       <td>True</td>
-      <td>1.112612</td>
+      <td>1.776849</td>
     </tr>
     <tr>
       <th>2</th>
       <td>0.0</td>
-      <td>0.628177</td>
-      <td>1.448514</td>
-      <td>-0.126484</td>
-      <td>0.329410</td>
-      <td>0.498247</td>
-      <td>3</td>
+      <td>0.257282</td>
+      <td>-1.308327</td>
+      <td>1.547448</td>
+      <td>-0.995869</td>
+      <td>-0.904392</td>
+      <td>0</td>
       <td>True</td>
-      <td>3.348269</td>
+      <td>0.436070</td>
     </tr>
     <tr>
       <th>3</th>
       <td>0.0</td>
-      <td>0.038758</td>
-      <td>1.613230</td>
-      <td>-1.136255</td>
-      <td>-0.818521</td>
-      <td>0.476712</td>
-      <td>3</td>
+      <td>0.418268</td>
+      <td>-2.240148</td>
+      <td>0.774776</td>
+      <td>-1.044587</td>
+      <td>-1.388704</td>
+      <td>0</td>
       <td>False</td>
-      <td>1.148678</td>
+      <td>-1.452268</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.0</td>
-      <td>0.913642</td>
-      <td>0.656800</td>
-      <td>-0.932051</td>
-      <td>0.890903</td>
-      <td>0.285856</td>
-      <td>1</td>
+      <td>1.0</td>
+      <td>0.566311</td>
+      <td>-0.552725</td>
+      <td>-0.426374</td>
+      <td>-0.460174</td>
+      <td>-1.011157</td>
+      <td>3</td>
       <td>True</td>
-      <td>2.210334</td>
+      <td>1.907566</td>
     </tr>
     <tr>
       <th>...</th>
@@ -727,63 +727,63 @@ <h1>DoWhy: Interpreters for Causal Estimators<a class="headerlink" href="#DoWhy:
     </tr>
     <tr>
       <th>9995</th>
-      <td>0.0</td>
-      <td>0.977562</td>
-      <td>0.001779</td>
-      <td>0.070824</td>
-      <td>1.416120</td>
-      <td>0.177606</td>
+      <td>1.0</td>
+      <td>0.532125</td>
+      <td>-1.826497</td>
+      <td>2.749183</td>
+      <td>-0.531833</td>
+      <td>-0.714357</td>
       <td>3</td>
       <td>True</td>
-      <td>3.725697</td>
+      <td>2.050131</td>
     </tr>
     <tr>
       <th>9996</th>
       <td>0.0</td>
-      <td>0.560897</td>
-      <td>1.904908</td>
-      <td>-1.730092</td>
-      <td>-0.805939</td>
-      <td>0.013775</td>
-      <td>2</td>
+      <td>0.774825</td>
+      <td>-0.537176</td>
+      <td>2.724776</td>
+      <td>-0.928348</td>
+      <td>1.096883</td>
+      <td>3</td>
       <td>True</td>
-      <td>1.110946</td>
+      <td>3.010047</td>
     </tr>
     <tr>
       <th>9997</th>
-      <td>0.0</td>
-      <td>0.948574</td>
-      <td>-0.252089</td>
-      <td>0.609017</td>
-      <td>1.770531</td>
-      <td>1.559547</td>
-      <td>1</td>
+      <td>1.0</td>
+      <td>0.285903</td>
+      <td>-1.000688</td>
+      <td>-0.396921</td>
+      <td>1.056061</td>
+      <td>0.455609</td>
+      <td>3</td>
       <td>True</td>
-      <td>4.394935</td>
+      <td>1.949979</td>
     </tr>
     <tr>
       <th>9998</th>
       <td>0.0</td>
-      <td>0.472237</td>
-      <td>0.603944</td>
-      <td>-0.890167</td>
-      <td>0.221969</td>
-      <td>0.023440</td>
+      <td>0.364756</td>
+      <td>-0.711436</td>
+      <td>0.945276</td>
+      <td>-0.430878</td>
+      <td>-0.168530</td>
       <td>2</td>
-      <td>False</td>
-      <td>0.973352</td>
+      <td>True</td>
+      <td>1.781639</td>
     </tr>
     <tr>
       <th>9999</th>
-      <td>0.0</td>
-      <td>0.697132</td>
-      <td>2.406748</td>
-      <td>0.145082</td>
-      <td>0.197808</td>
-      <td>-0.390090</td>
-      <td>2</td>
+      <td>1.0</td>
+      <td>0.068676</td>
+      <td>0.019125</td>
+      <td>0.541005</td>
+      <td>1.748337</td>
+      <td>1.798937</td>
+      <td>1</td>
       <td>True</td>
-      <td>2.136826</td>
+      <td>2.058443</td>
     </tr>
   </tbody>
 </table>
@@ -861,9 +861,9 @@ <h2>Identifying the causal estimand<a class="headerlink" href="#Identifying-the-
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W3,W4,W1,W0])
+─────(E[y|W1,W2,W0,W4,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)
 
 ### Estimand : 2
 Estimand name: iv
@@ -871,9 +871,9 @@ <h2>Identifying the causal estimand<a class="headerlink" href="#Identifying-the-
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+ ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 
 ### Estimand : 3
 Estimand name: frontdoor
@@ -911,18 +911,18 @@ <h2>Method 1: Propensity Score Stratification<a class="headerlink" href="#Method
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W3,W4,W1,W0])
+─────(E[y|W1,W2,W0,W4,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)
 
 ## Realized estimand
-b: y~v0+W2+W3+W4+W1+W0
+b: y~v0+W1+W2+W0+W4+W3
 Target units: att
 
 ## Estimate
-Mean value: 0.9849750790391165
+Mean value: 1.0072519271114253
 
-Causal Estimate is 0.9849750790391165
+Causal Estimate is 1.0072519271114253
 </pre></div></div>
 </div>
 <section id="Textual-Interpreter">
@@ -942,7 +942,7 @@ <h3>Textual Interpreter<a class="headerlink" href="#Textual-Interpreter" title="
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9849750790391165 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0072519271114253 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
 </pre></div></div>
 </div>
 </section>
@@ -999,18 +999,18 @@ <h2>Method 2: Propensity Score Matching<a class="headerlink" href="#Method-2:-Pr
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W3,W4,W1,W0])
+─────(E[y|W1,W2,W0,W4,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)
 
 ## Realized estimand
-b: y~v0+W2+W3+W4+W1+W0
+b: y~v0+W1+W2+W0+W4+W3
 Target units: atc
 
 ## Estimate
-Mean value: 1.0059102988172535
+Mean value: 0.987197165766296
 
-Causal Estimate is 1.0059102988172535
+Causal Estimate is 0.987197165766296
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1027,7 +1027,7 @@ <h2>Method 2: Propensity Score Matching<a class="headerlink" href="#Method-2:-Pr
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0059102988172535 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.987197165766296 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
 </pre></div></div>
 </div>
 <p>Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator.</p>
@@ -1062,18 +1062,18 @@ <h2>Method 3: Weighting<a class="headerlink" href="#Method-3:-Weighting" title="
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W3,W4,W1,W0])
+─────(E[y|W1,W2,W0,W4,W3])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)
 
 ## Realized estimand
-b: y~v0+W2+W3+W4+W1+W0
+b: y~v0+W1+W2+W0+W4+W3
 Target units: ate
 
 ## Estimate
-Mean value: 1.0267251598760518
+Mean value: 1.170035426800626
 
-Causal Estimate is 1.0267251598760518
+Causal Estimate is 1.170035426800626
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1090,7 +1090,7 @@ <h2>Method 3: Weighting<a class="headerlink" href="#Method-3:-Weighting" title="
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0267251598760518 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.170035426800626 in the expected value of the outcome [[&#39;y&#39;]], over the data distribution/population represented by the dataset.
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
diff --git a/main/example_notebooks/dowhy_interpreter.ipynb b/main/example_notebooks/dowhy_interpreter.ipynb
index 724bc2820..e55d0021f 100644
--- a/main/example_notebooks/dowhy_interpreter.ipynb
+++ b/main/example_notebooks/dowhy_interpreter.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "91bf56d5",
+   "id": "4782d6c6",
    "metadata": {},
    "source": [
     "# DoWhy: Interpreters for Causal Estimators\n",
@@ -16,13 +16,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6fd00d50",
+   "id": "656882ee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:10.548698Z",
-     "iopub.status.busy": "2024-10-25T14:49:10.548495Z",
-     "iopub.status.idle": "2024-10-25T14:49:10.563739Z",
-     "shell.execute_reply": "2024-10-25T14:49:10.563275Z"
+     "iopub.execute_input": "2024-10-25T14:50:08.145627Z",
+     "iopub.status.busy": "2024-10-25T14:50:08.145442Z",
+     "iopub.status.idle": "2024-10-25T14:50:08.162554Z",
+     "shell.execute_reply": "2024-10-25T14:50:08.161967Z"
     }
    },
    "outputs": [],
@@ -34,13 +34,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c3dc2921",
+   "id": "8ec4f244",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:10.565507Z",
-     "iopub.status.busy": "2024-10-25T14:49:10.565160Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.018540Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.017936Z"
+     "iopub.execute_input": "2024-10-25T14:50:08.164479Z",
+     "iopub.status.busy": "2024-10-25T14:50:08.164132Z",
+     "iopub.status.idle": "2024-10-25T14:50:09.702731Z",
+     "shell.execute_reply": "2024-10-25T14:50:09.702092Z"
     }
    },
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3442b96",
+   "id": "9d10adce",
    "metadata": {},
    "source": [
     "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome.\n",
@@ -67,13 +67,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cb28add2",
+   "id": "18916cff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.021117Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.020616Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.308024Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.307402Z"
+     "iopub.execute_input": "2024-10-25T14:50:09.705387Z",
+     "iopub.status.busy": "2024-10-25T14:50:09.704926Z",
+     "iopub.status.idle": "2024-10-25T14:50:09.999977Z",
+     "shell.execute_reply": "2024-10-25T14:50:09.997127Z"
     }
    },
    "outputs": [
@@ -81,7 +81,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8002\n"
+      "8979\n"
      ]
     },
     {
@@ -120,62 +120,62 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.993429</td>\n",
-       "      <td>0.769897</td>\n",
-       "      <td>-0.847178</td>\n",
-       "      <td>0.928229</td>\n",
-       "      <td>0.365930</td>\n",
+       "      <td>0.477734</td>\n",
+       "      <td>-1.192526</td>\n",
+       "      <td>1.272471</td>\n",
+       "      <td>1.959071</td>\n",
+       "      <td>0.463038</td>\n",
        "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.352911</td>\n",
+       "      <td>2.389759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.258673</td>\n",
-       "      <td>1.128014</td>\n",
-       "      <td>1.153442</td>\n",
-       "      <td>-0.152917</td>\n",
-       "      <td>-0.825393</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.718861</td>\n",
+       "      <td>-1.115188</td>\n",
+       "      <td>1.201292</td>\n",
+       "      <td>0.061369</td>\n",
+       "      <td>0.767281</td>\n",
+       "      <td>2</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.112612</td>\n",
+       "      <td>1.776849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.628177</td>\n",
-       "      <td>1.448514</td>\n",
-       "      <td>-0.126484</td>\n",
-       "      <td>0.329410</td>\n",
-       "      <td>0.498247</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.257282</td>\n",
+       "      <td>-1.308327</td>\n",
+       "      <td>1.547448</td>\n",
+       "      <td>-0.995869</td>\n",
+       "      <td>-0.904392</td>\n",
+       "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.348269</td>\n",
+       "      <td>0.436070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.038758</td>\n",
-       "      <td>1.613230</td>\n",
-       "      <td>-1.136255</td>\n",
-       "      <td>-0.818521</td>\n",
-       "      <td>0.476712</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.418268</td>\n",
+       "      <td>-2.240148</td>\n",
+       "      <td>0.774776</td>\n",
+       "      <td>-1.044587</td>\n",
+       "      <td>-1.388704</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.148678</td>\n",
+       "      <td>-1.452268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.913642</td>\n",
-       "      <td>0.656800</td>\n",
-       "      <td>-0.932051</td>\n",
-       "      <td>0.890903</td>\n",
-       "      <td>0.285856</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.566311</td>\n",
+       "      <td>-0.552725</td>\n",
+       "      <td>-0.426374</td>\n",
+       "      <td>-0.460174</td>\n",
+       "      <td>-1.011157</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>2.210334</td>\n",
+       "      <td>1.907566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -191,63 +191,63 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9995</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.977562</td>\n",
-       "      <td>0.001779</td>\n",
-       "      <td>0.070824</td>\n",
-       "      <td>1.416120</td>\n",
-       "      <td>0.177606</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.532125</td>\n",
+       "      <td>-1.826497</td>\n",
+       "      <td>2.749183</td>\n",
+       "      <td>-0.531833</td>\n",
+       "      <td>-0.714357</td>\n",
        "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>3.725697</td>\n",
+       "      <td>2.050131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9996</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.560897</td>\n",
-       "      <td>1.904908</td>\n",
-       "      <td>-1.730092</td>\n",
-       "      <td>-0.805939</td>\n",
-       "      <td>0.013775</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0.774825</td>\n",
+       "      <td>-0.537176</td>\n",
+       "      <td>2.724776</td>\n",
+       "      <td>-0.928348</td>\n",
+       "      <td>1.096883</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.110946</td>\n",
+       "      <td>3.010047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9997</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.948574</td>\n",
-       "      <td>-0.252089</td>\n",
-       "      <td>0.609017</td>\n",
-       "      <td>1.770531</td>\n",
-       "      <td>1.559547</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.285903</td>\n",
+       "      <td>-1.000688</td>\n",
+       "      <td>-0.396921</td>\n",
+       "      <td>1.056061</td>\n",
+       "      <td>0.455609</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>4.394935</td>\n",
+       "      <td>1.949979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9998</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.472237</td>\n",
-       "      <td>0.603944</td>\n",
-       "      <td>-0.890167</td>\n",
-       "      <td>0.221969</td>\n",
-       "      <td>0.023440</td>\n",
+       "      <td>0.364756</td>\n",
+       "      <td>-0.711436</td>\n",
+       "      <td>0.945276</td>\n",
+       "      <td>-0.430878</td>\n",
+       "      <td>-0.168530</td>\n",
        "      <td>2</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.973352</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.781639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9999</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.697132</td>\n",
-       "      <td>2.406748</td>\n",
-       "      <td>0.145082</td>\n",
-       "      <td>0.197808</td>\n",
-       "      <td>-0.390090</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.068676</td>\n",
+       "      <td>0.019125</td>\n",
+       "      <td>0.541005</td>\n",
+       "      <td>1.748337</td>\n",
+       "      <td>1.798937</td>\n",
+       "      <td>1</td>\n",
        "      <td>True</td>\n",
-       "      <td>2.136826</td>\n",
+       "      <td>2.058443</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -256,30 +256,30 @@
       ],
       "text/plain": [
        "       Z0        Z1        W0        W1        W2        W3 W4     v0  \\\n",
-       "0     0.0  0.993429  0.769897 -0.847178  0.928229  0.365930  3   True   \n",
-       "1     0.0  0.258673  1.128014  1.153442 -0.152917 -0.825393  1   True   \n",
-       "2     0.0  0.628177  1.448514 -0.126484  0.329410  0.498247  3   True   \n",
-       "3     0.0  0.038758  1.613230 -1.136255 -0.818521  0.476712  3  False   \n",
-       "4     0.0  0.913642  0.656800 -0.932051  0.890903  0.285856  1   True   \n",
+       "0     0.0  0.477734 -1.192526  1.272471  1.959071  0.463038  3   True   \n",
+       "1     0.0  0.718861 -1.115188  1.201292  0.061369  0.767281  2   True   \n",
+       "2     0.0  0.257282 -1.308327  1.547448 -0.995869 -0.904392  0   True   \n",
+       "3     0.0  0.418268 -2.240148  0.774776 -1.044587 -1.388704  0  False   \n",
+       "4     1.0  0.566311 -0.552725 -0.426374 -0.460174 -1.011157  3   True   \n",
        "...   ...       ...       ...       ...       ...       ... ..    ...   \n",
-       "9995  0.0  0.977562  0.001779  0.070824  1.416120  0.177606  3   True   \n",
-       "9996  0.0  0.560897  1.904908 -1.730092 -0.805939  0.013775  2   True   \n",
-       "9997  0.0  0.948574 -0.252089  0.609017  1.770531  1.559547  1   True   \n",
-       "9998  0.0  0.472237  0.603944 -0.890167  0.221969  0.023440  2  False   \n",
-       "9999  0.0  0.697132  2.406748  0.145082  0.197808 -0.390090  2   True   \n",
+       "9995  1.0  0.532125 -1.826497  2.749183 -0.531833 -0.714357  3   True   \n",
+       "9996  0.0  0.774825 -0.537176  2.724776 -0.928348  1.096883  3   True   \n",
+       "9997  1.0  0.285903 -1.000688 -0.396921  1.056061  0.455609  3   True   \n",
+       "9998  0.0  0.364756 -0.711436  0.945276 -0.430878 -0.168530  2   True   \n",
+       "9999  1.0  0.068676  0.019125  0.541005  1.748337  1.798937  1   True   \n",
        "\n",
        "             y  \n",
-       "0     3.352911  \n",
-       "1     1.112612  \n",
-       "2     3.348269  \n",
-       "3     1.148678  \n",
-       "4     2.210334  \n",
+       "0     2.389759  \n",
+       "1     1.776849  \n",
+       "2     0.436070  \n",
+       "3    -1.452268  \n",
+       "4     1.907566  \n",
        "...        ...  \n",
-       "9995  3.725697  \n",
-       "9996  1.110946  \n",
-       "9997  4.394935  \n",
-       "9998  0.973352  \n",
-       "9999  2.136826  \n",
+       "9995  2.050131  \n",
+       "9996  3.010047  \n",
+       "9997  1.949979  \n",
+       "9998  1.781639  \n",
+       "9999  2.058443  \n",
        "\n",
        "[10000 rows x 9 columns]"
       ]
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be4c7348",
+   "id": "2c885e19",
    "metadata": {},
    "source": [
     "Note that we are using a pandas dataframe to load the data."
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad1a7989",
+   "id": "f9abeb14",
    "metadata": {},
    "source": [
     "## Identifying the causal estimand"
@@ -321,7 +321,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e0769ad",
+   "id": "314290fb",
    "metadata": {},
    "source": [
     "We now input a causal graph in the GML graph format."
@@ -330,13 +330,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f0b7456b",
+   "id": "2453757e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.310902Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.310373Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.354518Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.353904Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.005934Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.005390Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.049282Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.048722Z"
     }
    },
    "outputs": [],
@@ -354,13 +354,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1085e11e",
+   "id": "1b6a5704",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.357497Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.357024Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.541477Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.540854Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.052009Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.051419Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.232227Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.231670Z"
     }
    },
    "outputs": [
@@ -382,13 +382,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7c543ea6",
+   "id": "f6320675",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.543790Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.543545Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.585980Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.585334Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.234601Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.234156Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.276704Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.276114Z"
     }
    },
    "outputs": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4310abb3",
+   "id": "d4b956ed",
    "metadata": {},
    "source": [
     "We get a causal graph. Now identification and estimation is done."
@@ -419,13 +419,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "104b7d83",
+   "id": "63070c4c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.588416Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.588016Z",
-     "iopub.status.idle": "2024-10-25T14:49:12.851524Z",
-     "shell.execute_reply": "2024-10-25T14:49:12.850872Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.279016Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.278596Z",
+     "iopub.status.idle": "2024-10-25T14:50:10.551219Z",
+     "shell.execute_reply": "2024-10-25T14:50:10.550674Z"
     }
    },
    "outputs": [
@@ -439,9 +439,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -449,9 +449,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cd24c65",
+   "id": "ad70a2c2",
    "metadata": {},
    "source": [
     "## Method 1: Propensity Score Stratification\n",
@@ -478,13 +478,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2272009b",
+   "id": "563dcd39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:12.853504Z",
-     "iopub.status.busy": "2024-10-25T14:49:12.853316Z",
-     "iopub.status.idle": "2024-10-25T14:49:13.422757Z",
-     "shell.execute_reply": "2024-10-25T14:49:13.422162Z"
+     "iopub.execute_input": "2024-10-25T14:50:10.553408Z",
+     "iopub.status.busy": "2024-10-25T14:50:10.552970Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.182266Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.181617Z"
     }
    },
    "outputs": [
@@ -501,18 +501,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: att\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 0.9849750790391165\n",
+      "Mean value: 1.0072519271114253\n",
       "\n",
-      "Causal Estimate is 0.9849750790391165\n"
+      "Causal Estimate is 1.0072519271114253\n"
      ]
     }
    ],
@@ -526,7 +526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e9db2d6",
+   "id": "c56716f3",
    "metadata": {},
    "source": [
     "### Textual Interpreter\n",
@@ -537,13 +537,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "d99297ee",
+   "id": "55a78134",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:13.424859Z",
-     "iopub.status.busy": "2024-10-25T14:49:13.424415Z",
-     "iopub.status.idle": "2024-10-25T14:49:13.458624Z",
-     "shell.execute_reply": "2024-10-25T14:49:13.457942Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.184256Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.184056Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.219007Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.218328Z"
     }
    },
    "outputs": [
@@ -551,7 +551,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9849750790391165 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0072519271114253 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f384957",
+   "id": "e66205d8",
    "metadata": {},
    "source": [
     "### Visual Interpreter\n",
@@ -578,19 +578,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "05cc5f4f",
+   "id": "0407ba00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:13.460582Z",
-     "iopub.status.busy": "2024-10-25T14:49:13.460362Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.183525Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.182991Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.221240Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.220850Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.741755Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.740992Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29bb9d50",
+   "id": "4cf3c662",
    "metadata": {},
    "source": [
     "This plot shows how the SMD decreases from the unadjusted to the stratified units. "
@@ -614,7 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d5a9b2c",
+   "id": "0db5e1a0",
    "metadata": {},
    "source": [
     "## Method 2: Propensity Score Matching\n",
@@ -625,13 +625,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d664abac",
+   "id": "c03f0b1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.185706Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.185301Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.243200Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.242636Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.744010Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.743789Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.803046Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.802441Z"
     }
    },
    "outputs": [
@@ -648,18 +648,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.0059102988172535\n",
+      "Mean value: 0.987197165766296\n",
       "\n",
-      "Causal Estimate is 1.0059102988172535\n"
+      "Causal Estimate is 0.987197165766296\n"
      ]
     }
    ],
@@ -674,13 +674,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "dfb41720",
+   "id": "f3f01e14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.245101Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.244762Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.277002Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.276465Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.805066Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.804865Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.838984Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.838456Z"
     }
    },
    "outputs": [
@@ -688,7 +688,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0059102988172535 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.987197165766296 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -699,7 +699,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5e69bd0",
+   "id": "5c4e0141",
    "metadata": {},
    "source": [
     "Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator."
@@ -707,7 +707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f398aa07",
+   "id": "980676bd",
    "metadata": {},
    "source": [
     "## Method 3: Weighting\n",
@@ -721,13 +721,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "322eabb5",
+   "id": "486ab88d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.279043Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.278686Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.329134Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.328450Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.840868Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.840671Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.893559Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.892900Z"
     }
    },
    "outputs": [
@@ -744,18 +744,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W2,W3,W4,W1,W0])\n",
+      "─────(E[y|W1,W2,W0,W4,W3])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W4,W1,W0,U) = P(y|v0,W2,W3,W4,W1,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W2,W0,W4,W3,U) = P(y|v0,W1,W2,W0,W4,W3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W3+W4+W1+W0\n",
+      "b: y~v0+W1+W2+W0+W4+W3\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.0267251598760518\n",
+      "Mean value: 1.170035426800626\n",
       "\n",
-      "Causal Estimate is 1.0267251598760518\n"
+      "Causal Estimate is 1.170035426800626\n"
      ]
     }
    ],
@@ -771,13 +771,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "79310206",
+   "id": "8195b82c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.331113Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.330816Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.364771Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.364155Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.895780Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.895397Z",
+     "iopub.status.idle": "2024-10-25T14:50:11.931539Z",
+     "shell.execute_reply": "2024-10-25T14:50:11.930866Z"
     }
    },
    "outputs": [
@@ -785,7 +785,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0267251598760518 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
+      "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.170035426800626 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n"
      ]
     }
    ],
@@ -797,19 +797,19 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "7b0ceca9",
+   "id": "2e854085",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:14.366431Z",
-     "iopub.status.busy": "2024-10-25T14:49:14.366250Z",
-     "iopub.status.idle": "2024-10-25T14:49:14.719079Z",
-     "shell.execute_reply": "2024-10-25T14:49:14.718480Z"
+     "iopub.execute_input": "2024-10-25T14:50:11.933721Z",
+     "iopub.status.busy": "2024-10-25T14:50:11.933359Z",
+     "iopub.status.idle": "2024-10-25T14:50:12.276396Z",
+     "shell.execute_reply": "2024-10-25T14:50:12.275777Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -825,7 +825,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "833ce544",
+   "id": "1ef4bb9f",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/main/example_notebooks/dowhy_lalonde_example.html b/main/example_notebooks/dowhy_lalonde_example.html
index 27c84136a..1c34bbecf 100644
--- a/main/example_notebooks/dowhy_lalonde_example.html
+++ b/main/example_notebooks/dowhy_lalonde_example.html
@@ -613,7 +613,7 @@ <h2>2. Run DoWhy analysis: model, identify, estimate<a class="headerlink" href="
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Causal Estimate is 1639.7735077993884
+Causal Estimate is 1639.8254852564396
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -633,10 +633,10 @@ <h2>2. Run DoWhy analysis: model, identify, estimate<a class="headerlink" href="
   <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.743</td>
 </tr>
 <tr>
-  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00973</td>
+  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00972</td>
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:49:19</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>
+  <th>Time:</th>                 <td>14:50:17</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>
 </tr>
 <tr>
   <th>No. Observations:</th>      <td>   445</td>      <th>  AIC:               </th> <td>   9093.</td>
@@ -656,18 +656,18 @@ <h2>2. Run DoWhy analysis: model, identify, estimate<a class="headerlink" href="
         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>
 </tr>
 <tr>
-  <th>Intercept</th>     <td> 4555.0799</td> <td>  406.701</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.776</td> <td> 5354.383</td>
+  <th>Intercept</th>     <td> 4555.0717</td> <td>  406.705</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.761</td> <td> 5354.383</td>
 </tr>
 <tr>
-  <th>treat[T.True]</th> <td> 1639.7735</td> <td>  631.493</td> <td>    2.597</td> <td> 0.010</td> <td>  398.678</td> <td> 2880.869</td>
+  <th>treat[T.True]</th> <td> 1639.8255</td> <td>  631.496</td> <td>    2.597</td> <td> 0.010</td> <td>  398.725</td> <td> 2880.926</td>
 </tr>
 </table>
 <table class="simpletable">
 <tr>
-  <th>Omnibus:</th>       <td>303.258</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>
+  <th>Omnibus:</th>       <td>303.265</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>
 </tr>
 <tr>
-  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.328</td>
+  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.724</td>
 </tr>
 <tr>
   <th>Skew:</th>          <td> 2.709</td>  <th>  Prob(JB):          </th> <td>    0.00</td>
@@ -723,7 +723,7 @@ <h2>4. Sanity check: compare to manual IPW estimate<a class="headerlink" href="#
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Causal Estimate is 1639.7735077993912
+Causal Estimate is 1639.8254852564378
 </pre></div></div>
 </div>
 </section>
diff --git a/main/example_notebooks/dowhy_lalonde_example.ipynb b/main/example_notebooks/dowhy_lalonde_example.ipynb
index 666878c52..34be1cef5 100644
--- a/main/example_notebooks/dowhy_lalonde_example.ipynb
+++ b/main/example_notebooks/dowhy_lalonde_example.ipynb
@@ -21,10 +21,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:17.212952Z",
-     "iopub.status.busy": "2024-10-25T14:49:17.212510Z",
-     "iopub.status.idle": "2024-10-25T14:49:19.947589Z",
-     "shell.execute_reply": "2024-10-25T14:49:19.946909Z"
+     "iopub.execute_input": "2024-10-25T14:50:15.194386Z",
+     "iopub.status.busy": "2024-10-25T14:50:15.194192Z",
+     "iopub.status.idle": "2024-10-25T14:50:17.935105Z",
+     "shell.execute_reply": "2024-10-25T14:50:17.934424Z"
     }
    },
    "outputs": [],
@@ -46,10 +46,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:19.949917Z",
-     "iopub.status.busy": "2024-10-25T14:49:19.949720Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.004109Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.003471Z"
+     "iopub.execute_input": "2024-10-25T14:50:17.937992Z",
+     "iopub.status.busy": "2024-10-25T14:50:17.937688Z",
+     "iopub.status.idle": "2024-10-25T14:50:17.998148Z",
+     "shell.execute_reply": "2024-10-25T14:50:17.997435Z"
     }
    },
    "outputs": [
@@ -57,7 +57,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 1639.7735077993884\n"
+      "Causal Estimate is 1639.8254852564396\n"
      ]
     },
     {
@@ -75,10 +75,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.743</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00973</td>\n",
+       "  <th>Date:</th>             <td>Fri, 25 Oct 2024</td> <th>  Prob (F-statistic):</th>  <td>0.00972</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>14:49:19</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>\n",
+       "  <th>Time:</th>                 <td>14:50:17</td>     <th>  Log-Likelihood:    </th> <td> -4544.7</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   445</td>      <th>  AIC:               </th> <td>   9093.</td>\n",
@@ -98,18 +98,18 @@
        "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th>     <td> 4555.0799</td> <td>  406.701</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.776</td> <td> 5354.383</td>\n",
+       "  <th>Intercept</th>     <td> 4555.0717</td> <td>  406.705</td> <td>   11.200</td> <td> 0.000</td> <td> 3755.761</td> <td> 5354.383</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>treat[T.True]</th> <td> 1639.7735</td> <td>  631.493</td> <td>    2.597</td> <td> 0.010</td> <td>  398.678</td> <td> 2880.869</td>\n",
+       "  <th>treat[T.True]</th> <td> 1639.8255</td> <td>  631.496</td> <td>    2.597</td> <td> 0.010</td> <td>  398.725</td> <td> 2880.926</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td>303.258</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>\n",
+       "  <th>Omnibus:</th>       <td>303.265</td> <th>  Durbin-Watson:     </th> <td>   2.085</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.328</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4770.724</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td> 2.709</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
@@ -126,8 +126,8 @@
        "\\textbf{Dep. Variable:}    &       re78       & \\textbf{  R-squared:         } &     0.015   \\\\\n",
        "\\textbf{Model:}            &       WLS        & \\textbf{  Adj. R-squared:    } &     0.013   \\\\\n",
        "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     6.743   \\\\\n",
-       "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.00973    \\\\\n",
-       "\\textbf{Time:}             &     14:49:19     & \\textbf{  Log-Likelihood:    } &   -4544.7   \\\\\n",
+       "\\textbf{Date:}             & Fri, 25 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.00972    \\\\\n",
+       "\\textbf{Time:}             &     14:50:17     & \\textbf{  Log-Likelihood:    } &   -4544.7   \\\\\n",
        "\\textbf{No. Observations:} &         445      & \\textbf{  AIC:               } &     9093.   \\\\\n",
        "\\textbf{Df Residuals:}     &         443      & \\textbf{  BIC:               } &     9102.   \\\\\n",
        "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
@@ -137,13 +137,13 @@
        "\\begin{tabular}{lcccccc}\n",
        "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
        "\\midrule\n",
-       "\\textbf{Intercept}     &    4555.0799  &      406.701     &    11.200  &         0.000        &     3755.776    &     5354.383     \\\\\n",
-       "\\textbf{treat[T.True]} &    1639.7735  &      631.493     &     2.597  &         0.010        &      398.678    &     2880.869     \\\\\n",
+       "\\textbf{Intercept}     &    4555.0717  &      406.705     &    11.200  &         0.000        &     3755.761    &     5354.383     \\\\\n",
+       "\\textbf{treat[T.True]} &    1639.8255  &      631.496     &     2.597  &         0.010        &      398.725    &     2880.926     \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\\begin{tabular}{lclc}\n",
-       "\\textbf{Omnibus:}       & 303.258 & \\textbf{  Durbin-Watson:     } &    2.085  \\\\\n",
-       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4770.328  \\\\\n",
+       "\\textbf{Omnibus:}       & 303.265 & \\textbf{  Durbin-Watson:     } &    2.085  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4770.724  \\\\\n",
        "\\textbf{Skew:}          &   2.709 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
        "\\textbf{Kurtosis:}      &  18.097 & \\textbf{  Cond. No.          } &     2.47  \\\\\n",
        "\\bottomrule\n",
@@ -162,8 +162,8 @@
        "Dep. Variable:                   re78   R-squared:                       0.015\n",
        "Model:                            WLS   Adj. R-squared:                  0.013\n",
        "Method:                 Least Squares   F-statistic:                     6.743\n",
-       "Date:                Fri, 25 Oct 2024   Prob (F-statistic):            0.00973\n",
-       "Time:                        14:49:19   Log-Likelihood:                -4544.7\n",
+       "Date:                Fri, 25 Oct 2024   Prob (F-statistic):            0.00972\n",
+       "Time:                        14:50:17   Log-Likelihood:                -4544.7\n",
        "No. Observations:                 445   AIC:                             9093.\n",
        "Df Residuals:                     443   BIC:                             9102.\n",
        "Df Model:                           1                                         \n",
@@ -171,11 +171,11 @@
        "=================================================================================\n",
        "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
        "---------------------------------------------------------------------------------\n",
-       "Intercept      4555.0799    406.701     11.200      0.000    3755.776    5354.383\n",
-       "treat[T.True]  1639.7735    631.493      2.597      0.010     398.678    2880.869\n",
+       "Intercept      4555.0717    406.705     11.200      0.000    3755.761    5354.383\n",
+       "treat[T.True]  1639.8255    631.496      2.597      0.010     398.725    2880.926\n",
        "==============================================================================\n",
-       "Omnibus:                      303.258   Durbin-Watson:                   2.085\n",
-       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4770.328\n",
+       "Omnibus:                      303.265   Durbin-Watson:                   2.085\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4770.724\n",
        "Skew:                           2.709   Prob(JB):                         0.00\n",
        "Kurtosis:                      18.097   Cond. No.                         2.47\n",
        "==============================================================================\n",
@@ -226,10 +226,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:20.006285Z",
-     "iopub.status.busy": "2024-10-25T14:49:20.005844Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.304203Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.303527Z"
+     "iopub.execute_input": "2024-10-25T14:50:18.000537Z",
+     "iopub.status.busy": "2024-10-25T14:50:18.000107Z",
+     "iopub.status.idle": "2024-10-25T14:50:18.322478Z",
+     "shell.execute_reply": "2024-10-25T14:50:18.321799Z"
     }
    },
    "outputs": [
@@ -261,10 +261,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:20.306333Z",
-     "iopub.status.busy": "2024-10-25T14:49:20.305968Z",
-     "iopub.status.idle": "2024-10-25T14:49:20.311481Z",
-     "shell.execute_reply": "2024-10-25T14:49:20.310899Z"
+     "iopub.execute_input": "2024-10-25T14:50:18.324776Z",
+     "iopub.status.busy": "2024-10-25T14:50:18.324336Z",
+     "iopub.status.idle": "2024-10-25T14:50:18.330429Z",
+     "shell.execute_reply": "2024-10-25T14:50:18.329850Z"
     }
    },
    "outputs": [
@@ -272,7 +272,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Causal Estimate is 1639.7735077993912\n"
+      "Causal Estimate is 1639.8254852564378\n"
      ]
     }
    ],
diff --git a/main/example_notebooks/dowhy_mediation_analysis.html b/main/example_notebooks/dowhy_mediation_analysis.html
index 25305ac7f..adeb9f128 100644
--- a/main/example_notebooks/dowhy_mediation_analysis.html
+++ b/main/example_notebooks/dowhy_mediation_analysis.html
@@ -605,11 +605,11 @@ <h2>Creating a dataset<a class="headerlink" href="#Creating-a-dataset" title="Pe
 <div class="output_area docutils container">
 <div class="highlight"><pre>
          FD0        W0        v0          y
-0  -4.604887 -0.308790 -0.488776 -11.455013
-1  18.313037  0.774883  3.998435  43.731761
-2 -24.094361 -1.699439 -4.952121 -60.329111
-3  -9.730324 -0.124735 -2.170977 -22.063332
-4  -3.117294 -0.026310 -0.667598  -7.023355
+0  -1.622075  0.501889 -0.079710  -0.904602
+1   2.467555  0.612379  0.621440   5.619792
+2   2.989213  0.035365  0.807377   4.639184
+3 -12.350724 -0.415800 -2.783882 -19.974205
+4  -0.686259  0.542081 -0.183767   0.643876
 </pre></div></div>
 </div>
 </section>
@@ -761,7 +761,7 @@ <h3>Natural Indirect Effect<a class="headerlink" href="#Natural-Indirect-Effect"
 Target units: ate
 
 ## Estimate
-Mean value: 10.704622257788243
+Mean value: 6.073961318473499
 
 </pre></div></div>
 </div>
@@ -779,7 +779,7 @@ <h3>Natural Indirect Effect<a class="headerlink" href="#Natural-Indirect-Effect"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-10.704622257788243 10.727800892028872
+6.073961318473499 6.05342048266878
 </pre></div></div>
 </div>
 <p>The parameter is called <code class="docutils literal notranslate"><span class="pre">ate</span></code> because in the simulated dataset, the direct effect is set to be zero.</p>
@@ -829,7 +829,7 @@ <h3>Natural Direct Effect<a class="headerlink" href="#Natural-Direct-Effect" tit
 Target units: ate
 
 ## Estimate
-Mean value: 2.0167870465215287e-05
+Mean value: -2.0011491711713347e-05
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_mediation_analysis.ipynb b/main/example_notebooks/dowhy_mediation_analysis.ipynb
index 98913d2fb..04aa1db90 100644
--- a/main/example_notebooks/dowhy_mediation_analysis.ipynb
+++ b/main/example_notebooks/dowhy_mediation_analysis.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:22.052114Z",
-     "iopub.status.busy": "2024-10-25T14:49:22.051922Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.481762Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.481058Z"
+     "iopub.execute_input": "2024-10-25T14:50:20.201015Z",
+     "iopub.status.busy": "2024-10-25T14:50:20.200581Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.726294Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.725675Z"
     }
    },
    "outputs": [],
@@ -43,10 +43,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.484091Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.483770Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.493696Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.493166Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.728836Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.728298Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.738285Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.737783Z"
     }
    },
    "outputs": [
@@ -55,11 +55,11 @@
      "output_type": "stream",
      "text": [
       "         FD0        W0        v0          y\n",
-      "0  -4.604887 -0.308790 -0.488776 -11.455013\n",
-      "1  18.313037  0.774883  3.998435  43.731761\n",
-      "2 -24.094361 -1.699439 -4.952121 -60.329111\n",
-      "3  -9.730324 -0.124735 -2.170977 -22.063332\n",
-      "4  -3.117294 -0.026310 -0.667598  -7.023355\n"
+      "0  -1.622075  0.501889 -0.079710  -0.904602\n",
+      "1   2.467555  0.612379  0.621440   5.619792\n",
+      "2   2.989213  0.035365  0.807377   4.639184\n",
+      "3 -12.350724 -0.415800 -2.783882 -19.974205\n",
+      "4  -0.686259  0.542081 -0.183767   0.643876\n"
      ]
     }
    ],
@@ -88,10 +88,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.495452Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.495256Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.606510Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.605949Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.740185Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.739822Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.851795Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.851243Z"
     }
    },
    "outputs": [
@@ -144,10 +144,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.608796Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.608362Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.622041Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.621584Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.853929Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.853706Z",
+     "iopub.status.idle": "2024-10-25T14:50:21.867512Z",
+     "shell.execute_reply": "2024-10-25T14:50:21.866996Z"
     }
    },
    "outputs": [
@@ -182,10 +182,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.624037Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.623832Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.859053Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.858477Z"
+     "iopub.execute_input": "2024-10-25T14:50:21.869454Z",
+     "iopub.status.busy": "2024-10-25T14:50:21.869234Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.126541Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.125931Z"
     }
    },
    "outputs": [
@@ -233,10 +233,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.861024Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.860815Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.909287Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.908687Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.128567Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.128358Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.177913Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.177366Z"
     }
    },
    "outputs": [
@@ -264,7 +264,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.704622257788243\n",
+      "Mean value: 6.073961318473499\n",
       "\n"
      ]
     }
@@ -295,10 +295,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.911830Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.911524Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.915001Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.914473Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.180981Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.180429Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.184928Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.184397Z"
     }
    },
    "outputs": [
@@ -306,7 +306,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10.704622257788243 10.727800892028872\n"
+      "6.073961318473499 6.05342048266878\n"
      ]
     }
    ],
@@ -329,10 +329,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:23.917353Z",
-     "iopub.status.busy": "2024-10-25T14:49:23.917141Z",
-     "iopub.status.idle": "2024-10-25T14:49:23.985043Z",
-     "shell.execute_reply": "2024-10-25T14:49:23.984409Z"
+     "iopub.execute_input": "2024-10-25T14:50:22.187562Z",
+     "iopub.status.busy": "2024-10-25T14:50:22.187172Z",
+     "iopub.status.idle": "2024-10-25T14:50:22.254777Z",
+     "shell.execute_reply": "2024-10-25T14:50:22.254115Z"
     }
    },
    "outputs": [
@@ -360,7 +360,7 @@
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 2.0167870465215287e-05\n",
+      "Mean value: -2.0011491711713347e-05\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_multiple_treatments.html b/main/example_notebooks/dowhy_multiple_treatments.html
index a8aa934db..8272c2b5c 100644
--- a/main/example_notebooks/dowhy_multiple_treatments.html
+++ b/main/example_notebooks/dowhy_multiple_treatments.html
@@ -630,63 +630,63 @@ <h1>Estimating effect of multiple treatments<a class="headerlink" href="#Estimat
   <tbody>
     <tr>
       <th>0</th>
-      <td>-1.242831</td>
-      <td>1.456611</td>
-      <td>-1.029501</td>
-      <td>0.110232</td>
-      <td>3</td>
-      <td>1</td>
-      <td>14.644329</td>
-      <td>1.846584</td>
-      <td>166.026822</td>
+      <td>-0.344602</td>
+      <td>0.831968</td>
+      <td>0.392958</td>
+      <td>0.684496</td>
+      <td>0</td>
+      <td>2</td>
+      <td>4.484254</td>
+      <td>6.531018</td>
+      <td>142.642648</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.516329</td>
-      <td>-1.044592</td>
-      <td>0.063713</td>
-      <td>0.596371</td>
-      <td>0</td>
+      <td>-2.110287</td>
+      <td>2.776796</td>
+      <td>0.422762</td>
+      <td>0.292303</td>
       <td>3</td>
-      <td>13.124141</td>
-      <td>0.848538</td>
-      <td>135.730916</td>
+      <td>0</td>
+      <td>11.442877</td>
+      <td>4.339151</td>
+      <td>97.496255</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>-0.598166</td>
-      <td>0.939163</td>
-      <td>0.589856</td>
-      <td>-1.435998</td>
-      <td>3</td>
+      <td>-0.275387</td>
+      <td>-0.917356</td>
+      <td>1.883795</td>
+      <td>1.311776</td>
+      <td>1</td>
       <td>2</td>
-      <td>14.440459</td>
-      <td>4.869747</td>
-      <td>249.820705</td>
+      <td>12.374288</td>
+      <td>10.632938</td>
+      <td>-331.699923</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.445804</td>
-      <td>2.104228</td>
-      <td>0.324483</td>
-      <td>1.304685</td>
-      <td>2</td>
+      <td>0.253720</td>
+      <td>-0.052694</td>
+      <td>0.016866</td>
+      <td>0.523128</td>
+      <td>1</td>
       <td>0</td>
-      <td>15.488781</td>
-      <td>7.546051</td>
-      <td>1193.870840</td>
+      <td>4.865552</td>
+      <td>1.071125</td>
+      <td>69.992638</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>-0.422895</td>
-      <td>0.541271</td>
-      <td>-1.408185</td>
-      <td>2.583083</td>
-      <td>0</td>
-      <td>2</td>
-      <td>14.132825</td>
-      <td>-1.508823</td>
-      <td>133.706932</td>
+      <td>1.641157</td>
+      <td>0.437726</td>
+      <td>0.322939</td>
+      <td>1.671700</td>
+      <td>3</td>
+      <td>1</td>
+      <td>16.240063</td>
+      <td>8.003803</td>
+      <td>1513.835137</td>
     </tr>
   </tbody>
 </table>
@@ -746,9 +746,9 @@ <h1>Estimating effect of multiple treatments<a class="headerlink" href="#Estimat
 Estimand name: backdoor
 Estimand expression:
     d
-─────────(E[y|W0,W3,W2,W1])
+─────────(E[y|W3,W2,W0,W1])
 d[v₀  v₁]
-Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)
 
 ### Estimand : 2
 Estimand name: iv
@@ -791,16 +791,16 @@ <h2>Linear model<a class="headerlink" href="#Linear-model" title="Permalink to t
 Estimand name: backdoor
 Estimand expression:
     d
-─────────(E[y|W0,W3,W2,W1])
+─────────(E[y|W3,W2,W0,W1])
 d[v₀  v₁]
-Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)
 
 ## Realized estimand
-b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1
+b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1
 Target units: ate
 
 ## Estimate
-Mean value: 38.48325274778242
+Mean value: 45.69097368298819
 
 </pre></div></div>
 </div>
@@ -831,43 +831,43 @@ <h2>Linear model<a class="headerlink" href="#Linear-model" title="Permalink to t
 Estimand name: backdoor
 Estimand expression:
     d
-─────────(E[y|W0,W3,W2,W1])
+─────────(E[y|W3,W2,W0,W1])
 d[v₀  v₁]
-Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)
 
 ## Realized estimand
-b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1
+b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1
 Target units:
 
 ## Estimate
-Mean value: 38.48325274778242
+Mean value: 45.69097368298819
 ### Conditional Estimates
 __categorical__X0  __categorical__X1
-(-4.434, -1.311]   (-3.4379999999999997, 0.0683]    -72.917557
-                   (0.0683, 0.65]                   -41.452919
-                   (0.65, 1.155]                    -18.899074
-                   (1.155, 1.72]                     -0.874103
-                   (1.72, 5.039]                     30.952455
-(-1.311, -0.738]   (-3.4379999999999997, 0.0683]    -36.759444
-                   (0.0683, 0.65]                    -3.655510
-                   (0.65, 1.155]                     15.870641
-                   (1.155, 1.72]                     35.822712
-                   (1.72, 5.039]                     69.750816
-(-0.738, -0.221]   (-3.4379999999999997, 0.0683]    -14.294039
-                   (0.0683, 0.65]                    18.609205
-                   (0.65, 1.155]                     37.836481
-                   (1.155, 1.72]                     57.871563
-                   (1.72, 5.039]                     90.660415
-(-0.221, 0.384]    (-3.4379999999999997, 0.0683]      7.522082
-                   (0.0683, 0.65]                    41.146692
-                   (0.65, 1.155]                     61.207314
-                   (1.155, 1.72]                     80.871746
-                   (1.72, 5.039]                    113.599925
-(0.384, 3.11]      (-3.4379999999999997, 0.0683]     44.351338
-                   (0.0683, 0.65]                    76.905999
-                   (0.65, 1.155]                    100.413341
-                   (1.155, 1.72]                    118.424617
-                   (1.72, 5.039]                    149.087826
+(-3.509, -0.768]   (-3.3369999999999997, -0.439]    -97.545075
+                   (-0.439, 0.148]                  -56.362835
+                   (0.148, 0.662]                   -36.699819
+                   (0.662, 1.244]                   -16.351319
+                   (1.244, 4.414]                    18.098515
+(-0.768, -0.193]   (-3.3369999999999997, -0.439]    -43.268273
+                   (-0.439, 0.148]                   -9.193659
+                   (0.148, 0.662]                    13.083896
+                   (0.662, 1.244]                    34.970122
+                   (1.244, 4.414]                    69.062313
+(-0.193, 0.328]    (-3.3369999999999997, -0.439]    -10.999394
+                   (-0.439, 0.148]                   23.879168
+                   (0.148, 0.662]                    44.791434
+                   (0.662, 1.244]                    66.708583
+                   (1.244, 4.414]                   101.492109
+(0.328, 0.911]     (-3.3369999999999997, -0.439]     22.816677
+                   (-0.439, 0.148]                   56.431745
+                   (0.148, 0.662]                    78.275033
+                   (0.662, 1.244]                    99.508186
+                   (1.244, 4.414]                   134.902504
+(0.911, 3.758]     (-3.3369999999999997, -0.439]     71.878946
+                   (-0.439, 0.148]                  109.508433
+                   (0.148, 0.662]                   128.741806
+                   (0.662, 1.244]                   150.536299
+                   (1.244, 4.414]                   188.398120
 dtype: float64
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_multiple_treatments.ipynb b/main/example_notebooks/dowhy_multiple_treatments.ipynb
index 0b0e08816..6b3c2cdf7 100644
--- a/main/example_notebooks/dowhy_multiple_treatments.ipynb
+++ b/main/example_notebooks/dowhy_multiple_treatments.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:25.966002Z",
-     "iopub.status.busy": "2024-10-25T14:49:25.965823Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.410743Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.410120Z"
+     "iopub.execute_input": "2024-10-25T14:50:24.426785Z",
+     "iopub.status.busy": "2024-10-25T14:50:24.426586Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.058882Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.058141Z"
     }
    },
    "outputs": [],
@@ -32,10 +32,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.413059Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.412588Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.440737Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.440108Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.061526Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.060956Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.091853Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.091269Z"
     }
    },
    "outputs": [
@@ -74,82 +74,82 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.242831</td>\n",
-       "      <td>1.456611</td>\n",
-       "      <td>-1.029501</td>\n",
-       "      <td>0.110232</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>14.644329</td>\n",
-       "      <td>1.846584</td>\n",
-       "      <td>166.026822</td>\n",
+       "      <td>-0.344602</td>\n",
+       "      <td>0.831968</td>\n",
+       "      <td>0.392958</td>\n",
+       "      <td>0.684496</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.484254</td>\n",
+       "      <td>6.531018</td>\n",
+       "      <td>142.642648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.516329</td>\n",
-       "      <td>-1.044592</td>\n",
-       "      <td>0.063713</td>\n",
-       "      <td>0.596371</td>\n",
-       "      <td>0</td>\n",
+       "      <td>-2.110287</td>\n",
+       "      <td>2.776796</td>\n",
+       "      <td>0.422762</td>\n",
+       "      <td>0.292303</td>\n",
        "      <td>3</td>\n",
-       "      <td>13.124141</td>\n",
-       "      <td>0.848538</td>\n",
-       "      <td>135.730916</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.442877</td>\n",
+       "      <td>4.339151</td>\n",
+       "      <td>97.496255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.598166</td>\n",
-       "      <td>0.939163</td>\n",
-       "      <td>0.589856</td>\n",
-       "      <td>-1.435998</td>\n",
-       "      <td>3</td>\n",
+       "      <td>-0.275387</td>\n",
+       "      <td>-0.917356</td>\n",
+       "      <td>1.883795</td>\n",
+       "      <td>1.311776</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
-       "      <td>14.440459</td>\n",
-       "      <td>4.869747</td>\n",
-       "      <td>249.820705</td>\n",
+       "      <td>12.374288</td>\n",
+       "      <td>10.632938</td>\n",
+       "      <td>-331.699923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.445804</td>\n",
-       "      <td>2.104228</td>\n",
-       "      <td>0.324483</td>\n",
-       "      <td>1.304685</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0.253720</td>\n",
+       "      <td>-0.052694</td>\n",
+       "      <td>0.016866</td>\n",
+       "      <td>0.523128</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>15.488781</td>\n",
-       "      <td>7.546051</td>\n",
-       "      <td>1193.870840</td>\n",
+       "      <td>4.865552</td>\n",
+       "      <td>1.071125</td>\n",
+       "      <td>69.992638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.422895</td>\n",
-       "      <td>0.541271</td>\n",
-       "      <td>-1.408185</td>\n",
-       "      <td>2.583083</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14.132825</td>\n",
-       "      <td>-1.508823</td>\n",
-       "      <td>133.706932</td>\n",
+       "      <td>1.641157</td>\n",
+       "      <td>0.437726</td>\n",
+       "      <td>0.322939</td>\n",
+       "      <td>1.671700</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16.240063</td>\n",
+       "      <td>8.003803</td>\n",
+       "      <td>1513.835137</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         X0        X1        W0        W1 W2 W3         v0        v1  \\\n",
-       "0 -1.242831  1.456611 -1.029501  0.110232  3  1  14.644329  1.846584   \n",
-       "1  0.516329 -1.044592  0.063713  0.596371  0  3  13.124141  0.848538   \n",
-       "2 -0.598166  0.939163  0.589856 -1.435998  3  2  14.440459  4.869747   \n",
-       "3  0.445804  2.104228  0.324483  1.304685  2  0  15.488781  7.546051   \n",
-       "4 -0.422895  0.541271 -1.408185  2.583083  0  2  14.132825 -1.508823   \n",
+       "         X0        X1        W0        W1 W2 W3         v0         v1  \\\n",
+       "0 -0.344602  0.831968  0.392958  0.684496  0  2   4.484254   6.531018   \n",
+       "1 -2.110287  2.776796  0.422762  0.292303  3  0  11.442877   4.339151   \n",
+       "2 -0.275387 -0.917356  1.883795  1.311776  1  2  12.374288  10.632938   \n",
+       "3  0.253720 -0.052694  0.016866  0.523128  1  0   4.865552   1.071125   \n",
+       "4  1.641157  0.437726  0.322939  1.671700  3  1  16.240063   8.003803   \n",
        "\n",
        "             y  \n",
-       "0   166.026822  \n",
-       "1   135.730916  \n",
-       "2   249.820705  \n",
-       "3  1193.870840  \n",
-       "4   133.706932  "
+       "0   142.642648  \n",
+       "1    97.496255  \n",
+       "2  -331.699923  \n",
+       "3    69.992638  \n",
+       "4  1513.835137  "
       ]
      },
      "execution_count": 2,
@@ -174,10 +174,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.443263Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.442712Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.448805Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.448297Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.095424Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.094280Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.101708Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.101163Z"
     }
    },
    "outputs": [],
@@ -192,10 +192,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.450739Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.450518Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.617673Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.617108Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.104357Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.103873Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.284253Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.283730Z"
     }
    },
    "outputs": [
@@ -231,10 +231,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.619689Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.619410Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.640202Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.639709Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.287038Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.286481Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.309908Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.309231Z"
     }
    },
    "outputs": [
@@ -248,9 +248,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -284,10 +284,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.642411Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.642015Z",
-     "iopub.status.idle": "2024-10-25T14:49:27.704053Z",
-     "shell.execute_reply": "2024-10-25T14:49:27.703473Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.312339Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.312100Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.375794Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.374729Z"
     }
    },
    "outputs": [
@@ -304,16 +304,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
+      "b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 38.48325274778242\n",
+      "Mean value: 45.69097368298819\n",
       "\n"
      ]
     }
@@ -339,10 +339,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:27.706639Z",
-     "iopub.status.busy": "2024-10-25T14:49:27.706397Z",
-     "iopub.status.idle": "2024-10-25T14:49:28.055774Z",
-     "shell.execute_reply": "2024-10-25T14:49:28.055118Z"
+     "iopub.execute_input": "2024-10-25T14:50:26.378381Z",
+     "iopub.status.busy": "2024-10-25T14:50:26.378107Z",
+     "iopub.status.idle": "2024-10-25T14:50:26.735794Z",
+     "shell.execute_reply": "2024-10-25T14:50:26.735127Z"
     }
    },
    "outputs": [
@@ -359,43 +359,43 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "    d                      \n",
-      "─────────(E[y|W0,W3,W2,W1])\n",
+      "─────────(E[y|W3,W2,W0,W1])\n",
       "d[v₀  v₁]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W0,W3,W2,W1,U) = P(y|v0,v1,W0,W3,W2,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W2,W0,W1,U) = P(y|v0,v1,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+v1+W0+W3+W2+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
+      "b: y~v0+v1+W3+W2+W0+W1+v0*X0+v0*X1+v1*X0+v1*X1\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: 38.48325274778242\n",
+      "Mean value: 45.69097368298819\n",
       "### Conditional Estimates\n",
       "__categorical__X0  __categorical__X1            \n",
-      "(-4.434, -1.311]   (-3.4379999999999997, 0.0683]    -72.917557\n",
-      "                   (0.0683, 0.65]                   -41.452919\n",
-      "                   (0.65, 1.155]                    -18.899074\n",
-      "                   (1.155, 1.72]                     -0.874103\n",
-      "                   (1.72, 5.039]                     30.952455\n",
-      "(-1.311, -0.738]   (-3.4379999999999997, 0.0683]    -36.759444\n",
-      "                   (0.0683, 0.65]                    -3.655510\n",
-      "                   (0.65, 1.155]                     15.870641\n",
-      "                   (1.155, 1.72]                     35.822712\n",
-      "                   (1.72, 5.039]                     69.750816\n",
-      "(-0.738, -0.221]   (-3.4379999999999997, 0.0683]    -14.294039\n",
-      "                   (0.0683, 0.65]                    18.609205\n",
-      "                   (0.65, 1.155]                     37.836481\n",
-      "                   (1.155, 1.72]                     57.871563\n",
-      "                   (1.72, 5.039]                     90.660415\n",
-      "(-0.221, 0.384]    (-3.4379999999999997, 0.0683]      7.522082\n",
-      "                   (0.0683, 0.65]                    41.146692\n",
-      "                   (0.65, 1.155]                     61.207314\n",
-      "                   (1.155, 1.72]                     80.871746\n",
-      "                   (1.72, 5.039]                    113.599925\n",
-      "(0.384, 3.11]      (-3.4379999999999997, 0.0683]     44.351338\n",
-      "                   (0.0683, 0.65]                    76.905999\n",
-      "                   (0.65, 1.155]                    100.413341\n",
-      "                   (1.155, 1.72]                    118.424617\n",
-      "                   (1.72, 5.039]                    149.087826\n",
+      "(-3.509, -0.768]   (-3.3369999999999997, -0.439]    -97.545075\n",
+      "                   (-0.439, 0.148]                  -56.362835\n",
+      "                   (0.148, 0.662]                   -36.699819\n",
+      "                   (0.662, 1.244]                   -16.351319\n",
+      "                   (1.244, 4.414]                    18.098515\n",
+      "(-0.768, -0.193]   (-3.3369999999999997, -0.439]    -43.268273\n",
+      "                   (-0.439, 0.148]                   -9.193659\n",
+      "                   (0.148, 0.662]                    13.083896\n",
+      "                   (0.662, 1.244]                    34.970122\n",
+      "                   (1.244, 4.414]                    69.062313\n",
+      "(-0.193, 0.328]    (-3.3369999999999997, -0.439]    -10.999394\n",
+      "                   (-0.439, 0.148]                   23.879168\n",
+      "                   (0.148, 0.662]                    44.791434\n",
+      "                   (0.662, 1.244]                    66.708583\n",
+      "                   (1.244, 4.414]                   101.492109\n",
+      "(0.328, 0.911]     (-3.3369999999999997, -0.439]     22.816677\n",
+      "                   (-0.439, 0.148]                   56.431745\n",
+      "                   (0.148, 0.662]                    78.275033\n",
+      "                   (0.662, 1.244]                    99.508186\n",
+      "                   (1.244, 4.414]                   134.902504\n",
+      "(0.911, 3.758]     (-3.3369999999999997, -0.439]     71.878946\n",
+      "                   (-0.439, 0.148]                  109.508433\n",
+      "                   (0.148, 0.662]                   128.741806\n",
+      "                   (0.662, 1.244]                   150.536299\n",
+      "                   (1.244, 4.414]                   188.398120\n",
       "dtype: float64\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_optimize_backdoor_example.html b/main/example_notebooks/dowhy_optimize_backdoor_example.html
index 1b701381e..bdc37f807 100644
--- a/main/example_notebooks/dowhy_optimize_backdoor_example.html
+++ b/main/example_notebooks/dowhy_optimize_backdoor_example.html
@@ -557,9 +557,9 @@ <h2>Testing optimized backdoor search<a class="headerlink" href="#Testing-optimi
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Time taken for initializing model = 0.0045223236083984375
-Time taken for vanilla identification = 0.0002155303955078125
-Time taken for optimized backdoor identification = 0.00012540817260742188
+Time taken for initializing model = 0.005246877670288086
+Time taken for vanilla identification = 0.013742208480834961
+Time taken for optimized backdoor identification = 0.0015587806701660156
 </pre></div></div>
 </div>
 <p>It can be observed that the optimized backdoor search makes causal identification faster as compared to the vanilla implementation.</p>
diff --git a/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb b/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb
index e94415bd3..1796af42a 100644
--- a/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb
+++ b/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:29.923858Z",
-     "iopub.status.busy": "2024-10-25T14:49:29.923672Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.362428Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.361758Z"
+     "iopub.execute_input": "2024-10-25T14:50:28.558426Z",
+     "iopub.status.busy": "2024-10-25T14:50:28.558184Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.126055Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.125351Z"
     }
    },
    "outputs": [],
@@ -48,10 +48,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:31.364814Z",
-     "iopub.status.busy": "2024-10-25T14:49:31.364514Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.382356Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.381807Z"
+     "iopub.execute_input": "2024-10-25T14:50:30.128557Z",
+     "iopub.status.busy": "2024-10-25T14:50:30.128225Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.146005Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.145461Z"
     }
    },
    "outputs": [
@@ -96,10 +96,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:31.384401Z",
-     "iopub.status.busy": "2024-10-25T14:49:31.384044Z",
-     "iopub.status.idle": "2024-10-25T14:49:31.392877Z",
-     "shell.execute_reply": "2024-10-25T14:49:31.392380Z"
+     "iopub.execute_input": "2024-10-25T14:50:30.147933Z",
+     "iopub.status.busy": "2024-10-25T14:50:30.147729Z",
+     "iopub.status.idle": "2024-10-25T14:50:30.172392Z",
+     "shell.execute_reply": "2024-10-25T14:50:30.171817Z"
     }
    },
    "outputs": [
@@ -107,9 +107,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for initializing model = 0.0045223236083984375\n",
-      "Time taken for vanilla identification = 0.0002155303955078125\n",
-      "Time taken for optimized backdoor identification = 0.00012540817260742188\n"
+      "Time taken for initializing model = 0.005246877670288086\n",
+      "Time taken for vanilla identification = 0.013742208480834961\n",
+      "Time taken for optimized backdoor identification = 0.0015587806701660156\n"
      ]
     }
    ],
diff --git a/main/example_notebooks/dowhy_refutation_testing.html b/main/example_notebooks/dowhy_refutation_testing.html
index 24bc303d7..085730d66 100644
--- a/main/example_notebooks/dowhy_refutation_testing.html
+++ b/main/example_notebooks/dowhy_refutation_testing.html
@@ -1049,13 +1049,13 @@ <h2>IHDP<a class="headerlink" href="#id1" title="Permalink to this heading">#</a
 Estimand name: backdoor
 Estimand expression:
      d                                                                         ↪
-────────────(E[y_factual|x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x ↪
+────────────(E[y_factual|x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5 ↪
 d[treatment]                                                                   ↪
 
 ↪
-↪ 17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14])
+↪ ,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7])
 ↪
-Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14,U) = P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14)
+Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7,U) = P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1091,9 +1091,9 @@ <h2>Lalonde<a class="headerlink" href="#id2" title="Permalink to this heading">#
 Estimand name: backdoor
 Estimand expression:
    d
-────────(E[re78|hisp,age,nodegr,married,educ,black])
+────────(E[re78|nodegr,hisp,age,married,black,educ])
 d[treat]
-Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,age,nodegr,married,educ,black,U) = P(re78|treat,hisp,age,nodegr,married,educ,black)
+Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,nodegr,hisp,age,married,black,educ,U) = P(re78|treat,nodegr,hisp,age,married,black,educ)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1129,7 +1129,7 @@ <h2>IHDP<a class="headerlink" href="#id3" title="Permalink to this heading">#</a
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-The Causal Estimate is 4.028748218390157
+The Causal Estimate is 4.028748218389992
 </pre></div></div>
 </div>
 </section>
@@ -1153,7 +1153,7 @@ <h2>Lalonde<a class="headerlink" href="#id4" title="Permalink to this heading">#
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-The Causal Estimate is 1639.8231995062642
+The Causal Estimate is 1639.8090120835514
 </pre></div></div>
 </div>
 </section>
@@ -1184,8 +1184,8 @@ <h3>Add Random Common Cause<a class="headerlink" href="#Add-Random-Common-Cause"
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:4.028748218390157
-New effect:4.028748218390156
+Estimated effect:4.028748218389992
+New effect:4.028748218389992
 p value:1.0
 
 </pre></div></div>
@@ -1214,9 +1214,9 @@ <h3>Replace Treatment with Placebo<a class="headerlink" href="#Replace-Treatment
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:4.028748218390157
-New effect:-0.4567346745113159
-p value:0.02
+Estimated effect:4.028748218389992
+New effect:-0.47212791991095493
+p value:0.06
 
 </pre></div></div>
 </div>
@@ -1243,9 +1243,9 @@ <h3>Remove Random Subset of Data<a class="headerlink" href="#Remove-Random-Subse
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:4.028748218390157
-New effect:4.026103742836523
-p value:0.96
+Estimated effect:4.028748218389992
+New effect:4.027300878025508
+p value:0.98
 
 </pre></div></div>
 </div>
@@ -1275,8 +1275,8 @@ <h3>Add Random Common Cause<a class="headerlink" href="#id7" title="Permalink to
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:1639.8231995062642
-New effect:1639.8231995062638
+Estimated effect:1639.8090120835514
+New effect:1639.809012083551
 p value:1.0
 
 </pre></div></div>
@@ -1305,9 +1305,9 @@ <h3>Replace Treatment with Placebo<a class="headerlink" href="#id8" title="Perma
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:1639.8231995062642
-New effect:-177.44064343403886
-p value:0.8
+Estimated effect:1639.8090120835514
+New effect:-189.7811505416216
+p value:0.76
 
 </pre></div></div>
 </div>
@@ -1335,9 +1335,9 @@ <h3>Remove Random Subset of Data<a class="headerlink" href="#id9" title="Permali
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:1639.8231995062642
-New effect:1595.963972683793
-p value:0.94
+Estimated effect:1639.8090120835514
+New effect:1658.6773045421505
+p value:0.8
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/dowhy_refutation_testing.ipynb b/main/example_notebooks/dowhy_refutation_testing.ipynb
index e3d35060e..1e1b06fd3 100644
--- a/main/example_notebooks/dowhy_refutation_testing.ipynb
+++ b/main/example_notebooks/dowhy_refutation_testing.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:33.133469Z",
-     "iopub.status.busy": "2024-10-25T14:49:33.133289Z",
-     "iopub.status.idle": "2024-10-25T14:49:34.557871Z",
-     "shell.execute_reply": "2024-10-25T14:49:34.557255Z"
+     "iopub.execute_input": "2024-10-25T14:50:32.024076Z",
+     "iopub.status.busy": "2024-10-25T14:50:32.023615Z",
+     "iopub.status.idle": "2024-10-25T14:50:33.548126Z",
+     "shell.execute_reply": "2024-10-25T14:50:33.547503Z"
     }
    },
    "outputs": [],
@@ -80,10 +80,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:34.560159Z",
-     "iopub.status.busy": "2024-10-25T14:49:34.559871Z",
-     "iopub.status.idle": "2024-10-25T14:49:34.742983Z",
-     "shell.execute_reply": "2024-10-25T14:49:34.742414Z"
+     "iopub.execute_input": "2024-10-25T14:50:33.550770Z",
+     "iopub.status.busy": "2024-10-25T14:50:33.550250Z",
+     "iopub.status.idle": "2024-10-25T14:50:33.583876Z",
+     "shell.execute_reply": "2024-10-25T14:50:33.583178Z"
     }
    },
    "outputs": [
@@ -346,10 +346,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:34.744997Z",
-     "iopub.status.busy": "2024-10-25T14:49:34.744624Z",
-     "iopub.status.idle": "2024-10-25T14:49:35.612847Z",
-     "shell.execute_reply": "2024-10-25T14:49:35.612191Z"
+     "iopub.execute_input": "2024-10-25T14:50:33.585942Z",
+     "iopub.status.busy": "2024-10-25T14:50:33.585579Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.469445Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.468765Z"
     }
    },
    "outputs": [
@@ -516,10 +516,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:35.614978Z",
-     "iopub.status.busy": "2024-10-25T14:49:35.614538Z",
-     "iopub.status.idle": "2024-10-25T14:49:35.859803Z",
-     "shell.execute_reply": "2024-10-25T14:49:35.859168Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.471656Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.471186Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.720594Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.719917Z"
     }
    },
    "outputs": [
@@ -575,10 +575,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:35.862084Z",
-     "iopub.status.busy": "2024-10-25T14:49:35.861735Z",
-     "iopub.status.idle": "2024-10-25T14:49:36.012077Z",
-     "shell.execute_reply": "2024-10-25T14:49:36.011400Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.723201Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.722825Z",
+     "iopub.status.idle": "2024-10-25T14:50:34.861826Z",
+     "shell.execute_reply": "2024-10-25T14:50:34.861131Z"
     }
    },
    "outputs": [
@@ -634,10 +634,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:49:36.014657Z",
-     "iopub.status.busy": "2024-10-25T14:49:36.014240Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.895768Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.895210Z"
+     "iopub.execute_input": "2024-10-25T14:50:34.863972Z",
+     "iopub.status.busy": "2024-10-25T14:50:34.863767Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.871948Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.871282Z"
     }
    },
    "outputs": [
@@ -651,13 +651,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                                                                         ↪\n",
-      "────────────(E[y_factual|x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x ↪\n",
+      "────────────(E[y_factual|x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5 ↪\n",
       "d[treatment]                                                                   ↪\n",
       "\n",
       "↪                                        \n",
-      "↪ 17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14])\n",
+      "↪ ,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7])\n",
       "↪                                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14,U) = P(y_factual|treatment,x15,x6,x9,x22,x19,x4,x8,x18,x13,x24,x16,x25,x20,x21,x17,x23,x2,x12,x11,x5,x10,x3,x7,x1,x14)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7,U) = P(y_factual|treatment,x16,x18,x14,x6,x1,x3,x23,x20,x19,x13,x4,x24,x8,x21,x5,x10,x11,x2,x9,x17,x22,x12,x25,x15,x7)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -688,10 +688,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.897670Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.897342Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.910044Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.909544Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.874267Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.873866Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.887686Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.887155Z"
     }
    },
    "outputs": [
@@ -705,9 +705,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "   d                                                \n",
-      "────────(E[re78|hisp,age,nodegr,married,educ,black])\n",
+      "────────(E[re78|nodegr,hisp,age,married,black,educ])\n",
       "d[treat]                                            \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,age,nodegr,married,educ,black,U) = P(re78|treat,hisp,age,nodegr,married,educ,black)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,nodegr,hisp,age,married,black,educ,U) = P(re78|treat,nodegr,hisp,age,married,black,educ)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -745,10 +745,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.912109Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.911603Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.941315Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.940594Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.889830Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.889453Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.920278Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.919673Z"
     }
    },
    "outputs": [
@@ -756,7 +756,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The Causal Estimate is 4.028748218390157\n"
+      "The Causal Estimate is 4.028748218389992\n"
      ]
     }
    ],
@@ -781,10 +781,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.944033Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.943751Z",
-     "iopub.status.idle": "2024-10-25T14:50:30.975513Z",
-     "shell.execute_reply": "2024-10-25T14:50:30.975028Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.923151Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.922616Z",
+     "iopub.status.idle": "2024-10-25T14:51:29.956053Z",
+     "shell.execute_reply": "2024-10-25T14:51:29.955359Z"
     }
    },
    "outputs": [
@@ -792,7 +792,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The Causal Estimate is 1639.8231995062642\n"
+      "The Causal Estimate is 1639.8090120835514\n"
      ]
     }
    ],
@@ -831,10 +831,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:30.978051Z",
-     "iopub.status.busy": "2024-10-25T14:50:30.977697Z",
-     "iopub.status.idle": "2024-10-25T14:50:31.924467Z",
-     "shell.execute_reply": "2024-10-25T14:50:31.923793Z"
+     "iopub.execute_input": "2024-10-25T14:51:29.959369Z",
+     "iopub.status.busy": "2024-10-25T14:51:29.958331Z",
+     "iopub.status.idle": "2024-10-25T14:51:31.083429Z",
+     "shell.execute_reply": "2024-10-25T14:51:31.082738Z"
     }
    },
    "outputs": [
@@ -843,8 +843,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:4.028748218390156\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:4.028748218389992\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -872,10 +872,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:31.926474Z",
-     "iopub.status.busy": "2024-10-25T14:50:31.926261Z",
-     "iopub.status.idle": "2024-10-25T14:50:32.893080Z",
-     "shell.execute_reply": "2024-10-25T14:50:32.892503Z"
+     "iopub.execute_input": "2024-10-25T14:51:31.085425Z",
+     "iopub.status.busy": "2024-10-25T14:51:31.085203Z",
+     "iopub.status.idle": "2024-10-25T14:51:32.060341Z",
+     "shell.execute_reply": "2024-10-25T14:51:32.059746Z"
     }
    },
    "outputs": [
@@ -884,9 +884,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:-0.4567346745113159\n",
-      "p value:0.02\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:-0.47212791991095493\n",
+      "p value:0.06\n",
       "\n"
      ]
     }
@@ -914,10 +914,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:32.894976Z",
-     "iopub.status.busy": "2024-10-25T14:50:32.894764Z",
-     "iopub.status.idle": "2024-10-25T14:50:33.768531Z",
-     "shell.execute_reply": "2024-10-25T14:50:33.767949Z"
+     "iopub.execute_input": "2024-10-25T14:51:32.062498Z",
+     "iopub.status.busy": "2024-10-25T14:51:32.062067Z",
+     "iopub.status.idle": "2024-10-25T14:51:32.953005Z",
+     "shell.execute_reply": "2024-10-25T14:51:32.952292Z"
     }
    },
    "outputs": [
@@ -926,9 +926,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:4.028748218390157\n",
-      "New effect:4.026103742836523\n",
-      "p value:0.96\n",
+      "Estimated effect:4.028748218389992\n",
+      "New effect:4.027300878025508\n",
+      "p value:0.98\n",
       "\n"
      ]
     }
@@ -962,10 +962,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:33.770528Z",
-     "iopub.status.busy": "2024-10-25T14:50:33.770304Z",
-     "iopub.status.idle": "2024-10-25T14:50:34.661229Z",
-     "shell.execute_reply": "2024-10-25T14:50:34.660655Z"
+     "iopub.execute_input": "2024-10-25T14:51:32.955148Z",
+     "iopub.status.busy": "2024-10-25T14:51:32.954947Z",
+     "iopub.status.idle": "2024-10-25T14:51:33.888811Z",
+     "shell.execute_reply": "2024-10-25T14:51:33.888209Z"
     }
    },
    "outputs": [
@@ -974,8 +974,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:1639.8231995062638\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:1639.809012083551\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -1003,10 +1003,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:34.663129Z",
-     "iopub.status.busy": "2024-10-25T14:50:34.662921Z",
-     "iopub.status.idle": "2024-10-25T14:50:35.602892Z",
-     "shell.execute_reply": "2024-10-25T14:50:35.602291Z"
+     "iopub.execute_input": "2024-10-25T14:51:33.891036Z",
+     "iopub.status.busy": "2024-10-25T14:51:33.890585Z",
+     "iopub.status.idle": "2024-10-25T14:51:34.875052Z",
+     "shell.execute_reply": "2024-10-25T14:51:34.874337Z"
     }
    },
    "outputs": [
@@ -1015,9 +1015,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:-177.44064343403886\n",
-      "p value:0.8\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:-189.7811505416216\n",
+      "p value:0.76\n",
       "\n"
      ]
     }
@@ -1045,10 +1045,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:35.604984Z",
-     "iopub.status.busy": "2024-10-25T14:50:35.604547Z",
-     "iopub.status.idle": "2024-10-25T14:50:36.463055Z",
-     "shell.execute_reply": "2024-10-25T14:50:36.462391Z"
+     "iopub.execute_input": "2024-10-25T14:51:34.877427Z",
+     "iopub.status.busy": "2024-10-25T14:51:34.876941Z",
+     "iopub.status.idle": "2024-10-25T14:51:35.754559Z",
+     "shell.execute_reply": "2024-10-25T14:51:35.753814Z"
     }
    },
    "outputs": [
@@ -1057,9 +1057,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:1639.8231995062642\n",
-      "New effect:1595.963972683793\n",
-      "p value:0.94\n",
+      "Estimated effect:1639.8090120835514\n",
+      "New effect:1658.6773045421505\n",
+      "p value:0.8\n",
       "\n"
      ]
     }
diff --git a/main/example_notebooks/dowhy_simple_example.html b/main/example_notebooks/dowhy_simple_example.html
index 015bcbee6..a3ec2bb47 100644
--- a/main/example_notebooks/dowhy_simple_example.html
+++ b/main/example_notebooks/dowhy_simple_example.html
@@ -642,68 +642,68 @@ <h1>Basic Example for Calculating the Causal Effect<a class="headerlink" href="#
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.671234</td>
-      <td>1.0</td>
-      <td>0.572346</td>
-      <td>2.147272</td>
-      <td>-1.008626</td>
-      <td>-0.636455</td>
-      <td>-0.726492</td>
-      <td>2</td>
-      <td>True</td>
-      <td>18.593792</td>
+      <td>2.584789</td>
+      <td>0.0</td>
+      <td>0.416842</td>
+      <td>0.943169</td>
+      <td>-0.211056</td>
+      <td>-1.037660</td>
+      <td>1.462029</td>
+      <td>3</td>
+      <td>False</td>
+      <td>16.889292</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.964685</td>
-      <td>1.0</td>
-      <td>0.517017</td>
-      <td>-0.643717</td>
-      <td>-0.120277</td>
-      <td>0.304531</td>
-      <td>-1.707153</td>
-      <td>0</td>
-      <td>True</td>
-      <td>8.122055</td>
+      <td>-0.421155</td>
+      <td>0.0</td>
+      <td>0.746773</td>
+      <td>0.760769</td>
+      <td>-0.948665</td>
+      <td>-2.609936</td>
+      <td>2.330821</td>
+      <td>1</td>
+      <td>False</td>
+      <td>-3.205946</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.311971</td>
-      <td>1.0</td>
-      <td>0.152409</td>
-      <td>-0.436145</td>
-      <td>0.677291</td>
-      <td>1.221427</td>
-      <td>0.819015</td>
-      <td>0</td>
+      <td>2.264079</td>
+      <td>0.0</td>
+      <td>0.712215</td>
+      <td>0.601335</td>
+      <td>-0.642743</td>
+      <td>-1.544072</td>
+      <td>-0.253958</td>
+      <td>3</td>
       <td>True</td>
-      <td>19.239614</td>
+      <td>26.358782</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>1.299439</td>
-      <td>1.0</td>
-      <td>0.687731</td>
-      <td>1.184121</td>
-      <td>-0.530793</td>
-      <td>-0.577695</td>
-      <td>1.606986</td>
-      <td>1</td>
+      <td>0.807192</td>
+      <td>0.0</td>
+      <td>0.741959</td>
+      <td>2.537230</td>
+      <td>0.848927</td>
+      <td>-2.727143</td>
+      <td>0.768600</td>
+      <td>3</td>
       <td>True</td>
-      <td>20.986311</td>
+      <td>30.392742</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.232770</td>
-      <td>1.0</td>
-      <td>0.154202</td>
-      <td>-1.499937</td>
-      <td>-0.320013</td>
-      <td>-0.834136</td>
-      <td>2.449783</td>
-      <td>0</td>
+      <td>0.514467</td>
+      <td>0.0</td>
+      <td>0.331064</td>
+      <td>-0.036820</td>
+      <td>-0.617192</td>
+      <td>0.271595</td>
+      <td>0.782916</td>
+      <td>2</td>
       <td>True</td>
-      <td>13.098742</td>
+      <td>22.997458</td>
     </tr>
   </tbody>
 </table>
@@ -785,9 +785,9 @@ <h4>Identification<a class="headerlink" href="#Identification" title="Permalink
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W1,W3,W0,W2])
+─────(E[y|W4,W3,W2,W0,W1])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)
 
 ### Estimand : 2
 Estimand name: iv
@@ -833,16 +833,16 @@ <h4>Estimation<a class="headerlink" href="#Estimation" title="Permalink to this
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W1,W3,W0,W2])
+─────(E[y|W4,W3,W2,W0,W1])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)
 
 ## Realized estimand
-b: y~v0+W4+W1+W3+W0+W2
+b: y~v0+W4+W3+W2+W0+W1
 Target units: ate
 
 ## Estimate
-Mean value: 12.3093616611939
+Mean value: 13.111841490138564
 
 </pre></div></div>
 </div>
@@ -874,18 +874,18 @@ <h4>Estimation<a class="headerlink" href="#Estimation" title="Permalink to this
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W1,W3,W0,W2])
+─────(E[y|W4,W3,W2,W0,W1])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)
 
 ## Realized estimand
-b: y~v0+W4+W1+W3+W0+W2
+b: y~v0+W4+W3+W2+W0+W1
 Target units: atc
 
 ## Estimate
-Mean value: 12.225338634909248
+Mean value: 13.037286029881079
 
-Causal Estimate is 12.225338634909248
+Causal Estimate is 13.037286029881079
 </pre></div></div>
 </div>
 </section>
@@ -972,18 +972,18 @@ <h2>Interface 2: Specify common causes and instruments<a class="headerlink" href
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W1,W3,W0,W2])
+─────(E[y|W4,W3,W2,W0,W1])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)
 
 ## Realized estimand
-b: y~v0+W4+W1+W3+W0+W2
+b: y~v0+W4+W3+W2+W0+W1
 Target units: ate
 
 ## Estimate
-Mean value: 12.3093616611939
+Mean value: 13.111841490138564
 
-Causal Estimate is 12.3093616611939
+Causal Estimate is 13.111841490138564
 </pre></div></div>
 </div>
 </section>
@@ -1016,7 +1016,7 @@ <h3>Adding a random common cause variable<a class="headerlink" href="#Adding-a-r
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "595c9871fb114acba558d6a7db002403"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "ea8cb893ee004fa397825bf601616bce"}</script></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1024,8 +1024,8 @@ <h3>Adding a random common cause variable<a class="headerlink" href="#Adding-a-r
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:12.3093616611939
-New effect:12.3093616611939
+Estimated effect:13.111841490138564
+New effect:13.111841490138568
 p value:1.0
 
 </pre></div></div>
@@ -1047,7 +1047,7 @@ <h3>Replacing treatment with a random (placebo) variable<a class="headerlink" hr
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "8f219d973cfd4696933afeb56f1a47e2"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "e3afa9e9c4ef4f7f9b8a7d006e220466"}</script></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1055,9 +1055,9 @@ <h3>Replacing treatment with a random (placebo) variable<a class="headerlink" hr
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:12.3093616611939
-New effect:0.01173367700295361
-p value:0.94
+Estimated effect:13.111841490138564
+New effect:-0.011250168352115359
+p value:1.0
 
 </pre></div></div>
 </div>
@@ -1078,7 +1078,7 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "dabd0980e25e4488bba07e4eb3defa12"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "16a7b6646f684210a403e65627e64dc8"}</script></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1086,9 +1086,9 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:12.3093616611939
-New effect:12.277462311024122
-p value:0.82
+Estimated effect:13.111841490138564
+New effect:12.856005739426717
+p value:0.02
 
 </pre></div></div>
 </div>
@@ -1109,7 +1109,7 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 <div class="prompt empty docutils container">
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@@ -1117,16 +1117,16 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
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 <div class="highlight"><pre>
 [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
-[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.1s
-[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.5s
-[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.3s
-[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    4.8s
-[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.0s
-[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    6.8s
-[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.0s
-[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    8.9s
-[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.4s
-[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   11.7s
+[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.5s
+[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.9s
+[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.7s
+[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    5.3s
+[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.4s
+[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.2s
+[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.4s
+[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    9.6s
+[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.9s
+[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   12.3s
 </pre></div></div>
 </div>
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@@ -1135,9 +1135,9 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
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 <div class="highlight"><pre>
 Refute: Use a subset of data
-Estimated effect:12.3093616611939
-New effect:12.283713319247354
-p value:0.72
+Estimated effect:13.111841490138564
+New effect:12.875409322185174
+p value:0.12
 
 </pre></div></div>
 </div>
@@ -1146,7 +1146,7 @@ <h3>Removing a random subset of the data<a class="headerlink" href="#Removing-a-
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   12.5s finished
+[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   13.3s finished
 </pre></div></div>
 </div>
 </section>
@@ -1175,8 +1175,8 @@ <h3>Adding an unobserved common cause variable<a class="headerlink" href="#Addin
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 <div class="highlight"><pre>
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.3093616611939
-New effect:11.499907799537318
+Estimated effect:13.111841490138564
+New effect:12.013311802768278
 
 </pre></div></div>
 </div>
@@ -1205,8 +1205,8 @@ <h3>Adding an unobserved common cause variable<a class="headerlink" href="#Addin
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.3093616611939
-New effect:(8.216459566237983, 12.175990977887663)
+Estimated effect:13.111841490138564
+New effect:(11.221001774229803, 12.921868423571057)
 
 </pre></div></div>
 </div>
@@ -1237,8 +1237,8 @@ <h3>Adding an unobserved common cause variable<a class="headerlink" href="#Addin
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.3093616611939
-New effect:(4.633438858979389, 12.180186652249047)
+Estimated effect:13.111841490138564
+New effect:(5.738666451726319, 13.008344040681948)
 
 </pre></div></div>
 </div>
@@ -1276,14 +1276,14 @@ <h3>Adding an unobserved common cause variable<a class="headerlink" href="#Addin
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.3093616611939
-New effect:(6.999875970223543, 12.397733619781969)
+Estimated effect:13.111841490138564
+New effect:(-0.10165103240117264, 12.657755577247155)
 
 </pre></div></div>
 </div>
 <p><strong>Conclusion</strong>: Assuming that the unobserved confounder does not affect the treatment or outcome more strongly than any observed confounder, the causal effect can be concluded to be positive.</p>
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diff --git a/main/example_notebooks/dowhy_simple_example.ipynb b/main/example_notebooks/dowhy_simple_example.ipynb
index 2d12635ca..6f47d1906 100644
--- a/main/example_notebooks/dowhy_simple_example.ipynb
+++ b/main/example_notebooks/dowhy_simple_example.ipynb
@@ -16,10 +16,10 @@
    "execution_count": 1,
    "metadata": {
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-     "iopub.execute_input": "2024-10-25T14:50:43.270472Z",
-     "iopub.status.busy": "2024-10-25T14:50:43.270290Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.729758Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.729120Z"
+     "iopub.execute_input": "2024-10-25T14:51:43.301755Z",
+     "iopub.status.busy": "2024-10-25T14:51:43.301575Z",
+     "iopub.status.idle": "2024-10-25T14:51:44.927089Z",
+     "shell.execute_reply": "2024-10-25T14:51:44.926426Z"
     }
    },
    "outputs": [],
@@ -44,10 +44,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.732271Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.731785Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.879933Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.879289Z"
+     "iopub.execute_input": "2024-10-25T14:51:44.930025Z",
+     "iopub.status.busy": "2024-10-25T14:51:44.929635Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.081574Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.080866Z"
     }
    },
    "outputs": [],
@@ -68,10 +68,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.882541Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.882327Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.897724Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.897121Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.084881Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.084201Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.098906Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.098147Z"
     }
    },
    "outputs": [
@@ -111,87 +111,87 @@
        "  <tbody>\n",
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        "      <th>0</th>\n",
-       "      <td>1.671234</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.572346</td>\n",
-       "      <td>2.147272</td>\n",
-       "      <td>-1.008626</td>\n",
-       "      <td>-0.636455</td>\n",
-       "      <td>-0.726492</td>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>18.593792</td>\n",
+       "      <td>2.584789</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.416842</td>\n",
+       "      <td>0.943169</td>\n",
+       "      <td>-0.211056</td>\n",
+       "      <td>-1.037660</td>\n",
+       "      <td>1.462029</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>16.889292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.964685</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.517017</td>\n",
-       "      <td>-0.643717</td>\n",
-       "      <td>-0.120277</td>\n",
-       "      <td>0.304531</td>\n",
-       "      <td>-1.707153</td>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>8.122055</td>\n",
+       "      <td>-0.421155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.746773</td>\n",
+       "      <td>0.760769</td>\n",
+       "      <td>-0.948665</td>\n",
+       "      <td>-2.609936</td>\n",
+       "      <td>2.330821</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-3.205946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.311971</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.152409</td>\n",
-       "      <td>-0.436145</td>\n",
-       "      <td>0.677291</td>\n",
-       "      <td>1.221427</td>\n",
-       "      <td>0.819015</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2.264079</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.712215</td>\n",
+       "      <td>0.601335</td>\n",
+       "      <td>-0.642743</td>\n",
+       "      <td>-1.544072</td>\n",
+       "      <td>-0.253958</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>19.239614</td>\n",
+       "      <td>26.358782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.299439</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.687731</td>\n",
-       "      <td>1.184121</td>\n",
-       "      <td>-0.530793</td>\n",
-       "      <td>-0.577695</td>\n",
-       "      <td>1.606986</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.807192</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.741959</td>\n",
+       "      <td>2.537230</td>\n",
+       "      <td>0.848927</td>\n",
+       "      <td>-2.727143</td>\n",
+       "      <td>0.768600</td>\n",
+       "      <td>3</td>\n",
        "      <td>True</td>\n",
-       "      <td>20.986311</td>\n",
+       "      <td>30.392742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.232770</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.154202</td>\n",
-       "      <td>-1.499937</td>\n",
-       "      <td>-0.320013</td>\n",
-       "      <td>-0.834136</td>\n",
-       "      <td>2.449783</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.514467</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.331064</td>\n",
+       "      <td>-0.036820</td>\n",
+       "      <td>-0.617192</td>\n",
+       "      <td>0.271595</td>\n",
+       "      <td>0.782916</td>\n",
+       "      <td>2</td>\n",
        "      <td>True</td>\n",
-       "      <td>13.098742</td>\n",
+       "      <td>22.997458</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         X0   Z0        Z1        W0        W1        W2        W3 W4    v0  \\\n",
-       "0  1.671234  1.0  0.572346  2.147272 -1.008626 -0.636455 -0.726492  2  True   \n",
-       "1  0.964685  1.0  0.517017 -0.643717 -0.120277  0.304531 -1.707153  0  True   \n",
-       "2  0.311971  1.0  0.152409 -0.436145  0.677291  1.221427  0.819015  0  True   \n",
-       "3  1.299439  1.0  0.687731  1.184121 -0.530793 -0.577695  1.606986  1  True   \n",
-       "4  1.232770  1.0  0.154202 -1.499937 -0.320013 -0.834136  2.449783  0  True   \n",
+       "         X0   Z0        Z1        W0        W1        W2        W3 W4     v0  \\\n",
+       "0  2.584789  0.0  0.416842  0.943169 -0.211056 -1.037660  1.462029  3  False   \n",
+       "1 -0.421155  0.0  0.746773  0.760769 -0.948665 -2.609936  2.330821  1  False   \n",
+       "2  2.264079  0.0  0.712215  0.601335 -0.642743 -1.544072 -0.253958  3   True   \n",
+       "3  0.807192  0.0  0.741959  2.537230  0.848927 -2.727143  0.768600  3   True   \n",
+       "4  0.514467  0.0  0.331064 -0.036820 -0.617192  0.271595  0.782916  2   True   \n",
        "\n",
        "           y  \n",
-       "0  18.593792  \n",
-       "1   8.122055  \n",
-       "2  19.239614  \n",
-       "3  20.986311  \n",
-       "4  13.098742  "
+       "0  16.889292  \n",
+       "1  -3.205946  \n",
+       "2  26.358782  \n",
+       "3  30.392742  \n",
+       "4  22.997458  "
       ]
      },
      "execution_count": 3,
@@ -231,10 +231,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.901070Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.900269Z",
-     "iopub.status.idle": "2024-10-25T14:50:44.907081Z",
-     "shell.execute_reply": "2024-10-25T14:50:44.906572Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.101339Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.101101Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.107555Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.106802Z"
     }
    },
    "outputs": [],
@@ -253,10 +253,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:44.909537Z",
-     "iopub.status.busy": "2024-10-25T14:50:44.909333Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.078909Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.078261Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.110111Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.109609Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.281474Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.280707Z"
     }
    },
    "outputs": [
@@ -280,10 +280,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.081502Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.081227Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.088033Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.087482Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.283837Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.283609Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.288784Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.288238Z"
     },
     "scrolled": true
    },
@@ -329,10 +329,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.090235Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.090032Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.260750Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.260177Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.291014Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.290636Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.463887Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.463349Z"
     }
    },
    "outputs": [
@@ -346,9 +346,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -391,10 +391,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.262760Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.262421Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.583717Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.583059Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.466110Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.465769Z",
+     "iopub.status.idle": "2024-10-25T14:51:45.811564Z",
+     "shell.execute_reply": "2024-10-25T14:51:45.810854Z"
     },
     "scrolled": true
    },
@@ -412,16 +412,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.3093616611939\n",
+      "Mean value: 13.111841490138564\n",
       "\n"
      ]
     }
@@ -444,10 +444,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.585673Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.585419Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.856249Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.855671Z"
+     "iopub.execute_input": "2024-10-25T14:51:45.813867Z",
+     "iopub.status.busy": "2024-10-25T14:51:45.813485Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.107675Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.106952Z"
     }
    },
    "outputs": [
@@ -464,18 +464,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: atc\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.225338634909248\n",
+      "Mean value: 13.037286029881079\n",
       "\n",
-      "Causal Estimate is 12.225338634909248\n"
+      "Causal Estimate is 13.037286029881079\n"
      ]
     }
    ],
@@ -500,10 +500,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.858245Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.857870Z",
-     "iopub.status.idle": "2024-10-25T14:50:45.861052Z",
-     "shell.execute_reply": "2024-10-25T14:50:45.860599Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.110002Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.109540Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.112976Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.112504Z"
     },
     "scrolled": true
    },
@@ -523,10 +523,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:45.862837Z",
-     "iopub.status.busy": "2024-10-25T14:50:45.862415Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.004591Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.003956Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.114807Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.114512Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.260743Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.260115Z"
     }
    },
    "outputs": [
@@ -550,10 +550,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.007016Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.006596Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.011381Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.010855Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.263443Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.263138Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.268183Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.267691Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.013455Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.013060Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.126355Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.125792Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.270116Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.269905Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.428174Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.427576Z"
     }
    },
    "outputs": [],
@@ -610,10 +610,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.128472Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.128088Z",
-     "iopub.status.idle": "2024-10-25T14:50:46.396440Z",
-     "shell.execute_reply": "2024-10-25T14:50:46.395799Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.430434Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.429980Z",
+     "iopub.status.idle": "2024-10-25T14:51:46.710661Z",
+     "shell.execute_reply": "2024-10-25T14:51:46.710063Z"
     }
    },
    "outputs": [
@@ -630,18 +630,18 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W4,W1,W3,W0,W2])\n",
+      "─────(E[y|W4,W3,W2,W0,W1])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W1,W3,W0,W2,U) = P(y|v0,W4,W1,W3,W0,W2)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W2,W0,W1,U) = P(y|v0,W4,W3,W2,W0,W1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W1+W3+W0+W2\n",
+      "b: y~v0+W4+W3+W2+W0+W1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 12.3093616611939\n",
+      "Mean value: 13.111841490138564\n",
       "\n",
-      "Causal Estimate is 12.3093616611939\n"
+      "Causal Estimate is 13.111841490138564\n"
      ]
     }
    ],
@@ -685,17 +685,17 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:50:46.398426Z",
-     "iopub.status.busy": "2024-10-25T14:50:46.398237Z",
-     "iopub.status.idle": "2024-10-25T14:51:13.224614Z",
-     "shell.execute_reply": "2024-10-25T14:51:13.223939Z"
+     "iopub.execute_input": "2024-10-25T14:51:46.712825Z",
+     "iopub.status.busy": "2024-10-25T14:51:46.712432Z",
+     "iopub.status.idle": "2024-10-25T14:52:14.800399Z",
+     "shell.execute_reply": "2024-10-25T14:52:14.799738Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "595c9871fb114acba558d6a7db002403",
+       "model_id": "ea8cb893ee004fa397825bf601616bce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -711,8 +711,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.3093616611939\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:13.111841490138568\n",
       "p value:1.0\n",
       "\n"
      ]
@@ -735,17 +735,17 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:51:13.226834Z",
-     "iopub.status.busy": "2024-10-25T14:51:13.226434Z",
-     "iopub.status.idle": "2024-10-25T14:51:39.854390Z",
-     "shell.execute_reply": "2024-10-25T14:51:39.853760Z"
+     "iopub.execute_input": "2024-10-25T14:52:14.802601Z",
+     "iopub.status.busy": "2024-10-25T14:52:14.802396Z",
+     "iopub.status.idle": "2024-10-25T14:52:40.458714Z",
+     "shell.execute_reply": "2024-10-25T14:52:40.458023Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f219d973cfd4696933afeb56f1a47e2",
+       "model_id": "e3afa9e9c4ef4f7f9b8a7d006e220466",
        "version_major": 2,
        "version_minor": 0
       },
@@ -761,9 +761,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:0.01173367700295361\n",
-      "p value:0.94\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:-0.011250168352115359\n",
+      "p value:1.0\n",
       "\n"
      ]
     }
@@ -786,17 +786,17 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:51:39.856297Z",
-     "iopub.status.busy": "2024-10-25T14:51:39.856108Z",
-     "iopub.status.idle": "2024-10-25T14:52:04.297987Z",
-     "shell.execute_reply": "2024-10-25T14:52:04.297336Z"
+     "iopub.execute_input": "2024-10-25T14:52:40.460682Z",
+     "iopub.status.busy": "2024-10-25T14:52:40.460477Z",
+     "iopub.status.idle": "2024-10-25T14:53:06.480478Z",
+     "shell.execute_reply": "2024-10-25T14:53:06.479816Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dabd0980e25e4488bba07e4eb3defa12",
+       "model_id": "16a7b6646f684210a403e65627e64dc8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -812,9 +812,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.277462311024122\n",
-      "p value:0.82\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.856005739426717\n",
+      "p value:0.02\n",
       "\n"
      ]
     }
@@ -841,17 +841,17 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:04.300093Z",
-     "iopub.status.busy": "2024-10-25T14:52:04.299721Z",
-     "iopub.status.idle": "2024-10-25T14:52:16.797936Z",
-     "shell.execute_reply": "2024-10-25T14:52:16.797225Z"
+     "iopub.execute_input": "2024-10-25T14:53:06.483001Z",
+     "iopub.status.busy": "2024-10-25T14:53:06.482576Z",
+     "iopub.status.idle": "2024-10-25T14:53:19.834952Z",
+     "shell.execute_reply": "2024-10-25T14:53:19.834126Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "157326ad6e5e496a89bca87f987ebfec",
+       "model_id": "2650a6a2475a49bfb7f8241fa41ced30",
        "version_major": 2,
        "version_minor": 0
       },
@@ -873,70 +873,70 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.1s\n"
+      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    3.5s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.5s\n"
+      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.9s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.3s\n"
+      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.7s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    4.8s\n"
+      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    5.3s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.0s\n"
+      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    6.8s\n"
+      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.2s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.0s\n"
+      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    8.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    8.9s\n"
+      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:    9.6s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.4s\n"
+      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   10.9s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   11.7s\n"
+      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   12.3s\n"
      ]
     },
     {
@@ -944,9 +944,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:12.283713319247354\n",
-      "p value:0.72\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.875409322185174\n",
+      "p value:0.12\n",
       "\n"
      ]
     },
@@ -954,7 +954,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   12.5s finished\n"
+      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   13.3s finished\n"
      ]
     }
    ],
@@ -984,10 +984,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:16.800100Z",
-     "iopub.status.busy": "2024-10-25T14:52:16.799882Z",
-     "iopub.status.idle": "2024-10-25T14:52:17.092581Z",
-     "shell.execute_reply": "2024-10-25T14:52:17.091987Z"
+     "iopub.execute_input": "2024-10-25T14:53:19.837480Z",
+     "iopub.status.busy": "2024-10-25T14:53:19.837078Z",
+     "iopub.status.idle": "2024-10-25T14:53:20.150031Z",
+     "shell.execute_reply": "2024-10-25T14:53:20.149352Z"
     }
    },
    "outputs": [
@@ -996,8 +996,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:11.499907799537318\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:12.013311802768278\n",
       "\n"
      ]
     }
@@ -1021,16 +1021,16 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:17.094897Z",
-     "iopub.status.busy": "2024-10-25T14:52:17.094320Z",
-     "iopub.status.idle": "2024-10-25T14:52:18.364723Z",
-     "shell.execute_reply": "2024-10-25T14:52:18.364084Z"
+     "iopub.execute_input": "2024-10-25T14:53:20.152158Z",
+     "iopub.status.busy": "2024-10-25T14:53:20.151955Z",
+     "iopub.status.idle": "2024-10-25T14:53:21.472784Z",
+     "shell.execute_reply": "2024-10-25T14:53:21.472014Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Tp+ep9+5fpIaGhnk+CAvSvn17xMbGYuvWrbKyrKws/P777zAyMoKfn5+Cvfn/doOCghASEiJXnpiYiE2bNqFu3br5jjgoO949e/bIXXZ78eJFBAcHy66CSktLw5s3b+T2cXFxgbGxseyy4DZt2sDExASzZ89GZmZmnpiePXtW6Ph79eqF9PR0rFu3DocPH0bPnj3lthf2tezs7FC3bl2sW7dO7nLbY8eO5Zmvkh8DAwNMmDAB4eHhmDBhQr4jGhs3bsTFixcB5LzvFy9eRGBgoGx7amoqVq5cCScnp1KzvlD9+vVhbW2NFStWyF3WfejQIYSHh6NDhw5KeR1F3z9NTc08dbZv357nkvDcRDZ3XhyQMxqzcuVKuXrJycl55hW5u7tDQ0ND1u9u3bpBU1MT06dPz/PaQgi8ePFCkS5TMeGIDKnEoUOHZH+JvqtRo0aoUqWKwu2NHz8e+/btw2effYYBAwbAy8sLqampCAsLw44dOxAVFQUrKyu0aNECffv2xdKlS3Hv3j20bdsWUqkU586dQ4sWLWS3BPDy8sLx48excOFC2Nvbw9nZOd/5DQAwbNgw/PnnnxgwYABCQkLg5OSEHTt24Pz581i8eHGhJzP+18SJE7F9+3Y0a9YMX331Fdzc3PD06VMEBAQgJiYGa9euLVK7isbr6uqKJk2a4JtvvkF6ejoWL14MS0tL2Wm8u3fvomXLlujZsydq1qwJLS0t7N69G3FxcejduzeAnPkQy5cvR9++fVGvXj307t0bFSpUQHR0NA4cOIDGjRvjjz/+KFT89erVg6urK3766Sekp6fLnVZS9LXmzJmDDh06oEmTJhg0aBASEhLw+++/o1atWnITigsyfvx43Lx5EwsWLMCpU6fw+eefw9bWFrGxsdizZw8uXryICxcuAMg5nps3b0a7du0wcuRIWFhYYN26dYiMjMTOnTvzPZWmCtra2vjtt98wcOBA+Pn5wd/fX3b5tZOTE0aPHq2011Lk/fvss88wY8YMDBw4EI0aNUJYWBg2bdqU5/OiVq1aaNiwISZNmiQbed2yZUuepOXkyZP47rvv0KNHD1SrVg1ZWVnYsGEDNDU10b17dwA5SdGsWbMwadIkREVFoUuXLjA2NkZkZCR2796NYcOGFesK41RIKrlWisqt911+DUCsXbtWVleRy6+FyLmUddKkScLV1VXo6OgIKysr0ahRIzF//ny5y1izsrLEvHnzhJubm9DR0REVKlQQ7dq1EyEhIbI6t2/fFs2aNRP6+voCwAcvxY6LixMDBw4UVlZWQkdHR7i7u8v15UN9Ksjjx4/FkCFDRMWKFYWWlpawsLAQn332mQgKCspT18/PT9SqVStPef/+/fNcnluYeHMvv543b55YsGCBcHBwELq6uqJp06bi2rVrsnrPnz8Xw4cPF25ubsLQ0FCYmpoKHx8fsW3btjyxnDp1SrRp00aYmpoKPT094eLiIgYMGCAuX74sF6+hoeF735effvpJABCurq4F1inMawkhxM6dO0WNGjWErq6uqFmzpti1a1e+79n77NixQ7Ru3VpYWFgILS0tYWdnJ3r16iVOnz4tV+/+/fvi888/F2ZmZkJPT094e3uL/fv354kb+VwWnPu7c+nSJbnyqVOnCgDi2bNnsrL8fkfePZ6Feb2tW7cKT09PoaurKywsLMQXX3whdxm+EAUfq9yYCqsw79+bN2/E2LFjhZ2dndDX1xeNGzcWgYGBws/PT/j5+cm1d//+fdGqVSuhq6srbGxsxI8//iiOHTsmd/n1gwcPxKBBg4SLi4vQ09MTFhYWokWLFuL48eN54tu5c6do0qSJMDQ0FIaGhsLNzU0MHz5c3Llzp9B9pOIjEaKYZwESERERFZPSMZZJREREVARMZIiIiEhtMZEhIiIitcVEhoiIiNQWExkiIiJSW0xkiIiISG1xQbx8SKVSPH36FMbGxkW+IzIREREpRgiBV69ewd7evtCLRDKRycfTp0/z3CGWiIiISsajR49QqVKlQtVlIpOP3CXaHz16JLvVPBERERWv5ORkODg4KHRrFyYy+cg9nWRiYsJEhoiIqIQpMq2Dk32JiIhIbXFE5j0yMjKQkZGRp1xDQwNaWlpy9QoikUigra1dpLqZmZn53tq+OOsCgI6OTpHqZmVlQSqVKqWutra2LCMvrrrZ2dnIzs5WSl0tLS3ZxLTSUFcqlea52++7NDU1oampWWrqCiGQmZmplLrv/n4WV13g/b/L/IzIvy4/I/gZ8aG67/v5LwgTmfdYsGAB9PT08pRXrVoVffr0kT2fP39+gR+Ajo6OGDBggOz5kiVLkJaWlm9de3t7DB06VPZ82bJlSEpKyrduhQoV8O2338qer1q1Cs+ePcu3rqmpKUaNGiV7HhAQgKdPn+Zb18DAAOPHj5c937RpEx4+fJhvXW1tbfz444+y59u2bcO9e/fyrQsAU6dOlf1/9+7duHXrVoF1J02aJPtQ279/P65du1Zg3XHjxsHQ0BAAcOTIEVy+fLnAut9//z3MzMwAACdOnEBgYGCBdb/55htYW1sDAM6dO4czZ84UWHfIkCGoWLEiACAoKAjHjx8vsG7//v3h5OQEAAgJCcGhQ4cKrOvv749q1aoBAMLCwrB3794C637++eeoVasWACA8PBw7duwosG7nzp1Rt25dAEBERAQ2b95cYN127drB29sbABAdHY1169YVWLdVq1Zo3LgxACAmJgarV68usK6fnx+aN28OAHj27BmWL19eYF1fX1+0bt0aAJCUlIQlS5YUWLd+/fro0KEDACAtLQ3z588vsK6Hhwe6dOkCIOcLec6cOQXWrVmzJnr06CF7/r66/IzIwc+I/8fPiBwf+ox48+ZNgfsWhKeWiIiISG1JxPvGBMup5ORkmJqa4tmzZ/lO9uWwcf51OWzMYWOeWlK8Lj8jilaXnxEfV7c0/N7nVzc5ORkVKlRAUlJSoS+2YSKTj9xERpE3koiIiD5OUb5/eWqJiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFRIaIiIjUFhMZIiIiUltMZIiIiEhtMZEpQcEPXiAhVfH7SBAREVH+eK+lEvI8JR3DNoRAQwL81KEmuterqNBtyomIiCgvjsiUkMS0DNiZ6uFlWibGbb+GPquC8eBZiqrDIiIiUmtMZEqIq7Ux/hnRBBPaukFPWwOBD16g7eJzWHL8HtKzCr43BhERERWMiUwJ0tbUwDfNXXB0lB+aVauAjGwpFh2/i/ZLziH4wQtVh0dERKR2mMioQGVLA6wb2AC/+3vCykgX95+lotfKIPyw4xpecjIwERFRoTGRURGJRIKOHvY4MdYPfXwqAwC2XX6MlgvPYNeVx+BNyYmIiD6MiYyKmeprY3ZXd+z8xhfVbIyQkJqBMduu4cs1wYh8nqrq8IiIiEo1JjKlhJejBfaPaIof2laHrpYGzke8QJvFZ/H7iXvIyJKqOjwiIqJSiYlMKaKjpYFvm7vi6OhmaFrVChlZUiw4dhftl57DxcgEVYdHRERU6jCRKYUcLQ2xfpA3lvSuCysjHUTEp6Dnn4GYuPM6EtM4GZiIiCgXE5lSSiKRoHPdijgxpjn8vR0AAFsuPULLBWew5+oTTgYmIiICE5lSz9RAG3O61cH2r31R1doIL1IzMGprKPr9dREPX3AyMBERlW9MZNREAycLHBjZFOPbVIeOlgbO3XuO1ovOYtmpCE4GJiKickuliczZs2fRsWNH2NvbQyKRYM+ePbJtmZmZmDBhAtzd3WFoaAh7e3v069cPT58+/WC7y5Ytg5OTE/T09ODj44OLFy8WYy9Kjo6WBoa3cMXRUc3QxNUK6VlSzDtyBx2WnsOlKE4GJiKi8qdIicyGDRvQuHFj2Nvb4+HDhwCAxYsXY+/evQq1k5qaCg8PDyxbtizPtrS0NFy5cgVTpkzBlStXsGvXLty5cwedOnV6b5tbt27FmDFjMHXqVFy5cgUeHh5o06YN4uPjFYqtNHOyMsSGwd5Y3KsuLA11cC8+BT1WBGLSrutISstUdXhEREQlRiIUnDW6fPly/Pzzzxg1ahR++eUX3LhxA1WqVEFAQADWrVuHU6dOFS0QiQS7d+9Gly5dCqxz6dIleHt74+HDh6hcuXK+dXx8fNCgQQP88ccfAACpVAoHBweMGDECEydOLFQsycnJMDU1RVJSEkxMTBTuS0lKTMvAr4duY8ulRwAAKyMdTPmsJjp55IxyERERqYuifP8qPCLz+++/Y9WqVfjpp5+gqakpK69fvz7CwsIUbU4hSUlJkEgkMDMzy3d7RkYGQkJC0KpVK1mZhoYGWrVqhcDAwALbTU9PR3JystxDXZgZ6ODX7nWwdVhDuFQwxPOUDHy/hZOBiYiofFA4kYmMjISnp2eecl1dXaSmFt8X55s3bzBhwgT4+/sXmKU9f/4c2dnZsLGxkSu3sbFBbGxsgW3PmTMHpqamsoeDg4NSYy8JPlUscfD7phj7aTVOBiYionJD4UTG2dkZoaGhecoPHz6MGjVqKCOmPDIzM9GzZ08IIbB8+XKltz9p0iQkJSXJHo8ePVL6a5QEXS1NjGhZFUdGNUMjF0vZZOCOv/+LkIecDExERGWPlqI7jBkzBsOHD8ebN28ghMDFixexefNmzJkzB6tXr1Z6gLlJzMOHD3Hy5Mn3njOzsrKCpqYm4uLi5Mrj4uJga2tb4H66urrQ1dVVWsyq5mxliE1DfLD76hPMOhCOO3Gv0H15IPr4VMaENm4wNdBWdYhERERKofCIzJAhQ/Dbb79h8uTJSEtLQ58+fbB8+XIsWbIEvXv3VmpwuUnMvXv3cPz4cVhaWr63vo6ODry8vHDixAlZmVQqxYkTJ+Dr66vU2Eo7iUSCbvUq4cQYP/SsXwkA8HdwNFouPIN9155yZWAiIioTFL5q6V1paWlISUmBtbV1kfZPSUlBREQEAMDT0xMLFy5EixYtYGFhATs7O3z++ee4cuUK9u/fLzfvxcLCAjo6OgCAli1bomvXrvjuu+8A5Fx+3b9/f/z555/w9vbG4sWLsW3bNty+fTvP3JmCqNNVS4UV9OAFftwdhgfPcuYx+VWrgFldasPBwkDFkREREeUoyvevwonM69evIYSAgUHOF+DDhw+xe/du1KxZE61bt1Yo4NOnT6NFixZ5yvv3749p06bB2dk53/1OnTqF5s2bAwCcnJwwYMAATJs2Tbb9jz/+wLx58xAbG4u6deti6dKl8PHxKXRcZTGRAYD0rGysOP0gZwJwthR62hr4vmU1DGnqDG1NLvJMRESqVSKJTOvWrdGtWzd8/fXXSExMRPXq1aGjo4Pnz59j4cKF+Oabb4oUfGlSVhOZXPefpWDy7hsIfPACAOBma4xfurrDy9FcxZEREVF5ViLryFy5cgVNmzYFAOzYsQO2trZ4+PAh1q9fj6VLlyraHKmASwUj/D3UB/N7eMDcQBu3Y1/h8xUXMHlPGJJec2VgIiJSHwonMmlpaTA2NgYAHD16FN26dYOGhgYaNmwou10BlX4SiQSfe1XCibHN8blXJQgBbAyKRquFZ7D/OicDExGRelA4kXF1dcWePXvw6NEjHDlyRDYvJj4+vkyehinrLAx1ML+HB/4e6oMqVoZ49iod3/19FYMCLuFRQpqqwyMiInovhROZn3/+GePGjYOTkxN8fHxklzUfPXo03xV/ST00crHCwe+b4vuWVaGjqYFTd57h00Vn8OeZ+8jM5srARERUOhXp8uvY2FjExMTAw8MDGho5udDFixdhYmICNzc3pQdZ0sr6ZN8PiYhPwU+7wxAcmbMasJutMeZ0c4dnZU4GJiKi4lMiVy2VB+U9kQEAIQS2hzzG7IPhSEzLhEQC9G3oiHFtqsNEjysDExGR8pVIItOiRQtIJJICt588eVKR5kolJjL/70VKOn45GI5dV54AAKyNdTGtUy20q2373p8DIiIiRRXl+1fhey3VrVtX7nlmZiZCQ0Nx48YN9O/fX9HmqJSzNNLFwp518Xm9Svhpzw1EPk/Ft5uu4BM3a0zvVIsrAxMRkUop7dTStGnTkJKSgvnz5yujOZXiiEz+3mRm43+nIrD8zH1kZgvoa2ti9KdVMaixM7S4MjAREX0klc6RiYiIgLe3NxISEpTRnEoxkXm/iPhX+HHXDVyMyjnWNexMMKebO+o6mKk2MCIiUmslsrJvQQIDA6Gnp6es5qgUc7U2xpZhDfFbd3eY6msjPCYZXf93HlP33sCrN1wZmIiISo7Cc2S6desm91wIgZiYGFy+fBlTpkxRWmBUumloSNCrQWW0rGGDXw6EY/fVJ1gX+BCHb8ZiWsdaaMvJwEREVAIUPrU0cOBAuecaGhqoUKECPvnkE4Xvfl1a8dSS4v699xyT94Qh6kXOasCtalhjeufaqGimr+LIiIhIXXAdGSVhIlM0bzKzsexUBFa8nQxsoKOJMZ9Ww4BGTpwMTEREH6TSOTJEetqaGNu6Og6ObIoGTuZIy8jGrAPh6LzsPK4/TlR1eEREVAYpPCKTnZ2NRYsWYdu2bYiOjkZGRobcdl61RAAglQpsu/wIsw+GI/lNFjQkQD9fJ4xtXQ3GXBmYiIjyUSIjMtOnT8fChQvRq1cvJCUlYcyYMejWrRs0NDQwbdo0RZujMkpDQ4Le3pVxYmxzdKlrD6kAAi5E4dOFZ3H4RqyqwyMiojJC4REZFxcXLF26FB06dICxsTFCQ0NlZUFBQfj777+LK9YSwxEZ5Tt37xkm77mBh28nA39a0wbTO9WCPScDExHRWyUyIhMbGwt3d3cAgJGREZKSkgAAn332GQ4cOKBoc1RONK1aAUdGNcPwFi7Q0pDg2K04tFp4Bmv+jURWtlTV4RERkZpSOJGpVKkSYmJiAOSMzhw9ehQAcOnSJejq6io3OipT9LQ1Mb6NGw5+3xRejjmTgWfuv4Uu/zuPsMdJqg6PiIjUkMKJTNeuXXHixAkAwIgRIzBlyhRUrVoV/fr1w6BBg5QeIJU91WyMsf0rX8zu6g4TPS3ceJKMzsv+xYx/biElPUvV4RERkRr56HVkgoKCcOHCBVStWhUdO3ZUVlwqxTkyJSf+1RvM2h+OfdeeAgDsTPUwvVMttK5lq+LIiIiopHFBPCVhIlPyztx9hsl7wvAo4TUAoHVNG0zvXAt2ppwMTERUXpTIZN85c+bgr7/+ylP+119/4bffflO0OSIAgF+1Cjg6yg/fNM+ZDHz0VhxaLTiDtecjkS1lrk1ERPlTOJH5888/4ebmlqe8Vq1aWLFihVKCovJJX0cTE9q6Yf/IJqhX2QypGdmY/s8tdP3fedx4wsnARESUV5Euv7azs8tTXqFCBdnVTEQfw83WBDu+boRZXWrDWE8L1x8nodMf/2Lm/ltI5WRgIiJ6h8KJjIODA86fP5+n/Pz587C3t1dKUEQaGhJ82dARJ8b44bM6dpAKYM2/kfh04RkcuxWn6vCIiKiU0FJ0h6FDh2LUqFHIzMzEJ598AgA4ceIEfvjhB4wdO1bpAVL5Zm2ihz/61EN3r3hM2XMDj1++xtD1l9G2li2mdaoFW1M9VYdIREQqpPBVS0IITJw4EUuXLpXdMFJPTw8TJkzAzz//XCxBljRetVQ6vc7IxuITd7H6XM4EYCNdLYxrXQ19fZ2gqSFRdXhERPSRSvTy65SUFISHh0NfXx9Vq1YtU6v6MpEp3cJjkjFpVxhCHyUCADwqmWJ2N3fUsjdVbWBERPRRuI6MkjCRKf2ypQJ/X4zG3EO38So9C5oaEgxq7IRRrarBUFfhM6ZERFQKlMg6MkSlgaaGBH0bOuL4WD90cLdDtlRg1blItF50FifCORmYiKi8YCJDas3GRA/LvqiHvwbUR0UzfTxJfI3B6y7j200hiEt+o+rwiIiomDGRoTLhEzcbHBvTDMOaVYGmhgQHw2LRcsEZbAiMgpQrAxMRlVlMZKjMMNDRwo/ta2Dfd43h4WCGlPQsTNl7E71XBSHyeaqqwyMiomJQpMm+9+7dw6lTpxAfHw+pVCq3TZFLsM+ePYt58+YhJCQEMTEx2L17N7p06SLbvmvXLqxYsQIhISFISEjA1atXUbdu3fe2GRAQgIEDB8qV6erq4s2bwp9m4GRf9ZctFVgfGIW5h+/gdWY2dLU0MK51dQxq4sxLtYmISqmifP8qfHnHqlWr8M0338DKygq2traQSP7/S0EikSiUyKSmpsLDwwODBg1Ct27d8t3epEkT9OzZE0OHDi10uyYmJrhz545cXFS+aGpIMLCxM1rVsMHEXddxPuIFfjkYjv1hMZj3eR1UszFWdYhERKQECicys2bNwi+//IIJEyZ89Iu3a9cO7dq1K3B73759AQBRUVEKtSuRSGBra/sxoVEZ4WBhgI2DfbD10iP8ciAc1x4losPScxj5SVV83dwF2po8u0pEpM4U/hR/+fIlevToURyxKE1KSgocHR3h4OCAzp074+bNm++tn56ejuTkZLkHlR0SiQS9vSvj6JhmaOlmjcxsgQXH7qLTH7yrNhGRulM4kenRoweOHj1aHLEoRfXq1fHXX39h79692LhxI6RSKRo1aoTHjx8XuM+cOXNgamoqezg4OJRgxFRS7Ez1sbp/fSzuVRdmBtoIj0lG52XnMffwbbzJzFZ1eEREVAQKT/adM2cOFi5ciA4dOsDd3R3a2tpy20eOHFm0QCSSPJN9c0VFRcHZ2blQk33/KzMzEzVq1IC/vz9mzpyZb5309HSkp6fLnicnJ8PBwYGTfcuwZ6/SMW3fTRwIiwEAuFQwxNzP68DL0ULFkRERlV8lMtl35cqVMDIywpkzZ3DmzBm5bRKJpMiJTHHR1taGp6cnIiIiCqyjq6tbpu4VRR9WwVgXy76oh443YjB5z03cf5aKz1cEYmAjZ4xrUw0GOrzNARGROlD40zoyMrI44ig22dnZCAsLQ/v27VUdCpVCbWvboWEVS8zYfwu7rjzBX+cjcTw8Dr92c0cjVytVh0dERB/wUX925p6VKurlzSkpKXIjJZGRkQgNDYWFhQUqV66MhIQEREdH4+nTpwAgu6Ta1tZWdlVSv379ULFiRcyZMwcAMGPGDDRs2BCurq5ITEzEvHnz8PDhQwwZMqTI/aSyzcxABwt71kVHD3v8uCsM0Qlp6LM6GP7elTGpvRtM9LQ/3AgREalEka49Xb9+Pdzd3aGvrw99fX3UqVMHGzZsULidy5cvw9PTE56engCAMWPGwNPTU7YWzb59++Dp6YkOHToAAHr37g1PT0+sWLFC1kZ0dDRiYmJkz1++fImhQ4eiRo0aaN++PZKTk3HhwgXUrFmzKF2lcqRFdWscHd0MX/hUBgBsvhiNNovO4tTteBVHRkREBVF4su/ChQsxZcoUfPfdd2jcuDEA4N9//8WyZcswa9YsjB49ulgCLUlc2ZcC77/AhJ3XEZ2QBgDo5lkRP3esCTMDHRVHRkRUdhXl+1fhRMbZ2RnTp09Hv3795MrXrVuHadOmqd0cmvwwkSEASMvIwoKjd/HX+UgIAVgZ6WJm51po526n6tCIiMqkonz/KnxqKSYmBo0aNcpT3qhRI7lTPETqzkBHC1M+q4kdXzeCq7URnqek45tNV/DtphA8e5X+4QaIiKjYKZzIuLq6Ytu2bXnKt27diqpVqyolKKLSxMvRHPtHNMHwFi7Q1JDgYFgsPl10BruvPkYR7rlKRERKpPCppZ07d6JXr15o1aqVbI7M+fPnceLECWzbtg1du3YtlkBLEk8tUUFuPEnC+B3XER6TcxuLT9ys8UvX2rAz1VdxZERE6q9E5sgAQEhICBYtWoTw8HAAQI0aNTB27FjZ1UfqjokMvU9mthR/nrmPpScikJEthbGuFn7sUAO9GzjwTutERB+hxBKZso6JDBXG3bhXGL/jOq49SgQANHa1xK/d6sDBwkC1gRERqakSTWTi4+MRHx8PqVQqV16nTp2iNFeqMJGhwsqWCqw9H4l5R+4gPUsKfW1N/NC2Ovr7OkFDg6MzRESKKJFEJiQkBP3790d4eHieiY4SiQTZ2ep/F2EmMqSoqOepmLDzOoIjEwAA9R3N8dvndeBSwUjFkRERqY8SSWQ8PDzg4uKCCRMmwMbGJs+cAEdHR0WaK5WYyFBRSKUCmy5G49eD4UjNyIaOlgZGt6qGoU2doaVZpEW0iYjKlRJJZIyNjXH16lW4uroWKUh1wESGPsbjl2n4cfcNnL37DADgXtEUcz+vgxp2/FkiInqfElkQr2XLlrh27ZrCwRGVF5XMDbBuYAPM+7wOTPS0EPYkCR1//xeLjt1FRpb0ww0QEVGhKTwi8/z5c/Tv3x/e3t6oXbs2tLXl7wzcqVMnpQaoChyRIWWJT36Dn/bcwLFbcQCA6jbGmPt5HXg4mKk2MCKiUqhETi39888/6Nu3L5KTk/M2xsm+RHkIIbD/egym7ruJhNQMaEiAoU2rYPSn1aCnranq8IiISo0SObU0YsQIfPnll4iJiYFUKpV7lIUkhkjZJBIJOnrY49joZujkYQ+pAP48+wDtlpzDpagEVYdHRKTWijTZNzQ0FC4uLsUVk8pxRIaK07FbcfhpdxjiX6VDIgH6NXTED23dYKirperQiIhUqkRGZLp164ZTp04pHBwR5fi0pg2OjfFDr/oOEAJYF/gQbRafxb/3nqs6NCIitaPwn4DVqlXDpEmT8O+//8Ld3T3PZN+RI0cqLTiisspUXxu/fV4Hn3nYYeLOMDx++RpfrglGr/oO+LFDDZjqa3+4ESIiUvzUkrOzc8GNSSR48ODBRwelajy1RCUpNT0Lcw/fxrrAhwAAGxNd/NLFHa1q2qg4MiKiksWbRioJExlShYuRCZiw8zoin6cCADrXtcfUjrVgYaij4siIiEpGicyRIaLi4e1sgUPfN8VXzapAQwLsDX2KTxeewf7rT/Pc14yIiHIoPCIjhMCOHTtw6tSpfO9+vWvXLqUGqAockSFVu/YoEeN3XMPduBQAQJtaNpjZuTasTfRUHBkRUfEpkRGZUaNGoW/fvoiMjISRkRFMTU3lHkT08TwczPDPiCYY2bIqtDQkOHIzDq0WnsGOkMccnSEieofCIzIWFhbYuHEj2rdvX1wxqRxHZKg0ufU0GT/svIYbT3JW0/arVgGzu7mjopm+iiMjIlKuEhmRMTU1RZUqVRQOjoiKpqa9CfZ82xg/tK0OHS0NnLn7DK0XnsHGoIeQSjk6Q0Tlm8KJzLRp0zB9+nS8fv26OOIhonxoaWrg2+auODiyKbwczZGakY3Je27Af1UQot5e5UREVB4pfGrp9evX6Nq1K86fPw8nJ6c8C+JduXJFqQGqAk8tUWmWLRVYHxiFuYfv4HVmNvS0NTCudXUMbOwMTQ2JqsMjIiqyonz/Kryyb//+/RESEoIvv/wSNjY2kEj4wUlUkjQ1JBjY2Bkt3Wwwcdd1XLj/ArMOhONAWAzmdq+DqjbGqg6RiKjEKDwiY2hoiCNHjqBJkybFFZPKcUSG1IUQAlsuPcIvB8KRkp4FHU0NjGzpiq/8XKCtyWWiiEi9lMhkXwcHB365E5USEokE/t6VcWxMM7SoXgEZ2VLMP3oXnf84jxtPklQdHhFRsVM4kVmwYAF++OEHREVFFUM4RFQUdqb6+GtAAyzq5QEzA23ciklG52XnMf/IHaRnZas6PCKiYqPwqSVzc3OkpaUhKysLBgYGeSb7JiQkKDVAVeCpJVJnz16lY+q+GzgYFgsAcLU2wtzP66BeZXMVR0ZE9H4lMtl38eLFiu5CRCWogrEu/veFFw6FxWDK3huIiE9B9+UXMLixM8a2rg59HU1Vh0hEpDS8+3U+OCJDZcXL1AzM3H8Lu64+AQA4Whrg12514OtiqeLIiIjyKsr3b5ESmezsbOzZswfh4eEAgFq1aqFTp07Q1Cwbf+kxkaGy5tTtePy4OwwxSW8AAF/4VMbEdm4w1tP+wJ5ERCWnRBKZiIgItG/fHk+ePEH16tUBAHfu3IGDgwMOHDgAFxcXxSMvZZjIUFn06k0m5hy6jb+DowEA9qZ6mN3NHc2rW6s4MiKiHCVy+fXIkSPh4uKCR48e4cqVK7hy5Qqio6Ph7OyMkSNHKtTW2bNn0bFjR9jb20MikWDPnj1y23ft2oXWrVvD0tISEokEoaGhhWp3+/btcHNzg56eHtzd3XHw4EGF4iIqi4z1tDG7qzv+HuqDyhYGeJr0BgPWXsLYbdeQmJah6vCIiIpE4UTmzJkzmDt3LiwsLGRllpaW+PXXX3HmzBmF2kpNTYWHhweWLVtW4PYmTZrgt99+K3SbFy5cgL+/PwYPHoyrV6+iS5cu6NKlC27cuKFQbERlVSMXKxwe1RSDGjtDIgF2XnmMTxedxeEbsaoOjYhIYQqfWrKwsMD+/fvRqFEjufLz58+jY8eORb78WiKRYPfu3ejSpUuebVFRUXB2dsbVq1dRt27d97bTq1cvpKamYv/+/bKyhg0bom7dulixYkWhYuGpJSovQh4m4Icd13H/Wc6NJzvUscP0TrVgZaSr4siIqDwqkVNLn332GYYNG4bg4GAIISCEQFBQEL7++mt06tRJ4aCVLTAwEK1atZIra9OmDQIDAwvcJz09HcnJyXIPovLAy9ECB0Y2xbfNXaCpIcGB6zH4dOEZ7A19Al7QSETqQOFEZunSpXBxcYGvry/09PSgp6eHxo0bw9XVFUuWLCmOGBUSGxsLGxsbuTIbGxvExhY8bD5nzhyYmprKHg4ODsUdJlGpoaetiR/aumHv8MZwszXGy7RMfL8lFEPXX0bs26uciIhKK4UTGTMzM+zduxd3797Fjh07sGPHDty5cwe7d++GqalpccRY7CZNmoSkpCTZ49GjR6oOiajE1a5oin3fNcGYT6tBW1OC4+Hx+HTRGWy9FM3RGSIqtRRe2TeXq6srXF1dlRmLUtja2iIuLk6uLC4uDra2tgXuo6urC11dzgkg0tHSwMiWVdGmli1+2HEN1x4nYcLOMPxzLQZzurnDwcJA1SESEclReESme/fu+V5FNHfuXPTo0UMpQX0MX19fnDhxQq7s2LFj8PX1VVFEROqnuq0xdn7TCD+2d4Oulgb+jXiONovPYt2FKEilHJ0hotJD4UTm7NmzaN++fZ7ydu3a4ezZswq1lZKSgtDQUNn6MJGRkQgNDUV0dM6CXQkJCQgNDcWtW7cA5Cy8FxoaKjffpV+/fpg0aZLs+ffff4/Dhw9jwYIFuH37NqZNm4bLly/ju+++U7SrROWalqYGhjVzweFRzeDtZIG0jGxM3XcTvVYG4sGzFFWHR0QEoAiJTEpKCnR0dPKUa2trK3y1z+XLl+Hp6QlPT08AwJgxY+Dp6Ymff/4ZALBv3z54enqiQ4cOAIDevXvD09NT7jLq6OhoxMTEyJ43atQIf//9N1auXAkPDw/s2LEDe/bsQe3atRXtKhEBcLYyxJZhDTGzcy0Y6GjiUtRLtFtyDn+euY+sbKmqwyOick7hdWS8vb3x2WefyZKNXNOmTcM///yDkJAQpQaoClxHhih/jxLS8OPuMJy79xwAUKeSKeZ+Xgdutvw9IaKPV5TvX4Un+06ZMgXdunXD/fv38cknnwAATpw4gc2bN2P79u2KNkdEasTBwgDrB3lje8hjzNx/C9cfJ6Hj7//iuxZV8U1zF+hoKTzIS0T0UYp09+sDBw5g9uzZCA0Nhb6+PurUqYOpU6fCz8+vOGIscRyRIfqwuOQ3+Gn3DRwPz7lK0M3WGPM+94B7JfVchoGIVK9E7n5dHjCRISocIQT+uR6DaftuIiE1A5oaEgxtWgWjWlWFnramqsMjIjVTIrcoICLKJZFI0MnDHsdGN0NHD3tkSwVWnLmP9kvP4XJU0e67RkSkCCYyRPTRLI108bu/J1b29YK1sS4ePEtFjz8DMW3fTaRlZKk6PCIqw5jIEJHStK5li2Oj/dCzfiUIAQRciEKbxWdxPuK5qkMjojKKiQwRKZWpgTbmfu6B9YO8UdFMH48SXuOL1cGYtOs6kt9kqjo8IipjipzIZGRk4M6dO8jK4rAxEeXVrFoFHBndDP18HQEAmy8+QuuFZ3HydtwH9iQiKjyFE5m0tDQMHjwYBgYGqFWrlux2AiNGjMCvv/6q9ACJSH0Z6WphRufa2DqsIZwsDRCb/AaDAi5j9NZQvEzNUHV4RFQGKJzITJo0CdeuXcPp06ehp6cnK2/VqhW2bt2q1OCIqGzwqWKJQ983w7BmVaAhAXZffYJPF53BwbCYD+9MRPQeCicye/bswR9//IEmTZpAIpHIymvVqoX79+8rNTgiKjv0dTTxY/sa2PVtY1SzMcLzlAx8u+kKvtkYgvhXb1QdHhGpKYUTmWfPnsHa2jpPeWpqqlxiQ0SUn7oOZvhnRBOM/MQVWhoSHLoRi08XnsWuK4/B9TmJSFEKJzL169fHgQMHZM9zk5fVq1fD19dXeZERUZmlq6WJMa2rY993TVDL3gRJrzMxZts1DAq4hKeJr1UdHhGpEYVvGjl79my0a9cOt27dQlZWFpYsWYJbt27hwoULOHPmTHHESERlVE17E+wZ3hgrzz7AkuP3cOrOM7RedBY/tq8Bf28HjvIS0QcpPCLTpEkThIaGIisrC+7u7jh69Cisra0RGBgILy+v4oiRiMowbU0NDG/hioPfN0G9ymZISc/Cj7vD0GdVMB6+SFV1eERUyvGmkfngTSOJVCNbKhBwIQrzjtzGm0wp9LQ1ML6NGwY0coKmBkdniMq6Erv7tVQqRUREBOLj4yGVSuW2NWvWTNHmSh0mMkSq9fBFKibuDEPggxcAgHqVzbCwZ104WRmqODIiKk4lksgEBQWhT58+ePjwYZ4rDCQSCbKzsxVprlRiIkOkelKpwJZLjzD7YDhS0rNgZaSLLcN84GptrOrQiKiYFOX7V+E5Ml9//TXq16+PGzduICEhAS9fvpQ9EhISFA6aiCg/GhoS9PGpjKOjm6GmnQmep6Sj98pgRMSnqDo0IipFFB6RMTQ0xLVr1+Dq6lpcMakcR2SISpeXqRnoszoY4THJqGCsiy3DGsKlgpGqwyIiJSuRERkfHx9EREQoHBwRUVGZG+pg0xAfuNka49mrdPivDML9ZxyZIaJCjshcv35d9v/79+9j8uTJGD9+PNzd3aGtrS1Xt06dOsqPsoRxRIaodEpIzUCfVUG4HfsK1m9HZqpwZIaozCi2yb4aGhqQSCQFLh+eu42TfYmouL1ISUefVcG4E/cKNia62DLMF868momoTCi2RObhw4eFDsLR0bHQdUsrJjJEpdvzlHT0WRWEu3EpsDXRw5ZhDXlpNlEZUGxzZBwdHWWPhw8fomLFinJljo6OqFixokIJDxFRUVkZ6eLvoQ1R1doIsclv4L8qiKsAE5VTCk/2bdGiRb6XWSclJaFFixZKCYqI6EPeTWZikt6g90omM0TlkcKJTO5cmP968eIFDA05tEtEJaeCcU4y4/o2mfFfGYToF2mqDouISlCh737drVs3ADkTewcMGABdXV3ZtuzsbFy/fh2NGjVSfoRERO+Rk8z4vL0kOxX+q4KwZVhDOFgYqDo0IioBhR6RMTU1hampKYQQMDY2lj03NTWFra0thg0bho0bNxZnrERE+bI21sPmoQ1RpYIhniS+Ru+VQXiUwJEZovJA4ZV9p0+fjnHjxpXp00i8aolIPcUn58yVefA8FRXN9DkyQ6RmSuzu12UdExki9RX3NpmJfJ6KSuY5yUwlcyYzROqgRG5RQERUmtmY5JxmcrYyxOOXr+G/KghPEl+rOiwiKiZMZIiozLE1zUlmnCwN8CjhNXqvDMRTJjNEZRITGSIqk2xN9bB5WEM4ypKZICYzRGUQExkiKrPsTPWxeWhDVLYwQHRCGvxXBSEmickMUVmi8GTfpUuX5t+QRAI9PT24urqiWbNm0NTU/GBbZ8+exbx58xASEoKYmBjs3r0bXbp0kW0XQmDq1KlYtWoVEhMT0bhxYyxfvhxVq1YtsM1p06Zh+vTpcmXVq1fH7du3C9dBcLIvUVnz9O0l2dEJaXCyNMCWYb6wNdVTdVhE9B9F+f4t9IJ4uRYtWoRnz54hLS0N5ubmAICXL1/CwMAARkZGiI+PR5UqVXDq1Ck4ODi8t63U1FR4eHhg0KBBsgX33jV37lwsXboU69atg7OzM6ZMmYI2bdrg1q1b0NMr+EOoVq1aOH78+P93UkvhbhJRGWJvpo/Nwxqi98pARL1IQ++VgUxmiMoIhU8tzZ49Gw0aNMC9e/fw4sULvHjxAnfv3oWPjw+WLFmC6Oho2NraYvTo0R9sq127dpg1axa6du2aZ5sQAosXL8bkyZPRuXNn1KlTB+vXr8fTp0+xZ8+e97arpaUFW1tb2cPKykrRbhJRGVPRLOc0UyVzfUS9yDnNFJf8RtVhEdFHUjiRmTx5MhYtWgQXFxdZmaurK+bPn49JkyahUqVKmDt3Ls6fP/9RgUVGRiI2NhatWrWSlZmamsLHxweBgYHv3ffevXuwt7dHlSpV8MUXXyA6Ovq99dPT05GcnCz3IKKyp5K5ATYPbYiKZvqIfJ4K/5VMZojUncKJTExMDLKysvKUZ2VlITY2FgBgb2+PV69efVRguW3Z2NjIldvY2Mi25cfHxwcBAQE4fPgwli9fjsjISDRt2vS98cyZM0fulgsfOiVGROrLwcIAW4blJDMPnufcmymeyQyR2lI4kWnRogW++uorXL16VVZ29epVfPPNN/jkk08AAGFhYXB2dlZelApo164devTogTp16qBNmzY4ePAgEhMTsW3btgL3mTRpEpKSkmSPR48elWDERFTS5JKZtzeajH/FZIZIHSmcyKxZswYWFhbw8vKCrq4udHV1Ub9+fVhYWGDNmjUAACMjIyxYsOCjArO1tQUAxMXFyZXHxcXJthWGmZkZqlWrhoiIiALr6OrqwsTERO5BRGWbg0XOaSZ7U72cu2avDMKzV+mqDouIFKRwImNra4tjx47h1q1b2L59O7Zv345bt27h6NGjstNALVq0QOvWrT8qMGdnZ9ja2uLEiROysuTkZAQHB8PX17fQ7aSkpOD+/fuws7P7qHiIqOyp/PZSbLvcZGYVkxkidVPkBfHc3NzQqVMndOrUCdWrVy9SGykpKQgNDUVoaCiAnAm+oaGhiI6OhkQiwahRozBr1izs27cPYWFh6NevH+zt7eXWmmnZsiX++OMP2fNx48bhzJkziIqKwoULF9C1a1doamrC39+/qF0lojIsJ5lpCDtTPUTEp6DPqiA8T2EyQ6QuFF5gJTs7GwEBAThx4gTi4+MhlUrltp88ebLQbV2+fBktWrSQPR8zZgwAoH///ggICMAPP/yA1NRUDBs2DImJiWjSpAkOHz4st4bM/fv38fz5c9nzx48fw9/fHy9evECFChXQpEkTBAUFoUKFCop2lYjKCUdLQ2we2hC9Vwbh3ttk5u+hDWFlpKvq0IjoAxRe2fe7775DQEAAOnToADs7O0gkErntixYtUmqAqsCVfYnKp8jnqei9MhBxyemobmOMv4f6wJLJDFGJKcr3r8KJjJWVFdavX4/27dsXKUh1wESGqPyKfJ6KXn8GIv5VOtxsjbFpCJMZopJSlO9fhefI6OjowNXVVeHgiIjUgbOVIbYMawhrY13cjn2FL1YHIyE1Q9VhEVEBFE5kxo4diyVLlkDBgRwiIrVRpYIRNg9riApvk5k+q4LwkskMUamk8Kmlrl274tSpU7CwsECtWrWgra0tt33Xrl1KDVAVeGqJiAAgIj5Fdkl2DTsT/D3EB+aGOqoOi6jMKpFTS2ZmZujatSv8/PxgZWUlt7S/qampwkETEZVWrtZG2Pz26qXwmGR8sToYiWkcmSEqTRQekSkPOCJDRO+KiH+F3iuD8DwlA7XsTbBpiA/MDDgyQ6RsJTIiQ0RU3rhaG78dmdHBzafJ+HINR2aISotCjcjUq1cPJ06cgLm5OTw9PfOsHfOuK1euKDVAVeCIDBHl527cK/ivDMKL1Ay4VzTFxsE+MDXQ/vCORFQoRfn+LdTKvp07d4aubs46Cu/eHoCIqDypZmOMv4c2RJ9VQQh7koS+fwVjw2AfmOozmSFSFc6RyQdHZIjofW7HJqPPqpz1ZTwqmWI9kxkipeAcGSKiEuBmmzPh19xAG9ceJ6HfXxeR/CZT1WERlUuFGpExNzd/77yYdyUkJHx0UKrGERkiKozwmOScxfLSMlHXwQzrB3vDRI8jM0RFVWxzZBYvXiz7/4sXLzBr1iy0adMGvr6+AIDAwEAcOXIEU6ZMUTxqIiI1VcPOBJuGNESf1UEIfZSI/n9dxPpB3jBmMkNUYhSeI9O9e3e0aNEC3333nVz5H3/8gePHj2PPnj3KjE8lOCJDRIq4+TTp7WJ5mahX2QzrmMwQFUmJzJE5cuQI2rZtm6e8bdu2OH78uKLNERGpvVr2by/F1tfGlehEDFh7CSnpWaoOi6hcUDiRsbS0xN69e/OU7927F5aWlkoJiohI3dSuaIpNQ3KSmZCHL9H/r4tMZohKQKHmyLxr+vTpGDJkCE6fPg0fHx8AQHBwMA4fPoxVq1YpPUAiInVR++0ieV+sDkLIw5cY8NdFBAzyhpGuwh+1RFRICo/IDBgwAOfPn4eJiQl27dqFXbt2wcTEBP/++y8GDBhQDCESEakP90qm2DjEByZ6Wrj88CUGrr2IVI7MEBUbLoiXD072JaKPde1RIr5cE4xXb7Lg7WSBtQMbwJAjM0TvVZTv30IlMsnJyYUOoix88TORISJlCH2UiL6rg/EqPQvezhYIGNgABjpMZogKUmyJjIaGRqEXxMvOzi5UvdKMiQwRKcu7yUzDKhb4awCTGaKCFNuCeKdOnZL9PyoqChMnTsSAAQPkFsRbt24d5syZU4SwiYjKrtwVf/uuuYigBwkYHHAZfw1oAH0dTVWHRlQmKDxHpmXLlhgyZAj8/f3lyv/++2+sXLkSp0+fVmZ8KsERGSJStivRL9FvTc4l2b5VLJnMEOWjRBbECwwMRP369fOU169fHxcvXlS0OSKicqFeZXOsG+QNQx1NBD54gcHrLuF1hvqfiidSNYUTGQcHh3zXi1m9ejUcHByUEhQRUVnk5WiO9YNzkpkL919g6PrLeJPJZIboYyh8aungwYPo3r07XF1dZQviXbx4Effu3cPOnTvRvn37Ygm0JPHUEhEVp8tRCej310WkZWSjaVUrrOpXH3raPM1EVCKnltq3b4979+6hU6dOSEhIQEJCAjp27Ii7d++WiSSGiKi41XeywLpB3jDQ0cS5e885MkP0EbggXj44IkNEJeFiZAIGrM0ZmWlWrQJW9vXiyAyVa8W2jkx+0tLSEB0djYyMDLnyOnXqFKW5UoWJDBGVlOAHLzBg7SW8zsyGX7UK+JPJDJVjJZLIPHv2DAMHDsShQ4fy3c4F8YiIFBP04AUGvk1mmlfPSWZ0tZjMUPlTInNkRo0ahcTERAQHB0NfXx+HDx/GunXrULVqVezbt0/hoImIyruGb9eV0dPWwOk7z/D1hhCkZ6n/H4VEJUHhRObkyZNYuHAh6tevDw0NDTg6OuLLL7/E3LlzubIvEVER+br8fzJz6s4zfLPxCpMZokJQOJFJTU2FtbU1AMDc3BzPnj0DALi7u+PKlSvKjY6IqBxp5GKFv/o3gK6WBk7ejse3TGaIPkjhRKZ69eq4c+cOAMDDwwN//vknnjx5ghUrVsDOzk7pARIRlSeNXK3w14CcZObE7XgM33QFGVlSVYdFVGopnMh8//33iImJAQBMnToVhw4dQuXKlbF06VLMnj1b6QESEZU3jV2tsObtyMzx8HgM/5vJDFFBFE5kvvzySwwYMAAA4OXlhYcPH+LSpUt49OgRevXqpVBbZ8+eRceOHWFvbw+JRII9e/bIbRdC4Oeff4adnR309fXRqlUr3Lt374PtLlu2DE5OTtDT04OPjw/vAUVEaqfJ2xV/dbQ0cOxWHL5jMkOUL4UTmf8yMDBAvXr1YGVlpfC+qamp8PDwwLJly/LdPnfuXCxduhQrVqxAcHAwDA0N0aZNG7x586bANrdu3YoxY8Zg6tSpuHLlCjw8PNCmTRvEx8crHB8RkSo1q1YBq98mM0dvxWHE5ivIzGYyQ/SuUrOyr0Qiwe7du9GlSxcAOaMx9vb2GDt2LMaNGwcASEpKgo2NDQICAtC7d+982/Hx8UGDBg3wxx9/AACkUikcHBwwYsQITJw4sVCxcB0ZIipNztx9hqHrLyMjS4q2tWzxex9PaGt+9N+hRKVOiawjU1IiIyMRGxuLVq1aycpMTU3h4+ODwMDAfPfJyMhASEiI3D4aGhpo1apVgfsAQHp6OpKTk+UeRESlhd/b2xfoaGrg8M1YjNx8lSMzRG+V2kQmNjYWAGBjYyNXbmNjI9v2X8+fP0d2drZC+wDAnDlzYGpqKns4ODh8ZPRERMrVvLo1/nybzBy6EYvvtzCZIQIUTGSysrIwY8YMPH78uLjiUYlJkyYhKSlJ9nj06JGqQyIiyqOFmzVW9K0HHU0NHAyLxagtochiMkPlnEKJjJaWFubNm4esrKziikfG1tYWABAXFydXHhcXJ9v2X1ZWVtDU1FRoHwDQ1dWFiYmJ3IOIqDT6xM0Gy7+sB21NCQ6ExWDUViYzVL4pfGrpk08+wZkzZ4ojFjnOzs6wtbXFiRMnZGXJyckIDg6Gr69vvvvo6OjAy8tLbh+pVIoTJ04UuA8RkbppWcMGy7/wgramBPuvx2D0tmtMZqjc0lJ0h3bt2mHixIkICwuDl5cXDA0N5bZ36tSp0G2lpKQgIiJC9jwyMhKhoaGwsLBA5cqVMWrUKMyaNQtVq1aFs7MzpkyZAnt7e9mVTQDQsmVLdO3aFd999x0AYMyYMejfvz/q168Pb29vLF68GKmpqRg4cKCiXSUiKrVa1bTB/77wwrebQvDPtaeQAFjY0wNavJqJyhmFE5lvv/0WALBw4cI82yQSCbKzC39fkMuXL6NFixay52PGjAEA9O/fHwEBAfjhhx+QmpqKYcOGITExEU2aNMHhw4ehp6cn2+f+/ft4/vy57HmvXr3w7Nkz/Pzzz4iNjUXdunVx+PDhPBOAiYjU3ac1bbCsTz18u+kK9l17CokEWNizLjQ1JKoOjajElJp1ZEoTriNDROrkyM1YDN90BVlSga6eFTG/hweTGVJLJb6OzPtW2CUiopLRppYt/uhTD1oaEuy++gTjt19DtpR/o1L5oHAik52djZkzZ6JixYowMjLCgwcPAABTpkzBmjVrlB4gERF9WNvatvjd3xOaGhLsuvoE43cwmaHyQeFE5pdffkFAQADmzp0LHR0dWXnt2rWxevVqpQZHRESF187d7v+TmStP8MOO60xmqMxTOJFZv349Vq5ciS+++AKampqycg8PD9y+fVupwRERkWLau9thae+cZGbnlceYuPM6pExmqAxT+KqlJ0+ewNXVNU+5VCpFZmamUoIiIqKi61DHDgIC328JxfaQx5BIgF+71YEGJwBTGaTwiEzNmjVx7ty5POU7duyAp6enUoIiIqKP81kdeyzuVRcaEmDb5ceYtCuMIzNUJik8IvPzzz+jf//+ePLkCaRSKXbt2oU7d+5g/fr12L9/f3HESERERdDRwx4CwKgtV7H18iNIJMDsru4cmaEyReERmc6dO+Off/7B8ePHYWhoiJ9//hnh4eH4559/8OmnnxZHjEREVESdPOyx6O3IzJZLj/DTnhscmaEyhQvi5YML4hFRWbPn6hOM2RYKqQD6+FTGrM61OTJDpU6JLIhXpUoVvHjxIk95YmIiqlSpomhzRERUArp4VsSCnh6QSIC/g6MxZe8N8O9YKgsUTmSioqLyvZ9Seno6njx5opSgiIhI+bp6VsKCHjnJzCYmM1RGFHqy7759+2T/P3LkCExNTWXPs7OzceLECTg5OSk1OCIiUq5u9SpBKoDxO65hY1A0NCQSTO9UCxIJTzOReip0ItOlSxfZ//v37y+3TVtbG05OTliwYIHSAiMiouLxuVclCCHww87rWB/4EBIA05jMkJoqVCJz/fp1ZGZmQlNTE87Ozrh06RKsrKyKOzYiIiomPeo7QACYsPM61gU+hEQiwdSONZnMkNop1BwZT09PJCQkAAAkEgl/0ImIyoCe9R3wW7c6AICAC1GYsf8W58yQ2ilUImNmZia7y/XDhw8hlUqLNSgiIioZPRs44Lfu7gCAteejMHN/OJMZUiuFOrXUvXt3+Pn5wc7ODgBQv359uRtGvis34SEiIvXQq0FlSAUwaVcY/jofCQ0J8FOHGhx9J7VQqERm5cqV6NatGyIiIjBy5EgMHToUxsbGxR0bERGVEH/vyhAC+HF3GFb/GwmJBPixPZMZKv0KfdVS27ZtAQAhISH4/vvvmcgQEZUxfXwqQ0Dgp903sOpcJCQSCSa1c2MyQ6WawjeNXLt2bXHEQUREpcAXPo6QCmDKnhtYefYBJBJgYlsmM1R6KZzIAMDly5exbds2REdHIyMjQ27brl27lBIYERGpRt+GjoAQmLL3Jv488wASSDChbXUmM1QqKXyLgi1btqBRo0YIDw/H7t27kZmZiZs3b+LkyZNyq/0SEZH66uvrhBmdawEAVpy5j3lH7vBqJiqVFE5kZs+ejUWLFuGff/6Bjo4OlixZgtu3b6Nnz56oXLlyccRIREQq0M/XCdM75SQz/zt9H/OPMpmh0kfhROb+/fvo0KEDAEBHRwepqamQSCQYPXo0Vq5cqfQAiYhIdfo3csLUjjUBAMtO3cfCY3eZzFCponAiY25ujlevXgEAKlasiBs3bgAAEhMTkZaWptzoiIhI5QY2dsbPn+UkM7+fjMAiJjNUiig82bdZs2Y4duwY3N3d0aNHD3z//fc4efIkjh07hpYtWxZHjEREpGKDmjhDKgRmHQjH0pMROSPxn1ZTdVhEiicyf/zxB968eQMA+Omnn6CtrY0LFy6ge/fumDx5stIDJCKi0mFI0yoAgFkHwrHkxD1IJMCoVkxmSLUkguODeSQnJ8PU1BRJSUkwMTFRdThERKXKqrMP8MvBcADA6FbV8H2rqiqOiMqKonz/KjxHhoiIyrehzapgUjs3AMCi43fx+4l7Ko6IyjMmMkREpLCv/Fww8W0ys+DYXfxxkskMqQYTGSIiKpKv/VwwoW1OMjP/6F0sOxWh4oioPGIiQ0RERfZNcxeMb1MdADDvyB387zSTGSpZCicygwYNkq0j867U1FQMGjRIKUEREZH6GN7CVZbMzD18ByvO3FdxRFSeKJzIrFu3Dq9fv85T/vr1a6xfv14pQRERkXoZ3sIVY9+uK/Prodv4k8kMlZBCryOTnJwMIQSEEHj16hX09PRk27Kzs3Hw4EFYW1sXS5BERFT6jWhZFQLAwmN3MefQbUgkwLBmLqoOi8q4Qo/ImJmZwcLCAhKJBNWqVYO5ubnsYWVlhUGDBmH48OFKD/DVq1cYNWoUHB0doa+vj0aNGuHSpUsF1j99+jQkEkmeR2xsrNJjIyIieSNbVsWot+vKzD54G6vPPVBxRFTWFXpE5tSpUxBC4JNPPsHOnTthYWEh26ajowNHR0fY29srPcAhQ4bgxo0b2LBhA+zt7bFx40a0atUKt27dQsWKFQvc786dO3KL6XC0iIioZIxqVQ1CAEtO3MOsAzkL5+WuCkykbAqv7Pvw4UNUrlwZEomkuGKSef36NYyNjbF3717ZHbcBwMvLC+3atcOsWbPy7HP69Gm0aNECL1++hJmZWZFelyv7EhF9HCEEFh27i6Unc65imvJZTQxu4qziqKi0K5GVfU+ePIkdO3bkKd++fTvWrVunaHPvlZWVhezsbLn5OACgr6+Pf//997371q1bF3Z2dvj0009x/vz599ZNT09HcnKy3IOIiIou96aSIz5xBQDM3H8La89HqjgqKosUTmTmzJkDKyurPOXW1taYPXu2UoLKZWxsDF9fX8ycORNPnz5FdnY2Nm7ciMDAQMTExOS7j52dHVasWIGdO3di586dcHBwQPPmzXHlypUCX2fOnDkwNTWVPRwcHJTaDyKi8kgikWDMp9XwXYucZGb6P7cQwGSGlEzhU0t6enq4ffs2nJyc5MqjoqJQo0aNfC/N/hj379/HoEGDcPbsWWhqaqJevXqoVq0aQkJCEB4eXqg2/Pz8ULlyZWzYsCHf7enp6UhPT5c9T05OhoODA08tEREpgRDi7WJ5OZdkT+9UC/0bOak2KCqVSuTUkrW1Na5fv56n/Nq1a7C0tFS0uQ9ycXHBmTNnkJKSgkePHuHixYvIzMxElSqFnzjm7e2NiIiCV5vU1dWFiYmJ3IOIiJRDIpFgfJvq+KZ5zqXYU/fdxPrAKNUGRWWGwomMv78/Ro4ciVOnTiE7OxvZ2dk4efIkvv/+e/Tu3bs4YgQAGBoaws7ODi9fvsSRI0fQuXPnQu8bGhoKOzu7YouNiIjeTyKR4Ic21fGVX84foT/vvYkNQQ9VHBWVBYW+/DrXzJkzERUVhZYtW0JLK2d3qVSKfv36KX2ODAAcOXIEQghUr14dERERGD9+PNzc3DBw4EAAwKRJk/DkyRPZqsKLFy+Gs7MzatWqhTdv3mD16tU4efIkjh49qvTYiIio8CQSCSa2dQME8OfZB5iy5wYkAL5s6Kjq0EiNKZzI6OjoYOvWrZg5cyauXbsGfX19uLu7w9GxeH4Qk5KSMGnSJDx+/BgWFhbo3r07fvnlF2hrawMAYmJiEB0dLaufkZGBsWPH4smTJzAwMECdOnVw/PhxtGjRoljiIyKiwpNIJJjYzg1SIbDqXCQm77kBDYkEfXwqqzo0UlMKT/bNlZGRgcjISLi4uMhGZsoKriNDRFS8hBD45UA4Vv+bcxXTnG7u8PdmMlPelchk37S0NAwePBgGBgaoVauWbDRkxIgR+PXXXxVtjoiIyiGJRIKfOtSQLZI3aVcYtlyM/sBeRHkpnMhMmjQJ165dw+nTp+UWqmvVqhW2bt2q1OCIiKjskkgkmNyhBgY2dgIATNwVhm2XHqk2KFI7Cp8T2rNnD7Zu3YqGDRvK3aagVq1auH+ft20nIqLCk0gk+PmzmhACCLgQhQm7rgMSoGd9LkxKhaNwIvPs2bN8b8CYmppaIvdfIiKiskUikWBqx5oA3iYzO69DAqAHkxkqBIVPLdWvXx8HDhyQPc9NXlavXg1fX1/lRUZEROVGbjLTz9cRQgA/7LyOHSGPVR0WqQGFR2Rmz56Ndu3a4datW8jKysKSJUtw69YtXLhwAWfOnCmOGImIqByQSCSY3qkWhAA2BD3E+B3XIAHQ3auSqkOjUkzhEZkmTZogNDQUWVlZcHd3x9GjR2FtbY3AwEB4eXkVR4xERFROSCQSzOhcC182rAwhgHE7rmH3VY7MUMEKNSIzZswYzJw5E4aGhjh79iwaNWqEVatWFXdsRERUDkkkEszoVBtSAfwdHI2x265BAgm6eFZUdWhUChVqROb3339HSkoKAKBFixZISEgo1qCIiKh809CQYFbn2vD3rgypAMZsC8Xe0CeqDotKoUKNyDg5OWHp0qVo3bo1hBAIDAyEubl5vnWbNWum1ACJiKh80tCQ4JcutQEIbL74CKO3hgIAOtflyAz9v0LdomDPnj34+uuvER8fD4lEgoJ2kUgkyM7OVnqQJY23KCAiKj2kUoEfd4dhy6VH0JAAi3t7opOHvarDomJQlO9fhe61lJKSAhMTE9y5cyfftWQAwNTUtLDNlVpMZIiIShepVGDiruvYdvkxNCTAkt6e6MhkpswptnstjRkzBqmpqTAyMsKpU6fg7OwMU1PTfB9ERETKpqEhwa/d6qCHVyVIBTBqaygOXI9RdVhUCig82feTTz7hZF8iIipxGhoS/Na9Dj73qoRsqcDILVdxMIzJTHnHyb5ERKQ2cpMZqRDYdeUJRmy+CgmAdu52qg6NVISTffPBOTJERKVbtlRg/PZr2HX1CbQ0JPijjyfa1mYyo+442VdJmMgQEZV+2VKBcduvYbcsmamHtrVtVR0WfYSifP8qdK+ldyf7amkpfJsmIiIipdHUkGB+Dw8IIbAn9Cm++/sKln1RD21qMZkpTwp9r6Vt27YhIyMDfn5+0NLSwuPHjyGVSmXb09LSMHfu3GIJkoiIKD+aGhIs6FkXnevaI0sqMHzTFRy9GavqsKgEFTqR8ff3R2Jioux5zZo1ERUVJXv+6tUrTJo0SZmxERERfZCmhgQLenigo8fbZObvKzh+K07VYVEJKXQi89+pNApMrSEiIipWWpoaWNTTA5/VsUNmtsA3m0JwIpzJTHlQ6ESGiIioNNPS1MDiXnXRITeZ2XgFJ28zmSnrmMgQEVGZoaWpgSW96qKDux0ysqX4esMVnLodr+qwqBgpdOnRkSNHZJdXS6VSnDhxAjdu3AAAufkzREREqqKlqYHFvetCKgQO3YjFVxtC8Gc/L7Sonv+yIaTeCr2OjIbGhwdvuCAeERGVFpnZUozcfBWHbsRCR0sDK/t6oTmTmVKt2G4aCeSMwHzoURaSGCIiKhu0NTWw1N8TbWrZICNLimEbQnDm7jNVh0VKxjkyRERUZmlrauB3/3poXTMnmRm6/jLOMpkpU5jIEBFRmaajpYE/+tTDp+8kM+fuMZkpK5jIEBFRmaejpYFlfeqhVQ0bpGdJMWTdZfx777mqwyIlYCJDRETlgo6WBv73RT20qmGN9CwpBq+7hPMRTGbUHRMZIiIqN3S0NLDsi3r4xO3/k5kLTGbUGhMZIiIqV3S1NLH8y5xk5k2mFIPWXULg/ReqDouKqFDryJibm0MikRSqwYSEhI8OStW4jgwRUdmXnpWNrzeE4NSdZ9DX1sTagQ3QsIqlqsMq14ry/VuolX0XL14s+/+LFy8wa9YstGnTBr6+vgCAwMBAHDlyBFOmTFE8aiIiIhXIGZnxwldv15cZuPYSkxk1VOiVfXN1794dLVq0wHfffSdX/scff+D48ePYs2ePMuNTCY7IEBGVH28yszFsQwjO3s0ZmQkY2AA+TGZUolhX9s115MgRtG3bNk9527Ztcfz4cUWb+6BXr15h1KhRcHR0hL6+Pho1aoRLly69d5/Tp0+jXr160NXVhaurKwICApQeFxERlQ162ppY2dcLTata4XVmNgYGXMLFSPWfJlFeKJzIWFpaYu/evXnK9+7dC0tL5WewQ4YMwbFjx7BhwwaEhYWhdevWaNWqFZ48eZJv/cjISHTo0AEtWrRAaGgoRo0ahSFDhuDIkSNKj42IiMoGPW1NrOpXH02rWiEtIxsD1l7EpSgmM+pA4VNLAQEBGDJkCNq1awcfHx8AQHBwMA4fPoxVq1ZhwIABSgvu9evXMDY2xt69e9GhQwdZuZeXF9q1a4dZs2bl2WfChAk4cOCA7K7cANC7d28kJibi8OHDhXpdnloiIiqf3mRm5yyWF/EchjqaWDfIG/WdLFQdVrlRIqeWBgwYgPPnz8PExAS7du3Crl27YGJign///VepSQwAZGVlITs7G3p6enLl+vr6+Pfff/PdJzAwEK1atZIra9OmDQIDAwt8nfT0dCQnJ8s9iIio/MkdmWnsaonUjGz0/+siQh5yZKY0K9I6Mj4+Pti0aROuXLmCK1euYNOmTbLRGWUyNjaGr68vZs6ciadPnyI7OxsbN25EYGAgYmJi8t0nNjYWNjY2cmU2NjZITk7G69ev891nzpw5MDU1lT0cHByU3hciIlIP+jqaWN2vARq55CYzlxDy8KWqw6ICFCmRuX//PiZPnow+ffogPj4eAHDo0CHcvHlTqcEBwIYNGyCEQMWKFaGrq4ulS5fC398fGhrKW8tv0qRJSEpKkj0ePXqktLaJiEj96OtoYk3/BvCtYomU9Cz0/+sirkQzmSmNFM4Gzpw5A3d3dwQHB2Pnzp1ISUkBAFy7dg1Tp05VeoAuLi44c+YMUlJS8OjRI1y8eBGZmZmoUqVKvvVtbW0RFxcnVxYXFwcTExPo6+vnu4+uri5MTEzkHkREVL7p62hizYD6aFjFIieZWXMRV5nMlDoKJzITJ07ErFmzcOzYMejo6MjKP/nkEwQFBSk1uHcZGhrCzs4OL1++xJEjR9C5c+d86/n6+uLEiRNyZceOHZMt3kdERFRYBjpa+GtAA/g4W+BVehb6rbmI0EeJqg6L3qFwIhMWFoauXbvmKbe2tsbz58q/8daRI0dw+PBhREZG4tixY2jRogXc3NwwcOBAADmnhfr16yer//XXX+PBgwf44YcfcPv2bfzvf//Dtm3bMHr0aKXHRkREZZ+BjhbWDmwA77fJTN81wbjGZKbUUDiRMTMzy3ei7dWrV1GxYkWlBPWupKQkDB8+HG5ubujXrx+aNGmCI0eOQFtbGwAQExOD6OhoWX1nZ2ccOHAAx44dg4eHBxYsWIDVq1ejTZs2So+NiIjKBwMdLawd0ADeThZ49SYLX64JxvXHiaoOi1CEdWTGjRuH4OBgbN++HdWqVcOVK1cQFxeHfv36oV+/fsUyT6akcR0ZIiLKT2p61tvF8l7CRE8Lm4Y0hHslU1WHVWaUyDoys2fPhpubGxwcHJCSkoKaNWuiWbNmaNSoESZPnqxw0EREROrCUFcLawd6o76jOZLfjszceJKk6rDKNYVHZHI9evQIYWFhSElJgaenJ6pWrars2FSGIzJERPQ+uZdkhzx8CVN9bWwa4oPaFTky87FKZERmxowZSEtLg4ODA9q3b4+ePXuiatWqeP36NWbMmKFw0EREROrGSFcLAQMboF5lMyS9zsSXa4Jx8ylHZlRB4REZTU1NxMTEwNraWq78xYsXsLa2RnZ2tlIDVAWOyBARUWG8epOJfn9dxNXoRJgZ5IzM1LLnyExRlciIjBACEokkT/m1a9dgYcEbaxERUflhrKeNdYO8UdfBDIlpmfhidTBuPeX9+kpSoRMZc3NzWFhYQCKRoFq1arCwsJA9TE1N8emnn6Jnz57FGSsREVGpY6KnjfWDveEhS2aCEB7DZKakFPrU0rp16yCEwKBBg7B48WKYmv7/0JmOjg6cnJzKzOq5PLVERESKSnqdiX5rgnHtcRIsDHXw91AfuNnyO0QRRfn+VXiOzJkzZ9CoUSPZgnRlERMZIiIqiqTXmei7JhjX3yYzm4c2RHVbY1WHpTZKZI6Mn5+fLIl58+YNkpOT5R5ERETllam+NjYM8oF7RVMkpGagz6og3I17peqwyjSFE5m0tDR89913sLa2hqGhIczNzeUeRERE5ZmpgTY2DvZB7YomePE2mbnHZKbYKJzIjB8/HidPnsTy5cuhq6uL1atXY/r06bC3t8f69euLI0YiIiK1kpvM1LI3wfOUDPgzmSk2Cs+RqVy5MtavX4/mzZvDxMQEV65cgaurKzZs2IDNmzfj4MGDxRVrieEcGSIiUobEtAz0WRWMWzHJsDLSxZZhPnC15pyZgpTIHJmEhARUqVIFAGBiYoKEhAQAQJMmTXD27FlFmyMiIiqzzAx0sGmID2rameB5Sjp6rwxGRHyKqsMqUxROZKpUqYLIyEgAgJubG7Zt2wYA+Oeff2BmZqbU4IiIiNSduWFOMlPjbTLjvyoI958xmVEWhROZgQMH4tq1awCAiRMnYtmyZdDT08Po0aMxfvx4pQdIRESk7nKTGTdbYzx7lQ7/lUxmlKXId7/O9fDhQ4SEhMDV1RV16tRRVlwqxTkyRERUHHIvyb4d+wrWxrrYMqwhqlQwUnVYpUaJLIhXHjCRISKi4vIiJR19VgXjTtwr2JjoYsswXzhbGao6rFKhxBKZS5cu4dSpU4iPj4dUKpXbtnDhQkWbK3WYyBARUXF6npL+drG8FNia6GHLsIZwYjJTpO9fLUVfZPbs2Zg8eTKqV68OGxsbuTth53dXbCIiIpJnZaSLv4c2hP/KINyLT4H/qiBsGdYQjpZMZhSl8IiMjY0NfvvtNwwYMKCYQlI9jsgQEVFJePYqZ2TmXnwK7Ez1yn0yUyLryGhoaKBx48YKB0dERETyKhjnjMy4WhshJukN/FcGIfpFmqrDUisKJzKjR4/GsmXLiiMWIiKicicnmfGBSwVDPE16A/9VQXiUwGSmsBQ+tSSVStGhQwfcvXsXNWvWlN0JO9euXbuUGqAq8NQSERGVtPjkN+i9KggPnqWiopk+tgxrCAcLA1WHVaJK5NTSyJEjcerUKVSrVg2WlpYwNTWVexAREZHirE30sGVoQ1SxMsSTxNfovZIjM4Wh8IiMsbExtmzZgg4dOhRXTCrHERkiIlKVuOQ36L0yCJHPU1HJPGdkppJ5+RiZKZERGQsLC7i4uCgcHBEREX2YjYkeNg9tCGcrQzx++Rr+q4LwJPG1qsMqtRROZKZNm4apU6ciLY3DXURERMXB1jQnmXGyNMCjhNfovTIQT5nM5EvhU0uenp64f/8+hBBwcnLKM9n3ypUrSg1QFXhqiYiISoOYpJy5Mg9fpKGyhQG2DGsIezN9VYdVbEpkZd8uXboougsREREVgZ2pPjYPbYjeK4MQnZAmWwHYzrTsJjOK4k0j88ERGSIiKk2evr2KKTohDY6WBtg6zBe2pnqqDkvpSmSyLxEREZUsezN9bB7WEA4W+nj4Ig29VwYiNumNqsMqFQqVyFhYWOD58+cAAHNzc1hYWBT4ICIiIuWraJZzmqmSuT6iXuScZopLZjJTqDkyixYtgrGxsez/vMs1ERFRyatkbiCbMxP5PBX+K4OweVhD2JiUvdNMhcU5MvngHBkiIirNHiWkoffKnPVlqlQwxJahDWFdBpKZEpkjo6mpifj4+DzlL168gKampqLNERERkYIc3l6KXdFMHw+epcJ/VRDiX5XP00wKJzIFDeCkp6dDR0fnowN6V3Z2NqZMmQJnZ2fo6+vDxcUFM2fOLDAGADh9+jQkEkmeR2xsrFJjIyIiUiUHi5zTTPamerj/LOc0U3lMZgq9jszSpUsBABKJBKtXr4aRkZFsW3Z2Ns6ePQs3NzelBvfbb79h+fLlWLduHWrVqoXLly9j4MCBMDU1xciRI9+77507d+SGpaytrZUaGxERkapVtjTAlmG+6LUyEPefpaLPqmBsHtoQFYx1VR1aiSl0IrNo0SIAOSMyK1askDuNpKOjAycnJ6xYsUKpwV24cAGdO3eW3aDSyckJmzdvxsWLFz+4r7W1NczMzJQaDxERUWmTk8zkTACOiE9Bn1VB+LscJTOFPrUUGRmJyMhI+Pn54dq1a7LnkZGRuHPnDo4cOQIfHx+lBteoUSOcOHECd+/eBQBcu3YN//77L9q1a/fBfevWrQs7Ozt8+umnOH/+/HvrpqenIzk5We5BRESkLhwtDbF5aEPYmujhXnwKvlgdhOcp6aoOq0QoPEfm1KlTMDc3lz3Pzs5GaGgoXr58qdTAAGDixIno3bs33NzcoK2tDU9PT4waNQpffPFFgfvY2dlhxYoV2LlzJ3bu3AkHBwc0b978vfeAmjNnDkxNTWUPBwcHpfeFiIioODlZGb69FFsXd+NS8MWqYLwoB8mMwpdfjxo1Cu7u7hg8eDCys7PRrFkzBAYGwsDAAPv370fz5s2VFtyWLVswfvx4zJs3D7Vq1UJoaChGjRqFhQsXon///oVux8/PD5UrV8aGDRvy3Z6eno709P8/2MnJyXBwcODl10REpHYin6ei15+BiH+VDjdbY2wa4gNLI/U4zVQil19v374dHh4eAIB//vkHUVFRuH37NkaPHo2ffvpJ0ebea/z48bJRGXd3d/Tt2xejR4/GnDlzFGrH29sbERERBW7X1dWFiYmJ3IOIiEgdOVsZYsuwhrA21sXt2Ff4YnUwElIzVB1WsVE4kXnx4gVsbW0BAAcPHkSPHj1QrVo1DBo0CGFhYUoNLi0tDRoa8iFqampCKpUq1E5oaCjs7OyUGRoREVGpVaWCETa/k8z0WRWEl2U0mVE4kbGxscGtW7eQnZ2Nw4cP49NPPwWQk3Qoe0G8jh074pdffsGBAwcQFRWF3bt3Y+HChejatauszqRJk9CvXz/Z88WLF2Pv3r2IiIjAjRs3MGrUKJw8eRLDhw9XamxERESlmUsFI9nVS7djX6HP6uAymcwU+vLrXAMHDkTPnj1hZ2cHiUSCVq1aAQCCg4OVvo7M77//jilTpuDbb79FfHw87O3t8dVXX+Hnn3+W1YmJiUF0dLTseUZGBsaOHYsnT57AwMAAderUwfHjx9GiRQulxkZERFTauVobye7NFB6TjC9WB2PTEB+YGyp3AVtVKtK9lnbs2IFHjx6hR48eqFSpEgBg3bp1MDMzQ+fOnZUeZEnjvZaIiKgsiYh/hd4rg/A8JQO17E2waYgPzAxKXzJTlO9f3jQyH0xkiIiorLkX9wr+q3KSmdoVTbBxcOlLZor1qqX27dsjKSlJ9vzXX39FYmKi7PmLFy9Qs2bNwkdLREREJaaqjTH+HtoQloY6uPEkGX3XXERSWqaqw/pohU5kjhw5IrfWyuzZs5GQkCB7npWVhTt37ig3OiIiIlKaau8kM2FPktD3r2AkvVbvZKbQicx/z0DxjBQREZH6qW6bk8xYGOrg+uMk9Fuj3smMwpdfExERkXrLSWZ8YG6gjWuPk9Dvr4tIfqOeyUyhExmJRAKJRJKnjIiIiNSPm60J/h7aMCeZeZSIvmvUM5kp9DoyQggMGDAAuro592t48+YNvv76axgaGgKA3PwZIiIiKv1q2Jlg05CG6LM6CNceJaLfmovYMNgbxnraqg6t0Ap9+fXAgQML1eDatWs/KqDSgJdfExFReXLzaRK+WB2MxLRMeFY2w/pBqklmuI6MkjCRISKi8ubGk5xkJul1JupVNsP6wT4w0lX4BgAfpUTufk1ERERlT+2Kptg0xAem+tq4Ep2I/n9dREp6lqrD+iAmMkRERAQgJ5nZONgHJnpaCHn4EgPUIJlhIkNEREQy7pVMsXFITjJz+eFLDFx7EamlOJlhIkNERERy6lQyw8YhPjDW08KlqJcYuPZSqU1mmMgQERFRHnUqmWHjYB8Y62rhYlQCBgZcQlpG6UtmmMgQERFRvjwczLBhyNtkJjIBA9eWvmSGiQwREREVqK6DGdYP9oaRrhaCIxMwKOASXmdkqzosGSYyRERE9F6elc1lyUzQg9KVzDCRISIiog+qV9kc6wZ5w1BHE4EPXmDwutKRzDCRISIiokLxcswZmTHU0cSF+y8w78gdVYfERIaIiIgKz8vRAusGeaOxqyW+b1VV1eEU/u7XRERERABQ38kCGwf7QCKRqDoUjsgQERGR4kpDEgMwkSEiIiI1xkSGiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFRIaIiIjUFhMZIiIiUltMZIiIiEhtMZEhIiIitcVEhoiIiNQWExkiIiJSW0xkiIiISG0xkSEiIiK1xUSGiIiI1JaWqgMojYQQAIDk5GQVR0JERFR+5H7v5n4PFwYTmXy8evUKAODg4KDiSIiIiMqfV69ewdTUtFB1JUKRtKeckEqlePr0KYyNjSGRSFQdjtIlJyfDwcEBjx49gomJiarDKTbloZ/loY9A+ehneegjUD76yT4WnRACr169gr29PTQ0Cjf7hSMy+dDQ0EClSpVUHUaxMzExKbO/ZO8qD/0sD30Eykc/y0MfgfLRT/axaAo7EpOLk32JiIhIbTGRISIiIrXFRKYc0tXVxdSpU6Grq6vqUIpVeehneegjUD76WR76CJSPfrKPJYuTfYmIiEhtcUSGiIiI1BYTGSIiIlJbTGSIiIhIbTGRISIiIrXFREYNLFu2DE5OTtDT04OPjw8uXrz43vrbt2+Hm5sb9PT04O7ujoMHD8ptF0Lg559/hp2dHfT19dGqVSvcu3dPtj0qKgqDBw+Gs7Mz9PX14eLigqlTpyIjI0OujkQiyfMICgpSm34CgJOTU54+/Prrr3J1rl+/jqZNm0JPTw8ODg6YO3eu2vTx9OnT+R4niUSCS5cuAVD+sVR2H3ft2oXWrVvD0tISEokEoaGhedp48+YNhg8fDktLSxgZGaF79+6Ii4uTqxMdHY0OHTrAwMAA1tbWGD9+PLKysorUR1X0MyEhASNGjED16tWhr6+PypUrY+TIkUhKSpKrl9+x3LJli1r0EQCaN2+eJ/6vv/5aro66H8uCfuckEgm2b98uq1daj2VmZiYmTJgAd3d3GBoawt7eHv369cPTp0/l2khISMAXX3wBExMTmJmZYfDgwUhJSZGro5TPV0Gl2pYtW4SOjo7466+/xM2bN8XQoUOFmZmZiIuLy7f++fPnhaamppg7d664deuWmDx5stDW1hZhYWGyOr/++qswNTUVe/bsEdeuXROdOnUSzs7O4vXr10IIIQ4dOiQGDBggjhw5Iu7fvy/27t0rrK2txdixY2VtREZGCgDi+PHjIiYmRvbIyMhQm34KIYSjo6OYMWOGXB9SUlJk25OSkoSNjY344osvxI0bN8TmzZuFvr6++PPPP9Wij+np6XJ9i4mJEUOGDBHOzs5CKpUKIZR7LIujj+vXrxfTp08Xq1atEgDE1atX87Tz9ddfCwcHB3HixAlx+fJl0bBhQ9GoUSPZ9qysLFG7dm3RqlUrcfXqVXHw4EFhZWUlJk2apHAfVdXPsLAw0a1bN7Fv3z4REREhTpw4IapWrSq6d+8uVw+AWLt2rdyxfPdnvjT3UQgh/Pz8xNChQ+XiT0pKkm0vC8cyKysrz+/l9OnThZGRkXj16pWsXmk9lomJiaJVq1Zi69at4vbt2yIwMFB4e3sLLy8vuXbatm0rPDw8RFBQkDh37pxwdXUV/v7+su3K+nxlIlPKeXt7i+HDh8ueZ2dnC3t7ezFnzpx86/fs2VN06NBBrszHx0d89dVXQgghpFKpsLW1FfPmzZNtT0xMFLq6umLz5s0FxjF37lzh7Owse5775ZffB1FRqKqfjo6OYtGiRQXG9b///U+Ym5uL9PR0WdmECRNE9erVFeqfEKXjWGZkZIgKFSqIGTNmyMqUeSyV3cd3FRRnYmKi0NbWFtu3b5eVhYeHCwAiMDBQCCHEwYMHhYaGhoiNjZXVWb58uTAxMZE7tqW5n/nZtm2b0NHREZmZmbIyAGL37t2F68h7qKqPfn5+4vvvvy8wrrJ6LOvWrSsGDRokV6YOxzLXxYsXBQDx8OFDIYQQt27dEgDEpUuXZHUOHTokJBKJePLkiRBCeZ+vPLVUimVkZCAkJAStWrWSlWloaKBVq1YIDAzMd5/AwEC5+gDQpk0bWf3IyEjExsbK1TE1NYWPj0+BbQJAUlISLCws8pR36tQJ1tbWaNKkCfbt26dQ/3Kpup+//vorLC0t4enpiXnz5skNUQcGBqJZs2bQ0dGRe507d+7g5cuXatPHXPv27cOLFy8wcODAPNs+9lgWRx8LIyQkBJmZmXLtuLm5oXLlyrJ2AgMD4e7uDhsbG7nXSU5Oxs2bNwv9WoDq+pmfpKQkmJiYQEtL/rZ5w4cPh5WVFby9vfHXX39BKLhcmKr7uGnTJlhZWaF27dqYNGkS0tLS5F6nrB3LkJAQhIaGYvDgwXm2qcuxTEpKgkQigZmZmawNMzMz1K9fX1anVatW0NDQQHBwsKyOMj5fedPIUuz58+fIzs6W+4UFABsbG9y+fTvffWJjY/OtHxsbK9ueW1ZQnf+KiIjA77//jvnz58vKjIyMsGDBAjRu3BgaGhrYuXMnunTpgj179qBTp05q08+RI0eiXr16sLCwwIULFzBp0iTExMRg4cKFsnacnZ3ztJG7zdzcvNT38V1r1qxBmzZt5G6KqqxjWRx9LIzY2Fjo6OjIPkDza6eg18ndpghV9TO/OGbOnIlhw4bJlc+YMQOffPIJDAwMcPToUXz77bdISUnByJEjFWpbVX3s06cPHB0dYW9vj+vXr2PChAm4c+cOdu3a9d7Xyd2miNJyLNesWYMaNWqgUaNGcuXqcizfvHmDCRMmwN/fX3YDydjYWFhbW8vV09LSgoWFhdzvpTI+X5nI0Hs9efIEbdu2RY8ePTB06FBZuZWVFcaMGSN73qBBAzx9+hTz5s1TOJFRpXf7UKdOHejo6OCrr77CnDlzSsXS28r0+PFjHDlyBNu2bZMrLyvHsjxJTk5Ghw4dULNmTUybNk1u25QpU2T/9/T0RGpqKubNm6fQl58qvZuYubu7w87ODi1btsT9+/fh4uKiwsiKx+vXr/H333/LHbdc6nAsMzMz0bNnTwghsHz5cpXEwFNLpZiVlRU0NTXzXH0RFxcHW1vbfPextbV9b/3cfwvT5tOnT9GiRQs0atQIK1eu/GC8Pj4+iIiI+GC9/1J1P//bh6ysLERFRb33dd59jcIoDX1cu3YtLC0tC5WcFOVYFkcfC8PW1hYZGRlITEwssB1lHUdAdf3M9erVK7Rt2xbGxsbYvXs3tLW131vfx8cHjx8/Rnp6eqFfQ9V9fJePjw8AyH4ey9KxBIAdO3YgLS0N/fr1+2Dd0nYsc5OYhw8f4tixY7LRmNw24uPj5epnZWUhISFB6b+XTGRKMR0dHXh5eeHEiROyMqlUihMnTsDX1zfffXx9feXqA8CxY8dk9Z2dnWFraytXJzk5GcHBwXJtPnnyBM2bN4eXlxfWrl0LDY0P/6iEhobCzs5OoT4Cqu1nfn3Q0NCQDYn6+vri7NmzyMzMlHud6tWrF3rYszT0UQiBtWvXol+/fh/84gOKdiyLo4+F4eXlBW1tbbl27ty5g+joaFk7vr6+CAsLk/tgzf3grVmzZqFfC1BdP4Gc49u6dWvo6Ohg37590NPT++A+oaGhMDc3V2iEUZV9/K/cS5dzfx7LyrHMtWbNGnTq1AkVKlT4YN3SdCxzk5h79+7h+PHjsLS0zNNGYmIiQkJCZGUnT56EVCqVJafK+nzlVUul3JYtW4Surq4ICAgQt27dEsOGDRNmZmayGft9+/YVEydOlNU/f/680NLSEvPnzxfh4eFi6tSp+V6ya2ZmJvbu3SuuX78uOnfuLHfJ7uPHj4Wrq6to2bKlePz4sdylf7kCAgLE33//LcLDw0V4eLj45ZdfhIaGhvjrr7/Upp8XLlwQixYtEqGhoeL+/fti48aNokKFCqJfv36yNhITE4WNjY3o27evuHHjhtiyZYswMDAo8uXXJd3HXMePHxcARHh4eJ64lHksi6OPL168EFevXhUHDhwQAMSWLVvE1atX5X4ev/76a1G5cmVx8uRJcfnyZeHr6yt8fX1l23Mv2W3durUIDQ0Vhw8fFhUqVPioS3ZLup9JSUnCx8dHuLu7i4iICLnfy6ysLCGEEPv27ROrVq0SYWFh4t69e+J///ufMDAwED///LNa9DEiIkLMmDFDXL58WURGRoq9e/eKKlWqiGbNmsnaKAvHMte9e/eERCIRhw4dyhNXaT6WGRkZolOnTqJSpUoiNDRU7mfx3SuQ2rZtKzw9PUVwcLD4999/RdWqVeUuv1bW5ysTGTXw+++/i8qVKwsdHR3h7e0tgoKCZNv8/PxE//795epv27ZNVKtWTejo6IhatWqJAwcOyG2XSqViypQpwsbGRujq6oqWLVuKO3fuyLavXbtWAMj3kSsgIEDUqFFDGBgYCBMTE+Ht7S13+as69DMkJET4+PgIU1NToaenJ2rUqCFmz54t3rx5I9fOtWvXRJMmTYSurq6oWLGi+PXXX9Wmj7n8/f3l1lV5l7KPpbL7WNDP49SpU2V1Xr9+Lb799lthbm4uDAwMRNeuXfN8aURFRYl27doJfX19YWVlJcaOHSt32XJp7+epU6cK/L2MjIwUQuRc3lq3bl1hZGQkDA0NhYeHh1ixYoXIzs5Wiz5GR0eLZs2aCQsLC6GrqytcXV3F+PHj5daREUL9j2WuSZMmCQcHh3yPT2k+lrmXlef3OHXqlKzeixcvhL+/vzAyMhImJiZi4MCBcuvkCKGcz1eJEApey0VERERUSnCODBEREaktJjJERESktpjIEBERkdpiIkNERERqi4kMERERqS0mMkRERKS2mMgQERGR2mIiQ0RERGqLiQwRgObNm2PUqFGqDgNCCAwbNgwWFhaQSCSy+8woYsCAAejSpYvSYyMiKo2YyJBa69ixI9q2bZvvtnPnzkEikeD69eslHFXRHT58GAEBAdi/fz9iYmJQu3btPHVOnz4NiUSS527PuZYsWYKAgIDiDfQjxcbGYsSIEahSpQp0dXXh4OCAjh075rlRXXEqzoRPVYnxtGnTIJFI3vsoDlFRUUVOvIuLk5MTFi9erOowqAQwkSG1NnjwYBw7dgyPHz/Os23t2rWoX78+6tSpo4LIiub+/fuws7NDo0aNYGtrCy0tLYXbMDU1hZmZmfKDU1BGRka+5VFRUfDy8sLJkycxb948hIWF4fDhw2jRogWGDx9ewlGWLePGjUNMTIzsUalSJcyYMUOu7F0FHSMitaLw3ZmISpHMzExhY2MjZs6cKVf+6tUrYWRkJJYvXy6eP38uevfuLezt7YW+vr6oXbu2+Pvvv+Xq+/n5ie+//172HIDYvXu3XB1TU1Oxdu1a2fPo6GjRo0cPYWpqKszNzUWnTp1kN+8ryOnTp0WDBg2Ejo6OsLW1FRMmTJDd7K5///5yN19zdHTMt43cmwe+fPky3+39+/cXnTt3luvbiBEjxPjx44W5ubmwsbHJc/O6ly9fisGDBwsrKythbGwsWrRoIUJDQ2XbIyIiRKdOnYS1tbUwNDQU9evXF8eOHZNrw9HRUcyYMUP07dtXGBsb57kJXa527dqJihUripSUlDzb3u3Tw4cPRadOnYShoaEwNjYWPXr0kN2tVwghpk6dKjw8PMT69euFo6OjMDExEb169RLJycmyOtu3bxe1a9cWenp6wsLCQrRs2VKkpKSIqVOnFnizux9++EFUrVpV6OvrC2dnZzF58mSRkZFR6Nf973HEOzd1/K+EhATRt29fYWZmJvT19UXbtm3F3bt3ZdvXrl0rTE1NxeHDh4Wbm5swNDQUbdq0EU+fPs23vf9ydHQUixYtkj338/MTw4cPF99//72wtLQUzZs3F0IIERYWJtq2bSsMDQ2FtbW1+PLLL8WzZ89k+x06dEg0btxYmJqaCgsLC9GhQwcREREh2/7f/vr5+cnei86dO4tffvlFWFtbC1NTUzF9+nSRmZkpxo0bJ8zNzUXFihXz3Gn9Q79bue3OmzdP2NraCgsLC/Htt9/KjpOfn1+BN7ylsocjMqTWtLS00K9fPwQEBEC8c//T7du3Izs7G/7+/njz5g28vLxw4MAB3LhxA8OGDUPfvn1x8eLFIr9uZmYm2rRpA2NjY5w7dw7nz5+HkZER2rZtW+BfuU+ePEH79u3RoEEDXLt2DcuXL8eaNWswa9YsADmnhGbMmIFKlSohJiYGly5dKnJ8/7Vu3ToYGhoiODgYc+fOxYwZM3Ds2DHZ9h49eiA+Ph6HDh1CSEgI6tWrh5YtWyIhIQEAkJKSgvbt2+PEiRO4evUq2rZti44dOyI6OlrudebPnw8PDw9cvXoVU6ZMyRNHQkICDh8+jOHDh8PQ0DDP9tyRJKlUis6dOyMhIQFnzpzBsWPH8ODBA/Tq1Uuu/v3797Fnzx7s378f+/fvx5kzZ/Drr78CAGJiYuDv749BgwYhPDwcp0+fRrdu3SCEwLhx49CzZ0+0bdtWNlLRqFEjAICxsTECAgJw69YtLFmyBKtWrcKiRYsK/bpLliyBr68vhg4dKmvbwcEh3+MyYMAAXL58Gfv27UNgYCCEEGjfvj0yMzNlddLS0jB//nxs2LABZ8+eRXR0NMaNG5dve4Wxbt066Ojo4Pz581ixYgUSExPxySefwNPTE5cvX8bhw4cRFxeHnj17yvZJTU3FmDFjcPnyZZw4cQIaGhro2rUrpFIpAMh+l44fP46YmBjs2rVLtu/Jkyfx9OlTnD17FgsXLsTUqVPx2WefwdzcHMHBwfj666/x1VdfyUZVC/u7derUKdy/fx+nTp3CunXrEBAQIDulumvXrjyjUVSGqTiRIvpo4eHheW4f37RpU/Hll18WuE+HDh3E2LFjZc8VHZHZsGGDqF69upBKpbLt6enpQl9fXxw5ciTf1/zxxx/z7LNs2TJhZGQksrOzhRBCLFq0qMCRmFxFGZFp0qSJXJ0GDRqICRMmCCGEOHfunDAxMRFv3ryRq+Pi4iL+/PPPAuOoVauW+P3332XPHR0dRZcuXd4be3BwsAAgdu3a9d56R48eFZqamiI6OlpWdvPmTQFAXLx4UQiRMzJiYGAgNwIzfvx44ePjI4QQIiQkRAAQUVFR+b7Gf9+ngsybN094eXnJnn/odYXI+/OUn7t37woA4vz587Ky58+fC319fbFt2zYhRM6IDAC50Y9ly5YJGxubD8YtRP4jMp6ennJ1Zs6cKVq3bi1X9ujRIwFA3LlzJ992nz17JgCIsLAwIYQQkZGRAoC4evWqXL3+/fsLR0dH2c+3EEJUr15dNG3aVPY8KytLGBoais2bNwshCve7ldtuVlaWrE6PHj1Er169Cuw7lV0ckSG15+bmhkaNGuGvv/4CAERERODcuXMYPHgwACA7OxszZ86Eu7s7LCwsYGRkhCNHjuQZTVDEtWvXEBERAWNjYxgZGcHIyAgWFhZ48+YN7t+/n+8+4eHh8PX1lZtw2bhxY6SkpOQ7x0eZ/jtPyM7ODvHx8QBy+pKSkgJLS0tZX4yMjBAZGSnrS0pKCsaNG4caNWrAzMwMRkZGCA8Pz/Me1q9f/71xiHdGzd4nPDwcDg4OciMZNWvWhJmZGcLDw2VlTk5OMDY2zrdfHh4eaNmyJdzd3dGjRw+sWrUKL1++/OBrb926FY0bN4atrS2MjIwwefLkPP183+sWVnh4OLS0tODj4yMrs7S0RPXq1eX6aGBgABcXl496rXd5eXnJPb927RpOnTold+zd3NwAQHb87927B39/f1SpUgUmJiZwcnICgEL9DtWqVQsaGv//VWNjYwN3d3fZc01NTVhaWsr9PBbmd6tWrVrQ1NSUPf/Y94XUl+IzCYlKocGDB2PEiBFYtmwZ1q5dCxcXF/j5+QEA5s2bhyVLlmDx4sVwd3eHoaEhRo0a9d6JjhKJJM+X7rvD/SkpKfDy8sKmTZvy7FuhQgUl9Up5tLW15Z5LJBLZaYGUlBTY2dnh9OnTefbLPdUzbtw4HDt2DPPnz4erqyv09fXx+eef53kP8ztd9K6qVatCIpHg9u3bRe/MO97XL01NTRw7dgwXLlzA0aNH8fvvv+Onn35CcHAwnJ2d820vMDAQX3zxBaZPn442bdrA1NQUW7ZswYIFCwr9usqW32sVNiHMz3+PUUpKCjp27Ijf/q+d+wtpqo0DOP7VREhKXDWrWW0YMyaTakWwxPUHaxTVqov+qIQyJPtnubwJR0EXQYRdjCTKi6CL0Isp3gwKilhuMFuWBZmZNB1R0Sq7iaCW74V0YDZzW73v2+T3gXOxcw7P83t2dji/58/Z+fM/nbtw4UJg/O1ArVZLa2srGo2G79+/YzQaE1osHC/+qX6Pidxb/+U1EH83SWTEtLBnzx6OHz/OjRs3uH79OocOHVJGPnw+HzabjaqqKmB8/cXz588pLi6etDy1Wh0zrz44OMjnz5+VzyaTifb2dvLz88nNzU0oRoPBgNvtZmxsLCa22bNns2jRoqTb/KeYTCbevHlDVlaW0tOeyOfzUV1dza5du4Dxh00oFEq6rjlz5mC1WmlpaaG+vv6nh+ro6Ch5eXkYDAbC4TDhcFgZlXn69Cmjo6O/vG4TZWRkUFpaSmlpKadPn0ar1dLZ2YnD4SA7O5toNBpzvt/vR6vV0tTUpOwbHh5Oup3xyp7IYDDw7ds3AoGAsj7n/fv3DAwMJNXG32UymXC73eh0urhvyf2IqbW1lbKyMgC6u7tjzsnOzgaYss2JxpPsvRVPItdATA8ytSSmhVmzZrF3715OnTrF69evqa6uVo7p9XqlZ97f38/Bgwd5+/btL8vbuHEjly5d4uHDhwSDQerq6mJ6gJWVlcybNw+bzca9e/d4+fIld+/epb6+ftJposOHDxMOhzl27BjPnj2jq6uLM2fO4HA4YobeE/XkyRMePXqkbH19fUmXAVBeXo7ZbGbnzp3cunWLUCiE3++nqamJYDAIjH+HHR0dSj0VFRUp935bWlqIRqOsWbMGt9vN4OAg/f39uFwuzGazElNJSQmVlZX09vbS09PDgQMHWLdu3ZTTVz8EAgHOnTtHMBhkZGSEjo4O3r17h8FgAManhx4/fszAwACRSISvX7+i1+sZGRmhra2NoaEhXC4XnZ2dSbdRp9MRCAQIhUJEIpG435Ver8dms1FbW0t3dzd9fX1UVVVRUFCAzWZLus5UHTlyhA8fPrB//37u37/P0NAQN2/epKamhmg0ikqlYu7cuVy9epUXL15w584dHA5HTBn5+fnMnDlTWSj86dOnlONJ5d6KR6fT4fV6efXqFZFIJOV4xN9PEhkxbdjtdj5+/IjVakWj0Sj7nU4nJpMJq9XK+vXrWbBgwZR/hNbc3MzixYspKyujoqKCxsZGcnJylOM5OTl4vV6WLFnC7t27MRgM2O12vnz5MmkvsqCgAI/HQ09PD8uXL6eurg673Y7T6UypvRaLhZUrVyrbxLUPicrIyMDj8WCxWKipqaGoqIh9+/YxPDzM/PnzAbh48SIqlYq1a9eyfft2rFYrJpMppfoKCwvp7e1lw4YNnDx5EqPRyKZNm7h9+zaXL19WYurq6kKlUmGxWCgvL6ewsJD29vaE68nNzcXr9bJ161aKiopwOp00NzezZcsWAGpra1m2bBmrV69GrVbj8/nYsWMHDQ0NHD16lBUrVuD3++O+fTWVxsZGZsyYQXFxMWq1etK1JNeuXWPVqlVs27YNs9nM2NgYHo/np2mTf5NGo8Hn8xGNRtm8eTMlJSWcOHGCvLw8MjMzyczMpK2tjQcPHmA0GmloaODChQsxZWRlZeFyubhy5Qoajea3ErFU7q14zp49SygUYunSpX/ldK/4czLGfmeyVQghhBDifyQjMkIIIYRIW5LICCGEECJtSSIjhBBCiLQliYwQQggh0pYkMkIIIYRIW5LICCGEECJtSSIjhBBCiLQliYwQQggh0pYkMkIIIYRIW5LICCGEECJtSSIjhBBCiLT1D9UtKYwpYl8IAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ]
@@ -1043,8 +1043,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(8.216459566237983, 12.175990977887663)\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:(11.221001774229803, 12.921868423571057)\n",
       "\n"
      ]
     }
@@ -1070,16 +1070,16 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:18.366818Z",
-     "iopub.status.busy": "2024-10-25T14:52:18.366433Z",
-     "iopub.status.idle": "2024-10-25T14:52:22.758493Z",
-     "shell.execute_reply": "2024-10-25T14:52:22.757843Z"
+     "iopub.execute_input": "2024-10-25T14:53:21.475117Z",
+     "iopub.status.busy": "2024-10-25T14:53:21.474722Z",
+     "iopub.status.idle": "2024-10-25T14:53:26.072615Z",
+     "shell.execute_reply": "2024-10-25T14:53:26.071953Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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xY0cIITBlypRs1739L1YrK6tsfzHmpmXLloiJicHatWvVZZmZmfjpp59gbW2NgIAAia35X70nT55ERESERnl8fDz+/PNP1KxZM8ceCbnj3bx5s8Yy39OnT+PUqVPqVVavXr1CamqqxnvKly8PGxsb9TLkoKAg2NraYvr06cjIyMgW07Nnz7SOv2vXrkhLS8OKFSuwc+dOdOnSReO8tp/l7OyMmjVrYsWKFRrLe/fs2ZNtvktOihUrhrFjx+L69esYO3Zsjj0ef/zxB06fPg3gzX0/ffo0Tpw4oT6fkpKCpUuXwsPDo8Dsb+Tr64vSpUtjyZIlGsvId+zYgevXr6NVq1ayfI7U+2dsbJztmvXr12dbgp6V2GbNqwPe9NYsXbpU47rExMRs85J8fHxgZGSkbneHDh1gbGyMKVOmZPtsIQRevHghpclEANhjU2Tt2LFD/S/Vt9WvXx/lypWTXN+YMWOwZcsWtG7dGn369EHt2rWRkpKCy5cvY8OGDbh//z4cHBzQuHFjfP7551iwYAFu376N5s2bQ6VS4ciRI2jcuLH6EQa1a9fG3r17MWfOHLi4uMDT0zPH+REA8MUXX+CXX35Bnz59EBERAQ8PD2zYsAHHjh3DvHnztJ4c+W/jxo3D+vXr8fHHH+PLL79EpUqV8OTJEyxfvhzR0dFYtmyZTvVKjdfLywsNGzbEoEGDkJaWhnnz5qFkyZLqYb9bt26hSZMm6NKlC6pUqQITExNs2rQJT58+Rbdu3QC8mU+xePFifP755/joo4/QrVs3lCpVClFRUdi2bRsaNGiAhQsXahX/Rx99BC8vL3z77bdIS0vTGIaS+lnh4eFo1aoVGjZsiH79+iEuLg4//fQTqlatqjFBOTdjxozB1atXMXv2bBw4cACdOnWCk5MTYmJisHnzZpw+fRrHjx8H8Obn+Z///ActWrTAsGHDYG9vjxUrViAyMhJ//fVXjkNv+mBqaooffvgBffv2RUBAALp3765e7u3h4YGRI0fK9llS7l/r1q0xdepU9O3bF/Xr18fly5fx559/Zvv7omrVqqhXrx5CQ0PVPbNr1qzJlsTs378fQ4YMQefOnVGhQgVkZmZi1apVMDY2RseOHQG8SZK+++47hIaG4v79+2jXrh1sbGwQGRmJTZs24YsvvsjXHdCpiNLLWizKN+9a7g1ALFu2TH2tlOXeQrxZOhsaGiq8vLyEmZmZcHBwEPXr1xezZs3SWDabmZkpfvzxR1GpUiVhZmYmSpUqJVq0aCEiIiLU19y4cUN8/PHHwtLSUgB479Lvp0+fir59+woHBwdhZmYmfHx8NNryvjbl5tGjR2LAgAHC1dVVmJiYCHt7e9G6dWtx8uTJbNcGBASIqlWrZivv3bt3tuXA2sSbtdz7xx9/FLNnzxZubm7C3NxcNGrUSFy8eFF93fPnz8XgwYNFpUqVhJWVlbCzsxN+fn5i3bp12WI5cOCACAoKEnZ2dsLCwkKUL19e9OnTR5w9e1YjXisrq3fel2+//VYAEF5eXrleo81nCSHEX3/9JSpXrizMzc1FlSpVxMaNG3O8Z++yYcMG0axZM2Fvby9MTEyEs7Oz6Nq1qzh48KDGdXfv3hWdOnUSxYsXFxYWFqJu3bpi69at2eJGDsuQs/7fOXPmjEZ5WFiYACCePXumLsvp/5G3f57afN7atWtFrVq1hLm5ubC3txc9e/bUWPYvRO4/q6yYtKXN/UtNTRWjRo0Szs7OwtLSUjRo0ECcOHFCBAQEiICAAI367t69KwIDA4W5ublwdHQU48ePF3v27NFY7n3v3j3Rr18/Ub58eWFhYSHs7e1F48aNxd69e7PF99dff4mGDRsKKysrYWVlJSpVqiQGDx4sbt68qXUbibIohMjnWY9EREREH0jB6JslIiIikgETGyIiIioymNgQERFRkcHEhoiIiNQOHz6Mzz77DC4uLlAoFNi8ebP6XEZGhvrBv1ZWVnBxcUFwcDCePHmic51ZkpOTMWTIEJQpUwaWlpaoUqUKlixZIjl+JjZERESklpKSgho1amDRokXZzr169Qrnzp3DxIkTce7cOWzcuBE3b95EmzZtdK4zS0hICHbu3Ik//vgD169fx4gRIzBkyBBs2bJFUvxcFUVEREQ5UigU2LRp0zsfy3HmzBnUrVsXDx48yPYMNSl1VqtWDV27dsXEiRPVZbVr10aLFi3w3XffaR0zN+jTkUqlwpMnT2BjY6PzU6uJiKjwEUIgKSkJLi4u+bbxY2pqKtLT02WrTwiR7XeVubk5zM3N81x3QkICFAoFihcvnqd66tevjy1btqBfv35wcXHBwYMHcevWLcydO1daRfrcRKcwe/jw4Ts3wuPBgwcPHkX7ePjwYb78fnn9+rVwKm0sa6zW1tbZysLCwt4bCwCxadOmd8b60UcfiR49emjdvtzqTE1NFcHBwQKAMDExEWZmZmLFihVa15uFPTY6ytoWv1r3STA2s3jP1YVfiWsp+g6hQDCJjNZ3CKSjzOd87hDJIxMZOIrtOj/O5X3S09MRE6tEZIQ7bG3y3iOUmKSCZ+0HePjwIWxtbdXlee2tycjIQJcuXSCEwOLFi/MaJn766SecPHkSW7Zsgbu7Ow4fPozBgwfDxcVF0sN9mdjoKKtLz9jMwiASGxMTpb5DKBBMjMz0HQLpSmGq7wioqBBv/pPf0xBsbYxkSWzU9dnaaiQ2eZGV1Dx48AD79+/Pc72vX7/G+PHjsWnTJvWDYKtXr44LFy5g1qxZTGyIiIgKO6VQQSnkqUdOWUnN7du3ceDAAZQsWVKWOjMyMrLNWTI2NoZKJS1+JjZERESklpycjDt37qhfR0ZG4sKFC7C3t4ezszM6deqEc+fOYevWrVAqlYiJiQEA2Nvbw8zsTa92kyZN0L59ewwZMuS9dZYtWxa2trYICAjAmDFjYGlpCXd3dxw6dAgrV67EnDlzJMXPxIaIiKgAUkFAhbx32Uit4+zZs2jcuLH6dUhICACgd+/emDx5snpfmZo1a2q878CBA/jkk08AAHfv3sXz58+1qnP58uUAgDVr1iA0NBQ9e/ZEXFwc3N3d8f333+Orr76SFD8TGyIiogJIBRXkGESSWssnn3wC8Y4t7t51Lsv9+/cl1QkATk5OWLZsmVYxvgt3HiYiIqIigz02REREBZBSCChleDiAHHUUJkxsiIiICiB9zbEp7DgURUREREUGe2yIiIgKIBUElOyxkYyJDRERUQHEoSjdcCiKiIiIigz22BARERVAXBWlG/bYEBERUZHBHhsiIqICSPXfQ456DAkTGyIiogJIKdOqKDnqKEw4FEVERERFBntsiIiICiCleHPIUY8hYWJDRERUAHGOjW44FEVERERFBntsiIiICiAVFFBCIUs9hoSJDRERUQGkEm8OOeoxJByKIiIioiKDPTZEREQFkFKmoSg56ihMmNgQEREVQExsdMOhKCIiIioy2GNDRERUAKmEAiohw6ooGeooTNhjQ0REREUGe2yIiIgKIM6x0Q0TGyIiogJICSMoZRhYUcoQS2HCoSgiIiIqMthjQ0REVAAJmSYPCwObPMzEhoiIqADiHBvdcCiKiIiIigz22BARERVASmEEpZBh8rCBPQSTiQ0REVEBpIICKhkGVlQwrMyGQ1FERERUZLDHhoiIqADi5GHdsMeGiIiIigz22BARERVA8k0eNqw5NkxsiIiICqA3k4dleLo3h6KIiIiICif22BARERVAKpkegmloy72Z2BARERVAnGOjGw5FERERUZHBHhsiIqICSAUj7jysAyY2REREBZBSKKAUMmzQJ0MdhQmHooiIiKjIYI8NERFRAaSUaVWUkkNRREREpG8qYQSVDKuiVFwVRURERFQ4sceGiIioAOJQlG7YY0NERERFBntsiIiICiAV5Fmqrcp7KIUKExsiIqICSL4N+gxrcMawWktERERFGntsiIiICiD5HoJpWH0YTGyIiIgKIBUUUEGOOTZ8pAIRERFRocQeGyIiogKIQ1G6YWJDRERUAMm3QZ9hJTaG1VoiIiIq0thjQ0REVACphAIqOTbok6GOwoQ9NkRERFRksMeGiIioAFLJNMfG0HYeZmJDRERUAKmEEVQyrGiSo47CRHJrjY2NERsbm638xYsXMDY2liUoIiIiIl1I7rERQuRYnpaWBjMzszwHRERERIASCihl2DVYjjoKE60TmwULFgAAFAoFfv31V1hbW6vPKZVKHD58GJUqVZI/QiIiIgPEoSjdaJ3YzJ07F8CbHpslS5ZoDDuZmZnBw8MDS5YskT9CIiIiIi1pndhERkYCABo3boyNGzeiRIkS+RYUERGRoVNCnmEkZd5DKVQk908dOHBA9qRm0aJF8PDwgIWFBfz8/HD69Ol3Xr9+/XpUqlQJFhYW8PHxwfbt29XnMjIyMHbsWPj4+MDKygouLi4IDg7GkydPNOqIi4tDz549YWtri+LFi6N///5ITk6WtV1ERES6yhqKkuMwJJJbq1Qq8dtvv6FHjx4IDAzEp59+qnFItXbtWoSEhCAsLAznzp1DjRo1EBQUlOPKKwA4fvw4unfvjv79++P8+fNo164d2rVrhytXrgAAXr16hXPnzmHixIk4d+4cNm7ciJs3b6JNmzYa9fTs2RNXr17Fnj17sHXrVhw+fBhffPGF5PiJiIio4FCI3JY55WLIkCFYvnw5WrVqBWdnZygUmt1kWXNxtOXn54c6depg4cKFAACVSgU3NzcMHToU48aNy3Z9165dkZKSgq1bt6rL6tWrh5o1a+Y6x+fMmTOoW7cuHjx4gLJly+L69euoUqUKzpw5A19fXwDAzp070bJlSzx69AguLi7vjTsxMRF2dnao0Xs6jM0sJLW5MLK/kqLvEAoEk7uP9R0C6Sjz2XN9h0BFRKbIwEH8jYSEBNja2spef9bvl9ATzWFhbZrn+lKTMxDuvzPf4i1oJC/3XrNmDdatW4eWLVvm+cPT09MRERGB0NBQdZmRkRECAwNx4sSJHN9z4sQJhISEaJQFBQVh8+bNuX5OQkICFAoFihcvrq6jePHi6qQGAAIDA2FkZIRTp06hffv22epIS0tDWlqa+nViYqI2TSQiItKJgAIqGebYCANb7i15KMrMzAxeXl6yfPjz58+hVCrh6OioUe7o6IiYmJgc3xMTEyPp+tTUVIwdOxbdu3dXZ6oxMTEoXbq0xnUmJiawt7fPtZ7w8HDY2dmpDzc3N63aSERERB+O5MRm1KhRmD9/fq4b9RUkGRkZ6NKlC4QQWLx4cZ7qCg0NRUJCgvp4+PChTFESERFlpxRGsh2GRPJQ1NGjR3HgwAHs2LEDVatWhamp5vjfxo0bta7LwcEBxsbGePr0qUb506dP4eTklON7nJyctLo+K6l58OAB9u/frzGu6OTklG1ycmZmJuLi4nL9XHNzc5ibm2vdNiIiIvrwJKdxxYsXR/v27REQEAAHBweN4Rk7OztJdZmZmaF27drYt2+fukylUmHfvn3w9/fP8T3+/v4a1wPAnj17NK7PSmpu376NvXv3omTJktnqiI+PR0REhLps//79UKlU8PPzk9QGIiKi/KASCtkOQyK5x2bZsmWyBhASEoLevXvD19cXdevWxbx585CSkoK+ffsCAIKDg+Hq6orw8HAAwPDhwxEQEIDZs2ejVatWWLNmDc6ePYulS5cCeJPUdOrUCefOncPWrVuhVCrV82bs7e1hZmaGypUro3nz5hg4cCCWLFmCjIwMDBkyBN26ddNqRRQREVF+U8IISun9DznWY0gkJzbAm2GbgwcP4u7du+jRowdsbGzw5MkT2NraajxDShtdu3bFs2fPMGnSJMTExKBmzZrYuXOneoJwVFQUjIz+90OpX78+Vq9ejQkTJmD8+PHw9vbG5s2bUa1aNQDA48ePsWXLFgBAzZo1NT7rwIED+OSTTwAAf/75J4YMGYImTZrAyMgIHTt2VD8Pi4iIiAonyfvYPHjwAM2bN0dUVBTS0tJw69YtlCtXDsOHD0daWprBPC+K+9gYJu5jU3hxHxuSy4fax2bY0bYwl2Efm7TkDCxomH/xFjSS+6eGDx8OX19fvHz5EpaWlury9u3bZ5v7QkRERLpRwUi2w5BIHoo6cuQIjh8/DjMzM41yDw8PPH7Mf80SERGR/khObFQqFZTK7M8KffToEWxsbGQJioiIyNAphQJKGVY0yVFHYSK5f6pZs2aYN2+e+rVCoUBycjLCwsJkecwCERERcbm3riT32MyePRtBQUGoUqUKUlNT0aNHD9y+fRsODg74z3/+kx8xEhEREWlFcmJTpkwZXLx4EWvWrMGlS5eQnJyM/v37o2fPnhqTiYmIiEh3QhhBJcPjEAQfqaDFm0xM0KtXL7ljISIiIsoTnRKbJ0+e4OjRo4iNjYVKpdI4N2zYMFkCIyIiMmRKKKCEDJOHZaijMJGc2CxfvhxffvklzMzMULJkSSgU/7thCoWCiQ0REZEMVAKyTPxVSdqGt/CTnNhMnDgRkyZNQmhoqMajDoiIiIj0TXJi8+rVK3Tr1o1JDRERUT5SyTR5WI46ChPJre3fvz/Wr1+fH7EQERHRf6mgkO0wJJJ7bMLDw9G6dWvs3LkTPj4+MDXVfEDXnDlzZAuOiIiISAqdEptdu3ahYsWKAJBt8jARERHlHR+poBuddh7+/fff0adPn3wIh4iIiADOsdGV5Naam5ujQYMG+RELERERUZ5ITmyGDx+On376KT9iISIiov9SQaaHYHLy8LudPn0a+/fvx9atW1G1atVsk4c3btwoW3BERESGSsi0okkwsXm34sWLo0OHDvkRCxEREVGeSE5sli1blh9xEBER0VuyhpLkqMeQSJ5j8+mnnyI+Pj5beWJiIj799FM5YiIiIiLSieQem4MHDyI9PT1beWpqKo4cOSJLUERERIaOy711o3Vic+nSJfWfr127hpiYGPVrpVKJnTt3wtXVVd7oiIiIDBSHonSjdWJTs2ZNKBQKKBSKHIecLC0tuQyciIiI9ErrxCYyMhJCCJQrVw6nT59GqVKl1OfMzMxQunRpGBsb50uQREREhkauB1hyH5tcuLu7AwBUKlW+BUNERERvcChKN5InD2e5du0aoqKisk0kbtOmTZ6DIiIiItKF5MTm3r17aN++PS5fvgyFQgEhBID/PdlbqVTKGyEREZEBYo+NbnR6VpSnpydiY2NRrFgxXL16FYcPH4avry8OHjyYDyESEREZHlmeEyVTclSYSO6xOXHiBPbv3w8HBwcYGRnByMgIDRs2RHh4OIYNG4bz58/nR5xERERE7yW5x0apVMLGxgYA4ODggCdPngB4M7n45s2b8kZHRERkoNhjoxvJPTbVqlXDxYsX4enpCT8/P8ycORNmZmZYunQpypUrlx8xEhEREWlFcmIzYcIEpKSkAACmTp2K1q1bo1GjRihZsiTWrl0re4BERESGSECePWhE3kMpVCQnNkFBQeo/e3l54caNG4iLi0OJEiXUK6OIiIgob7gqSjc6Pxnrzp072LVrF16/fg17e3s5YyIiIiLSieTE5sWLF2jSpAkqVKiAli1bIjo6GgDQv39/jBo1SvYAiYiIDBEnD+tGcmIzcuRImJqaIioqCsWKFVOXd+3aFTt37pQ1OCIiIkPFxEY3kufY7N69G7t27UKZMmU0yr29vfHgwQPZAiMiIiKSSnJik5KSotFTkyUuLg7m5uayBEVERGToOHlYN5KHoho1aoSVK1eqXysUCqhUKsycORONGzeWNTgiIiJDJYRCtsOQSO6xmTlzJpo0aYKzZ88iPT0d33zzDa5evYq4uDgcO3YsP2IkIiIi0orkHptq1arh1q1baNiwIdq2bYuUlBR06NAB58+fR/ny5fMjRiIiIoOjgkK2w5BI6rHJyMhA8+bNsWTJEnz77bf5FRMREZHB4xwb3UjqsTE1NcWlS5fyKxYiIiKiPJE8FNWrVy/89ttv+RELERER/RcnD+tG8uThzMxM/P7779i7dy9q164NKysrjfNz5syRLTgiIiIiKbRObIyNjREdHY0rV67go48+AgDcunVL4xo+BJOIiEgenGOjG60TGyHePPj8wIED+RYMERERvSHXMJKhDUXp/HRvIiIiooJG0hybX3/9FdbW1u+8ZtiwYXkKiIiIiN70tMgxjGRoPTaSEpslS5bA2Ng41/MKhYKJDRERkQwEgP/OAslzPYZEUmJz9uxZlC5dOr9iISIiIsoTrRMbrngiIiL6cFRQQCHD4xD4SIVcCDn6w4iIiEgrXBWlG61XRYWFhb134jARERGRPmndYxMWFpafcRAREdFbVEIBBTfok4z72BAREVGRIflZUURERJT/hJBpubeBTZFlYkNERFQAcfKwbjgURUREREWG5MTm6dOn+Pzzz+Hi4gITExMYGxtrHERERJR3WT02chyGRPJQVJ8+fRAVFYWJEyfC2dmZG/cRERHlA66K0o3kxObo0aM4cuQIatasmQ/hEBEREelOcmLj5ubGXYiJiIjyGVdF6UbyHJt58+Zh3LhxuH//fj6EQ0REREBWYiPHHBt9t+TDktxj07VrV7x69Qrly5dHsWLFYGpqqnE+Li5OtuCIiIiIpJCc2MybNy8fwiAiIqK3cR8b3UhObHr37p0fcRAREdFbxH8POeoxJDpt0KdUKvHXX3/hu+++w3fffYdNmzZBqVTKHRsRERF9YElJSRgxYgTc3d1haWmJ+vXr48yZM7lev3HjRjRt2hSlSpWCra0t/P39sWvXLo1rFi9ejOrVq8PW1lZ9zY4dO/IlfsmJzZ07d1C5cmUEBwdj48aN2LhxI3r16oWqVavi7t27+REjERGRwdHXBn0DBgzAnj17sGrVKly+fBnNmjVDYGAgHj9+nOP1hw8fRtOmTbF9+3ZERESgcePG+Oyzz3D+/Hn1NWXKlMGMGTMQERGBs2fP4tNPP0Xbtm1x9erVPN2jnCiExLXbLVu2hBACf/75J+zt7QEAL168QK9evWBkZIRt27bJHmRBlJiYCDs7O3j8PgFGxSz0HU6+Uzwopu8QCoRiMfqOgHSVaq/vCKioUKam4t7345GQkABbW1vZ68/6/VJu5XgYy/D7RfkqFfeCp2sV7+vXr2FjY4O///4brVq1UpfXrl0bLVq0wHfffafVZ1atWhVdu3bFpEmTcr3G3t4eP/74I/r3769dQ7QkeY7NoUOHcPLkSXVSAwAlS5bEjBkz0KBBA1mDIyIiMlgyT7JJTEzUKDY3N4e5ublGWWZmJpRKJSwsNBMqS0tLHD16VKuPU6lUSEpK0sgT3qZUKrF+/XqkpKTA399fy0ZoT/JQlLm5OZKSkrKVJycnw8zMTJagiIiIDJ5cw1D/HYpyc3ODnZ2d+ggPD8/2kTY2NvD398e0adPw5MkTKJVK/PHHHzhx4gSio6O1CnvWrFlITk5Gly5dNMovX74Ma2trmJub46uvvsKmTZtQpUqVvN+nf5Gc2LRu3RpffPEFTp06BSEEhBA4efIkvvrqK7Rp00b2AImIiCjvHj58iISEBPURGhqa43WrVq2CEAKurq4wNzfHggUL0L17dxgZvT9lWL16NaZMmYJ169ahdOnSGucqVqyICxcu4NSpUxg0aBB69+6Na9euydK2t0lObBYsWIDy5cvD398fFhYWsLCwQIMGDeDl5YX58+fLHiAREZEhynqkghwHAPWKpKzj38NQWcqXL49Dhw4hOTkZDx8+xOnTp5GRkYFy5cq9M941a9ZgwIABWLduHQIDA7OdNzMzg5eXF2rXro3w8HDUqFEjX/IGyXNsihcvjr///ht37tzB9evXAQCVK1eGl5eX7MEREREZKn1v0GdlZQUrKyu8fPkSu3btwsyZM3O99j//+Q/69euHNWvWaEw6fheVSoW0tDSdYnsXyYlNFi8vLyYzRERERcyuXbsghEDFihVx584djBkzBpUqVULfvn0BAKGhoXj8+DFWrlwJ4M3wU+/evTF//nz4+fkhJubN8lFLS0vY2dmp39OiRQuULVsWSUlJWL16NQ4ePJhtvxs56LRBHxEREeWzrIm/chwSJCQkYPDgwahUqRKCg4PRsGFD7Nq1S/1syOjoaERFRamvX7p0KTIzMzF48GA4Ozurj+HDh6uviY2NRXBwMCpWrIgmTZrgzJkz2LVrF5o2bSrPvXqLzj02RERElH/enh+T13qk6NKlS7YVTW9bvny5xuuDBw++t87ffvtNWhB5wB4bIiIiKjIkJzZRUVHIabNiIYRG1xQRERHlgZDxMCCSExtPT088e/YsW3lcXBw8PT1lCYqIiIhIF5Ln2AghoFBkn4iUnJycbQtmIiIi0o2+l3sXVlonNiEhIQAAhUKBiRMnolix/z0UUalU4tSpU6hZs6bsARIRERksAxtGkoPWiU3W48eFELh8+bLGc6HMzMxQo0YNjB49Wv4IiYiIiLSkdWJz4MABAEDfvn0xf/78fHlUOxEREb3BoSjdSJ5js2zZsvyIg4iIiN4m14omAxvOkpzYpKSkYMaMGdi3bx9iY2OhUqk0zt+7d0+24IiIiIikkJzYDBgwAIcOHcLnn38OZ2fnHFdIERERUV4p/nvIUY/hkJzY7NixA9u2bUODBg3yIx4iIiICOBSlI8kb9JUoUQL29vb5EQsRERFRnkhObKZNm4ZJkybh1atX+REPERERAXykgo4kD0XNnj0bd+/ehaOjIzw8PNSPMc9y7tw52YIjIiIyWELx5pCjHgMiObFp165dPoRBRERElHeSE5uwsLD8iIOIiIjeIsSbQ456DInkOTZEREREBZXkHhulUom5c+di3bp1iIqKQnp6usb5uLg42YIjIiIyWFzurRPJPTZTpkzBnDlz0LVrVyQkJCAkJAQdOnSAkZERJk+enA8hEhERGaCsycNyHAZEcmLz559/4v/+7/8watQomJiYoHv37vj1118xadIknDx5Mj9iJCIiItKK5MQmJiYGPj4+AABra2skJCQAAFq3bo1t27bJGx0REZGBUgj5DkMiObEpU6YMoqOjAQDly5fH7t27AQBnzpyBubm5vNEREREZKm7QpxPJiU379u2xb98+AMDQoUMxceJEeHt7Izg4GP369ZM9QCIiIiJtSV4VNWPGDPWfu3btCnd3dxw/fhze3t747LPPZA2OiIjIYHHnYZ1ITmwOHz6M+vXrw8TkzVvr1auHevXqITMzE4cPH8bHH38se5BEREQGh8u9dSJ5KKpx48Y57lWTkJCAxo0byxIUERERkS4k99gIIaBQZO/WevHiBaysrGQJioiIyOCxx0YnWic2HTp0AAAoFAr06dNHYwWUUqnEpUuXUL9+ffkjJCIiItKS1omNnZ0dgDc9NjY2NrC0tFSfMzMzQ7169TBw4ED5IyQiIjJE7LHRidaJzbJlywAAHh4eGD16NIediIiI8hNXRelE8uThb775RmOOzYMHDzBv3jz1Rn1ERERE+iI5sWnbti1WrlwJAIiPj0fdunUxe/ZstG3bFosXL5Y9QCIiIkPERyroRnJic+7cOTRq1AgAsGHDBjg5OeHBgwdYuXIlFixYIHuAREREBomPVNCJ5MTm1atXsLGxAQDs3r0bHTp0gJGREerVq4cHDx7IHiARERGRtiQnNl5eXti8eTMePnyIXbt2oVmzZgCA2NhY2Nrayh4gERERkbYkJzaTJk3C6NGj4eHhAT8/P/j7+wN403tTq1Yt2QMkIiIyRArINMdG3w35wCTvPNypUyc0bNgQ0dHRqFGjhrq8SZMmaN++vazBEREREUkhObEBACcnJzg5OWmU1a1bV5aAiIiICNzHRkeSE5uUlBTMmDED+/btQ2xsLFQqlcb5e/fuyRYcERGRweLOwzqRnNgMGDAAhw4dwueffw5nZ+ccH4hJREREpA+SE5sdO3Zg27ZtaNCgQX7EQ0RERAB7bHQkeVVUiRIlYG9vnx+xEBEREeWJ5MRm2rRpmDRpEl69epUf8RARERH4SAVdSR6Kmj17Nu7evQtHR0d4eHjA1NRU4/y5c+dkC46IiMhgcShKJ5ITm3bt2uVDGERERER5JzmxCQsLy484iIiI6G3ssdGJThv0AUBERASuX78OAKhatSofp0BERCQjuebHcI7Ne8TGxqJbt244ePAgihcvDgCIj49H48aNsWbNGpQqVUruGImIiIi0InlV1NChQ5GUlISrV68iLi4OcXFxuHLlChITEzFs2LD8iJGIiMjwZD1SQY7DgEjusdm5cyf27t2LypUrq8uqVKmCRYsWoVmzZrIGR0REZLA4x0YnkntsVCpVtiXeAGBqaprtuVFEREREH5LkxObTTz/F8OHD8eTJE3XZ48ePMXLkSDRp0kTW4IiIiAwVN+jTjeTEZuHChUhMTISHhwfKly+P8uXLw9PTE4mJifjpp5/yI0YiIiIirUieY+Pm5oZz585h7969uHHjBgCgcuXKCAwMlD04IiIig8U5NjrRaR8bhUKBpk2bomnTpnLHQ0RERAAg1zCSgSU2Wg9F7d+/H1WqVEFiYmK2cwkJCahatSqOHDkia3BEREREUmid2MybNw8DBw6Era1ttnN2dnb48ssvMWfOHFmDIyIiMlhCxsOAaJ3YXLx4Ec2bN8/1fLNmzRARESFLUERERAaPiY1OtE5snj59muP+NVlMTEzw7NkzWYIiIiIi0oXWiY2rqyuuXLmS6/lLly7B2dlZlqCIiIgMHfex0Y3WiU3Lli0xceJEpKamZjv3+vVrhIWFoXXr1rIGR0RERCSF1su9J0yYgI0bN6JChQoYMmQIKlasCAC4ceMGFi1aBKVSiW+//TbfAiUiIiJ6H60TG0dHRxw/fhyDBg1CaGgohHjTt6VQKBAUFIRFixbB0dEx3wIlIiIyKNygTyeSNuhzd3fH9u3b8fLlS9y5cwdCCHh7e6NEiRL5FR8REZFBkmt+DOfYaKFEiRKoU6cO6tatm+ekZtGiRfDw8ICFhQX8/Pxw+vTpd16/fv16VKpUCRYWFvDx8cH27ds1zm/cuBHNmjVDyZIloVAocOHChWx1pKamYvDgwShZsiSsra3RsWNHPH36NE/tICIiIv3TKbGRy9q1axESEoKwsDCcO3cONWrUQFBQEGJjY3O8/vjx4+jevTv69++P8+fPo127dmjXrp3Gaq2UlBQ0bNgQP/zwQ66fO3LkSPzzzz9Yv349Dh06hCdPnqBDhw6yt4+IiChPuIeNZAqRNVlGD/z8/FCnTh0sXLgQAKBSqeDm5oahQ4di3Lhx2a7v2rUrUlJSsHXrVnVZvXr1ULNmTSxZskTj2vv378PT0xPnz59HzZo11eUJCQkoVaoUVq9ejU6dOgF4MwG6cuXKOHHiBOrVq6dV7ImJibCzs4PH7xNgVMxCatMLHcWDYvoOoUAoFqPvCEhXqfb6joCKCmVqKu59Px4JCQk57safV1m/X7zGTYexed5/vyjTUnFnRv7FW9DorccmPT0dERERGk8FNzIyQmBgIE6cOJHje06cOJHtKeJBQUG5Xp+TiIgIZGRkaNRTqVIllC1b9p31pKWlITExUeMgIiLKN9x5WCeSEpuMjAz069cPkZGRef7g58+fQ6lUZltJ5ejoiJiYnP9ZHBMTI+n63OowMzND8eLFJdUTHh4OOzs79eHm5qb1ZxIREUnFDfp0IymxMTU1xV9//ZVfsRRooaGhSEhIUB8PHz7Ud0hERET0L5KHotq1a4fNmzfn+YMdHBxgbGycbTXS06dP4eTklON7nJycJF2fWx3p6emIj4+XVI+5uTlsbW01DiIionzDoSidSNrHBgC8vb0xdepUHDt2DLVr14aVlZXG+WHDhmlVj5mZGWrXro19+/ahXbt2AN5MHt63bx+GDBmS43v8/f2xb98+jBgxQl22Z88e+Pv7ax1/7dq1YWpqin379qFjx44AgJs3byIqKkpSPURERPmJ+9joRnJi89tvv6F48eKIiIhARESExjmFQqF1YgMAISEh6N27N3x9fVG3bl3MmzcPKSkp6Nu3LwAgODgYrq6uCA8PBwAMHz4cAQEBmD17Nlq1aoU1a9bg7NmzWLp0qbrOuLg4REVF4cmTJwDeJC3Am54aJycn2NnZoX///ggJCYG9vT1sbW0xdOhQ+Pv7a70iioiIiAomyYmNHBOHs3Tt2hXPnj3DpEmTEBMTg5o1a2Lnzp3qCcJRUVEwMvrfaFn9+vWxevVqTJgwAePHj4e3tzc2b96MatWqqa/ZsmWLOjECgG7dugEAwsLCMHnyZADA3LlzYWRkhI4dOyItLQ1BQUH4+eefZWsXERFRnvGRCjrR6z42hRn3sTFM3Mem8OI+NiSXD7WPTYUQ+faxuTXHcPaxkdxjAwCPHj3Cli1bEBUVhfT0dI1zc+bMkSUwIiIiIqkkJzb79u1DmzZtUK5cOdy4cQPVqlXD/fv3IYTARx99lB8xEhERGRxOHtaN5OXeoaGhGD16NC5fvgwLCwv89ddfePjwIQICAtC5c+f8iJGIiIhIK5ITm+vXryM4OBgAYGJigtevX8Pa2hpTp05954MniYiISALuY6MTyYmNlZWVel6Ns7Mz7t69qz73/Plz+SIjIiIyZExsdCJ5jk29evVw9OhRVK5cGS1btsSoUaNw+fJlbNy4kfvAEBERkV5JTmzmzJmD5ORkAMCUKVOQnJyMtWvXwtvbmyuiiIiIZMLJw7qRnNiUK1dO/WcrKyssWbJE1oCIiIgI3KBPR5Ln2ABAfHw8fv31V4SGhiIuLg4AcO7cOTx+/FjW4IiIiIikkNxjc+nSJQQGBsLOzg7379/HwIEDYW9vj40bNyIqKgorV67MjziJiIgMCoeidCO5xyYkJAR9+vTB7du3YWHxv62eW7ZsicOHD8saHBERkcHiqiidSE5szpw5gy+//DJbuaurK2Ji+CAdIiIi0h/JQ1Hm5uZITEzMVn7r1i2UKlVKlqCIiIgMHicP60Ryj02bNm0wdepUZGRkAAAUCgWioqIwduxYdOzYUfYAiYiIDJFCxsOQSE5sZs+ejeTkZJQuXRqvX79GQEAAvLy8YGNjg++//z4/YiQiIiLSiuShKDs7O+zZswdHjx7FpUuXkJycjI8++giBgYH5ER8REZFh4lCUTiQnNlkaNmyIhg0byhkLERERUZ7olNjs27cP+/btQ2xsLFQqlca533//XZbAiIiIDBn3sdGN5MRmypQpmDp1Knx9feHs7AyFwtCmJREREX0AHIrSieTEZsmSJVi+fDk+//zz/IiHiIiISGeSE5v09HTUr18/P2IhIiKitxlYb4scJC/3HjBgAFavXp0fsRAREdF/Zc2xkeMwJJJ7bFJTU7F06VLs3bsX1atXh6mpqcb5OXPmyBYcERERkRQ6Pd27Zs2aAIArV65onONEYiIiIplw8rBOJCc2Bw4cyI84iIiI6C1c7q0byXNsiIiIiAoqrXpsOnTogOXLl8PW1hYdOnR457UbN26UJTAiIiKDxqEonWiV2NjZ2annz9jZ2eVrQERERES60iqxWbZsWY5/JiIiovzBOTa6kW2OzaVLl2BmZiZXdURERIZNyHgYENkSGyEEMjMz5aqOiIiISDKdnu6dG0Pcx2Z27XWwsjHWdxj57nYNJ32HUCBcSC6r7xBIR49fcX4gySMzJQ33PsQHcfKwTmRNbIiIiEgenGOjG60Tm8TExHeeT0pKynMwRERERHmhdWJTvHjxdw41CSEMciiKiIgoX3AoSidaJzZ8lAIREdGHoxACCpH3rESOOgoTrRObgICA/IyDiIiIKM84eZiIiKgg4lCUTpjYEBERFUBcFaUbPt2biIiIigytEptLly5BpVLldyxERESUhY9U0IlWiU2tWrXw/PlzAEC5cuXw4sWLfA2KiIiISBdaJTbFixdHZGQkAOD+/fvsvSEiIspnWXNs5DgMiVaThzt27IiAgAA4OztDoVDA19cXxsY5Px/p3r0P8gQNIiKioo2ronSiVWKzdOlSdOjQAXfu3MGwYcMwcOBA2NjY5HdsRERERJJovdy7efPmAICIiAgMHz6ciQ0REVE+4nJv3Ujex2bZsmXqPz969AgAUKZMGfkiIiIiIg5F6UjyPjYqlQpTp06FnZ0d3N3d4e7ujuLFi2PatGmcVExERER6JbnH5ttvv8Vvv/2GGTNmoEGDBgCAo0ePYvLkyUhNTcX3338ve5BERESGyNCGkeQgObFZsWIFfv31V7Rp00ZdVr16dbi6uuLrr79mYkNERCQHId4cctRjQCQPRcXFxaFSpUrZyitVqoS4uDhZgiIiIiLSheTEpkaNGli4cGG28oULF6JGjRqyBEVERGTouEGfbiQPRc2cOROtWrXC3r174e/vDwA4ceIEHj58iO3bt8seIBEREZG2JPfYBAQE4NatW2jfvj3i4+MRHx+PDh064ObNm2jUqFF+xEhERGR4+BBMnUjusQEAFxcXThImIiLKRwrVm0OOegyJ5B4bIiIiooJKpx4bIiIiymfceVgnTGyIiIgKID4rSjcciiIiIqIiQ6fEJjMzE3v37sUvv/yCpKQkAMCTJ0+QnJwsa3BEREQGK2vnYTkOAyJ5KOrBgwdo3rw5oqKikJaWhqZNm8LGxgY//PAD0tLSsGTJkvyIk4iIyKBwKEo3kntshg8fDl9fX7x8+RKWlpbq8vbt22Pfvn2yBkdEREQkheQemyNHjuD48eMwMzPTKPfw8MDjx49lC4yIiMigcVWUTiQnNiqVCkqlMlv5o0ePYGNjI0tQREREho5DUbqRPBTVrFkzzJs3T/1aoVAgOTkZYWFhaNmypZyxEREREUkiucdm9uzZCAoKQpUqVZCamooePXrg9u3bcHBwwH/+85/8iJGIiMjwyLWiycBWRUnusSlTpgwuXryI8ePHY+TIkahVqxZmzJiB8+fPo3Tp0joFsWjRInh4eMDCwgJ+fn44ffr0O69fv349KlWqBAsLC/j4+GR7qrgQApMmTYKzszMsLS0RGBiI27dva1zj4eEBhUKhccyYMUOn+ImIiKhg0GnnYRMTE/Tq1UuWANauXYuQkBAsWbIEfn5+mDdvHoKCgnDz5s0cE6Xjx4+je/fuCA8PR+vWrbF69Wq0a9cO586dQ7Vq1QAAM2fOxIIFC7BixQp4enpi4sSJCAoKwrVr12BhYaGua+rUqRg4cKD6NecIERFRQcE5NrqRnNisXLnyneeDg4Ml1TdnzhwMHDgQffv2BQAsWbIE27Ztw++//45x48Zlu37+/Plo3rw5xowZAwCYNm0a9uzZg4ULF2LJkiUQQmDevHmYMGEC2rZtq47Z0dERmzdvRrdu3dR12djYwMnJSVK8REREHwRXRelEcmIzfPhwjdcZGRl49eoVzMzMUKxYMUmJTXp6OiIiIhAaGqouMzIyQmBgIE6cOJHje06cOIGQkBCNsqCgIGzevBkAEBkZiZiYGAQGBqrP29nZwc/PDydOnNBIbGbMmIFp06ahbNmy6NGjB0aOHAkTk5xvSVpaGtLS0tSvExMTtW4nERERfRiSE5uXL19mK7t9+zYGDRqk7kXR1vPnz6FUKuHo6KhR7ujoiBs3buT4npiYmByvj4mJUZ/PKsvtGgAYNmwYPvroI9jb2+P48eMIDQ1FdHQ05syZk+PnhoeHY8qUKZLaR0REpCsORelGlqd7e3t7Y8aMGejVq1euCUlB83avT/Xq1WFmZoYvv/wS4eHhMDc3z3Z9aGioxnsSExPh5ub2QWIlIiIDpBJvDjnqMSCyPd3bxMQET548kfQeBwcHGBsb4+nTpxrlT58+zXXui5OT0zuvz/qvlDoBwM/PD5mZmbh//36O583NzWFra6txEBERUcEiucdmy5YtGq+FEIiOjsbChQvRoEEDSXWZmZmhdu3a2LdvH9q1awfgzc7G+/btw5AhQ3J8j7+/P/bt24cRI0aoy/bs2QN/f38AgKenJ5ycnLBv3z7UrFkTwJvelVOnTmHQoEG5xnLhwgUYGRnpvGSdiIhIVpw8rBPJiU1WApJFoVCgVKlS+PTTTzF79mzJAYSEhKB3797w9fVF3bp1MW/ePKSkpKhXSQUHB8PV1RXh4eEA3kxeDggIwOzZs9GqVSusWbMGZ8+exdKlS9XxjBgxAt999x28vb3Vy71dXFzUsZ84cQKnTp1C48aNYWNjgxMnTmDkyJHo1asXSpQoIbkNREREclNApjk2ea+iUNHpWVFy6tq1K549e4ZJkyYhJiYGNWvWxM6dO9WTf6OiomBk9L8Rs/r162P16tWYMGECxo8fD29vb2zevFm9hw0AfPPNN0hJScEXX3yB+Ph4NGzYEDt37lTvYWNubo41a9Zg8uTJSEtLg6enJ0aOHJlttRUREREVLgohDGyvZZkkJibCzs4Of12sACsbY32Hk+9up3G/HwC4kFxW3yGQjh6/stN3CFREZKakYW/LX5CQkJAv8y2zfr80aDIZJiYW73/De2RmpuLYvsn5Fm9Bo1WPjZSejNyWSxMREVHh8PjxY4wdOxY7duzAq1ev4OXlhWXLlsHX1zfH66OjozFq1CicPXsWd+7cwbBhwzQemA0Ay5cvV08zyWJubo7U1FRZY9cqsTl//rxWlSkUhjaSR0RElD/0tY/Ny5cv0aBBAzRu3Bg7duxAqVKlcPv27XfOQU1LS0OpUqUwYcIEzJ07N9frbG1tcfPmzf/Flg95g1aJzYEDB2T/YCIiInoHPa2K+uGHH+Dm5oZly5apyzw9Pd/5Hg8PD8yfPx8A8Pvvv+d6nUKhyPdHGcm2jw0REREVXImJiRrH248JetuWLVvg6+uLzp07o3Tp0qhVqxb+7//+T5YYkpOT4e7uDjc3N7Rt2xZXr16Vpd636bTz8NmzZ7Fu3TpERUUhPT1d49zGjRtlCYyIiMiQKYSAQob1PVl1/Hu3/LCwMEyePDnb9ffu3cPixYsREhKC8ePH48yZMxg2bBjMzMzQu3dvneOoWLEifv/9d1SvXh0JCQmYNWsW6tevj6tXr6JMmTI61/tvkhObNWvWIDg4GEFBQdi9ezeaNWuGW7du4enTp2jfvr1sgRERERk01X8POeoB8PDhQ41VUTk9Pgh4s62Lr68vpk+fDgCoVasWrly5giVLluQpsfH391dvpgu82b6lcuXK+OWXXzBt2jSd6/03yUNR06dPx9y5c/HPP//AzMwM8+fPx40bN9ClSxeULculsERERAXRvx8LlFti4+zsjCpVqmiUVa5cGVFRUbLGY2pqilq1auHOnTuy1is5sbl79y5atWoF4M0jEVJSUqBQKDBy5Ej17r9ERESUN1lDUXIcUjRo0EBj5RIA3Lp1C+7u7nI2D0qlEpcvX4azs7Os9UpObEqUKIGkpCQAgKurK65cuQIAiI+Px6tXr2QNjoiIyGAJGQ8JRo4ciZMnT2L69Om4c+cOVq9ejaVLl2Lw4MHqa0JDQxEcHKzxvgsXLuDChQtITk7Gs2fPcOHCBVy7dk19furUqdi9ezfu3buHc+fOoVevXnjw4AEGDBggLcD3kDzH5uOPP8aePXvg4+ODzp07Y/jw4di/fz/27NmDJk2ayBocERERfVh16tTBpk2bEBoaiqlTp8LT0xPz5s1Dz5491ddER0dnG5qqVauW+s8RERFYvXo13N3dcf/+fQBv9scZOHAgYmJiUKJECdSuXRvHjx/PNuyVV1o/UuHKlSuoVq0a4uLikJqaChcXF6hUKsycORPHjx+Ht7c3JkyYYDAPkeQjFQwTH6lQePGRCiSXD/VIhY8bTJTtkQqHj03jIxX+rXr16qhTpw4GDBiAbt26AQCMjIwwbty4fAuOiIjIUOlr5+HCTus5NocOHULVqlUxatQoODs7o3fv3jhy5Eh+xkZEREQkidaJTaNGjfD7778jOjoaP/30E+7fv4+AgABUqFABP/zwA2JiYvIzTiIiIsMihHyHAZG8KsrKygp9+/bFoUOHcOvWLXTu3BmLFi1C2bJl0aZNm/yIkYiIiEgrOj1SIYuXlxfGjx8Pd3d3hIaGYtu2bXLFRUREZNAUqjeHHPUYEp0Tm8OHD+P333/HX3/9BSMjI3Tp0gX9+/eXMzYiIiLDJdcwkoENRUlKbJ48eYLly5dj+fLluHPnDurXr48FCxagS5cusLKyyq8YiYiIiLSidWLTokUL7N27Fw4ODggODka/fv1QsWLF/IyNiIjIcOmwa3Cu9RgQrRMbU1NTbNiwAa1bt4axcdHfkI6IiEifdHnOU271GBKtE5stW7bkZxxEREREeZanVVFERESUTzh5WCdMbIiIiAoiAUCOpdqGldcwscmrTy0zYWtZ9L81n1jc13cIBUKm3V19h0BEepaYpIKLvoOgXDGxISIiKoA4eVg3kh+pQERERFRQsceGiIioIBKQafJw3qsoTJjYEBERFURcFaUTDkURERFRkcEeGyIiooJIBUAhUz0GhIkNERFRAcRVUbrhUBQREREVGeyxISIiKog4eVgnTGyIiIgKIiY2OuFQFBERERUZ7LEhIiIqiNhjoxMmNkRERAURl3vrhENRREREVGSwx4aIiKgA4j42umGPDRERERUZ7LEhIiIqiDh5WCdMbIiIiAoilQAUMiQlKsNKbDgURUREREUGe2yIiIgKIg5F6YSJDRERUYEkU2IDw0psOBRFRERERQZ7bIiIiAoiDkXphIkNERFRQaQSkGUYiauiiIiIiAon9tgQEREVREL15pCjHgOi9x6bRYsWwcPDAxYWFvDz88Pp06ffef369etRqVIlWFhYwMfHB9u3b9c4L4TApEmT4OzsDEtLSwQGBuL27dsa13z//feoX78+ihUrhuLFi8vdJCIiItITvSY2a9euRUhICMLCwnDu3DnUqFEDQUFBiI2NzfH648ePo3v37ujfvz/Onz+Pdu3aoV27drhy5Yr6mpkzZ2LBggVYsmQJTp06BSsrKwQFBSE1NVV9TXp6Ojp37oxBgwblexuJiIh0kjV5WI7DgCiE0F+L/fz8UKdOHSxcuBAAoFKp4ObmhqFDh2LcuHHZru/atStSUlKwdetWdVm9evVQs2ZNLFmyBEIIuLi4YNSoURg9ejQAICEhAY6Ojli+fDm6deumUd/y5csxYsQIxMfHS449MTERdnZ2eHmrHGxtjCW/v7DJEJn6DqFAyIRS3yEQkZ4lJqngUvEREhISYGtrK3/9//39Euj6FUyMzPNcX6YqDXsfL8m3eAsavfXYpKenIyIiAoGBgf8LxsgIgYGBOHHiRI7vOXHihMb1ABAUFKS+PjIyEjExMRrX2NnZwc/PL9c6tZWWlobExESNg4iIiAoWvSU2z58/h1KphKOjo0a5o6MjYmJicnxPTEzMO6/P+q+UOrUVHh4OOzs79eHm5pan+oiIiN6JQ1E60fvk4cIiNDQUCQkJ6uPhw4f6DomIiIoyAZkSG3035MPSW2Lj4OAAY2NjPH36VKP86dOncHJyyvE9Tk5O77w+679S6tSWubk5bG1tNQ4iIiIqWPSW2JiZmaF27drYt2+fukylUmHfvn3w9/fP8T3+/v4a1wPAnj171Nd7enrCyclJ45rExEScOnUq1zqJiIgKJA5F6USvG/SFhISgd+/e8PX1Rd26dTFv3jykpKSgb9++AIDg4GC4uroiPDwcADB8+HAEBARg9uzZaNWqFdasWYOzZ89i6dKlAACFQoERI0bgu+++g7e3Nzw9PTFx4kS4uLigXbt26s+NiopCXFwcoqKioFQqceHCBQCAl5cXrK2tP+g9ICIiypFKBUCGzfVUhrVBn14Tm65du+LZs2eYNGkSYmJiULNmTezcuVM9+TcqKgpGRv/rVKpfvz5Wr16NCRMmYPz48fD29sbmzZtRrVo19TXffPMNUlJS8MUXXyA+Ph4NGzbEzp07YWFhob5m0qRJWLFihfp1rVq1AAAHDhzAJ598ks+tJiIiovyi131sCjPuY2OYuI8NEX2wfWxK9YeJkVme68tUpWPvs98MZh8bPiuKiIioIJJrfoyB9V9wuTcREREVGeyxISIiKohUArJsQqNijw0RERFRocQeGyIiogJICBWEyPtSbTnqKEyY2BARERVEQsgzjMTJw0RERESFE3tsiIiICiIh0+RhA+uxYWJDRERUEKlUgEKG+TEGNseGQ1FERERUZLDHhoiIqCDiUJROmNgQEREVQEKlgpBhKMrQlntzKIqIiIiKDPbYEBERFUQcitIJe2yIiIioyGCPDRERUUGkEoCCPTZSMbEhIiIqiIQAIMc+NoaV2HAoioiIiIoM9tgQEREVQEIlIGQYihIG1mPDxIaIiKggEirIMxTFfWyIiIiIMGPGDCgUCowYMeKd18XHx2Pw4MFwdnaGubk5KlSogO3bt6vPL168GNWrV4etrS1sbW3h7++PHTt25EvM7LEhIiIqgPQ9FHXmzBn88ssvqF69+juvS09PR9OmTVG6dGls2LABrq6uePDgAYoXL66+pkyZMpgxYwa8vb0hhMCKFSvQtm1bnD9/HlWrVtUpvtwwsSEiIiqI9DgUlZycjJ49e+L//u//8N13373z2t9//x1xcXE4fvw4TE1NAQAeHh4a13z22Wcar7///nssXrwYJ0+eZGJTUGRlwInJhjF2mWFgY7S5yZTjLxkiKtSS/vv3fn5Pys1EhiwbD2ciAwCQmJioUW5ubg5zc/Mc3zN48GC0atUKgYGB701stmzZAn9/fwwePBh///03SpUqhR49emDs2LEwNjbOdr1SqcT69euRkpICf39/HVuVOyY2OkpKSgIAuH90X7+BEBGRXiQlJcHOzk72es3MzODk5ISjMdvff7GWrK2t4ebmplEWFhaGyZMnZ7t2zZo1OHfuHM6cOaNV3ffu3cP+/fvRs2dPbN++HXfu3MHXX3+NjIwMhIWFqa+7fPky/P39kZqaCmtra2zatAlVqlTJU7tyohCGtg5MJiqVCk+ePIGNjQ0UCsUH+czExES4ubnh4cOHsLW1/SCfqS+G1Na84H0iOfH7pB0hBJKSkuDi4gIjo/xZg5Oamor09HTZ6hNCZPtdlVOPzcOHD+Hr64s9e/ao59Z88sknqFmzJubNm5dj3RUqVEBqaioiIyPVPTRz5szBjz/+iOjoaPV16enpiIqKQkJCAjZs2IBff/0Vhw4dkj25YWJTiCQmJsLOzg4JCQlF/i8dQ2prXvA+kZz4faLNmzejffv2GkNISqUSCoUCRkZGSEtLyza8FBAQAFNTU+zdu1ddtmPHDrRs2RJpaWkwMzPL8bMCAwNRvnx5/PLLL7K2gUNRREREBABo0qQJLl++rFHWt29fVKpUKdc5Mw0aNMDq1auhUqnUPVi3bt2Cs7NzrkkN8GbkIy0tTd4GgIkNERER/ZeNjQ2qVaumUWZlZYWSJUuqy4ODg+Hq6orw8HAAwKBBg7Bw4UIMHz4cQ4cOxe3btzF9+nQMGzZMXUdoaChatGiBsmXLIikpCatXr8bBgwexa9cu2dvAxKYQMTc3R1hYWK6z2IsSQ2prXvA+kZz4fSJtREVFacwtcnNzw65duzBy5EhUr14drq6uGD58OMaOHau+JjY2FsHBwYiOjoadnR2qV6+OXbt2oWnTprLHxzk2REREVGTwkQpERERUZDCxISIioiKDiQ0REREVGUxsiIiIqMhgYkNERERFBhMbIgPBBZAkJ36fqKBiYlPEKJVKfYfwQdy9exfbt8v3gLiiKiMjQ/3nD/VMMyq63v77hd8nKqiY2BQRDx48QGxsLIyNjYt8cnPhwgVUqFBB4+FqlN21a9fQrVs3BAUFoXnz5jh69CgSEhL0HRYVUjdu3MAXX3yB7t27Y+DAgXj48CF7bahAYmJTBNy8eRPe3t6oUaMGHj9+XKSTm4sXL6Jhw4YYOXIk+vfvn+28SqXSQ1QFz+3bt+Hv7w9bW1vUrVsXQgh07twZc+bMwYMHD/QdHhUyN2/eRN26dZGamgpTU1OcP38eNWrUwLJly/Dy5Ut9h0ekgTsPF3KxsbHo2bMnFAoFMjIy8OjRIxw4cABlypSBUqnM8YFlhdWNGzfg5+eH4OBg/PTTT1AqlVi3bh0eP34MExMTfP311+984JohCQ0NxZUrV/DPP/+oy6ZOnYp169ahefPmCAkJgYuLix4jpMJCCIGvv/4az58/x/r169XlgwYNwt9//43x48ejd+/esLGx0WOURP/DZ0UVctevX0eJEiXw1VdfwcbGBuPGjUPjxo3VyU1mZiZMTIrGj3nt2rVISkrCxx9/jBcvXqBLly54/fo1nj17hvT0dMyfPx87duxApUqVIIQw6DkAGRkZePXqFTIyMmBkZARjY2NMmjQJFhYW+PXXX+Ht7Y0vv/zS4O8TvZ9CoUBKSgosLS0BvPlumZqaYvHixTA3N8fkyZNRoUIFNGvWjN8nKhDYY1MEHD16FA0bNgQAnDp1CuPHj0dUVBT2798PNzc3dc/N24+UL6wGDx6MnTt3wtTUFN7e3liwYAFKlCiB169fo2fPnoiNjcWFCxeKTDKnq5kzZ2L+/Pm4fPky7O3tkZaWpn6w4bBhw7BhwwZcv34ddnZ2eo6UCoPhw4djx44duHXrFgBofJ86d+6Mixcv4urVqzA1NdVnmERvCCpyTp06JT799FPh5eUlHj58KIQQYurUqeLAgQP6DUwmgwYNEr6+vuLatWsa5YcPHxb29vbi2LFjeopM/1QqlfrP1apVEx9//LH69evXr4UQQiQmJopSpUqJtWvXfvD4qHB6/PixKF++vOjWrZu67NWrV0IIIa5duyacnJzEoUOH9BUekYbC/c93A3Tnzh3MnTsX33zzDXbs2IGnT5+qz2VNGK5bty7Cw8NRtmxZNG3aFH369EFYWBgcHR31FbZO/t3WR48eAQB+/vln/PDDD/D09ATwv/000tPT4eDgUOjamVfx8fFIS0sD8GbYIOt7sHDhQjx48ACBgYEAAAsLCwBASkoKHBwcUKJECf0ETAVaVFQU/vjjD8yYMQMREREAAAcHB3z77be4dOmSetJ+1tCUqakpihUrpv5+EemdvjMr0t7ly5dFiRIlRMOGDYWfn58wNzcX3bt3F9u3b1dfk5mZqf7zsWPHhK2trbC3txfnz5/XQ8S6y62tW7ZsyfU9Y8aMEQEBASIuLu4DRqpfV69eFSVKlBATJkzQ+NkLIUR6errYtm2bKF++vPDx8RE7duwQhw4dEhMmTBDOzs7iwYMHeoqaCqpLly6JcuXKiXr16glvb29hamoqtm3bJoQQ4uXLl2LhwoWiQoUKokmTJuL69eviypUrYtKkScLd3V08fvxYz9ETvcHEppB49eqVaN26tRg6dKj6F9iOHTtEs2bNxCeffCI2btyovlapVAohhBg8eLAwNzcXV65c0UvMupLSViGEiIiIEKNHjxZ2dnbi4sWL+ghZLx4/fixq164tqlevLiwsLMTEiROzJTeZmZni9u3bonnz5sLd3V14enqKqlWrioiICD1FTQXVvXv3RNmyZcW4ceNEYmKieP36tQgJCRHe3t7i2bNnQgghkpOTxZ49e0TdunVFyZIlhZeXlyhXrhy/T1SgGPYMy0LEzMwMjx8/Rr169dRLuJs3b47ixYsjPDwcS5cuhYuLC/z8/GBkZIQzZ87g3LlzOH78OKpWrarn6KWR0tZ79+5h9erV2L59Ow4dOoTq1avrOfoPQ6VS4ejRo/D09MSkSZNw4cIF9O3bFwAQFhamvm/Gxsbw8vLCjh07cP36dVhYWMDGxgYODg76DJ8KmIyMDCxduhR169bFxIkTUaxYMQBAy5YtsWnTJvWiAysrKwQGBiIwMBDHjh2Dra0tSpUqBScnJ32GT6SBiU0hoFKpkJaWBmdnZzx//hwA1Cud6tWrh9GjR+Orr77C5s2b4efnBwCoU6cOtm3bVujmUUhtq6urKwYNGoTRo0cb1F+uRkZG+Oijj2BrawsfHx/4+PhACIF+/foBACZNmqReGZa15L9y5cr6DJkKMFNTU1SpUgUA1EkNANSqVQuvX7/GkydPYGdnB2NjY/WS7gYNGugrXKJ303eXEWlv4cKFwszMTOzatUsI8b8hJyGE+Pnnn4WNjY2IjY3VKC+stGnr06dP9RVegZF1X7L+u3LlSmFsbKwelkpPTxcrV64U586d02eYVEi8vapOCCGePXsmXFxcxNWrV9VlZ8+eFUlJSR86NCKtscemgHr06BGuXr2KxMRE+Pr6wtPTE4MHD8aZM2fQqVMn7NixQ+NfTF5eXvDw8ICxsXGh26tG17Ya2l41b9+nOnXqwMPDA0ZGRhqbMH7++ecAgL59+0IIgadPn2Lt2rW4dOmSPkOnAiin71PWDuampqZQKpVIT0+HiYkJrK2tAQBjx47Fr7/+ips3b6rLiAocfWdWlN2lS5eEo6OjqFOnjjA2Nha+vr5iyJAhQog3k0G7dOkiihUrJlasWCEiIyNFZmamGDVqlKhRo4Z4+fKlfoOXyJDamhc53aehQ4eqz2dkZGhcv2LFCqFQKETx4sXF2bNnP3S4VMC97/uUNQk9q8cmMjJSTJw4UVhZWYlTp07pK2wirTCxKWDi4+NFjRo1xIgRI0R8fLx49OiRmDZtmqhatapo3bq1+rpRo0YJe3t7UbZsWeHr6ytKlixZ6IYbDKmteZHbfapWrZpo1aqV+rqsX0ZpaWli0KBBws7OLtsmhkTafp+yrq1cubJo1aqVMDMzY5JMhQITmwLmwYMHokKFCuL48ePqsqSkJLFu3TpRoUIF0blzZ3X5sWPHxPr168Wff/4pIiMj9RBt3hhSW/PiXfepYsWKGvdJpVKJvXv3ChcXF3H69Gl9hEsFnJTv0507d4RCoRBWVlbiwoUL+giXSDImNgVMXFyc8PT0FLNmzdIoT01NFStWrBA+Pj5i0aJFeopOXobU1rx4332qXr26WLJkibo8JiaGE6spV1K/TzNmzBCXLl360GES6axwzTI1AMWKFcPHH3+MvXv34vLly+pyc3NzdOrUCZ6enjhy5IgeI5SPIbU1L953nzw8PHDw4EF1uaOjI0qXLq2HSKkwkPp9Gjt2LHx8fPQQKZFumNgUMObm5hg9ejTOnz+P7777Dnfv3lWfK1asGAICAnDr1i28evVKj1HKw5Damhe8TyQnbb9PKSkpeoySSHeGtV62EFCpVKhWrRr+/vtvNGnSBCqVCl9//TUaN24MALhx4wbKlClTJJY6G1Jb84L3ieSk7ffJ1NRUz5ES6UYhxH8fjUwflEqlghBCvfV9VpmRkZF6p92IiAgMGDBAXebh4YEDBw7g8OHDqFGjhh6jl8aQ2poXvE8kJ36fyFAxsdGDa9euYfr06YiJiYG3tzdat26NVq1aAfjf4wOy/hsVFYWIiAjs378fbm5uaNOmDSpVqqTnFmjPkNqaF7xPJCd+n8iQMbH5wG7evAk/Pz+0aNECHh4e2LFjB0xNTdGwYUPMnTsXAJCeng4zMzP1M1kKK0Nqa17wPpGc+H0iQ8fE5gMSQmDChAm4c+cO1q5dCwBISkrCggULsGHDBtSpUwdLly5VX//333/D39+/UK5wMaS25gXvE8mJ3yciror6oBQKBZ48eYKYmBh1mY2NDYYNG4ZevXrh/PnzmDFjBgBg27ZtGDJkCBYsWACVSqWvkHVmSG3NC94nkhO/T0RMbD6YrI6xjz76CEqlEjdv3lSfs7GxQb9+/VCrVi38888/SE9PR6tWrdCvXz/069ev0D3U0pDamhe8TyQnfp+I/uvD7QVIQrzZotzBwUH069dPJCUlCSHebIMvhBBRUVFCoVCIf/75R58hysaQ2poXvE8kJ36fyNBx44sPrHz58li3bh1atGgBS0tLTJ48GQ4ODgAAU1NTVK9eHSVLltRzlPIwpLbmBe8TyYnfJzJ0TGz0oHHjxli/fj06d+6M6OhodOnSBdWrV8fKlSsRGxsLNzc3fYcoG0Nqa17wPpGc+H0iQ8ZVUXp07tw5hISE4P79+zAxMYGxsTHWrFmDWrVq6Ts02RlSW/OC94nkxO8TGSImNnqWmJiIuLg4JCUlwdnZWd1lXBQZUlvzgveJ5MTvExkaJjZERERUZHCNHxERERUZTGyIiIioyGBiQ0REREUGExsiIiIqMpjYEBERUZHBxIaIiIiKDCY2REREVGQwsSG9+eSTTzBixAh9hwEhBL744gvY29tDoVDgwoULkuvo06cP2rVrJ3tsREQkDRMbkuyzzz5D8+bNczx35MgRKBQKXLp06QNHpbudO3di+fLl2Lp1K6Kjo1GtWrVs1xw8eBAKhQLx8fE51jF//nwsX748fwPNo5iYGAwdOhTlypWDubk53Nzc8Nlnn2Hfvn0fLIb8TAALQqK8YsUK1KlTB8WKFYONjQ0CAgKwdetWyfUwUSbSHRMbkqx///7Ys2cPHj16lO3csmXL4Ovri+rVq+shMt3cvXsXzs7OqF+/PpycnGBiIv3ZsHZ2dihevLj8wUmUnp6eY/n9+/dRu3Zt7N+/Hz/++CMuX76MnTt3onHjxhg8ePAHjrJoGj16NL788kt07doVly5dwunTp9GwYUO0bdsWCxcu1Hd4RIZDEEmUkZEhHB0dxbRp0zTKk5KShLW1tVi8eLF4/vy56Natm3BxcRGWlpaiWrVqYvXq1RrXBwQEiOHDh6tfAxCbNm3SuMbOzk4sW7ZM/ToqKkp07txZ2NnZiRIlSog2bdqIyMjId8Z78OBBUadOHWFmZiacnJzE2LFjRUZGhhBCiN69ewsA6sPd3T3HOg4cOCAAiJcvX+Z4vnfv3qJt27YabRs6dKgYM2aMKFGihHB0dBRhYWEa73n58qXo37+/cHBwEDY2NqJx48biwoUL6vN37twRbdq0EaVLlxZWVlbC19dX7NmzR6MOd3d3MXXqVPH5558LGxsb0bt37xzja9GihXB1dRXJycnZzr3dpgcPHog2bdoIKysrYWNjIzp37ixiYmLU58PCwkSNGjXEypUrhbu7u7C1tRVdu3YViYmJ6mvWr18vqlWrJiwsLIS9vb1o0qSJSE5OFmFhYRr3GoA4cOCAEEKIb775Rnh7ewtLS0vh6ekpJkyYINLT07X+3H//HAHk+r2Ii4sTn3/+uShevLiwtLQUzZs3F7du3VKfX7ZsmbCzsxM7d+4UlSpVElZWViIoKEg8efIkx/qEEOLEiRMCgFiwYEG2cyEhIcLU1FRERUVptOVtc+fOVX/33nWfHj58KLp16yZKlCghihUrJmrXri1Onjyprufnn38W5cqVE6ampqJChQpi5cqVGp8DQCxZskS0atVKWFpaikqVKonjx4+L27dvi4CAAFGsWDHh7+8v7ty5o/G+zZs3i1q1aglzc3Ph6ekpJk+erP5/iKigYWJDOhkzZowoX768UKlU6rLff/9dWFpaivj4ePHo0SPx448/ivPnz4u7d++KBQsWCGNjY3Hq1Cn19VITm/T0dFG5cmXRr18/cenSJXHt2jXRo0cPUbFiRZGWlpZjnI8ePRLFihUTX3/9tbh+/brYtGmTcHBwUCcZ8fHxYurUqaJMmTIiOjpaxMbG5liPLomNra2tmDx5srh165ZYsWKFUCgUYvfu3eprAgMDxWeffSbOnDkjbt26JUaNGiVKliwpXrx4IYQQ4sKFC2LJkiXi8uXL4tatW2LChAnCwsJCPHjwQF1H1i/5WbNmiTt37mT7hSSEEC9evBAKhUJMnz49x9izKJVKUbNmTdGwYUNx9uxZcfLkSVG7dm0REBCgviYsLExYW1uLDh06iMuXL4vDhw8LJycnMX78eCGEEE+ePBEmJiZizpw5IjIyUly6dEksWrRIJCUliaSkJNGlSxfRvHlzER0dLaKjo9U/t2nTpoljx46JyMhIsWXLFuHo6Ch++OEHrT83Pj5e+Pv7i4EDB6rrzszMzLGdbdq0EZUrVxaHDx8WFy5cEEFBQcLLy0udSC1btkyYmpqKwMBAcebMGRERESEqV64sevTokeu9GzZsmLC2ts7xe/j48WMBQMydO1fdlnclNrndp6SkJFGuXDnRqFEjceTIEXH79m2xdu1acfz4cSGEEBs3bhSmpqZi0aJF4ubNm2L27NnC2NhY7N+/X/05AISrq6tYu3atuHnzpmjXrp3w8PAQn376qdi5c6e4du2aqFevnmjevLn6PYcPHxa2trZi+fLl4u7du2L37t3Cw8NDTJ48Odf7QaRPTGxIJ9evX9f4l6QQQjRq1Ej06tUr1/e0atVKjBo1Sv1aamKzatUqUbFiRY1kKi0tTVhaWopdu3bl+Jnjx4/P9p5FixYJa2troVQqhRCav1Ryo0ti07BhQ41r6tSpI8aOHSuEEOLIkSPC1tZWpKamalxTvnx58csvv+QaR9WqVcVPP/2kfu3u7i7atWv3zthPnTolAIiNGze+87rdu3cLY2Njdc+CEEJcvXpVABCnT58WQrz5pVysWDGNHpoxY8YIPz8/IYQQERERAoC4f/9+jp/x7/uUmx9//FHUrl1b/fp9nytE9u9TTm7duiUAiGPHjqnLnj9/LiwtLcW6deuEEG8SGwAaSeKiRYuEo6NjrvU2b948W7LyNltbWzFo0CB1W96V2AiR83365ZdfhI2NjTrx/bf69euLgQMHapR17txZtGzZUv0agJgwYYL6dVZP02+//aYu+89//iMsLCzUr5s0aZItKV61apVwdnbOtb1E+sQ5NqSTSpUqoX79+vj9998BAHfu3MGRI0fQv39/AIBSqcS0adPg4+MDe3t7WFtbY9euXYiKitL5My9evIg7d+7AxsYG1tbWsLa2hr29PVJTU3H37t0c33P9+nX4+/tDoVCoyxo0aIDk5OQc5wjJ6d/zjJydnREbGwvgTVuSk5NRsmRJdVusra0RGRmpbktycjJGjx6NypUro3jx4rC2tsb169ez3UNfX993xiGE0Cre69evw83NDW5ubuqyKlWqoHjx4rh+/bq6zMPDAzY2Njm2q0aNGmjSpAl8fHzQuXNn/N///R9evnz53s9eu3YtGjRoACcnJ1hbW2PChAnZ2vmuz9XW9evXYWJiAj8/P3VZyZIlUbFiRY02FitWDOXLl5f0WdreZ11duHABtWrVgr29fY7nr1+/jgYNGmiUNWjQQKNdgOb30tHREQDg4+OjUZaamorExEQAb76rU6dO1fieDhw4ENHR0Xj16pUsbSOSk/RZkkT/1b9/fwwdOhSLFi3CsmXLUL58eQQEBAAAfvzxR8yfPx/z5s2Dj48PrKysMGLEiFwntwKAQqHI9sshIyND/efk5GTUrl0bf/75Z7b3lipVSqZWycfU1FTjtUKhgEqlAvCmLc7Ozjh48GC292VNQh49ejT27NmDWbNmwcvLC5aWlujUqVO2e2hlZfXOOLy9vaFQKHDjxg3dG/OWd7XL2NgYe/bswfHjx7F792789NNP+Pbbb3Hq1Cl4enrmWN+JEyfQs2dPTJkyBUFBQbCzs8OaNWswe/ZsrT9Xbjl91rsSlwoVKuDo0aNIT0+HmZmZxrknT54gMTERFSpUAAAYGRm983ueG0tLS23Df6e325aV8OdU9vZ3dcqUKejQoUO2uiwsLGSJiUhO7LEhnXXp0gVGRkZYvXo1Vq5ciX79+qn/Ujx27Bjatm2LXr16oUaNGihXrhxu3br1zvpKlSqF6Oho9evbt29r/Ivwo48+wu3bt1G6dGl4eXlpHHZ2djnWWblyZZw4cULjF8mxY8dgY2ODMmXK5KX5efLRRx8hJiYGJiYm2dri4OCgjrNPnz5o3749fHx84OTkhPv370v+LHt7ewQFBWHRokVISUnJdj5rCXvlypXx8OFDPHz4UH3u2rVriI+PR5UqVbT+PIVCgQYNGmDKlCk4f/48zMzMsGnTJgCAmZkZlEqlxvXHjx+Hu7s7vv32W/j6+sLb2xsPHjyQ3M6c6v63ypUrIzMzE6dOnVKXvXjxAjdv3pTUxn/r1q0bkpOT8csvv2Q7N2vWLJiamqJjx44A3nzPY2JiNL6T/947Kae2VK9eHRcuXEBcXFyOMVSuXBnHjh3TKDt27Fie2gW8+a7evHkz2/fUy8sLRkb8FUIFD7+VpDNra2t07doVoaGhiI6ORp8+fdTnvL291f9yv379Or788ks8ffr0nfV9+umnWLhwIc6fP4+zZ8/iq6++0viXZM+ePeHg4IC2bdviyJEjiIyMxMGDBzFs2LBch5W+/vprPHz4EEOHDsWNGzfw999/IywsDCEhITr9pXz58mVcuHBBfVy8eFFyHQAQGBgIf39/tGvXDrt378b9+/dx/PhxfPvttzh79iyAN/dw48aN6s/p0aOHzj0UixYtglKpRN26dfHXX3/h9u3buH79OhYsWAB/f391TD4+PujZsyfOnTuH06dPIzg4GAEBAe8d7spy6tQpTJ8+HWfPnkVUVBQ2btyIZ8+eoXLlygDeDCddunQJN2/exPPnz5GRkQFvb29ERUVhzZo1uHv3LhYsWKBOhKTw8PDAqVOncP/+fTx//jzHe+Xt7Y22bdti4MCBOHr0KC5evIhevXrB1dUVbdu2lfyZWfz9/TF8+HCMGTMGs2fPxt27d3Hjxg1MmDAB8+fPx+zZs9VDfJ988gmePXuGmTNn4u7du1i0aBF27NiRrS3/vk/du3eHk5MT2rVrh2PHjuHevXv466+/cOLECQDAmDFjsHz5cixevBi3b9/GnDlzsHHjRowePVrndgHApEmTsHLlSkyZMgVXr17F9evXsWbNGkyYMCFP9RLlGz3O76Ei4Pjx4wKAxgRFId6sxGnbtq2wtrYWpUuXFhMmTBDBwcHZJti+Pdnz8ePHolmzZsLKykp4e3uL7du3Z1vuHR0dLYKDg4WDg4MwNzcX5cqVEwMHDhQJCQm5xviu5d5CSJs8/O/D2NhYCJHz5OF/T2Rt27atxnLsxMREMXToUOHi4iJMTU2Fm5ub6Nmzp3rybmRkpGjcuLGwtLQUbm5uYuHChdnqdXd3V6+2eZ8nT56IwYMHC3d3d2FmZiZcXV1FmzZtNCaAa7vc+21v379r166JoKAgUapUKWFubi4qVKigMdk5NjZWNG3aVFhbW2tMPh8zZowoWbKksLa2Fl27dhVz584VdnZ2Wn+uEELcvHlT1KtXT1haWmq13NvOzk5YWlqKoKCgHJd7v23Tpk1Cm78uf/vtN1G7dm1hYWEhrKysRKNGjcSWLVuyXbd48WLh5uYmrKysRHBwsPj+++812pLbfbp//77o2LGjsLW1FcWKFRO+vr4aKw21We799gT9yMhIAUCcP39eXZbTRPmdO3eK+vXrC0tLS2Frayvq1q0rli5d+t77QaQPCiHyecYbERER0QfCoSgiIiIqMpjYEBERUZHBxIaIiIiKDCY2REREVGQwsSEiIqIig4kNERERFRlMbIiIiKjIYGJDRERERQYTGyIiIioymNgQERFRkcHEhoiIiIoMJjZERERUZPw/F7324lUD5qIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 600x500 with 2 Axes>"
       ]
@@ -1092,8 +1092,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(4.633438858979389, 12.180186652249047)\n",
+      "Estimated effect:13.111841490138564\n",
+      "New effect:(5.738666451726319, 13.008344040681948)\n",
       "\n"
      ]
     }
@@ -1118,10 +1118,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:22.760646Z",
-     "iopub.status.busy": "2024-10-25T14:52:22.760412Z",
-     "iopub.status.idle": "2024-10-25T14:52:50.114991Z",
-     "shell.execute_reply": "2024-10-25T14:52:50.113951Z"
+     "iopub.execute_input": "2024-10-25T14:53:26.075637Z",
+     "iopub.status.busy": "2024-10-25T14:53:26.075038Z",
+     "iopub.status.idle": "2024-10-25T14:53:50.166694Z",
+     "shell.execute_reply": "2024-10-25T14:53:50.166085Z"
     }
    },
    "outputs": [
@@ -1135,7 +1135,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
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@@ -1148,8 +1148,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:12.3093616611939\n",
-      "New effect:(6.999875970223543, 12.397733619781969)\n",
+      "Estimated effect:13.111841490138564\n",
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diff --git a/main/example_notebooks/gcm_401k_analysis.html b/main/example_notebooks/gcm_401k_analysis.html
index bb9ffeda6..e6418de67 100644
--- a/main/example_notebooks/gcm_401k_analysis.html
+++ b/main/example_notebooks/gcm_401k_analysis.html
@@ -960,7 +960,7 @@ <h2>Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income<
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 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06&lt;00:00,  2.65it/s]
+Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06&lt;00:00,  2.57it/s]
 </pre></div></div>
 </div>
 <p>Before computing CATE, we first divide households into equi-width bins of income percentiles. This allows us to study the impact on various income groups.</p>
@@ -1017,7 +1017,7 @@ <h2>Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income<
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 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43&lt;00:00,  2.30it/s]
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   plt.plot((x, x), (ci[0], ci[1]), &#39;ro-&#39;, color=&#39;orange&#39;)
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+     "iopub.status.busy": "2024-10-25T14:53:57.852904Z",
+     "iopub.status.idle": "2024-10-25T14:53:58.082489Z",
+     "shell.execute_reply": "2024-10-25T14:53:58.081747Z"
     }
    },
    "outputs": [
@@ -369,10 +369,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:56.964666Z",
-     "iopub.status.busy": "2024-10-25T14:52:56.964175Z",
-     "iopub.status.idle": "2024-10-25T14:52:58.438404Z",
-     "shell.execute_reply": "2024-10-25T14:52:58.437810Z"
+     "iopub.execute_input": "2024-10-25T14:53:58.085461Z",
+     "iopub.status.busy": "2024-10-25T14:53:58.084977Z",
+     "iopub.status.idle": "2024-10-25T14:53:59.682064Z",
+     "shell.execute_reply": "2024-10-25T14:53:59.681362Z"
     }
    },
    "outputs": [
@@ -416,10 +416,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:58.440226Z",
-     "iopub.status.busy": "2024-10-25T14:52:58.440042Z",
-     "iopub.status.idle": "2024-10-25T14:52:58.443661Z",
-     "shell.execute_reply": "2024-10-25T14:52:58.443069Z"
+     "iopub.execute_input": "2024-10-25T14:53:59.684480Z",
+     "iopub.status.busy": "2024-10-25T14:53:59.683952Z",
+     "iopub.status.idle": "2024-10-25T14:53:59.688008Z",
+     "shell.execute_reply": "2024-10-25T14:53:59.687391Z"
     }
    },
    "outputs": [],
@@ -453,10 +453,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:52:58.445632Z",
-     "iopub.status.busy": "2024-10-25T14:52:58.445261Z",
-     "iopub.status.idle": "2024-10-25T14:53:05.234222Z",
-     "shell.execute_reply": "2024-10-25T14:53:05.233719Z"
+     "iopub.execute_input": "2024-10-25T14:53:59.690074Z",
+     "iopub.status.busy": "2024-10-25T14:53:59.689648Z",
+     "iopub.status.idle": "2024-10-25T14:54:06.685852Z",
+     "shell.execute_reply": "2024-10-25T14:54:06.685299Z"
     }
    },
    "outputs": [
@@ -481,7 +481,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node e401:   6%|▌         | 1/18 [00:01<00:21,  1.26s/it]"
+      "Fitting causal mechanism of node e401:   6%|▌         | 1/18 [00:01<00:22,  1.29s/it]"
      ]
     },
     {
@@ -489,7 +489,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node net_tfa:   6%|▌         | 1/18 [00:01<00:21,  1.26s/it]"
+      "Fitting causal mechanism of node net_tfa:   6%|▌         | 1/18 [00:01<00:22,  1.29s/it]"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node net_tfa:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node net_tfa:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node age:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]    "
+      "Fitting causal mechanism of node age:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]    "
      ]
     },
     {
@@ -513,7 +513,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node inc:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node inc:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -521,7 +521,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node fsize:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node fsize:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -529,7 +529,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node educ:  11%|█         | 2/18 [00:06<01:00,  3.76s/it] "
+      "Fitting causal mechanism of node educ:  11%|█         | 2/18 [00:06<01:02,  3.88s/it] "
      ]
     },
     {
@@ -537,7 +537,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node male:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node male:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -545,7 +545,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node db:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node db:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -553,7 +553,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node marr:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node marr:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -561,7 +561,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node twoearn:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node twoearn:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -569,7 +569,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node pira:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]   "
+      "Fitting causal mechanism of node pira:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]   "
      ]
     },
     {
@@ -577,7 +577,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hown:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hown:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -585,7 +585,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hval:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hval:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -593,7 +593,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hequity:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node hequity:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -601,7 +601,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hmort:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node hmort:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -609,7 +609,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node nohs:  11%|█         | 2/18 [00:06<01:00,  3.76s/it] "
+      "Fitting causal mechanism of node nohs:  11%|█         | 2/18 [00:06<01:02,  3.88s/it] "
      ]
     },
     {
@@ -617,7 +617,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node hs:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]  "
+      "Fitting causal mechanism of node hs:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]  "
      ]
     },
     {
@@ -625,7 +625,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node smcol:  11%|█         | 2/18 [00:06<01:00,  3.76s/it]"
+      "Fitting causal mechanism of node smcol:  11%|█         | 2/18 [00:06<01:02,  3.88s/it]"
      ]
     },
     {
@@ -633,7 +633,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06<00:00,  2.65it/s]"
+      "Fitting causal mechanism of node smcol: 100%|██████████| 18/18 [00:06<00:00,  2.57it/s]"
      ]
     },
     {
@@ -660,10 +660,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:05.236194Z",
-     "iopub.status.busy": "2024-10-25T14:53:05.235831Z",
-     "iopub.status.idle": "2024-10-25T14:53:05.240191Z",
-     "shell.execute_reply": "2024-10-25T14:53:05.239738Z"
+     "iopub.execute_input": "2024-10-25T14:54:06.687912Z",
+     "iopub.status.busy": "2024-10-25T14:54:06.687532Z",
+     "iopub.status.idle": "2024-10-25T14:54:06.691938Z",
+     "shell.execute_reply": "2024-10-25T14:54:06.691470Z"
     }
    },
    "outputs": [],
@@ -691,10 +691,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:05.241983Z",
-     "iopub.status.busy": "2024-10-25T14:53:05.241639Z",
-     "iopub.status.idle": "2024-10-25T14:53:48.725196Z",
-     "shell.execute_reply": "2024-10-25T14:53:48.724542Z"
+     "iopub.execute_input": "2024-10-25T14:54:06.693728Z",
+     "iopub.status.busy": "2024-10-25T14:54:06.693391Z",
+     "iopub.status.idle": "2024-10-25T14:54:52.986976Z",
+     "shell.execute_reply": "2024-10-25T14:54:52.986291Z"
     }
    },
    "outputs": [
@@ -711,7 +711,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   1%|          | 1/100 [00:00<00:44,  2.21it/s]"
+      "Estimating bootstrap interval...:   1%|          | 1/100 [00:00<00:46,  2.14it/s]"
      ]
     },
     {
@@ -719,7 +719,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   2%|▏         | 2/100 [00:00<00:43,  2.26it/s]"
+      "Estimating bootstrap interval...:   2%|▏         | 2/100 [00:00<00:48,  2.04it/s]"
      ]
     },
     {
@@ -727,7 +727,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   3%|▎         | 3/100 [00:01<00:42,  2.28it/s]"
+      "Estimating bootstrap interval...:   3%|▎         | 3/100 [00:01<00:46,  2.09it/s]"
      ]
     },
     {
@@ -735,7 +735,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   4%|▍         | 4/100 [00:01<00:42,  2.28it/s]"
+      "Estimating bootstrap interval...:   4%|▍         | 4/100 [00:01<00:45,  2.12it/s]"
      ]
     },
     {
@@ -743,7 +743,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   5%|▌         | 5/100 [00:02<00:41,  2.29it/s]"
+      "Estimating bootstrap interval...:   5%|▌         | 5/100 [00:02<00:44,  2.13it/s]"
      ]
     },
     {
@@ -751,7 +751,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   6%|▌         | 6/100 [00:02<00:41,  2.29it/s]"
+      "Estimating bootstrap interval...:   6%|▌         | 6/100 [00:02<00:44,  2.12it/s]"
      ]
     },
     {
@@ -759,7 +759,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   7%|▋         | 7/100 [00:03<00:40,  2.28it/s]"
+      "Estimating bootstrap interval...:   7%|▋         | 7/100 [00:03<00:44,  2.11it/s]"
      ]
     },
     {
@@ -767,7 +767,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   8%|▊         | 8/100 [00:03<00:40,  2.26it/s]"
+      "Estimating bootstrap interval...:   8%|▊         | 8/100 [00:03<00:43,  2.10it/s]"
      ]
     },
     {
@@ -775,7 +775,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:   9%|▉         | 9/100 [00:03<00:40,  2.26it/s]"
+      "Estimating bootstrap interval...:   9%|▉         | 9/100 [00:04<00:43,  2.10it/s]"
      ]
     },
     {
@@ -783,7 +783,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  10%|█         | 10/100 [00:04<00:40,  2.24it/s]"
+      "Estimating bootstrap interval...:  10%|█         | 10/100 [00:04<00:42,  2.10it/s]"
      ]
     },
     {
@@ -791,7 +791,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  11%|█         | 11/100 [00:04<00:40,  2.21it/s]"
+      "Estimating bootstrap interval...:  11%|█         | 11/100 [00:05<00:42,  2.11it/s]"
      ]
     },
     {
@@ -799,7 +799,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  12%|█▏        | 12/100 [00:05<00:39,  2.22it/s]"
+      "Estimating bootstrap interval...:  12%|█▏        | 12/100 [00:05<00:41,  2.12it/s]"
      ]
     },
     {
@@ -807,7 +807,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  13%|█▎        | 13/100 [00:05<00:39,  2.22it/s]"
+      "Estimating bootstrap interval...:  13%|█▎        | 13/100 [00:06<00:40,  2.13it/s]"
      ]
     },
     {
@@ -815,7 +815,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  14%|█▍        | 14/100 [00:06<00:38,  2.24it/s]"
+      "Estimating bootstrap interval...:  14%|█▍        | 14/100 [00:06<00:40,  2.13it/s]"
      ]
     },
     {
@@ -823,7 +823,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  15%|█▌        | 15/100 [00:06<00:37,  2.26it/s]"
+      "Estimating bootstrap interval...:  15%|█▌        | 15/100 [00:07<00:39,  2.13it/s]"
      ]
     },
     {
@@ -831,7 +831,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  16%|█▌        | 16/100 [00:07<00:36,  2.28it/s]"
+      "Estimating bootstrap interval...:  16%|█▌        | 16/100 [00:07<00:39,  2.13it/s]"
      ]
     },
     {
@@ -839,7 +839,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  17%|█▋        | 17/100 [00:07<00:36,  2.29it/s]"
+      "Estimating bootstrap interval...:  17%|█▋        | 17/100 [00:08<00:38,  2.14it/s]"
      ]
     },
     {
@@ -847,7 +847,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  18%|█▊        | 18/100 [00:07<00:35,  2.29it/s]"
+      "Estimating bootstrap interval...:  18%|█▊        | 18/100 [00:08<00:37,  2.16it/s]"
      ]
     },
     {
@@ -855,7 +855,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  19%|█▉        | 19/100 [00:08<00:35,  2.29it/s]"
+      "Estimating bootstrap interval...:  19%|█▉        | 19/100 [00:08<00:37,  2.18it/s]"
      ]
     },
     {
@@ -863,7 +863,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  20%|██        | 20/100 [00:08<00:34,  2.30it/s]"
+      "Estimating bootstrap interval...:  20%|██        | 20/100 [00:09<00:36,  2.17it/s]"
      ]
     },
     {
@@ -871,7 +871,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  21%|██        | 21/100 [00:09<00:34,  2.30it/s]"
+      "Estimating bootstrap interval...:  21%|██        | 21/100 [00:09<00:36,  2.15it/s]"
      ]
     },
     {
@@ -879,7 +879,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  22%|██▏       | 22/100 [00:09<00:33,  2.30it/s]"
+      "Estimating bootstrap interval...:  22%|██▏       | 22/100 [00:10<00:36,  2.15it/s]"
      ]
     },
     {
@@ -887,7 +887,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  23%|██▎       | 23/100 [00:10<00:33,  2.28it/s]"
+      "Estimating bootstrap interval...:  23%|██▎       | 23/100 [00:10<00:35,  2.15it/s]"
      ]
     },
     {
@@ -895,7 +895,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  24%|██▍       | 24/100 [00:10<00:33,  2.29it/s]"
+      "Estimating bootstrap interval...:  24%|██▍       | 24/100 [00:11<00:35,  2.16it/s]"
      ]
     },
     {
@@ -903,7 +903,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  25%|██▌       | 25/100 [00:10<00:32,  2.30it/s]"
+      "Estimating bootstrap interval...:  25%|██▌       | 25/100 [00:11<00:34,  2.15it/s]"
      ]
     },
     {
@@ -911,7 +911,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  26%|██▌       | 26/100 [00:11<00:32,  2.31it/s]"
+      "Estimating bootstrap interval...:  26%|██▌       | 26/100 [00:12<00:34,  2.14it/s]"
      ]
     },
     {
@@ -919,7 +919,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  27%|██▋       | 27/100 [00:11<00:31,  2.31it/s]"
+      "Estimating bootstrap interval...:  27%|██▋       | 27/100 [00:12<00:34,  2.14it/s]"
      ]
     },
     {
@@ -927,7 +927,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  28%|██▊       | 28/100 [00:12<00:31,  2.32it/s]"
+      "Estimating bootstrap interval...:  28%|██▊       | 28/100 [00:13<00:33,  2.14it/s]"
      ]
     },
     {
@@ -935,7 +935,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  29%|██▉       | 29/100 [00:12<00:30,  2.32it/s]"
+      "Estimating bootstrap interval...:  29%|██▉       | 29/100 [00:13<00:33,  2.14it/s]"
      ]
     },
     {
@@ -943,7 +943,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  30%|███       | 30/100 [00:13<00:30,  2.32it/s]"
+      "Estimating bootstrap interval...:  30%|███       | 30/100 [00:14<00:32,  2.14it/s]"
      ]
     },
     {
@@ -951,7 +951,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  31%|███       | 31/100 [00:14<00:32,  2.11it/s]"
      ]
     },
     {
@@ -959,7 +959,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  32%|███▏      | 32/100 [00:15<00:32,  2.09it/s]"
      ]
     },
     {
@@ -967,7 +967,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  33%|███▎      | 33/100 [00:14<00:28,  2.32it/s]"
+      "Estimating bootstrap interval...:  33%|███▎      | 33/100 [00:15<00:32,  2.04it/s]"
      ]
     },
     {
@@ -975,7 +975,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  34%|███▍      | 34/100 [00:16<00:32,  2.06it/s]"
      ]
     },
     {
@@ -983,7 +983,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  35%|███▌      | 35/100 [00:16<00:30,  2.10it/s]"
      ]
     },
     {
@@ -991,7 +991,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  36%|███▌      | 36/100 [00:15<00:27,  2.31it/s]"
+      "Estimating bootstrap interval...:  36%|███▌      | 36/100 [00:16<00:30,  2.12it/s]"
      ]
     },
     {
@@ -999,7 +999,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  37%|███▋      | 37/100 [00:16<00:27,  2.32it/s]"
+      "Estimating bootstrap interval...:  37%|███▋      | 37/100 [00:17<00:29,  2.15it/s]"
      ]
     },
     {
@@ -1007,7 +1007,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  38%|███▊      | 38/100 [00:17<00:28,  2.15it/s]"
      ]
     },
     {
@@ -1015,7 +1015,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  39%|███▉      | 39/100 [00:18<00:28,  2.15it/s]"
      ]
     },
     {
@@ -1023,7 +1023,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  40%|████      | 40/100 [00:18<00:27,  2.16it/s]"
      ]
     },
     {
@@ -1031,7 +1031,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  41%|████      | 41/100 [00:19<00:27,  2.17it/s]"
      ]
     },
     {
@@ -1039,7 +1039,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  42%|████▏     | 42/100 [00:19<00:26,  2.17it/s]"
      ]
     },
     {
@@ -1047,7 +1047,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  43%|████▎     | 43/100 [00:20<00:26,  2.16it/s]"
      ]
     },
     {
@@ -1055,7 +1055,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  44%|████▍     | 44/100 [00:19<00:24,  2.31it/s]"
+      "Estimating bootstrap interval...:  44%|████▍     | 44/100 [00:20<00:25,  2.18it/s]"
      ]
     },
     {
@@ -1063,7 +1063,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  45%|████▌     | 45/100 [00:21<00:24,  2.20it/s]"
      ]
     },
     {
@@ -1071,7 +1071,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  46%|████▌     | 46/100 [00:21<00:24,  2.20it/s]"
      ]
     },
     {
@@ -1079,7 +1079,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  47%|████▋     | 47/100 [00:21<00:24,  2.19it/s]"
      ]
     },
     {
@@ -1087,7 +1087,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  48%|████▊     | 48/100 [00:22<00:24,  2.12it/s]"
      ]
     },
     {
@@ -1095,7 +1095,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  49%|████▉     | 49/100 [00:22<00:23,  2.14it/s]"
      ]
     },
     {
@@ -1103,7 +1103,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  50%|█████     | 50/100 [00:23<00:23,  2.16it/s]"
      ]
     },
     {
@@ -1111,7 +1111,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  51%|█████     | 51/100 [00:23<00:22,  2.16it/s]"
      ]
     },
     {
@@ -1119,7 +1119,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  52%|█████▏    | 52/100 [00:22<00:20,  2.32it/s]"
+      "Estimating bootstrap interval...:  52%|█████▏    | 52/100 [00:24<00:22,  2.17it/s]"
      ]
     },
     {
@@ -1127,7 +1127,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  53%|█████▎    | 53/100 [00:23<00:20,  2.32it/s]"
+      "Estimating bootstrap interval...:  53%|█████▎    | 53/100 [00:24<00:21,  2.18it/s]"
      ]
     },
     {
@@ -1135,7 +1135,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  54%|█████▍    | 54/100 [00:23<00:19,  2.32it/s]"
+      "Estimating bootstrap interval...:  54%|█████▍    | 54/100 [00:25<00:21,  2.13it/s]"
      ]
     },
     {
@@ -1143,7 +1143,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  55%|█████▌    | 55/100 [00:23<00:19,  2.32it/s]"
+      "Estimating bootstrap interval...:  55%|█████▌    | 55/100 [00:25<00:20,  2.14it/s]"
      ]
     },
     {
@@ -1151,7 +1151,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  56%|█████▌    | 56/100 [00:24<00:18,  2.32it/s]"
+      "Estimating bootstrap interval...:  56%|█████▌    | 56/100 [00:26<00:20,  2.16it/s]"
      ]
     },
     {
@@ -1159,7 +1159,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  57%|█████▋    | 57/100 [00:26<00:19,  2.17it/s]"
      ]
     },
     {
@@ -1167,7 +1167,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  58%|█████▊    | 58/100 [00:27<00:19,  2.16it/s]"
      ]
     },
     {
@@ -1175,7 +1175,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  59%|█████▉    | 59/100 [00:25<00:17,  2.32it/s]"
+      "Estimating bootstrap interval...:  59%|█████▉    | 59/100 [00:27<00:18,  2.16it/s]"
      ]
     },
     {
@@ -1183,7 +1183,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  60%|██████    | 60/100 [00:28<00:18,  2.17it/s]"
      ]
     },
     {
@@ -1191,7 +1191,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  61%|██████    | 61/100 [00:28<00:18,  2.16it/s]"
      ]
     },
     {
@@ -1199,7 +1199,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  62%|██████▏   | 62/100 [00:28<00:17,  2.17it/s]"
      ]
     },
     {
@@ -1207,7 +1207,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  63%|██████▎   | 63/100 [00:27<00:15,  2.32it/s]"
+      "Estimating bootstrap interval...:  63%|██████▎   | 63/100 [00:29<00:16,  2.18it/s]"
      ]
     },
     {
@@ -1215,7 +1215,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  64%|██████▍   | 64/100 [00:29<00:16,  2.18it/s]"
      ]
     },
     {
@@ -1223,7 +1223,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  65%|██████▌   | 65/100 [00:28<00:15,  2.31it/s]"
+      "Estimating bootstrap interval...:  65%|██████▌   | 65/100 [00:30<00:16,  2.17it/s]"
      ]
     },
     {
@@ -1231,7 +1231,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  66%|██████▌   | 66/100 [00:30<00:15,  2.17it/s]"
      ]
     },
     {
@@ -1239,7 +1239,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  67%|██████▋   | 67/100 [00:31<00:15,  2.13it/s]"
      ]
     },
     {
@@ -1247,7 +1247,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  68%|██████▊   | 68/100 [00:31<00:15,  2.13it/s]"
      ]
     },
     {
@@ -1255,7 +1255,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  69%|██████▉   | 69/100 [00:32<00:14,  2.14it/s]"
      ]
     },
     {
@@ -1263,7 +1263,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  70%|███████   | 70/100 [00:32<00:13,  2.15it/s]"
      ]
     },
     {
@@ -1271,7 +1271,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  71%|███████   | 71/100 [00:33<00:13,  2.16it/s]"
      ]
     },
     {
@@ -1279,7 +1279,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  72%|███████▏  | 72/100 [00:33<00:12,  2.17it/s]"
      ]
     },
     {
@@ -1287,7 +1287,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  73%|███████▎  | 73/100 [00:34<00:12,  2.17it/s]"
      ]
     },
     {
@@ -1295,7 +1295,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  74%|███████▍  | 74/100 [00:32<00:11,  2.31it/s]"
+      "Estimating bootstrap interval...:  74%|███████▍  | 74/100 [00:34<00:12,  2.17it/s]"
      ]
     },
     {
@@ -1303,7 +1303,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  75%|███████▌  | 75/100 [00:34<00:11,  2.17it/s]"
      ]
     },
     {
@@ -1311,7 +1311,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  76%|███████▌  | 76/100 [00:35<00:11,  2.17it/s]"
      ]
     },
     {
@@ -1319,7 +1319,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  77%|███████▋  | 77/100 [00:35<00:10,  2.18it/s]"
      ]
     },
     {
@@ -1327,7 +1327,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  78%|███████▊  | 78/100 [00:33<00:09,  2.32it/s]"
+      "Estimating bootstrap interval...:  78%|███████▊  | 78/100 [00:36<00:10,  2.18it/s]"
      ]
     },
     {
@@ -1335,7 +1335,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  79%|███████▉  | 79/100 [00:34<00:09,  2.32it/s]"
+      "Estimating bootstrap interval...:  79%|███████▉  | 79/100 [00:36<00:09,  2.20it/s]"
      ]
     },
     {
@@ -1343,7 +1343,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  80%|████████  | 80/100 [00:37<00:09,  2.20it/s]"
      ]
     },
     {
@@ -1351,7 +1351,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  81%|████████  | 81/100 [00:37<00:08,  2.21it/s]"
      ]
     },
     {
@@ -1359,7 +1359,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  82%|████████▏ | 82/100 [00:38<00:08,  2.21it/s]"
      ]
     },
     {
@@ -1367,7 +1367,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  83%|████████▎ | 83/100 [00:38<00:07,  2.20it/s]"
      ]
     },
     {
@@ -1375,7 +1375,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  84%|████████▍ | 84/100 [00:39<00:07,  2.20it/s]"
      ]
     },
     {
@@ -1383,7 +1383,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  85%|████████▌ | 85/100 [00:39<00:06,  2.21it/s]"
      ]
     },
     {
@@ -1391,7 +1391,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  86%|████████▌ | 86/100 [00:39<00:06,  2.21it/s]"
      ]
     },
     {
@@ -1399,7 +1399,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  87%|████████▋ | 87/100 [00:40<00:05,  2.22it/s]"
      ]
     },
     {
@@ -1407,7 +1407,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  88%|████████▊ | 88/100 [00:40<00:05,  2.21it/s]"
      ]
     },
     {
@@ -1415,7 +1415,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  89%|████████▉ | 89/100 [00:41<00:04,  2.22it/s]"
      ]
     },
     {
@@ -1423,7 +1423,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  90%|█████████ | 90/100 [00:41<00:04,  2.23it/s]"
      ]
     },
     {
@@ -1431,7 +1431,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Estimating bootstrap interval...:  91%|█████████ | 91/100 [00:42<00:04,  2.24it/s]"
      ]
     },
     {
@@ -1439,7 +1439,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  92%|█████████▏| 92/100 [00:39<00:03,  2.30it/s]"
+      "Estimating bootstrap interval...:  92%|█████████▏| 92/100 [00:42<00:03,  2.23it/s]"
      ]
     },
     {
@@ -1447,7 +1447,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  93%|█████████▎| 93/100 [00:40<00:03,  2.31it/s]"
+      "Estimating bootstrap interval...:  93%|█████████▎| 93/100 [00:43<00:03,  2.23it/s]"
      ]
     },
     {
@@ -1455,7 +1455,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  94%|█████████▍| 94/100 [00:40<00:02,  2.31it/s]"
+      "Estimating bootstrap interval...:  94%|█████████▍| 94/100 [00:43<00:02,  2.23it/s]"
      ]
     },
     {
@@ -1463,7 +1463,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  95%|█████████▌| 95/100 [00:41<00:02,  2.32it/s]"
+      "Estimating bootstrap interval...:  95%|█████████▌| 95/100 [00:43<00:02,  2.22it/s]"
      ]
     },
     {
@@ -1471,7 +1471,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  96%|█████████▌| 96/100 [00:41<00:01,  2.32it/s]"
+      "Estimating bootstrap interval...:  96%|█████████▌| 96/100 [00:44<00:01,  2.22it/s]"
      ]
     },
     {
@@ -1479,7 +1479,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  97%|█████████▋| 97/100 [00:42<00:01,  2.32it/s]"
+      "Estimating bootstrap interval...:  97%|█████████▋| 97/100 [00:44<00:01,  2.21it/s]"
      ]
     },
     {
@@ -1487,7 +1487,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  98%|█████████▊| 98/100 [00:42<00:00,  2.32it/s]"
+      "Estimating bootstrap interval...:  98%|█████████▊| 98/100 [00:45<00:00,  2.21it/s]"
      ]
     },
     {
@@ -1495,7 +1495,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...:  99%|█████████▉| 99/100 [00:43<00:00,  2.32it/s]"
+      "Estimating bootstrap interval...:  99%|█████████▉| 99/100 [00:45<00:00,  2.20it/s]"
      ]
     },
     {
@@ -1503,7 +1503,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00,  2.32it/s]"
+      "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:46<00:00,  2.20it/s]"
      ]
     },
     {
@@ -1511,15 +1511,15 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00,  2.30it/s]"
+      "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:46<00:00,  2.16it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'ate': 6579.210517369218, '0%-20%': 3941.620563059555, '20%-40%': 3472.827070248335, '40%-60%': 5551.872539342875, '60%-80%': 7535.200629399749, '80%-100%': 12227.572489811779}\n",
-      "{'ate': array([4859.15619427, 8363.63710589]), '0%-20%': array([2425.3999363 , 5605.11577024]), '20%-40%': array([1463.60903336, 5794.94940105]), '40%-60%': array([3306.12004311, 8036.63811783]), '60%-80%': array([ 4810.31782312, 10781.24257438]), '80%-100%': array([ 5546.67902188, 18650.67127681])}\n"
+      "{'ate': 6599.824328516486, '0%-20%': 4039.699222520601, '20%-40%': 3339.4679058647803, '40%-60%': 5794.671860700217, '60%-80%': 7659.605099728334, '80%-100%': 11995.431482001522}\n",
+      "{'ate': array([5071.87036132, 8545.70698262]), '0%-20%': array([2461.44199557, 5631.10422336]), '20%-40%': array([1300.76540705, 5646.82387151]), '40%-60%': array([3720.3467779 , 8486.96668456]), '60%-80%': array([ 4765.22282317, 11213.38070644]), '80%-100%': array([ 6000.76420438, 18819.65878719])}\n"
      ]
     },
     {
@@ -1569,10 +1569,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:48.727280Z",
-     "iopub.status.busy": "2024-10-25T14:53:48.726895Z",
-     "iopub.status.idle": "2024-10-25T14:53:48.821294Z",
-     "shell.execute_reply": "2024-10-25T14:53:48.820697Z"
+     "iopub.execute_input": "2024-10-25T14:54:52.988838Z",
+     "iopub.status.busy": "2024-10-25T14:54:52.988637Z",
+     "iopub.status.idle": "2024-10-25T14:54:53.114015Z",
+     "shell.execute_reply": "2024-10-25T14:54:53.113314Z"
     }
    },
    "outputs": [
@@ -1580,13 +1580,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_4021/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n",
+      "/tmp/ipykernel_4017/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n",
       "  plt.plot((x, x), (ci[0], ci[1]), 'ro-', color='orange')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
diff --git a/main/example_notebooks/gcm_basic_example.html b/main/example_notebooks/gcm_basic_example.html
index c4ca7a152..94df8e4cc 100644
--- a/main/example_notebooks/gcm_basic_example.html
+++ b/main/example_notebooks/gcm_basic_example.html
@@ -633,33 +633,33 @@ <h2>Step 1: Modeling cause-effect relationships as a structural causal model (SC
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.216788</td>
-      <td>0.410497</td>
-      <td>0.318602</td>
+      <td>-1.004880</td>
+      <td>-0.701488</td>
+      <td>-2.403116</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-0.476031</td>
-      <td>-1.903199</td>
-      <td>-4.402106</td>
+      <td>-0.734725</td>
+      <td>0.451607</td>
+      <td>1.933812</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.656258</td>
-      <td>1.192341</td>
-      <td>4.106536</td>
+      <td>-0.233025</td>
+      <td>-0.405104</td>
+      <td>-2.811089</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>-1.751869</td>
-      <td>-4.711298</td>
-      <td>-13.801311</td>
+      <td>-1.204772</td>
+      <td>-1.922767</td>
+      <td>-6.003365</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>-0.945124</td>
-      <td>-0.398115</td>
-      <td>0.019949</td>
+      <td>1.501223</td>
+      <td>3.122603</td>
+      <td>10.429561</td>
     </tr>
   </tbody>
 </table>
@@ -715,19 +715,19 @@ <h2>Step 1: Modeling cause-effect relationships as a structural causal model (SC
 Node Y is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the causal relationship as Y := f(X) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 0.9742765024277729
+LinearRegression: 1.0448453469461705
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 0.9800239824248347
-HistGradientBoostingRegressor: 1.0913790854666052
+                (&#39;linearregression&#39;, LinearRegression)]): 1.0478728496088803
+HistGradientBoostingRegressor: 1.1349141690770985
 
 --- Node: Z
 Node Z is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the causal relationship as Z := f(Y) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 0.9500102471330812
+LinearRegression: 1.0165052626309083
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 0.9504305607115737
-HistGradientBoostingRegressor: 1.4228887507027141
+                (&#39;linearregression&#39;, LinearRegression)]): 1.017072889305317
+HistGradientBoostingRegressor: 1.421134931479623
 
 ===Note===
 Note, based on the selected auto assignment quality, the set of evaluated models changes.
@@ -764,7 +764,7 @@ <h2>Step 2: Fitting the SCM to the data<a class="headerlink" href="#Step-2:-Fitt
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00&lt;00:00, 404.57it/s]
+Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00&lt;00:00, 427.09it/s]
 </pre></div></div>
 </div>
 <p>Fitting means, we learn the generative models of the variables in the SCM according to the data.</p>
@@ -782,8 +782,8 @@ <h2>Step 2: Fitting the SCM to the data<a class="headerlink" href="#Step-2:-Fitt
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00&lt;00:00, 3684.60it/s]
-Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 18.68it/s]
+Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00&lt;00:00, 2787.53it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 18.08it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -812,21 +812,21 @@ <h2>Step 2: Fitting the SCM to the data<a class="headerlink" href="#Step-2:-Fitt
 We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.
 
 --- Node X
-- The KL divergence between generated and observed distribution is 0.017201478484168996.
+- The KL divergence between generated and observed distribution is 0.04486035655302496.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Y
-- The MSE is 0.976443943612139.
-- The NMSE is 0.4483833647839692.
-- The R2 coefficient is 0.7985032436842728.
-- The normalized CRPS is 0.2555275355528893.
+- The MSE is 1.0467465612232283.
+- The NMSE is 0.47323453236211643.
+- The R2 coefficient is 0.7742617784338882.
+- The normalized CRPS is 0.272445898490441.
 The estimated CRPS indicates a good model performance.
 
 --- Node Z
-- The MSE is 0.9526589247617239.
-- The NMSE is 0.1451098038253918.
-- The R2 coefficient is 0.9789264378438249.
-- The normalized CRPS is 0.08325239327425824.
+- The MSE is 1.0162458498820515.
+- The NMSE is 0.15398077945269872.
+- The R2 coefficient is 0.9762621570148147.
+- The normalized CRPS is 0.08788013934735178.
 The estimated CRPS indicates a very good model performance.
 
 ==== Evaluation of Invertible Functional Causal Model Assumption ====
@@ -834,13 +834,13 @@ <h2>Step 2: Fitting the SCM to the data<a class="headerlink" href="#Step-2:-Fitt
 --- The model assumption for node Y is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
---- The model assumption for node Z is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node Z is not rejected with a p-value of 0.5259643809464394 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
 Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.
 
 ==== Evaluation of Generated Distribution ====
-The overall average KL divergence between the generated and observed distribution is 0.012872322533934848
+The overall average KL divergence between the generated and observed distribution is 0.016592406474661824
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 ==== Evaluation of the Causal Graph Structure ====
@@ -910,33 +910,33 @@ <h2>Step 3: Answering a causal query based on the SCM<a class="headerlink" href=
   <tbody>
     <tr>
       <th>0</th>
-      <td>0.396680</td>
+      <td>0.501525</td>
       <td>2.34</td>
-      <td>6.897156</td>
+      <td>7.645349</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-1.075865</td>
+      <td>0.999885</td>
       <td>2.34</td>
-      <td>6.658478</td>
+      <td>7.319836</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.565186</td>
+      <td>-0.464415</td>
       <td>2.34</td>
-      <td>6.775646</td>
+      <td>5.997534</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>-2.294770</td>
+      <td>0.903415</td>
       <td>2.34</td>
-      <td>5.928846</td>
+      <td>8.018945</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>-0.059750</td>
+      <td>-0.751885</td>
       <td>2.34</td>
-      <td>6.726993</td>
+      <td>6.773647</td>
     </tr>
   </tbody>
 </table>
diff --git a/main/example_notebooks/gcm_basic_example.ipynb b/main/example_notebooks/gcm_basic_example.ipynb
index 626e24c1d..96a76c547 100644
--- a/main/example_notebooks/gcm_basic_example.ipynb
+++ b/main/example_notebooks/gcm_basic_example.ipynb
@@ -25,10 +25,10 @@
    "id": "f22337ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:51.740493Z",
-     "iopub.status.busy": "2024-10-25T14:53:51.740087Z",
-     "iopub.status.idle": "2024-10-25T14:53:52.022190Z",
-     "shell.execute_reply": "2024-10-25T14:53:52.021668Z"
+     "iopub.execute_input": "2024-10-25T14:54:57.030287Z",
+     "iopub.status.busy": "2024-10-25T14:54:57.030106Z",
+     "iopub.status.idle": "2024-10-25T14:54:57.374207Z",
+     "shell.execute_reply": "2024-10-25T14:54:57.373587Z"
     }
    },
    "outputs": [],
@@ -51,10 +51,10 @@
    "id": "0367caeb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:52.024540Z",
-     "iopub.status.busy": "2024-10-25T14:53:52.024176Z",
-     "iopub.status.idle": "2024-10-25T14:53:53.148136Z",
-     "shell.execute_reply": "2024-10-25T14:53:53.147513Z"
+     "iopub.execute_input": "2024-10-25T14:54:57.376878Z",
+     "iopub.status.busy": "2024-10-25T14:54:57.376521Z",
+     "iopub.status.idle": "2024-10-25T14:54:58.797105Z",
+     "shell.execute_reply": "2024-10-25T14:54:58.796415Z"
     }
    },
    "outputs": [],
@@ -87,10 +87,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:53.150607Z",
-     "iopub.status.busy": "2024-10-25T14:53:53.150113Z",
-     "iopub.status.idle": "2024-10-25T14:53:53.160107Z",
-     "shell.execute_reply": "2024-10-25T14:53:53.159512Z"
+     "iopub.execute_input": "2024-10-25T14:54:58.800006Z",
+     "iopub.status.busy": "2024-10-25T14:54:58.799409Z",
+     "iopub.status.idle": "2024-10-25T14:54:58.811767Z",
+     "shell.execute_reply": "2024-10-25T14:54:58.811053Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -129,33 +129,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.216788</td>\n",
-       "      <td>0.410497</td>\n",
-       "      <td>0.318602</td>\n",
+       "      <td>-1.004880</td>\n",
+       "      <td>-0.701488</td>\n",
+       "      <td>-2.403116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.476031</td>\n",
-       "      <td>-1.903199</td>\n",
-       "      <td>-4.402106</td>\n",
+       "      <td>-0.734725</td>\n",
+       "      <td>0.451607</td>\n",
+       "      <td>1.933812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.656258</td>\n",
-       "      <td>1.192341</td>\n",
-       "      <td>4.106536</td>\n",
+       "      <td>-0.233025</td>\n",
+       "      <td>-0.405104</td>\n",
+       "      <td>-2.811089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-1.751869</td>\n",
-       "      <td>-4.711298</td>\n",
-       "      <td>-13.801311</td>\n",
+       "      <td>-1.204772</td>\n",
+       "      <td>-1.922767</td>\n",
+       "      <td>-6.003365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.945124</td>\n",
-       "      <td>-0.398115</td>\n",
-       "      <td>0.019949</td>\n",
+       "      <td>1.501223</td>\n",
+       "      <td>3.122603</td>\n",
+       "      <td>10.429561</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -163,11 +163,11 @@
       ],
       "text/plain": [
        "          X         Y          Z\n",
-       "0 -0.216788  0.410497   0.318602\n",
-       "1 -0.476031 -1.903199  -4.402106\n",
-       "2  0.656258  1.192341   4.106536\n",
-       "3 -1.751869 -4.711298 -13.801311\n",
-       "4 -0.945124 -0.398115   0.019949"
+       "0 -1.004880 -0.701488  -2.403116\n",
+       "1 -0.734725  0.451607   1.933812\n",
+       "2 -0.233025 -0.405104  -2.811089\n",
+       "3 -1.204772 -1.922767  -6.003365\n",
+       "4  1.501223  3.122603  10.429561"
       ]
      },
      "execution_count": 3,
@@ -214,10 +214,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:53.161979Z",
-     "iopub.status.busy": "2024-10-25T14:53:53.161651Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.936099Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.934878Z"
+     "iopub.execute_input": "2024-10-25T14:54:58.814011Z",
+     "iopub.status.busy": "2024-10-25T14:54:58.813573Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.026608Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.025470Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -245,10 +245,10 @@
    "id": "4f1493d8-9a0b-4ed5-97be-7b5f39cbd7d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.938962Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.938727Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.946490Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.945891Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.030360Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.029764Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.039636Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.039033Z"
     }
    },
    "outputs": [
@@ -282,19 +282,19 @@
       "Node Y is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Y := f(X) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 0.9742765024277729\n",
+      "LinearRegression: 1.0448453469461705\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 0.9800239824248347\n",
-      "HistGradientBoostingRegressor: 1.0913790854666052\n",
+      "                ('linearregression', LinearRegression)]): 1.0478728496088803\n",
+      "HistGradientBoostingRegressor: 1.1349141690770985\n",
       "\n",
       "--- Node: Z\n",
       "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Z := f(Y) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 0.9500102471330812\n",
+      "LinearRegression: 1.0165052626309083\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 0.9504305607115737\n",
-      "HistGradientBoostingRegressor: 1.4228887507027141\n",
+      "                ('linearregression', LinearRegression)]): 1.017072889305317\n",
+      "HistGradientBoostingRegressor: 1.421134931479623\n",
       "\n",
       "===Note===\n",
       "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n",
@@ -322,10 +322,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.948598Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.948395Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.951785Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.951216Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.041966Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.041541Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.045490Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.044929Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -369,10 +369,10 @@
    "id": "fe5f99f0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.953803Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.953383Z",
-     "iopub.status.idle": "2024-10-25T14:53:56.966843Z",
-     "shell.execute_reply": "2024-10-25T14:53:56.966277Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.047672Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.047251Z",
+     "iopub.status.idle": "2024-10-25T14:55:03.060839Z",
+     "shell.execute_reply": "2024-10-25T14:55:03.060164Z"
     }
    },
    "outputs": [
@@ -413,7 +413,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 404.57it/s]"
+      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 427.09it/s]"
      ]
     },
     {
@@ -450,10 +450,10 @@
    "id": "671cd8d8-75b0-4019-b503-8aa83ad91615",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:56.969009Z",
-     "iopub.status.busy": "2024-10-25T14:53:56.968611Z",
-     "iopub.status.idle": "2024-10-25T14:53:58.016098Z",
-     "shell.execute_reply": "2024-10-25T14:53:58.015530Z"
+     "iopub.execute_input": "2024-10-25T14:55:03.063207Z",
+     "iopub.status.busy": "2024-10-25T14:55:03.062967Z",
+     "iopub.status.idle": "2024-10-25T14:55:04.196416Z",
+     "shell.execute_reply": "2024-10-25T14:55:04.195750Z"
     }
    },
    "outputs": [
@@ -470,7 +470,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3684.60it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 2787.53it/s]"
      ]
     },
     {
@@ -493,7 +493,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.21it/s]"
+      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.18it/s]"
      ]
     },
     {
@@ -501,7 +501,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.64it/s]"
+      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.15it/s]"
      ]
     },
     {
@@ -509,7 +509,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.68it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.08it/s]"
      ]
     },
     {
@@ -521,7 +521,7 @@
     },
     {
      "data": {
-      "image/png": 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jI4OwsDCqVauGqakpzZs3L5bO2H379iUyMlJtWWRkJH379lVbduTIEWbOnEl4eDjz5s3Dy8sLR0dH2rdvz+bNmzXKF5cLFy6wc+dOvvnmG5o3b06rVq348ssvWb9+Pbdu3cp3u4EDB/LWW2/h6OhIo0aNmDFjBn/99ZfqCUnFihX55JNPaNKkCTVq1KBdu3aEhIRw4MCBEjkOUTBJFIQQQghR7kRHR6Onp8eRI0dYvHgxCxYs4JtvvsmzrIWFBZ07d2bdunVqy9euXUv37t0xMTEBwNzcnKioKM6fP8/ixYtZtWoVCxcufKk4hwwZQlxcHOvXr+f06dN89NFHdOjQgUuXLr1UvV27duWff/7h4MGDABw8eJB//vmHLl26qJVbu3YtZmZmhISE5FlPhQoV8t1HvXr1MDMzy/f17rvv5rttXFwcFSpUoEmTJqplvr6+6OjocPjw4UId4+PHj4mMjMTJyQl7e/s8y9y6dYstW7bQpo3MeVUW3vhRj4QQQghR/tjb27Nw4UIUCgW1a9fmzJkzLFy4kAEDBuRZPjAwkN69e5OWloaJiQkpKSls376drVu3qspMmDBB9W9HR0fCwsJYv349Y8aMKVKMiYmJREZGkpiYiJ2dHQBhYWHs3LmTyMhIZs6cWaR6AfT19enVqxerV6+mVatWrF69ml69eqGvr69W7tKlS9SsWVNjeWHs2LGjwCcqxsbG+a67ffs2Vaqoz8qup6eHlZUVt2/fLnC/y5cvZ8yYMTx+/JjatWuze/duDAwM1Mr07NmTH3/8kSdPntClS5d8k0RRst74JwqGRqaca7+Oc+3XYWhkWtbhCCGEEAJo0aIFCoVC9b5ly5ZcunSJ7OxsZs6cqXbnOzExkY4dO6Kvr89PP/0E5HbutbCwwNfXV1XHhg0b8Pb2xsbGBjMzMyZMmEBiYmKRYzxz5gzZ2dm4urqqxbN//36uXLlS9IP/P/3792fTpk3cvn2bTZs20b9/f40y+TXHKowaNWrg7Oyc7yuv/hDFITAwkJMnT7J//35cXV3x9/cnPT1drczChQs5ceIEP/74I1euXCE0NLREYhEFe+OfKOjq6VHPu1NZhyGEEEKIQho8eDD+/v6q93Z2dujp6fHhhx+ybt06evTowbp16wgICEBPL/dSJy4ujsDAQKZOnYqfnx+WlpasX7+e8PDwfPejo6OjcSH+/B341NRUdHV1OX78OLq6umrlzMzMXvo4GzRogJubGz179qROnTrUr1+f+Ph4tTKurq4cPHiQrKwsrZ8q1KtXj//973/5rm/durVGv49nbGxsuHv3rtqyp0+f8uDBA2xsbArcr6WlJZaWlri4uNCiRQsqVqzI1q1b6dmzp1r9NjY2uLm5YWVlRevWrZk4cSK2trZaHKF4WW98oiCEEEKI8uff7dz/+OMPXFxc0NXVxcrKCisrK41tAgMDad++PefOneO3335jxowZqnWHDh2iRo0afP7556plBV0kA1hbW5OUlKR6n52dzdmzZ2nbti0Anp6eZGdnc/fuXVq3bl2k43yR/v37ExISwooVK/Jc//HHH7NkyRKWL1/O8OHDNdY/fPgw334KL9P0qGXLljx8+JDjx4/TuHFjAH777TdycnJo3rx5AUekTqlUolQqycjIyLdMTk4OQIFlRMl44xOFrMwMTmzN7cjU6L2R6BsYlnFEQgghhEhMTCQ0NJRBgwZx4sQJvvzyywLv/gO89dZb2NjYEBgYiJOTk9oFq4uLC4mJiaxfv56mTZtq9F/Iy9tvv01oaCjbt2+nVq1aLFiwgIcPH6rWu7q6EhgYSJ8+fQgPD8fT05O///6bvXv34u7uTqdO+bdYuHnzpsbTgRo1amiUGzBgAB999FG+F/vNmzdnzJgxjBo1ips3b/Lee+9hZ2fH5cuX+eqrr2jVqlWeCUR++yusOnXq0KFDBwYMGMBXX31FVlYWQ4YMoUePHqr+Gjdv3qRdu3asWbOGZs2acfXqVTZs2MA777yDtbU1N27cYPbs2RgbG9OxY0cgN3m5c+cOTZs2xczMjHPnzjF69Gi8vb1xdHQscryiaN74PgpZmek0vzCL5hdmkZWZ/uINhBBCCFHi+vTpw5MnT2jWrBmffvopw4cPf+EY/QqFgp49e3Lq1CkCAwPV1nXt2pWRI0cyZMgQPDw8OHToEBMnTiywvv79+9O3b1/69OlDmzZtqFmzpuppwjORkZH06dOHUaNGUbt2bbp3787Ro0dxcHAosO758+fj6emp9tq+fbtGOT09PSpXrqxqQpWXOXPmsG7dOg4fPoyfnx/16tUjNDQUd3f3EhseFXJHXHJzc6Ndu3Z07NiRVq1a8fXXX6vWZ2VlkZCQoBqe1sjIiAMHDtCxY0ecnZ0JCAjA3NycQ4cOqTpGGxsbs2rVKlq1akWdOnUYOXIkXbt25b///W+JHYfIn0L5Mr1gylhKSgqWlpYkJydjYWFRpDrSUpMxmZ/7nzktLBETM8viDFEIIV5LSUlJTJi1EAevrlhUqvLiDf7PzcvnObVmEssH++DioF1b46T7Kaw8cJtB42a+VDvl4vjb8bz09HSuXbuGk5MTRkZGL12fyJ2/wMPDI9/Zi4UQL6ewv1tv/BMFIYQQQgghhCZJFIQQQgghhBAa3vjOzEIIIYQoX2JiYso6BCEE8kRBCCGEEEIIkQdJFIQQQgghhBAa3vimRwaGxpx6ayUA9Qzzn1hECCGEEEKIN8kbnyjo6RvQ8O0eZR2GEEIIIYQQ5Yo0PRJCCCGEEEJoeOOfKGRlZnBye+4sgp6dBqJvYFjGEQkhhBBCCFH2JFHITKfZqQkApLXvI4mCEEIIIYQQSNMjIYQQQgghRB4kURBCCCGEEEJokERBCCGEEEIIoUESBSGEEEIIIYQGSRSEEEIIIYQQGiRREEIIIYQQQmh444dHNTA05nizRQA0NDQu22CEEEIIIYQoJ974REFP34DGHfuVdRhCCCGEEEKUK9L0SAghhBBCCKHhjX+i8DQrk1O71wLQsH0gevoGZRyREEIIIYQQZe+NTxQyM57Q+MgIANLeel8SBSGEEEIIIZCmR0IIIYQQQog8SKIghBBCCCGE0CCJghBCCCGEEEKDJApCCCGEEEIIDW98Z2YhhBCipCQnJ5OWllYq+zIxMcHS0rLY650yZQrbtm0jPj6+UOWvX7+Ok5MTJ0+exMPDo8j7La56Strt27fp3bs3hw4dQl9fn4cPH5Z1SIWWlpZG79692b17N48ePeKff/6hQoUKZR1WuRIVFcWIESNeqc+1OGmdKDx58gSlUomJiQkA//vf/9i6dSt169blnXfe0aquFStWsGLFCq5fvw5AvXr1mDRpEu+++662YQkhhBDlSnJyMl/MXcj9R6WTKFQyN+HzMSMLnSx06dKFrKwsdu7cqbHuwIEDvPXWW5w6dYqwsDCGDh1a3OGqCQoK4uHDh2zbtk21zN7enqSkJCpXrlyi+35ZCxcuJCkpifj4+DzPvaOjI//73//y3b5v375ERUWhUChUyywsLKhfvz7Tp0/n7bffLpG4AaKjozlw4ACHDh2icuXKJZJoloXSvrh//rMzMTHBzs4Ob29vhg4dSuPGjTXK37hxg5o1a+Lq6srZs2c11iuVSr755htWr17NuXPnyMnJoUaNGvj6+jJ06FCcnZ1L9Hiep3Wi0K1bN95//30GDx7Mw4cPad68Ofr6+ty7d48FCxbwySefFLqu6tWrM3v2bFxcXFAqlURHR9OtWzdOnjxJvXr1tA2tSPQNjDjScAYAngZGpbJPIYQQr7+0tDTuP0rDql4rzCytSnRfqckPuH/uIGlpaYW+2AsODuaDDz7gxo0bVK9eXW1dZGQkTZo0wd3dHQAzM7Nij/lFdHV1sbGxKfX9auvKlSs0btwYFxeXPNcfPXqU7OxsAA4dOsQHH3xAQkICFhYWABgbG6vKRkZG0qFDB+7du8fnn39O586dOXv2LDVr1iyx2OvUqUP9+vVLpP6CZGVloa+vX+r7LSnPPrv09HQuXrzI119/TfPmzVm9ejV9+vRRKxsVFYW/vz+///47hw8fpnnz5qp1SqWSjz/+mG3btvHZZ5+xcOFC7OzsuHXrFlu3bmXGjBlERUWV2nFp3UfhxIkTtG7dGoAffviBqlWr8r///Y81a9awZMkSrerq0qULHTt2xMXFBVdXV7744gvMzMz4448/tA2ryPQNDGn23lCavTcUfQPDUtuvEEKIN4OZpRUWlaqU6KsoiUjnzp2xtrbWuOhITU1l06ZNBAcHA7lNj55v+pOTk8O0adOoXr06hoaGeHh45PlU4pns7GyCg4NxcnLC2NiY2rVrs3jxYtX6KVOmEB0dzY8//ohCoUChUBATE8P169dRKBRqTZ72799Ps2bNMDQ0xNbWlnHjxvH06VPVeh8fH4YNG8aYMWOwsrLCxsaGKVOmqNYrlUqmTJmCg4MDhoaG2NnZMWzYsALP04oVK6hVqxYGBgbUrl2bb7/9VrXO0dGRzZs3s2bNGhQKBUFBQRrbW1tbY2Njg42NDVZWuZ9TlSpVVMueT+wqVKiAjY0N9evXZ8WKFTx58oTdu3dz//59evbsSbVq1TAxMaFBgwZ8//33BcYNsHnzZurVq4ehoSGOjo6Eh4ernavw8HB+//13FAoFPj4+edbx7PNfuXIl9vb2mJiY4O/vT3Jyslq5b775hjp16mBkZISbmxvLly9XrXv2WW7YsIE2bdpgZGTE2rVrCQoKonv37sycOZOqVatSoUIFpk2bxtOnTxk9ejRWVlZUr16dyMhIVV0xMTEoFAq1pwXx8fEoFAquX79OTEwM/fr1Izk5WfV9evYdyMjIICwsjGrVqmFqakrz5s2JiYlRO46oqCgcHBwwMTHhvffe4/79+y88z/D/PztHR0feeecdfvjhBwIDAxkyZAj//POPqpxSqSQyMpLevXvz8ccfExERoVbPhg0bWL9+PRs2bGDixIm0aNECBwcHWrRowZw5czTORbNmzTA1NaVChQp4e3sX+PSqKLROFNLS0jA3Nwfg119/5f3330dHR4cWLVq8VHDZ2dmsX7+ex48f07JlyyLXI4QQQogX09PTo0+fPkRFRaFUKlXLN23aRHZ2Nj179sxzu8WLFxMeHs78+fM5ffo0fn5+dO3alUuXLuVZPicnh+rVq7Np0ybOnz/PpEmT+Oyzz9i4cSMAYWFh+Pv706FDB5KSkkhKSsLLy0ujnps3b9KxY0eaNm3KqVOnWLFiBREREcyYMUOtXHR0NKamphw+fJi5c+cybdo0du/eDeReOC9cuJCVK1dy6dIltm3bRoMGDfI9R1u3bmX48OGMGjWKs2fPMmjQIPr168e+ffuA3KcFHTp0wN/fn6SkJLUE6GU9e9KQmZlJeno6jRs3Zvv27Zw9e5aBAwfSu3dvjhw5ku/2x48fx9/fnx49enDmzBmmTJnCxIkTVYnhli1bGDBgAC1btiQpKYktW7bkW9fly5fZuHEjP//8Mzt37uTkyZOEhISo1q9du5ZJkybxxRdfcOHCBWbOnMnEiROJjo5Wq2fcuHEMHz6cCxcu4OfnB8Bvv/3GrVu3+P3331mwYAGTJ0+mc+fOVKxYkcOHDzN48GAGDRrEjRs3CnXevLy8WLRoERYWFqrvU1hYGABDhgwhLi6O9evXc/r0aT766CM6dOig+u4ePnyY4OBghgwZQnx8PG3bttX4fmlj5MiRPHr0SPX9A9i3bx9paWn4+vrSq1cv1bXvM99//z21a9ema9euedb5rJnT06dP6d69O23atOH06dPExcUxcOBAtWZQxUHrpkfOzs5s27aN9957j127djFy5EgA7t69q3qMpo0zZ87QsmVL0tPTMTMzU/V3yEtGRgYZGRmq9ykpKVrv79+eZmVy7kDuf456rWVmZiGEEG+O/v37M2/ePPbv36+6oxwZGckHH3yQbxOm+fPnM3bsWHr06AHAnDlz2LdvH4sWLWLZsmUa5fX19Zk6darqvZOTE3FxcWzcuBF/f3/MzMwwNjYmIyOjwKZGy5cvx97enqVLl6JQKHBzc+PWrVuMHTuWSZMmoaOTe+/T3d2dyZMnA+Di4sLSpUvZu3cv7du3JzExERsbG3x9fdHX18fBwYFmzZrlu8/58+cTFBSkuigODQ3ljz/+YP78+bRt2xZra2sMDQ0xNjYu1mZSaWlpTJgwAV1dXdq0aUO1atVUF7sAQ4cOZdeuXWzcuDHf+BcsWEC7du2YOHEiAK6urpw/f5558+YRFBSElZUVJiYmGBgYvDD29PR01qxZQ7Vq1QD48ssv6dSpE+Hh4djY2DB58mTCw8N5//33gdzP+Pz586xcuZK+ffuq6hkxYoSqzDNWVlYsWbIEHR0dateuzdy5c0lLS+Ozzz4DYPz48cyePZuDBw+qvnMFMTAwwNLSEoVCoXZciYmJREZGkpiYiJ2dHZCbpO7cuZPIyEhmzpzJ4sWL6dChA2PGjFGds0OHDhX4xKwgbm5uAKq+uAARERH06NEDXV1d6tevT82aNdm0aZPqadTFixepXbu2Wj0jRozgm2++AXKfXNy4cYOUlBSSk5Pp3LkztWrVAqBOnTpFirMgWj9RmDRpEmFhYTg6OtK8eXPV3f9ff/0VT09PrQOoXbs28fHxHD58mE8++YS+ffty/vz5PMvOmjULS0tL1cve3l7r/f1bZsYTGv4+iIa/DyIz48lL1yeEEEK8Ktzc3PDy8mL16tVA7p3jAwcOqJod/VtKSgq3bt3C29tbbbm3tzcXLlzIdz/Lli2jcePGWFtbY2Zmxtdff01iYqJWsV64cIGWLVuq3TH19vYmNTVV7W7zs34Vz9ja2nL37l0APvroI548eULNmjUZMGAAW7duVWu6lNc+tT3Wl9GzZ0/MzMwwNzdn8+bNRERE4O7uTnZ2NtOnT6dBgwZYWVlhZmbGrl27CjyH+cV+6dIlVZ+JwnJwcFAlCQAtW7YkJyeHhIQEHj9+zJUrVwgODsbMzEz1mjFjBleuXFGrp0mTJhp116tXT5XkAVStWlXtKY+uri6VKlVSfYZFdebMGbKzs3F1dVWLc//+/ao4L1y4oNZf4NmxFtWzJ3XPvrMPHz5ky5Yt9OrVS1WmV69eGs2P/u3zzz8nPj6eSZMmkZqaCuQmWEFBQfj5+dGlSxcWL15MUlJSkWPNj9ZPFD788ENatWpFUlISDRs2VC1v164d7733ntYBGBgYqHpvN27cmKNHj7J48WJWrlypUXb8+PGEhoaq3qekpBRLsiCEEEK8qYKDgxk6dCjLli0jMjKSWrVq0aZNm2Krf/369YSFhREeHk7Lli0xNzdn3rx5HD58uNj28bx/d5BVKBTk5OQAuSMpJSQksGfPHnbv3k1ISIjqiUp56Fi7cOFCfH19sbS0xNraWrV83rx5LF68mEWLFtGgQQNMTU0ZMWIEmZmZZRhtrmcXrqtWrdK4yNbV1VV7b2pqqrF9Xp9XQZ/hs6Ti+eZyWVlZhYpTV1eX48ePa8RVUp31nyWUTk5OAKxbt4709HSNzss5OTlcvHgRV1dXXFxcSEhIUKvH2toaa2trqlSporY8MjKSYcOGsXPnTjZs2MCECRPYvXs3LVq0KLZjKNKEazY2Nnh6eqplgM2aNVM9YnkZOTk5as2LnmdoaIiFhYXaSwghhBBF5+/vj46ODuvWrWPNmjX0798/33bOFhYW2NnZERsbq7Y8NjY232bDsbGxeHl5ERISgqenJ87Ozhp3mg0MDF54l7tOnTrExcWpXSDGxsZibm6uMWpTQYyNjenSpQtLliwhJiaGuLg4zpw5k+8+tTnWl2VjY4Ozs7NakvBsn926daNXr140bNiQmjVrcvHixQLryi92V1dXjQvlF0lMTOTWrVuq93/88YeqqVDVqlWxs7Pj6tWrODs7q72eXSAXp2fn5vm75/+e4yOv75OnpyfZ2dncvXtXI85nTZTq1KmjkcC+zAA7z/pK+Pr6ArnNjkaNGkV8fLzqderUKVq3bq16qtezZ08SEhL48ccfC7UPT09Pxo8fz6FDh6hfvz7r1q0rcrx50fqJwuPHj5k9ezZ79+7l7t27qgzvmatXrxa6rvHjx/Puu+/i4ODAo0ePWLduHTExMezatUvbsIQQQghRBGZmZgQEBDB+/HhSUlLyHLnneaNHj2by5MnUqlULDw8PIiMjiY+PZ+3atXmWd3FxYc2aNezatQsnJye+/fZbjh49qnYR6ejoyK5du0hISKBSpUp59o8ICQlh0aJFDB06lCFDhpCQkMDkyZMJDQ1Vu3FZkKioKLKzs2nevDkmJiZ89913GBsbU6NGjXyP1d/fH09PT3x9ffn555/ZsmULe/bsKdT+iouLiws//PADhw4domLFiixYsIA7d+4UmLCMGjWKpk2bMn36dAICAoiLi2Pp0qVqoxEVlpGREX379mX+/PmkpKQwbNgw/P39VRfYU6dOZdiwYVhaWtKhQwcyMjI4duwY//zzj1pLkOLg7OyMvb09U6ZM4YsvvuDixYtqozlB7vcpNTWVvXv30rBhQ0xMTHB1dSUwMJA+ffoQHh6Op6cnf//9N3v37sXd3Z1OnToxbNgwvL29mT9/Pt26dWPXrl2F7p/w8OFDbt++TUZGBhcvXmTlypVs27aNNWvWUKFCBeLj4zlx4gRr167VuLHes2dPpk2bxowZM+jRowdbtmyhR48ejB8/Hj8/P9UIoxs2bFAledeuXePrr7+ma9eu2NnZkZCQwKVLlzSGYn1ZWicK//nPf9i/fz+9e/fG1tb2pXpX3717lz59+pCUlISlpSXu7u7s2rWL9u3bF7lOIYQQojxJTX5Q7vcRHBxMREQEHTt2VHX0zM+wYcNITk5m1KhR3L17l7p16/LTTz/lO4/AoEGDOHnyJAEBASgUCnr27ElISAi//PKLqsyAAQOIiYmhSZMmpKamsm/fPhwdHdXqqVatGjt27GD06NE0bNgQKysrgoODmTBhQqGPs0KFCsyePZvQ0FCys7Np0KABP//8M5UqVcqzfPfu3Vm8eDHz589n+PDhODk5ERkZme9QoiVlwoQJXL16FT8/P0xMTBg4cCDdu3fXGKL0eY0aNWLjxo1MmjSJ6dOnY2try7Rp016YCObF2dmZ999/n44dO/LgwQM6d+6slnD85z//wcTEhHnz5jF69GhMTU1p0KABI0aMKMLRFkxfX5/vv/+eTz75BHd3d5o2bcqMGTP46KOPVGW8vLwYPHgwAQEB3L9/n8mTJzNlyhQiIyOZMWMGo0aN4ubNm1SuXJkWLVrQuXNnAFq0aMGqVauYPHkykyZNwtfXlwkTJjB9+vQXxtWvXz8gN6mqVq0arVq14siRIzRq1AjIfZpQt27dPFvfvPfeewwZMoQdO3bQtWtXNmzYwKpVq4iMjGTu3LlkZWVRvXp12rVrx4IFC4Dcid3+/PNPoqOjuX//Pra2tnz66acMGjTopc/x8xTK55/hFUKFChXYvn27RgeZspCSkoKlpSXJyclFboaUlpqMyXyH3H+HJWJi9nrMSiiEECUpKSmJCbMW4uDVFYtKVV68wf+5efk8p9ZMYvlgH1wcbLXb5/0UVh64zaBxM7G11W7b5xXH347npaenc+3aNZycnDAy+v8Td5b3mZmFKIwpU6awbds2jeY94tWW3+/Wv2n9RKFixYqqCUOEEEIIkTdLS0s+HzOStLTSSRRMTEwkSRBCFCutE4Xp06czadIkoqOjMTExKYmYSpW+gRGH64wHoJFB/hmVEEIIoa1nw3kLIcSrSOtEITw8nCtXrlC1alUcHR01hrA6ceJEsQVXGvQNDGkeMK6swxBCCCGEKHemTJnClClTyjoMUUa0ThS6d+9eAmEIIYQQQgghyhOtE4Vn06K/LrKfPuXPw7nDsbo190NXT+tTIoQQQgghxGunyFfFx48fV804V69ePTw9PYstqNKUkf6Yers/BiCtoYx6JIQQQgghBBQhUbh79y49evQgJiaGChUqALmTTLRt25b169drzCYohBBCCCGEePUUbirD5wwdOpRHjx5x7tw5Hjx4wIMHDzh79qxqpj4hhBBCCCHEq0/rJwo7d+5kz5491KlTR7Wsbt26LFu2jHfeeadYgxNCCCGEEEKUDa0ThZycHI0hUSF3Su2cnJxiCUoIIYR4HSQnJ7/yE65pOzPv9evXcXJy4uTJk3h4eBR5v8VVT0m7ffs2vXv35tChQ+jr6/Pw4cOyDumlxMbGMnjwYP788086derEtm3byjqkcsfHxwcPDw8WLVpU1qGUOK0Thbfffpvhw4fz/fffY2dnB8DNmzcZOXIk7dq1K/YAhRBCiFdRcnIyS+fNIOvRvVLZn755ZYaMnlDoZKFLly5kZWWxc+dOjXUHDhzgrbfe4tSpU4SFhTF06NDiDldNUFAQDx8+VLsotbe3JykpicqVK5fovl/WwoULSUpKIj4+Pt9z/6Jky8fHh/379zNr1izGjVOf26lTp07s2LGDyZMnq81ncPnyZb744gt2797N33//jZ2dHS1atGDUqFE0adKkyMcTGhqKh4cHv/zyC2ZmZkWup7wpzYv7qKgo+vXrB4COjg4WFha4urrSqVMnhg8fnuf3ZNasWUyYMIHZs2czevRojfW3b99m1qxZbN++nRs3bmBpaYmzszO9evWib9++JTYJstaJwtKlS+natSuOjo7Y29sD8Ndff1G/fn2+++67Yg9QCCGEeBWlpaWR9ege7zcwx7qCaYnu6++Hj9ly5h5paWmFThSCg4P54IMPuHHjBtWrV1dbFxkZSZMmTXB3dwcokwtGXV1dbGxsSn2/2rpy5QqNGzfGxcXlpeqxt7cnKipKLVG4efMme/fuxdbWVq3ssWPHaNeuHfXr12flypW4ubnx6NEjfvzxR0aNGsX+/fuLHMeVK1cYPHiwxneipGVmZmJgYFCq+yxJFhYWJCQkoFQqefjwIYcOHWLWrFlERkYSGxurutn+zOrVqxkzZgyrV6/WSBSuXr2Kt7c3FSpUYObMmTRo0ABDQ0POnDnD119/TbVq1ejatWuJHIfWnZnt7e05ceIE27dvZ8SIEYwYMYIdO3Zw4sSJUv9SFQc9fUP+qDWcP2oNR0/fsKzDEUII8ZqxrmCKbSWLEn0VJRHp3Lkz1tbWREVFqS1PTU1l06ZNBAcHA7l3w59v+pOTk8O0adOoXr06hoaGeHh45PlU4pns7GyCg4NxcnLC2NiY2rVrs3jxYtX6KVOmEB0dzY8//ohCoUChUBATE8P169dRKBRqd+H3799Ps2bNMDQ0xNbWlnHjxvH06VPVeh8fH4YNG8aYMWOwsrLCxsZG7S68UqlkypQpODg4YGhoiJ2d3QsHYlmxYgW1atXCwMCA2rVr8+2336rWOTo6snnzZtasWYNCoSAoKKjAugrSuXNn7t27R2xsrGpZdHQ077zzDlWqVFE7hqCgIFxcXDhw4ACdOnWiVq1aeHh4MHnyZH788cd895GRkcGwYcOoUqUKRkZGtGrViqNHjwKozvf9+/fp378/CoVC47vx/HFPnz6dnj17YmpqSrVq1Vi2bJlamYcPH/Kf//wHa2trLCwsePvttzl16pRq/bPv1TfffIOTkxNGRkYAKBQKVq5cSefOnTExMaFOnTrExcVx+fJlfHx8MDU1xcvLiytXrqjqCgoK0pgQeMSIEfj4+KjW79+/n8WLF6u+Y9evXwfg7NmzvPvuu5iZmVG1alV69+7NvXv//yng48eP6dOnD2ZmZtja2hIeHp7v+X2eQqHAxsYGW1tb6tSpQ3BwMIcOHSI1NZUxY8aold2/fz9Pnjxh2rRppKSkcOjQIbX1ISEh6OnpcezYMfz9/alTpw41a9akW7dubN++nS5dugBF+36/iNaJAuQefPv27Rk6dChDhw7F19f3pYIoSwaGRrToPY0WvadhYGhU1uEIIYQQpUJPT48+ffoQFRWFUqlULd+0aRPZ2dn07Nkzz+0WL15MeHg48+fP5/Tp0/j5+dG1a1cuXbqUZ/mcnByqV6/Opk2bOH/+PJMmTeKzzz5j48aNAISFheHv70+HDh1ISkoiKSkJLy8vjXpu3rxJx44dadq0KadOnWLFihVEREQwY8YMtXLR0dGYmppy+PBh5s6dy7Rp09i9ezcAmzdvZuHChaxcuZJLly6xbds2GjRokO852rp1K8OHD2fUqFGcPXuWQYMG0a9fP/bt2wfA0aNH6dChA/7+/iQlJaklQNoyMDAgMDCQyMhI1bKoqCj69++vVi4+Pp5z584xatQodHQ0L+OeDV2flzFjxrB582aio6M5ceIEzs7O+Pn58eDBA1VTLwsLCxYtWkRSUhIBAQH51jVv3jwaNmzIyZMnGTduHMOHD1edZ4CPPvqIu3fv8ssvv3D8+HEaNWpEu3btePDggarM5cuX2bx5M1u2bFFLCKdPn06fPn2Ij4/Hzc2Njz/+mEGDBjF+/HiOHTuGUqlkyJAhBZ1ONYsXL6Zly5YMGDBA9R2zt7fn4cOHvP3223h6enLs2DF27tzJnTt38Pf3V207evRo9u/fz48//sivv/5KTEwMJ06cKPS+n1elShUCAwP56aefyM7OVi2PiIigZ8+e6Ovr07NnTyIiIlTr7t+/z6+//sqnn36KqWneNwQUCgWg/fe7MArV9GjJkiUMHDgQIyMjlixZUmBZGSJVCCGEeDX079+fefPmsX//ftXd18jISD744IN8mzDNnz+fsWPH0qNHDwDmzJnDvn37WLRokcZdZcgd7GTq1Kmq905OTsTFxbFx40b8/f0xMzPD2NiYjIyMApsaLV++HHt7e5YuXYpCocDNzY1bt24xduxYJk2apLpodnd3Z/LkyQC4uLiwdOlS9u7dS/v27UlMTMTGxgZfX1/09fVxcHCgWbNm+e5z/vz5BAUFERISAuS23//jjz+YP38+bdu2xdraGkNDQ4yNjYulmVT//v1p3bo1ixcv5vjx4yQnJ9O5c2e1pyLPEjI3Nzet6n78+DErVqwgKiqKd999F4BVq1axe/duIiIiGD16NDY2NigUCiwtLV94PN7e3qpmUq6ursTGxrJw4ULat2/PwYMHOXLkCHfv3sXQMLe1xvz589m2bRs//PADAwcOBHKbG61Zs0ZjDq5+/fqpLtbHjh1Ly5YtmThxIn5+fgAMHz5c1QegMCwtLTEwMMDExETtuJYuXYqnpyczZ85ULVu9ejX29vZcvHgROzs7IiIi+O6771T9cKOjo1+qBc2zZmL379+nSpUqpKSk8MMPPxAXFwdAr169VN8BMzMzLl++jFKppHbt2mr1VK5cmfT0dAA+/fRT5syZo/X3uzAKlSgsXLiQwMBAjIyMWLhwYb7lFArFK5coZD99ypXTuY/5arl7o6tX5MmqhRBCiFeKm5sbXl5erF69Gh8fHy5fvsyBAweYNm1anuVTUlK4desW3t7easu9vb3VmpX827Jly1i9ejWJiYk8efKEzMxMrUcyunDhAi1btlTdPX2239TUVG7cuIGDgwOAql/FM7a2tty9exfIvcu9aNEiatasSYcOHejYsSNdunRBL5+//RcuXFBd1D6/z5d5clCQhg0b4uLiwg8//MC+ffvo3bu3RmzPP/3RxpUrV8jKylL77PT19WnWrBkXLlzQur6WLVtqvH/WUfjUqVOkpqZSqVIltTJPnjxRazJUo0aNPCfqff4zrFq1KoDanfGqVauSnp5OSkoKFhYWWsf+zKlTp9i3b1+efXCuXLmi+q42b95ctdzKykrjol0bzz6/Z9/j77//nlq1atGwYUMAPDw8qFGjBhs2bFA1/8vLkSNHyMnJITAwkIyMDED773dhFGrLa9eu5fnv10FG+mNcf8rtAJLmmoiJWfEPLSeEEEKUV8HBwQwdOpRly5YRGRlJrVq1aNOmTbHVv379esLCwggPD6dly5aYm5szb948Dh8+XGz7eN6/h3BXKBSq4dvt7e1JSEhgz5497N69m5CQENUTlbyGfi8L/fv3Z9myZZw/f54jR45orHd1dQXgzz//xNPTs7TDK5TU1FRsbW2JiYnRWPd806j8mtI8/1k8u6DOa9mzz1VHR0cjgcrKyipUnF26dGHOnDka62xtbbl8+fIL69DWhQsXsLCwUCVRERERnDt3Tu1iPicnh9WrVxMcHIyzszMKhYKEhAS1emrWrAmAsbGxallJfL+17qMwbdq0PMeEftYJQwghhBCvDn9/f3R0dFi3bh1r1qxRdWTNi4WFBXZ2dmodbiF37P26devmuU1sbCxeXl6EhITg6emJs7Oz2l1lyG2f/3yb7bw869T6/AVhbGws5ubmWjUFMTY2pkuXLixZsoSYmBji4uI4c+ZMvvvU5liLw8cff8yZM2eoX79+nvvx8PCgbt26hIeH5zl/VX7zODzrkP388WRlZXH06NEiHc8ff/yh8f7ZZLyNGjXi9u3b6Onp4ezsrPYqieFura2tSUpKUlv276Fo8/qONWrUiHPnzuHo6KgRp6mpKbVq1UJfX18tqf3nn3+4ePFikeK8e/cu69ato3v37ujo6HDmzBmOHTtGTEwM8fHxqtez7+Wff/5JpUqVaN++PUuXLuXx48cv3Ic23+/C0DpRmDp1KqmpqRrL09LS1NogCiGEEKL8MzMzIyAggPHjx5OUlPTCkXtGjx7NnDlz2LBhAwkJCYwbN474+HiGDx+eZ3kXFxeOHTvGrl27uHjxIhMnTlSNtPOMo6Mjp0+fJiEhgXv37uV5NzgkJIS//vqLoUOH8ueff/Ljjz8yefJkQkND8+zUm5eoqCgiIiI4e/YsV69e5bvvvsPY2JgaNWrke6xRUVGsWLGCS5cusWDBArZs2UJYWFih9ve8J0+eqF0MxsfHayRMABUrViQpKYm9e/fmWY9CoSAyMpKLFy/SunVrduzYwdWrVzl9+jRffPEF3bp1y3M7U1NTPvnkE0aPHs3OnTs5f/48AwYMIC0trcAmLvmJjY1l7ty5XLx4kWXLlrFp0ybVd8DX15eWLVvSvXt3fv31V65fv86hQ4f4/PPPOXbsmNb7epG3336bY8eOsWbNGi5dusTkyZM5e/asWhlHR0cOHz7M9evXuXfvHjk5OXz66ac8ePCAnj17cvToUa5cucKuXbvo168f2dnZmJmZERwczOjRo/ntt984e/YsQUFBhfq+KZVKbt++TVJSEhcuXGD16tV4eXlhaWnJ7NmzgdynCc2aNeOtt96ifv36qtdbb71F06ZNVZ2aly9fztOnT2nSpAkbNmzgwoULJCQk8N133/Hnn3+iq6sLaP/9LgytGy0plco87zScOnUKKyurIgcihBBCvI7+fvjiu4BlvY/g4GAiIiLo2LGjxvju/zZs2DCSk5MZNWoUd+/epW7duvz000/5ziMwaNAgTp48SUBAAAqFgp49exISEsIvv/yiKjNgwABiYmJo0qQJqamp7Nu3D0dHR7V6qlWrxo4dOxg9ejQNGzbEysqK4OBgJkyYUOjjrFChArNnzyY0NJTs7GwaNGjAzz//rNGW/pnu3buzePFi5s+fz/Dhw3FyciIyMlLV8VsbFy9e1Ggq1K5dO/bs2ZNnnAVp1qwZx44d44svvmDAgAHcu3cPW1tbvLy8CpxQbPbs2eTk5NC7d28ePXpEkyZN2LVrFxUrVtT6eEaNGsWxY8eYOnUqFhYWLFiwQNXZWKFQsGPHDj7//HP69evH33//jY2NDW+99Zaqz0Fx8vPzY+LEiYwZM4b09HT69+9Pnz591O6kh4WF0bdvX+rWrcuTJ0+4du0ajo6OxMbGMnbsWN555x0yMjKoUaMGHTp0UCUD8+bNUzVRMjc3Z9SoUSQnJ78wppSUFGxtbVEoFFhYWFC7dm369u3L8OHDsbCwIDMzk++++46xY8fmuf0HH3xAeHg4M2fOpFatWpw8eZKZM2cyfvx4bty4gaGhIXXr1iUsLEzV2V7b73dhKJSF7BVTsWJFFAoFycnJWFhYqCUL2dnZpKamMnjw4DxHPCgpKSkpWFpaqmIqirTUZEzm53aASguTPgpCCFEYSUlJTJi1EAevrlhUqvLiDf7PzcvnObVmEssH++DiYPviDZ7f5/0UVh64zaBxMzUmoNJGcfzteF56ejrXrl1TGwseyv/MzEIUlaOjo2ouLfFqyu93698K/URh0aJFKJVK+vfvz9SpU9V+iAwMDHB0dNToAS+EEEK8qSwtLRkyekKe/fpKgomJiSQJQohiVehEoW/fvkDu+MdeXl7lZnQAIYQQoryytLSUi3chxCtL6z4Kzw+Zlp6eTmZmptr64niMW5r09A2Jsx8AQGN9wzKORgghhBCifLt+/XpZhyBKidaJQlpaGmPGjGHjxo3cv39fY/2LhjcrbwwMjWgZPL+swxBCCCGEEKJc0Xp41GdDRK1YsQJDQ0O++eYbpk6dip2dHWvWrCmJGIUQQgghhBClTOsnCj///DNr1qzBx8eHfv360bp1a5ydnalRowZr164lMDCwJOIsMTnZ2SRePAmAg6snOv83Fq0QQgghhBBvMq2fKDx48EA1bbSFhQUPHjwAoFWrVvz+++/FG10pSH+SiuOGdjhuaEf6E82J5IQQQgghhHgTaZ0o1KxZk2vXrgHg5ubGxo0bgdwnDS+aIEQIIYQQQgjxatA6UejXrx+nTp0CYNy4cSxbtgwjIyNGjhzJ6NGjiz1AIYQQQgghROnTOlEYOXIkw4YNA8DX15c///yTdevWcfLkSYYPH17sAQohhBCi7EyZMgUPD49Cl79+/ToKhYL4+PiX2m9x1VPSbt++Tfv27TE1NX3lWlakpaXxwQcfYGFhgUKh4OHDh0WqJygoiO7duxdrbKJ80DpRWLNmDRkZGar3NWrU4P3338fNzU1GPRJCCCFeEV26dKFDhw55rjtw4AAKhYLTp08TFhbG3r17SzSWvC407e3tSUpKon79+iW675e1cOFCkpKSiI+P5+LFixrrHR0dUSgU+b6CgoIA1JZZWlri7e3Nb7/9VqKxR0dHc+DAAQ4dOkRSUlKRJwdcvHgxUVFRxRtcEfj4+KjOoaGhIdWqVaNLly5s2bIl323c3NwwNDTk9u3bea7ft28fnTt3xtraGiMjI2rVqkVAQMAr2S+3KIrU9Cg5OVlj+aNHj+jXr1+xBCWEEEKIkhUcHMzu3bu5ceOGxrrIyEiaNGmCu7s7ZmZmVKpUqdTj09XVxcbGBj09rQdoLFVXrlyhcePGuLi4UKVKFY31R48eJSkpiaSkJDZv3gxAQkKCatnixYtVZSMjI0lKSiI2NpbKlSvTuXNnrl69WqKx16lTh/r162NjY4NCoShSPZaWluXmacqAAQNISkriypUrbN68mbp169KjRw8GDhyoUfbgwYM8efKEDz/8kOjoaI31y5cvp127dlSqVIkNGzaQkJDA1q1b8fLyYuTIkaVxOGVO60RBqVTm+UW6ceOGTFMvhBBCvCKe3SX9953g1NRUNm3aRHBwMKDZ9CgnJ4dp06ZRvXp1DA0N8fDwYOfOnfnuJzs7m+DgYJycnDA2NqZ27dpqF8dTpkwhOjqaH3/8UXU3OCYmJs+mR/v376dZs2YYGhpia2vLuHHjePr0qWq9j48Pw4YNY8yYMVhZWWFjY8OUKVNU65VKJVOmTMHBwQFDQ0Ps7OxUzanzs2LFCmrVqoWBgQG1a9fm22+/Va1zdHRk8+bNrFmzRu3pwPOsra2xsbHBxsYGKysrAKpUqaJa9vy1U4UKFbCxsaF+/fqsWLGCJ0+esHv3bu7fv0/Pnj2pVq0aJiYmNGjQgO+//77AuAE2b95MvXr1MDQ0xNHRkfDwcLVzFR4ezu+//45CocDHxyffembMmEGVKlUwNzfnP//5D+PGjVP7Tjz/ROjrr7/Gzs6OnJwctTq6detG//79Ve9//PFHGjVqhJGRETVr1mTq1Klqn6VCoeCbb77hvffew8TEBBcXF3766acXHrOJiQk2NjZUr16dFi1aMGfOHFauXMmqVavYs2ePWtmIiAg+/vhjevfuzerVq9XWJSYmMmLECEaMGEF0dDRvv/02NWrUwN3dneHDh3Ps2LEXxvI6KHSi4OnpSaNGjVAoFLRr145GjRqpXg0bNqR169b4+vqWZKwlQk/fkD9sAvnDJhA9fcOyDkcIIcRrJC3zab6v9KzsYi2rLT09Pfr06UNUVBRKpVK1fNOmTWRnZ9OzZ888t1u8eDHh4eHMnz+f06dP4+fnR9euXbl06VKe5XNycqhevTqbNm3i/PnzTJo0ic8++0w1amJYWBj+/v506NBBdZfdy8tLo56bN2/SsWNHmjZtyqlTp1ixYgURERHMmDFDrVx0dDSmpqYcPnyYuXPnMm3aNHbv3g3kXjgvXLiQlStXcunSJbZt20aDBg3yPUdbt25l+PDhjBo1irNnzzJo0CD69evHvn37gNynBR06dMDf31/j6cDLMjY2BiAzM5P09HQaN27M9u3bOXv2LAMHDqR3794cOXIk3+2PHz+Ov78/PXr04MyZM0yZMoWJEyeqEsMtW7YwYMAAWrZsSVJSUr7Nc9auXcsXX3zBnDlzOH78OA4ODqxYsSLf/X700Ufcv39fdY4gd2j9nTt3qubaOnDgAH369GH48OGcP3+elStXEhUVxRdffKFW19SpU/H39+f06dN07NiRwMBA1bD82ujbty8VK1ZUO8ZHjx6xadMmevXqRfv27UlOTubAgQOq9Zs3byYrK4sxY8bkWWdRn768agr9PO9ZphgfH4+fnx9mZmaqdQYGBjg6OvLBBx8Ue4AlzcDQiBaDl5d1GEIIIV5DdSftyndd29rWRPZrpnrfePoenvwrIXimuZMVGwa1VL1vNWcfDx5nqpW5PruT1vH179+fefPmsX//ftUd5cjISD744IN8WwnMnz+fsWPH0qNHDwDmzJnDvn37WLRoEcuWLdMor6+vz9SpU1XvnZyciIuLY+PGjfj7+2NmZoaxsTEZGRnY2NjkG+vy5cuxt7dn6dKlKBQK3NzcuHXrFmPHjmXSpEno6OTe+3R3d2fy5MkAuLi4sHTpUvbu3Uv79u1JTEzExsYGX19f9PX1cXBwoFmzZvnuc/78+QQFBRESEgJAaGgof/zxB/Pnz6dt27ZYW1tjaGiIsbFxgbFrKy0tjQkTJqCrq0ubNm2oVq0aYWFhqvVDhw5l165dbNy4Md/4FyxYQLt27Zg4cSIArq6unD9/nnnz5hEUFISVlRUmJiYYGBgUGPuXX35JcHCwqnn5pEmT+PXXX0lNzXvuqYoVK/Luu++ybt062rVrB8APP/xA5cqVadu2LZCbAIwbN46+ffsCuUPvT58+nTFjxqg+O8h9UvEsYZ05cyZLlizhyJEj+fatyY+Ojg6urq5cv35dtWz9+vW4uLhQr149AHr06EFERAStW7cG4OLFi1hYWKidm82bN6tiBoiLiysw0XwdFDpRePbBOTo6EhAQgJGRUYkFJYQQQoiS5+bmhpeXF6tXr8bHx4fLly9z4MABpk2blmf5lJQUbt26hbe3t9pyb29v1dDpeVm2bBmrV68mMTGRJ0+ekJmZqdVISgAXLlygZcuWandyvb29SU1N5caNGzg4OAC5icLzbG1tuXv3LpB7t3vRokXUrFmTDh060LFjR7p06ZJvP4gLFy5otG339vYu1icHz+vZsye6uro8efIEa2trIiIicHd3Jzs7m5kzZ7Jx40Zu3rxJZmYmGRkZmJiY5FvXhQsX6Natm0bsixYtIjs7G11d3ULFlJCQoEqUnmnWrFmBHa0DAwMZMGAAy5cvx9DQkLVr19KjRw9VMnfq1CliY2PVniBkZ2eTnp5OWlqa6rie/yxNTU2xsLBQfZba+nfT+dWrV9OrVy/V+169etGmTRu+/PJLzM3NAc2nBn5+fsTHx3Pz5k18fHzIzs47sX+daN1D6PlM6nWQk53N7b8uA2Bj74xOIf/jCCGEEC9yfppfvut0/nURcnxi/s13/1324Ni2LxfYc4KDgxk6dCjLli0jMjKSWrVq0aZNm2Krf/369YSFhREeHk7Lli0xNzdn3rx5HD58uNj28Tx9fX219wqFQtVe3t7enoSEBPbs2cPu3bsJCQlRPVH593ZlYeHChfj6+mJpaYm1tbVq+bx581i8eDGLFi2iQYMGmJqaMmLECDIzMwuorex06dIFpVLJ9u3badq0KQcOHGDhwoWq9ampqUydOpX3339fY9vnb0QX9FlqIzs7m0uXLtG0aVMAzp8/zx9//MGRI0cYO3asWrn169czYMAAXFxcSE5O5vbt26qnCmZmZjg7O5f7DvbFSevOzDo6Oujq6ub7etWkP0nFLqoZdlHNSH+S92M0IYQQoihMDPTyfRnp6xZr2aLy9/dHR0eHdevWsWbNGvr3759v+2sLCwvs7OyIjY1VWx4bG0vdunXz3CY2NhYvLy9CQkLw9PTE2dmZK1euqJUxMDB44d3ZOnXqEBcXp9afIjY2FnNzc6pXr16YQwVy2/536dKFJUuWEBMTQ1xcHGfOnMl3n9oc68uysbHB2dlZLUl4ts9u3brRq1cvGjZsSM2aNfMcivV5+cXu6uqq1fVa7dq1OXr0qNqyf7//NyMjI95//33Wrl3L999/T+3atWnUqJFqfaNGjUhISMDZ2Vnj9eypQ3GKjo7mn3/+UTWRj4iI4K233uLUqVPEx8erXqGhoURERADw4Ycfoq+vz5w5c4o9nleJ1r8sW7ZsUfsBycrK4uTJk0RHR6u1QRRCCCFE+WdmZkZAQADjx48nJSUlz5F7njd69GgmT55MrVq18PDwIDIykvj4eNauXZtneRcXF9asWcOuXbtwcnLi22+/5ejRozg5OanKODo6smvXLhISEqhUqVKe/SNCQkJYtGgRQ4cOZciQISQkJDB58mRCQ0MLfXEZFRVFdnY2zZs3x8TEhO+++w5jY2Nq1KiR77H6+/vj6emJr68vP//8M1u2bNEYPaekubi48MMPP3Do0CEqVqzIggULuHPnToEJy6hRo2jatCnTp08nICCAuLg4li5dyvLl2vXLHDp0KAMGDKBJkyZ4eXmxYcMGTp8+Tc2aNQvcLjAwkM6dO3Pu3Dm1Jj6Q28+hc+fOODg48OGHH6Kjo8OpU6c4e/asRud0baWlpXH79m2ePn3KjRs32Lp1KwsXLuSTTz6hbdu2ZGVl8e233zJt2jSNOTr+85//sGDBAs6dO0e9evUIDw9n+PDhPHjwgKCgIJycnHjw4AHfffcdwCt5g1xbWicKec289+GHH1KvXj02bNigGk5NCCGEEK+G4OBgIiIi6NixI3Z2dgWWHTZsGMnJyYwaNYq7d+9St25dfvrpJ1xcXPIsP2jQIE6ePElAQAAKhYKePXsSEhLCL7/8oiozYMAAYmJiaNKkCampqezbtw9HR0e1eqpVq8aOHTsYPXo0DRs2xMrKiuDgYCZMmFDo46xQoQKzZ88mNDSU7OxsGjRowM8//5zvPBHdu3dn8eLFzJ8/n+HDh+Pk5ERkZGSBQ4mWhAkTJnD16lX8/PwwMTFh4MCBdO/ePc95rZ5p1KgRGzduZNKkSUyfPh1bW1umTZv2wkTw3wIDA7l69SphYWGkp6fj7+9PUFBQgSMuAbz99ttYWVmRkJDAxx9/rLbOz8+P//73v0ybNo05c+agr6+Pm5sb//nPf7SKLS+rVq1i1apVGBgYUKlSJRo3bsyGDRt47733APjpp5+4f/++6v3z6tSpQ506dYiIiGDBggUMHTqUOnXqsGDBAj788ENSUlKoVKkSLVu2ZOfOna99R2YAhfL5Z3gv4erVq7i7u+fbC74kpKSkYGlpSXJyMhYWFkWqIy01GZP5uR2g0sISMTGTuSCEEOJFkpKSmDBrIQ5eXbGopDnJVH5uXj7PqTWTWD7YBxcHW+32eT+FlQduM2jcTGxttdv2ecXxt+N56enpXLt2DScnJxnoQ7wR2rdvj42NjdqcEuLVUtjfrWLpjfHkyROWLFlCtWrViqM6IYQQQghRDqSlpfHVV1/h5+eHrq4u33//vaozuHj9aZ0oVKxYUa2PglKp5NGjR6q2ftqYNWsWW7Zs4c8//8TY2BgvLy/mzJlD7dq1tQ1LCCGEEEIUM4VCwY4dO/jiiy9IT0+ndu3abN68+ZWcZFdoT+tEYdGiRWrvdXR0sLa2pnnz5lSsWFGruvbv38+nn35K06ZNefr0KZ999hnvvPMO58+fx9TUVNvQhBBCCCFEMTI2Ni71ztui/CjTeRR27typ9j4qKooqVapw/Phx3nrrrWLbT0F09fQ5XDl3HF8PvbIfQ1kIIYQQQojyoEh9FNLT0zl9+jR3797VmPiia9euRQ7mWe99KyurItehLUMjE5oPiSy1/QkhhBBCCPEq0DpR2LlzJ7179+b+/fsa6xQKRZGns87JyWHEiBF4e3trjGv7TEZGBhkZGar3KSkpRdpXYSQnJ5OWllakbbOysoo0w6OJiUmeY0cLIYQQQghR2rROFIYOHYq/vz+TJk2iatWqxRbIp59+ytmzZzl48GC+ZWbNmlXsk7opc3L4514SABUr26LQ0SE5OZkv5i7k/iPtE4XMjAzuJByjSV0nDLRMFvTNKzNk9ARJFoQQQgghRJnTOlG4c+cOoaGhxZokDBkyhP/+97/8/vvvBU7DPn78eEJDQ1XvU1JSsLe3f6l9P0l7hNXy3JkNn82jkJaWxv1HaVjVa4WZpXbNoG4nXub26X10cTPC0c76xRv8n78fPmbLmXukpaVJoiCEEEIIIcqc1onChx9+SExMDLVq1XrpnSuVSoYOHcrWrVuJiYlRm849L4aGhhgaGr70fgvLzNJKq4mEAB79cw+AypYm2FbSdiKfR1qWF0IIIYQQomToaLvB0qVL2bJlC0FBQYSHh7NkyRK1lzY+/fRTvvvuO9atW4e5uTm3b9/m9u3bPHnyRNuwhBBCCFECpkyZgoeHR6HLX79+HYVCQXx8/Evtt7jqKWm3b9+mffv2mJqaUqFChbIO56XFxsbSoEED9PX16d69e5HrUSgUbNu2rdjiEmVD60Th+++/59dff2Xz5s18+eWXLFy4UPX69xwLL7JixQqSk5Px8fHB1tZW9dqwYYO2YQkhhBBCC126dKFDhw55rjtw4AAKhYLTp08TFhbG3r17SzSWoKAgjYtSe3t7kpKS8h3gpLxYuHAhSUlJxMfHc/HixTzLvCjZ8vHxQaFQMHv2bI11nTp1QqFQMGXKFLXlly9fpl+/flSvXh1DQ0OcnJzo2bMnx44de5nDITQ0FA8PD65du0ZUVFSR60lKSuLdd999qVhe1rNk89nL3NycevXq8emnn3Lp0qU8t4mLi0NXV5dOnTrluT4zM5N58+bRqFEjTE1NsbS0pGHDhkyYMIFbt26V5OGUCa0Thc8//5ypU6eSnJzM9evXuXbtmup19epVrepSKpV5voKCgrQNSwghhBBaCA4OZvfu3dy4cUNjXWRkJE2aNMHd3R0zMzMqVapU6vHp6upiY2ODnl6RRnIvNVeuXKFx48a4uLhQpYp2zZWfZ29vr3FhfvPmTfbu3Yutra3a8mPHjtG4cWMuXrzIypUrOX/+PFu3bsXNzY1Ro0YVOQbIPZ63336b6tWrv9QTEhsbm1JtLl6QPXv2kJSUxKlTp5g5cyYXLlygYcOGeSbAERERDB06lN9//13jwj8jI4P27dszc+ZMgoKC+P333zlz5gxLlizh3r17fPnll6V1SKVG60QhMzOTgIAAdHS03lQIIYQQ5UTnzp2xtrbWuDhNTU1l06ZNBAcHA5p3w3Nycpg2bZrqTraHh4fGBKrPy87OJjg4GCcnJ4yNjalduzaLFy9WrZ8yZQrR0dH8+OOPqju/MTExeTY92r9/P82aNcPQ0BBbW1vGjRvH06dPVet9fHwYNmwYY8aMwcrKChsbG7U78UqlkilTpuDg4IChoSF2dnYMGzaswPO0YsUKatWqhYGBAbVr1+bbb79VrXN0dGTz5s2sWbMGhULxUjc6O3fuzL1794iNjVUti46O5p133lFLQJ7dUHVxceHAgQN06tSJWrVq4eHhweTJk/nxxx/z3UdGRgbDhg2jSpUqGBkZ0apVK44ePQr8/7vv9+/fp3///igUinyfKCQlJdGpUyeMjY1xcnJi3bp1ODo6qrUseb7pkZeXF2PHjlWr4++//0ZfX5/ff/9dFVtYWBjVqlXD1NSU5s2bExMToyofFRVFhQoV2LVrF3Xq1MHMzIwOHTqQlJT0wnNbqVIlbGxsqFmzJt26dWPPnj00b96c4OBgtWH9U1NT2bBhA5988gmdOnXSOP6FCxdy8OBBfvvtN4YNG0bjxo1xcHCgTZs2fPXVV8ycOfOFsbxqtL7a79u3rzQNEkIIIQoj83H+r6x0Lco+eXFZLenp6dGnTx+ioqJQKpWq5Zs2bSI7O5uePXvmud3ixYsJDw9n/vz5nD59Gj8/P7p27ZpvU46cnByqV6/Opk2bOH/+PJMmTeKzzz5j48aNAISFheHv76+66EtKSsLLy0ujnps3b9KxY0eaNm3KqVOnWLFiBREREcyYMUOtXHR0NKamphw+fJi5c+cybdo0du/eDcDmzZtZuHAhK1eu5NKlS2zbto0GDRrke462bt3K8OHDGTVqFGfPnmXQoEH069ePffv2AXD06FE6dOiAv78/SUlJagmQtgwMDAgMDCQy8v9PAhsVFUX//v3VysXHx3Pu3DlGjRqV503bgp4CjBkzhs2bNxMdHc2JEydwdnbGz8+PBw8eqJp6WVhYsGjRIpKSkggICMiznj59+nDr1i1iYmLYvHkzX3/9NXfv3s13v4GBgaxfv17te7Zhwwbs7Oxo3bo1kDsCZlxcHOvXr+f06dN89NFHdOjQQe17lZaWxvz58/n222/5/fffSUxMJCwsLN/95kdHR4fhw4fzv//9j+PHj6uWb9y4ETc3N2rXrk2vXr1YvXq1Wszff/897du3x9PTM896FQqF1rGUd1o/z8vOzmbu3Lns2rULd3d3jYnFFixYUGzBlQZdPX2OWua20XTX036SNCGEECJfM+3yX+fyDgRu+v/v5zlDVj7z99RoBf22///3ixpA2r8mPp2SrHV4/fv3Z968eezfvx8fHx8gt9nRBx98kO9Q3fPnz2fs2LH06NEDgDlz5rBv3z4WLVrEsmXLNMrr6+urzYHk5OREXFwcGzduxN/fHzMzM4yNjcnIyMDGxibfWJcvX469vT1Lly5FoVDg5ubGrVu3GDt2LJMmTVJdNLu7uzN58mQAXFxcWLp0KXv37qV9+/YkJiZiY2ODr68v+vr6ODg40KxZs3z3OX/+fIKCgggJCQFy2+//8ccfzJ8/n7Zt22JtbY2hoSHGxsYFxl5Y/fv3p3Xr1ixevJjjx4+TnJxM586d1Z6KPLtwdnNz06rux48fs2LFCqKiolR9B1atWsXu3buJiIhg9OjR2NjYoFAosLS0zPd4/vzzT/bs2cPRo0dp0qQJAN988w0uLi757tvf358RI0Zw8OBBVWKwbt06evbsiUKhIDExkcjISBITE7Gzy/0/ExYWxs6dO4mMjFTdqc/KyuKrr75Sjbw5ZMgQpk2bptV5eObZ+bt+/brqOxAREUGvXr0A6NChA8nJyWr/Ny5evKj69zPvvfeeKhF1d3fn0KFDRYqnvNL6icKZM2fw9PRER0eHs2fPcvLkSdWrvI9MkBdDIxOajtxA05EbMDQyKetwhBBCiFLj5uaGl5cXq1evBnI7yB44cEDV7OjfUlJSuHXrFt7e3mrLvb29uXDhQr77WbZsGY0bN8ba2hozMzO+/vprEhMTtYr1woULtGzZUu2urbe3N6mpqWr9LNzd3dW2s7W1Vd3t/uijj3jy5Ak1a9ZkwIABbN26Va3pUl771PZYX0bDhg1xcXHhhx9+YPXq1fTu3Vujj8bzd7i1ceXKFbKystSOR19fn2bNmml1PAkJCejp6dGoUSPVMmdnZypWrJjvNtbW1rzzzjusXbsWgGvXrhEXF0dgYCCQe22ZnZ2Nq6srZmZmqtf+/fu5cuWKqh4TExO14fmf/2y19ew8Pvs+JSQkcOTIEdWTND09PQICAoiIiCiwnuXLlxMfH0///v1JS9N+ot7yTqsnCtnZ2UydOpUGDRoU+IUQQgghBPBZAaOgKHTV34++XEDZf93XG3Gm6DH9S3BwMEOHDmXZsmVERkZSq1Yt2rRpU2z1r1+/nrCwMMLDw2nZsiXm5ubMmzePw4cPF9s+nvfvlg4KhYKcnBwgt8NwQkICe/bsYffu3YSEhKieqPx7u7LSv39/li1bxvnz5zly5IjGeldXVyD3zn5+TWDKo8DAQIYNG8aXX37JunXraNCggarZV2pqKrq6uhw/fhxdXfX/F2ZmZqp/5/XZFjVxepYcPZvDKyIigqdPn6qeaEBuMmFoaMjSpUuxtLTExcWFhIQEtXqedTS3stJugt5XhVZPFHR1dXnnnXd4+PBhCYVT+pQ5OaSlJpOWmozy/35IhBBCiGJhYJr/S99Ii7LGLy5bRP7+/ujo6LBu3TrWrFmj6siaFwsLC+zs7NQ63ELu2Pt169bNc5vY2Fi8vLwICQnB09MTZ2dntbvEkNs+//lOpXmpU6cOcXFxaheGsbGxmJubU7169cIcKgDGxsZ06dKFJUuWEBMTQ1xcHGfO5J141alTR6tjLQ4ff/wxZ86coX79+nnux8PDg7p16xIeHq5KgJ6X3zXasw7Zzx9PVlYWR48e1ep4ateuzdOnTzl58qRq2eXLl/nnn38K3K5bt26kp6ezc+dO1q1bp3qaAODp6Ul2djZ3797F2dlZ7VUcTbr+LScnhyVLluDk5ISnpydPnz5lzZo1hIeHEx8fr3qdOnUKOzs7vv/+ewB69uzJ7t271Y79dad1H4X69etz9erVF86i/Kp4kvYIk/kOAKSFJWJilnebTCGEEOJ1ZGZmRkBAAOPHjyclJeWFI/eMHj2ayZMnq0baiYyMJD4+XtWs5N9cXFxYs2YNu3btwsnJiW+//ZajR4+qXUc4Ojqya9cuEhISqFSpUp79I0JCQli0aBFDhw5lyJAhJCQkMHnyZEJDQws9EmNUVBTZ2dk0b94cExMTvvvuO4yNjalRo0a+x+rv74+npye+vr78/PPPbNmyhT179hRqf8978uSJRhNtc3NztaY0ABUrViQpKSnfJxwKhYLIyEh8fX1p3bo1n3/+OW5ubqSmpvLzzz/z66+/sn//fo3tTE1N+eSTTxg9ejRWVlY4ODgwd+5c0tLS8m1qlhc3Nzd8fX0ZOHAgK1asQF9fn1GjRmFsbFxgZ15TU1O6d+/OxIkTuXDhglpneVdXVwIDA+nTpw/h4eF4enry999/s3fvXtzd3fOd06Cw7t+/z+3bt0lLS+Ps2bMsWrSII0eOsH37dnR1ddm2bRv//PMPwcHBGt+9Dz74gIiICAYPHszIkSPZvn077dq1Y/LkybRu3ZqKFSty8eJFfvnlF42nIa8DrROFGTNmEBYWxvTp02ncuDGmpup3MSwsLIotOCGEEEKUvODgYCIiIujYsaNa04u8DBs2jOTkZEaNGsXdu3epW7cuP/30U76dWQcNGsTJkycJCAhAoVDQs2dPQkJC+OWXX1RlBgwYQExMDE2aNCE1NZV9+/bh6OioVk+1atXYsWMHo0ePpmHDhlhZWREcHMyECRMKfZwVKlRg9uzZhIaGkp2dTYMGDfj555/znSeie/fuLF68mPnz5zN8+HCcnJyIjIzU6NBaGBcvXtRoKtSuXbs8k44XzV/QrFkzjh07xhdffMGAAQO4d+8etra2eHl5FTj57ezZs8nJyaF37948evSIJk2asGvXLq2bk69Zs4bg4GDeeustbGxsmDVrFufOncPIyKjA7QIDA+nYsSNvvfUWDg4OausiIyOZMWMGo0aN4ubNm1SuXJkWLVrQuXNnrWLLi6+vL5Dbx6FGjRq0bduWr7/+GmdnZyC32ZGvr2+eCeoHH3zA3LlzOX36NO7u7uzdu5dFixYRGRnJ+PHjycnJwcnJiXfffZeRI0e+dKzljUKpZeOu57P25zNHpVKJQqF44aPD4pSSkoKlpSXJyclFTlDSUpM1nigkJSUxYdZCHLy6YlFJu8lTbl4+z6k1k1g+2AcXB9sXb/B/ku6nsPLAbQaNm6kxsYoQQpQ3Rf2dLOpvJBTf72Rx/O14Xnp6OteuXcPJyemFF0pCvI5u3LiBvb09e/bsoV27dmUdjiiEwv5uaf1E4dnYwUIIIYQQ4s3z22+/kZqaSoMGDUhKSmLMmDE4Ojry1ltvlXVoophpnSgU50gIQgghhBDi1ZKVlcVnn33G1atXMTc3x8vLi7Vr15abkaNE8dE6UQA4cOAAK1eu5OrVq2zatIlq1arx7bff4uTkRKtWrYo7RiGEEEIIUU74+fnh5+dX1mGIUqD1hGubN2/Gz88PY2NjTpw4QUZGBgDJycmqmfOEEEIIIYQQrzatE4UZM2bw1VdfsWrVKrVHTN7e3pw4caJYgysNOrp6nDB7ixNmb6GjW6QHLEIIIYQQQrx2tL4yTkhIyLOziqWl5Ss5EZuRsSmNwn4u6zCEEEK84vKa/EoIIcqjwv5eaZ0o2NjYcPnyZY3xjQ8ePEjNmjW1rU4IIYR4pRkYGKCjo8OtW7ewtrbGwMCgwImnhBCirCiVSjIzM/n777/R0dHBwMCgwPJaJwoDBgxg+PDhrF69GoVCwa1bt4iLiyMsLIyJEycWOXAhhBDiVaSjo4OTkxNJSUncunWrrMMRQogXMjExwcHB4YWzmmudKIwbN46cnBzatWtHWloab731FoaGhoSFhTF06NAiB1xW8ppwTQghhNCGgYEBDg4OPH36tFQnHhVCCG3p6uqip6dXqCefWicKCoWCzz//nNGjR3P58mVSU1OpW7cuZmZmRQpWCCGEeB0oFAr09fVlLHkhxGuj0KMePX78mE8++YRq1aphbW1Nnz59sLa2plmzZpIkCCGEEEII8ZopdKIwceJEvv32Wzp37szHH3/Mb7/9xsCBA0syNiGEEEIIIUQZKXTTo61btxIZGclHH30EQJ8+fWjRogVPnz5FT0/mHxBCCCGEEOJ1UugnCjdu3MDb21v1vnHjxujr68sID0IIIYQQQryGCp0o5OTkaHTQ0tPTk9EdhBBCCCGEeA0Vus2QUqmkXbt2as2M0tLS6NKli9pkDSdOnCjeCEuYjq4ep4ybAVBbV5pQCSGEEEIIAVokCpMnT9ZY1q1bt2INpiwYGZvScOzusg5DCCGEEEKIcuWlEgUhhBBCCCHE66nQfRSEEEIIIYQQb443vlF+WmoyzHPJfTP6EiZmlmUbkBBCCCGEEOXAG58oAJgoMgBIK+M4hBBCCCGEKC+k6ZEQQgghhBBCgyQKQgghhBBCCA1FShSGDBnCgwcPijsWIYQQQgghRDlR6EThxo0bqn+vW7eO1NRUABo0aMBff/1V/JEJIYQQQgghykyhOzO7ublRqVIlvL29SU9P56+//sLBwYHr16+TlZVVkjEKIYQQQgghSlmhnyg8fPiQTZs20bhxY3JycujYsSOurq5kZGSwa9cu7ty5U5JxlhgdHV3OGTTgnEEDdHR0yzocIYQQQgghyoVCJwpZWVk0a9aMUaNGYWxszMmTJ4mMjERXV5fVq1fj5ORE7dq1SzLWEmFkYka9zw5S77ODGJmYlXU4QgghhBBClAuFbnpUoUIFPDw88Pb2JjMzkydPnuDt7Y2enh4bNmygWrVqHD16tCRjFUIIIYQQQpSSQj9RuHnzJhMmTMDQ0JCnT5/SuHFjWrduTWZmJidOnEChUNCqVauSjFUIIYQQQghRSgqdKFSuXJkuXbowa9YsTExMOHr0KEOHDkWhUBAWFoalpSVt2rQpyVhLRFpqMv9MseefKfakpSaXdThCCCGEEEKUC0WecM3S0hJ/f3/09fX57bffuHbtGiEhIcUZW6mpSAoVSSnrMIQQQgghhCg3Ct1H4XmnT5+mWrVqANSoUQN9fX1sbGwICAgo1uCEEEIIIYQQZaNIiYK9vb3q32fPni22YIQQQgghhBDlQ5GbHhWH33//nS5dumBnZ4dCoWDbtm1lGY4QQgghhBDi/5RpovD48WMaNmzIsmXLyjIMIYQQQgghxL8UqelRcXn33Xd59913yzIEIYQQQgghRB7KNFEoD3R0dLmk5wKAvY5uGUcjhBBCCCFE+fBKJQoZGRlkZGSo3qekvPyQpkYmZrhMOPbS9QghhBBCCPE6KdM+CtqaNWsWlpaWqtfzoy8JIYQQQgghis8rlSiMHz+e5ORk1euvv/4q65CEEEIIIYR4Lb1STY8MDQ0xNDQs1jqfPH7Ew3meAFQYfRJjU/NirV8IIYQQQohXUZkmCqmpqVy+fFn1/tq1a8THx2NlZYWDg0OpxKBU5mDL3wCkKXNKZZ9CCCGEEEKUd2WaKBw7doy2bduq3oeGhgLQt29foqKiyigqIYQQQgghRJkmCj4+PiiVyrIMQQghhBBCCJGHV6ozsxBCCCGEEKJ0SKIghBBCCCGE0CCJghBCCCGEEELDKzU8aklQKHS4rpM7cVtVheRNQgghhBBCgCQKGJua4zjpbFmHIYQQQgghRLkit9CFEEIIIYQQGiRREEIIIYQQQmh445sePXn8iDvhLQGoOioOY1PzMo5ICCGEEEKIsvfGJwpKZQ6OOX8BkKbMKeNohBBCCCGEKB+k6ZEQQgghhBBCgyQKQgghhBBCCA2SKAghhBBCCCE0SKIghBBCCCGE0CCJghBCCCGEEELDGz/qkUKhQxLWAFRQSN4khBBCCCEESKKAsak5xlMul3UYQgghhBBClCtyC10IIYQQQgihQRIFIYQQQgghhIY3vulReloqfy3wAcA+NAYjE7OyDUgIIYQQQohy4I1PFHJysnF5egmAtJzsMo5GCCGEEEKI8kGaHgkhhBBCCCE0SKIghBBCCCGE0CCJghBCCCGEEEKDJApCCCGEEEIIDZIoCCGEEEIIITS88aMeAfyDBQCGZRyHEEIIIYQQ5cUbnyiYmFliMuWvsg5DCCGEEEKIckWaHgkhhBBCCCE0SKIghBBCCCGE0PDGNz1KT0vlyqIOANQasRMjE7MyjkgIIYQQQoiy98YnCjk52dTLPANAWk52GUcjhBBCCCFE+SBNj4QQQgghhBAaJFEQQgghhBBCaJBEQQghhBBCCKFBEgUhhBBCCCGEBkkUhBBCCCGEEBre+FGPANKUhmUdghBCCCGEEOXKG58omJhZwtS7ZR2GEEIIIYQQ5Yo0PRJCCCGEEEJokERBCCGEEEIIoeGNb3qU/uQxCUu6A1B72DaMjE3LNiAhhBBCCCHKgTc+UcjJfkrDJ0cASMt+WsbRCCGEEEIIUT5I0yMhhBBCCCGEhnKRKCxbtgxHR0eMjIxo3rw5R44cKeuQhBBCCCGEeKOVeaKwYcMGQkNDmTx5MidOnKBhw4b4+flx964MWSqEEEIIIURZKfNEYcGCBQwYMIB+/fpRt25dvvrqK0xMTFi9enVZhyaEEEIIIcQbq0wThczMTI4fP46vr69qmY6ODr6+vsTFxZVhZEIIIYQQQrzZynTUo3v37pGdnU3VqlXVlletWpU///xTo3xGRgYZGRmq98nJyQCkpKQUOYa01BSeZihz/52SwtMcBY8ePSIzM4P7t2+QnvZYq/r++TuJp0+z+d+dhygVhT+995LTyMjI5NGjR5ia5j1E66NHj3j8WLt4AJRKJQqFQuvtAExNTTE3Ny/WeF63mMpbPOUxpoLieZmYXqdzVB5jelE8RfmdLOpvJBTud7Iwnv3NUCqVRa5DCCHeBAplGf5S3rp1i2rVqnHo0CFatmypWj5mzBj279/P4cOH1cpPmTKFqVOnlnaYQgghXkN//fUX1atXL+swhBCi3CrTJwqVK1dGV1eXO3fuqC2/c+cONjY2GuXHjx9PaGio6n1OTg4PHjygUqVKRb6DB7l3l+zt7fnrr7+wsLAocj2vOzlPLybn6MXkHL2YnKPCKep5UiqVPHr0CDs7uxKMTgghXn1lmigYGBjQuHFj9u7dS/fu3YHci/+9e/cyZMgQjfKGhoYYGhqqLatQoUKxxWNhYSF/lAtBztOLyTl6MTlHLybnqHCKcp4sLS1LKBohhHh9lPnMzKGhofTt25cmTZrQrFkzFi1axOPHj+nXr19ZhyaEEEIIIcQbq8wThYCAAP7++28mTZrE7du38fDwYOfOnRodnIUQQgghhBClp8wTBYAhQ4bk2dSotBgaGjJ58mSNZk1CnZynF5Nz9GJyjl5MzlHhyHkSQoiSVaajHgkhhBBCCCHKpzKfmVkIIYQQQghR/kiiIIQQQgghhNAgiYIQQgghhBBCwxuTKCxbtgxHR0eMjIxo3rw5R44cKbD8pk2bcHNzw8jIiAYNGrBjx45SirTsaHOOVq1aRevWralYsSIVK1bE19f3hef0daHtd+mZ9evXo1AoVHOGvM60PUcPHz7k008/xdbWFkNDQ1xdXV/7/3PanqNFixZRu3ZtjI2Nsbe3Z+TIkaSnp5dStKXv999/p0uXLtjZ2aFQKNi2bdsLt4mJiaFRo0YYGhri7OxMVFRUiccphBCvNeUbYP369UoDAwPl6tWrlefOnVMOGDBAWaFCBeWdO3fyLB8bG6vU1dVVzp07V3n+/HnlhAkTlPr6+sozZ86UcuSlR9tz9PHHHyuXLVumPHnypPLChQvKoKAgpaWlpfLGjRulHHnp0vY8PXPt2jVltWrVlK1bt1Z269atdIItI9qeo4yMDGWTJk2UHTt2VB48eFB57do1ZUxMjDI+Pr6UIy892p6jtWvXKg0NDZVr165VXrt2Tblr1y6lra2tcuTIkaUceenZsWOH8vPPP1du2bJFCSi3bt1aYPmrV68qTUxMlKGhocrz588rv/zyS6Wurq5y586dpROwEEK8ht6IRKFZs2bKTz/9VPU+OztbaWdnp5w1a1ae5f39/ZWdOnVSW9a8eXPloEGDSjTOsqTtOfq3p0+fKs3NzZXR0dElFWK5UJTz9PTpU6WXl5fym2++Ufbt2/e1TxS0PUcrVqxQ1qxZU5mZmVlaIZY5bc/Rp59+qnz77bfVloWGhiq9vb1LNM7yojCJwpgxY5T16tVTWxYQEKD08/MrwciEEOL19to3PcrMzOT48eP4+vqqluno6ODr60tcXFye28TFxamVB/Dz88u3/KuuKOfo39LS0sjKysLKyqqkwixzRT1P06ZNo0qVKgQHB5dGmGWqKOfop59+omXLlnz66adUrVqV+vXrM3PmTLKzs0sr7FJVlHPk5eXF8ePHVc2Trl69yo4dO+jYsWOpxPwqeNN+t4UQojSUiwnXStK9e/fIzs7WmOm5atWq/Pnnn3luc/v27TzL3759u8TiLEtFOUf/NnbsWOzs7DT+UL9OinKeDh48SEREBPHx8aUQYdkryjm6evUqv/32G4GBgezYsYPLly8TEhJCVlYWkydPLo2wS1VRztHHH3/MvXv3aNWqFUqlkqdPnzJ48GA+++yz0gj5lZDf73ZKSgpPnjzB2Ni4jCITQohX12v/REGUvNmzZ7N+/Xq2bt2KkZFRWYdTbjx69IjevXuzatUqKleuXNbhlFs5OTlUqVKFr7/+msaNGxMQEMDnn3/OV199VdahlRsxMTHMnDmT5cuXc+LECbZs2cL27duZPn16WYcmhBDiNfbaP1GoXLkyurq63LlzR235nTt3sLGxyXMbGxsbrcq/6opyjp6ZP38+s2fPZs+ePbi7u5dkmGVO2/N05coVrl+/TpcuXVTLcnJyANDT0yMhIYFatWqVbNClrCjfJVtbW/T19dHV1VUtq1OnDrdv3yYzMxMDA4MSjbm0FeUcTZw4kd69e/Of//wHgAYNGvD48WMGDhzI559/jo6O3PPJ73fbwsJCniYIIUQRvfZ/XQwMDGjcuDF79+5VLcvJyWHv3r20bNkyz21atmypVh5g9+7d+ZZ/1RXlHAHMnTuX6dOns3PnTpo0aVIaoZYpbc+Tm5sbZ86cIT4+XvXq2rUrbdu2JT4+Hnt7+9IMv1QU5bvk7e3N5cuXVUkUwMWLF7G1tX3tkgQo2jlKS0vTSAaeJVZKpbLkgn2FvGm/20IIUSrKujd1aVi/fr3S0NBQGRUVpTx//rxy4MCBygoVKihv376tVCqVyt69eyvHjRunKh8bG6vU09NTzp8/X3nhwgXl5MmT34jhUbU5R7Nnz1YaGBgof/jhB2VSUpLq9ejRo7I6hFKh7Xn6tzdh1CNtz1FiYqLS3NxcOWTIEGVCQoLyv//9r7JKlSrKGTNmlNUhlDhtz9HkyZOV5ubmyu+//1559epV5a+//qqsVauW0t/fv6wOocQ9evRIefLkSeXJkyeVgHLBggXKkydPKv/3v/8plUqlcty4ccrevXuryj8bHnX06NHKCxcuKJctWybDowohxEt6IxIFpVKp/PLLL5UODg5KAwMDZbNmzZR//PGHal2bNm2Uffv2VSu/ceNGpaurq9LAwEBZr1495fbt20s54tKnzTmqUaOGEtB4TZ48ufQDL2Xafpee9yYkCkql9ufo0KFDyubNmysNDQ2VNWvWVH7xxRfKp0+flnLUpUubc5SVlaWcMmWKslatWkojIyOlvb29MiQkRPnPP/+UfuClZN++fXn+xjw7L3379lW2adNGYxsPDw+lgYGBsmbNmsrIyMhSj1sIIV4nCqVSnlsLIYQQQggh1L32fRSEEEIIIYQQ2pNEQQghhBBCCKFBEgUhhBBCCCGEBkkUhBBCCCGEEBokURBCCCGEEEJokERBCCGEEEIIoUESBSGEEEIIIYQGSRSEEEIIIYQQGiRREKIYxMTEoFAoePjwYVmHQmxsLA0aNEBfX5/u3bsXqY6goCCtti2u4y9P51EIIYR400miIF5pQUFBKBQKjdfly5dLbJ8+Pj6MGDFCbZmXlxdJSUlYWlqW2H4LKzQ0FA8PD65du0ZUVFSR6li8eHGRty2s8n4ehRBCiDedXlkHIMTL6tChA5GRkWrLrK2tNcplZmZiYGBQIjEYGBhgY2NTInVr68qVKwwePJjq1asXuY6yulAvT+dRCCGEeNPJEwXxyjM0NMTGxkbtpauri4+PD0OGDGHEiBFUrlwZPz8/ABYsWECDBg0wNTXF3t6ekJAQUlNT1eqMjY3Fx8cHExMTKlasiJ+fH//88w9BQUHs37+fxYsXq55eXL9+Pc8mM5s3b6ZevXoYGhri6OhIeHi42j4cHR2ZOXMm/fv3x9zcHAcHB77++usCjzUjI4Nhw4ZRpUoVjIyMaNWqFUePHgXg+vXrKBQK7t+/T//+/VEoFHk+Ffjss89o3ry5xvKGDRsybdo0QLPpUUH7zcv9+/fp2bMn1apVw8TEhAYNGvD999+r1pfmeczMzGTIkCHY2tpiZGREjRo1mDVrVoHnWQghhBCSKIjXXHR0NAYGBsTGxvLVV18BoKOjw5IlSzh37hzR0dH89ttvjBkzRrVNfHw87dq1o27dusTFxXHw4EG6dOlCdnY2ixcvpmXLlgwYMICkpCSSkpKwt7fX2O/x48fx9/enR48enDlzhilTpjBx4kSNC/fw8HCaNGnCyZMnCQkJ4ZNPPiEhISHf4xkzZgybN28mOjqaEydO4OzsjJ+fHw8ePMDe3p6kpCQsLCxYtGgRSUlJBAQEaNQRGBjIkSNHuHLlimrZuXPnOH36NB9//LHW+81Leno6jRs3Zvv27Zw9e5aBAwfSu3dvjhw5AlCq53HJkiX89NNPbNy4kYSEBNauXYujo2O+51gIIYQQ/0cpxCusb9++Sl1dXaWpqanq9eGHHyqVSqWyTZs2Sk9PzxfWsWnTJmWlSpVU73v27Kn09vbOt3ybNm2Uw4cPV1u2b98+JaD8559/lEqlUvnxxx8r27dvr1Zm9OjRyrp166re16hRQ9mrVy/V+5ycHGWVKlWUK1asyHO/qampSn19feXatWtVyzIzM5V2dnbKuXPnqpZZWloqIyMj841fqVQqGzZsqJw2bZrq/fjx45XNmzdXve/bt6+yW7duhd7vv48/L506dVKOGjVK9b60zuPQoUOVb7/9tjInJ6eAMyKEEEKIf5MnCuKV17ZtW+Lj41WvJUuWqNY1btxYo/yePXto164d1apVw9zcnN69e3P//n3S0tKA//9E4WVcuHABb29vtWXe3t5cunSJ7Oxs1TJ3d3fVvxUKBTY2Nty9ezfPOq9cuUJWVpZavfr6+jRr1owLFy5oFV9gYCDr1q0DQKlU8v333xMYGFhs+83Ozmb69Ok0aNAAKysrzMzM2LVrF4mJiVrFWRznMSgoiPj4eGrXrs2wYcP49ddftYpBCCGEeFNJoiBeeaampjg7O6tetra2auued/36dTp37oy7uzubN2/m+PHjLFu2DMhtyw5gbGxcarHr6+urvVcoFOTk5JT4fnv27ElCQgInTpzg0KFD/PXXX3k2UyqqefPmsXjxYsaOHcu+ffuIj4/Hz89PdY6LW0HnsVGjRly7do3p06fz5MkT/P39+fDDD0skDiGEEOJ1IomCeKMcP36cnJwcwsPDadGiBa6urty6dUutjLu7O3v37s23DgMDA7W72XmpU6cOsbGxastiY2NxdXVFV1e3SLHXqlVL1d/imaysLI4ePUrdunW1qqt69eq0adOGtWvXsnbtWtq3b0+VKlWKbb+xsbF069aNXr160bBhQ2rWrMnFixfVypTmebSwsCAgIIBVq1axYcMGNm/enG//CiGEEELkkuFRxRvF2dmZrKwsvvzyS7p06aLWyfmZ8ePH06BBA0JCQhg8eDAGBgbs27ePjz76iMqVK+Po6Mjhw4e5fv06ZmZmWFlZaexn1KhRNG3alOnTpxMQEEBcXBxLly5l+fLlRY7d1NSUTz75hNGjR2NlZYWDgwNz584lLS2N4OBgresLDAxk8uTJZGZmsnDhwmLdr4uLCz/88AOHDh2iYsWKLFiwgDt37qglFqV1HhcsWICtrS2enp7o6OiwadMmbGxsqFChQqHrEEIIId5E8kRBvFEaNmzIggULmDNnDvXr12ft2rUaQ2W6urry66+/curUKZo1a0bLli358ccf0dPLzavDwsLQ1dWlbt26WFtb59nuvlGjRmzcuJH169dTv359Jk2axLRp0wgKCnqp+GfPns0HH3xA7969adSoEZcvX2bXrl1UrFhR67o+/PBDVd+MF83CrO1+J0yYQKNGjfDz88PHxwcbGxuNfZTWeTQ3N2fu3Lk0adKEpk2bcv36dXbs2IGOjvz8CSGEEAVRKJVKZVkHIYQQQgghhChf5JaaEEIIIYQQQoMkCkIIIYQQQggNkigIIYQQQgghNEiiIIQQQgghhNAgiYIQQgghhBBCgyQKQgghhBBCCA2SKAghhBBCCCE0SKIghBBCCCGE0CCJghBCCCGEEEKDJApCCCGEEEIIDZIoCCGEEEIIITRIoiCEEEIIIYTQ8P8A8KNli8n0RHkAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -547,21 +547,21 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node X\n",
-      "- The KL divergence between generated and observed distribution is 0.017201478484168996.\n",
+      "- The KL divergence between generated and observed distribution is 0.04486035655302496.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Y\n",
-      "- The MSE is 0.976443943612139.\n",
-      "- The NMSE is 0.4483833647839692.\n",
-      "- The R2 coefficient is 0.7985032436842728.\n",
-      "- The normalized CRPS is 0.2555275355528893.\n",
+      "- The MSE is 1.0467465612232283.\n",
+      "- The NMSE is 0.47323453236211643.\n",
+      "- The R2 coefficient is 0.7742617784338882.\n",
+      "- The normalized CRPS is 0.272445898490441.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Z\n",
-      "- The MSE is 0.9526589247617239.\n",
-      "- The NMSE is 0.1451098038253918.\n",
-      "- The R2 coefficient is 0.9789264378438249.\n",
-      "- The normalized CRPS is 0.08325239327425824.\n",
+      "- The MSE is 1.0162458498820515.\n",
+      "- The NMSE is 0.15398077945269872.\n",
+      "- The R2 coefficient is 0.9762621570148147.\n",
+      "- The normalized CRPS is 0.08788013934735178.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
@@ -569,13 +569,13 @@
       "--- The model assumption for node Y is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Z is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Z is not rejected with a p-value of 0.5259643809464394 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.012872322533934848\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.016592406474661824\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -637,10 +637,10 @@
    "id": "52452496",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:53:58.018245Z",
-     "iopub.status.busy": "2024-10-25T14:53:58.017880Z",
-     "iopub.status.idle": "2024-10-25T14:53:58.029343Z",
-     "shell.execute_reply": "2024-10-25T14:53:58.028872Z"
+     "iopub.execute_input": "2024-10-25T14:55:04.198725Z",
+     "iopub.status.busy": "2024-10-25T14:55:04.198484Z",
+     "iopub.status.idle": "2024-10-25T14:55:04.211526Z",
+     "shell.execute_reply": "2024-10-25T14:55:04.210903Z"
     }
    },
    "outputs": [
@@ -673,33 +673,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.396680</td>\n",
+       "      <td>0.501525</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.897156</td>\n",
+       "      <td>7.645349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.075865</td>\n",
+       "      <td>0.999885</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.658478</td>\n",
+       "      <td>7.319836</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.565186</td>\n",
+       "      <td>-0.464415</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.775646</td>\n",
+       "      <td>5.997534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-2.294770</td>\n",
+       "      <td>0.903415</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>5.928846</td>\n",
+       "      <td>8.018945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.059750</td>\n",
+       "      <td>-0.751885</td>\n",
        "      <td>2.34</td>\n",
-       "      <td>6.726993</td>\n",
+       "      <td>6.773647</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -707,11 +707,11 @@
       ],
       "text/plain": [
        "          X     Y         Z\n",
-       "0  0.396680  2.34  6.897156\n",
-       "1 -1.075865  2.34  6.658478\n",
-       "2  0.565186  2.34  6.775646\n",
-       "3 -2.294770  2.34  5.928846\n",
-       "4 -0.059750  2.34  6.726993"
+       "0  0.501525  2.34  7.645349\n",
+       "1  0.999885  2.34  7.319836\n",
+       "2 -0.464415  2.34  5.997534\n",
+       "3  0.903415  2.34  8.018945\n",
+       "4 -0.751885  2.34  6.773647"
       ]
      },
      "execution_count": 9,
diff --git a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html
index 09051886f..620b8feae 100644
--- a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html
+++ b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html
@@ -706,7 +706,7 @@ <h2>Modeling of the graph<a class="headerlink" href="#Modeling-of-the-graph" tit
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00&lt;00:00, 29.25it/s]
+Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00&lt;00:00, 21.91it/s]
 </pre></div></div>
 </div>
 <p>Optionally, we can now also evaluate the fitted causal model:</p>
@@ -723,8 +723,8 @@ <h2>Modeling of the graph<a class="headerlink" href="#Modeling-of-the-graph" tit
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00&lt;00:00, 3549.48it/s]
-Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 89.90it/s]
+Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00&lt;00:00, 3090.87it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 88.61it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -761,15 +761,15 @@ <h2>Modeling of the graph<a class="headerlink" href="#Modeling-of-the-graph" tit
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Vision
-- The MSE is 0.003261806190982879.
-- The NMSE is 0.18253366556893805.
-- The R2 coefficient is 0.9666790551530827.
-- The normalized CRPS is 0.10648111139984254.
+- The MSE is 0.0032631168083106163.
+- The NMSE is 0.1826058582974253.
+- The R2 coefficient is 0.9666536471105938.
+- The normalized CRPS is 0.10639145853760663.
 The estimated CRPS indicates a very good model performance.
 
 ==== Evaluation of Invertible Functional Causal Model Assumption ====
 
---- The model assumption for node Vision is not rejected with a p-value of 0.5421442804573395 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node Vision is not rejected with a p-value of 0.6092815181807543 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
 Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.
@@ -882,7 +882,7 @@ <h2>Answering Alice’s counterfactual queries<a class="headerlink" href="#Answe
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;matplotlib.legend.Legend at 0x7fb03f528a00&gt;
+&lt;matplotlib.legend.Legend at 0x7f5db9719730&gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
index 4dd608208..73e16fefe 100644
--- a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
+++ b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb
@@ -34,10 +34,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:02.795937Z",
-     "iopub.status.busy": "2024-10-25T14:54:02.795760Z",
-     "iopub.status.idle": "2024-10-25T14:54:03.032257Z",
-     "shell.execute_reply": "2024-10-25T14:54:03.031648Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.027157Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.026967Z",
+     "iopub.status.idle": "2024-10-25T14:55:09.268293Z",
+     "shell.execute_reply": "2024-10-25T14:55:09.267663Z"
     }
    },
    "outputs": [
@@ -128,10 +128,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:03.034275Z",
-     "iopub.status.busy": "2024-10-25T14:54:03.033923Z",
-     "iopub.status.idle": "2024-10-25T14:54:03.575119Z",
-     "shell.execute_reply": "2024-10-25T14:54:03.574518Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.270377Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.270048Z",
+     "iopub.status.idle": "2024-10-25T14:55:09.826985Z",
+     "shell.execute_reply": "2024-10-25T14:55:09.826344Z"
     }
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    "outputs": [
@@ -181,10 +181,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:03.577584Z",
-     "iopub.status.busy": "2024-10-25T14:54:03.577313Z",
-     "iopub.status.idle": "2024-10-25T14:54:07.021109Z",
-     "shell.execute_reply": "2024-10-25T14:54:07.020379Z"
+     "iopub.execute_input": "2024-10-25T14:55:09.829344Z",
+     "iopub.status.busy": "2024-10-25T14:55:09.828744Z",
+     "iopub.status.idle": "2024-10-25T14:55:13.551143Z",
+     "shell.execute_reply": "2024-10-25T14:55:13.550341Z"
     }
    },
    "outputs": [
@@ -227,7 +227,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Vision:  67%|██████▋   | 2/3 [00:00<00:00, 19.92it/s]"
+      "Fitting causal mechanism of node Vision:  67%|██████▋   | 2/3 [00:00<00:00, 14.92it/s]"
      ]
     },
     {
@@ -235,7 +235,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Condition:  67%|██████▋   | 2/3 [00:00<00:00, 19.92it/s]"
+      "Fitting causal mechanism of node Condition:  67%|██████▋   | 2/3 [00:00<00:00, 14.92it/s]"
      ]
     },
     {
@@ -243,7 +243,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 29.25it/s]"
+      "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 21.91it/s]"
      ]
     },
     {
@@ -279,10 +279,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:07.023466Z",
-     "iopub.status.busy": "2024-10-25T14:54:07.023059Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.811677Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.811016Z"
+     "iopub.execute_input": "2024-10-25T14:55:13.553797Z",
+     "iopub.status.busy": "2024-10-25T14:55:13.553300Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.674607Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.673945Z"
     }
    },
    "outputs": [
@@ -299,7 +299,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3549.48it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3090.87it/s]"
      ]
     },
     {
@@ -322,7 +322,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.90it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 88.61it/s]"
      ]
     },
     {
@@ -368,15 +368,15 @@
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Vision\n",
-      "- The MSE is 0.003261806190982879.\n",
-      "- The NMSE is 0.18253366556893805.\n",
-      "- The R2 coefficient is 0.9666790551530827.\n",
-      "- The normalized CRPS is 0.10648111139984254.\n",
+      "- The MSE is 0.0032631168083106163.\n",
+      "- The NMSE is 0.1826058582974253.\n",
+      "- The R2 coefficient is 0.9666536471105938.\n",
+      "- The normalized CRPS is 0.10639145853760663.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
       "\n",
-      "--- The model assumption for node Vision is not rejected with a p-value of 0.5421442804573395 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Vision is not rejected with a p-value of 0.6092815181807543 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
@@ -420,10 +420,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.813689Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.813324Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.821322Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.820845Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.676818Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.676383Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.685502Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.684844Z"
     }
    },
    "outputs": [
@@ -506,17 +506,17 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.823085Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.822906Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.980064Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.979584Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.687467Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.687264Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.882040Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.881367Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb03f528a00>"
+       "<matplotlib.legend.Legend at 0x7f5db9719730>"
       ]
      },
      "execution_count": 6,
@@ -620,10 +620,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.982139Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.981767Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.994410Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.993955Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.884610Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.884162Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.897187Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.896585Z"
     }
    },
    "outputs": [],
@@ -663,10 +663,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:20.996243Z",
-     "iopub.status.busy": "2024-10-25T14:54:20.995897Z",
-     "iopub.status.idle": "2024-10-25T14:54:20.999893Z",
-     "shell.execute_reply": "2024-10-25T14:54:20.999272Z"
+     "iopub.execute_input": "2024-10-25T14:55:27.899641Z",
+     "iopub.status.busy": "2024-10-25T14:55:27.899143Z",
+     "iopub.status.idle": "2024-10-25T14:55:27.903356Z",
+     "shell.execute_reply": "2024-10-25T14:55:27.902747Z"
     }
    },
    "outputs": [],
diff --git a/main/example_notebooks/gcm_cps2015_dist_change_robust.html b/main/example_notebooks/gcm_cps2015_dist_change_robust.html
index fd0464161..0d8908ea1 100644
--- a/main/example_notebooks/gcm_cps2015_dist_change_robust.html
+++ b/main/example_notebooks/gcm_cps2015_dist_change_robust.html
@@ -503,7 +503,7 @@ <h2>Read and prepare data<a class="headerlink" href="#Read-and-prepare-data" tit
 <section id="Causal-Model">
 <h2>Causal Model<a class="headerlink" href="#Causal-Model" title="Permalink to this heading">#</a></h2>
 <p>To use our causal change attribution method, we need a causal model linking the outcome (wage) and explanatory variables. (In practice, the method only requires knowing a <em>causal ordering</em>.) We will work with the following DAG:</p>
-<p><img alt="7509fec964be43e5b89dca6db0e2f7f9" class="no-scaled-link" src="../_images/gender_gap_DAG.png" style="width: 250px;" /></p>
+<p><img alt="49009f7fcb03423f8de548c27a83f37b" class="no-scaled-link" src="../_images/gender_gap_DAG.png" style="width: 250px;" /></p>
 <p>The DAG above implies the following causal Markov factorization of the distribution of the data:</p>
 <div class="math notranslate nohighlight">
 \[P(\mathtt{wage} \mid \mathtt{occup}, \mathtt{educ}) \times P(\mathtt{occup} \mid \mathtt{educ}) \times P(\mathtt{educ})\]</div>
@@ -561,7 +561,7 @@ <h2>Implementation with <code class="docutils literal notranslate"><span class="
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating set functions...: 100%|██████████| 8/8 [00:02&lt;00:00,  3.80it/s]
+Evaluating set functions...: 100%|██████████| 8/8 [00:02&lt;00:00,  3.54it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb b/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
index 1e184367b..2cd02e645 100644
--- a/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
+++ b/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb
@@ -27,10 +27,10 @@
    "id": "6e926b16-37e2-4921-9afb-1c99db9c9feb",
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-     "shell.execute_reply": "2024-10-25T14:54:25.847349Z"
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@@ -78,10 +78,10 @@
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-     "shell.execute_reply": "2024-10-25T14:54:27.023282Z"
+     "iopub.execute_input": "2024-10-25T14:55:32.954668Z",
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@@ -117,10 +117,10 @@
    "id": "39dcb294-25df-4358-81cb-3ee5a41e0419",
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-     "shell.execute_reply": "2024-10-25T14:54:30.543491Z"
+     "iopub.execute_input": "2024-10-25T14:55:34.282920Z",
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@@ -138,7 +138,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.80it/s]"
+      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]"
      ]
     },
     {
@@ -146,7 +146,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.80it/s]"
+      "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]"
      ]
     },
     {
@@ -216,10 +216,10 @@
    "id": "f7febdc3-8c1e-46cf-816b-4a62251d641b",
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-     "shell.execute_reply": "2024-10-25T14:54:30.559904Z"
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@@ -250,10 +250,10 @@
    "id": "5f9e1bad-ae6e-432d-bbb8-1f65ab7bbab6",
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-     "shell.execute_reply": "2024-10-25T14:54:32.815490Z"
+     "iopub.execute_input": "2024-10-25T14:55:38.136192Z",
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@@ -344,10 +344,10 @@
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-     "shell.execute_reply": "2024-10-25T14:54:37.350630Z"
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+     "shell.execute_reply": "2024-10-25T14:55:45.056946Z"
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@@ -396,10 +396,10 @@
    "id": "95ec0312-84c2-4405-a823-740c98482090",
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-     "shell.execute_reply": "2024-10-25T14:54:37.503268Z"
+     "iopub.execute_input": "2024-10-25T14:55:45.060083Z",
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@@ -491,10 +491,10 @@
    "id": "a5b5dbf8-046b-4d9d-848c-694eb742aafb",
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-     "shell.execute_reply": "2024-10-25T14:54:37.668647Z"
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@@ -571,10 +571,10 @@
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-     "shell.execute_reply": "2024-10-25T14:54:37.862478Z"
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     "tags": []
    },
diff --git a/main/example_notebooks/gcm_draw_samples.html b/main/example_notebooks/gcm_draw_samples.html
index 223c5375e..e1cfe5b5c 100644
--- a/main/example_notebooks/gcm_draw_samples.html
+++ b/main/example_notebooks/gcm_draw_samples.html
@@ -613,33 +613,33 @@ <h1>Basic Example for generating samples from a GCM<a class="headerlink" href="#
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.121542</td>
-      <td>1.657360</td>
-      <td>3.616523</td>
+      <td>1.202890</td>
+      <td>3.041920</td>
+      <td>8.717822</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.503374</td>
-      <td>1.251506</td>
-      <td>4.587577</td>
+      <td>-1.050020</td>
+      <td>-3.235161</td>
+      <td>-8.177542</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>-0.473361</td>
-      <td>1.553348</td>
-      <td>4.065011</td>
+      <td>0.048552</td>
+      <td>-0.068086</td>
+      <td>0.254594</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.865151</td>
-      <td>1.475716</td>
-      <td>3.616999</td>
+      <td>0.065656</td>
+      <td>-1.161772</td>
+      <td>-2.940748</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.641329</td>
-      <td>2.701932</td>
-      <td>11.109621</td>
+      <td>-1.968502</td>
+      <td>-3.431138</td>
+      <td>-10.633668</td>
     </tr>
   </tbody>
 </table>
@@ -664,7 +664,7 @@ <h1>Basic Example for generating samples from a GCM<a class="headerlink" href="#
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00&lt;00:00, 423.32it/s]
+Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00&lt;00:00, 459.01it/s]
 </pre></div></div>
 </div>
 <p>We now learned the generative models of the variables, based on the defined causal graph and the additive noise model assumption. To generate new samples from this model, we can now simply call:</p>
@@ -708,33 +708,33 @@ <h1>Basic Example for generating samples from a GCM<a class="headerlink" href="#
   <tbody>
     <tr>
       <th>0</th>
-      <td>0.481421</td>
-      <td>1.807229</td>
-      <td>6.236546</td>
+      <td>0.377113</td>
+      <td>0.796241</td>
+      <td>2.942921</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.010589</td>
-      <td>-0.653087</td>
-      <td>-2.223590</td>
+      <td>0.029839</td>
+      <td>0.995039</td>
+      <td>3.948314</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>-0.327882</td>
-      <td>0.178439</td>
-      <td>0.210575</td>
+      <td>1.537772</td>
+      <td>3.331072</td>
+      <td>9.241248</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>-0.234144</td>
-      <td>-0.272059</td>
-      <td>-0.227509</td>
+      <td>0.077872</td>
+      <td>-1.938379</td>
+      <td>-7.001653</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.925122</td>
-      <td>3.424511</td>
-      <td>9.974717</td>
+      <td>0.892098</td>
+      <td>1.337169</td>
+      <td>5.719637</td>
     </tr>
   </tbody>
 </table>
@@ -754,7 +754,7 @@ <h1>Basic Example for generating samples from a GCM<a class="headerlink" href="#
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 18.14it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00&lt;00:00, 17.05it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -772,7 +772,7 @@ <h1>Basic Example for generating samples from a GCM<a class="headerlink" href="#
 Evaluated and the overall average KL divergence between generated and observed distribution and the graph structure. The results are as follows:
 
 ==== Evaluation of Generated Distribution ====
-The overall average KL divergence between the generated and observed distribution is 0.0133688297726812
+The overall average KL divergence between the generated and observed distribution is 0.019128396107247297
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 ==== Evaluation of the Causal Graph Structure ====
diff --git a/main/example_notebooks/gcm_draw_samples.ipynb b/main/example_notebooks/gcm_draw_samples.ipynb
index 419a71c88..65b15d5da 100644
--- a/main/example_notebooks/gcm_draw_samples.ipynb
+++ b/main/example_notebooks/gcm_draw_samples.ipynb
@@ -29,10 +29,10 @@
    "id": "f22337ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:42.167628Z",
-     "iopub.status.busy": "2024-10-25T14:54:42.167428Z",
-     "iopub.status.idle": "2024-10-25T14:54:42.398255Z",
-     "shell.execute_reply": "2024-10-25T14:54:42.397619Z"
+     "iopub.execute_input": "2024-10-25T14:55:50.307983Z",
+     "iopub.status.busy": "2024-10-25T14:55:50.307806Z",
+     "iopub.status.idle": "2024-10-25T14:55:50.566746Z",
+     "shell.execute_reply": "2024-10-25T14:55:50.566061Z"
     }
    },
    "outputs": [
@@ -65,33 +65,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.121542</td>\n",
-       "      <td>1.657360</td>\n",
-       "      <td>3.616523</td>\n",
+       "      <td>1.202890</td>\n",
+       "      <td>3.041920</td>\n",
+       "      <td>8.717822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.503374</td>\n",
-       "      <td>1.251506</td>\n",
-       "      <td>4.587577</td>\n",
+       "      <td>-1.050020</td>\n",
+       "      <td>-3.235161</td>\n",
+       "      <td>-8.177542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.473361</td>\n",
-       "      <td>1.553348</td>\n",
-       "      <td>4.065011</td>\n",
+       "      <td>0.048552</td>\n",
+       "      <td>-0.068086</td>\n",
+       "      <td>0.254594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.865151</td>\n",
-       "      <td>1.475716</td>\n",
-       "      <td>3.616999</td>\n",
+       "      <td>0.065656</td>\n",
+       "      <td>-1.161772</td>\n",
+       "      <td>-2.940748</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.641329</td>\n",
-       "      <td>2.701932</td>\n",
-       "      <td>11.109621</td>\n",
+       "      <td>-1.968502</td>\n",
+       "      <td>-3.431138</td>\n",
+       "      <td>-10.633668</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -99,11 +99,11 @@
       ],
       "text/plain": [
        "          X         Y          Z\n",
-       "0  1.121542  1.657360   3.616523\n",
-       "1  1.503374  1.251506   4.587577\n",
-       "2 -0.473361  1.553348   4.065011\n",
-       "3  0.865151  1.475716   3.616999\n",
-       "4  0.641329  2.701932  11.109621"
+       "0  1.202890  3.041920   8.717822\n",
+       "1 -1.050020 -3.235161  -8.177542\n",
+       "2  0.048552 -0.068086   0.254594\n",
+       "3  0.065656 -1.161772  -2.940748\n",
+       "4 -1.968502 -3.431138 -10.633668"
       ]
      },
      "execution_count": 1,
@@ -136,10 +136,10 @@
    "id": "0367caeb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:42.400580Z",
-     "iopub.status.busy": "2024-10-25T14:54:42.400081Z",
-     "iopub.status.idle": "2024-10-25T14:54:47.481474Z",
-     "shell.execute_reply": "2024-10-25T14:54:47.480788Z"
+     "iopub.execute_input": "2024-10-25T14:55:50.568793Z",
+     "iopub.status.busy": "2024-10-25T14:55:50.568551Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.040184Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.039494Z"
     }
    },
    "outputs": [
@@ -180,7 +180,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 423.32it/s]"
+      "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 459.01it/s]"
      ]
     },
     {
@@ -220,10 +220,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:47.484135Z",
-     "iopub.status.busy": "2024-10-25T14:54:47.483406Z",
-     "iopub.status.idle": "2024-10-25T14:54:47.495226Z",
-     "shell.execute_reply": "2024-10-25T14:54:47.494671Z"
+     "iopub.execute_input": "2024-10-25T14:55:56.042918Z",
+     "iopub.status.busy": "2024-10-25T14:55:56.042179Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.054149Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.053578Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -262,33 +262,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.481421</td>\n",
-       "      <td>1.807229</td>\n",
-       "      <td>6.236546</td>\n",
+       "      <td>0.377113</td>\n",
+       "      <td>0.796241</td>\n",
+       "      <td>2.942921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.010589</td>\n",
-       "      <td>-0.653087</td>\n",
-       "      <td>-2.223590</td>\n",
+       "      <td>0.029839</td>\n",
+       "      <td>0.995039</td>\n",
+       "      <td>3.948314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.327882</td>\n",
-       "      <td>0.178439</td>\n",
-       "      <td>0.210575</td>\n",
+       "      <td>1.537772</td>\n",
+       "      <td>3.331072</td>\n",
+       "      <td>9.241248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.234144</td>\n",
-       "      <td>-0.272059</td>\n",
-       "      <td>-0.227509</td>\n",
+       "      <td>0.077872</td>\n",
+       "      <td>-1.938379</td>\n",
+       "      <td>-7.001653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.925122</td>\n",
-       "      <td>3.424511</td>\n",
-       "      <td>9.974717</td>\n",
+       "      <td>0.892098</td>\n",
+       "      <td>1.337169</td>\n",
+       "      <td>5.719637</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -296,11 +296,11 @@
       ],
       "text/plain": [
        "          X         Y         Z\n",
-       "0  0.481421  1.807229  6.236546\n",
-       "1  0.010589 -0.653087 -2.223590\n",
-       "2 -0.327882  0.178439  0.210575\n",
-       "3 -0.234144 -0.272059 -0.227509\n",
-       "4  1.925122  3.424511  9.974717"
+       "0  0.377113  0.796241  2.942921\n",
+       "1  0.029839  0.995039  3.948314\n",
+       "2  1.537772  3.331072  9.241248\n",
+       "3  0.077872 -1.938379 -7.001653\n",
+       "4  0.892098  1.337169  5.719637"
       ]
      },
      "execution_count": 3,
@@ -332,10 +332,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:47.497131Z",
-     "iopub.status.busy": "2024-10-25T14:54:47.496943Z",
-     "iopub.status.idle": "2024-10-25T14:54:48.212265Z",
-     "shell.execute_reply": "2024-10-25T14:54:48.211666Z"
+     "iopub.execute_input": "2024-10-25T14:55:56.056555Z",
+     "iopub.status.busy": "2024-10-25T14:55:56.056028Z",
+     "iopub.status.idle": "2024-10-25T14:55:56.816691Z",
+     "shell.execute_reply": "2024-10-25T14:55:56.816041Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -358,7 +358,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 12.23it/s]"
+      "Test permutations of given graph:  33%|███▎      | 2/6 [00:00<00:00, 11.49it/s]"
      ]
     },
     {
@@ -366,7 +366,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 12.19it/s]"
+      "Test permutations of given graph:  67%|██████▋   | 4/6 [00:00<00:00, 11.45it/s]"
      ]
     },
     {
@@ -374,7 +374,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.14it/s]"
+      "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 17.05it/s]"
      ]
     },
     {
@@ -401,7 +401,7 @@
       "Evaluated and the overall average KL divergence between generated and observed distribution and the graph structure. The results are as follows:\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.0133688297726812\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.019128396107247297\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
diff --git a/main/example_notebooks/gcm_falsify_dag.html b/main/example_notebooks/gcm_falsify_dag.html
index 14a550a4b..5f57ef42c 100644
--- a/main/example_notebooks/gcm_falsify_dag.html
+++ b/main/example_notebooks/gcm_falsify_dag.html
@@ -637,7 +637,7 @@ <h2>Synthetic Data<a class="headerlink" href="#Synthetic-Data" title="Permalink
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Test permutations of given graph: 100%|██████████| 20/20 [00:16&lt;00:00,  1.19it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:17&lt;00:00,  1.17it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -723,7 +723,7 @@ <h2>Synthetic Data<a class="headerlink" href="#Synthetic-Data" title="Permalink
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Test permutations of given graph: 100%|██████████| 20/20 [00:18&lt;00:00,  1.11it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:18&lt;00:00,  1.06it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -843,7 +843,7 @@ <h2>Real Data (Protein Network dataset by Sachs et al., 2005)<a class="headerlin
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Test permutations of given graph: 100%|██████████| 100/100 [12:03&lt;00:00,  7.24s/it]
+Test permutations of given graph: 100%|██████████| 100/100 [12:13&lt;00:00,  7.33s/it]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -888,7 +888,7 @@ <h2>Edge Suggestions<a class="headerlink" href="#Edge-Suggestions" title="Permal
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Test permutations of given graph: 100%|██████████| 20/20 [00:18&lt;00:00,  1.06it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:21&lt;00:00,  1.05s/it]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
diff --git a/main/example_notebooks/gcm_falsify_dag.ipynb b/main/example_notebooks/gcm_falsify_dag.ipynb
index d024eec82..9f2f66ba4 100644
--- a/main/example_notebooks/gcm_falsify_dag.ipynb
+++ b/main/example_notebooks/gcm_falsify_dag.ipynb
@@ -18,10 +18,10 @@
    "id": "13a5f7f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:52.411936Z",
-     "iopub.status.busy": "2024-10-25T14:54:52.411500Z",
-     "iopub.status.idle": "2024-10-25T14:54:53.813945Z",
-     "shell.execute_reply": "2024-10-25T14:54:53.813345Z"
+     "iopub.execute_input": "2024-10-25T14:56:01.147169Z",
+     "iopub.status.busy": "2024-10-25T14:56:01.146746Z",
+     "iopub.status.idle": "2024-10-25T14:56:02.697341Z",
+     "shell.execute_reply": "2024-10-25T14:56:02.696670Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "id": "08d67fd1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:53.816412Z",
-     "iopub.status.busy": "2024-10-25T14:54:53.815902Z",
-     "iopub.status.idle": "2024-10-25T14:54:53.924049Z",
-     "shell.execute_reply": "2024-10-25T14:54:53.923441Z"
+     "iopub.execute_input": "2024-10-25T14:56:02.700190Z",
+     "iopub.status.busy": "2024-10-25T14:56:02.699632Z",
+     "iopub.status.idle": "2024-10-25T14:56:02.819904Z",
+     "shell.execute_reply": "2024-10-25T14:56:02.819206Z"
     }
    },
    "outputs": [
@@ -105,10 +105,10 @@
    "id": "953753d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:54:53.926517Z",
-     "iopub.status.busy": "2024-10-25T14:54:53.926093Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.608516Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.607875Z"
+     "iopub.execute_input": "2024-10-25T14:56:02.822376Z",
+     "iopub.status.busy": "2024-10-25T14:56:02.821922Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.089046Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.088372Z"
     }
    },
    "outputs": [
@@ -125,7 +125,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 1/20 [00:01<00:25,  1.33s/it]"
+      "Test permutations of given graph:   5%|▌         | 1/20 [00:01<00:26,  1.40s/it]"
      ]
     },
     {
@@ -133,7 +133,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 2/20 [00:02<00:24,  1.35s/it]"
+      "Test permutations of given graph:  10%|█         | 2/20 [00:02<00:24,  1.37s/it]"
      ]
     },
     {
@@ -141,7 +141,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 3/20 [00:03<00:22,  1.31s/it]"
+      "Test permutations of given graph:  15%|█▌        | 3/20 [00:04<00:22,  1.34s/it]"
      ]
     },
     {
@@ -149,7 +149,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 4/20 [00:04<00:18,  1.15s/it]"
+      "Test permutations of given graph:  20%|██        | 4/20 [00:04<00:18,  1.17s/it]"
      ]
     },
     {
@@ -157,7 +157,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 5/20 [00:05<00:15,  1.06s/it]"
+      "Test permutations of given graph:  25%|██▌       | 5/20 [00:05<00:16,  1.08s/it]"
      ]
     },
     {
@@ -165,7 +165,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 6/20 [00:06<00:13,  1.00it/s]"
+      "Test permutations of given graph:  30%|███       | 6/20 [00:06<00:14,  1.02s/it]"
      ]
     },
     {
@@ -173,7 +173,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 7/20 [00:07<00:14,  1.09s/it]"
+      "Test permutations of given graph:  35%|███▌      | 7/20 [00:08<00:14,  1.12s/it]"
      ]
     },
     {
@@ -181,7 +181,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 8/20 [00:08<00:12,  1.02s/it]"
+      "Test permutations of given graph:  40%|████      | 8/20 [00:09<00:12,  1.05s/it]"
      ]
     },
     {
@@ -189,7 +189,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 9/20 [00:10<00:12,  1.11s/it]"
+      "Test permutations of given graph:  45%|████▌     | 9/20 [00:10<00:12,  1.14s/it]"
      ]
     },
     {
@@ -197,7 +197,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 10/20 [00:11<00:11,  1.17s/it]"
+      "Test permutations of given graph:  50%|█████     | 10/20 [00:11<00:11,  1.19s/it]"
      ]
     },
     {
@@ -205,7 +205,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 12/20 [00:12<00:06,  1.21it/s]"
+      "Test permutations of given graph:  60%|██████    | 12/20 [00:12<00:06,  1.18it/s]"
      ]
     },
     {
@@ -213,7 +213,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:13<00:05,  1.18it/s]"
+      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:13<00:06,  1.16it/s]"
      ]
     },
     {
@@ -221,7 +221,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 14/20 [00:14<00:05,  1.17it/s]"
+      "Test permutations of given graph:  70%|███████   | 14/20 [00:14<00:05,  1.14it/s]"
      ]
     },
     {
@@ -229,7 +229,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  85%|████████▌ | 17/20 [00:14<00:01,  1.78it/s]"
+      "Test permutations of given graph:  85%|████████▌ | 17/20 [00:15<00:01,  1.75it/s]"
      ]
     },
     {
@@ -237,7 +237,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 18/20 [00:15<00:01,  1.59it/s]"
+      "Test permutations of given graph:  90%|█████████ | 18/20 [00:16<00:01,  1.56it/s]"
      ]
     },
     {
@@ -245,7 +245,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  95%|█████████▌| 19/20 [00:16<00:00,  1.45it/s]"
+      "Test permutations of given graph:  95%|█████████▌| 19/20 [00:17<00:00,  1.42it/s]"
      ]
     },
     {
@@ -253,7 +253,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00,  1.19it/s]"
+      "Test permutations of given graph: 100%|██████████| 20/20 [00:17<00:00,  1.17it/s]"
      ]
     },
     {
@@ -317,10 +317,10 @@
    "id": "529de3f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.610385Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.610207Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.613608Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.613101Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.091337Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.090939Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.094668Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.094056Z"
     }
    },
    "outputs": [
@@ -350,10 +350,10 @@
    "id": "4623dbc3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.615360Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.615024Z",
-     "iopub.status.idle": "2024-10-25T14:55:14.708019Z",
-     "shell.execute_reply": "2024-10-25T14:55:14.707473Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.096406Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.096202Z",
+     "iopub.status.idle": "2024-10-25T14:56:24.193214Z",
+     "shell.execute_reply": "2024-10-25T14:56:24.192560Z"
     }
    },
    "outputs": [
@@ -382,10 +382,10 @@
    "id": "fcd847fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:14.710076Z",
-     "iopub.status.busy": "2024-10-25T14:55:14.709865Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.500191Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.499450Z"
+     "iopub.execute_input": "2024-10-25T14:56:24.195920Z",
+     "iopub.status.busy": "2024-10-25T14:56:24.195506Z",
+     "iopub.status.idle": "2024-10-25T14:56:45.926358Z",
+     "shell.execute_reply": "2024-10-25T14:56:45.925587Z"
     }
    },
    "outputs": [
@@ -402,7 +402,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:46,  2.46s/it]"
+      "Test permutations of given graph:   5%|▌         | 1/20 [00:02<00:48,  2.57s/it]"
      ]
     },
     {
@@ -410,7 +410,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 2/20 [00:04<00:44,  2.46s/it]"
+      "Test permutations of given graph:  10%|█         | 2/20 [00:05<00:46,  2.56s/it]"
      ]
     },
     {
@@ -418,7 +418,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:35,  2.06s/it]"
+      "Test permutations of given graph:  15%|█▌        | 3/20 [00:06<00:36,  2.15s/it]"
      ]
     },
     {
@@ -426,7 +426,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 4/20 [00:08<00:30,  1.91s/it]"
+      "Test permutations of given graph:  20%|██        | 4/20 [00:08<00:31,  1.99s/it]"
      ]
     },
     {
@@ -434,7 +434,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 5/20 [00:09<00:26,  1.80s/it]"
+      "Test permutations of given graph:  25%|██▌       | 5/20 [00:10<00:28,  1.87s/it]"
      ]
     },
     {
@@ -442,7 +442,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:23,  1.65s/it]"
+      "Test permutations of given graph:  30%|███       | 6/20 [00:11<00:23,  1.70s/it]"
      ]
     },
     {
@@ -450,7 +450,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 7/20 [00:12<00:19,  1.53s/it]"
+      "Test permutations of given graph:  35%|███▌      | 7/20 [00:12<00:20,  1.57s/it]"
      ]
     },
     {
@@ -458,7 +458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 8/20 [00:13<00:17,  1.44s/it]"
+      "Test permutations of given graph:  40%|████      | 8/20 [00:14<00:18,  1.51s/it]"
      ]
     },
     {
@@ -466,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 9/20 [00:15<00:15,  1.41s/it]"
+      "Test permutations of given graph:  45%|████▌     | 9/20 [00:15<00:16,  1.46s/it]"
      ]
     },
     {
@@ -474,7 +474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 12/20 [00:16<00:06,  1.18it/s]"
+      "Test permutations of given graph:  60%|██████    | 12/20 [00:16<00:07,  1.13it/s]"
      ]
     },
     {
@@ -482,7 +482,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:17<00:05,  1.18it/s]"
+      "Test permutations of given graph:  65%|██████▌   | 13/20 [00:17<00:06,  1.12it/s]"
      ]
     },
     {
@@ -490,7 +490,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:18<00:03,  1.44it/s]"
+      "Test permutations of given graph:  75%|███████▌  | 15/20 [00:18<00:03,  1.37it/s]"
      ]
     },
     {
@@ -498,7 +498,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.11it/s]"
+      "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.06it/s]"
      ]
     },
     {
@@ -557,10 +557,10 @@
    "id": "3fe68e25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.502196Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.502006Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.595059Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.594431Z"
+     "iopub.execute_input": "2024-10-25T14:56:45.928545Z",
+     "iopub.status.busy": "2024-10-25T14:56:45.928282Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.032809Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.032125Z"
     }
    },
    "outputs": [
@@ -604,10 +604,10 @@
    "id": "03e8ffa3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.597521Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.597116Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.767492Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.766953Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.035589Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.035182Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.069198Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.068419Z"
     }
    },
    "outputs": [],
@@ -624,10 +624,10 @@
    "id": "3d30a01b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.769719Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.769347Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.895092Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.894430Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.072783Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.072257Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.217919Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.217241Z"
     }
    },
    "outputs": [
@@ -660,10 +660,10 @@
    "id": "072544c1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.897463Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.897092Z",
-     "iopub.status.idle": "2024-10-25T14:55:35.901575Z",
-     "shell.execute_reply": "2024-10-25T14:55:35.901005Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.221083Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.220461Z",
+     "iopub.status.idle": "2024-10-25T14:56:46.224798Z",
+     "shell.execute_reply": "2024-10-25T14:56:46.224229Z"
     }
    },
    "outputs": [],
@@ -692,10 +692,10 @@
    "id": "bbfe658d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T14:55:35.904014Z",
-     "iopub.status.busy": "2024-10-25T14:55:35.903524Z",
-     "iopub.status.idle": "2024-10-25T15:07:50.708246Z",
-     "shell.execute_reply": "2024-10-25T15:07:50.707586Z"
+     "iopub.execute_input": "2024-10-25T14:56:46.227173Z",
+     "iopub.status.busy": "2024-10-25T14:56:46.226755Z",
+     "iopub.status.idle": "2024-10-25T15:09:10.934004Z",
+     "shell.execute_reply": "2024-10-25T15:09:10.933345Z"
     }
    },
    "outputs": [
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   1%|          | 1/100 [00:10<17:50, 10.81s/it]"
+      "Test permutations of given graph:   1%|          | 1/100 [00:11<18:28, 11.20s/it]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 2/100 [00:21<17:42, 10.84s/it]"
+      "Test permutations of given graph:   2%|▏         | 2/100 [00:22<18:27, 11.30s/it]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   3%|▎         | 3/100 [00:32<17:12, 10.65s/it]"
+      "Test permutations of given graph:   3%|▎         | 3/100 [00:33<17:55, 11.08s/it]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 4/100 [00:42<17:07, 10.71s/it]"
+      "Test permutations of given graph:   4%|▍         | 4/100 [00:44<17:41, 11.05s/it]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   5%|▌         | 5/100 [00:53<17:08, 10.83s/it]"
+      "Test permutations of given graph:   5%|▌         | 5/100 [00:55<17:37, 11.14s/it]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 6/100 [01:03<16:19, 10.42s/it]"
+      "Test permutations of given graph:   6%|▌         | 6/100 [01:05<16:48, 10.73s/it]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   7%|▋         | 7/100 [01:11<14:46,  9.53s/it]"
+      "Test permutations of given graph:   7%|▋         | 7/100 [01:13<15:08,  9.76s/it]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 8/100 [01:22<15:19,  9.99s/it]"
+      "Test permutations of given graph:   8%|▊         | 8/100 [01:24<15:37, 10.19s/it]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   9%|▉         | 9/100 [01:33<15:37, 10.30s/it]"
+      "Test permutations of given graph:   9%|▉         | 9/100 [01:35<15:58, 10.54s/it]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 10/100 [01:43<15:34, 10.38s/it]"
+      "Test permutations of given graph:  10%|█         | 10/100 [01:46<15:51, 10.57s/it]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  11%|█         | 11/100 [01:54<15:26, 10.41s/it]"
+      "Test permutations of given graph:  11%|█         | 11/100 [01:57<15:41, 10.58s/it]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 12/100 [02:04<15:17, 10.42s/it]"
+      "Test permutations of given graph:  12%|█▏        | 12/100 [02:07<15:29, 10.57s/it]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  13%|█▎        | 13/100 [02:13<14:34, 10.05s/it]"
+      "Test permutations of given graph:  13%|█▎        | 13/100 [02:16<14:45, 10.18s/it]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 14/100 [02:21<13:25,  9.37s/it]"
+      "Test permutations of given graph:  14%|█▍        | 14/100 [02:24<13:38,  9.52s/it]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  15%|█▌        | 15/100 [02:32<13:43,  9.69s/it]"
+      "Test permutations of given graph:  15%|█▌        | 15/100 [02:35<13:57,  9.85s/it]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 16/100 [02:42<13:52,  9.91s/it]"
+      "Test permutations of given graph:  16%|█▌        | 16/100 [02:46<14:05, 10.07s/it]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  17%|█▋        | 17/100 [02:49<12:36,  9.12s/it]"
+      "Test permutations of given graph:  17%|█▋        | 17/100 [02:53<12:45,  9.22s/it]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 18/100 [03:00<12:56,  9.47s/it]"
+      "Test permutations of given graph:  18%|█▊        | 18/100 [03:03<13:09,  9.63s/it]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  19%|█▉        | 19/100 [03:10<13:06,  9.71s/it]"
+      "Test permutations of given graph:  19%|█▉        | 19/100 [03:14<13:23,  9.92s/it]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 20/100 [03:18<12:12,  9.16s/it]"
+      "Test permutations of given graph:  20%|██        | 20/100 [03:22<12:26,  9.33s/it]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  21%|██        | 21/100 [03:28<12:30,  9.50s/it]"
+      "Test permutations of given graph:  21%|██        | 21/100 [03:33<12:47,  9.72s/it]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 22/100 [03:38<12:41,  9.76s/it]"
+      "Test permutations of given graph:  22%|██▏       | 22/100 [03:43<12:57,  9.96s/it]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  23%|██▎       | 23/100 [03:45<11:18,  8.81s/it]"
+      "Test permutations of given graph:  23%|██▎       | 23/100 [03:50<11:29,  8.95s/it]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 24/100 [03:53<11:01,  8.71s/it]"
+      "Test permutations of given graph:  24%|██▍       | 24/100 [03:58<11:09,  8.80s/it]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  25%|██▌       | 25/100 [04:04<11:26,  9.15s/it]"
+      "Test permutations of given graph:  25%|██▌       | 25/100 [04:08<11:33,  9.25s/it]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 26/100 [04:12<11:07,  9.01s/it]"
+      "Test permutations of given graph:  26%|██▌       | 26/100 [04:17<11:14,  9.12s/it]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  27%|██▋       | 27/100 [04:20<10:34,  8.70s/it]"
+      "Test permutations of given graph:  27%|██▋       | 27/100 [04:25<10:40,  8.78s/it]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 28/100 [04:30<10:41,  8.91s/it]"
+      "Test permutations of given graph:  28%|██▊       | 28/100 [04:35<10:47,  8.99s/it]"
      ]
     },
     {
@@ -936,7 +936,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  29%|██▉       | 29/100 [04:40<10:59,  9.29s/it]"
+      "Test permutations of given graph:  29%|██▉       | 29/100 [04:45<11:06,  9.39s/it]"
      ]
     },
     {
@@ -944,7 +944,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 30/100 [04:47<10:01,  8.60s/it]"
+      "Test permutations of given graph:  30%|███       | 30/100 [04:52<10:07,  8.68s/it]"
      ]
     },
     {
@@ -952,7 +952,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  31%|███       | 31/100 [04:54<09:29,  8.25s/it]"
+      "Test permutations of given graph:  31%|███       | 31/100 [05:00<09:33,  8.32s/it]"
      ]
     },
     {
@@ -960,7 +960,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 32/100 [05:03<09:21,  8.26s/it]"
+      "Test permutations of given graph:  32%|███▏      | 32/100 [05:08<09:25,  8.32s/it]"
      ]
     },
     {
@@ -968,7 +968,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  33%|███▎      | 33/100 [05:10<09:05,  8.14s/it]"
+      "Test permutations of given graph:  33%|███▎      | 33/100 [05:16<09:07,  8.18s/it]"
      ]
     },
     {
@@ -976,7 +976,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 34/100 [05:18<08:42,  7.92s/it]"
+      "Test permutations of given graph:  34%|███▍      | 34/100 [05:23<08:44,  7.95s/it]"
      ]
     },
     {
@@ -984,7 +984,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  35%|███▌      | 35/100 [05:27<08:59,  8.30s/it]"
+      "Test permutations of given graph:  35%|███▌      | 35/100 [05:32<09:00,  8.31s/it]"
      ]
     },
     {
@@ -992,7 +992,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 36/100 [05:36<09:02,  8.48s/it]"
+      "Test permutations of given graph:  36%|███▌      | 36/100 [05:41<09:01,  8.45s/it]"
      ]
     },
     {
@@ -1000,7 +1000,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  37%|███▋      | 37/100 [05:44<08:37,  8.21s/it]"
+      "Test permutations of given graph:  37%|███▋      | 37/100 [05:49<08:37,  8.22s/it]"
      ]
     },
     {
@@ -1008,7 +1008,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 38/100 [05:49<07:36,  7.36s/it]"
+      "Test permutations of given graph:  38%|███▊      | 38/100 [05:54<07:41,  7.44s/it]"
      ]
     },
     {
@@ -1016,7 +1016,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  39%|███▉      | 39/100 [05:59<08:23,  8.25s/it]"
+      "Test permutations of given graph:  39%|███▉      | 39/100 [06:05<08:27,  8.32s/it]"
      ]
     },
     {
@@ -1024,7 +1024,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 40/100 [06:07<08:11,  8.19s/it]"
+      "Test permutations of given graph:  40%|████      | 40/100 [06:13<08:15,  8.26s/it]"
      ]
     },
     {
@@ -1032,7 +1032,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  41%|████      | 41/100 [06:13<07:25,  7.55s/it]"
+      "Test permutations of given graph:  41%|████      | 41/100 [06:19<07:28,  7.60s/it]"
      ]
     },
     {
@@ -1040,7 +1040,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 42/100 [06:23<07:49,  8.10s/it]"
+      "Test permutations of given graph:  42%|████▏     | 42/100 [06:28<07:53,  8.17s/it]"
      ]
     },
     {
@@ -1048,7 +1048,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  43%|████▎     | 43/100 [06:28<06:56,  7.31s/it]"
+      "Test permutations of given graph:  43%|████▎     | 43/100 [06:34<07:00,  7.38s/it]"
      ]
     },
     {
@@ -1056,7 +1056,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 44/100 [06:36<06:50,  7.33s/it]"
+      "Test permutations of given graph:  44%|████▍     | 44/100 [06:41<06:54,  7.40s/it]"
      ]
     },
     {
@@ -1064,7 +1064,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  45%|████▌     | 45/100 [06:44<06:55,  7.55s/it]"
+      "Test permutations of given graph:  45%|████▌     | 45/100 [06:50<06:59,  7.63s/it]"
      ]
     },
     {
@@ -1072,7 +1072,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 46/100 [06:47<05:33,  6.17s/it]"
+      "Test permutations of given graph:  46%|████▌     | 46/100 [06:53<05:36,  6.23s/it]"
      ]
     },
     {
@@ -1080,7 +1080,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  47%|████▋     | 47/100 [06:55<05:58,  6.75s/it]"
+      "Test permutations of given graph:  47%|████▋     | 47/100 [07:01<06:00,  6.80s/it]"
      ]
     },
     {
@@ -1088,7 +1088,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 48/100 [07:01<05:45,  6.65s/it]"
+      "Test permutations of given graph:  48%|████▊     | 48/100 [07:07<05:47,  6.68s/it]"
      ]
     },
     {
@@ -1096,7 +1096,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  49%|████▉     | 49/100 [07:10<06:18,  7.42s/it]"
+      "Test permutations of given graph:  49%|████▉     | 49/100 [07:16<06:20,  7.45s/it]"
      ]
     },
     {
@@ -1104,7 +1104,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 50/100 [07:15<05:31,  6.63s/it]"
+      "Test permutations of given graph:  50%|█████     | 50/100 [07:21<05:33,  6.66s/it]"
      ]
     },
     {
@@ -1112,7 +1112,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  51%|█████     | 51/100 [07:24<05:50,  7.16s/it]"
+      "Test permutations of given graph:  51%|█████     | 51/100 [07:30<05:52,  7.19s/it]"
      ]
     },
     {
@@ -1120,7 +1120,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 52/100 [07:33<06:12,  7.77s/it]"
+      "Test permutations of given graph:  52%|█████▏    | 52/100 [07:39<06:15,  7.83s/it]"
      ]
     },
     {
@@ -1128,7 +1128,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  53%|█████▎    | 53/100 [07:38<05:32,  7.08s/it]"
+      "Test permutations of given graph:  53%|█████▎    | 53/100 [07:44<05:35,  7.14s/it]"
      ]
     },
     {
@@ -1136,7 +1136,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 54/100 [07:45<05:26,  7.10s/it]"
+      "Test permutations of given graph:  54%|█████▍    | 54/100 [07:52<05:29,  7.16s/it]"
      ]
     },
     {
@@ -1144,7 +1144,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  55%|█████▌    | 55/100 [07:54<05:35,  7.46s/it]"
+      "Test permutations of given graph:  55%|█████▌    | 55/100 [08:00<05:38,  7.53s/it]"
      ]
     },
     {
@@ -1152,7 +1152,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 56/100 [07:58<04:50,  6.61s/it]"
+      "Test permutations of given graph:  56%|█████▌    | 56/100 [08:05<04:53,  6.67s/it]"
      ]
     },
     {
@@ -1160,7 +1160,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  57%|█████▋    | 57/100 [08:05<04:43,  6.60s/it]"
+      "Test permutations of given graph:  57%|█████▋    | 57/100 [08:11<04:46,  6.66s/it]"
      ]
     },
     {
@@ -1168,7 +1168,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  58%|█████▊    | 58/100 [08:12<04:41,  6.71s/it]"
+      "Test permutations of given graph:  58%|█████▊    | 58/100 [08:18<04:44,  6.78s/it]"
      ]
     },
     {
@@ -1176,7 +1176,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  59%|█████▉    | 59/100 [08:16<04:06,  6.02s/it]"
+      "Test permutations of given graph:  59%|█████▉    | 59/100 [08:23<04:09,  6.08s/it]"
      ]
     },
     {
@@ -1184,7 +1184,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 60/100 [08:24<04:21,  6.54s/it]"
+      "Test permutations of given graph:  60%|██████    | 60/100 [08:30<04:20,  6.52s/it]"
      ]
     },
     {
@@ -1192,7 +1192,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  61%|██████    | 61/100 [08:29<03:52,  5.95s/it]"
+      "Test permutations of given graph:  61%|██████    | 61/100 [08:35<03:52,  5.97s/it]"
      ]
     },
     {
@@ -1200,7 +1200,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 62/100 [08:34<03:43,  5.89s/it]"
+      "Test permutations of given graph:  62%|██████▏   | 62/100 [08:41<03:44,  5.92s/it]"
      ]
     },
     {
@@ -1208,7 +1208,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  63%|██████▎   | 63/100 [08:40<03:38,  5.91s/it]"
+      "Test permutations of given graph:  63%|██████▎   | 63/100 [08:47<03:39,  5.93s/it]"
      ]
     },
     {
@@ -1216,7 +1216,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 64/100 [08:47<03:46,  6.28s/it]"
+      "Test permutations of given graph:  64%|██████▍   | 64/100 [08:54<03:49,  6.37s/it]"
      ]
     },
     {
@@ -1224,7 +1224,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  65%|██████▌   | 65/100 [08:53<03:32,  6.08s/it]"
+      "Test permutations of given graph:  65%|██████▌   | 65/100 [09:00<03:35,  6.15s/it]"
      ]
     },
     {
@@ -1232,7 +1232,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 66/100 [08:58<03:13,  5.70s/it]"
+      "Test permutations of given graph:  66%|██████▌   | 66/100 [09:05<03:17,  5.80s/it]"
      ]
     },
     {
@@ -1240,7 +1240,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  67%|██████▋   | 67/100 [09:04<03:12,  5.85s/it]"
+      "Test permutations of given graph:  67%|██████▋   | 67/100 [09:11<03:16,  5.94s/it]"
      ]
     },
     {
@@ -1248,7 +1248,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 68/100 [09:08<02:51,  5.37s/it]"
+      "Test permutations of given graph:  68%|██████▊   | 68/100 [09:16<02:55,  5.49s/it]"
      ]
     },
     {
@@ -1256,7 +1256,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  69%|██████▉   | 69/100 [09:16<03:05,  5.98s/it]"
+      "Test permutations of given graph:  69%|██████▉   | 69/100 [09:23<03:09,  6.11s/it]"
      ]
     },
     {
@@ -1264,7 +1264,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 70/100 [09:25<03:27,  6.92s/it]"
+      "Test permutations of given graph:  70%|███████   | 70/100 [09:32<03:32,  7.09s/it]"
      ]
     },
     {
@@ -1272,7 +1272,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  71%|███████   | 71/100 [09:30<03:07,  6.45s/it]"
+      "Test permutations of given graph:  71%|███████   | 71/100 [09:38<03:13,  6.67s/it]"
      ]
     },
     {
@@ -1280,7 +1280,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 72/100 [09:37<03:04,  6.59s/it]"
+      "Test permutations of given graph:  72%|███████▏  | 72/100 [09:45<03:09,  6.77s/it]"
      ]
     },
     {
@@ -1288,7 +1288,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  73%|███████▎  | 73/100 [09:41<02:34,  5.71s/it]"
+      "Test permutations of given graph:  73%|███████▎  | 73/100 [09:49<02:37,  5.85s/it]"
      ]
     },
     {
@@ -1296,7 +1296,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 74/100 [09:47<02:34,  5.94s/it]"
+      "Test permutations of given graph:  74%|███████▍  | 74/100 [09:55<02:37,  6.05s/it]"
      ]
     },
     {
@@ -1304,7 +1304,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  75%|███████▌  | 75/100 [09:52<02:19,  5.58s/it]"
+      "Test permutations of given graph:  75%|███████▌  | 75/100 [10:00<02:21,  5.67s/it]"
      ]
     },
     {
@@ -1312,7 +1312,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 76/100 [09:57<02:07,  5.33s/it]"
+      "Test permutations of given graph:  76%|███████▌  | 76/100 [10:05<02:09,  5.39s/it]"
      ]
     },
     {
@@ -1320,7 +1320,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  77%|███████▋  | 77/100 [10:02<02:02,  5.33s/it]"
+      "Test permutations of given graph:  77%|███████▋  | 77/100 [10:10<02:03,  5.38s/it]"
      ]
     },
     {
@@ -1328,7 +1328,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 78/100 [10:08<01:58,  5.40s/it]"
+      "Test permutations of given graph:  78%|███████▊  | 78/100 [10:16<02:00,  5.47s/it]"
      ]
     },
     {
@@ -1336,7 +1336,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  79%|███████▉  | 79/100 [10:12<01:48,  5.16s/it]"
+      "Test permutations of given graph:  79%|███████▉  | 79/100 [10:21<01:49,  5.22s/it]"
      ]
     },
     {
@@ -1344,7 +1344,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 80/100 [10:19<01:52,  5.61s/it]"
+      "Test permutations of given graph:  80%|████████  | 80/100 [10:27<01:53,  5.65s/it]"
      ]
     },
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      "output_type": "stream",
      "text": [
       "\r",
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      ]
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      ]
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      "output_type": "stream",
      "text": [
       "\r",
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+      "Test permutations of given graph:  83%|████████▎ | 83/100 [10:41<01:25,  5.02s/it]"
      ]
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      "output_type": "stream",
      "text": [
       "\r",
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      ]
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      "output_type": "stream",
      "text": [
       "\r",
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      ]
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      "output_type": "stream",
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      ]
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      "output_type": "stream",
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      ]
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      ]
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      "output_type": "stream",
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      ]
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    "id": "58650ea8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:07:50.710303Z",
-     "iopub.status.busy": "2024-10-25T15:07:50.710116Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.286854Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.286195Z"
+     "iopub.execute_input": "2024-10-25T15:09:10.936127Z",
+     "iopub.status.busy": "2024-10-25T15:09:10.935770Z",
+     "iopub.status.idle": "2024-10-25T15:09:35.872512Z",
+     "shell.execute_reply": "2024-10-25T15:09:35.871810Z"
     }
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@@ -1600,7 +1600,7 @@
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      "output_type": "stream",
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      ]
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+      "Test permutations of given graph:  55%|█████▌    | 11/20 [00:15<00:07,  1.27it/s]"
      ]
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      ]
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      ]
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       "\r",
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    "id": "ab8ef360",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:13.288895Z",
-     "iopub.status.busy": "2024-10-25T15:08:13.288505Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.376022Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.375468Z"
+     "iopub.execute_input": "2024-10-25T15:09:35.875021Z",
+     "iopub.status.busy": "2024-10-25T15:09:35.874524Z",
+     "iopub.status.idle": "2024-10-25T15:09:35.960051Z",
+     "shell.execute_reply": "2024-10-25T15:09:35.959317Z"
     }
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    "outputs": [
@@ -1800,10 +1800,10 @@
    "id": "073a54c4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:13.378137Z",
-     "iopub.status.busy": "2024-10-25T15:08:13.377924Z",
-     "iopub.status.idle": "2024-10-25T15:08:13.479623Z",
-     "shell.execute_reply": "2024-10-25T15:08:13.479014Z"
+     "iopub.execute_input": "2024-10-25T15:09:35.962346Z",
+     "iopub.status.busy": "2024-10-25T15:09:35.962106Z",
+     "iopub.status.idle": "2024-10-25T15:09:36.042389Z",
+     "shell.execute_reply": "2024-10-25T15:09:36.041649Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/gcm_icc.html b/main/example_notebooks/gcm_icc.html
index b19592fdf..297200edb 100644
--- a/main/example_notebooks/gcm_icc.html
+++ b/main/example_notebooks/gcm_icc.html
@@ -627,7 +627,7 @@ <h2>Intrinsic influence on car MPG consumption<a class="headerlink" href="#Intri
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00&lt;00:00, 28.62it/s]
+Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00&lt;00:00, 28.99it/s]
 </pre></div></div>
 </div>
 <p>Optionally, we can get some insights into the performance of the causal mechanisms by using the evaluation method:</p>
@@ -644,7 +644,7 @@ <h2>Intrinsic influence on car MPG consumption<a class="headerlink" href="#Intri
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00&lt;00:00, 429.87it/s]
+Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00&lt;00:00, 3254.93it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -670,35 +670,35 @@ <h2>Intrinsic influence on car MPG consumption<a class="headerlink" href="#Intri
 The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.
 
 --- Node displacement
-- The MSE is 1066.8802205998074.
-- The NMSE is 0.3150091474285043.
-- The R2 coefficient is 0.9002474266291071.
-- The normalized CRPS is 0.17522937047621454.
+- The MSE is 1060.1157400952736.
+- The NMSE is 0.31274920184264293.
+- The R2 coefficient is 0.9015843974287858.
+- The normalized CRPS is 0.17714313434646875.
 The estimated CRPS indicates a very good model performance.
 
 --- Node weight
-- The MSE is 79949.61282051282.
-- The NMSE is 0.3359350937278292.
-- The R2 coefficient is 0.8852962623585189.
-- The normalized CRPS is 0.18398226376156224.
+- The MSE is 77927.59152872444.
+- The NMSE is 0.3293392447432768.
+- The R2 coefficient is 0.8909086867528749.
+- The normalized CRPS is 0.18431008611167252.
 The estimated CRPS indicates a very good model performance.
 
 --- Node horsepower
-- The MSE is 209.29113924050634.
-- The NMSE is 0.38098501624901415.
-- The R2 coefficient is 0.8513378579863071.
-- The normalized CRPS is 0.20296699411876892.
+- The MSE is 207.14212917883805.
+- The NMSE is 0.37926574409578384.
+- The R2 coefficient is 0.8557617347249853.
+- The normalized CRPS is 0.20152482768695754.
 The estimated CRPS indicates a good model performance.
 
 --- Node mpg
-- The MSE is 15.699538297818407.
-- The NMSE is 0.5134945988653445.
-- The R2 coefficient is 0.7353328271586502.
-- The normalized CRPS is 0.2830433213216262.
+- The MSE is 15.620289636388742.
+- The NMSE is 0.5121045268547029.
+- The R2 coefficient is 0.7344865131314628.
+- The normalized CRPS is 0.2793091596647558.
 The estimated CRPS indicates a good model performance.
 
 ==== Evaluation of Generated Distribution ====
-The overall average KL divergence between the generated and observed distribution is 0.9578045815757854
+The overall average KL divergence between the generated and observed distribution is 0.9394536430054833
 The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.
 
 ==== NOTE ====
@@ -738,7 +738,7 @@ <h2>Intrinsic influence on car MPG consumption<a class="headerlink" href="#Intri
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating set functions...: 100%|██████████| 32/32 [00:14&lt;00:00,  2.13it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:14&lt;00:00,  2.15it/s]
 </pre></div></div>
 </div>
 <p>For a better interpretation of the results, we convert the variance attribution to percentages by normalizing it over the total sum.</p>
@@ -778,7 +778,7 @@ <h2>Intrinsic influence on car MPG consumption<a class="headerlink" href="#Intri
 <h2>Intrinsic influence on river flow<a class="headerlink" href="#Intrinsic-influence-on-river-flow" title="Permalink to this heading">#</a></h2>
 <p>In the next example, we look at different recordings taken of the river flows (<span class="math notranslate nohighlight">\(m^3/s\)</span>) at a 15 minute frequency across 5 different measuring stations in England at Henthorn, New Jumbles Rock, Hodder Place, Whalley Weir and Samlesbury. Here, obtaining a better understanding of how the river flows behave can help to plan potential mitigation steps to avoid overflows. The data is taken from the <a class="reference external" href="https://environment.data.gov.uk/hydrology/explore">UK Department for Environment Food &amp; Rural Affairs
 website</a>. Here is a map of the rivers:</p>
-<p><img alt="542ac19c834d42ce861c676d4a9eb6c7" class="no-scaled-link" src="../_images/river-map.jpg" style="width: 600px;" /></p>
+<p><img alt="842918f6d15945c0817cad92cd096584" class="no-scaled-link" src="../_images/river-map.jpg" style="width: 600px;" /></p>
 <p>New Jumbles Rock lies at a confluence point of the 3 rivers passing Henthorn, Hodder Place, and Whalley Weir and New Jumbles Rock flows into Samlesbury. The water passing a certain measuring station is certainly a mixture of some fraction of the amount observed at the next stations further upstream plus some amount contributed by streams and little rivers entering the river in between. This defines our causal graph as:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -801,7 +801,7 @@ <h2>Intrinsic influence on river flow<a class="headerlink" href="#Intrinsic-infl
 </div>
 </div>
 <p>In this setting, we are interested in the causal influence of the upstream rivers on the Samlesbury river. Similar to the example before, we would expect these nodes to be heavily confounded by, e.g., the weather. That is, the true graph is more likely to be along the lines of:</p>
-<p><img alt="b1a414fd1b4e421a8f167f215a1ef5e7" class="no-scaled-link" src="../_images/river-confounded.png" style="width: 400px;" /></p>
+<p><img alt="074defe25ef94aefb5429ee009513a01" class="no-scaled-link" src="../_images/river-confounded.png" style="width: 400px;" /></p>
 <p>Nevertheless, we still expect the ICC algorithm to provide some insights into the contribution to the river flow of Samlesbury, even with the hidden confounder in place:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -823,8 +823,8 @@ <h2>Intrinsic influence on river flow<a class="headerlink" href="#Intrinsic-infl
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00&lt;00:00, 369.38it/s]
-Evaluating set functions...: 100%|██████████| 32/32 [00:00&lt;00:00, 240.35it/s]
+Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00&lt;00:00, 379.97it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:00&lt;00:00, 247.72it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/main/example_notebooks/gcm_icc.ipynb b/main/example_notebooks/gcm_icc.ipynb
index 5d2433943..8435550ba 100644
--- a/main/example_notebooks/gcm_icc.ipynb
+++ b/main/example_notebooks/gcm_icc.ipynb
@@ -46,10 +46,10 @@
    "id": "6bfa09f9-67e2-4c65-a27c-903c2742fe0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:18.048145Z",
-     "iopub.status.busy": "2024-10-25T15:08:18.047729Z",
-     "iopub.status.idle": "2024-10-25T15:08:19.590651Z",
-     "shell.execute_reply": "2024-10-25T15:08:19.589971Z"
+     "iopub.execute_input": "2024-10-25T15:09:40.817638Z",
+     "iopub.status.busy": "2024-10-25T15:09:40.817437Z",
+     "iopub.status.idle": "2024-10-25T15:09:42.539565Z",
+     "shell.execute_reply": "2024-10-25T15:09:42.538950Z"
     }
    },
    "outputs": [
@@ -101,10 +101,10 @@
    "id": "04f25d22-df49-45c8-9e4d-6fe05bc30e88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:19.593146Z",
-     "iopub.status.busy": "2024-10-25T15:08:19.592799Z",
-     "iopub.status.idle": "2024-10-25T15:08:23.679118Z",
-     "shell.execute_reply": "2024-10-25T15:08:23.678382Z"
+     "iopub.execute_input": "2024-10-25T15:09:42.543235Z",
+     "iopub.status.busy": "2024-10-25T15:09:42.542808Z",
+     "iopub.status.idle": "2024-10-25T15:09:47.069123Z",
+     "shell.execute_reply": "2024-10-25T15:09:47.068392Z"
     }
    },
    "outputs": [
@@ -153,7 +153,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 23.46it/s]"
+      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 23.83it/s]"
      ]
     },
     {
@@ -161,7 +161,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 23.46it/s]       "
+      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 23.83it/s]       "
      ]
     },
     {
@@ -169,7 +169,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 28.62it/s]"
+      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 28.99it/s]"
      ]
     },
     {
@@ -200,10 +200,10 @@
    "id": "ec3eadc5-e305-4291-864a-fab105c27443",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:23.681422Z",
-     "iopub.status.busy": "2024-10-25T15:08:23.681042Z",
-     "iopub.status.idle": "2024-10-25T15:08:24.784489Z",
-     "shell.execute_reply": "2024-10-25T15:08:24.783852Z"
+     "iopub.execute_input": "2024-10-25T15:09:47.071599Z",
+     "iopub.status.busy": "2024-10-25T15:09:47.071092Z",
+     "iopub.status.idle": "2024-10-25T15:09:48.315271Z",
+     "shell.execute_reply": "2024-10-25T15:09:48.314678Z"
     }
    },
    "outputs": [
@@ -220,7 +220,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 429.87it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 3254.93it/s]"
      ]
     },
     {
@@ -252,35 +252,35 @@
       "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "--- Node displacement\n",
-      "- The MSE is 1066.8802205998074.\n",
-      "- The NMSE is 0.3150091474285043.\n",
-      "- The R2 coefficient is 0.9002474266291071.\n",
-      "- The normalized CRPS is 0.17522937047621454.\n",
+      "- The MSE is 1060.1157400952736.\n",
+      "- The NMSE is 0.31274920184264293.\n",
+      "- The R2 coefficient is 0.9015843974287858.\n",
+      "- The normalized CRPS is 0.17714313434646875.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node weight\n",
-      "- The MSE is 79949.61282051282.\n",
-      "- The NMSE is 0.3359350937278292.\n",
-      "- The R2 coefficient is 0.8852962623585189.\n",
-      "- The normalized CRPS is 0.18398226376156224.\n",
+      "- The MSE is 77927.59152872444.\n",
+      "- The NMSE is 0.3293392447432768.\n",
+      "- The R2 coefficient is 0.8909086867528749.\n",
+      "- The normalized CRPS is 0.18431008611167252.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node horsepower\n",
-      "- The MSE is 209.29113924050634.\n",
-      "- The NMSE is 0.38098501624901415.\n",
-      "- The R2 coefficient is 0.8513378579863071.\n",
-      "- The normalized CRPS is 0.20296699411876892.\n",
+      "- The MSE is 207.14212917883805.\n",
+      "- The NMSE is 0.37926574409578384.\n",
+      "- The R2 coefficient is 0.8557617347249853.\n",
+      "- The normalized CRPS is 0.20152482768695754.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node mpg\n",
-      "- The MSE is 15.699538297818407.\n",
-      "- The NMSE is 0.5134945988653445.\n",
-      "- The R2 coefficient is 0.7353328271586502.\n",
-      "- The normalized CRPS is 0.2830433213216262.\n",
+      "- The MSE is 15.620289636388742.\n",
+      "- The NMSE is 0.5121045268547029.\n",
+      "- The R2 coefficient is 0.7344865131314628.\n",
+      "- The normalized CRPS is 0.2793091596647558.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.9578045815757854\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.9394536430054833\n",
       "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "==== NOTE ====\n",
@@ -307,16 +307,16 @@
    "id": "6257a9e2-a1f4-4c16-b629-b5a13bbf32dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:24.786752Z",
-     "iopub.status.busy": "2024-10-25T15:08:24.786284Z",
-     "iopub.status.idle": "2024-10-25T15:08:25.400709Z",
-     "shell.execute_reply": "2024-10-25T15:08:25.400051Z"
+     "iopub.execute_input": "2024-10-25T15:09:48.317389Z",
+     "iopub.status.busy": "2024-10-25T15:09:48.317064Z",
+     "iopub.status.idle": "2024-10-25T15:09:48.907181Z",
+     "shell.execute_reply": "2024-10-25T15:09:48.906448Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -352,10 +352,10 @@
    "id": "9cbc1002-57e4-4b79-8b8b-cab7dff25d4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:25.403081Z",
-     "iopub.status.busy": "2024-10-25T15:08:25.402865Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.536283Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.535521Z"
+     "iopub.execute_input": "2024-10-25T15:09:48.909438Z",
+     "iopub.status.busy": "2024-10-25T15:09:48.909205Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.195167Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.194428Z"
     }
    },
    "outputs": [
@@ -372,7 +372,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.36it/s]"
+      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.29it/s]"
      ]
     },
     {
@@ -380,7 +380,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.87it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.88it/s]"
      ]
     },
     {
@@ -388,7 +388,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.79it/s]"
+      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.82it/s]"
      ]
     },
     {
@@ -396,7 +396,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  1.74it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  1.75it/s]"
      ]
     },
     {
@@ -404,7 +404,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.13it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.15it/s]"
      ]
     },
     {
@@ -433,10 +433,10 @@
    "id": "885195c7-f9d7-449e-a96f-894ce09562d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.538879Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.538499Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.542131Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.541519Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.197880Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.197489Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.201286Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.200692Z"
     }
    },
    "outputs": [],
@@ -452,16 +452,16 @@
    "id": "a80ca005-23aa-46b0-889d-4de7144adb33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.544226Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.543793Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.659374Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.658767Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.203221Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.202925Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.328892Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.328110Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -530,10 +530,10 @@
    "id": "c3cb5016-edca-4314-ae34-dc64e3686ce5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.662052Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.661658Z",
-     "iopub.status.idle": "2024-10-25T15:08:48.753150Z",
-     "shell.execute_reply": "2024-10-25T15:08:48.752586Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.331920Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.331681Z",
+     "iopub.status.idle": "2024-10-25T15:10:12.428376Z",
+     "shell.execute_reply": "2024-10-25T15:10:12.427696Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "id": "5c5a439d-4b43-4a38-bb78-975b0694a2c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:48.755606Z",
-     "iopub.status.busy": "2024-10-25T15:08:48.755076Z",
-     "iopub.status.idle": "2024-10-25T15:08:54.025050Z",
-     "shell.execute_reply": "2024-10-25T15:08:54.024439Z"
+     "iopub.execute_input": "2024-10-25T15:10:12.430879Z",
+     "iopub.status.busy": "2024-10-25T15:10:12.430518Z",
+     "iopub.status.idle": "2024-10-25T15:10:17.910638Z",
+     "shell.execute_reply": "2024-10-25T15:10:17.910003Z"
     }
    },
    "outputs": [
@@ -647,7 +647,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 369.38it/s]"
+      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 379.97it/s]"
      ]
     },
     {
@@ -670,7 +670,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 188.24it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 190.31it/s]"
      ]
     },
     {
@@ -678,7 +678,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 240.35it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 247.72it/s]"
      ]
     },
     {
@@ -690,7 +690,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/main/example_notebooks/gcm_online_shop.html b/main/example_notebooks/gcm_online_shop.html
index 4a5acd36b..ac14aaba9 100644
--- a/main/example_notebooks/gcm_online_shop.html
+++ b/main/example_notebooks/gcm_online_shop.html
@@ -853,67 +853,67 @@ <h2>Step 1: Define causal model<a class="headerlink" href="#Step-1:-Define-causa
 Node Shopping Event? is a root node. Therefore, assigning &#39;Empirical Distribution&#39; to the node representing the marginal distribution.
 
 --- Node: Unit Price
-Node Unit Price is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
+Node Unit Price is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using Pipeline&#39; to the node.
 This represents the causal relationship as Unit Price := f(Shopping Event?) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 138.94088619006752
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 138.9540857127003
-HistGradientBoostingRegressor: 423.7141382351113
+                (&#39;linearregression&#39;, LinearRegression)]): 144.66690660144045
+LinearRegression: 144.67363407450733
+HistGradientBoostingRegressor: 423.78543034447205
 
 --- Node: Ad Spend
 Node Ad Spend is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the causal relationship as Ad Spend := f(Shopping Event?) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 15987.54106716137
+LinearRegression: 16128.70662145468
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 16077.092422884758
-HistGradientBoostingRegressor: 81230.60985063826
+                (&#39;linearregression&#39;, LinearRegression)]): 16140.622755139828
+HistGradientBoostingRegressor: 80441.31192441305
 
 --- Node: Page Views
 Node Page Views is a non-root node with discrete data. Assigning &#39;Discrete AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the discrete causal relationship as Page Views := f(Ad Spend,Shopping Event?) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 80093.59713032305
+LinearRegression: 89538.34945420861
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 103808.892264037
-HistGradientBoostingRegressor: 1422131.7607968026
+                (&#39;linearregression&#39;, LinearRegression)]): 96004.15431450964
+HistGradientBoostingRegressor: 1466488.6168275173
 
 --- Node: Sold Units
 Node Sold Units is a non-root node with discrete data. Assigning &#39;Discrete AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the discrete causal relationship as Sold Units := f(Page Views,Shopping Event?,Unit Price) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 8897.337998967068
+LinearRegression: 8558.37003116247
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 15093.623441629079
-HistGradientBoostingRegressor: 259744.19440229205
+                (&#39;linearregression&#39;, LinearRegression)]): 14786.380409459769
+HistGradientBoostingRegressor: 234793.98953644396
 
 --- Node: Revenue
 Node Revenue is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using Pipeline&#39; to the node.
 This represents the causal relationship as Revenue := f(Sold Units,Unit Price) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 6.992361408303774e-19
-LinearRegression: 72430705.45956144
-HistGradientBoostingRegressor: 149689722723.5923
+                (&#39;linearregression&#39;, LinearRegression)]): 4.717022054539674e-19
+LinearRegression: 74897935.84700143
+HistGradientBoostingRegressor: 124795953359.75322
 
 --- Node: Operational Cost
 Node Operational Cost is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the causal relationship as Operational Cost := f(Ad Spend,Sold Units) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 38.74700556676281
+LinearRegression: 38.7763118955903
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 38.84743309762714
-HistGradientBoostingRegressor: 14171555085.146936
+                (&#39;linearregression&#39;, LinearRegression)]): 39.52633219472173
+HistGradientBoostingRegressor: 14356916125.0501
 
 --- Node: Profit
 Node Profit is a non-root node with continuous data. Assigning &#39;AdditiveNoiseModel using LinearRegression&#39; to the node.
 This represents the causal relationship as Profit := f(Operational Cost,Revenue) + N.
 For the model selection, the following models were evaluated on the mean squared error (MSE) metric:
-LinearRegression: 1.8102834561483138e-18
+LinearRegression: 1.2133410476877163e-18
 Pipeline(steps=[(&#39;polynomialfeatures&#39;, PolynomialFeatures(include_bias=False)),
-                (&#39;linearregression&#39;, LinearRegression)]): 1.1168538088104242e-06
-HistGradientBoostingRegressor: 25059619203.962067
+                (&#39;linearregression&#39;, LinearRegression)]): 1.0878901127713091e-05
+HistGradientBoostingRegressor: 26792154395.605186
 
 ===Note===
 Note, based on the selected auto assignment quality, the set of evaluated models changes.
@@ -938,7 +938,7 @@ <h2>Step 2: Fit causal models to data<a class="headerlink" href="#Step-2:-Fit-ca
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00&lt;00:00, 408.02it/s]
+Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00&lt;00:00, 333.22it/s]
 </pre></div></div>
 </div>
 <p>The fit method learns the parameters of the generative models in each node. Before we continue, let’s have a quick look into the performance of the causal mechanisms and how well they capture the distribution:</p>
@@ -955,8 +955,8 @@ <h2>Step 2: Fit causal models to data<a class="headerlink" href="#Step-2:-Fit-ca
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00&lt;00:00, 1109.35it/s]
-Test permutations of given graph: 100%|██████████| 50/50 [00:15&lt;00:00,  3.17it/s]
+Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00&lt;00:00, 785.23it/s]
+Test permutations of given graph: 100%|██████████| 50/50 [00:15&lt;00:00,  3.15it/s]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -985,67 +985,67 @@ <h2>Step 2: Fit causal models to data<a class="headerlink" href="#Step-2:-Fit-ca
 We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.
 
 --- Node Shopping Event?
-- The KL divergence between generated and observed distribution is 0.01006454331352404.
+- The KL divergence between generated and observed distribution is 0.019379875172409668.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Unit Price
-- The MSE is 139.41387612465752.
-- The NMSE is 0.5616885215223719.
-- The R2 coefficient is 0.6669227941580237.
-- The normalized CRPS is 0.10313819496164527.
+- The MSE is 184.91813678408536.
+- The NMSE is 1.1255525660980945.
+- The R2 coefficient is -1.0000963529058176.
+- The normalized CRPS is 0.18132262173243546.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Ad Spend
-- The MSE is 16157.220051300485.
-- The NMSE is 0.4764603364312297.
-- The R2 coefficient is 0.7629774538505525.
-- The normalized CRPS is 0.27307929207717874.
+- The MSE is 15986.775126115423.
+- The NMSE is 0.5271546552545335.
+- The R2 coefficient is 0.6967044526958617.
+- The normalized CRPS is 0.30524001266694467.
 The estimated CRPS indicates a good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Page Views
-- The MSE is 77696.70136986303.
-- The NMSE is 0.3061734788455607.
-- The R2 coefficient is 0.8202746841221045.
-- The normalized CRPS is 0.153542404367848.
+- The MSE is 80358.36712328767.
+- The NMSE is 0.16122920792518886.
+- The R2 coefficient is 0.9721511406886085.
+- The normalized CRPS is 0.06207081187872663.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Sold Units
-- The MSE is 8761.94794520548.
-- The NMSE is 0.28035764227531546.
-- The R2 coefficient is 0.816290015483671.
-- The normalized CRPS is 0.12098919626226576.
+- The MSE is 8843.898630136986.
+- The NMSE is 0.12925595992014843.
+- The R2 coefficient is 0.9816809669321052.
+- The normalized CRPS is 0.0563944284124813.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Revenue
-- The MSE is 8.381096663330535e-16.
-- The NMSE is 1.875736833919074e-14.
+- The MSE is 1.6686495222103205e-16.
+- The NMSE is 1.1067668020372116e-14.
 - The R2 coefficient is 1.0.
-- The normalized CRPS is 4.907414383535057e-15.
+- The normalized CRPS is 2.8144957901556516e-15.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Operational Cost
-- The MSE is 38.73103242267744.
-- The NMSE is 1.945709517377356e-05.
-- The R2 coefficient is 0.9999999995726991.
-- The normalized CRPS is 1.0734009606877667e-05.
+- The MSE is 39.15134109515263.
+- The NMSE is 1.8022101153815417e-05.
+- The R2 coefficient is 0.9999999996670821.
+- The normalized CRPS is 9.99642316493373e-06.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 --- Node Profit
-- The MSE is 1.2873786891923771e-18.
-- The NMSE is 4.5101982377797984e-15.
+- The MSE is 1.1732032891789207e-18.
+- The NMSE is 4.050097390974477e-15.
 - The R2 coefficient is 1.0.
-- The normalized CRPS is 3.650963853919452e-16.
+- The normalized CRPS is 3.9305545120030674e-16.
 The estimated CRPS indicates a very good model performance.
 The mechanism is better or equally good than all 7 baseline mechanisms.
 
 ==== Evaluation of Generated Distribution ====
-The overall average KL divergence between the generated and observed distribution is 1.0693674038028511
+The overall average KL divergence between the generated and observed distribution is 1.0386964291919851
 The estimated KL divergence indicates some mismatches between the distributions.
 
 ==== Evaluation of the Causal Graph Structure ====
@@ -1054,9 +1054,9 @@ <h2>Step 2: Fit causal models to data<a class="headerlink" href="#Step-2:-Fit-ca
 +-------------------------------------------------------------------------------------------------------+
 | The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |
 | equivalence class of the given DAG (p-value: 0.00).                                                   |
-| The given DAG violates 6/18 LMCs and is better than 74.0% of the permuted DAGs (p-value: 0.26).       |
+| The given DAG violates 6/18 LMCs and is better than 80.0% of the permuted DAGs (p-value: 0.20).       |
 | Based on the provided significance level (0.2) and because the DAG is informative,                    |
-| we reject the DAG.                                                                                    |
+| we do not reject the DAG.                                                                             |
 +-------------------------------------------------------------------------------------------------------+
 
 ==== NOTE ====
@@ -1120,111 +1120,111 @@ <h3>Generate new samples<a class="headerlink" href="#Generate-new-samples" title
       <th>0</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,249.40</td>
-      <td>11648</td>
-      <td>2274</td>
-      <td>$2,271,726.00</td>
-      <td>$1,638,259.65</td>
-      <td>$633,466.35</td>
+      <td>$1,176.23</td>
+      <td>11531</td>
+      <td>2318</td>
+      <td>$2,315,682.00</td>
+      <td>$1,660,178.10</td>
+      <td>$655,503.90</td>
     </tr>
     <tr>
       <th>1</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,188.04</td>
-      <td>11516</td>
-      <td>2274</td>
-      <td>$2,271,726.00</td>
-      <td>$1,638,193.71</td>
-      <td>$633,532.29</td>
+      <td>$1,390.23</td>
+      <td>11805</td>
+      <td>2356</td>
+      <td>$2,353,644.00</td>
+      <td>$1,679,398.64</td>
+      <td>$674,245.36</td>
     </tr>
     <tr>
       <th>2</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,164.94</td>
-      <td>11533</td>
-      <td>2270</td>
-      <td>$2,267,730.00</td>
-      <td>$1,636,166.52</td>
-      <td>$631,563.48</td>
+      <td>$1,156.13</td>
+      <td>11678</td>
+      <td>2296</td>
+      <td>$2,293,704.00</td>
+      <td>$1,649,158.30</td>
+      <td>$644,545.70</td>
     </tr>
     <tr>
       <th>3</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,394.90</td>
-      <td>11856</td>
-      <td>2024</td>
-      <td>$2,021,976.00</td>
-      <td>$1,513,408.87</td>
-      <td>$508,567.13</td>
+      <td>$1,100.22</td>
+      <td>11592</td>
+      <td>2312</td>
+      <td>$2,309,688.00</td>
+      <td>$1,657,100.84</td>
+      <td>$652,587.16</td>
     </tr>
     <tr>
       <th>4</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,254.98</td>
-      <td>11657</td>
-      <td>2342</td>
-      <td>$2,339,658.00</td>
-      <td>$1,672,262.08</td>
-      <td>$667,395.92</td>
+      <td>$1,449.15</td>
+      <td>11905</td>
+      <td>2417</td>
+      <td>$2,414,583.00</td>
+      <td>$1,709,959.83</td>
+      <td>$704,623.17</td>
     </tr>
     <tr>
       <th>5</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,290.24</td>
-      <td>11718</td>
-      <td>2295</td>
-      <td>$2,292,705.00</td>
-      <td>$1,648,803.22</td>
-      <td>$643,901.78</td>
+      <td>$1,171.43</td>
+      <td>11635</td>
+      <td>2292</td>
+      <td>$2,289,708.00</td>
+      <td>$1,647,174.03</td>
+      <td>$642,533.97</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>False</td>
-      <td>$999.00</td>
-      <td>$1,183.30</td>
-      <td>11720</td>
-      <td>2360</td>
-      <td>$2,357,640.00</td>
-      <td>$1,681,187.43</td>
-      <td>$676,452.57</td>
+      <td>True</td>
+      <td>$913.46</td>
+      <td>$2,499.94</td>
+      <td>20426</td>
+      <td>5966</td>
+      <td>$5,449,707.95</td>
+      <td>$3,485,503.60</td>
+      <td>$1,964,204.36</td>
     </tr>
     <tr>
       <th>7</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,458.28</td>
-      <td>11713</td>
-      <td>2345</td>
-      <td>$2,342,655.00</td>
-      <td>$1,673,960.31</td>
-      <td>$668,694.69</td>
+      <td>$1,111.98</td>
+      <td>11715</td>
+      <td>2355</td>
+      <td>$2,352,645.00</td>
+      <td>$1,678,623.63</td>
+      <td>$674,021.37</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>False</td>
-      <td>$999.00</td>
-      <td>$1,379.30</td>
-      <td>11727</td>
-      <td>2292</td>
-      <td>$2,289,708.00</td>
-      <td>$1,647,380.83</td>
-      <td>$642,327.17</td>
+      <td>True</td>
+      <td>$913.46</td>
+      <td>$2,690.98</td>
+      <td>20295</td>
+      <td>5894</td>
+      <td>$5,383,938.77</td>
+      <td>$3,449,695.95</td>
+      <td>$1,934,242.81</td>
     </tr>
     <tr>
       <th>9</th>
       <td>False</td>
       <td>$999.00</td>
-      <td>$1,326.60</td>
-      <td>11694</td>
-      <td>2387</td>
-      <td>$2,384,613.00</td>
-      <td>$1,694,832.18</td>
-      <td>$689,780.82</td>
+      <td>$1,296.94</td>
+      <td>11796</td>
+      <td>2369</td>
+      <td>$2,366,631.00</td>
+      <td>$1,685,800.08</td>
+      <td>$680,830.92</td>
     </tr>
   </tbody>
 </table>
@@ -1324,7 +1324,7 @@ <h3>What are the key factors influencing the variance in profit?<a class="header
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating set functions...: 100%|██████████| 121/121 [02:46&lt;00:00,  1.38s/it]
+Evaluating set functions...: 100%|██████████| 119/119 [02:43&lt;00:00,  1.37s/it]
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1363,7 +1363,7 @@ <h3>What are the key factors influencing the variance in profit?<a class="header
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;matplotlib.collections.LineCollection at 0x7fe1be8d5af0&gt;
+&lt;matplotlib.collections.LineCollection at 0x7f43edba2e20&gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1430,7 +1430,7 @@ <h3>What are the key factors explaining the Profit drop on a particular day?<a c
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Evaluating set functions...: 100%|██████████| 116/116 [00:02&lt;00:00, 39.68it/s]
+Evaluating set functions...: 100%|██████████| 121/121 [00:03&lt;00:00, 32.56it/s]
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
diff --git a/main/example_notebooks/gcm_online_shop.ipynb b/main/example_notebooks/gcm_online_shop.ipynb
index dcadf0581..c3ed8ced9 100644
--- a/main/example_notebooks/gcm_online_shop.ipynb
+++ b/main/example_notebooks/gcm_online_shop.ipynb
@@ -53,10 +53,10 @@
    "id": "3e2cc4f9-539c-415d-98a5-3a35ebe226a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:58.985667Z",
-     "iopub.status.busy": "2024-10-25T15:08:58.985154Z",
-     "iopub.status.idle": "2024-10-25T15:08:58.996389Z",
-     "shell.execute_reply": "2024-10-25T15:08:58.995815Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.352828Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.352401Z",
+     "iopub.status.idle": "2024-10-25T15:10:23.364146Z",
+     "shell.execute_reply": "2024-10-25T15:10:23.363558Z"
     }
    },
    "outputs": [
@@ -136,10 +136,10 @@
    "id": "49665408-6239-4d17-ab3f-fca7a8bfbbd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:58.998409Z",
-     "iopub.status.busy": "2024-10-25T15:08:58.997995Z",
-     "iopub.status.idle": "2024-10-25T15:08:59.282289Z",
-     "shell.execute_reply": "2024-10-25T15:08:59.281746Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.366220Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.365794Z",
+     "iopub.status.idle": "2024-10-25T15:10:23.684446Z",
+     "shell.execute_reply": "2024-10-25T15:10:23.683677Z"
     }
    },
    "outputs": [],
@@ -175,10 +175,10 @@
    "id": "47243f27-c8ef-40e2-9d88-ac9418a96393",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:08:59.284777Z",
-     "iopub.status.busy": "2024-10-25T15:08:59.284199Z",
-     "iopub.status.idle": "2024-10-25T15:09:00.560857Z",
-     "shell.execute_reply": "2024-10-25T15:09:00.560207Z"
+     "iopub.execute_input": "2024-10-25T15:10:23.687080Z",
+     "iopub.status.busy": "2024-10-25T15:10:23.686600Z",
+     "iopub.status.idle": "2024-10-25T15:10:25.215492Z",
+     "shell.execute_reply": "2024-10-25T15:10:25.214845Z"
     }
    },
    "outputs": [
@@ -213,10 +213,10 @@
    "id": "9d4f5efd-5873-46ed-9c3a-d5b644380f84",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:00.563239Z",
-     "iopub.status.busy": "2024-10-25T15:09:00.562874Z",
-     "iopub.status.idle": "2024-10-25T15:09:00.580409Z",
-     "shell.execute_reply": "2024-10-25T15:09:00.579799Z"
+     "iopub.execute_input": "2024-10-25T15:10:25.219018Z",
+     "iopub.status.busy": "2024-10-25T15:10:25.217893Z",
+     "iopub.status.idle": "2024-10-25T15:10:25.236761Z",
+     "shell.execute_reply": "2024-10-25T15:10:25.236200Z"
     }
    },
    "outputs": [
@@ -371,10 +371,10 @@
    "id": "ffe4eb1d-3bf5-486b-947d-5d25115b5ab3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:00.582576Z",
-     "iopub.status.busy": "2024-10-25T15:09:00.582343Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.411247Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.410581Z"
+     "iopub.execute_input": "2024-10-25T15:10:25.239330Z",
+     "iopub.status.busy": "2024-10-25T15:10:25.238940Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.701678Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.700871Z"
     }
    },
    "outputs": [],
@@ -404,10 +404,10 @@
    "id": "941a24d8-cb6c-442e-81d9-0d64bb5ec933",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.413577Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.413250Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.423905Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.423268Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.704482Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.703980Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.715632Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.714981Z"
     }
    },
    "outputs": [
@@ -438,67 +438,67 @@
       "Node Shopping Event? is a root node. Therefore, assigning 'Empirical Distribution' to the node representing the marginal distribution.\n",
       "\n",
       "--- Node: Unit Price\n",
-      "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
+      "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n",
       "This represents the causal relationship as Unit Price := f(Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 138.94088619006752\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 138.9540857127003\n",
-      "HistGradientBoostingRegressor: 423.7141382351113\n",
+      "                ('linearregression', LinearRegression)]): 144.66690660144045\n",
+      "LinearRegression: 144.67363407450733\n",
+      "HistGradientBoostingRegressor: 423.78543034447205\n",
       "\n",
       "--- Node: Ad Spend\n",
       "Node Ad Spend is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Ad Spend := f(Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 15987.54106716137\n",
+      "LinearRegression: 16128.70662145468\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 16077.092422884758\n",
-      "HistGradientBoostingRegressor: 81230.60985063826\n",
+      "                ('linearregression', LinearRegression)]): 16140.622755139828\n",
+      "HistGradientBoostingRegressor: 80441.31192441305\n",
       "\n",
       "--- Node: Page Views\n",
       "Node Page Views is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the discrete causal relationship as Page Views := f(Ad Spend,Shopping Event?) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 80093.59713032305\n",
+      "LinearRegression: 89538.34945420861\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 103808.892264037\n",
-      "HistGradientBoostingRegressor: 1422131.7607968026\n",
+      "                ('linearregression', LinearRegression)]): 96004.15431450964\n",
+      "HistGradientBoostingRegressor: 1466488.6168275173\n",
       "\n",
       "--- Node: Sold Units\n",
       "Node Sold Units is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the discrete causal relationship as Sold Units := f(Page Views,Shopping Event?,Unit Price) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 8897.337998967068\n",
+      "LinearRegression: 8558.37003116247\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 15093.623441629079\n",
-      "HistGradientBoostingRegressor: 259744.19440229205\n",
+      "                ('linearregression', LinearRegression)]): 14786.380409459769\n",
+      "HistGradientBoostingRegressor: 234793.98953644396\n",
       "\n",
       "--- Node: Revenue\n",
       "Node Revenue is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n",
       "This represents the causal relationship as Revenue := f(Sold Units,Unit Price) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 6.992361408303774e-19\n",
-      "LinearRegression: 72430705.45956144\n",
-      "HistGradientBoostingRegressor: 149689722723.5923\n",
+      "                ('linearregression', LinearRegression)]): 4.717022054539674e-19\n",
+      "LinearRegression: 74897935.84700143\n",
+      "HistGradientBoostingRegressor: 124795953359.75322\n",
       "\n",
       "--- Node: Operational Cost\n",
       "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Operational Cost := f(Ad Spend,Sold Units) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 38.74700556676281\n",
+      "LinearRegression: 38.7763118955903\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 38.84743309762714\n",
-      "HistGradientBoostingRegressor: 14171555085.146936\n",
+      "                ('linearregression', LinearRegression)]): 39.52633219472173\n",
+      "HistGradientBoostingRegressor: 14356916125.0501\n",
       "\n",
       "--- Node: Profit\n",
       "Node Profit is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n",
       "This represents the causal relationship as Profit := f(Operational Cost,Revenue) + N.\n",
       "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n",
-      "LinearRegression: 1.8102834561483138e-18\n",
+      "LinearRegression: 1.2133410476877163e-18\n",
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n",
-      "                ('linearregression', LinearRegression)]): 1.1168538088104242e-06\n",
-      "HistGradientBoostingRegressor: 25059619203.962067\n",
+      "                ('linearregression', LinearRegression)]): 1.0878901127713091e-05\n",
+      "HistGradientBoostingRegressor: 26792154395.605186\n",
       "\n",
       "===Note===\n",
       "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n",
@@ -540,10 +540,10 @@
    "id": "2b032fa2-9348-418c-b62b-9ae77f49c342",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.425886Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.425513Z",
-     "iopub.status.idle": "2024-10-25T15:09:05.450071Z",
-     "shell.execute_reply": "2024-10-25T15:09:05.449545Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.717999Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.717611Z",
+     "iopub.status.idle": "2024-10-25T15:10:30.747801Z",
+     "shell.execute_reply": "2024-10-25T15:10:30.747122Z"
     }
    },
    "outputs": [
@@ -624,7 +624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 408.02it/s]"
+      "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 333.22it/s]"
      ]
     },
     {
@@ -653,10 +653,10 @@
    "id": "ab315928-4d60-4008-abb9-a9030e4cad44",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:05.451941Z",
-     "iopub.status.busy": "2024-10-25T15:09:05.451505Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.649430Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.648824Z"
+     "iopub.execute_input": "2024-10-25T15:10:30.749937Z",
+     "iopub.status.busy": "2024-10-25T15:10:30.749592Z",
+     "iopub.status.idle": "2024-10-25T15:11:04.741859Z",
+     "shell.execute_reply": "2024-10-25T15:11:04.741199Z"
     }
    },
    "outputs": [
@@ -673,7 +673,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 1109.35it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 785.23it/s]"
      ]
     },
     {
@@ -696,7 +696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
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@@ -704,7 +704,7 @@
      "output_type": "stream",
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      ]
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@@ -712,7 +712,7 @@
      "output_type": "stream",
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@@ -720,7 +720,7 @@
      "output_type": "stream",
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@@ -728,7 +728,7 @@
      "output_type": "stream",
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@@ -744,7 +744,7 @@
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@@ -760,7 +760,7 @@
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@@ -768,7 +768,7 @@
      "output_type": "stream",
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@@ -776,7 +776,7 @@
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      ]
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@@ -832,7 +832,7 @@
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      ]
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@@ -840,7 +840,7 @@
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@@ -848,7 +848,7 @@
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       "\r",
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@@ -856,7 +856,7 @@
      "output_type": "stream",
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@@ -864,7 +864,7 @@
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      ]
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@@ -872,7 +872,7 @@
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@@ -880,7 +880,7 @@
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@@ -888,7 +888,7 @@
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      ]
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@@ -896,7 +896,7 @@
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      ]
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      ]
     },
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@@ -912,7 +912,7 @@
      "output_type": "stream",
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       "\r",
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      ]
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@@ -920,7 +920,7 @@
      "output_type": "stream",
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      ]
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      ]
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@@ -936,7 +936,7 @@
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       "\r",
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      ]
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      ]
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@@ -952,7 +952,7 @@
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@@ -1000,7 +1000,7 @@
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      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 41/50 [00:14<00:01,  4.98it/s]"
+      "Test permutations of given graph:  80%|████████  | 40/50 [00:13<00:02,  4.22it/s]"
      ]
     },
     {
@@ -1016,7 +1016,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 42/50 [00:14<00:01,  4.93it/s]"
+      "Test permutations of given graph:  82%|████████▏ | 41/50 [00:14<00:02,  4.10it/s]"
      ]
     },
     {
@@ -1024,7 +1024,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 43/50 [00:14<00:01,  5.76it/s]"
+      "Test permutations of given graph:  84%|████████▍ | 42/50 [00:14<00:01,  4.29it/s]"
      ]
     },
     {
@@ -1032,7 +1032,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 44/50 [00:14<00:01,  5.43it/s]"
+      "Test permutations of given graph:  86%|████████▌ | 43/50 [00:14<00:01,  4.36it/s]"
      ]
     },
     {
@@ -1040,7 +1040,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 45/50 [00:14<00:00,  6.00it/s]"
+      "Test permutations of given graph:  88%|████████▊ | 44/50 [00:14<00:01,  4.96it/s]"
      ]
     },
     {
@@ -1048,7 +1048,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 46/50 [00:15<00:00,  6.45it/s]"
+      "Test permutations of given graph:  90%|█████████ | 45/50 [00:14<00:00,  5.48it/s]"
      ]
     },
     {
@@ -1056,7 +1056,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 47/50 [00:15<00:00,  6.67it/s]"
+      "Test permutations of given graph:  94%|█████████▍| 47/50 [00:15<00:00,  5.55it/s]"
      ]
     },
     {
@@ -1064,7 +1064,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 48/50 [00:15<00:00,  4.98it/s]"
+      "Test permutations of given graph:  96%|█████████▌| 48/50 [00:15<00:00,  5.34it/s]"
      ]
     },
     {
@@ -1072,7 +1072,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  6.68it/s]"
+      "Test permutations of given graph:  98%|█████████▊| 49/50 [00:15<00:00,  5.74it/s]"
      ]
     },
     {
@@ -1080,7 +1080,15 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.17it/s]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  4.98it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.15it/s]"
      ]
     },
     {
@@ -1092,7 +1100,7 @@
     },
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -1118,67 +1126,67 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node Shopping Event?\n",
-      "- The KL divergence between generated and observed distribution is 0.01006454331352404.\n",
+      "- The KL divergence between generated and observed distribution is 0.019379875172409668.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Unit Price\n",
-      "- The MSE is 139.41387612465752.\n",
-      "- The NMSE is 0.5616885215223719.\n",
-      "- The R2 coefficient is 0.6669227941580237.\n",
-      "- The normalized CRPS is 0.10313819496164527.\n",
+      "- The MSE is 184.91813678408536.\n",
+      "- The NMSE is 1.1255525660980945.\n",
+      "- The R2 coefficient is -1.0000963529058176.\n",
+      "- The normalized CRPS is 0.18132262173243546.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Ad Spend\n",
-      "- The MSE is 16157.220051300485.\n",
-      "- The NMSE is 0.4764603364312297.\n",
-      "- The R2 coefficient is 0.7629774538505525.\n",
-      "- The normalized CRPS is 0.27307929207717874.\n",
+      "- The MSE is 15986.775126115423.\n",
+      "- The NMSE is 0.5271546552545335.\n",
+      "- The R2 coefficient is 0.6967044526958617.\n",
+      "- The normalized CRPS is 0.30524001266694467.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Page Views\n",
-      "- The MSE is 77696.70136986303.\n",
-      "- The NMSE is 0.3061734788455607.\n",
-      "- The R2 coefficient is 0.8202746841221045.\n",
-      "- The normalized CRPS is 0.153542404367848.\n",
+      "- The MSE is 80358.36712328767.\n",
+      "- The NMSE is 0.16122920792518886.\n",
+      "- The R2 coefficient is 0.9721511406886085.\n",
+      "- The normalized CRPS is 0.06207081187872663.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Sold Units\n",
-      "- The MSE is 8761.94794520548.\n",
-      "- The NMSE is 0.28035764227531546.\n",
-      "- The R2 coefficient is 0.816290015483671.\n",
-      "- The normalized CRPS is 0.12098919626226576.\n",
+      "- The MSE is 8843.898630136986.\n",
+      "- The NMSE is 0.12925595992014843.\n",
+      "- The R2 coefficient is 0.9816809669321052.\n",
+      "- The normalized CRPS is 0.0563944284124813.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Revenue\n",
-      "- The MSE is 8.381096663330535e-16.\n",
-      "- The NMSE is 1.875736833919074e-14.\n",
+      "- The MSE is 1.6686495222103205e-16.\n",
+      "- The NMSE is 1.1067668020372116e-14.\n",
       "- The R2 coefficient is 1.0.\n",
-      "- The normalized CRPS is 4.907414383535057e-15.\n",
+      "- The normalized CRPS is 2.8144957901556516e-15.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Operational Cost\n",
-      "- The MSE is 38.73103242267744.\n",
-      "- The NMSE is 1.945709517377356e-05.\n",
-      "- The R2 coefficient is 0.9999999995726991.\n",
-      "- The normalized CRPS is 1.0734009606877667e-05.\n",
+      "- The MSE is 39.15134109515263.\n",
+      "- The NMSE is 1.8022101153815417e-05.\n",
+      "- The R2 coefficient is 0.9999999996670821.\n",
+      "- The normalized CRPS is 9.99642316493373e-06.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "--- Node Profit\n",
-      "- The MSE is 1.2873786891923771e-18.\n",
-      "- The NMSE is 4.5101982377797984e-15.\n",
+      "- The MSE is 1.1732032891789207e-18.\n",
+      "- The NMSE is 4.050097390974477e-15.\n",
       "- The R2 coefficient is 1.0.\n",
-      "- The normalized CRPS is 3.650963853919452e-16.\n",
+      "- The normalized CRPS is 3.9305545120030674e-16.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "The mechanism is better or equally good than all 7 baseline mechanisms.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 1.0693674038028511\n",
+      "The overall average KL divergence between the generated and observed distribution is 1.0386964291919851\n",
       "The estimated KL divergence indicates some mismatches between the distributions.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -1187,9 +1195,9 @@
       "+-------------------------------------------------------------------------------------------------------+\n",
       "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |\n",
       "| equivalence class of the given DAG (p-value: 0.00).                                                   |\n",
-      "| The given DAG violates 6/18 LMCs and is better than 74.0% of the permuted DAGs (p-value: 0.26).       |\n",
+      "| The given DAG violates 6/18 LMCs and is better than 80.0% of the permuted DAGs (p-value: 0.20).       |\n",
       "| Based on the provided significance level (0.2) and because the DAG is informative,                    |\n",
-      "| we reject the DAG.                                                                                    |\n",
+      "| we do not reject the DAG.                                                                             |\n",
       "+-------------------------------------------------------------------------------------------------------+\n",
       "\n",
       "==== NOTE ====\n",
@@ -1235,10 +1243,10 @@
    "id": "74ed17eb-45ec-444d-9364-2aa850911a05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.651772Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.651268Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.667304Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.666740Z"
+     "iopub.execute_input": "2024-10-25T15:11:04.744096Z",
+     "iopub.status.busy": "2024-10-25T15:11:04.743727Z",
+     "iopub.status.idle": "2024-10-25T15:11:04.760025Z",
+     "shell.execute_reply": "2024-10-25T15:11:04.759543Z"
     }
    },
    "outputs": [
@@ -1278,111 +1286,111 @@
        "      <th>0</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,249.40</td>\n",
-       "      <td>11648</td>\n",
-       "      <td>2274</td>\n",
-       "      <td>$2,271,726.00</td>\n",
-       "      <td>$1,638,259.65</td>\n",
-       "      <td>$633,466.35</td>\n",
+       "      <td>$1,176.23</td>\n",
+       "      <td>11531</td>\n",
+       "      <td>2318</td>\n",
+       "      <td>$2,315,682.00</td>\n",
+       "      <td>$1,660,178.10</td>\n",
+       "      <td>$655,503.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,188.04</td>\n",
-       "      <td>11516</td>\n",
-       "      <td>2274</td>\n",
-       "      <td>$2,271,726.00</td>\n",
-       "      <td>$1,638,193.71</td>\n",
-       "      <td>$633,532.29</td>\n",
+       "      <td>$1,390.23</td>\n",
+       "      <td>11805</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>$2,353,644.00</td>\n",
+       "      <td>$1,679,398.64</td>\n",
+       "      <td>$674,245.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,164.94</td>\n",
-       "      <td>11533</td>\n",
-       "      <td>2270</td>\n",
-       "      <td>$2,267,730.00</td>\n",
-       "      <td>$1,636,166.52</td>\n",
-       "      <td>$631,563.48</td>\n",
+       "      <td>$1,156.13</td>\n",
+       "      <td>11678</td>\n",
+       "      <td>2296</td>\n",
+       "      <td>$2,293,704.00</td>\n",
+       "      <td>$1,649,158.30</td>\n",
+       "      <td>$644,545.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,394.90</td>\n",
-       "      <td>11856</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>$2,021,976.00</td>\n",
-       "      <td>$1,513,408.87</td>\n",
-       "      <td>$508,567.13</td>\n",
+       "      <td>$1,100.22</td>\n",
+       "      <td>11592</td>\n",
+       "      <td>2312</td>\n",
+       "      <td>$2,309,688.00</td>\n",
+       "      <td>$1,657,100.84</td>\n",
+       "      <td>$652,587.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,254.98</td>\n",
-       "      <td>11657</td>\n",
-       "      <td>2342</td>\n",
-       "      <td>$2,339,658.00</td>\n",
-       "      <td>$1,672,262.08</td>\n",
-       "      <td>$667,395.92</td>\n",
+       "      <td>$1,449.15</td>\n",
+       "      <td>11905</td>\n",
+       "      <td>2417</td>\n",
+       "      <td>$2,414,583.00</td>\n",
+       "      <td>$1,709,959.83</td>\n",
+       "      <td>$704,623.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,290.24</td>\n",
-       "      <td>11718</td>\n",
-       "      <td>2295</td>\n",
-       "      <td>$2,292,705.00</td>\n",
-       "      <td>$1,648,803.22</td>\n",
-       "      <td>$643,901.78</td>\n",
+       "      <td>$1,171.43</td>\n",
+       "      <td>11635</td>\n",
+       "      <td>2292</td>\n",
+       "      <td>$2,289,708.00</td>\n",
+       "      <td>$1,647,174.03</td>\n",
+       "      <td>$642,533.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>False</td>\n",
-       "      <td>$999.00</td>\n",
-       "      <td>$1,183.30</td>\n",
-       "      <td>11720</td>\n",
-       "      <td>2360</td>\n",
-       "      <td>$2,357,640.00</td>\n",
-       "      <td>$1,681,187.43</td>\n",
-       "      <td>$676,452.57</td>\n",
+       "      <td>True</td>\n",
+       "      <td>$913.46</td>\n",
+       "      <td>$2,499.94</td>\n",
+       "      <td>20426</td>\n",
+       "      <td>5966</td>\n",
+       "      <td>$5,449,707.95</td>\n",
+       "      <td>$3,485,503.60</td>\n",
+       "      <td>$1,964,204.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,458.28</td>\n",
-       "      <td>11713</td>\n",
-       "      <td>2345</td>\n",
-       "      <td>$2,342,655.00</td>\n",
-       "      <td>$1,673,960.31</td>\n",
-       "      <td>$668,694.69</td>\n",
+       "      <td>$1,111.98</td>\n",
+       "      <td>11715</td>\n",
+       "      <td>2355</td>\n",
+       "      <td>$2,352,645.00</td>\n",
+       "      <td>$1,678,623.63</td>\n",
+       "      <td>$674,021.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>False</td>\n",
-       "      <td>$999.00</td>\n",
-       "      <td>$1,379.30</td>\n",
-       "      <td>11727</td>\n",
-       "      <td>2292</td>\n",
-       "      <td>$2,289,708.00</td>\n",
-       "      <td>$1,647,380.83</td>\n",
-       "      <td>$642,327.17</td>\n",
+       "      <td>True</td>\n",
+       "      <td>$913.46</td>\n",
+       "      <td>$2,690.98</td>\n",
+       "      <td>20295</td>\n",
+       "      <td>5894</td>\n",
+       "      <td>$5,383,938.77</td>\n",
+       "      <td>$3,449,695.95</td>\n",
+       "      <td>$1,934,242.81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>False</td>\n",
        "      <td>$999.00</td>\n",
-       "      <td>$1,326.60</td>\n",
-       "      <td>11694</td>\n",
-       "      <td>2387</td>\n",
-       "      <td>$2,384,613.00</td>\n",
-       "      <td>$1,694,832.18</td>\n",
-       "      <td>$689,780.82</td>\n",
+       "      <td>$1,296.94</td>\n",
+       "      <td>11796</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>$2,366,631.00</td>\n",
+       "      <td>$1,685,800.08</td>\n",
+       "      <td>$680,830.92</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1390,28 +1398,28 @@
       ],
       "text/plain": [
        "   Shopping Event?  Unit Price  Ad Spend  Page Views  Sold Units  \\\n",
-       "0            False     $999.00 $1,249.40       11648        2274   \n",
-       "1            False     $999.00 $1,188.04       11516        2274   \n",
-       "2            False     $999.00 $1,164.94       11533        2270   \n",
-       "3            False     $999.00 $1,394.90       11856        2024   \n",
-       "4            False     $999.00 $1,254.98       11657        2342   \n",
-       "5            False     $999.00 $1,290.24       11718        2295   \n",
-       "6            False     $999.00 $1,183.30       11720        2360   \n",
-       "7            False     $999.00 $1,458.28       11713        2345   \n",
-       "8            False     $999.00 $1,379.30       11727        2292   \n",
-       "9            False     $999.00 $1,326.60       11694        2387   \n",
+       "0            False     $999.00 $1,176.23       11531        2318   \n",
+       "1            False     $999.00 $1,390.23       11805        2356   \n",
+       "2            False     $999.00 $1,156.13       11678        2296   \n",
+       "3            False     $999.00 $1,100.22       11592        2312   \n",
+       "4            False     $999.00 $1,449.15       11905        2417   \n",
+       "5            False     $999.00 $1,171.43       11635        2292   \n",
+       "6             True     $913.46 $2,499.94       20426        5966   \n",
+       "7            False     $999.00 $1,111.98       11715        2355   \n",
+       "8             True     $913.46 $2,690.98       20295        5894   \n",
+       "9            False     $999.00 $1,296.94       11796        2369   \n",
        "\n",
-       "        Revenue  Operational Cost      Profit  \n",
-       "0 $2,271,726.00     $1,638,259.65 $633,466.35  \n",
-       "1 $2,271,726.00     $1,638,193.71 $633,532.29  \n",
-       "2 $2,267,730.00     $1,636,166.52 $631,563.48  \n",
-       "3 $2,021,976.00     $1,513,408.87 $508,567.13  \n",
-       "4 $2,339,658.00     $1,672,262.08 $667,395.92  \n",
-       "5 $2,292,705.00     $1,648,803.22 $643,901.78  \n",
-       "6 $2,357,640.00     $1,681,187.43 $676,452.57  \n",
-       "7 $2,342,655.00     $1,673,960.31 $668,694.69  \n",
-       "8 $2,289,708.00     $1,647,380.83 $642,327.17  \n",
-       "9 $2,384,613.00     $1,694,832.18 $689,780.82  "
+       "        Revenue  Operational Cost        Profit  \n",
+       "0 $2,315,682.00     $1,660,178.10   $655,503.90  \n",
+       "1 $2,353,644.00     $1,679,398.64   $674,245.36  \n",
+       "2 $2,293,704.00     $1,649,158.30   $644,545.70  \n",
+       "3 $2,309,688.00     $1,657,100.84   $652,587.16  \n",
+       "4 $2,414,583.00     $1,709,959.83   $704,623.17  \n",
+       "5 $2,289,708.00     $1,647,174.03   $642,533.97  \n",
+       "6 $5,449,707.95     $3,485,503.60 $1,964,204.36  \n",
+       "7 $2,352,645.00     $1,678,623.63   $674,021.37  \n",
+       "8 $5,383,938.77     $3,449,695.95 $1,934,242.81  \n",
+       "9 $2,366,631.00     $1,685,800.08   $680,830.92  "
       ]
      },
      "execution_count": 9,
@@ -1453,10 +1461,10 @@
    "id": "efe77b22-e99c-43e9-876f-4f7198d5b112",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.669278Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.668944Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.931709Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.931099Z"
+     "iopub.execute_input": "2024-10-25T15:11:04.761998Z",
+     "iopub.status.busy": "2024-10-25T15:11:04.761643Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.074470Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.073753Z"
     }
    },
    "outputs": [
@@ -1499,10 +1507,10 @@
    "id": "b3ff0966-ddaf-4b19-b9ba-1fe854092f43",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.933806Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.933380Z",
-     "iopub.status.idle": "2024-10-25T15:09:38.979244Z",
-     "shell.execute_reply": "2024-10-25T15:09:38.978767Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.077024Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.076587Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.126806Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.126213Z"
     }
    },
    "outputs": [
@@ -1539,16 +1547,16 @@
    "id": "ecef5abd-f551-444b-8ec4-53e224f6d529",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:38.981093Z",
-     "iopub.status.busy": "2024-10-25T15:09:38.980768Z",
-     "iopub.status.idle": "2024-10-25T15:09:39.307324Z",
-     "shell.execute_reply": "2024-10-25T15:09:39.306770Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.128900Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.128534Z",
+     "iopub.status.idle": "2024-10-25T15:11:05.422439Z",
+     "shell.execute_reply": "2024-10-25T15:11:05.421744Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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o1WrlQHbkyBHa2tqAj1bIuFzOsSRJIhQKcerUKerq6rBYLIwbNw6LxTLYTbsg8vBbX18f27dvx2w2ExsbS2Fh4XkfEwwGWbduHQsWLKCoqOjSNXYI8Pl87N69m40bN9Le3k4oFEKn0wEffWdaWlqQJInu7m7Gjx9Pdnb2ILf4dDqdTlmfNjs7m46ODqWHrqamhqqqKlavXo3b7SYnJ4fMzEwSEhLEyZpwyYkeO+GS6H8w37p1K8uWLWP37t1MnDiRmTNncs8995CdnT2kz3IlSUKSJF566SVeffVV1q9fzze/+U1mz57NxIkTiYqKorOzk4qKCtavX8/vf/971Go1V1xxBWvWrDnr+b773e+yYcMGqqqqqKmpwWq1XhYlFSRJwu128/vf/56XXnqJzMxM/vCHP5CVlTXYTbtgPT09VFRUcOWVV5KZmcn8+fP53e9+d977O51OcnNz+d73vsd1111Hfn7+JWztxSMX/VWr1Uqu2Zmampp44IEHOHbsGJMmTeL+++9n2rRp9PX1UVZWxgsvvMAbb7zBjBkzWLRoEY899hgwtEsUSZKEy+Viz549rFy5kueeew6r1cqECRO4/fbbuf766zEYDKjV6iH9OoSRZegeRYURxeVy8dJLL/H3v/+drq4uUlJSeP311xk3bhxxcXHo9fphcWZbVlbGxo0b2bNnD1/+8pf52te+RlpamrJ6hTxLd9KkSfT29rJ9+/bBbvKQJOcthcNhJWAWhq+vfOUrlJaWkpuby6uvvvqxva8PPPAAU6dOZebMmWi1WiwWC9HR0RQVFVFSUkJJSQnd3d3cddddREVFDemTPZVKhcViYdasWUydOpUHHniAZcuWsW3bNh588EFuuukmvvrVrzJlyhTMZvNgN1e4TAzdb4ww7MkH70OHDrFt2zZeeeUVUlJSuPLKK5k1axaTJk0iKioKo9E42E391BoaGuju7kalUpGWlobFYlGGk+CjHb1arcZsNjNt2jROnjxJOBwexBYPTQaDgeuvv54xY8Zgs9mIi4sb7CZdEkajkaeffpqxY8eSkJAw2M0ZMC6XS0lBOF+QHhERwbe+9S0SExOJjY1Vvjfy0n82m42kpCTa29txuVw0NjZit9uHdGAH/26/RqMhNTWVm2++mTFjxrB79262bdvG0qVLOXLkCLfffjtRUVHD4gRWGN6G9jdGGLbknXtNTQ27du1i9erV9PT0cO211zJr1ixmzZo1LIcnXC4Xfr//E3uZVCoVY8eOpbi4GJfLdQlbOPTJB8Lx48czfvz4wW7OJVFVVUU4HCYiIoLbbrtt2G33A8FsNnPllVee8zaVSoVGo8FoNKLRaAiFQrhcrmF1UiSf0I0fP57k5GSSk5OVepzd3d1kZ2czY8YMLBbLZZFyIQweEdgJF40kSfzlL39h69atNDc388c//pEZM2bgcDgGu2mfWVRUFCaTCa/Xy549e7jnnnvOCvDkXrsLDVzkYFF+vjMP/mdePvP/9r98rsf2v/189z3f9fLf/UtAfFK7Pq4N53rPzvRZ23iu+11omwc68Prf//1fvF4vV111Fbfddhtarfa878X5/v/53rMzP9sz73u+7eizfpYf93j5cv+A7NPMaJe3+6amJlwuF3a7nYiIiGHbuxUTE8OMGTP4xz/+wZNPPsm6dev42te+xjvvvENOTo4y018QLgYR2AkXRVtbG6+99hrvvPMOU6dO5b/+679YsGCBMnN0uCoqKiI7Oxuj0cjWrVt57LHHmDVrFnPnzqW4uPgzHYjC4TBtbW0sXbqUPXv2cPjwYdRqNampqRQWFvK1r32NpKSk04Z84aPyC93d3fzzn//kwIED1NTU0NHRgclkIjk5mfz8fG677TbGjx+PxWLB6/Xy0ksv8corr3Dq1Cml+PNXv/pVsrKy+Ne//sWxY8fw+XxotVoKCgq44447KCwsJCMjA4B169axceNGXnnlFQDsdjt5eXk88cQT/OMf/6C8vJzGxkZCoRC5ublMnDiRxYsXM378eDQaDd3d3ezevZsf/OAHdHV1Ka9l+fLlFBQUIEkSW7Zs4emnn+bIkSPK7d///veZNGkS//rXv9izZw8+nw+DwcDYsWO57777GDVqFDExMWe9t9XV1axdu5atW7dy8OBBgsEgUVFRZGRkcO+997J69WoOHz7M8ePHAZg2bRpz5szhP/7jPy74c7xQoVAIp9PJlVdeSXt7+2m3TZw4kUWLFnHvvfciSRIrV67k0UcfVW6Pioriiiuu4Oc//zkOh4NwOEx3dzevvvoqBw4coKysjLa2NkwmEzExMYwaNYr777+fzMxMHA4HkiSxbds23nrrLd555x3lea+55hp+8IMf8Mwzz3Do0CHa2trQaDQUFhayaNEi5syZQ2JiIiqViiNHjrB+/XqeeeYZWlpa8Pv9dHR0UFBQoARzGo2GrVu3kpyc/LHvRUtLC1u2bKGyshKDwUBWVhY5OTno9fqBfdMvIZVKRWxsLI899hhXXHEFP/rRj/jhD3/Ifffdx5IlS4bliIUwPIjAThhwLpeLuro6li9fTn5+PtOmTaO4uBiTyTTsd2RWq5Vp06bR1tbG22+/zcGDB+ns7KSsrIzc3FxSUlKIi4sjOTmZ3NzcTzUpJBgMsnLlStRqNRMnTmTMmDE0NzdTUVHBtm3bsNvtPPjgg0otPAC3282xY8f44IMP2LZtGwkJCUyfPh2bzYbf76elpYWamhr++te/8uUvf5kxY8aQmJhIYWEhgUCAffv2sXLlSiorK9m8eTNtbW0UFhZSUFBAX18fnZ2dHDx4kFdeeYXq6mpuuOEGUlNTSU9PZ+bMmZhMJp5//nnlgP7OO++QmJhIYmIiwWBQWed3586dtLS08O1vf5ukpCQMBgMZGRnceeed7Ny5k9LSUiorK/H5fMBHw1lpaWncdNNNpKamsnnzZk6cOMH27duRJInc3FySk5Pp6emhtbWV3bt3ExUVxYIFC/jCF76gvD/9g97Dhw/T2dnJjBkziIuLU2Ypbt68mZKSEiorK2lra+O73/0uubm5n7nMRnNzMzt27ODHP/7xWbft3buXpKSk066TS+TccccdbNmyhdLSUurq6rjzzjuZOnUqhYWFyvclKyuLe+65hxdffJG0tDRGjRrFggULMBqN+Hw+Wltbef7559m3bx9arZbJkycTFRWF3++nu7ub6upq/v73v7No0SKmTp1KfHw8ycnJzJ07F5PJxPLly6murqa0tJRVq1aRkZFBYmIiLpeL1tZWdu3aBXw0q/eBBx4APuqVmjBhAvfddx8vvfQSjY2NxMTEcMcddygBmVqtxmq1nvP9CofDdHR0cOLECQ4dOsTKlSsJhUJMnz6da665ZlgHdfDR56vVaomPj2f8+PHcfvvtvPbaaxw4cIC8vDzGjRs32E0URigR2AkDrquri1OnTrFz505+/OMfc8UVV5CamjrYzfrcVCoVer2eyZMno9frqaiooLa2liNHjnDgwAFMJhMFBQXk5OQoQ7BxcXHYbLaPnREXDAbZtWsXN910E+PGjSMlJYWysjL++te/sm7dOl599VVuu+220xKv5YPts88+i9Vq5corr2TevHmMGjWKzs5ONm7cyNq1a3nllVdwOByo1WqSk5OZOnUq06ZNY+PGjezbt4/KykoqKyuJjo7mgQceIDs7G6fTyalTp6iqqmLNmjW0t7eTnp5OSkoK+fn55Ofns3DhQjZt2kRFRQVdXV3s37+fRx99lLy8PBwOBzU1NfzpT39i586d7Nixg9mzZzNz5kwlKBk1ahQvvPACPT09VFZWKu+FVqtV/se4ceNoaGigsrKSU6dOkZiYyJe//GWlhtjBgwdZsWIFK1euxGKxsHDhQqUXRF6e7sUXX0StVjNq1CjuuusuRo8eTTAYpKGhgd/97nc0NjbS29uL0Wjk8ccfx2azfebto6urC6/Xq9Qm7K+tre2s59ZoNJjNZr797W8TGxtLIBCgrq6O66+/nmnTpinfGZVKxahRo3jsscfYvHkzCxYsYO7cucycORP4qLervLycv/3tb1gsFubMmcMtt9xCcXExnZ2dHDp0iOeff55XXnkFvV5PVFQUCQkJZGVlkZWVxcyZMykvL6elpYX29nZKSkr42te+Rnp6OoBSvmf79u309vZy3333oVarlUB+zpw57N69G6fTSUZGBt///vfPG8z1Jxcp//DDD9m/fz87duwgNTWV+fPnc8011wz7k0CZXq8nNTWV22+/nddff53jx4+ze/duEdgJF40I7IQBt2/fPjZu3Eh0dDS33HILmZmZg92kAZWSkkJycjJXXnkla9asoaSkhH379rF9+3a2bdvGli1bAHA4HNx111184Qtf4Kqrrjrv8+n1er74xS8yffp04uPjAZg8eTJHjx6lubmZVatW0draSnx8vFJG4pVXXmH16tW0trby//7f/2PevHnKGrpWq5W77rqLqVOnsnXrVv75z3/i8/koKioiNjb2tP/t8Xi47rrruP3228nLywM+Gl6Ni4vjt7/9LXfccQclJSX8+te/5pprrjkr6TsQCBAZGcn//M//MHr0aGWofezYsXzve9/jrbfe4gc/+AG/+tWv+NWvfvWZ1/mdNGkSixcvpri4WGmjzWZj3rx5bNy4kVOnTtHd3a3kb9bU1PDb3/6WlpYWvvzlL3P77bczY8YM5fliYmL43e9+x+233059ff2ApAiMHj2a+fPn89vf/vas2x5++GG8Xu95Hzt16lT8fj+rVq3i7bffRqVSnXYy1NXVxeHDh2lqaiI3N5dp06Ypt23bto2XX36ZhoYGfvrTn3L11VczefJk4KNVTeLi4hg3bhxTpkxRimrPnj37nO1IT0/nlltuYcKECcpnHRsby7x589iyZQvHjh2jq6uLyMjIzz1bta+vjxtvvBGj0Uh6ejp33303jzzyCImJiSOuNIjRaCQ3N5fFixdz4MAB3nvvPaXnUxAGmgjshAFXX19PY2MjM2fOxGw2j5gzb5n8evR6PXPmzGHSpEncdddd9PT0cPz4cY4fP87OnTv58MMPefXVVzl58iRpaWlkZWWdlScHHw1XyWU/+r9XVqtVCVQ6OztxuVxKYHfgwAGOHj2KRqNh+vTpxMbGnvZYjUaDzWZj8uTJbNmyhbq6Ovbt28fChQtPu5+cj5eenn7a9VqtlpycHBISEqivr+fEiRO0tbURGxt7WhCk0+mw2+3k5+ej1+tPe46UlBTS09OJjo7m1KlTNDY20t3dTWRk5AW/50lJSWRlZZ31GuPj49HpdHi9Xtrb24mKikKlUilLVwUCATIyMhg/fvxpj1Wr1URFRZGSkkJsbCy9vb0X3Kbz+Szbe1paGhMnTiQ/P5+dO3eSmZnJ1VdfrWwTNTU1vPbaa9x4441kZ2efFmBXVlaye/duVCoVxcXF5OTknNYGvV5PTEwMmZmZVFZWcuLECVpaWoiOjj4rOIuIiGDUqFGn5X9JkkRsbCwWi4Xe3l7a2tqwWq0DUoZEkiQefvhhFixYQE5OjlIGZaTuM6644goqKio4fvw4TqdTzJAVLgoR2AkDzuVy4Xa7lZUkRtpOGv49009eYkieCRgXF0dGRgZJSUnKMOOJEyc4cuQIKSkp5wzs5Oc58zatVqtcFwgElGXHwuEwXV1dyhBidHT0WT1OckmR+Ph41Gq1kit15n2MRiMmk+msWoJy6QabzYZOp8Pj8eB0OomKijorsDOZTOfsYTEYDFgsFmw2Gx0dHTidTtxu92cK7OS2nNl+eX3dUCiE3+8HPsrd8vl8tLe3o1KpsFqtZ/3P/rXTzGbzgAZ25zJr1iwCgcBZQZfMaDQSGxvLtGnTWLlyJVVVVZSVlSkrMzQ2NnL48GG+973vkZCQoMyEldctlidfbNq0iaqqqrO2pVAoRGdnJ263G6fTSXt7+zl73fR6PZGRkWe10WAwKDN55XI/AyUpKYmcnJwRka7xSeQcT4/Hg8vlwmQyicBOGHAisBMGXCAQIBAIjIjJEv3Jy6L5fD5CoRB2u125TZ4BmJaWRlpaGlOmTKGtrY0333xTyVuaP3/+OSvyywHKmTv4/mUi+h9Ig8Gg0gatVqtMBjiTnLiuVquVBczPPCDLdcMkSTrnZ2UymZQDUV9fH8Fg8LTb5f9/vhIn8soCKpUKn8+Hx+M553v7SfR6/TmHS+UVP+Df71EoFCIQCODz+dDr9ej1+nMG1PLznu+2gXTHHXd84n2sViuLFi1i06ZNyqSWKVOm0NraSk1NDQ0NDUybNu20STRyQCtPPvn73//+sT1pOp0Oi8VCZ2fnOWvEabVaTCbTWdf3f5+HU225ocZoNCoBstvtFu+lcFEMzyJBwpAWGRlJREQEJ0+eJBAIDHZzBlRpaSnf/e53ueGGGz6210KtVjNt2jTi4+MJBoN0d3cP2E5cr9djsVjQ6/X4fL5zBlzwUQDY1tZGKBRSyl6cWQ+uq6tLCQrOpbu7m76+PlQqFTExMWfNVHS73R/b2+Xz+ejo6ECSJGw2GxEREZ/hFV8YrVaL0WjEarUSCATweDznDSh7enpwOp0XvU2fhs1mY/HixWRnZ1NfX8/LL7+Mz+dj+fLlHD9+nNtvv/2sXjadTofZbMZut6PRaFi1ahUNDQ00Nzef92fHjh3MmDFjQFd8+TS16oSPClX39PRgMBjOWcJIEAaCCOyEAZeVlUV2djZ79uyhtbUVt9s92E0aMOFwGKfTSXNzM8ePHz9vQrwkSVRVVSnDpVlZWQOSkyQfQMeOHUtubi6hUIj9+/fT2dl52v3kyv2lpaUEg0ESExMZN27cWQffQCBAU1MTtbW1p10fDAaprq6mra0NlUpFRkYGDofjrMAuFArR19dHRUXFWUF8c3MzDQ0NdHV1kZaWRnx8/Gm9nBeLSqXCbrczceJEtFotdXV1HD169LT7yMPZzc3NZ713F5PX6+V73/ser7zyCocOHTqr3Tqdjvnz55OTk0NLSwvvvvsu69ato6uri+uvv/601AZ5W0hLS6O4uJhwOExdXR0dHR1KT6X8o9VqWb16NT//+c/5+c9/PuBr88pLavU/waisrOShhx7i7bff5uTJk2c9xmq18tZbb7Fo0aJz1iAcaSRJYvPmzfj9fsaOHXtWTqogDBQR2AkDLi0tjdzcXHp6ejh48CANDQ0jZqF3SZKUHrgNGzZQVVVFR0cHXq+XUCiE1+uls7OTU6dOsW/fPtxuN7GxsRQUFAxoXa7i4mLGjRuHwWBg69atVFRU0NnZSTAYxOVyUVVVRWlpKQ0NDWRkZJCXl3dWHTX4qGexsrKSgwcP0tXVhd/vx+1209raytatW+ns7MThcDBlypTzDhd7vV52795NS0sLLpdLqSFXUlJCWVkZoVCIqVOnkpiYeMlqk0VFRTFr1izMZjOVlZXs3LmTtrY2fD4fXq+Xrq4u9uzZQ1dXF6FQ6JK0CT4KhHfs2EFFRcVZRYnlFUsmT55MdnY2Ho+HVatWKZMV8vPzzznknpWVxRVXXIHJZGL//v0cPHiQtrY2AoEAfr8fp9OpFGo+cuTIOcuxfF42mw2j0YjT6cTj8dDb20tdXR0rV66kvb1dyX+U9fX1UV9fj0qloqOj46K0aSjxer3U1dVx6NAhTCaTUsxcBHbCxSBy7IQBl5eXh8/nIzY2lpdffhlJksjMzPzYXK7hQq1Wo9Vq6enp4cknn8Tj8TBhwgRycnKIjIykq6uL6upqtm3bxhtvvEFsbCyTJk1i/vz56PV6JcANBoOEQiHlst/vV3o75Jw4+T7wUQ+afJ1Go2HhwoWEw2F2797NCy+8gFarJRgMMm7cOFpbW1m9ejWrVq3C6XRy8803s2DBgrOGQVUqFWazmZKSEoLBIA6Hg9zcXHp7ezl+/Di//e1v6erqYvbs2dx7773n7HGUh4OXLVumzI6NiYnh5MmTPP/88+zfvx+r1cp9991Hbm7uaa8/GAwqw9NyXqYc3Mh5cvLJgPz6NRoNGo2GcDiM3+9X3sNwOKy8P1qtlsTERO666y7efPNN9u/fT1tbG9nZ2YwfP16pF/fHP/6Rnp4erFbrxw5Hn4/cZrmdcjvODGL68/v9nzgkP3PmTI4dO8bbb7/NsmXLuPrqqxk3btxpuXX9FRcXY7Vaeffdd3nnnXdobm5Wil1LkkRzczMffvghb731FkuWLOGmm25S3kP5fQ6Hw6e1X6vVKkFk//e5/2cXCoWUQD8lJQWHw0FlZSVNTU0EAgGOHz9Oe3s7OTk5xMXFndbm+vp61qxZw49//GOuvvpqrr32Wu67774LefuHjXA4TGdnJ6tXr+bIkSPcfvvtLFq0aLCbJYxkkiAMsHA4LHV1dUkffPCBlJ2dLc2bN0/69a9/LYVCISkcDg928z6zcDgs+Xw+6ciRI9Ibb7whfeMb35CuvvpqadKkSVJeXp4UExMjJSYmSrm5udLMmTOl//zP/5Q2bdoktbe3S+FwWAqHw1J9fb303nvvSVFRUZLBYJA0Go2kUqmkyMhI6atf/ar08ssvS36/X8rOzpYsFouk1+slQLJYLNL48eOlr33ta1IoFJJCoZDU29srlZeXS9/4xjekuXPnSrm5uVJsbKyUnJwsTZw4Ubrtttukd999V2ptbZUCgcBpr2XDhg3SuHHjpOTkZOmXv/yltHz5cmnx4sVSdna2lJiYKCUnJ0uzZs2S/vnPf0oHDx6U/H7/aZ9dIBCQ5syZI2VnZ0tXXHGFtHfvXunee++Vpk2bJqWkpEhxcXHSpEmTpIceekhas2aN5PF4pFAoJHV0dEgrVqyQsrKyJLPZLGm1WgmQbDabNGnSJOnrX/+6tGvXLunqq6+WbDabpNPpJEAym83SqFGjpJ/97GdSZ2en9OSTT0qRkZGS0WiUVCqVpNPpJIfDIX3xi1+Udu3aJYXDYSkQCEgHDhyQfvrTn0oLFiyQEhMTpcTERKmwsFBavHixtGrVKunOO++Uxo4dK0VHR0u9vb0XtD3cddddUnx8vGSz2SRA0mg0ksFgkKKios77ExkZKWk0Gulb3/qWtGHDhvNuZxs3bpTuv/9+SaVSSX/84x+lEydOfOJ2WVVVJf30pz+VlixZIo0ZM0aKjY2V0tPTpfHjx0vXX3+99Morr0gVFRXKZ3no0CHp29/+thQZGSnpdDpJpVJJer1eiouLk/77v/9bKisrk7Zu3SpFRUVJRqNR0mg0klqtlux2u7Ro0SJp6dKlShtOnDghPffcc9K0adOk5ORkKSsrS5o1a5b05JNPSp2dnVIoFDqtzUePHpV+97vfSVarVbr55pulf/zjHxf03g8nR48elf70pz9JDodDeuCBB6S1a9dKgUBgWO8LhaFN9NgJA07uCSoqKuKLX/wiR44c4b333iMmJoaFCxcSFxc3bKf463Q6UlNTsdvtpKSk0NbWhsfjUYb41Go1er0eq9WqzJCNiIhQeikjIiIYO3Ysv/zlL8967qysLNLS0lCr1fz4xz8+K3/PbrcrpS7k91iuaD9//nx6e3vxer1KDTuHw8GoUaM+tpisvF5scXExDzzwAO3t7QSDQdRqNdHR0YwZM0apLXY+BoOB7Oxs7rjjDqWsiSRJREZGkpCQoKz5KZdQKSgo4Mc//vFZvWQREREkJiaSnp7Oww8/zJIlS0673Ww2M2rUKEwmE/PmzTur/Im8uoZck0+j0ZCVlcX1119PUVERbW1thMNhTCYTERERjBs3jldeeYVAIKDM3L0Qd955J7Nnz/5MQ7mFhYXKyg5nkleauPvuu5k8eTKzZs1SClef7/46nY6EhASuv/56pkyZQnt7Oy6XS5lIEhERQUFBAdHR0eh0OiRJIjExkRtvvFEpTC1Tq9UUFhaSkJBARETEObfVxMREcnJylMsJCQnMmjULh8NBR0cHWq0Wm81GXl4eFovlrCHkhIQEpRc5JSXlMxeuHspCoRAlJSW8++67lJaWMnr0aG6++WYKCgoGJN9WEM5HJUkjIPFJGJKk/1to/N1332Xt2rVkZWVx//33U1BQQGJiIkajcVgPyw5nGzdu5LHHHqOzs5Mnn3ySL33pSxf0+GAwyJVXXkl9fT0ZGRmsX7/+IrX0swmHwwSDQY4fP058fPxZBZyl/xv+/spXvkJJSQmRkZFs2bLlnOVoBOFCyBOXWlpaeP3111m/fj19fX0sWrSIxx577LQTPUG4GMRpg3DRqFQqZs2aRWJiIkVFRTz66KMcP36cK6+8kocffpjRo0efdl9BGCiBQIC2tjZuuukmHnvsMR588MHTeklCoRBNTU00NDQAMHHixHNOTBCET6N//4jX62Xfvn3885//5PXXX2fhwoXcdtttPPTQQ4DY1wkXnwjshIsuPT1dWdLoL3/5Czt27GDNmjUsWbKExYsXM2bMmM+0GoEgfBKn08k777xDX18fd911F3a7nd7eXiorK3nyySepr6+nqKiIr33ta5dsxq4w8kiShMvl4oMPPuD111/n5MmTeDwennrqKWbOnElWVtZgN1G4jIjATrjo5PVECwoKuPnmmzly5AiHDx9m//79OJ1OsrOzKS4uPm9JDWFgBAIBSkpK2L9/P/v27aOlpQWPx8Pbb79Nc3Mz48ePZ9q0aedceUB2/PhxTpw4QUlJCTU1NfT29iJJEj/96U+VtU6zs7Mv4as6NznPcMmSJfT29lJeXs5zzz2nrNTQ19eH3+9n0aJFTJo0iYyMDNFjJ1wwSZKUtZQPHjzIhx9+SE9PD3l5eRQWFjJnzhzS0tIuSf1GQZCJwE64JORF12+88UYmT57Mvn37eOqpp1i3bh3bt2+nqqqKyMhI4uPjsVqtyuLYYthi4ASDQQ4fPsybb77JqVOnMBgMGAwGdu/eTXV1NcFgkOLi4o8N7Gpqati2bRuvvPIK8FGR2WAwyLPPPovX68VisQyJwE6r1WK327nnnnvYvn07paWlrF69mvb2djQaDXa7nSlTpnDLLbeQn59PVFTUYDdZGAakfuWJfD4fLpeLffv2sWXLFjZv3kx3dzcLFixg9uzZXHfddTgcDrEPEy45MXlCuKT6b25ut5v333+fDRs28Nprr2EymZg6dSqzZs3i1ltvJSEhQQyPDaD+7/25vvb9VzT4pOf4rI+/lM5s45mX+7dzqLRZGNokScLj8bBv3z527tzJa6+9Rnl5ORkZGcycOZPHH3+c5OTk05ZrE9uWcKmJwE4YFNL/FUPt7u6mo6OD6upqVq5cSXl5OU1NTeh0OqZOncr48eOZOHEixcXFYj1KQRAGRVtbG9XV1Xz44YesXbtWWYM5JyeHefPmkZ+fT1ZWFklJSUppH0EYLGIoVhgUcp2x6OhoIiIiSEhIIBAIkJyczMmTJzlx4gSnTp2ip6eHyspKTp06RVJSEtHR0cTFxREVFSV2noIgXBTysmhtbW3U1dVRVVVFXV2dsj50WloaCQkJTJgwgRkzZpCYmCiG84UhQ/TYCUNKKBSip6eHLVu28NZbb3HkyBGqqqpISEhg2rRpFBYWMmXKFMaNG6ecGWu1WmXChejREwThQsh5c6FQiFAoRDAYpKWlhZMnT7Jv3z7Wr1/PiRMn8Pv9JCUlcfvttzNz5kwKCgrEbH5hSBKBnTCkyJujvI5le3s71dXVvP3222zfvp3a2lo6OjpIT09n8uTJFBUVMWfOHCXQE4GdIAgXQvq/9XSPHDnC/v37lTVdvV4vZrOZefPmMWvWLMaNG8ekSZOUE0mRGiIMVSKwE4Y0v9+Py+WisbGR9vZ2WltbqaqqoqysjNbWVlwuF2q1mvT0dFJTU0lNTWX06NHk5uYSERGB2Wwe7JcgCMIQEQ6HCQQCVFRUUFNTo5QqOXHiBL29vQSDQeLi4sjMzCQlJUXJm4uJiSEiIkIZbhUBnTCUicBOGFacTicNDQ3s2rWLY8eOUV1dTW1trbI2aUxMDGlpaeTl5REbG0t0dDRRUVHYbDYsFgtWqxW1Wi12zIIwwsllSTweD11dXfT29tLX10d3dzdHjx6loaGBtrY2mpqacDqd2Gw24uLimDBhAsXFxWRkZJCRkYFWqxX7C2FYEYGdMKy5XC4qKirYsGEDZWVllJeXU1paSigUIjIykvT0dGbNmsX48ePJzc2loKAAs9msDKOcazhF7MQFYfg4swSPnDMXDodpa2ujqqqKbdu2UVpaysmTJykvL8fn8xEfH09aWhqTJk1i7ty5jBkzhqysLHQ6ndgHCMOaCOyEYU1Oevb7/QSDQYLBIE6nk6NHj1JZWcnx48fZt28fdXV1uFwuDAYDGRkZZGdnk5mZydixYxk1ahSJiYnExsYCIrAThOEkGAzidrs5ceIElZWVVFVVcfDgQcrKyujo6MDlcmGz2cjPzycjI4MxY8YwY8YM4uPjsdls6HQ6dDodGo1GTMISRgQR2AkjiiRJBINBOjo66O7upr29ncbGRpqammhra6OxsZGenh4CgQDBYFDp2YuKiiImJobU1FQSExNxOBzExsaSmJiIyWRCp9MN9ksThMuWJEkEAgHa29uV73VdXR0tLS10dnbS2tpKZ2cnoVAISZLQ6/UYDAZsNhsOh4OMjAxiYmKU73lKSgomk0kUQBdGJBHYCZeFnp4e2traqKio4OjRo9TW1tLQ0EBVVRV9fX2o1WqMRiPp6emkpKQQFxdHUlISWVlZREVFYTKZlCW49Ho9Op1O+Vvk7AnC5ycHb/KPz+fD5/Ph9/vx+/04nU7q6+tpbW2lsbGR6upqmpqa6OzspLu7G5PJpNS5zM/PJzc3l9TUVNLT08nKyhKzWIXLhgjshMtWOBzG5/NRVlZGVVUVJ0+eZP/+/ZSVlSk9AQARERHExMSQnZ3NmDFjSEtLIyUlhby8PLKysjCbzZ9YLFkcUITL2ac9zNTX1ysnXRUVFRw/fpza2lrq6+upqakhFAqh1WqxWCwUFRUxatQoMjMzKSgoYMKECURGRoqZ8MJlTwR2wmWr/4LeZ/YSuN1uuru7OXnypDKM29TURH19Pe3t7TidTlQqFSaTCYfDoSRix8fHEx0dTUJCAunp6cTFxREREYHFYhnslysIgyYYDOLz+WhsbKSxsZGOjg5aW1upra2lubmZ9vZ2Ghoa6O7uJhAIoFKpcDgcJCcnExMTQ2xsLGlpacqQakxMDEajUek91+v1GI1G1Gq1WJFGuOyJwE4QziEQCOD1emlpaVFKJXR0dNDW1nba5Z6eHkKhkLJEmjzco9FoiIyMxG63Y7FYiIiIIDIyUim5EhERgdVqxWQyYTKZsNlsmEwmtFqxyp8wfMgnRi6XC6/Xi8vlwul00tfXh8vlore3l56eHlwuF319fXR2dtLT04PP5yMYDBIOh5UfSZKwWCzY7XbsdjvR0dHExsYq353Y2FhiYmKwWq1YLBYxtCoI5yECO0G4QKFQCK/XS21tLVVVVTQ1NdHU1ERtbe1phZQ9Hg+hUAi1Wo3FYiE5OZno6Giio6OVWbjyxI2kpCQiIyMxGo3K7Dy59+HMy/J14sAmDDT5cBAKhZRgS15qSw7A+l8OhULKSU5XVxft7e20tLTQ0dFBZ2cnzc3NNDU10d3djcvlQqVSodPpsFqtOBwO0tPTSUhIIC4ujpSUFHJzc5U1oUUvtyB8NiKwE4QLdK6vTP/r5INhfX09TU1NtLa2UlNTQ1VVFc3NzbS0tFBdXU1rayt+v59wOAyAXq/HYrEQFxenDEE5HA4SExNPCwpjY2OJjY3FbDZjNBov2esWRr5QKKTMPpVnlre1tVFfX09HRwcdHR00NDQokxY6Ojro7e09bfuXUxPi4uJIS0sjMzOThIQE4uPjyczMJC0tDYvFctZMc1FPUhAGhgjsBGGAyV8peUafnLvn9XqVy16vF6/Xi9vtVmbsdnd309fXh9PpVIavPB4Pvb29OJ1OwuEwarVaqbul0+kwGo1ER0djtVqVId7IyEjlcmRkpJLjJ19nsVjQ6/Wi1MMIJfekOZ1O3G43Ho8Hl8tFZ2enMkTa3d2tbG99fX1KeoHb7aavr++0ckDBYBCLxYLZbMZisWCz2bDb7cplh8OBw+HAbrcTFRWlbF/yzPH+uXBGoxGDwSBy4QThIhKBnSAMklAohM/nU3KP5CWPenp66OzsVHKV5IOwx+NRAkKfz0coFAJQDpparVY5mGq1WrRaLXq9HrPZrOTyWSwWpX6XXK6l/33lgLH/ZXnRc71erwwJy9f1/63VasUB+wLIk3fkwtryEGf/vwOBgPK3vL3I9w8EAkphbvnv/uVC5BMDn8+nnCDIt/W/bzAYVC7LQ6z9S/sYjUYiIyOx2WxYrVaioqKUkwe5TpzdbsdqtWK329Hr9aK3TRAGkQjsBGGIk7+iLpeLnp4eZZjM6XTS3d1NS0sLPT09OJ3O04bQenp66OjowOfzKflS8vPJPX9yb57ZbFaS1uWeGPlgbjQaT5vgodfrMZlM5w0Y5Qkg8sG9/0H+Qv8+l897u+zT7vrOd78zh98/zW3nWvaqr68Pj8ej/LjdbmV2tjzRQA7O5Fw1t9uN0+mkt7cXl8ulbBtOp1MJ/vvnYKpUKrRaLTabTQnGYmNjlc88Pj5emezjcDiUYr7yJAaxzJYgDB8isBOEYeB862Ge7+/+l+WDv9PppKurSxmOk4d8+/r6lEBBHgKWh+t6e3uVQKGnpwf4+IBMntghD/0ajUalDIU8JCf/yGUqNBoNZrNZmRBit9uVQORcNQJtNtvHFoWWey7Pl38o94p5PJ6PDe76+vqUlQxkgUAAj8dDOBzG6/UqPV7ysLvcm+Z2u08bEgWU2aPyDFK/34/P5zvtcz3X3/LwpclkUmZWm81mbDabEpjLM68jIiKUwDwqKkoZerfZbMTExCgL2p8Z9J0ZhJ9rHWUR2AnC8CACO0EY4eTgo/8wnTzMd67LcnDSf3hOvl0OXs5cFUAeHpYfK/+4XC7a2to4ePAgOTk5xMfHYzAYTpt1KQdA/Ycm4aNcMflvWf/7ni/QCAaDpwW3Z5KvNxgMZ90mv1ZJkjAYDMrQs6z/zGQ5uJQD0v73lQMoQOntkoNeechao9Gg0+lO6+nUaDSsW7eOiooKmpubmT17Ntdeey2pqalKYCwPe/cfNj/X5f731Wq1GAwGMZNaEC4DomiWIIxw8gHfZDJ97ufy+XynTQaRgzs5/08O/jweD+3t7Zw6dYrGxkaMRiM5OTnk5+ej1+uVAFHuPZOHi8PhsNKTFgqF8Hg8yv+Wg0Cv16vMJD6XQCBAR0cHhw4dIjExkejoaOx2+2n3UalU5yynIQ9l19bWMm/ePOx2+2mzN/vnGvYPouTASg7aTCaTEkTJNde0Wq3Sgyn3WhoMBsxmMwaDQVmIXqvVEhcXx9GjR7HZbERHR5OdnU12drbSmykIgnA+osdOEIQBJe9Sjhw5wnvvvceTTz7Jddddx3/8x38wbdq0SzK54sMPP+Tuu+/m7rvv5rrrrmPixImf6nEbN25k7dq1PPPMM+zZs4fs7OwBCYgv1IkTJ9i2bRv/+Z//icFgYNq0aTz66KNMnTpVCTRFgCcIwrmI6WuCIAyoYDDI0aNH+a//+i/eeOMN5s2bx69//WsmTpx4yYKRQCBAZ2cndrv9nEOu5zNx4kRmz56NSqVi7dq1VFRUXMRWnl9mZia33norGzduZPHixdTU1HDbbbfxs5/9jAMHDpw1RC0IgiATQ7GCIAwYt9tNc3Mzf/rTn+jq6mLMmDF86UtfIiYm5pLNrJTzAb1er1JT7dOyWCzEx8czfvx4SkpKyM3NZfz48Rextecm59tlZGRwyy23kJuby7Zt29i4cSNdXV1MmjSJa6+9lujoaDQazSVvnyAIQ5fosRMEYUAEAgGam5spKSnhgw8+ICYmhpkzZ7Jw4cJLWi6jf522c61w8HG0Wi1RUVFMnjyZ8vJy6uvr8fl8n7o0ykBSqVSYzWZmzpzJjTfeyOLFi/H5fOzYsYPly5ezb98+WlpacLvdHztZRBCEy4sI7ARB+NwkSaK9vZ033niD73//+yQmJvLd736Xe++995IXrJVLuQBERERc0FAsQHR0NDfeeCN1dXUcPXqUU6dOXYxmfmoqlYrU1FS+9KUvsWzZMmbPns3x48f54he/yD/+8Q+OHDkyqO0TBGFoEUOxgiB8LsFgkK6uLh5//HFOnTpFamoqzzzzDFlZWYOybJm8TJZKpSIiIuKC19O1Wq1MmjSJjIwM6uvrWbduHaNHj75Irf30VCoV6enpfO9732PJkiW8++67vPzyy2zevJnJkyfzne98B4fDcUE9lIIgjDwisBME4TMLBoO0tbXx9ttvU1FRQVJSEnPmzCE3N1dZE/RS83q9SpkUq9V6wYGOWq3GYrFQWFhIU1MT+/fvJxQKKfXqBotKpcJgMJCQkIDJZCIQCOB2u6mpqWHfvn08++yzzJs3j8zMTBITEwetnYIgDC4xFCsIwgWTc7p6e3upqKjgb3/7GwDTp0/nK1/5CiaTadCS+uWluYALzrGDf6+6MG3aNDQaDYcOHcLlcn1s7bxLSa1WExUVxbx583jkkUeYNm0afX19/PGPf2TlypUcOnSI7u7u05aREwTh8iF67ARB+EwCgQDLli3jzTffpKOjg5deeolx48YRFRU1qO3q6+vD6XSi0Wiw2WwXnGMnu+666zh8+DD79+9nzZo1XHXVVYP+2s5UUFBAbm4uX/3qV/nNb37DP//5T1577TVmzZrF//7v/xIRETEow+GCIAwe0WMnCMIFkVeFeOqpp3j//fdxuVz88pe/pKCgQFkZYTCHLN1uNz6fD7vd/rl6DR0OB3l5eeTk5LBy5Up6e3sHsJWfn/w+63Q64uLi+PrXv87//M//MHv2bPbs2cNDDz3E8uXLqaysHOymCoJwCYkeO0EQLkhvby/Hjx9nw4YNhEIhiouLmT17NlFRUUMicV9e2sxqtSrruV4oOZ8tPT2d3Nxc9u7dS2dnJwkJCZ+5B/BiUavVGI1GcnNzkSQJs9lMIBDg8OHDrF+/ntbWVqZPn05BQcElLTsjCMLgED12giB8asFgkNraWpYuXcqBAwcoKirigQceICMjY0gEdfBRjp3P58Nms33uICY7O5tJkyZRVlZGfX39kOu1O1NeXh4333wzP/3pTykoKGDNmjU8/fTTLF26lPb2dmVNXkEQRi7RYycIwqe2fPlyVq1axSuvvMJvfvMb5s6dS15e3mA36zR9fX14vV6io6M/d2CXl5eHWq0mJiaGjRs3otPpWLhw4QC19OIwGo2kpqby7LPPsm3bNjZs2KD8fffdd3PttdcOuc9MEISBIwI7QRA+UTAYZPv27bzzzjvU1NRwxx13MHfuXFJSUobcklYejwe/34/NZvvc5VY0Gg12u50ZM2ZQVlZGWloaV1999aDnEX4cuW1Go5Fx48YRGRlJSkoKr7/+OitWrGD//v3ccMMNXHXVVcpwtSAII4cI7ARB+Fg+n4/Ozk7Wrl1LVVUVNpuN66+/nqysrCGXbwan59h93uBLpVJhMpmYNm0aL730EjU1NfT09BAZGTkwjb3I4uLiiIiIIDExkdraWkpKSjhy5AgajYaoqCiys7OJj4/HYDAM2UBVEIQLI07VBEE4L0mSaG1tZceOHTz99NOkpaVxxx13cP311w/JoA4+WlLM5/MRERExIMGKxWLh+uuvJxAIcOrUKQ4ePDis6sMZDAaSkpL45S9/yQ9/+EOuvPJKXnnlFb73ve/x8ssv09TUJNaaFYQRRAR2giCcV21tLe+88w7f//73WbBgAQ8++CBLliwZ7GZ9rL6+PjweD1FRUQMyzKjT6cjOzmb8+PH4/X7eeeedYTkBQaVSMWPGDL7zne+wcuVKHA4HL774IjfffDPLli2jra1tsJsoCMIAEIGdIAhnCYfD9Pb28uabb7Jt2zZsNht33nkn+fn5mM3mwW7ex/J6vUqO3UD02Mm14oqKirBarZSUlOB2uwmFQgPQ2ktDzrszGAw4HA7Gjh3LPffcw7XXXktsbCzLli3jpZdeYsuWLbhcLtF7JwjDmMixEwThNOFwGJ/PR3l5OStXrsTpdDJhwgS+8IUvYDKZhnwulsfjIRAIYLFYBrStRUVFVFZWsn37dlpbW0lKShryQe65aLVaYmJi+OIXv0h6ejp2u52lS5fi8/loa2vDarWSm5uL2WxGqxWHCEEYbkSPnSAIpwkEAtTV1fHQQw/R3NzMnDlz+M1vfoPZbB7yQR18lGPn9XqJiooa0PbOmjWLcePGEQ6HWblyJa2trQP23Jea3As5ffp0vv/97/Pcc8+hVqt54YUXWLJkiVLYWBCE4UecjgmCAHw0USIcDrNu3TreeustWltb+e///m9mzJiB3W4HGNKBnSRJeDwevF4vkiQRGRk5oKU8jEYjWVlZTJ8+nffee4+pU6eSkZExYM9/qcmfpV6vZ8qUKfz+979n//79vP/++3zve99j9uzZzJ49my996UsYjcYh/dkLgvBvIrATBEGxe/duPvzwQ44dO8bChQuZNGkSaWlpQ65W3blIkoTb7SYYDKLRaAak3El/arWahIQEJkyYwHPPPUdjYyM9PT1EREQM2P8YDGq1Whl+1ev1qFQqvF4v9fX1rF27llAoxIIFC4iNjcVqtQ52cwVB+AQisBMEgXA4jNfr5a233mL37t14PB4eeeQR8vPzsVgsg928T83pdCqB3UBNnugvMTGRqVOn8otf/IKqqioaGxuHfWAnMxgM5OXlkZWVRXR0NEuXLqWkpIQDBw5gsVgoKioiPT0dk8kEDO3eW0G4nInAThAE2traeOONN3jppZeYP38+d999N0VFRcPq4C1JEl1dXfj9fnQ6HQ6HY8DbHx8fz5QpU8jJyWHfvn1YLBZGjx49oP9jsGk0GhYuXMjUqVMpLS3lt7/9LQ899BDTpk1j4cKFfP3rXxeTKgRhCBOTJwThMldfX8+OHTv461//ylVXXcUNN9zA5MmTh/SyWeciSRLd3d0EAgG0Wi0REREDvlyWSqVCr9dz1VVXUVdXx/79+/H5fCOqPIhKpUKtVmO32ykuLuanP/0pjz76KBqNhmXLlvGtb32L0tJSuru7B7upgiCcgzjtEoTLlDzZoLS0lO3bt+N0Opk9ezbjx48nOjp6sJt3wSRJwuVyEQqFMBgMF20Wr1arZerUqezatYv6+noaGhpIT08fFnmIF0Kn0xEVFUVkZCR9fX1otVr27t3L3r17SU5OZvz48YwZM0aZQDKcTgIEYSQTPXaCcBmSl5BqbGzkrbfe4t1332X69OksXrx42A4tSpJEb28vkiSh1+svWs09nU7HvHnziIyMpK2tjV27dhEMBgf8/wwFcu/dnDlzePTRR3n88cfRarU89dRTPPXUU6xcuRKv10s4HB5RvZaCMJyJwE4QLkOhUIju7m4efPBBDh48yKRJk/jDH/5ATEzMYDftM5Mkic7OTgwGAzab7aL9H5VKRWxsLJMnTyY6OpqXX34Zv99/0f7fUBETE8PMmTNZuXIlP/nJT7BYLDzxxBPcfvvtbNmyRQzNCsIQIYZiBeEyI0kSlZWVvP3229TX13Pttddy1VVXKXXfhuuQmiRJ9PX1odPpLuqKEHLuYXFxMR0dHbz//vs0NTWh0WiG5UoUn5a8bdjtdq6++mqSkpIYM2YMa9eu5U9/+hPjxo3j2muvZcKECWJyhSAMIvHtE4TLTGtrK0eOHGHVqlUkJCQwbdo0pk+fPiIOxi6XC61Wq5TkuJjy8vI4deoUnZ2dnDx5EpvNNqIDO/h3UJubm4vD4SA5OZlTp05x8uRJuru70Wq1WCwWkpKSsNvtIy7vUBCGg+G/JxcE4VORc6BWrVrFqlWrOHDgAG+++Sbjx4/H4XAMcus+P3lWrE6nuyS190aPHk1TUxOxsbGsXr2aqKgoEhIShm2P54WKjo7G4XDwr3/9i3/+85+sXr2an/zkJ+zbt4/77ruPWbNmDYsVSwRhpBE5doJwmQgEAqxbt46lS5dSXV3NT37yE6ZNmzas8+r6kwM7vV5/UXPsZCqViqSkJJYsWcKqVauoqKggEAhc9P871BgMBu6++25+/etf8/e//51jx47xX//1Xzz44IPs2rXrssg/FIShRAR2gnAZ8Hq9NDU1sWzZMtRqNePHj2fBggVYLJYRM1wmSRJOpxOdTndJhmJVKhUxMTHMnj0bp9NJVVUVp06duuj/dyiRZ83abDbS09OZMWMG9913H/n5+bS2tvLnP/+Z5cuXc/z4cYLBoJg5KwiXgBiKFYQRTpIkOjo6OHLkCKtXr+aaa65hzpw5jB8/frCbNuD6+vqIiIi4JIEdQEREBBMnTsRgMFBTU0NZWdmwLRfzeVksFnJzc7nvvvuIjIxkw4YNrF+/nkAggMvlQq/Xk5SUhE6nG/DC0YIg/Jv4dgnCCOf3+9m4cSM//vGPSUhI4Ktf/Spf/OIXB7tZA+5SD8UC6PV64uLimDVrFg0NDaxZs+aS/N+hLCoqivvvv58//OEP/OpXv2LXrl088cQT3HfffRw9ehSv1zvYTRSEEU0EdoIwwv3zn//k3Xffxel08utf/5rCwkJ0Ot1gN2vAXerJE/DvocgbbrgBjUbDwYMHqaysvCxz7eDfs2ZVKhVRUVFcd911PPfcc9xxxx14PB6++tWv8swzz7Bt2zZR1FgQLhIxFCsII5TP5+PUqVNs2bKFnp4errjiCoqKii7KGqqDTV5Jw+VyXbIcO5lKpaKwsJDY2Fjq6+s5cOAAcXFxIzJ4vhBarRaHw0FxcTHhcBiz2czGjRvZuXMnXV1ddHZ2Mnv2bCwWy2X/XgnCQBKBnSCMQOFwmN7eXtasWcO+ffsoLi7mrrvuIjY2dsQFdbJQKKQEdpeynpxKpSI/P5/MzEzKy8vZuHEjs2bNwmKxXPZlPuSeuwULFjBp0iTi4+P561//SllZGfv27SMhIYHMzEwiIiLQ6/WD3VxBGBFG5h5eEC5zLS0tSm7TggUL+MpXvsKCBQtGbKDh9/vp7e0lGAxisVguWY6dTKVSMWXKFKZOncqrr75Ke3v7ZTscez4RERHcf//9vPLKKzz66KO0tbWxaNEifvGLX7Bx48bBbp4gjBiix04QRhBJknC73axatYrly5eTk5PDzTffTFFR0YjtqYOPavQ5nU4kScJsNl+yHDv4d/HdgoICuru7efbZZ9m3bx96vZ6cnJxL1o6hTH6PNBoNqamp3HDDDYwaNYqXXnqJPXv2cPToUUpLS7n33nuJjIwUvXeC8DmM3D29IFxm5ET00tJS9u7dS2VlJQsWLKCgoIDY2NhBbt3FJZfUgI8K5hqNxkvehri4ODIyMoiJieHw4cPU1dVd8jYMBxaLhfT0dGbOnMk111xDeno6brebjRs3snbtWsrKyujo6BjsZgrCsCUCO0EYISRJIhAI8I9//IN9+/YRGRnJd7/7XeLj40d0bx18FNj19vYOSo+dzGw2k5iYyBVXXMHu3bs5fvy4mPl5HhqNBqvVyl133cUPfvADbrnlFk6ePMnjjz/OCy+8QGlpKYFAQJkUIwjCpzey9/aCcBlpaGjg97//PatXr2bixIn84he/IC4uDq125Gdc+Hw+urq6ALBarZc8x04WHR3NXXfdRVVVFQcPHrzsVqL4LMaMGcO9997Lxo0bWbRoEevXr+e+++7j8ccfp7a2VuQqCsIFGvl7fEG4DDQ2NlJSUsIbb7zBnDlzmDNnDqNHj1Zqio10co6dXq/HYDAMWvkMk8lEcXExcXFxtLa2sn37dpFn9wm0Wq3S23nPPfcwevRoDh06xNatWwmFQsyYMYPp06eTkpJyWWzLgvB5icBOEIYxSZIIBoMcO3aMvXv3Ul1dzX333UdRURExMTGD3bxLJhAI4Ha7laBusNa/1el0JCUlkZWVhdPpZO/evdx5551oNBoRlHwMtVqNwWBgxowZREdHk5CQQHV1Ndu2bcPj8QAwffp0YmNjMRgM4r0UhI8hhmIFYZiS8486Ojp46aWXeOutt1iwYAG33HLLZbdeqd/vp6enZ9Drock9pAsXLsRgMLB+/Xr6+voIhUKD1qbhZtSoUdx66628+uqrFBcXs337dr72ta/xhz/8gbq6OkKhkMi9E4SPIXrsBGGYklda+Pa3v82JEycoKCjg5z//OXa7fbCbdsnJdewiIyOHxCoGixYtorKykpKSEpYvX861115LXFzcYDdr2NBqtcTGxvLLX/6S0tJStmzZwtKlSykpKWHOnDl85StfIS0tTfTcCcI5iMBOEIappqYmdu/eTWlpKZMmTWL27NkkJycP2jDkYJLLnVit1iExWcThcJCRkUF6ejqbNm1ShhFFIPLpqFQqNBoNsbGxFBYWYjKZcLvdHDp0iF27duF0Olm8eDGjRo0iOjpavK+C0M/g7wEFQbhgHo+HkydPsnz5crxeLzNmzOCaa67BYDAMdtMGxVAK7FQqFQaDgfT0dAoLC9m4cSPt7e2kp6eLwrsXQA7WEhMTiY2NJTExkWeeeYa9e/fy6quvKj2zo0ePJiIiQuQxCsL/EYGdIAxDu3btYvny5bz++uv86U9/Yv78+aSkpAx2swaN3++nq6trSK1aMG7cOMLhMH//+9/Zv38/UVFRjBo1arCbNSxpNBqys7P53e9+x+HDh3njjTd46qmnWLVqFbNnz+b73/8+8fHxl2VvtSCcSQR2gjCMhEIhmpqaePbZZ6mpqWHJkiUsXLjwsh+OknPsUlJShkSOHXxU0y4/P5+xY8eye/dubDabCOw+o/5LkuXl5fHQQw+Rn5/PihUr+PDDDzly5AiPPfYY48ePJzU19VM9ZzgcBhjxxbuFy48I7ARhmJDXgV2zZg1VVVVERkayaNEiYmNjh0wwM1jkcicWi2XQh2JlOp2OiIgIJk+ezKlTp6ioqMDtdmMymS7rIPzzUKlUWCwWTCYTs2fPxufzUVJSwqFDh3jvvfeor69n3LhxTJw4EZ1Od86gTZIkPB4PR48exe12M336dDGMK4woQ2MPKAjCJ/L7/bS1tfH3v/8dgOLiYpYsWTJkApnBFAgE6Ovrw2q1Dqkg12g0ctVVV7Fz506OHz9Oa2urmM05ANRqNZmZmdx9993MmjWLpUuX8vzzz7Nv3z6mTJlCamoqMTEx6PX604Zn5TIpzc3NvPnmmzQ3N1NQUEBkZKQYxhVGDNEHLQjDxM6dO/nd737HsWPHePTRR3nooYcGZbH7oUheUiwqKmrIBXY33HAD6enptLS08Prrr4uadgNIr9eTn5/Pz3/+c1566SXGjRvHBx98wPz583nnnXeoqqo66zGBQIDHH3+cZcuWsWbNGn7yk5/Q3d196RsvCBeJCOwEYYiTJImysjI2bdrEli1buP/++ykuLlbKZ1zuvT/hcBi/34/b7cZmsw2pwE6eITtlyhQiIyNZu3Ytfr9fye8SPh+VSoVarUar1VJcXMxDDz3Ej370I1JSUvjrX//K//7v//Liiy/i8/kIh8O0tLSwevVq9u3bR3t7O52dnXzwwQfs2LGD+vr6wX45gjAgxBiOIAxh4XCYQCDAjh07OHbsGOFwmIULF5KcnIzJZBrs5g0JgUAAv99PMBgcUjl28O96bOPHj6epqYnVq1fT1NREUlISZrN5sJs3YqhUKmJjY7FarSQkJNDS0sK6des4fvw4breb5ORkMjIyqK+vZ/Xq1TQ3N+Pz+QCorq5m48aNmEwmYmJixJJlwrA3dPaAgiCcRpIkQqEQ7e3t/POf/0SSJObOncuVV14pZvL143K58Hg8SJKE3W4fUj12shkzZlBXV8err77Kzp07mTt3rgjsLgKTyURmZiZPPPEEY8eOZcWKFbz33nscPXqUu+66i46ODp577jn8fr/yGJVKxT/+8Q90Oh15eXmkpqYiSZII7oRhSxwdBGGIkiSJuro6Hn30UXp7e7n66qt54oknxAHnDL29vbhcLlQqFQ6HY8jUsesvNjaWMWPGMGPGDJYtW0Z1dfVgN2nE+8IXvsAvfvELli9fjt1uZ9myZbzyyitnDYOHw2F8Ph/Lly/nRz/6kciBFIY9EdgJwhB17NgxNmzYwIEDB/jCF77A7NmziYqKGuxmDTlutxuv14tKpcJmsw2poViZWq0mKSmJ2bNnc+zYMaqqqmhvbx/sZo1oBoOB6OhosrOzycvLw+Vy0dbWds78Rrk+ZGlpKWvXrqWnp2cQWiwIA0MEdoIwxMj16g4ePMiHH34IwIIFCygsLESv14seuzP0D+yGWo5df7GxsUyePJmuri6qq6upq6sb7CaNeHKOo0ajwe1243K5zhnYSZJEX18ftbW1fPDBBzQ1NSk5eIIw3AzNPaAgXKbkOlvl5eW899577Ny5k29961tMmTIFh8Mx2M0bkvr6+nC73ahUKiIiIoZkjh18FNhNnTqV9PR0Dh06REREBEVFRQAiWL8IJEmis7OTQ4cOsXTp0tPy6s53/56eHv7yl79QXFyMTqcjOztbfDbCsCN67ARhCAmFQnR3d/Of//mfNDQ0MG/ePL761a8SEREx2E0bsnp7ewkEAkqpk6F8INbr9XzpS1+iubmZLVu24HQ6kSRpsJs1Ivl8PjZs2MAPf/hDwuHwp94uVCoVTz75JEuXLhWfjzAsicBOEIaQtrY2Vq5cSXV1Nbm5uSxcuJDIyEgxC/ZjeDwepdTJUK7rJw8Lzpw5E6vVSmtrKwcOHCAYDA5200akXbt2sWvXLk6ePEk4HP7UAZokSTQ2NnLgwAHWrl0rag4Kw444WgjCEOHxeKisrOS1115Dr9czbdo0rrnmmiEdrAwFbrebUCiE1Wod7KZ8Io1Gw8SJE0lKSsLr9bJlyxb8fr/oFboISkpKOHHiBOFw+Lzrxp6LvJbssWPHePPNN3E6nSL4FoYVkWMnCEPE5s2bWblyJWvXruXll19WVisQPp7T6SQUChEVFTUsAmCLxcKMGTPweDz861//4sEHH8RsNg+Ltg8nV199NWlpaUyaNIl169ZRUVFBT08PkiSh1WoJhULn7Y0LhUJUV1fT1tbGG2+8wTXXXENaWtolfgWC8NmIwE4QBlkoFKKmpobXX3+d8vJyvvKVrzBt2jRiYmLEwf5T6OvrIxgMYrfbB7spn0j+PKdPn47L5WLFihXs2rWLiRMnkpqaOsitG1mys7NJSkpi6tSp3HLLLfT09NDR0UF1dTV79uyhrKyMuro6urq6UKvVSq9p/95Tj8fDU089hc1mw2g0EhcX96n+tyRJhMNhpcaix+Ohr6+Pnp4eXC4XLpcLp9OJ1+vF7/fj8/lwuVz4/X5lJRWPx4PH4yEQCAAf9UyfOaQcCoUIBAIEAgHlenmZNb1ef9ZEIpVKpZxEaLVaDAYDRqMRvV6PXq/HZDJhMpmUx5pMJmw2GyaTCYvFgt1ux2azYTabsVgsREREoNVq0Wg0n+uzEgaWCOwEYRCFw2G8Xi9r166lpqYGq9XKtddeqyxtJHwyj8dDKBRScuyGg8TERLKysoiOjmbfvn0kJiaKwG6Amc1mzGYz0dHRpKWl4ff7cTqdZGdnEx8fz5gxY2hoaKCpqYm2tja6urro6uqitbX1tJ68EydO8OGHH2K1WlmwYAEejwefz4fP58Pj8eB2u5XLcukdv9+P3+9XZmzLt/X19REIBAgGgwSDQaXXMBwOEwwGlb/l64PBoBKwyQFef/Is+nOV+JGXIzzX9XJ6h1qtRqPRoFarlTV35ctyTqhOp0Oj0aDVapUAz2g0YjabsVqtpwWFZrMZg8GAXq/HbDYrQaLRaMRisWAwGNBoNMPmezpcicBOEAaR3++nvb2dP//5z9jtdmbOnMkNN9wgzoAvgMvlIhQKYbPZBrspn1pkZCTp6elMnDiRrVu3UlBQwKRJk8TnfhHp9Xqio6OJjo6msLAQAK/XS21tLTt27ODw4cOUlZWxe/duZYk6tVqN1+vl/fffp6enh1GjRtHS0kJHRwddXV20tLTQ3NysBIXNzc20t7fjdDpxOp1KANM/kLJYLFitViIjI5Vgx2g0Kr2Cer0eg8GA1WpVAiO1Wo3RaFSCLJkcmFksltNeayAQwOfznZUbKPciyveRewV9Ph9erxeXy3VaoOpyueju7lZ6GPvng8pBpcFgwGw2ExsbS0JCAlFRUURGRpKQkEBsbCxRUVHExMSQnJxMdHQ0JpNJCe7kAFIOLPvnE4vg77MTgZ0gDKJ9+/bx3HPP0dTUxDe/+U0WLlwoDu4XSC534nA4htXBICkpifvuu4+vfOUr7N+/nwkTJpCXlzesXsNwZzAYyMnJITMzk87OTlpaWjh+/Lgym/bkyZNUV1fT2NjIq6++yltvvXVaL1l0dDTx8fFERUXhcDi44ooriI2NJSIigoiICGJiYrDb7VitVux2Ow6HA61We94A5nx/n+vyZ3XmRJ3+l8/1d//fvb29dHd309fXR29vL52dnfT09NDb20t7eztNTU10dnbS0NDA+vXraW9vx+fzKb2EBoMBu91OSkoKmZmZJCUlER8fT0ZGBunp6SQkJBAXFzcklwUcTkRgJwiD5ODBg2zZsoXt27fzwAMPMHHiRKKjo8WB/QK5XC6CweCwmBXbn81mY8KECaSnp1NXV8eWLVvIy8sb7GaNWPJs146ODmpqamhpaaG9vZ2Ghgbq6uro6emhr6+Pvr4+/H4/arWaqKgo0tLSMJvNqNVq/H4/U6ZMIS4ujoiICGVYsv9wpMFgQKfTKdfpdDq0Wq1y3XCd5S5JEjqdDpvNRjAYVHIB5Rw/eWhaHob2eDx4vV48Hg9Op5P29nal90/uATxy5AglJSV4vV6MRqOS05eSkkJiYiIxMTEkJCSQnp5OXFycUqtS+HgisBOES0ySJHw+H7t27aKsrAz4aAZfamoqRqNxkFs3/AzHHDv4aGgwLi6O0aNH09XVRUlJCXffffcFleYQziZPXOjr68Pj8eByuZSgrbe3l5aWFqqrq5Xepo6ODjo6OgiHw2g0GkwmEwkJCURGRhIdHU1ycjI2mw2NRoPf72f69OkkJCQM6VVOLgaVSoVOp7ug1xwOh/H5fDidTlpbW2lvb6erq4u2tjYaGhqUIe3W1lbcbjdOp5OmpiZaW1txOBzY7XYluE5ISFCus9vtWCwWzGYzdrsdrVYrvjP9iMBOEC4hSZIIBoM0Nzfz0ksv4fV6ueGGG5gxY8ZldZAYCPIQUV9fH8CwmBV7JpVKxbXXXsuyZcvYsWMHnZ2dxMTEiIPUp3Tm0KEc1Hm9XsrKyjh16hTl5eXs2bOH8vJyOjs78fl86HQ6UlJSSE5OJj8/n0WLFpGWlkZqaio5OTlYLBaREjEA1Gq1MtP2fDOKJUkiEAhQW1tLY2MjNTU1HDlyhIqKCg4dOkRVVRW9vb3KWtCZmZlMnjyZvLw8cnNzmTRpEg6HQ9l/ijw9EdgJwiXX3t7OY489RldXF1deeSXf/e53h+zC9UNdIBDA7XZjMpmGTR27/lQqFddddx2lpaVUVlaybNkyvvrVrxITEzPYTRsW5Lyv48ePs337dioqKqisrOTo0aN4PB6MRiORkZGMGzeOe+65h5SUFLKyssjLy8NoNKLVapUfeTaomLV56el0OjIyMkhLS2Py5MnceOONhEIhpZxLbW0tNTU11NfXc+LECQ4fPsyGDRuUUjXyY3Nzc5k+fTrjx48nLi4Ok8k02C9tUIijiSBcQpWVlezdu5cDBw5www03MG/ePBwOx2A3a1iSh9uCwSBarXZYzYrtz2q1kpeXR05ODuvWrWPJkiU4HA7Ra3cOPp+Prq4u6uvrOX78OLW1tbS0tNDQ0IDb7Uar1RIREcGCBQuUWZjR0dEkJibicDiIiIggMjISh8MhArghQv4MznVyK/fIysPjo0ePZsKECcyZM4euri46Ozupqamht7cXn8/HkSNHKCsrIy4ujri4OFJTU8nPzyc1NVWZoXs5EIGdIFwCkiTh9/spLy9n8+bNBAIB5syZw8SJE8UQ7GckSRJ9fX2EQiF0Ot2wy7EDlEKxOTk5jB07lqVLl9LS0kJcXNywmwwy0OShVTkpv7e3l46ODhoaGigvL6e0tJSqqiq6uroIh8NkZGSQkpJCRkYGY8eOZcyYMcTExBARETHYL0X4jOTvszzLuD+5LmFZWRnHjh2jpqaGyspKysrKKC8vx2g0kpKSQk1NDfn5+aSnp5OWlqYUWDaZTMoM5ZFGBHaCcInU1NSwevVqli9fzte//nWuuOIKkpKSBrtZw1Y4HKarq4tgMIherx/WZ+MTJ05EpVLx1FNP8eGHH6LX65k4ceJgN+uSO7MUh8fj4fjx45SUlPDuu++yZ88euru7UavVTJ06lSuuuIKxY8cyffp0cnJyRErDZUSuSzh79mxmz54NfLRP6OzspKSkhCNHjrB7926WLl1KT08ParWa3Nxcrr/+eqZOnaqs2dx/mxspQZ5KEqtPC8JFJVeAf+CBB6iursZut/Pcc88RFRUleus+B6/Xy65du3jkkUcYN24czzzzzLBdhk1em/TRRx8lFApx00038fDDDw92sy4puYfu5MmTHD9+nC1btrBjxw46OjoIBoPKMFxubi6jR48mNjYWo9GIwWBQCvkOx89eGDiSJBEKhU5bGcTpdFJdXc2pU6fYu3cv+/fvx+PxoNfrKSoqYubMmYwdO5aioiKsVuuI2IbE6Y0gXGS9vb3s3r2bsrIyMjMzmT9//rANQIYSOccuHA6j0+kwm82D3aTPTKPRYLfbmTNnDi+//DKVlZU0NzcTHx8/4reTQCCA0+mktraW0tJSTpw4QV1dHW1tbdjtdjIyMkhMTKSgoIC8vDwSExNJSkrCaDSO+PdGuDByaoNWq8VisSizpB0Oh1IbLzc3l8bGRlpbW+np6WHTpk0cPnyYXbt2UVhYSHZ2tlKncLgSgZ0gXESBQIDm5mZef/11+vr6GDduHDfeeOOwLVI6lEiShNPpBFAWLB/O5LVIly5dSk1NDRUVFcTHxw92sy4KeU1UucZcbW0t27dv56233qKtrQ1Jkhg1ahTXXHMNEyZMoLCwcFjOehYGl7zerTyZoqCggMWLF1NfX095eTkrV65kx44d7Nmzh1AoxNy5c5k/fz6FhYVkZmZisVjOWsZtOBCBnSBcRBUVFWzcuJHnn3+e3/zmN8yfP5/ExERxgBoAcj6NvDD5cH9PjUYjkyZNoqioiPb2dl588UVmzJgx7A4qn4bb7aapqYm//vWvrF27ltraWlQqFTfffDNXX301kydPJiMjAxg5eU/C4JPXpU1LSyMtLY2rrrqKvr4+jh8/zrp163j++ed57733MJlMTJs2jYceeojCwkJSUlIGu+kXRAR2gnARyDM2ly9fzoYNG5g1axZXX3016enp4kA1QMLhMD09PcpSRMP9fZXbf+2117JmzRq2b99OfX098fHxI2JFknA4jNPpZNWqVezdu5c9e/bgdrsZN24ct956K1OmTCEjIwOHw4HNZhPlXoSL4sz1eC0WC6NGjSI+Pp758+dz+PBhysvLOXDgAD/84Q/Jzc2lqKiIW2+9ldTUVAwGwyC2/tMRgZ0gXAThcJg9e/Zw6NAhenp6uPXWW0lJSbnsS1gMJDnHzmAwjIjAR1ZUVERZWRnbt2/n4MGDTJ8+fdi+PnlChLyE1KFDh/jwww9pbGxEkiSmTp3KpEmTGD16NOPGjcNsNg/7AF0YXjQaDTabDavVSkpKCjExMaSmpmK329m5cyft7e3s2rULjUbDhAkTlLI6QznHUwR2gjDA5PURly5dyqlTp0hOTub+++8XQd0Ak3Ps5B67kaKwsJCCggIiIyN5//33yc3NJTo6esgeRM5HTlx3uVzs37+fdevW8eKLL2IymSguLubee+/l9ttvx2AwiN45YdDJec85OTnk5ORwzTXXsHPnTlasWMGGDRv4n//5H+bNm8fVV1/NddddR1pamrJayVAjAjtBGGB1dXWsX7+eFStW8MADD7BkyZJhXWNtqAqFQnR2dmI2m7FYLIPdnAGjVqspLCzk5ptv5qmnnuK6664jJSVl2K2s4fF4OHHiBP/5n/9JRUUFgUCAhx56iNtuu420tDTMZrOoOycMaXK9u29+85usX7+ev/3tbzz99NP86U9/4kc/+hFXXXUViYmJg93Ms4hvlSAMoK6uLg4dOsQLL7zA7NmzmT59OqNGjRp2vS3DgbxOqN1uH1E9diqVivT0dObOncszzzxDSUkJDoeDmTNnDnbTPpE89Lp//35KSkp477336Onp4corr2TKlClMmTKF9PR0zGbziJwUIowc8oxatVqNw+Fgzpw5REdHs3//fnbv3s1f/vIXDh06xNSpU7n66qux2+1DZj8vAjtBGAByne+KigoOHDhARUUFjz/+OKNHjxZrwV4kco5dTEzMsEhovhCRkZHk5uaSmprK8ePHSUpK4oorrkCtVg+Zg8eZ5OKw5eXlbN26lT179lBZWcnMmTNZsGABs2fPJi4ubsi2XxDORaVSodfrSU1NJT4+nri4OAwGA6+++ip79uyhr68Pu93OFVdcMWR6oQe/BYIwQgQCAV566SV2795NSkoKd911lxiCvYjkWbEmk2lE9djBRwui2+12brjhBlasWIHRaOSuu+4asq9TDuqcTie/+MUv2Lt3LxqNhocffpi7776biIiIIRHQySdg4XAY+Hf5i4F4LtlnCb77LwAl93qei/y8Z/4eyc73Pn+ez+6zkpf6KywsZNq0aTz55JOsW7eOTZs28eKLL5Kbm0tkZOSgfy5DL+tPEIYhj8fDH//4R7Zu3UpUVBRPPfUUERERQzKxdqQ4s47dSGMymbjnnnuIiori1KlTvPvuuwSDwcFu1jm5XC42b97MV77yFbZu3cqSJUv4y1/+wv3334/dbh/s5incbjfHjx9n1KhRzJgxg+985zuf+bl6e3tZvnw5kyZNIjU1ldTUVLKzs9m9e/dner5wOMz69ev59a9/zeLFixk7dqySyD9x4kRuv/12nnjiCV599VXq6urw+/2fue3DSTgc5oMPPuCLX/yi8j5nZWXx5z//edDapNPpmDJlCn/84x/56U9/SnR0NHfeeSfPPvssZWVlg9YumeixE4TPSV4O6b333iM9PZ0pU6YwatQoNBrNoJ+5jWRyrcCRVu5EplariY+PZ8yYMRw/fpy1a9dy4403otVqh8x2JUkSwWCQFStWsG3bNurq6rj77rtZsGABBQUFQy7g7uzsZNeuXTQ0NNDW1obNZqOlpYWYmJgLzvkzGAyMGjWKhx56iO3bt7N9+3YaGxs/U8Dl8/l46623WLduHV1dXSQlJTF58mT0ej1qtZpAIEBDQwPl5eWUlJSwe/duHnvsMaWI80imUqnIy8vjtttuIycnh3fffZfGxkb6+voGtU16vZ74+HimTJnCAw88wLPPPsuHH35Id3c3jzzyCHFxcYM2LCsCO0H4HMLhMK2trRw4cIDDhw/z8MMPM2vWLGJiYga7aSOaPPTncrkwGAwjLscOPjp4mM1mxo0bpwQk3d3daLVa9Hr9YDcP+Gj7b2hoYNWqVRw9epTY2FjuuOMOUlNTh9wsXkmS6OjoYNeuXWi1Wvr6+mhoaODUqVNERERccGBnNBoZNWoUo0aNwuFwUF1dTWNj4wW3KxgM0tvby+uvv87hw4dJTk5m8eLFXHXVVUqvf2dnJ6tXr2b16tXs3r2bPXv2cNddd10WgZ1arSYvL4+8vDwKCwvZv38/LS0tg90s4KOh2czMTOLj4zl69ChbtmyhtbWV6dOnM2fOHMxm86CM2ohxIkH4HHw+Hxs3buRnP/sZkyZN4qabbmL27NmD3awRLxAI4PF48Pl8WCyWIdczNJCuvfZapk2bRk1NDVu2bKG5uXmwm6RwuVz8/Oc/Z/fu3aSlpfG3v/2NUaNGDbmgDj4K7BobG3n//ffJysoiIiKCrq4u3nnnHdxu96C1q6Ojg4MHD7Jy5Ury8/P50pe+xD333KOUuLFYLKSmpvLAAw9w//33c8MNNwyZHlvhowLHVquVX/3qV9xyyy0YDAYeeeQRqqqq8Hq9g9ImEdgJwufw4osvsmbNGgKBAE888QQ5OTmD3aTLgtfrpaenBwCbzYbZbB7kFl08CQkJjB49mqlTp/Laa69x6tSpwW4SgJL39+6777J48WIeeughUlJShmzQcfToUSoqKjAYDPzgBz+guLiY7u5uVqxYQV9f31nJ+ZdKT08PVVVVSJKkzLqUi+We+ZOdnS1OHIcotVrNLbfcwte+9jUkSeJ3v/sdW7ZsGZy2DMp/FYRhLhAIUFdXx5YtW+jt7WXmzJmMGjVKLIl0ifj9ftxuN5IkYTabR2SOnUyn05GUlMTMmTMpLy+nqqqK9vb2QW1TMBjk5MmTbNiwgbS0NCZPnkxBQQF6vX7Ibv/l5eU0NTVRUFBAcXExGRkZmM1mGhsbqaqqoqura1Da1X92Z1dXF93d3ee9b2xsLBMmTOChhx4iPj7+ErVQ+CRy4J2QkEBhYSFXX301hw8f5vDhw8ryeZeSyLEThAskSRIej4eSkhL27t3LmDFjuOWWW4iKihKzYC8Rv9+vJE+P9MAOID4+ngULFvDHP/6RiooKqqurBzWPs6+vj2PHjrF582a+/OUvU1xcTEpKyqC15+PI5UMOHz5Ma2srs2bNIj09nYyMDBISEjh69CiHDx8mOjqa6Ojocz5HKBQiGAzi8XgIBAJIkqQUsDWbzYRCoc/cPqPRqOw7ampqOHnyJN3d3ZhMJrRa7WnlUxwOBw6Hg6KiotNen8fjwePxKLOm1Wo1JpMJnU6H2+0mGAwqwYVOp8NoNKLT6c5K7pffK7/fr/z0Lw3T/7FyTmIgEDjt+yj/f4fDgdvtJhAIEAwGlfdLp9NhMpnOObksHA4rubPBYFD531qtFqPROGRnhcuMRiPp6encfffdrFmzhrKyMo4dO3bJV6cQgZ0gXCCfz0dlZSXf/va3ycnJ4Qtf+AKLFi0asj0VI5HH41F6Nux2+4jOsQOIjo5m2rRpFBcXc+jQIXQ6HZMmTRq09mzatImSkhJCoRBf//rXh/RkIUmS6O7uZvfu3Wi1Wm6++WalXEV7eztHjx5lxYoVxMTEMHbs2HM+R1NTEwcOHOC5555jx44d+Hw+ZVbsY489Rm1t7WduX3x8PJMnTyYuLo7y8nKam5s5duwYjzzyCIWFhSQkJHzsvsXv9/OPf/yDpUuXcuTIEeCj4fsHHniAiRMn8vvf/57Dhw/jdrvRarUUFRVx7733MnnyZPLz8896vq6uLjZs2MDatWvZtGkTbW1tqFQqbDYbU6dO5fbbb2fChAlkZWUBUFZWxsaNG3n88ceV50hKSmLLli387W9/Y+fOnRw5cgSNRkNeXh7Tpk3j4YcfJj09/azAsru7m8rKSn71q1+xf/9+Ojs7gY/WT7711luH9HYms1qtTJ8+nRkzZtDa2srrr7/O/PnzL2kbRGAnCBdox44drF69GrfbzZ133sn06dNFT90l5vP56OvrQ6vVYjKZRuSs2P5UKhVarZYbb7yR9957jz179tDY2DhoJRX279+P2+1m4sSJREVFDYlq++cTCATYtGkTVquVlJQUEhMTUavVjBkzho6ODjQaDQcOHGDWrFn09PSctjRUOBymubmZP/zhD+zYsYPOzk6+8Y1vkJ6eTmRkJB6Ph61bt3L8+PHPPFNTq9XicDj40Y9+xAsvvMCJEyfYsWMHdXV1xMXFER8fz+jRoykqKiInJ4e0tLTThrx1Oh033XQTU6dOZe/evTz99NN0dnayfv162trauPvuu4mMjMTlctHY2MiyZcv4wx/+wOTJk3nooYcYM2YMGo2GQCBAT08PP/zhDzlx4gSBQIBHHnmE9PR04KNSMW+88QZ/+MMfGDt2LI8++ih5eXlkZ2cTFRXFuHHjePrppzl06BDd3d38+c9/ZtSoUUydOhWDwUBFRQXLly9n7dq1dHd389vf/paIiAjlfWhpaeGDDz5g2bJlNDU1cdNNNzFq1CiSk5NxuVyUlZWxY8cOmpqaPlcP6cUmfy5z5sxh9erVlJaW4vP5lNI1l8LQ/TYKwhAjSRLt7e0cOHCAkpISioqKKCoqIjk5ebCbdtmRc+zkYaHLIbBWq9VMmTKFLVu2UFtbS2lpKfPmzRuUoKqmpoZAIEBubu4lPWB9FoFAgN27dxMdHU12drayekd0dDRpaWkkJyfT3NxMXV0d1dXVjBs3TnlsKBRi79697Nq1i+rqaoqLi5k/fz4ZGRlYrVY6Ozuprq7m5MmTymSeC6VWqzEajcydO5fOzk4OHjxIZWUlTU1NNDQ0oNfrqaqqora2VilYXFxcTHx8PAaDAbVaTVpaGmlpaUiShNVqpbm5GY1Gg8PhYPLkySQlJeHz+WhoaODYsWOsWrWKXbt2MXr0aPLz89FoNPT19VFSUsKWLVswGo0UFBQwd+5csrKylH1fVVUV77zzDrt27aKwsJDMzExsNhs2m420tDTefPNNTp06RXd3t7J9yIFfamoq+/bto7a2lg8//JC+vj6sVqsypHv48GF27drF/v37mT59OjNnzmT8+PEkJibS3t6O2+2moaGBzs7OQZvociHy8/P58MMPaW9vp6uri5iYmEv2PRm630ZBGELk3JPS0lJ27tzJqVOneOihh0hPTx+yyzyNZD6fD7fbjd1uH1IFey8mtVrN5MmTlZnXb7/9Ni6X65InZgPU1dURCoXIyckZ0u+9JEn4fD42bdpEWloa48ePV24zmUzExcUxZcoUtFotlZWV7Ny587TH+/1+XnnlFU6cOEFMTAz33nsvEyZMIDExUQlm7r77biZOnEhvb+9nbqdWq2XUqFF873vf41e/+hXf+MY3mDNnDomJiXg8Hvbs2cM//vEPnnjiCb7zne+wadMmenp6zhvg+P1+pk6dyh133EF+fj42m42YmBjGjBnDN7/5TZKSkjh58iT/+te/lJzBtrY2XnzxRerr6xkzZgx33HEHxcXFREZGEhUVRXZ2Nt/85jfJzMyktraWpUuX4vP5zrn9GY1GrrzySgoLC0lOTsZsNlNYWKjkYsrBn8/nUx6zYsUKdu3ahSRJ3HvvvcycOZOsrCxMJhOpqaksXryYxYsX09PTMyjb/IXKzs7G4XDg9Xqpqak57bVebCKwE4RPIRQK0d3dzRNPPEFHRwc333wz11133ZBaLuly4vV6cTqdOByOIT0MONA0Gg1XXXUVc+fO5bXXXqO6unpQKvDLB9fzTTYYKjo7Ozl27BiVlZWMGzfurLzE6OhovvSlL2GxWDh8+DDvvffeabcHg0E2b95Me3s7UVFRzJ07F51Op9yuUqmIiYkhNTVVGbL8PLRaLenp6XzpS19SVjIoLS3lrbfe4s477yQjI4OqqioeeeQR1q9fT0NDwzmfRw6Gziy/JAeQKSkpqNVqjh49SnNzM16vl66uLlatWoXP5yM3N5cZM2ac9liVSkVsbCxZWVnY7XYOHTpEQ0PDOWsA6nQ6iouLz8p9dTgcxMTEKIFk/8fu3r2bkydPotfrmTt3LpGRkac9NiIigqSkJLKysobFdz46Ohqz2Uw4HKajo+OSDh8P/XdHEIaApqYm3nzzTTo7O/nCF77ATTfddNn0FA1FPp8Pl8uFzWa74BUDhit5WxszZgydnZ3861//YvPmzWg0GoqLiy9pW3Q6HSqVasivV1pfX8+2bdtwuVw8/fTTvPrqq6dtL36/n9bWVpxOJ5IkUV1dTWVlJcnJyej1egKBAH19fUiShMFgwGKxnPWdl4dSB+IkTy6boVarlRmoBoOBiRMnEh0dzahRo5RF5w8dOkRSUhKpqalnPY/ZbEav15/13ZBnplqtVkwmEx6Ph56eHhwOBz6fD6fTSTgcZvny5Zw8efKcAdTevXtpbW0lFArR3NxMbGzsWQGcWq3GarWeNfSo0WiUNoVCIWUkJBgMKjNo9Xo9FovlnG3X6XRERkZ+rskql4rf7ycUCqFSqTAYDJf0WCECO0H4BE6nk6qqKj744AMSEhIYP378aeUGhEtPzrGz2WxDOr/rYoiPjyc3N5fMzEx2795NZmYmY8eOvaQnGnKA83mGHy82SZKU2az5+fm4XC5qamrOed+4uDi6urro6OigrKwMh8OBTqdTAo/+Ade5qNXqz9yL5Pf7cblcnDhxgry8vNN6quSgIDExkcTEREKhEJ2dnWzcuJGGhgZaW1vP+Zxye+W2n6u98msJhUKEw2EkSVKGdnt6eqipqTnnY+WyMBqN5rxL28lB2JnvV///CyhDqv3/d/+CzOd63uFyQt3b24vX61VmFF/KE1AR2AnCecg7nWPHjrFlyxY2b97Mv/71L6ZPn47Vah3k1l3evF4vLpeLyMjIYTEsM5C0Wi0JCQncdttt/Pa3v2X06NHMnTsXh8NxydqQlJREb2+vsmLC+QKIwRQMBqmurmbfvn08//zzFBYWnjW8Bx99z3/2s5+xcuVKjhw5wsqVK5X76vV6zGazkg/mdrvP6rWT8/icTudnamdHRwelpaXcd999vPTSS8ybN++872VCQoJSksXtdp93ySqn06nkv53ZVvl2l8uFSqUiKioKs9mMwWDAZrPhcrl48MEHefzxx89Za65/MDYQn7lKpVLeZ41Gg9/vx+PxoNFoTvtuy+tD9/b2DovJE9XV1XR2dqLT6UhPT7+k6ztfXqe6gnCBurq6+Ne//sV7773Hl7/8ZRYsWHDOoQ/h0vJ4PPT29g75UhsXS0xMDHfccQfR0dEcPnyYN99885L+/4kTJ6LT6di8efOQLRq7a9cuampqiImJYeLEiR87VHr99dczduxYPB4Py5cvp6WlhWAwiFarZc6cOcTExNDV1cXWrVsJBALK4yRJoqenh6ampvP2Bn4Subeqo6NDyQc8n9bWVo4dOwZAVlbWeWfk+3w+amtrqaioOO36UCjE8ePHaWhoQJIkRo8eTXx8vFIkeeHCheh0Ourq6jh8+PA5n/uDDz7ghz/8IbNmzaKrq2vAJjJMmTKFnJwc/H4/W7ZsOWsFDpfLRWtrK5WVlUN2m+tPnuCSl5dHTEzMJd1PicBOEM4jGAwquSZWq5Wbb775suwhGorks/rLKceuP61WS1RUFFOmTMHr9fLhhx/i9XovWU9GUVERCQkJtLa2cuDAgSE5JHvgwAFcLhcTJkxQyoKcjzz5QR6SraiooL6+Hq1Wy3XXXUd6ejrd3d289dZblJeXK+U3WltbWbFiBQcPHvxcKQFyb9TatWt59913OXjwIN3d3Xi9Xnw+H11dXezatYsNGzawfft2kpKSKCoqUooEn8lgMFBeXs769eupra3F7XYrxX9ffvllWlpaSE5O5oYbblBq4jkcDhYvXkxCQgLHjx/n7bffpqKigt7eXjweD06nkz179rBmzRqOHTtGUVGRkms5EObNm8f48eNRqVS8/fbbHDhwgIaGBnw+H62trWzevJn169efthLHUBQMBunp6aGkpAS1Ws3EiRPP2fN5MYkjlCCcg9/vp6uri3feeQe3201BQQFXXnnlJe1OF85PzrE7V4L25UCtVqPX65k1axbvvPMOpaWltLS0kJiYeEmKNY8ZM4b09HTC4TBbtmwhNjb2tJpkgyUUCuHz+aivr2fv3r0EAgHy8/Opq6sjPj7+rNJEcuK+XIMuIiKCpqYmSkpKMJvNSJLE/Pnz2bVrF9u2bWPbtm1MnjyZvLw8oqKi6O7uZtu2bdTX12OxWPB4PNTX11NVVYXFYvlUtcu0Wi1ms5nU1FTKy8sJBoOYzWb6+vqUYd/u7m527NjBnj17qK2tZfLkyRQVFZ139MBoNNLe3k5ZWRkZGRkkJSXhcrmorq7m/fffR6vVMn78eL7whS8oJ6qRkZEsWLCAN998k7q6OrZu3arUzTOZTAQCAbZv387Ro0ex2+1cf/31yjJfbreblpYWenp68Pv9BAIBTp48qWwXFouFyspKmpublSHr+vp6oqKigI9yHKdPn05DQwNHjhxh27ZtZGVl4Xa7ycjIoLOzk927d3P48GGsViter5f29nZOnjyJzWZTciKHAnllovLyciZPnsyUKVMufSMkQRDOcvLkSekvf/mLZDQapSeeeEI6cOCAFA6HpXA4PNhNEyRJ+t3vfifl5eVJTz/9tNTY2DjYzRkU4XBY6unpkf7rv/5LysjIkH7+859fsvciHA5LGzdulO69914pISFBevPNN6X29vZL8r8/TldXl7Rp0yYJUH60Wq00ZcoUadeuXWfd3+v1SpWVlVJKSsppjwEkk8kk5ebmSl6vV6qvr5dWrFgh3XDDDVJ0dLRksVikqKgoafLkydLzzz8v/eQnP5FmzpypPDYrK0t6+OGHJY/H84ltDofDktfrlbZt2yb9+te/lu655x5pypQpUkpKimS1WiW9Xi/ZbDZp/Pjx0p133in94Q9/kPr6+qRgMHjW/mjHjh3SuHHjpJSUFOlnP/uZtGHDBumaa66R4uLiJIvFItntdmn27NnSCy+8IB07duy0fZr8d2dnp/Tmm29KDz74oJSVlSWZzWbJYrFICQkJ0lVXXSX99a9/PW1/eOrUKem555476/0DpLvvvlt68803pZ6eHslkMp11+5gxY6SHH35Yea6Ojg5p37590k033SSlp6dLVqtVMpvN0oQJE6Sf//zn0t///ndp/vz5ksFgkADJZrNJ9913n1RdXT0wG9AAKC8vl2666SYpLy9PeuqppySv13vJjxuix04QziAPe/z5z39myZIlzJs3j/z8/CHd/X+5kP4vn+dynjzRn8ViYcKECdTU1PDiiy9y9dVXEx0dfdF7llUqFUVFRVitVnbu3Mlzzz1HVVUV3/jGNwZ0eO5C2Ww2Jk2axIEDB05rq8lkOmc+mk6nIykpidWrV5+WOyc/Tq/Xo9PpiIuLY9asWYwZM0YpCi3PWI2JicHv9/PlL38Zl8sFgF6vx2azferPQafTUVRURG5uLl6vF6/XSyAQUEqCyG0xmUyYzWZMJtN5Z47KoqOjmTx5Mn/605/weDzKML3JZFJqrPV/vDyL1mazsWDBAqZMmcI3v/lNJZ9NrVZjMpmIiIg4bQKJPKTb/z2XRUVFKfffuXPnWfl4RqPxtOey2+2MHj2a3/72t3g8HuV/GwwG5bs+b9485TNQq9VERkaSkJDwqd7ni23Hjh1s3LiRzZs388QTTzBv3rxB6Um8fPeIgnAGeaezd+9eSkpKcDqdLFy4UKl+LgwNfr9fqRF1uQ7Fwr9rkuXm5jJ16lRWrFjB8ePHiYmJISMj46L/f5vNRlZWFtdddx07duxg9+7dbNq0iblz5562lumlJNdo+7TliNRqNQaDgYKCgk+8X0RExGlrm57pswYXcoBmsVjOqgf3eeh0OmW5rwtpi1arJTIy8pwziM/FYDBgMBg+sVh1/1U/zker1aLVasnMzDzvfYZiUexwOMzJkyfZvHkze/fuZfTo0UydOlUpBH2picBOEPrxeDy8//77lJaWkpKSIlaXGII8Hg8+n49wOHzZTp7oLzs7m1AohFarZe/evURHRyurIFzM4EoOAB566CFOnDjB8ePHWbp0KWPGjCEmJgaj0Sh6uYURLxgM4vF42LRpE6tXr6a9vZ2HH36YgoKCQTt2XJ6nuoJwDi6XixdffJF169Zhs9l48sknsdvtl22P0FDV09ODx+NBrVZfdkuKnYs8zHj77bezadMmNm3adNaw4sWiVqvJysriRz/6Ebfeeitr167l3nvvZd26dedcakoQRppTp07x+9//nu985ztERkby6KOP8h//8R8X1FM60C7vPaIg/B+5Kv2zzz5Lfn4+c+bMYfTo0UN+av3lyOl04vV6leGxyz2wU6lUWK1Wbr31VrZt20ZFRQVbtmxhwYIFF33blZ8/NzcXnU5HVFQUf/nLX3j22WcpLS3l3nvvJSEh4bL/jC6mQCDA+++/z5o1aygtLaWqqopAIMBTTz3F2rVrWbRoEYsXL/7UQ6vCJ5P+ryj1+++/z9atW1m/fv3/Z++8w+Mqrzz8m96rpNFo1Ea9V1uSu2zLNjY2xkDAoTqBbMiSZAPZZDcJ2UASQno2MSSEULJgCC0UG1fcu2RZ1ZLVu0Zteu9z9w+4N5ZtjPvMSPd9Hj0qM6M5c8v5zncqHnjgAaxZswbl5eVhjyLQdxvNrIbMq9PpdDh16hSGhoZw6623Yu7cuZfMp6EJHy6XCz6f73PnUc5GuFwu8vLykJiYCIfDgWPHjmHp0qVfmGB/vZBKpcjIyACfz6fuoxMnTkCtVmPhwoVISEigWlvQG6XrDzneKz4+HvHx8dTfeTzeTe+hNtMh5+s2NjZi37596OvrQ0xMDG655RbMmTMHSUlJ4RYRDOL8MhUamlkE8Vlj0HfffRcvv/wy9Ho9XnrpJaqzPk1kQRAEDhw4gL///e84evQozp49Cz6fH/YdcqTwy1/+EsePH8fo6CiOHTsGoVB4Uw3fUCgEnU6Hv/zlL9i1axdVKbtq1SpUVlZSFbO0oUETTZBmUjAYxOjoKFpaWvDf//3fsNvtKC0txTe/+U2sXLkyYtYM2mNHM+vZunUrtm3bhu7ubrz++uvIzc2lQ0cRjMVigd/vB5/Pp9o+0HzKPffcA5/Ph+PHj+Pjjz9GdXU1NBrNTXt/BoOBxMREPPnkk3jggQfw5ptv4tVXX8VHH32E0tJS/OAHP6CrzGmijlAoBJvNhldeeQX79+9HfX09Kioq8Nhjj6GsrAzx8fERtWZEjiQ0NDeZQCAAu92OrVu3wmKxYOnSpSgsLLxgyDdNZEH2sBKLxQDo0N65xMfHIycnBwUFBdi6dSuys7OhVqtvmteO9MYJhUKkpKTg7rvvhkKhQGtrKwYGBvA///M/WLhwIYqLi1FeXg6lUkmfP5qIhCAI+P1+dHd3o62tDfv370dPTw9EIhE2btyI9evXo6ioCDExMRHjqSOhDTuaWYvH40FbWxtaWlqQlpaG5cuXIy4ujl5oIhyy2vJ69vyaKYjFYqSlpaGiogLbtm3D0NAQMjIybnriPJPJhEQiQWlpKWQyGRQKBfx+PxobG+F2uzE1NQWfz4f8/HwolUoIBAJ6XB9NRBAKheD1emG1WjE8PIzTp0/j9OnTOHHiBOLj41FQUICVK1eipqYmYvN7acOOZlZCEAQmJibwi1/8AqFQCPPnz8d9990XbrFoLgNy4LxEIqGN8IuQm5uL+++/Hy+++CKOHz8OmUyG5cuXA7j53k0Gg4H09HSkpaVh48aN2LlzJ/76179iy5YteOGFF/DAAw9gw4YNKCwsREJCwrTX0dDcLM4tNfB4PBgZGcHu3bvx5z//GUajERKJBJs2bcJXvvIVJCQkgM/nh1HaL4Y27GhmJY2NjThw4ACOHj2K3//+91i8eDHtMYgSyIHtdPuGiyORSJCZmYnbb78dp06dgt/vx5IlS8KeAyQSibBu3TosXLgQnZ2dOHLkCN5//33s2rULCoUCFRUV2LhxI/Lz8yNmRBTN7MDlcmFiYgLvvvsujhw5goGBATgcDixduhTLly9HRUUFkpKSIBaLw34fXQ6RLyENzXWEIAg4HA6cOnUKJ06cQGlpKcrKypCYmBixbnWa6ZDzOOlQ7MUhZ3quXbsWL730Enp7e9HS0oLS0tKwLUrnjs0iw65isRhKpRJdXV2YmprCwMAAXn31VaSnp0Or1SI3NxfZ2dmQSCTg8XhhkZtmZkKGW/v7+9HT04Ph4WH09PSgt7cXTCYT5eXlyMzMxNy5c5GTk4Pk5GQIhcJwi33Z0IYdzaxjeHgY9fX16OzsxP3334/MzEza+xNFuFwuEARBG3aXgMPhYNmyZfjggw8wOjqKI0eOID8/PyJ6mjGZTKhUKsTFxWHu3Llobm5GU1MTPvnkExw7dgyNjY1Qq9VYtGgR3G43NBoNFAoFRCIR1dqG3oTRXAkEQVBNhZ1OJ1wuF8xmM44dO4ba2lr09fVhaGgIWq0W1dXVqKysxNKlS6N28hDdx45mVhEIBPDwww+jo6MDcXFxePfdd296ry+aq4cgCHz5y19GKBRCQUEBnnrqqbAbKpEIqdb//Oc/Y//+/WhtbcXx48cRFxcXMT3/zl96CIJAa2srDhw4gOPHj2P//v1wuVxITk5GcXExNmzYgCVLlkCtVtPtUmiuiFAoBLvdjsbGRmzduhX19fVoa2uD0+nEnDlzMGfOHKxYsQK33noruFzuNJ0SjfqF9tjRzBqmpqZw8uRJHDp0CKtXr8Ztt91G90GLQmw2G0QiUVhnMUY65DW9atUqhEIhHDx4ELt27cKSJUuQkZERZuk+5WL3XXZ2NhISErB+/XoYjUY0NTWhv78f/f39+NWvfoXnnnsOKpUKGRkZKCsrQ2FhIVJSUuicPJppeL1emEwmdHd3o66uDp2dnRgaGsLo6CgkEgmSk5Px7//+71i6dCk0Gg1kMhmkUill1EX7mkAbdjSzAq/Xi9HRUezatQuxsbEoLCxEcXFxxHgvaC4fp9MJkUhEh2Ivg4SEBGRmZiItLQ1HjhxBamoqtFptRF73ZP87oVAIlUqF1NRUSKVSpKWlIT09HQqFAuPj4/D7/ejv74der8fZs2ehVquh1WqpkK1cLqd6i0X7Ak1zacgQq8Vigc1mg8VigU6nw9TUFPR6PUZGRqDX6+F0OsHlclFSUoKMjAykpaUhMzMT5eXlUVMQcSXMrE9DQ/M5mM1mnD17Fh988AHuu+8+VFRUIDU1Ndxi0VwBZOju/AbFNJ+PRCKhejT+85//RFVVFSoqKiLe28lgMMDhcJCXl4e8vDyEQiFYrVbU1tbi7NmzaG5uxrFjx7B3716wWCwkJiZiwYIFyM7ORkZGBoqLiyGRSMBms8FisajvTCaTNvaiFNKICwaD0768Xi96enowODiIrq4unDx5Et3d3bBarWCz2SgrK0NRUREKCgqwaNEiaLXaGd8Bgc6xo5kV/OY3v8H+/fvR39+Pffv2Qa1W05V2UQap1PPy8lBSUoJNmzZh3bp19EL9BbjdbkxMTGDRokVYtmwZbrvtNmzcuDHcYl0R5DJFEARCoRCVCN/Z2UmF244cOQKdTgeLxQI2m42cnBykp6cjJycHVVVVKCgogEqlglQqDfOnobka/H4/rFYrurq6cOrUKXR3d6OnpwfNzc1wOBzgcDiQy+VYsGABKioqkJ+fj7KyMiiVSqpo6NyvmQztsaOZ0fj9ftTW1uLIkSNwOBx47LHHInIEDM0XEwwGYbfbEQgEwOPx6AX6MuFyuVCpVFi5ciXGxsawe/du3H777eDxeFGzwJFyMhgMMJlMEAQBFouF7OxsaDQalJSU4K677oLZbIbRaMTg4CCmpqZgNptRX1+Pffv2QSwWQywWQ6FQIC0tDWq1GnFxcUhOToZWq4VEIqGLMsJMKBRCIBDAxMQEdDod9Ho9xsbGMDg4iLGxMRiNRpjNZhAEAYlEAoVCgXvuuYfKs9RoNIiNjYVcLodEIoFMJpuVIXnasKOZsQSDQbhcLuzfvx8WiwXx8fFYtmwZ+Hw+XQUbhYRCITidToRCIXA4HDrH7jJhsVjg8/lYsmQJ/vnPf6KrqwsDAwPIyMiI2pAU6XWRSqWQSqXQaDQAPvVO2u12DAwMoL+/H6OjoxgaGkJvby9sNhscDgf0ej1MJhP1WpVKBa1WC6VSCYlEQvXa4/F44PP5VJsVLpcbtccrUggGg/D7/fB4PHA6nfB4PPB6vXC73dTvLpcL4+PjmJiYgNVqhdVqhcFggNVqhd/vB5/Ph1qtRkJCAhITE5GZmYn09HSqhc5s8Mh9EbRhRzNj8Xq9mJycxMsvv4yqqiqsWLECpaWl4RaL5ioJBoOw2WwIhULg8Xh0jt0VwGQycfvtt+P06dPYv38/tm3bhn/7t3+DQqGYUYugQCCAQCCASqVCVVUVgE83BBaLBZ2dnRgeHkZfXx/OnDmDvr4+jI2NYWJigsrZVCqVSE9PR3JyMtRqNRITE5Geno6kpCTExsYiJibmglYY53oTz/1+7nNmMueGyb/ou8vlgtVqxcjICPr6+jA+Po7JyUkMDQ2hv78fU1NTMBqN1PmIjY1FRkYGCgsLkZ6ejpSUFOTl5SElJWXGFTxcT+gjQzNjOXXqFF566SWw2Wzce++9qKmpCbdINNdAIBCA0WhEKBSCQCCgm0pfIQqFAkuXLoXL5cLzzz+PdevWQSQSzfhcUwaDAblcjoqKCsyZM4cK94VCIfh8PlitVnR0dECn02FsbAwjIyMYGRlBU1MTpqamYLfbwWQyKS9xUlISlEollEol1Go14uPjoVAoqN/JPD6pVBqR1cfXG6fTCYfDAYPBgImJCRiNRphMJkxMTECv18NsNsNkMmFkZARmsxkejwehUAgsFguxsbGIjY1FUlISli9fjvj4eGg0GuTn5yMhIYHqMcpisajiF7pB9RdDG3Y0M5LOzk6cOnUKjY2NuPfee5GbmxvxlYA0l4bMsQuFQtRIKprLg/QsFRcXw2azYevWrThx4gSYTCby8vLCLd4NhfzsFzMGQqEQJBIJhEIhcnJy4HA4YLfbYbfb4XK54HQ6YTKZYDKZqL/7/X4EAgE4nU50dnaitbWVKugQCAQQCoVU2FYsFlNhXbKVC+lVFAqF4PF44HA44HA44PP54HA4YLPZYLPZVPj3XIOGrO4lYTKZYDKZ05rqkkVGgUAAwWCQei5ZeOL1egGAqij1+XwIBAIIBALw+XzweDzUZyTDpB6PBx6PBw6HAy6Xa9rv5P9wu91wuVzwer0IBoMQi8Xgcrlgs9mIiYmhGkuLRCIolUrExMRALBZDKBRCIpFAKpVCKBRCLBYjJiYGQqGQ9spdJfRRo5lRkMqrsbERZ8+ehcfjwa233gqNRkMriSgnFArB4XCAwWCAy+XOeE/TjSAlJQVFRUWIjY1FXV0d4uPjkZ2dPWvbgDCZTPD5fCpH73zINiuTk5MwGAyYnJy8IPdLr9dT+Xterxd+vx/BYBChUAhCoRB8Ph98Ph9CoZDK3yNz98437LhcLjgcDmUI8vn8aS1byOeSMBgMygg8F9JIO9ewC4VCCAaDcLvd1HNIo83n81FG3ecZdm63+6KGHSkHh8Oheso5HA5kZ2ejpKQEarUaSqUS8fHxUKlUlJEXHx8fESPuZiL0SkczoyBHx7z22muwWq1Yt24d5s+fTxt1M4BgMAiLxQI+nw+BQEBXNl8FQqEQKSkpuO+++/D6669DpVJhyZIlkMlk4RYtImEymVAoFFAoFJd8XigUgt/vx8TEBKampijDb2pqijL6LBYLTCYTlWNmNpupggGy6IP0/IUTiUQCPp8PHo8HkUgEuVwOsVgMiURCNYEmPWxKpRIymQwymQwqlQp+vx9vvvkm3nrrLUilUnzzm99EYWEhda/SRtzNgV7taGYUFosFv/nNb6DT6bBgwQI88cQTsyLPZTYQCARgNpuphYfm6oiJicEjjzyCHTt2oKWlBa+//jq+/e1vh1usqIb0Ims0GsTHxyMUClEeMvLn87+CwSDVdJeMNPh8PrS2tuKVV16BwWDA+vXrUV5eToVQzw+vklWmZHiVhMlkgsfjXVDFy2KxqGpyDodDeQ/J5woEAsp7S4avyS8Gg0HlupGPkWFi8meCIPDwww+juLgY3//+9/HTn/4Ua9aswcMPP0xvxG4itGFHM2OwWCzo6+vD/v37UVBQgMrKSiQkJIRbLJrrBBmKJXOWaK4ONpuN+Ph4VFVVYWBgAIcOHcL9998PmUxGe7avEtITdX6o9EpxOp3o7+/H5OQk5s2bh6qqKpSWllIFH6QxSEIaiIFA4AJ5OBzOBZtaBoNB3TssFouSl8zrux7tXBITEwEAd955JxoaGnD48GHExsZi/fr1s7KnXDig72KaqIdUdDqdjsqt27RpE6qqquiGozOIYDBIGXZ0P7Grh0y2J8eMnTx5EgMDA8jNzaULUsKM3W7HxMQE+vv78cQTTyAnJyfqNqdisRhpaWl45JFH0N/fj66uLrz11luoqqpCXFxcVDXGjlbommGaGYHX68XOnTuxefNmLFu2DKtXr0ZhYWG4xaK5jpwbiqUN9mvn9ttvR3V1NWQyGX7/+99Dp9OFW6RZz+HDh3H8+HEIhUKsWrUKycnJ4RbpquBwOCgtLcUvf/lL3Hbbbdi/fz9++MMforW1New5hLMB2mNHMyN46623cOLECTAYDPzXf/0X1Go1vSucYZDFE2KxmM6xuw6wWCzMmTMHd999NzZv3ozbbrsNQqEwao2JaIbMszt58iSGhoawZs0aSCSSqA2Nk7o3MzMTGzZsgEQiwXPPPQepVIqRkRHcddddYZZwZkN77GiimkAgAJPJhIMHD8LlcqGkpARFRUW0R2cGQubYkW0haK4eMjk+OTkZlZWV4HA4aGpqQnt7O2Vk0Nw8QqEQdDodBgYG4PP5sGDBgmm96aIVkUgErVaLpUuXoqCgAL29vTh48CDa2trg9/vDLd6MhTbsaKIWgiDgdrvR3d2Nw4cPQyqV4o477oBCoYjanS7N50M2KCYbu9JcO2q1GuXl5SgtLcWJEydw/PhxesENA4FAAI2NjdDpdBAIBKiurp4xOkyhUKCkpASPPvoofD4fDh48iK1bt8LhcEyr8KW5ftCGHU3UEgqF0NfXhyeeeALJyclYs2YN7rjjjnCLRXODIEeK0Tl21xeJRIInn3wSTqcTx48fx969exEKhcIt1qzC6/Viy5YtUCgUqKioQFZW1owam8Vms7Fu3Tp8//vfx/Lly/Hzn/8cb731Fnp6esIt2oxkZmwJaGYlTU1NOHjwIPr7+/HUU0+hsrKS7lk3gwkGg7DZbLTH7jrD4XCQm5uLyspK9Pf3Y8uWLVi6dCnV04zmxuJ2uzE1NYX6+nqsXbsWc+bMmXHHneyBN3fuXLBYLAwPD+ONN96gpmDk5OREfdg5kphZVw/NrIAgCLhcLjQ1NaGhoQFxcXGoqqpCcnIylTtEM7Mg51+6XC66j911hslkQqlUorKyEiqVCg0NDRgaGoLL5Qq3aLMCi8WC3t5emEwmZGRkIDs7O9wi3TDi4+NRVFSEW2+9FWazGY2NjTh27Bjsdjsdlr2O0IYdTVRBJnaPjo5i3759aGxsxEMPPYTs7Gx6LNIMhuyw73K5IBaLacPuBrB27VrMnz8fer0eO3fuxNjYGF1EcYMhCAJDQ0PYu3cvZDIZSkpKkJubG26xbigajQaPPPIIVq1ahc7OTmzevBnd3d3w+Xz09XadoA07mqjD7/fjqaeewvDwMMrKyvCNb3yDbqw6w3E6nbBarSAIAjKZjM6xuwHEx8ejoqICGzduxF/+8he0tLTQXrsbTCgUQmdnJ3bu3ImNGzciKSlpVqSTsFgsPP300/jyl78MiUSCBx98EI2NjbBareEWbUZAG3Y0UYXRaMSJEyfQ1NSE3NxcrF27FmKxmA6/znA8Hg9lZNAeuxsDk8lESkoK1q5dC4/Hg9OnT+PUqVPhFmtG093djd7eXlitVlRXVyMmJmbG6zLy80mlUixfvhz33HMPgsEg3n77bRw9ehRer5f23F0jdPEETdQQCAQwPj6OAwcOwOVyobi4GIsWLZoVO9zZjtfrpQw7oVBI97G7QcTGxlIzljs6OiCXyzF//nx6DNQNoq2tDSMjI+DxeCgpKYFEIgm3SDcFspiisLAQIpEIBw4cwLFjxyAUCpGdnY3s7GwQBEFfc1cJ7bGjiRqsVisaGxvx17/+FXfccQcWL16M9PT0cItFcxNwuVyw2+0APt3p06HYGwOXy4VSqcSjjz6KiYkJbN++HYODg3Ri+3WGzBXeu3cvJiYmsGDBAiQmJs66iSo8Hg/p6enYvHkzeDwe9uzZg1/+8pcIBALhFi2qoQ07mqhhy5Yt+PDDDyGXy/GNb3xjxicZ0/wL0rBjMpmQSqWzbgG8mXA4HHzpS1/CvHnzEAqF8Mwzz8DhcIRbrBlFIBDA8PAwGhoawOFwcOedd864FieXC4fDgUajwVNPPYWqqiocOHAAzz//PEZHR8MtWtRCh2JpIp5AIID+/n6cOnUKFosFa9asQWJiIu21mUV4vV54PB7w+Xyw2exZuwjeLORyOaqqquB0OnHs2DF0dXUhOzsbCoUi3KLNCDweD+rq6uD1eqkWILM17MhgMMDhcFBcXIyJiQmMjY1hx44dSEtLA4PBQFJSUrhFjDpow44moiEIAj6fDydOnEBnZyfi4uLw5S9/GUKhkM6tm0V4PB54PB6IRCKwWKxZuwjeDMhekPPmzYPL5cK7776L2tpaiEQiyOVy+thfIwRBwOl04sCBAxAIBEhNTUVGRka4xQo7iYmJWLhwIYLBIP7zP/8TR44cAZfLhUqlAofDoa+7K4A27GgiGr/fj6mpKfz0pz9FUVERVq5cifnz54dbLJqbjNPphNPphFKppA36m0R6ejo8Hg9uu+02PP/882Cz2cjIyACfz6cX2WsgEAjAYDDgvffew4MPPoiqqiraA/0ZWVlZUKvVaG9vx7Zt29DZ2QmVSoWysjL6vr8C6KuJJqJpa2vDG2+8AZfLhbVr12L16tX0dIlZiNvthsfjgVwupxfBmwQZBnvssccQCoVQV1eHHTt2hFusqKe3txcnT56Ey+XCokWLUFhYSOu0z2AwGBAKhXjsscdQXV0Nu92Op556CuPj4/B6veEWL2qgNSRNxGKz2dDZ2Ynjx4+jqKgI+fn5SExMDLdYNGHA6/XC6/XSPQtvMiKRCPn5+cjNzYXBYMDRo0fhdDrpKtlrYHh4GGfOnIFGo0FKSgqUSmW4RYooWCwWMjIysHTpUmRlZaGrqwuHDh3C+Pg43d/uMqENO5qIg2wFMDg4iMbGRpw+fRoPPPAAMjMzIRQKwy0eTRggPXZSqZT22N1E2Gw25HI5br/9dgQCAezfvx+jo6N0O4qrgJx33NXVhYaGBsyfPx/x8fF0Edh5MBgMMJlM3HHHHVi/fj2EQiFefPFFnDlzhvbaXSa0hqSJSAiCwO9+9zvU1taisrISX/7ylxEfHx9usWjCBJ1jFz4YDAYeeOABLFmyhBoFNTk5GW6xopLu7m60tLRgdHQUmzZtor11l0AkEmHJkiV44YUX0NnZiTfeeANvvfVWuMWKCmjDjibicDgc2L17N9ra2qBWq7Fp0yZwuVw6BDeLIatiaY9deODz+ViwYAHWrFmDY8eOoampie4zdhUcOnQIBoMBiYmJKC4upvsxXgIGgwGxWIzs7Gzcd9990Ov12LZtG1paWuDz+cItXkRDa0iaiCIQCMBisWDv3r0gCAIZGRmYP38+nVw8y6Fz7MIHGRrLzs5GVVUVvF4vTp8+jb6+PoRCITrv6TIgCAKBQAD19fXw+XzIysqCSqUCm003prgU5CSU1atXQy6XY2RkBAcPHoTdbqfTAS4BbdjRRBROpxMDAwP4+9//jgULFqC6uhrJycn0Yj7LcbvdcLvdtMcujCQlJaGiogKLFi3Ctm3bcPjwYfj9/nCLFRUEg0E4HA4cO3YMIpEINTU1AEDrtcuAxWJhzZo1WL16NRITE/HLX/4S/f39cDqd9Kbic6A1JE1EsXPnTjz//POQyWR44IEHsHjx4nCLRBMBnJtjRxt24SM2NhZPP/00AODYsWN4/fXX6cX1MjAYDPjggw/gcDhQUFCAW265JdwiRR133303vvnNb0KhUOBHP/oRtm/fjmAwSF9/F4HWkDQRAUEQ6OnpQUNDA3p7e/GlL30JycnJdBXsLIcgCPj9fni9XgSDQUgkEtqwCyMcDgdarRaLFi0Ci8XCjh07YDAY6JynL8BiseDQoUPIyspCWloa5HJ5uEWKOiQSCbKzs7Fp0yaMjY3hxIkTOHz4cLjFikhoDUkTdgiCQCgUwunTp9HT0wOfz4f169dDoVDQFZA08Hq98Pv9IAgCYrGYNuzCCIvFgkwmw9KlSxEXF4empiZ0dHTA6XSGW7SIxev1wmAw4PTp0ygqKkJqaip4PB4dhr1C2Gw24uPjceedd0IsFqO7uxv79u2Dy+Wi+yqeB60hacJOKBSCy+XCH/7wB9hsNqxZswZLliyBSCQKt2g0EYDZbIbX6wWbzYZCoaANuwjgjjvuwMqVKxETE4Nnn30WAwMD4RYpYhkcHERLSwu6urqwZs0a5ObmhlukqEUgECA7OxtPPPEE+Hw+3njjDZw6dQoulyvcokUUtIakCTujo6P485//jImJCcyfPx8PP/xwuEWiiRAIgoDVaoXX6wWLxaJHikUILBYL8+bNw7e+9S3U1tZi3759aGpqCrdYEUltbS1qa2uRm5uLwsJCxMbGhlukqGfNmjVYu3YtcnNz8eMf/xh9fX108+JzoDUkTVix2WwYGBjA3r17UVxcjKKiIiQlJYVbLJoIwuFwwO/3g8Vi0aHYCIHBYFDD2dPS0tDU1ITa2lp4vV46mf0zCIKA2+1Gd3c3RkdHUVVVBalUCg6HE27RohoGgwGZTIaysjIsW7YMQ0NDOHnyJLq7u8MtWsRAa0iasECODRsbG0NbWxvq6uqwevVqlJSUQCKR0PknNBTnGnZ08UTkIJPJkJmZiZqaGnR0dODIkSPQ6/W0YfcZBEHAaDSit7cXJpMJK1asAI/HC7dYM4aioiKsXbsWAoEA+/btQ11dHb2x+AxaQ9KEDYIgqDExhYWF2LhxI51/QjMNgiBgsVgQCATA5XJpoz/CEIlE+OEPfwitVouuri784Q9/oENinxEMBvH+++9Dp9NBrVbj9ttvpydNXEcEAgEyMjLw61//Gi0tLfjwww9x9OhR2rADbdjRhIlgMIidO3eivr4eoVAI3/3ud+lFm+aiWK1WsFgsiEQi+vqIMMiw2IYNG1BUVIStW7eira0NZrM53KKFFXLSxO7du6FUKlFVVQWBQEBfv9cRBoMBgUCAxYsXo7q6Gm63G5s3b4bVap31Uylow47mphMMBuF0OrF37154vV6kpaWhsrKSngdLcwEEQcDlcoHJZEIoFNKj5SIMBoMBDoeD8vJyFBYWwufz4fDhwxgbG0MoFAq3eGHD4/FgfHwc3d3dUKvVKCoqAovFoq/d6wyLxUJcXByWLFmCxMREdHR0oL6+HhaLJdyihRXasKO56ZBK77333kNCQgJWrFgBrVZL96yjuSh2u53y2NFEJsXFxViwYAEKCwuxZcsWtLe3z+qmxWazGc3NzZiYmEBWVhYqKirCLdKMZsOGDVi8eDFCoRD+7//+D0NDQ7O6tx09gZjmptPc3IwXXngBXC4XGzZswJo1a8ItEk2EQhAEzGYz2Gw2pFJpuMWhuQSFhYV45plnsGbNGuzYsQMAcM8994RZqvAwMDCAt956C+Xl5SguLkZKSkq4RZrRSKVS1NTUgMPh4LHHHkNubi4IgsDcuXPDLVpYoD12NDeVvr4+nD59GrW1tbjvvvuQl5dHjw2juSS0xy46EAqF0Gq1WL16NXQ6HXbt2gWDwTDrPCd2ux3Dw8Nobm7G0qVLodFo6EruGwyDwUB8fDzmz5+PiooK1NfXY8+ePfD5fLOymIK+2mhuCgRBIBgMorm5Ge3t7fB6vVixYgU0Gg3YbNpxTPP5OJ1OKseOJnLhcDiQyWRYsWIFWCwWOjo60NTUNOsW1/HxcQwPD8NgMKCiooJuSHyTEIvFSE1NxdKlS2EwGNDU1IShoaFZmetJG3Y0NwWCIOD1evGPf/wDzc3NWLZsGRYuXEgrPZpLQhAEbDYb1ZyYJrJhsVjYuHEjysvL4fV68atf/WrWJLKTvTmPHz+OlpYWSKVSLFq0CCqVKtyizRp4PB4ee+wxxMfHo7e3F2+99das7G1HG3Y0NwWHw4HNmzejs7MT6enp+OEPf0h3YKe5LOgcu+iCy+Xi3nvvxVe+8hXU1tZi69at6OjoCLdYN4VgMIj9+/dDp9PhS1/6EoRCIR2GvYkwGAzExcXhrrvuQklJCV588UX09fXB4XCEW7SbCn3F0dxwnE4ndDoddu7ciczMTFRVVSE5OZku/ae5LBwOBx2KjRLIdjTJyckoLS1FeXk5PvnkEzQ1NcHpdM5oz0kgEEBHRwdGRkbA4/GwZMkSsNlsWs/dRBgMBlgsFubOnYuKigowmUzs2rULg4OD4RbtpkIbdjQ3DDI0YTQacfbsWbS0tKC8vByVlZUQi8W0wqP5QgiCoAw7gUAQbnFoLhOFQoGMjAysWLECzc3NaG5uxsjISLjFuqH4/X40NDTAZrNBLpejrKyM9taFiaysLJSWliIjIwN79+5Fd3c33G53uMW6adBXHc0N58iRI3juueeQnZ2N9evXY/78+eEWiSaKsFqt4HA4dCg2ykhISMB3v/tdaDQa1NbW4s9//nO4RbqhuN1uvP3224iPj0d5eTmSk5Npwy5MsFgs5Obm4gc/+AHq6upw8OBBNDY2hlusmwZ91dHcUD755BPs378fQ0NDePLJJ5GSkkJ76mgui0AgAIfDAZ/PBz6fD4lEEm6RaK4AMnz++OOPIzExEXv37sWBAwdgs9nCLdpVEQqF4Pf7LxpOttvtGBoaQl1dHcrLyzFv3jxaz4URBoMBuVyOqqoq1NTUoL29Ha+++uqsqdCmDTuaqyYQCODs2bMwmUzw+/3THguFQvB4PDhw4ACmpqaQlZWF8vJyOgRLc9kEAgEqL4vH49E5dlEGg8EAk8lEeXk5cnNzIRKJsG3bNkxNTV2gL0gjvqWlBXq9PkwSXxqTyYT9+/dj9+7daG5uxuTkJJVuMjk5idbWVoRCIWRnZyMtLS3c4s56yIKrW265BQKBAJ2dnWhubobX6w23aDcc2rCjuSoIgoDf78e+ffvQ1dUFvV4Pj8dDKTq/34+pqSns3bsXfr8fK1asQHJyMng8XrhFp4kSAoEA7HY7CIIAn8+nDbsohMFgID09HXPnzkVhYSHee+899Pf3w263U8/x+Xyw2Wzo7+/Hzp07MTg4GJFelfHxcbz55pv461//ip07d6KlpQUmkwkulwu9vb04ceIEVCoVsrOzkZSUFG5xafCp13j9+vVITU2FyWTC7t27Z3wRD0CPFKO5SgiCgNPpxE9/+lMwGAwUFRXh61//Ou644w7weDyMj4/j6aefhtVqxfr16/H1r3893CLTRBl+vx8mkwkEQUAkEkEmk4VbJJqrZNmyZUhMTMSePXvw8ssvY2JiAg899BAAoLa2Fnv27MFf/vIX+P1+yOVylJaWRlw7JL/fD4PBgEOHDmH79u3g8XhITk7Gl7/8ZXR1deHUqVN48MEH6UkTEUZCQgJWr16NUCiE5557DrfffjtEIhH4fH64Rbth0IYdzVXhcDgwMjICp9OJUCiElpYW/PrXv8bx48dRVlaGUCiEvXv34v7778eSJUvo5rI0V4zf74fNZqM8dvRIseiFz+cjMTERjz32GD766CPs3bsXiYmJOHr0KE6ePImenh44HA6EQiHodDoMDAwgOzs73GJPg/QgB4NBKtVkdHQU//jHP+ByueByudDT04OWlhYQBEGHYyMAMu1nzpw58Pv9+Oc//4ldu3bB5/OhoqIizNLdOGjDjuaqcDgcGBsboxKKzWYzLBYLXC4XxsbGIBAI4HA4MHfuXGRkZNBjw2iumGAwCIfDAQaDAR6PN6N32DMdFotFDWrfsWMHent7sWPHDpw4cQJdXV3UdAoGgwG9Xo+RkZGIM+xCoRDcbjcVxiMIAi6XC/39/QA+HanW09ODffv2YXR0FHl5edBqtYiNjYVIJAKLxQqn+LOauLg4ZGVlIScnB/X19UhKSkJxcTG4XO6MzPmmV1uaq8JqtaK3t3faTUEQBPr7+9HX1wcejwetVousrCwolUoEg0EwmcwZeRPR3BhIDwmHwwGPxwOXyw23SDRXCUEQ4HK5qKioQHZ2No4fP44///nPCIVCCAaD1PPYbDbGxsbQ1dWFmpqaMEp8IcFgEC6X64L8LHIWqdfrRXNzMxoaGiCVSpGWloavfvWrqK6uRmZmJu1xDiM8Hg8JCQn40pe+hD/+8Y/QarVYsWIF4uPjwy3aDYFOBKC5KgwGA86cOUN1mic5N0zR19eH9evX4z/+4z+wffv2MEpLE434fD6YzWYoFAq66CbK8Xg8GBgYwDe/+U3s3bsXQ0ND8Pv9CAQC0wwlv9+Pjo4O1NfXh1HaixMMBr8w8Z78PDabDWfOnMF///d/48UXX8Thw4dvoqQ0FyMmJgYPP/wwEhIS0NbWhtdeey3cIt0waI8dzVVhsVjQ09NzgWImIStj9Xo9Dh8+DJ1Oh46ODtx6661ISUmBXC6/+ULTRBWkx04sFtPeuiiFIAi0tbXhxIkTOHbsGOrq6mCxWCgv18WYmprC0NAQvF4vOBxOxBQiBINBqvL/iyAIAgwGA0uWLMGiRYtQWFh4EySkuRRMJhNisRhLly5FW1sbDh06hG984xsQiUQzLlVoZn0amptCKBSCzWbDyMjItDDK+RAEAZ/Ph4GBAYyOjsJoNEKr1UIsFtOGHc0XQnpIhEJhxFVI0nwxgUAAVqsVhw8fxq5du3DkyBG43e5L6gzg0/xdg8EAk8mEuLi4iDHsQqHQZfdA4/F4iImJwerVqzF37lykpKTcYOlovghyjuyCBQug0+lQW1uLwcFBZGZmzjjDLjLuGJqogiyWGBoa+sLnkrvbQCCAlpYWNDY2YmBg4EaLSDMDIKtipVIp7bGLQmw2G44cOYKf/exn2LlzJxwOxxcadcC/Wik1NTXB4/HcBEm/GIIgLiie+DxYLBY0Gg1qamrw6KOPIisr6yZJSXM5rFy5EiUlJQiFQnjvvfdgMBhmXF872rCjuWL6+vqg0+kuGU4hYbFY4PF4SEpKwmuvvYZvfetbWLhw4U2QkibaIXPsZDIZnWMXhchkMtxyyy343e9+h/Xr14PP51+WZ4TFYsHr9eLEiRMRZdgFg8ELJmacD5vNRnZ2Nu644w78+te/piu5IxA+n485c+bg1ltvxeuvv06F/WcSM8v/SHNTGBsbg9FoBIPBuOROh8lkQiKRoKysDNXV1Zg/fz5UKhWt7GguC7LdiUajoUOxUQiLxYJAIMD8+fPB4XCQnJyMrVu3Qq/XX3JmZygUgsvlwpkzZyJmwfX7/fD5fJ/7OBnmUygUWL9+PWpqahAXF3dBcRlNeCHPR1paGhYuXIgtW7bg7NmzUKlUyM3NDbd41w3asKO5Ysh8OSaT+bmhFSaTCZFIhKysLKxevRobN25ESkoKreRoLhtyfqhIJKINuyiFwWAgKysL8fHxyM/Px8jICJqbm2E0GuF0Oi/6GjIU29HRAbfbjVAoFPY8O5/Pd0nDjslkQiAQID8/H+vXr0dZWdmMy9uaSWg0GpSVlUEkEuHMmTNISEhATk4OAMyINYoOxdJcMT09PRgdHf3cxxkMBiQSCSoqKvDaa6/h29/+Nm3U0Vwxfr8fFouFzrGbAUgkEhQVFeGdd97Bt771LcybN++S3iyfz4fe3l4YDAa43e6bLO2FeL3ezw0Lk57J5ORk/P3vf0d5eTkdlYhwuFwuVCoVbr/9dpw6dQpHjx6dUXl2tGFHc8V0d3dfsiK2pKQEDzzwAJ5//nlotVrweDzaqKO5Yvx+P6xWK6RSKZ1jF+WQRhyXy8VDDz2En/zkJ/j5z38OiURyyZYm7e3t0Ol0N1naC/F4PBcNCzMYDLDZbNxyyy349a9/DY1GM2OnGcwkGAwGhEIhNm7ciFAohM7OTtTV1V1W3ng0QPuKaS4bsn2JwWCAzWab9hiDwQCTyURVVRWWLl2KBQsWIDs7m542QXNVEASBQCBAtTuhw1rRD2ncxcfHg81mg8fjYWhoCHV1dRgZGYHFYpnmNWEwGOjv70dubm7Yx4t9Xo4d2atu6dKlKC8vp426KILD4SAnJwdqtRoejwenTp3C3LlzZ8ToN9pjR3PZhEIh2O12mM3mafkxTCYTXC4XCoUCmzZtwle+8hWsXbsWLBaLVnI0VwU5g9jlctENimcgMTExqKiowE9+8hOsWrUK6enpEAqF057DZDLR3d2NiYmJMEn5Ly6WY0cWh23atAlr1qyBRqOh9V0UwWKxkJCQgPz8fHC5XBw6dOhzG+5HG7RhR3PZeL1eNDU1TTPq2Gw2BAIBFixYgLfffhubNm1CZmZmGKWkmQk4nU44HA6EQiF6pNgMhcFgIDExEc888wxeffVVPProoxAIBJRnLxAI4OTJk+jr6wu3qPB4PBfk+iUmJuJ///d/ceuttyI9PT1MktFcKytXrkR6ejoOHz6M8fHxiGmxcy3Q8Q2ay8bn86G7uxter5cq79doNHjwwQexePFilJaWgsPh0LtWmmvG5XLB5XIBAF08MUMh9QSXy0VaWhoeeeQRKBQK7Ny5k2pObDAYMDk5CaPRCKVSGTbd4vP5puXYzZs3D8uWLcPKlSshFotpnRfFlJSUoLe3FxwOB4cOHUJNTQ1SU1PDLdY1QRt2EQKZU+T3+xEMBhEMBhEIBBAMBqmu536/H6FQCKFQiHrOua89H/K5F3Mts9nsC5QRuVMmQ6hMJhNMJhNsNhtMJhNWqxVdXV3wer3g8XhQKBSYP38+lixZgrKyMiiVyhtzcGhmHW63m9o5z8RZjjT/gqyiz83NxapVq+B2u8FkMtHa2gqHw4GJiQl0dXUhPT2dahQcDAYp/RYKhSj9R+rKiyXBX0ofkrruYgYak8lET08PFRKWyWRITU1Feno6gsEgpqamqNefqy9ZLBbYbDb1e7hbttBcnJiYGCQmJiIxMRH19fUoLS2lDTuaf3GuwiB//rzv5//t3Pw1l8sFt9sNm80Gr9dL7Rbtdjs8Hg98Ph/cbjc1d5GcqXk+Xq8Xfr9/mtFHvqdUKr0gSZTJZILD4VAd4smfJRIJeDwenE4nTpw4AYfDAblcjsLCQjz22GNQq9UIBoNwu93T8urOVZLn/+3zvtPQAP/y2JGLPu2xiw6uVgeSPxcUFEAul6OkpARPPfUU+vv70dPTg48//hgVFRXw+XzweDxwuVzTdKPT6QRBEPD7/Z9b6HAxfUjC4XDA5XIv2i+Rx+NhYmICQ0NDVF++mJgYOJ1O7Ny5k7o+SX0pFovB4/HA5/Mhl8up38/fTF+NnqS5/rDZbGg0GsyfPx8HDhzAunXrQBBEVB9z2rC7zgQCAej1elgsFpjNZoyNjcFgMMBqtcJkMsFoNMJms8HhcFCGnMPhgNvtht/vB0EQ075IGAwGlUTO5XLB4/EgFAopz5pEIrlAFtI4O39RDIVCGBsbu2BXGwgE4PV6EQgEqC/SoCSTh8l5j263GwaDAbW1tdROl8FgQC6XQyKRQCQSQSaTQS6XQyQSQSKRID4+HnFxcZDL5VAqlVCr1YiLi4NYLKb7PtFMw263w263AwCdYxdl2Gw2WCwWWK1WGAwGGAwGWCwWWCwWGI1GWCwWOJ1OOJ1OWCwW2O12uFwuOBwOKq8yEAhQzYkbGxtx5swZsNlsEAQBDocDgUBA6UIulwuBQAAWi0V5yS6mT0jv2fnXEkEQsNlsCAQCF+hEgiDgcrkovS0Wi2E0GrFr1y5s374dfr+fkvlcI+z8n9lsNoRCIWQyGUQiEaUfpVIpRCIRxGIx4uLiEBsbC6lUCoVCgfj4eCgUCojFYojF4ht0tmgAIDU1FevXr8ff//53dHR0IC8vL6rzJmnD7jLx+XxwuVyUsrJYLLDZbDCbzTAYDLDb7XA6nZSS8nq9VFPLc13ybDabGj0TFxdHKSOyyaVIJKKUFZ/PB4fDoV7P5XLBYrEoN/+5+WwX82iQ3rPzPXPkzvb8kAQZ5jg3xBEMBikPoc1mQ1dXFzIyMsDlcuH3++H1euF2u6nvZCiZVM7BYJBS6D09PdSsRRaLBZFIBD6fDz6fD5FIRBmFUqkUSqUSMTExkEqlkMlkUKlUEAgEdEhulkDeQ3w+H1wulw5jhRGCIOB2uylj22w2UxtSm80Gk8k0zThzOp3T9F8gEJims8jQJI/HQ2JiIqXTzt2I8ng8yngj9UtqauoFevTc15IbTPK9zocMh17sMbIa8mIh3EAggP7+fng8HsTGxkIsFk/TkT6fj4qckDqR9Bye61kk9eG5oWTyGAaDQbS2tlLpNWRRGo/HozbxMpkMQqEQIpEISqXyAn1JPk5vkq8cqVSK7OxsKBQKDA8Po7Ozkzbsoh3SwCFvRo/HQ4U8SVc/eQMaDAaMjY3BbDZPM+wcDgdcLhecTielcEjXvFwuh0AggFwup1zz5C5NIpFAKBRCKBRSRgyfzwePx6M8dKTCCycejwcWiwXNzc0oLi4Gn8+H1+ulPjP5ndyNn7sQkLtzo9EIg8EAl8sFj8czLSzC5XKpHaxEIqEMO7lcjpiYGCQlJVHH5vwdO2kc0nksMwe32w2fz0d5YujzeuMg+1OSBgkZ7vT5fJROJCMQZrMZU1NTMJvN1P1tMpmm6b9gMDhNBwoEAkrnKZVKSCQSCAQCSucJhULqOeRj5O98Pp+KaOTl5V2ymfGNpLe3F8FgEKmpqRdtuE4adaQDgFw7SP3ndrupSm8yzYD0UJLHzWg0wmq1Uvml526SWSwWZbgJhcILDLuEhARqI0yGfjkcDrUGkb+TIeNoDjPeCHg8HuLi4pCcnIzJyUn09PSEW6RrYtYZdp/Xo4ZMgh0bG0N3dzc6OjowPDyMkZER9Pf3Y3JyksrbYLPZlNs8OTkZ2dnZiIuLQ0xMDNRqNeLj4ynDJC4ubkY06eXz+VCr1Vi9evU1/y/SUB4fH8fU1BRMJhN17KempjA1NYXa2loMDg5SShIAxGIx5HI5EhMTUVBQAK1Wi+TkZGRlZSEvL4/ydn4e0X4OZhNOpxM+nw8KhYI+b9fIF/XlCgQCGBsbw8DAAMbGxiiPxcjICCYnJzE+Pg6r1UrlHbFYLKjVasTGxiI+Ph4pKSlQqVRUFILUhXK5HHFxcRfN5402vqiFE2nI8vl8SKXSa3ov0iCcmJiAyWSiokTj4+MwmUwwmUwYGhpCfX09ld5zrqdPIpEgNTUVGo0GGo0GmZmZyMrKgkajQWJiIpKSkr7wmpht9xwZLq+urkZdXR0aGxupYxSNx2LWGXZ2ux2Tk5NobW1Fb28vxsbGMDQ0hN7eXrhcLipEqdFooFKpkJ6ejsWLF0OlUiEmJgbx8fFITEykkmFJb9q5IdJzQwS0p+FCuFwutdvMyMiYFp44tyI4EAhQHoHR0VFMTk7CZDJBr9djcHAQp0+fhs1mg8/nA4/Hg1KphEqlohRZSkoK0tLSkJube9EqYJrIxeFwwOfzQS6X0+ftGiGr5vv6+jA8PIzR0VH09vZicHAQExMT0Ov1VI4Zl8uFRCJBcnIytFotysrKEBsbC7VajZiYGCiVSsTHx1ORBDJN5Hz9R/5M68Arh8PhUPrx3NQYMgfw3I4JZB70+Pg49Ho9zGYz9Ho9JicnYTAYMDg4iBMnTlCGOYfDgUwmQ1JSElQqFRITE5GbmwutVguNRoOUlJSoN8KvFjabjZUrV6K+vh79/f3o7e1FWlpaVKb/RJ/ElwFZeEAaAiaTCePj49QOyGg0YmJigip9ZzKZyMnJoUIBpKdNJpNBJpNNc3FLJBLI5XJ6qsI1QO76L0eBkOHe5ORkqujEZrNhamqKStAmQ7xk6EKn00Gn04HH40EikSApKQmxsbFQKpWIi4uDRqOhkpTpasvIhMzXlEgk9H12BdhsNqpQa2JigipWMBgMmJiYgM1mg9vtprwRCoUCMTExkEgkUCgUkEgkkMlkiI2NpdJGyL+JxWIIhUJIJJIZEYWIVK5EP5J50SqVispztNvtVA44uTE2Go2w2+3U3wDAarXCarWio6MDUqkUUqkUcXFxUKvVkMvlVAGHSqWizv1MhslkIjs7GzExMRgaGkJrayuSk5Npwy5ckEmvZF4Imcjf3t6OoaEhjIyMoKurC729vZSHRyKRID09HRqNBlqtFtnZ2UhOToZarUZKSgrt4YkQyORhhUJx0ce9Xi8mJyfR29tLGXS9vb3o6uqCyWSCzWajOtyr1Wqkp6cjNzeXOvdk3h6Zr0fmMtLnPryQhh3d/PXikAs6mctFJu2Pjo5Cp9NhaGgI3d3dGB4epsKpACAUCiGXy5GZmYm0tDSo1WokJCQgPT0dycnJkEqlEAgEYf50NJcLGUJUKpWf20eUIAg4nU4qnNvf30+F2UdHR9HZ2UmlPjAYDKSlpSEhIQEJCQnIyMhATk4OFbHi8XiUnuTxeDPKwcFgMJCUlISEhATodDqcPn0aq1atispiFAYxAwajkYn9J06cQH19Pc6cOYOTJ0/CYrFQiaZFRUXIz89Heno6cnJyUFVVRVWZfh4z5YKdyVzq8iUTkk+dOoX29nb09/ejvb0dZ86cQTAYhEgkQnZ2NpYtW4aSkhIUFxejsLBwWrsCmvDw9NNPo7W1FQqFAps3b4ZIJAq3SBGFz+eD0WhEfX09zp49i7Nnz+LYsWMYGxuD3+8Hh8NBeno6MjMzkZKSgqysLFRWViI9PR2xsbGXDI/S1/7M44uWeTLHubW1FWfOnMHg4CAGBwfR3t5OFTHFxMSgsrISZWVlyMvLQ3l5OZKSkmZM6JY8Rq+++ioOHjyI9vZ27N27F7GxsWGW7MqJSsPObDZjfHwcR48eRWtrK0ZGRqDT6eDxeKBSqRAXF4fU1FSUlJRQ1UJkGTj5RXoCaCU2cyFzUMiqNHLe4/j4OAYGBiivRn9/P3w+H5hMJhITE1FYWIjs7GwUFRVRY9LoPKGby/e+9z10dnYiOzsbzzzzzIwPA10Kv98Pk8mE3t5e1NbWoqenBzqdDqOjo/D5fJBKpYiNjYVWq0VmZiaVJE9WlZKV45/XKJeG5tyKXrK9jMfjgc1mQ29vL0ZHRzE8PIze3l7o9Xr4/X7w+Xykp6dTX2VlZSgsLKQq2aOVuro6fPLJJ/jNb36Dw4cPIzMz85oLYm42URGKJVtnkMpsfHwco6Oj6O7uht1uRyAQoHKnkpOTodFoKM+cUqmkmzvOUsg8lXMbkhIEAa1Wi9TUVIyNjSEtLQ09PT2YmpqC0WiE0+lEZ2cnxsfH0dHRgY6ODqqRckJCAlQq1YwKP0QqZLsHkUg06461z+ej+sPpdDpMTExQkw/GxsaoIi+yAj8+Ph5qtRoZGRnQarVU2Iw24GguF7I1yrmecbLoJjExEZOTk9DpdEhPT8fo6CiMRiPV5qa7uxtjY2Po6elBS0sLdf0lJiZGZf/RxMREJCcnw+/3Y3BwkKrsjiYi8miTOXPk7NSJiQkMDg7i4MGDOHjwIHQ6HaxWKzUGpKCgAKWlpaisrKTGYdHQXAxygkd2djays7NRXV0N4NNQxODgII4fP46TJ0+ira0NAwMDEAqFKC0tRWFhIRYtWoTKykqIRCKqJxTt9b0xkG1uZnrxBBkwObfBrdlsxsDAAFpbW3H48GG0tLTAZDIhGAyiqKgIhYWFyMnJQUlJCcrKyiAUCsPe55Jm5sFgMMDhcKDVaqHVaqm/e71eGAwGdHZ2ora2Fm1tbeju7sbHH38MPp8PrVaLvLw8LF68GBUVFYiPj4dEIqGaU0e6zkxISEBqairEYjE6OzuptlrRRESGYr1eL8bGxnDo0CG8+eabVCK8QqFATU0NysvLMWfOHJSXl1/QcRygc0RorozzZ/aSHeHHx8fx8ccf4+TJk+jq6kJ/fz/UajWqqqqwaNEi3HXXXYiPj6cX1RvA3XffDZ1Oh4cffhgPPvjgjB0pRk6BOXv2LJqamvDxxx/j+PHjcDgc4HA4qKiowLx581BUVIQ5c+ZQkxpIfUfPEqW52ZD6kuwqEQqFqJYrBw8eREtLC86cOYOGhgYqvaW8vBx33303KioqkJCQENHOF4Ig0NLSgh/84AeQy+V48MEHsXbt2nCLdUVEjGFHzuM7ePAgGhoaMDg4iPHxcahUKqSlpSE1NRX5+flQq9VQKBTUFAeAVmo0159AIACPx0O1jDAYDBgaGkJDQwN0Oh1sNhtkMhnmzp2LgoICFBUVIS8vL+J3o5EOWe15xx13YGpqCv/5n/+JDRs2zLi2NGRFYkNDA06ePImxsTHY7XbIZDJkZGQgJSUFWq0WSUlJUCqV1PxQPp9PX180EQfZlUKv18NqtcJisVAVt6OjoxgdHYXNZoNarUZycjLKysqwaNEiqNXqiAxz9vb24q9//SsOHjyIb3/72/jKV74SbpGuiLCazaSlT+bO9fT04MiRIxgYGIDX64VCoUBVVRVKSkqQnZ2NrKwsun8SzU2BzWZDLBYjMzMTmZmZ8Hq9mJqagkKhQEtLC7q6ujA8PIyGhgZMTU1Bp9PBZDIhMTGR2ngA9KbjaiBnawKYMTl2BEHA6/XCbrdjZGSEKtppa2tDb28v2Gw25HI5ysrKMG/ePGRkZCA1NRVCoXBGfH6amQ2TyYRQKERqaiqAT9d2p9OJjIwMdHd3o7W1FU1NTVT/UXIkXXp6OjUNQ6lURkz+skAgQFZWFt577z2YTCa43e6oagN00w27c8NePp8Po6Oj2LJlC44cOYLa2lrExcVh2bJlWLBgAW677Tao1eqorrChmRnweDwkJyfj3nvvxd133w2r1YqDBw/igw8+wLFjx/D6668jMzMT9913HxYuXIj58+dT120kKKpogSAI2O12+P1+sNlsyGSycIt01Zyr68iRhc3NzXj11VepqSkpKSlYv349Fi1ahJKSEmg0Gvp6oYl6mEwmJBIJqqqqUFVVhVAohImJCRw5cgR1dXXYtWsX/vnPf1JdCL7yla+guroaUqk0IlIMJBIJiouL4XQ6MTk5iYmJCWi12qi5N296KJYgCIyNjeHw4cPYt28ftm/fDqVSidLSUqxevRpr1qyBQCCgktNpDx1NpEFONiGbwk5NTaG7uxsvvvgimpubwWQykZ+fj29/+9tUyx2ayyMQCKC7uxuPPvoo2Gw2nn/+eeTm5kbl5i4YDMLhcGDnzp14//33qWrrqqoqrFq1CmVlZSguLqYavl7utAEammjj/IJIr9eLkydP4tChQ6irq0NraytycnIwb948rFixAqtXr6by58NBMBiEx+NBcXExFixYgDvvvBMbNmyIGlvkpnnsQqEQrFYrTp06hY8//hjDw8OwWq1YtWoV5s+fj6ysLGRkZFDNM6PlANLMPsg8OnIqBtkm4Bvf+Aba29sxODiIvr4+/OUvf0FhYSFKSkqwatWqGTEM/UZDeuyCwSD4fH5UVsUSBIH+/n60tLTg9OnTOH36NJhMJkpLS3HXXXdh7ty5SE9PR3x8/OdOVKGhmUmcOyaNy+VCJBKhvLwccXFxqKysREtLCzo6OqgUhdOnT2Pp0qXIysoKS0Uqk8kEl8uFVquF1+tFX1/fTZfhWrjhhh1BEHC73TAYDOjr68Pu3btx8OBBCIVCpKWlYcOGDaiurkZMTAzdBJYmKhEIBBAIBEhISEBxcTHa29uxZ88e7Nq1C1NTUxgZGYFSqURBQQHkcjmVqxFtBsvNgCAIOBwOBAIBsFisqJo44ff7qQTy48eP4/jx46ivr0cwGMTChQtRUVGB6upqaLVaWtfRzFpIvafRaKDRaFBaWorS0lJs3boVjY2NaGtrw8jICNWYOxgMQq1WU17tmyUjk8lEamoqLBYLhoeHb8r7Xi9uaCiWDFk1NTXhlVdewf79+zE6OoonnngCa9asQVVVVcQkS9LQXE+CwSBOnDiBV155BYcPH4bJZMIPfvADrFixAnPmzAl7Dkmk4vV6sX37dvziF79AYmIiPvzww6jQEQRBQK/Xo729Hb/73e9w6NAhpKamYuHChXjyySehVqujcuYkDc3NxGKxYHBwEM888wyOHDkCDoeDBQsW4Kc//SmSk5Nv6uzoUCiEP/zhDzh69Cj8fj927NgBIDp09g312A0NDeGTTz7BH/7wB4jFYlRUVOCvf/0r8vPz6bAUzYyGyWRi7ty50Gq16Ovrw9tvv41XXnkFu3fvxrx58/DjH/8YQqGQvgfOgyAImM1mKlwTDccnGAzigw8+wAcffIDm5mbw+Xz86le/QmlpKbKzs6lqPxoamksjkUiQl5eHzZs34/Tp06irq8OOHTtw55134s4770RNTQ1qampumjxpaWk4evQoxsbG4PV6qab0kc51N+zIJMmGhgacOnUKu3fvRnJyMiorKzFnzhyUlJRAJpPdNEXn8XgwNTWFN95446KPazQaLFy4EOnp6ZRMBEHg1KlT6OnpucAFy+Vy8eCDD0Iul0dd09QDBw6goaEBfr//c5/DYDDA5/MRExOD+Ph4FBUVIT4+nl6YrhAGgwGBQACNRgOBQIANGzaAzWajv78ftbW1eO2113DrrbciISEhqsrobzRkKJbD4UAgEES0EvX7/ZicnER9fT3ee+89WK1W5OTkoLq6GkuXLkViYiKUSuVNl6ulpQWffPLJF97nPB4PMpkMcXFxmDt3LmJjY2dcv0DgU8+LXq/H7t27odPppj3GZrMhkUhw//33X9BPjSx+efHFFxEIBKY9tmrVKsydO/eGyz7bIPPwNBoN5syZQ/Wr3bNnD1pbW2EymeByubB06dIbvjFmMBiUp93tdkOv10OtVkdFGsV1NexIo25qagq7d+/GsWPH0N7ejv/6r//CLbfcgpycnJuuqH0+H8bGxvDCCy/A4/FQQ45FIhFEIhEqKiqg0Wig1WqnXSTNzc3Yu3cvTpw4AaPRCD6fDx6PB6VSiVtvvRUikeiaDTsyVO1wOOByuaheVjeqK3dtbS1effVV2O12GI1GAJ+28Tg/gVsgECA+Ph7Z2dkIBAJYuHAhxGLxVctFtnswGAxgsVhUUvxsgMViITY2FrfccguSkpLw7rvvYuvWrXjxxRehUqkwZ84cpKSk0HM9P+N8wy4SIfWcwWBAc3MztmzZgrq6OtTU1GDVqlX48pe/HNbwcUdHB/72t7/BbrfDbDYjEAhQ9/m5MvH5fCiVSqSmplIe5piYmBk3SYU8Vx988AFqa2spfcvn8yGTyZCZmYkNGzZc1LCz2+148cUXYTAYEAgEqEIptVqN4uJiGI1GsNlsCIXCqMoHjQYSExORkJCAiooKcLlcfPTRR9i/fz/MZjOSk5ORmpp6QXuU601CQgLEYjE12jRq7g/iOhIIBAi9Xk+sW7eOSE5OJpYtW0Zs376d8Hq9RCgUIkKh0PV8u8uCfN9gMEjs2LGD+MpXvkIIBALie9/7HtHR0UEEg8GLyka+RqfTEdXV1cSzzz5LHDx48HOff7WyORwO4he/+AVRUFBArFu3jujr67vm/3up93M6nURDQwORlpZGpKenE5s2bSL8fj8RDAYJj8dD6PV64qOPPiJuu+02QqlUEjwej/jHP/5B9Pf3X/X7er1eoqenh8jOziZqamqI3//+99fxU0UPoVCIMJvNRF1dHVFSUkIkJSURX/3qV4mzZ88SwWAw3OJFBHa7nfjud79LrFmzhvj+978fbnEuSjAYJEZHR4mvfvWrRFZWFpGQkEC8//77xPDwMKUbwqHrSEKhEOHz+YjGxkZi8eLFhEqlIu68807CYrEQwWCQ8Pl8hMlkInbu3En827/9GxEbG0twuVziT3/6E3HmzJmwyX2jOHcNOH36NLFp0yaCw+EQa9euJf7yl79QOv1Sr3vmmWeIBx98kPjNb35D2O12wu12Ew0NDURWVhaxatUq4pVXXgnDJ5v5nHs/NTc3E3/84x+JmJgYorCwkPjDH/5ATE1N3bB7LRQKEV6vl3jiiSeIlJQU4v333ycsFssNea/rzXX1Kba0tOAvf/kLmpub8dBDD+G///u/MX/+/LAO/iXfl8lkXuBCPXfe4vmyXWwW46Wef7UQn+3+ySHgxEVqWU6ePImVK1fi0UcfxbFjx676vUi5z0/cJz8Xh8OBTCbDokWL8MADD+DOO++Ez+fDK6+8gvr6+qt+X+Izz+SlPuNsgMFgQCwWIzc3F3/5y19QWVmJ7u5u/OhHP8Lk5CS8Xm+4RQw7BEHAarWCx+NFpAfE4/FAp9Ph8ccfR1tbG4qKivDSSy9h8eLFUKlUF9xj4eBcXUX+Tn5nMplU+HHevHm4++678ZWvfAWBQADvvPMO9uzZEza5bxTnHo+EhASsWbMGHA4HbW1tOHny5CVfR16PHR0dMBqNuOWWW6giGFqn3XjOvZ9Iz+rLL78MkUiEjz76CE899RSsVusFofLrBZvNhkgkglAoxMTExA17n+vNdYv5DQ4O4vTp0zhy5AgKCwuxYMEClJWVhSXHJJpgs9koKirC+vXrERcXd9EQpdlsRm1tLex2+w0dRkwavzExMSgoKMD4+DgIgkB7ezt0Oh18Ph84HM4VL1osFgsSiQQbNmxATEwM8vLybtAniHzIRbW8vBwrV67EkSNH0NTUhAMHDqC6uhqJiYmzOiRLEAScTic4HE7EVZGGQiGMjIzg+PHjOHPmDMrLy7Fo0SLMnz8fcrk8KnJvgE8XSzabDYVCgaysLBiNRhAEge7ubgwMDFz1fR4NSKVSFBQUQCaTwWq1YmhoCOPj44iPj79oqkkoFEJ3dze8Xi9kMhlSUlLAZDLBYrGgVCpxxx13IDY2FpmZmWH4NLMLkUgELpcLoVCImpoaNDQ0oLGxEfv378eyZcsuSDW4VkiDUigUQigUwmKxIBgMXrf/fyO5ZsOO9MYcO3YM+/fvx5kzZ/DSSy+hoqICcXFx10PGGQtZqHD77bfj9ttvD7c409BoNNTcv4mJCRiNRng8nqvKL2Cz2VCr1fjd7353vcWMSsjzfv/990MikaCjowMvvPACNBoN4uPjoyOH4wZBEARsNhvkcjmEQmG4xZmG1+tFQ0MDXnzxRXC5XNx7771YvHgxNRc4GomJiaGMEoPBAL1eD5fLFdWj3C6FWCymmt52d3djYmICzc3NWLZs2UUNu0AggMOHD0MikSA1NZU610wmE2lpabROu8lwOBzExsbiO9/5Dl566SW89dZb2Lx5M7KysiAWi29I8Y9QKIREIqF66kUD17zFDAQCGB8fx+bNmzE+Po7vfOc7WLNmDWJjY6+HfDRh4vzQQrjDSzMRsViMlStX4ve//z1aWlqwbds2HDp0KNxihRWCIKhipUgrsHn33Xfx4YcfYnBwEC+//DKWLFkyIw2gmX6fs1gs3HnnnUhLS4PRaMTbb78Np9N50ef6/X68++67SEtLw7Jly26ypDSfR1xcHB566CE888wzqK+vxxtvvIGjR4/ekPcSCAQQiUSwWq0IhUI35D2uN9fssXM4HNi6dSt8Ph/Ky8uxfv36WVHh19PTg6amJrz33nvU3xITE/GjH/0IH3/8MXp7ezE2NgYmk4nk5GTk5+dj2bJliIuLA5PJhN1ux9mzZ/Hiiy/CbrdT/+PZZ59FWloaQqEQPvnkE+zbtw8NDQ3weDzo6enBr3/9a2zZsgXAp7vGO++8Exs3brzun0+n02FgYADAp5VBMTExYDAYqK+vx+uvv47JyUnquf/2b/8GhUKBI0eOoKOjAy6Xi2q1cO+996Kvrw+nTp2inr948WJ84xvfmBbuIUNwLS0tOHPmDNra2mCxWMBisSAUCqkqtIyMDOTm5lKvDQQCcDqd2LVrF3p6ejA6Ogqr1Uq1bElJScG6deuQkJAQcR4gBoMBmUyG3NxcLFu2DJ2dnRCJRKipqZm1hjTpseNyuRFTFRsKheByubBnzx6YTCbcdtttyMzMhFAojPpzpNfr0dXVBQBQqVRQqVQQCoUIhULo6elBb28vGhsbodPp4PF4QBAEYmNjMW/ePBQUFCAvL++i16rZbMbw8DD27NlDhTLJHLeamho4HA40Nzejo6MDBEEgJiYGZWVleOihhyAQCKhWI5988gm6u7sxMjJC5V4qFAqkpKTgtttug1qthlgsvqLPzGQyUV1djWPHjqGjowMnT56E2WyGTCab5vGxWq3o7++H0WiEVqtFTk4O1XB/z549aGxspJ67bt063HfffdPWPuKz8XinTp1Ce3s7urq6YLFYqLyt9PR0rFy5EsnJyYiJiQEAvPfee2hqakJPTw/1v/l8PrRaLb773e9CoVDA5/Ohq6sLr7zyCpRKJW677TaUlJSAyWRibGwMu3fvxv79++Hz+aj/8fvf/x4ajQZutxs7d+5Ee3s7pqamYLfbweFwoNFokJKSgrlz56KwsBA8Hi9ir21SrtjYWBQUFGD9+vWoq6uDTCbDggULwOfzr6vsHA4HPB4PLpdrdhh25GJ8+PBhKl8jJyfneskW0QQCAbjdbphMJpw9exZWqxXx8fFYs2YNhoeHMTk5CYPBAIfDge7ubuh0OojFYqxevRpMJpMaIm+xWNDV1YXx8XGYzWZ873vfg1arBUEQ8Hg8sFqtsNvtIAgCgUAADocDZrMZwKcKyuPxXLfPRBZxWCwWnDlzBmfOnAGDwUBJSQmSkpLAYrHg9/thtVrR29uLkZERGI1G5OTkIC0tDaOjozAajTCZTDAYDOjo6EBNTQ2sViumpqbQ0dFBNZ/9+te/Pu19fT4f6urqcOLECbS1tcFmsyEYDILNZoPP50Ov18NgMGBiYgJCoRDp6ekIBAIwm804deoUPvnkEypcHAwGwWKxMDk5iYGBAfD5fCxZsgQpKSkRl5BPttBZsmQJPvjgA3R0dMBkMkGpVEasYr2RnJtjFymGXSAQwMjICLq7u6FSqbB8+fKb2ovzekMm/VutVnR1deH06dNgMBjIzc1FRkYGWCwW9Ho96urq0NLSgtHRUWpwO9m7z263Uxuo9PT0af/b4/HgzJkzOH36NHbv3g2/3z/tfDY3N2NsbAxHjx5Fc3MzCgsLwWaz4XK5AHx6vC0WC+rq6vDJJ59gamrqove1QCDAwoULkZaWdkXGHZmIr9FowOfzMTIygtHRUSiVymmRJrPZjM7OTsjlcqjVasTGxlJ62+FwYGxsDN3d3TCbzUhKSsLGjRupcG4wGITX68WJEydw6NAh9PT0wOFwIBQKUYUco6OjYDAYmDNnDubMmQOJRAK3243JyUm0trait7cXarUaqampiImJoYwKn8+H1tZWbN++HfHx8UhJSUFRURGYTCaCwSBcLhd6e3upZP+8vDwEg0EYjUb09/djz549sFqt8Hq9CAQCYDKZsNlsGB8fh8FgQFxcHOLi4iJuI3w+ZFuulStX4g9/+AN6enowNjY27Xq8HrDZbLDZbHi93ugpkrmWktpgMEicPXuWUCgUxH/+538Se/fuvZZ/d8PZtWvXtHYnnZ2dX/iasbExorq6mvjlL39JHDp06ILHA4EA8eCDDxLp6emEVColNmzYQHz88cdET08PYTKZiH379hFVVVWEWq0mFi1aRDidzgvKs1977TWipqaGAEDU1tYSPp9v2uM7duwgxGIxUVVVRWzduvWajoHL5SIaGxupdicPPfQQ4fV6Cb/fT7hcLkKv1xMffvghcdtttxExMTEEn88n3nrrrQvanezatYtYs2YNwWAwiKKiIuLrX/86cfjwYcJqtRLd3d3E66+/TnC5XOLUqVOEw+Eg9Ho9sWHDBkKhUBD33Xcf4Xa7qePg8XiI4eFhory8nEhKSiLmzJlDbN++nRgfHyeMRiMxMDBAPPvss0RGRgZRUlJCbN68mfB6vYTJZCJ2795NpKSkEAkJCcS///u/EwcOHCDsdjvR3d1N/O1vfyMWL15MCAQC4re//S3R1tZ2TcfuRhEIBIienh6ipqaGmDt3LrFv3z7C6/WGW6ybTigUIvR6PSGTyYjvfe97EaNPzGYzsXnzZiI3N5f45je/Sdjt9rC2M7kc/H4/0dzcTLU7ueOOOwiTyUT4/X7C4/EQRqOR2LFjB/G1r32NiIuLI3g8HrF582aira2N8Pv9xI4dO4hFixYRGRkZxK9+9StidHSUMBgMRF9fH/Hcc89R9+lPfvITIhAIUMcjGAwSPT09xAMPPEAolUoiOTmZeOutt4iOjg7CbDYTbW1txCOPPEKUl5cTMTExBI/HI7Zt2zZNdrPZTBw8eJC6rx9++GFi3759hM1mI3p6eoj/+7//I5YsWUIIhULi5z//OdHa2npVx+j3v/89MW/ePAIA8bOf/Yyoq6ujHguFQsSxY8eIr3/968Tjjz9+0ffo7+8n1q9fT4jFYuLxxx+fptOcTifR09ND5OTkEBqNhqiuriZ27txJGAwGYnh4mDhw4ACxbNkyQqlUEhs3biRqa2uplkf9/f3E5s2bCYFAQDzyyCPEzp07p8ml1+uJTZs2EbGxsYRWqyU2bdpEtRQjz8H27duJJ554gvja175GvXb37t3E/fffT8jlcuJ///d/icbGRsLpdBITExPE3//+d+K+++4jGAwG8e677xKDg4NXdUxvNoFAgDCbzcS8efOImpoa4tVXXyUCgcB1fY9//OMfxH333UesW7eOGBgYuK7/+0ZxTR47vV6Pvr4+WCwWzJ07F9nZ2dfH2oxS5HI57rvvPixYsIBqnLh48WIsW7YM+/btQ2trKwwGA+Lj4yNiasXQ0BDluj8XoVAIjUaDDRs24J577sG8efMu6elau3Ytbr31VlRVVVHNOpOSkrBq1SoolUqw2Wy43e7Pff3g4CA2b96M7u5urFu3DnfddRdWrFhBhTVkMhn+4z/+A1NTU5iamqJet337dmzduhU6nQ4///nPUVNTg9LSUrDZbKSlpSEmJgalpaVYvnw5tmzZAr1ej1//+tfXfuCuM0wmE1qtFunp6ejs7ERdXR3mz58/I6cAXArSy+FyuaiE5UjA7Xbj6NGjyMrKQl5eXsR7Ms7HYDBg586dFzSIFwgEiI2NxdKlS/Hwww9jzpw505qV5+TkoKioCN/61reo8JZMJsPDDz+Mvr4+nDhxAlu2bMHjjz8OiUQCNpuNQCCAP/zhD6irq4NCocCPf/xj3HLLLZBIJGAymRCLxXjqqafw9ttv4+mnn76ovHv27MGHH36I0dFRPPXUU1ixYgXmzp0LNpsNrVaLmJgYFBcXY/ny5XjnnXdgMBjwxz/+8YqPS2VlJdVx4OOPP0ZSUhIqKysBfOoV6+3txd69e7FlyxYkJSVd0f8+e/YsXnrpJQwODuKBBx7AunXrsHz5cnA4HMjlcsTExOCFF17APffcg5MnT8LtduP999+nwtW33XYbfvazn6GpqQkKhQJr1qwB8C9v5p49eyAUCuHz+bB//37Y7XbIZDKw2WwQBIGPP/4YKpUK8+fPp2TS6XRoaGhAdnY25s2bR3lK+Xw+Nm7ciIyMDNTV1UWVJ5rJZEIqlWLOnDkYGRnByZMn8eCDD96w94uWKMo1GXYOhwMmkwkEQUClUkV8IvG5uSDEFbhUyed+0Unl8XjIzs6GUCikXPJcLhcxMTGQSCTw+XywWCxQKBQRYdjJ5XKkp6fjy1/+8rRWDXw+H3K5HCqVCvn5+V84s5PsAk4aImSvrMsNpdlsNpw8eRIejwdJSUkoLi6+YCafSCTCXXfdBafTSY046+3tRVtbGwiCQFlZGVJSUqbJIJFIkJiYCLVaDb1ej/7+flgsFsrojhTObT8hEomg1+ujJpfjekKGuAiCgEAgiJhQbDAYxPj4ODIzM6FUKiPq2rkcyNSFe++9d1oLGR6PB4lEgtjYWJSWllKGQSgUQl5eHu69914qH4yEvLdTUlLQ1taG/v5+jI+Pg8vlUq9tamqCXq9HSkoKKioqIBKJKH3IZDIRFxdHhTbPzdUlGRgYQEtLCwiCQElJCbRa7QX3dVJSEtRqNYxGI+VcuNL7Oi0tDYWFhRAIBBgcHMTQ0BCmpqYQFxeHnp4eTExMQCqVIi0t7YqNeYPBgFOnTiEQCCAjI4PKWyPh8/lISkpCQkICjEYjurq6YDabIZfLweFwoFQqkZubi6mpKXR2dsJms0EsFmNqago9PT1Qq9XIysrCxMQEWlpa0NLSgpKSEsjlcjidTpw9exZpaWkoKiqi3lMulyMpKQnt7e3Yv38/HA4HsrKykJiYCD6fj+zsbPzP//wPiouLL5hGFKmQazqp4ycnJ697uJT4rPNHtBh1wDUadl6vl6omkkgkEaOIPw8Wi0XltwUCgcu+APx+PzXD7lKw2WxoNJoLyuaFQiF1bJxOZ8Q0OZTL5cjPz8d3vvOda9qlKZXKq1YExGc5VT09PQiFQoiNjUVycvJFn7to0SLqNQRBYGJiAiMjIwA+vRbHxsag1+unvcbpdEIgEGBiYgJ6vR4mkyliPEHnIxKJwOfzqZzK2Ybf76f0CZ/Pj5g+dqFQiFpYIy1H83IQiUTIzMzEY489dsHYrIvBYDCQkpIChUJB3ZuBQAChUIjacDidTgSDQQQCARgMBmg0GqrZ+uDgIBwOB/h8PtLS0i7Qh3w+H1KpFDExMdM88OR9PTk5Sc3o9nq9GB8fp0Ygkni9XvD5fIyNjWFqagpGoxFisfiKDDuVSoX09HSoVCqMjo5ieHgYQ0NDiIuLw9mzZ2EwGKDVaq94hi7xWQFQT08PlZNnNptx5syZC57LZDIRCAQwNTUFk8lENcMVCAQoLi7G/v37MTw8TOWOjY+Po6urC7m5uaiqqsKZM2dQX1+PxsZGpKSkQCAQYHJyElarFXK5nGpZBXzawqqiogJdXV04deoU7HY7DAYDcnNzIRKJIBAIcMcdd1zxcYwEpFIpOBzOBfr/ekBe99F0TK7JsLtaD1i44PP54HK5lDFxOT1pQqEQ7HY7uFzuF3rZyN3k+RcAmXwJ/Et5zSQu59h8HoFAAB6PBw6HAzweDwKB4LKqmvx+P1wuF5VwvWHDhi98L7fbjeHhYSQnJ0dsuCHadobXE6/XC6vVCuDTVjBXWu14IyGnEMy0e/fzcDgc2L59O44fP46jR49ifHwcTqfzgukoMpmMKmwgi6BI45y8ny92PXM4nIsameff15dT8e/1ejE8PIykpKQrmmdN9kRbvXo1/vGPf6CjowN79uzB3LlzcfjwYbjdbqxbt+6KF3S/3w+Px0Mdh6eeegpPPfXUJV8jlUoxPDyM2NhYCIVCMJlMrFmzBl1dXeju7sbOnTvxyCOPoLu7GydOnMCGDRswd+5cMJlMvPHGG9i1axfmzZsHHo+HPXv2YM6cOdBqtdOcLVVVVSguLkZ+fj52796N3bt347nnngMAZGdno7i4GBs2bMDq1asj3klzM/F6vXC73ZDJZFFj3F2TYSeRSKgmxOPj40hJSYFarb4ugt0INBoNlEolQqEQent7P7d3EUkwGKSMAYVCQZWkh4OZutiTOR5isRgulwtutxtut/tzFwQSchi3SCSC1+vFzp07odFoLnnj8fl8qFSqK1L+NxO9Xg+bzUZVuM02vF4vLBYLCIKIKO8Ym81GYmIipqambohHINLwer34zne+g9OnT4PNZuOee+5BdXU11Q6EwWBg27Zt2LdvH+rr6yljl8lkgsvlQiQSwe12U/mSAoHgguvZ6/XCZDJd8N7n3tcejwcfffQR0tLSLnk/8Hi8q27sTU6P+Oijj9Dd3Q02m40HH3wQZ86cQWZmJlatWnXFm0ByaopEIoHL5cKzzz6LW2+99ZL/h8lkIikpifJSM5lMLFy4ENu2bcPZs2exbds2rFmzBoODg+jv70dNTQ1kMhnS09NRUVGBhoYGDA4OwufzYdu2bXjooYeQkZFxwfvw+XzcdtttWLZsGWV8NjU14fjx4+jo6MB//Md/4Hvf+x6WLVuG4uLiKzuYYWR0dBRutxsajea6/2+XywW73Y709PSIXTvO55qklEqlUKlU4PP5GBoaQlpaWkQbdkqlEnFxcZBKpRgaGoJer4fD4fhcz4DBYEB/fz/YbDbi4uLC1mGezMEKhUKUEiUIAv/85z8hFouh1WqRm5sblcYfg8GASCRCdnY2WltbodfrMTIyctFCnL1798JqtSIuLg4LFy5EQkICUlJS0N3dDbFYjKSkpAvyPAOBAJqamtDX1wepVDotNBEphEIhWK1W6PV6eL1eaLXaiPUo3kjIUCyTyYyoUCyHw0FmZiaampowMTExo0dukWkq9fX1mJiYQE5ODpYvX46CggKIRCLKeKqrq7sg74xs45GWlkZ5Ofr7+5GTkzMtlOl2u2G1WqlRZue+nsFgID4+HlqtFh0dHRCJRNBoNBekegSDQTQ1NaG/v5/q83Y154PP5yM3NxdxcXEYGRnB4OAgPvroI2paztVMTyKLTLKystDa2go+n4/Y2NiLro1tbW0YHh6GXq/H/fffP82AlUgk0Gq1SEhIQE9PD44dOwaj0UgdDzabDZVKhbKyMpw6dQqtra3Q6XQYHx9HTk7OBY6I5uZmNDQ0YMOGDdQGNxAIQCAQQCKRQCKR4Pjx42hqakJubm5UGHakl3hoaAgejwcFBQXX/b70eDxwu90XjcZFKtckpUQiQXx8PGJjY9Hd3Y3h4eGIDlfI5XJqVNbIyAjVi40MJZCyn9sLqKGhAQkJCUhISAhbcQjZpNfn88Hv9yMQCMDn8+HPf/4z3nnnHbS1tYVFruuFTCbD/PnzqZ5SLS0t8Hq9VHgnEAjAarXiH//4B1566SUcPHgQgUAAmZmZKC4uBoPBwNmzZ6HT6eD1eqmcCJ/PB6vViu3bt+Pll1/G1q1bI7IogSAISrkTBIHc3NxZa9g5HA6wWCwqbSIS4PF4KCkpgV6vx/Dw8EU9TTMJsliE+KwZcWlpKaRSKVVx6fP5YLPZLtpDk8lkory8HHFxcXC5XKivr4fD4aBy9Px+PyYmJqi+nRcjPT0dpaWlYDAY6OzsxMjIyLT72u/3w263Y8eOHXjllVfw/vvvX/V9zePxkJKSAq1WC6FQiKmpKfzf//0fVCoVsrKyvjBy8HmQTZzZbDaGh4fR3d09LWRNNlY/ePAg3njjDfztb3+blvdNbuYzMzORm5uLsbEx7NixAxaLBSUlJeBwOGAymYiPj8e8efPAYDBQV1eHY8eOIRAIQKvVXuCIOHbsGH7xi1+gp6cHdrud6hOanp6OFStWYPny5QA+7ZZgsViu6njebEKhEPR6PQYGBhAMBm+YYedyuSKu6O5SXLOUMpkMd911F44ePYra2toLcjAijcrKSnz/+98Hm83G7373O/zwhz/E7t27qea2Xq8Xk5OTeO+99/C///u/ePnll/E///M/SE5ODtsOnRxcPTo6ipGREQwNDaGxsREDAwMQiUQoLS0Ni1zXi9TUVPzgBz9AdnY2Tp48id/85jfYv38/VezQ19eHH//4x2hoaIBSqcRXv/pVcLlcrFu3Dl/72teQkpKCX/7yl/jb3/6GEydOwG63w2KxoLW1FVu2bMEf//hHVFRU4L777otIg8nr9eLvf/87HA4HMjMzqQVhtkE25FYoFBE1L1ckEuGOO+6AXC5HZ2cn3nzzzYjcIFwvOBwOqqqqwOFwqOk4Y2NjcDgcsFgsOHHiBN555x3U19df8Fo2m40nnngCVVVVsFqtePrpp/HJJ5+gt7eXaoj8q1/9Ctu2bfvcsZO33HILHn30UaSmpuL3v/89XnjhBRw7dgw2mw0WiwXt7e3YsmUL/vSnP6GgoACbNm265vt67dq1KCgogM/nQ3NzMxYsWICKioqr/n/5+fn4/ve/j4yMDHz00Ud49tlncejQIZhMJlitVuh0Orz55pt47rnn4HA48L3vfe+CTgAAUF5eThlcW7duRSAQoFqfAJ9OO1qxYgWkUinV3H3Dhg2fmyNnt9vxox/9CAcPHqQMZqPRiB07duDDDz8EQRCYO3fuFbd3CRc2mw0vv/wyLBYLMjIysGbNmutufJGh2NjY2KjRywziGt1rbrcbra2t+Pd//3ckJibi3nvvvaB9RiTh8XhgNpuxc+dOnDx5EuPj4/B4PFRHcDLnKzY2FklJScjMzMTq1asvWGxGRkZQV1eH5557Dl1dXbBarWAymZgzZw5uv/12LF68GBkZGbj77ruh0+lgNBphNptRXFyMkpIS5OfnY968efjd735HldZbLBaUlZUhLy8P5eXlePzxx6lO67W1tfjNb34D4NNdJpfLpXJAVq5ceVkFBy+++CL++c9/wmw2o729HcCnhnlOTg7i4+OxePFifPvb3/7c1/t8PvT09OBXv/oVWlpaoNPpYDKZUFBQAKVSCS6Xiw8++IBKiiYIAg0NDdi9ezd27dpFHSelUkmNWLv77ruRk5MDv9+PkydP4vjx42htbYXZbIbf76fOBzkupqioCBUVFeDxeNSUjKamJnz44YdUR3yfz0cVYUgkEpSWlmLZsmVIS0uLuIHt5NiiL33pS1iyZAmWL1+OBx54AMDMzav8PE6fPo2tW7fivffew4svvojq6upwiwTgX5Mafvvb36K2thY6nQ7/+Mc/kJSUFJE97Xbu3Ik//elPMBqN6O3thcvlokbXkWOYnnnmmc99fTAYxMmTJ7F161ZqtB+Hw6F6C2ZkZMBkMqG1tRWnT59Gfn4+8vLysHTpUnzjG9+Ax+NBU1MTdT7dbjc1eUKtVqOyshKjo6PYu3cvzp49i3feeQfr1q2j3j8QCMBms6GhoQHbtm3D+Pg4bDYbfD4f+Hw+BAIBxGIxSktLsWTJEmRmZl5Tew6CINDR0YHnn38eL7/8MuRyOd555x1UVFRckKYTCoVw6tQpfPTRRzh06BB6e3thsViQkJCA3NxcVFdX48EHH0RycjK8Xi9OnjyJI0eOoKurCyaTiQrj83g8asNeWlqKyspKamzjubjdbjQ2NmLjxo0IBAJ47LHH8J3vfAdSqZQq6HE4HHjooYdQW1uLxMRE/Pa3v8XChQsv8Hi3t7fj+PHjOHbsGOXEcLvdYDKZVEpMeno6HnroISQnJ0d8+zKj0Yj29nY88sgjWLRoEW655RZ86UtfAovFui66k/hsisqPfvQjfPDBB/jTn/6E5cuXX1Zlebi5ZvOTy+UiKysLubm5MBgM2LdvH5YtWwalUhkRvdrOh0ygX758OcRiMQYGBqDT6agBvywWi8pby8vLQ35+PuLi4i64UFgsFkQiERWmPRcyyZjJZEKtVkOlUk17XK1WQyaTgc/nIz4+HkKhECUlJdTj5/cElMlkKC8vx7p166DT6aiE5JqaGuTn5192BZNEIqHmvmZmZk57LCYm5rIuWC6Xi7i4OOTn5yM/P3/aY2w2+wKDnsvlQi6XIzk5eVobExaLBZlMRoUUuFwu5s6dCy6Xi9jYWLS3t1OzYkUiEbKysrBw4UKkpKRQuVdk77d58+ZRY85GR0dhNpshEAggk8mgVqtRU1ODtLS0iLsh/X4/hoaGcODAAfh8PhQUFFCh5dlIIBCAy+WCWCyOqJ0xueGbP38+DAYD2trasGfPHqxevXpa/8ZIgTSgFArFBfe5TCb7QiOIyWSipKQETqcTGo0Gvb29sNls4HA4kEgkSE1NRWFhIVJTU6HVagF8qtMkEgkYDAYEAgHy8/Op3p3d3d3w+XxgsVhITExEcXEx5WHj8XgX7SJApmc4HA709PRgeHgYZrOZapVC3tcXCzleDUlJSVi4cCHsdjukUim0Wu1Fi3cYDAY1r1ar1VKfH/hU15G9AMk80crKSjCZTKhUKnR2dsJsNlNN3BMTE7FkyRKkp6d/rvdSIBAgOTkZX/rSlxAIBFBeXj5Nj5HyrFmzBomJiVAqlcjKyrqoBzM5ORlLly4Fk8nE0NAQzGYzNSuWHJtWXl6OzMzMiFy7SUiD6+zZszh06BDcbjcqKytRUFBw3fWG3W6Hx+MBg8GAXC6PyIjPRbleIyzefvtt4t577yVUKhXx8ccfE5OTk9SIFBoamn8RCoWo0UB/+9vfiOzsbOKWW24hjh8/PqvvmcOHDxNf//rXiaVLlxK1tbXhFucC/H4/8dFHHxHLli0j0tPTibfeeosYHx8ngsFgxI8Yu1mQ17bH4yFcLhf1+7kEg0HinXfeIbKysgitVkt88sknYZKWJtoIhUJEMBgkBgYGiB/+8IdEZmYmsWbNGqKzs/O6685QKES0trYSDz74IJGRkUG0t7cTbrf7ur7HjeK6mbd33nknBAIBHA4Hvva1r+GZZ57B8uXLr/tAXhqaaIf4LHzy5JNPoqGhASwWC5s3b0ZiYuKs9dYB/8qxUyqVEZVjR8JisVBTU4PMzEzcc889ePbZZ3HgwAH85Cc/gUajmdXn7lz0ej2efvppjIyM4IMPPrjAizI5OUmNM7z99tuhVCrDJClNtEE2rH7kkUcwNTUFrVaLl1566aJRtWuFIAj09PTAYrFAKBQiIyMjIvXSxbhuiXBsNhvl5eX4+te/jtTUVCpPprGxMWImLdDQhBun04n+/n48/fTTaGxsRHJyMr773e9Co9FcVp7kTMbn88HhcFBzRyMNMsyYnJyMJ598EmlpaWhvb8eTTz6J+vr6C6YjzFaIzyYvdHV14eWXX8bIyAjsdjucTid6e3vx2muv4eDBgxCLxbjzzjuRmJgYbpFpooChoSHs27cPP/rRj2CxWLB06VJ8+9vfpooaboTuHB0dpVrwRFOLo+umPRkMBhISEiASiVBdXY0jR46gvr4eSqWSmjsqEomi5sDQ0FxPiM+mnfT29qKpqQn79u2DSqXC3LlzsWrVKvrewL9y7L5oNnE4IXNwb7nlFgwPD+PEiRM4deoUsrOz4XK5kJOTQ+XcztbzyeFwqHyvkydPQqlUUsn+Y2NjqK+vp/Ki5s6dG3EFTTSRA/HZmLrR0VHU1tairq4ODQ0NqKioQHV1Naqrqy9aTXy9mJycpOYbnztpK9K5rttiFosFuVyOZ599Fn/+85+xe/duPPXUU/B4PLj99tunddSPlgNEQ3MtEOc0lO7s7MTrr7+ODz74ALGxsfjhD3+I0tLSsE40iSS8Xi/sdjtVVBOpMJlMxMTE4PHHH8eyZcvw/PPP4xe/+AVqampw66234pFHHqE8jrNNzzEYDCiVSmzatAklJSV4//338fTTT8NoNMLpdEImk6GiogLr16/Hhg0bkJKSMuuOEc3lQRp1brcbr732Gt5//31MTU1h7dq1+NnPfoa4uLgbqicIgsDQ0BBYLNbnzi+PVG5IvIPFYuGRRx7BwoULsXXrVrzwwgv45JNPMG/ePHzrW99CUlJSRCtuGprrhd/vx+joKN588028/vrrEIlEWLJkCZ555hmo1eqIma4QCZCzYuVyeVToBx6Ph7KyMvzxj39EeXk5tm/fjmeffRbvvvsuHn/8cZSUlEyrmpxNZGVlQavVYvXq1fD7/dTUHCaTSbX7oK99mkvR2tqK48eP46233kJ3dzfWrFmD5cuX47bbbrtpzYJ7e3svqH6OBq67YUfuvsRiMTIyMrBu3Tp4PB50dnaiubkZv/3tb7F27Vrk5eUhNTWV6qFDQzOTCIVCGBgYQG1tLZqbm1FXV4eioiKUlJSgsrISiYmJVKsXmk8hB8hLJJKIDcWeC4PBAJfLhUKhoJrEtra2oqmpCa+//jpyc3NRWlpK9b6KBmP1esHhcKjedzQ0l4vb7YbRaMS+fftQW1uLsbExsNlsPPLII5g/fz7y8vKuqWfh5UJOnzIajcjOzr6gpVmkc0MzlBUKBSoqKiCRSPDPf/4TR44cwfbt2wF82h8mFAohNjYWYrH4ujUVpKEJJz6fj6ruPHHiBLZv34729nZwOBw88MADWLBgAQoKCgDMvjDdpSBHRXk8HohEoogsnvg8mEwmioqKoNFoUFhYCJ/Ph4MHD2J4eBgDAwNQKBRITU2FXC6HRCIBj8ejzz0NzWecO/5xcnISfX19ePfdd9Hf3081pv/a176G+Pj4i/YWvBEEAgFYLBbYbDYIhcKLzvmNZG649mQymcjLy8OTTz6Jr371q3jzzTfxpz/9Ce+++y4SExPx2GOPYf369VCpVFGxS6ehuRQjIyM4ffo03n33XWzbtg0FBQVYtmwZ/uu//gsJCQnUNU4v7NNxu91wu90IBAIRN1LsclEqlaisrERlZSU1ueGjjz7CO++8g/nz56O6uhp33XUXNQeVhobm081wV1cX/v73v+PEiRNobW1FamoqHnvsMSxZsiQsIzPtdjuam5sRCASQkJCA7Ozsmy7DtXBTtsUMBoMaWEy6VGtra3Hy5EkqJ6W8vBwrV67E0qVLLzrBgIYmUjGZTBgYGMBrr72GU6dOwel0Ii4uDps3b0ZpaSlSU1MRGxtLe6Uvgd1uh9vtpjq8R5PHjoQ8twRBUPl1GzduxCeffIIDBw7g448/xvvvv4/q6mrU1NSgqKgIubm5015LQzPTIQgCBEGgsbERx44dQ319PU6dOgW5XI7c3Fzcf//9WLlyZVg7aTgcDrS0tCA+Ph5qtZqq6o4Wbpr2JPNRYmNjUVJSAoFAgLi4OMTGxmJsbIyapdff34/U1FQkJiYiLS0NIpGINvJoIopQKASv14vR0VH09PRgcHAQ3d3dOHPmDDWTs7y8HEuWLEFSUlLEz1yMBFwuFzW6J9JGil0p5NxNgUAAhUKBUCgEuVyO/v5+DAwMYHR0FLt27UJTUxOysrKQkZGBpKQkxMfHXzCblIZmJkC2e9LpdBgfH8fAwACam5sxOjoKi8VCzUfPzc1FXl4ecnJywpp/73K50NvbC41GA4VCEXX66KZLS+7IKysrKS/dO++8g2PHjmHHjh04cOAAKisrMWfOHKxatYqaDcrhcGZtCwGa8EN8Ngje4/HA4/HAZDLhyJEj2LNnD7q7uzE2NoaCggKsW7cO8+bNQ0VFRbhFjiqcTie8Xi8YDEbENii+UphMJgQCAebOnYu5c+dCr9ejs7MTr7zyCk6dOgWDwQCZTIbly5djwYIFKCkpQUpKCng8HthsNtV0ldZ3NNEG6ZXz+/3w+/3URri2thZNTU04cuQI9Ho9UlJSUFhYiPvuuw8LFiyAWCwO+/VOEARcLhd6enpuWrHG9YZBkI22wsC5Pb5sNht0Oh22bNmCvXv3YmBgAB6PB2vWrEF1dTUqKiqogcrhPvE0sw+n04mhoSF88MEHOHz4MFpaWmCz2VBdXY0FCxZg6dKlmD9/PlgsFn2NXgXHjh3DW2+9hbfffhs9PT2QSqUzwrg7F1LfhUIhDA0NobOzE7t378a2bdtgMpnAYrFQVVWFdevWoaysDAUFBZDJZPS1RBN1BINBWK1WHD9+HKdPn8aRI0dw8uRJiEQiJCcno6amBnfffTfS0tIQGxs7bQMT7us9EAjg8OHD2LBhA37+859TaRPRRFgNu3MJBALUHLjJyUmMjo6isbERjY2NsNvtAIDExERUVFQgNzcX2dnZyM3NpXe0NDcEstT9xIkTOHPmDAYGBtDf349AIACNRgOtVouFCxciLS0NMTExUCqVVMiVvh6vnF27duH999/Hjh070N/fP+PHq7ndbrhcLhgMBkxMTKC3txe9vb1oa2vDxMQE1ey9qKgIBQUFyMjIQE5ODmJjY+nUFJqIw+/3w2g0orGxET09Pejr60NbWxtsNht4PB5UKhXKysqQn58PjUaDuLg4xMfHg8/ng8vlhlv8aYyMjGDfvn149NFHsXXrVpSWltLtTq4WMvSQmZkJrVaLrKwsqFQq8Pl89PX1YXJyEuPj4zh16hRGRkbQ2dmJvr4+qFQqyGQyyOVyxMTEUB4TGprLgdzXeDweOJ1O6PV6GI1G6PV6jI6Oorm5GVNTU3A4HOByucjNzUV+fj7y8/Mxf/78GelZCgdutxvBYBB8Pv+GzX2MJAQCAQQCAZRKJTIyMpCamor09HTI5XKcOXMGBoMBDocD7e3tVAj37Nmz1HOkUinkcjmV/0PrPJqbRTAYhNfrhc1mg9FohMVigcViwfDwMM6ePQuDwQCz2QyPxwONRgONRoPs7GwsXLgQmZmZUCgUEX296vV6TE5Ogs1mIyEhARKJJNwiXTERuSKx2WyoVCqoVCosW7YMer2eGgC8Z88eNDY2YmpqCnFxcZg3bx7y8/NRWlqK+fPnUz2wmEzmtCrEmb5Q0Fwe54bDyJE1gUAAExMTGBgYwNGjR1FXV4e+vj4MDw8jNjYWlZWVWL58Oaqrq1FZWUn3IbsBOBwOhEIhSKXScItyU2EwGGCz2VR3+2XLlsFut2NgYACtra3Yt28f6uvrMT4+Drfbjby8PBQWFiInJwclJSUoLy+HRCIBl8sFk8mk0gBovUdzrZybKnWuvnS73ZiamsLZs2dRV1eHtrY29PX1oa+vDzKZDNnZ2SgqKkJNTQ2qqqoQFxcXVVNGRkdHodPpoFAokJCQcNN6511PIiYUeynIiyoQCCAQCFCG3u7du3H69GkMDQ1Bp9NBIBAgJycHWVlZmDt3LhYtWoSkpCRqFiet5GgIgoDBYEBXVxc6OztRV1eH2tpa6PV6uFwuKJVKlJeXo6CgABUVFVi4cCHlRWKxWHQfuhvE888/j9raWoyOjmLv3r1R2cfuekEuoMFgkPKOTE5Ooq2tDQcPHsTZs2cxPDyMsbEx8Pl8JCcnIy0tDZWVlSgrK0NaWhq0Wi29AaG5JkKhEDweD86ePYu+vj50dHSgtrYW3d3dMJlM8Pv90Gq1yM7ORk5ODubPn4+KigqIxWKq2JF0rkTTdfjb3/4Wx48fB5PJxCuvvAK5XB5V8gMR6rE7H9Jty2KxwOPxwGKxIBaLqYoyk8mEqakp9PX1wWazwW6346OPPsKePXsglUqhUCiQnJyM1NRUqFQqJCQkICMjgw5hzHDcbjcsFgv6+/uh0+kwgTFJrgABAABJREFUNTWF0dFRDA0NwWKxwO12g8ViIS8vD9XV1dBoNMjKykJcXByUSiXi4uIgl8vpYoibgNPpRCgUioiquHDDYDCmbSJ4PB64XC7EYjFSUlJgNpthsVgwOTmJwcFBmM1mOBwOHDt2DIcPH4ZAIIBMJkNCQgLi4+MRExMDtVqN5ORkxMfH0+kDNBRk9Sp5TY2Pj2NkZAQGg4FyoBgMBng8HgQCAUgkEixcuBAxMTHQarVITU2FQqGATCZDfHw8YmNjweFwovIeJo/F6OgojEYjli1bFrVpIVF5d/P5fPD5fMTGxgL4tHO13W7HmTNn0NvbS4UxdDodBgcHwWKxoFarodFoEB8fj6SkJExOTkIqlVL/SygUUomcZOJ2NJ7Q2QR5I3q9Xrjdbni9Xupnl8sFq9WKqakpdHV1YXx8HEajERMTEzCbzeDz+ZBKpUhNTUV5eTnVTyw9PZ0+92HA7XYjFApFZdjjRsNkMiEUCiEUCpGYmAjg02R1m82Gs2fPYnBwEIODg+js7MTg4CAmJiYQCAQgk8kQGxsLuVyOuLg4pKWlITk5GSqVCmKxGAKBAFwuFzwej9KDpJeFZmYRCASotiNkyyafzwev1wu73Y6pqSkYDAaMjIxgfHwcZrMZZrMZExMT1KYiJiYG+fn5yM7ORlpaGnJychAXFzejnCN2ux16vR5utxvZ2dlRuwGKTqnPg8vlIiYmBkuXLsXSpUupnmPt7e3o6elBT08PmpqacOzYMUxNTcFms4HL5SIrKwvJycnIzMxEQUEBtFotEhMTkZGRAS6X+4ULPL3431gulSVAGnUejwc6nY4KT42MjKCjowNtbW0wmUxwOp0QCoXIyspCeno6ampqMHfuXOTk5CAxMZEeUh4h2O12BIPBqOwZFQ44HA5iYmKwePFiLF68mPo76XHp7u5Gc3Mzurq60NLSgr6+PrjdbnC5XEilUiplJTExEUlJScjOzkZ6ejoUCsU045rWf5HNF+lIEpvNRnWb6O7uRn9/P8bHxzE0NIS2tjZqYyUQCFBYWIj09HTMmzcPRUVFKCkpgUqlglKpvBkfKWyEQiF0d3djamoKbDYb5eXlUZsSMiMMu4tBhtiysrIQCATg8/ng9/upEEZLSwsGBgYwOTmJpqYmfPTRR/D7/QA+DX2kpaUhISEBiYmJ0Gq100q0k5OTKcOP5sZBEAQcDgfMZjOGh4cxPj4OvV4PnU6Hnp4ejIyMwGg0Tku8J0ME9957L5KSkpCWloa8vDyqyTWHwwGXy6U9ExGGzWZDMBiMynyWSIJcgMlm2X6/H4FAAB6PB729vRgeHqYiGcPDw2hoaIDJZILH4wGbzYZQKIRCoUBmZial89RqNZKSkhAbGwulUgm1Wh21C95Mgyz+IlNNTCYTJiYmMDo6iomJCSpcbzAY4PP5EAqFwOfzkZCQgNjYWOTl5WH9+vXUGpecnEzpShaLBS6XS/080wkGg6irq4PP54NarUZmZmbUfu4ZadiRCwOXy72gR45SqYRKpUJMTAyMRiOsVivMZjP0ej0sFgtsNhtMJhPlpu7r60Nvby+YTCZ4PB7EYjEUCgWkUinEYjGkUimUSiVEIhGEQiHEYjEkEgkVOiFn3dGL1b8gexbabDY4nU643W44nU7YbDY4HA44nU4qFOBwOOBwOGCxWCjFRB7PlJQUZGRkQCaTQaVSQSqVUuEnMgRFnm86Ty6yIXPs6FDstUHm5vF4POpvZDGGSCRCSkoKrFYrjEYjTCYT7HY77HY71aLC7XbD4/HA7/dTvRsJggCXy6V0GnmfiUQiiMViyOVyKrRL6kAytUUoFEIgEMyocN2NhjTEnU4nXC4X3G433G43bDYblWZCrlVOpxMOhwNWqxUOhwNerxd+v39adXR6ejqKioogEoko/SiTyag89fj4eGpNIzdWs1FXhkIhdHZ2Uk2Uo3nzMiMNu0vB4/HA4/GoSlngX4rPaDTCYDBgYGAAPT09GB8fx/j4OIaHh6HX6ymFFwqFIJFIIJVKqR2tTCajCjVIo4LsrUfueMgee+TX+b+f26KFvDFJhRgJivH8ViHkcbvUVzAYnPYzOZbL4XBQO0yr1QqLxTKtJ9Lk5CSmpqbg8/kAfBp6IhUPWfySlJSEhIQEaLVapKWlQSAQRPXNOJtxuVwIBoO0YXcDIIsxyBZS5xMMBqnCoqmpKeh0OgwMDGBsbAwGgwFTU1Mwm80IBALU/XyuIaBSqSjjjtzonttrTy6XU4Vq5HdSv507qeV8/Xfu30ljI1KNDlIfntsa5HL04/m6kqyCttvtMBqNMJvNsNvtlMOBNMTJnDi73Q6HwzHNoJdKpUhMTKTOd2pqKrRaLeLj46FSqRAXFxeRxzCckOlb3d3dUCgUSElJiepjNOsMu4tBKo+4uDjExcUhLy/vguf4fD5YLBaMjY1hYGAAOp1uWs6CXq+HyWSC0WhEMBikXsdmsylFR3oKSW+fUqmEQqGgvHwKhYLKcSF3w+cWdEQCfr+f2kGe62mz2+2UcUYqG4vFQikjq9WKyclJTE5Owul0UmFv4F9GG1nFp1KpsHTpUkoZaTQapKenQyqV0obbDMVms4HP50Mul4dblFkHk8mEWq2GWq3+3OeQSeV6vR4jIyMYGRmBXq/H1NQUhoaG0NTUBIvFAoPBAJfLNe21LBYLMpkMSqUS8fHxkMvlEIlEkEgkkMvlkEgklDeJ1IcikQhyuZxq5Mzn88Hj8SAQCCIyPOb3+6koj8PhoAoUSMPM6XRSG1gyKmGxWChPm91ux/j4OEwmE9xu9zT9SM4cTkj4f/buOzyu6lr48G96l2bUe7GKLbnIHeNecIxtaoDQIZBCEkLqDUluSCWkh04giUMH4wCmuuHeC+5FclW1ep2Rppfz/cE953PFBdmjst/n0WNLGmnWjGbOWWftvddOxeFwEBcXR1ZWFuPHjychIYHk5GRycnJITU0lNjb2nPOGe3PCcqnIi/B27drFLbfcQnFxcbRD+kJEYvd/zvVi1+l0xMXFYbPZyM3NVTY3llcayT32AoEAbW1tuFwunE4nzc3NyhtbHlaUV2vu378ft9utrFCSJOmMVTw58ZT7A8nzxCwWi/J9eePwEx+HvJvHqcPRcqk/EokoX5P7BPp8PgDlsQUCATweD36/n0AgQCgUOu1KVL6KlyRJGYKRh2Hk4eiEhASGDx+uDOHo9Xr+9a9/0draSkZGBt/61rcYN26cUnWTh9G1Wi06nU5pcyMOSn2LXAWWd/YQe6Nefud6viVJwmKxYDAYSElJYeDAgcrcPfkYIVfzQqGQsmPGzp07efPNNzGbzQwbNozi4mI8Ho9yPGlsbKS8vByfz6esZvf5fKeNaJw6gqHVapUWMCf+Kx//5FXEpz4ujUaj/Oyp5J1PTjwmwmfVTDlRlY91crzyvG154cGJTXxP7L0qTx8xGAzK8VFOUs1mM8nJyWRnZzN+/HglqZUTYXnI1Gw2K8fzE+e+ycfHE+fCiffPhevo6KCsrAy326305uvNRGJ3nuSDjU6nO+sVkfymlq/I5Ku0rq4uPB6P8nW5NYdc8ZLbdMgHjBMPmnJ5PhwOn9QcNxKJ4PF4lDdxV1fXafPIJElShjtOjVGuqvn9frKzs0/6nvz75Z+Xr5gjkYhyYD3xQz6oGAwG5QRgNBqVA5ec6MnD12azGZ1Oh9vtZsuWLXg8Hg4cOMDgwYOVip04OPUP8pVyMBhEo9GIodge6MS+evJ7/Ezk48euXbuU0Yvc3FymTp1KUVERWVlZSmInV7c8Ho9y3PP5fHi9XiVRlP+VP+SFAnJMchInN9JVq9XKMaurq+u0YdszHQ/lr8ujLKfe/sRj4onDrXKCZTQalQts+Tk6ceqNwWBQEi/5YldORuVqpHy8jImJwWKxYDKZsFgsyrFSTgSFS8flcnHkyBFMJhOJiYlKK7XeSiR23Ug+aMTGxiobwp+vSCSiJIIej0eZOCtfGfp8Pjo7O5XP5ZK/fOCR5yiduMT9xAPkidRqNYFAgKamJhobG5k0aZKSsJnNZiWBlZM1m82mzE2UrzRP/JAPSPKeeueTlEmSRGFhIa+99hrLli3jlVdeQa/XM2fOHGUo+nx/l9B7SZKE2+0mFAqh1WqxWq3RDkm4CHJ1qrOzkzVr1rBo0SLq6uqYMWMGDz/8MElJSefc7F0+lsnHPpfLpQxpnpj8nfpvIBDA6/UqownhcJiurq7Tfv+JoxCnOnEl6IlUKhU2m+2kOYAnJlvyopITq4cnXtzKFTe9Xt8jh5CFz7S3t3PgwAGl80Vvb7skErseQqVSYbValRPb2foTnW/fonNZtWoV8+fPZ8mSJfzqV79S9tj9vPgu5Ovnw2AwcP/99zNr1iw++ugjfvOb37Bu3TomT57Mb3/7W6VRtNB3hcNh2traCIVCGAwGHA6H+Jv3Qj6fj08//ZRnn32WRYsWMXfuXL72ta9x1113XVCTV7Vajc1mw2azkZycDFz6Y+G5XEwvP/Ea7l0aGhrYsGEDkyZNOuMCo95GJHY9xKkHgkt5YJAkibS0NOx2O+FwmMbGRrKzsy9ruV9+fPJqvWuuuYZIJMLatWtZtWoVXV1dfOc73yEnJ0epBAp9TyQSwel0EolE0Ov14m/dy4TDYRoaGnjttdfYunUrhw4d4uGHH2bixIkMGjRISerO53gmkiQhGtxuN42NjVRUVHDPPff0iUbMIrHrp+SFIJIk0dTURGpqatR2YTAajWRlZTFz5kxlvuHmzZvJz89n9OjRDBkyhLi4OHGA74PkOanhcBidTifm2PUibreb1tZWli9fzoYNG2hra2Po0KFcffXVFBQU9IkTpND3NTU10dTUhMfjYeDAgcTExEQ7pC8s+s3RhKhITExUViAeP34cv98f1XhUKhWDBg3ivvvu43/+53/QaDT8/e9/Z968eezZs0eZPyP0LZFIBJfLJSp2vYS8eCAcDlNXV8eaNWv42c9+RnV1NaNHj+Yvf/kLY8aMEUmd0GvIeyzr9XqGDBlywfPjeyJRseuHVCoVWq0Wu91OcnIy5eXleL3eaIcFgMPh4Morr+Tjjz/m0UcfZdu2bdx99938+Mc/5uabbyYrKyvaIQrdSJ5jJ682F6v/eoc333yTjz/+mJUrV3LVVVfx4IMPMnToUGJiYkRlXehVdu7cSVNTE2PGjMFms13QnNCeqvc/AuGiyIs1EhMTqauri3rFTiavyE1ISODuu++moKCA9evXs2DBAjo7O7nyyiuZOnUqOp1OnED6ALliJ68yFH/Tnsvv99Pc3Mzbb7/NJ598gkql4qabbuK2226jqKgIm83WI3bIEYTzIVefjx49SmdnJyUlJX2mD6BI7Poxs9lMfHw8jY2NZ2wBEC0qlQqdTse4ceOIjY3FaDTyzDPPsHbtWtxut7J5tWgh0PvJ7U5Er66eS5IkvF4vNTU17N+/n/feew+3283o0aO5/vrrmTJlikjohF5HPvbU1tYSDocpKirqM69jkdj1Y/I2Xps3b8bn8ykNPHsKlUpFcXExeXl55Obm8thjj/Hmm2+ybt06Xn75ZTIyMrBYLD0qZuHChMNhnE6nskuJ0LPI81rLy8v5z3/+wwcffEAgEODxxx9n7Nix5OTkRDdAQbhIwWCQsrIyGhsbycrK4oorrhCJndD7xcbGkp6eTmVl5Wn7O/Yker2eyZMnk5yczNq1a5k3bx533HEH999/PzNnzmTgwIHRDlG4SJFIhLa2NiwWi0jseqDW1lbWr1/P7373OyRJYvjw4TzyyCPk5eWJFcxCr+b3+1m7di1arZbMzEzy8vL6TJFAJHb9mMViITExUdnH1uv19siTq0qlwmw2k5eXhyRJBINB3nvvPdasWUNTUxM33HADQ4YMOWdne6Hnkbe3E0OxPYc892j//v3s2rWLDz/8ELPZzOjRo5kwYQIFBQWYzWYxDULo1YLBIPv37ychIYH09PQ+df4QiV0/ZrFYSEpKIhAI4HQ6cbvdPTKxk8XExDBs2DByc3M5fvw4mzdv5tixY5jNZuLi4khMTFTi7ytXXn2dnNjZ7XaMRmO0w+n35FYmDQ0NrF27lnXr1rFhwwbuuOMObrjhBiZMmNBnJpgL/VckEsHv93PgwAGGDBlCZmZmn3pNi8SuH4uNjVXah9TX19PY2EhiYmKUo/p8Go0Gu93OE088weLFi1myZAmPPPII+/bt49Zbb+Xqq69Gp9NFO0zhPEUiEdrb20lPT+/RFxX9hd/vp6mpiW9+85uUlZWRmJjI008/zXXXXack3n3pBCj0Tz6fj+bmZvbs2cONN97IoEGDoh1StxKJXT9mNBqJj4/HZrPR0tJCc3NztEM6J5VKpSzymDhxItnZ2QwYMIBXXnlF2e/v4Ycfxm6394l+RH1dOBymo6MDs9mMyWSKdjj92tGjR1m/fj1vvPEGDQ0NfPWrX2XixImMGjVKtKIR+pSqqiq2bNmCVqtl6NChFBQURDukbtU3loAIF0Wj0aDX64mLi6Ozs5OOjo5oh3ReVCoVKpUKu93OgAEDmDFjhjJEtG3bNt555x3Ky8vp7OyMdqjCOchbiok5dtETDAY5ePAgH3/8MWvWrKG1tZWZM2cyZcoUhg8fTlxcXJ9ZLSgIAI2NjRw8eJDMzEySkpKwWq3RDqlbiZJGP6fRaEhNTaWrq4v29vZoh3PBLBYLJSUl/PCHP2T+/Pm88847/OEPf0Cv13PFFVdQUFAgmhn3UJIknbR4Qsyxu3zkNibBYJCOjg6WLl3KvHnzCIVCjB07lp///OfY7fY+NaFcEOTXfW1tLQcOHGDEiBHY7fY+N31HXIb1cxqNhry8PNra2mhoaIh2OBctPz+fH/3oR7zxxhsMHDiQX/ziF/zP//wPCxcuJBKJRDs84QwikQjBYBCn04nVahXtMy6zQCDAsmXL+NGPfsTDDz9MSUkJjzzyCP/+979JTEzscyc7QQDo7Ozk2LFj7N+/nzlz5vTJfY1Fxa6f02g0ZGRkUFNTQ2tra7TDuSjy0KzZbCY3N5ff/OY3zJ8/nyNHjvDEE0/Q1NTE3LlzycnJES0aehC/309XVxeSJGG1WkXF7jIJhUK0t7fz/PPPs337dmpqavif//kfZs2aRX5+vlKlE1VuoS/as2cP1dXV6HQ6rrjiCmw2W7RD6nYisevn1Go1ycnJeDwenE4n4XAYtVrdKw/qGo0Gq9XKuHHjaG1txWAwsHr1apYtW0ZMTAxer5fCwkIxNNtDBAIBvF4vACaTScyxuwzcbjfNzc1s2bKFdevWEQgEKCwsZO7cuQwdOpSYmJhohygIl1RZWRlOp5PExMQ+179OJhK7fk6u2Hm9Xtrb2wkEAr26cqJSqdBoNFx33XUMHjyYIUOG8D//8z/U1tYyZcoUfvazn5GUlNRrk9e+xOfzKQtcrFarWBV7icjziiKRCFVVVWzatIlHHnmE5ORkbrnlFu6++26ysrLE+0Ho0+T3waefforP52PYsGFYLJY+uTBIJHb9nDzHzmg00tXVRVVVFQUFBX1iyDInJ4eUlBTy8vJ46qmnWLp0KZs3b+bZZ58lPz+/T86t6E28Xi9OpxP4rKei6GN36YTDYf7zn//w7rvvcvDgQSZNmsQjjzxCTk5On1sRKAhnEolE8Hg8bN26Vemm0FcvZvpeqipcELVaTWpqKkajEb/fT319vXJl09tpNBpMJhPFxcXce++93HLLLahUKp588kk++OADysrKoh1ivybPsQMwm81iKPYSCAQCHDt2jHnz5rFgwQLUajVXX301DzzwALm5uVitVrGThNAveL1e9u7dS1dXF4mJiQwbNizaIV0yomLXz6nVaux2OyaTCY/HQ2NjY59J7OCzx5eQkMD06dNJSkqisbGRtWvXotPpCIfDxMTEkJSUhFarFSe3y8zv9+P1etFqtRgMBrEKsxtJkoTf76eqqoodO3bw/vvv09DQwMyZM5kxYwZXXXVVtEMUhMvK6/WyZ88eNBoNycnJ5OTkRDukS0Ykdv2cSqXCYDAQHx9PMBikurq6T7YHsVqtjB07lhEjRvCnP/2JDz/8kGXLlnHkyBF+/OMfk5CQAIiVgJeTz+ejq6sLm82GXq/vE8P/PYHcH7C6upo//OEPbNq0ia6uLp5//nlGjx5NRkZGtEMUhMtKkiQ6OztZuXIleXl55OXl9cnVsDKR2AkApKam0tnZSWVlZZ+q2J1IpVKh1+t56KGHGDVqFOvWrePVV1+lqqqK6667jmuvvRar1SqSu8vE6/XS1dWFw+EQSV03qqurY9euXTzyyCOoVCrGjRvHt771LYYOHSrmMQr9UjAYpK2tjfXr1/PNb36T/Pz8aId0SYnETgAgLi4Ok8lEY2Njn6zYwf+vxsXFxTF8+HAMBgNtbW2UlpayZMkSXC4XN910U5/sRN4TyUOxVqu1T65Mu5wkSUKSJHbv3s327dtZu3YtZrOZyZMnM3bsWAYPHiyeZ6Hfamxs5NixY7hcLoqKikhPT492SJeUSOwEAOLj4zEajRw7dqzPVuxOlJGRQVxcHHFxcfziF79g9+7dHDlyhIKCAoYMGYLdbheT+S+xQCCAz+fDZrOJit0XEIlECIVCtLa2snz5ctavX8+uXbu48847ueuuuxg0aBBarTjUC/1XdXU1Bw8eRKfTMXDgQJKTk6Md0iUlLt8EANLS0rBarVRUVPTZit2pTCYTI0eO5OWXX+aBBx4gFArx5S9/mVdeeYX9+/dHO7w+z+v10tnZKYZivyCfz8fRo0e57777+Nvf/obX6+UPf/gDf/jDHxg8eLB4boV+b9euXWzdupXJkyeTlpbW56ckiMs4AYCkpCTsdjudnZ20t7ej1+v7fMVKpVIhSRLx8fHceOONDBkyhKeeeoq33nqLTZs2MXfuXO69916xU8Ul4vV6cbvd2O12kXxcBEmSqKys5KOPPmLJkiWUl5fzgx/8gCuuuIKSkhLRxkTo9yRJwul0cujQIaqrq/nWt76F2Wzu8+8LUbETAHA4HMTGxipDOn6/P9ohXRYqlQqdTkdKSgojRozg2muvJScnB6fTyZIlS1i5ciV1dXWEQqFoh9rnyEOxYu7XhfP5fJSXl/PBBx+wadMmWlpamD59OlOnTqWkpITExMQ+f/IShHORJIkjR47Q2NiISqVi5MiRfb5gAaJiJ/yfhIQEHA4HkiRRX19Pampqv9o3UqPREBsby/33309KSgqLFy9m/vz5eDwevv3tbzNhwgTi4uJQqVTihNlN/H4/Ho+HmJgYUbE7D/Lc11AoRHNzM6tWreLJJ58kJiaGkSNH8rvf/Q6HwyEW/gjC/5EkiU2bNtHa2kpcXBwjRowQiZ3QfzgcDhISErBYLFRUVFBQUEBKSkq0w4qKWbNmUVJSwuzZs3nwwQf53//9X0aPHs1vfvMbMjMzxUT0buLxeMQcuwsUDof54IMPWLRoEfPnz+fWW2/l5ptv5qqrrurVezwLwqUQiUT44IMPsNlsjBgxot+8R8T4hwB8NiRpMplITk6mrq4Oj8cT7ZCiRqPRkJCQwOjRo3nssccYNmwYR48e5eGHH2bLli00NTVFO8Q+wefziYrdeQoEAjQ3N/Poo4/y4osvUl5ezkMPPcQDDzzAmDFjlCqEqCYLwme8Xi/V1dUcO3aMlJQUxowZ029GXETpQQA+OyEYjUYSExNpaWnB6/VGO6SoMhgMJCUlMWvWLJxOp9JCYsWKFbjdboYMGUJaWlq/OEh0N7nnWiAQwO/3izl259DV1UVdXR27d+9mxYoVqNVqBgwYwLXXXsvIkSP7TRVCEC5EV1cXhw8fxuv1kpqaSmFhYbRDumxEYicozGYz6enp/b5iJ1OpVCQkJPD1r3+diRMn8sQTT/DEE0+wfft2rr32Wr7+9a8rCYlI8C5MMBjE7/cTCASIjY0VFbszkBPgY8eOsXTpUp5++mlsNhvf/e53mTNnDgMGDIh2iILQI0mSREtLC8uXL8fhcJCfn9/nd5s4kUjsBIXFYiEnJ4ePPvqIzs7OaIfTY+h0OoqLi3nyyScZO3YsH3zwAY899hg7d+7k4YcfJjMzE71eH+0we5WOjg68Xi8ajYa4uDiR2J2Bz+fjnXfe4YUXXqClpYVRo0bxpz/9ifT0dKxWa7TDE4QeS14E+PHHHzNnzhzy8vKiHdJlJRI7QWEymUhJSaGlpQW32004HBYnXD6rxmm1WmJiYpg2bRparZb09HS2bt3KCy+8wKRJk5g0aRJ2u11U7s5TV1cXgUAAtVotdp44RSgU4tChQ2zevJm33noLi8XC8OHDmTNnDtnZ2RiNRvF8CcLnqK2tpby8nKamJsaOHUtqamq/OjaLxE5QGI1GkpKS6OzsxOPx4Pf7+3yH7guhUqkYNGgQRqORhIQE9u3bx4oVK/B6vcTExDBq1ChMJpNYNXseTk3sxBy7z6oMwWCQqqoqNm/ezEcffURFRQU33HAD06ZNY/bs2f1m8rcgfBGVlZVUVFQgSRLFxcXExcVFO6TLSpyBBIXZbCY7OxtJkmhtbaW5uZns7Oxoh9Xj5OTkkJaWxrBhw/j+97/P4sWLWbJkCS+88AKjRo3C4XCIk+85uFwufD4farVa7DzB/0/q6uvr+elPf8r+/fsJhUI8++yzXHHFFcTHx0c7REHoFSRJYuvWrZSWljJs2DByc3P7VU9WEO1OhBNYLBays7NRq9U0NzdTV1cX7ZB6LJ1OR2ZmJk888QQ//elPycvL44EHHuBvf/sbK1asiHZ4PZ7T6SQQCChD3P09sauoqOD999/ny1/+MhUVFVx99dW88sorTJ48GbvdHu3wBKFXiEQiOJ1Otm3bRn19PV/+8pf75fxnUbETFFqtFovFgs1mw+1209raGu2QeiyVSoVeryc7O5sJEyagVqt566232LlzJy6Xi2AwyLRp0zAYDGKY8QzcbjeSJGEymfptUievet21axcbNmzg008/RaVSMXfuXMaPH8+QIUOwWCyi+isI5ykcDrN3716am5sxmUyMHTu2X06N6X+PWDgrtVqNXq8nPj4er9crErvzoNfrGTx4MAMGDCAYDPLKK6+wcuVKWltbld07LBaLSO5O0dXVBaBsyN3fkhd56NXpdLJ06VKWL19OZWUlc+fO5Wtf+xrp6en9stIgCF9EKBRi48aNeDwecnJyKCkpEYmdIKhUKnJzc/F6vdTW1kY7nF7DZDLx7W9/m5EjRyo9xyorK3nooYf40pe+REJCQrRD7DEkSaKjowOVStVvhxl9Ph/79u3jL3/5C8uWLWPatGn84he/4Gtf+1q/S3IFobsEAgEWLFhAYWEhkydPxmKxRDukqBCJnXAStVpNRkYGtbW1Yuus83Tiibi4uJjY2FgKCgp44okn+Ne//sWGDRt48MEHKSgoEFWY/9PZ2amsiO1PIpEItbW1vPHGG2zcuJFDhw7xv//7v1xxxRUUFRWJyq4gXKSWlhZKS0upqKjgpptuYuzYsf32IkkcRYTTJCUlEQgExFDsRYiJiSE3N5fJkyczY8YMDAYDpaWlvPfeexw8eJC2trZoh9gjeDweVCpVv2qn43a7qaqq4oMPPmDr1q10dHQwYcIErrrqKkpKSkhNTY12iILQazU3N7Nnzx6MRiM5OTlkZWVFO6SoEYmdcBKVSkVaWhp+v5+WlhZlgrdw/oxGIxkZGfzyl79k7ty5GAwG/vKXv/DRRx9x+PBh/H5/v3hew+Ew4XCYUCik/D8SiSBJkrKzSV/fQUH+O8utTNatW8djjz1GTU0NI0aM4A9/+AMjR44U7UwE4QuQJInjx4+zdu1aCgsLGTBgAMnJydEOK2rEUKxwEnmDcbVaTXt7O21tbaLP2EWyWCx861vf4pprruGNN97g6aefZvHixVx11VX84he/QKfTRTvES6qsrIyWlhY8Hg9xcXHYbDYsFgsxMTG0tbWhUqmIjY2NdpiXXCQS4aWXXlIWSdx0003cf//9SkPr/jpcJAjdxeVycfDgQdauXcvvfvc7MjIyoh1SVInETjiJSqUiNTUVs9lMa2srdXV1YsuniyCfrHU6HSkpKdx0000EAgF2797N4sWLkSSJO++8k8zMzD47HFldXc3mzZtZu3Yter0enU6HVqtFp9OxY8cONBoNFRUVdHV1YbFYMJlMmM1mbDYbw4cPJzMzk5SUlGg/jIvm9/tpbW3lpZdeYuXKlajVar761a9yxx13UFhYiMlkEnPqBKEbbN++ncOHD6PX65kwYUK/22niVCKxE06iUqlISEjAZDIRDodpbGykoKAg2mH1WvI8sqKiIubOnYtKpWL16tUsXbqUzMxMRo4cSWFhIVartc9VbiRJorGxkfXr15/x+1qtlsbGRuXiwWw2Y7VacTgcxMfH99qhFEmS8Hg8VFdXs3//fpYsWUIkEmHo0KFce+21jB07VlwoCUI3kKez7Nmzh/r6etLS0sjJyemzF8vnSyR2wmkSExOx2WxIkkRNTQ2hUCjaIfV6KpWKcePGkZOTw+TJk/n2t7/N73//e6ZNm8Z3v/tdRowYgVqt/tzk7tQ5eT09EZTnupxNKBQiFArh8XhO+rrBYOCuu+7qMa1QTnzez/Wcy3PqysvLmT9/PvPnz0eSJH7/+98zfvz4z30+BEG4cJIksX79elwuFzNmzMBsNvf7CycxDiCcRqVSkZiYiMPh4NixYyKx60aJiYlMmDCBRYsWMX36dPbu3cttt93GggULzrmFm9/vp62tjYqKCnw+32WK+OLl5OSQm5t73i1N1Go1VquVW265hYKCgh6zsCIYDHLkyBH8fv85b9vR0cG7777L3XffzYcffsiwYcNYtGgR1113Xb9epScIl4LP52PTpk0cOnQIu93OLbfcIqY3ICp2winkioTdbsdms1FfX08kEolyVH2HRqPBYDCQmZnJ7bffTm5uLhs2bOC1116jvr6eMWPGKFuUyX8LuQq0b98+1q9fj9vt5p577iErK6tHV+20Wi0Oh4PCwkL27NlzzgsESZLQaDRcddVVJCYm9ogDdDAYpLGxkWeeeYarr76aoqKi06pukiQpWxnt3LmThQsXkpSURElJCRMnTiQnJwej0djvqwiC0N28Xi+rVq3CbDaTnZ1NTk5Ojz4mXi4isRPOyG63ExMTQ0NDA+FwONrh9ClqtRqTycRVV11FSkoKOp2OF154gVAoREdHBxkZGWRkZKDVapXkpqWlhU8//ZQFCxYQCASYPHkyCQkJPbqzurzqtaioiAMHDhAOhz+3xYtWq8VmszFu3DgcDsdljPTs2tvbOXjwIG+88QbhcJhgMEhqaipGoxGVSkUkEiEUClFVVcW6detYt24d+/fv56tf/SozZ85k/Pjx5xxiFwThwoXDYTo7O1m/fj0pKSnk5eWJtkH/RyR2whklJCTgcDjYu3evGIq9RFQqFUOHDmXQoEEMGDCAp59+mhdffJEdO3bw/PPPk5ycjNFoBOCll15iyZIlbN++HUmSWLJkCZIkMXXq1Og+iHOIj49n7NixvPvuu597O41GQ2pqKmPGjCEvL6/H7O+4fv165s+fT0dHB//6179oamoiPT2dUaNGoVKp8Pv9NDY28u1vf5sjR44QExPD448/ztVXX60k3SKpE4Tu53K5qKysZPXq1fzhD39g3Lhx0Q6px+gZR0+hx0lMTCQuLo6amhoCgQCRSKRHDI31JSe2RLn66qtJTU1l48aNvPzyy9x///3cfvvtjBs3jsrKSl577TUqKiqUYfE333yTrq4uSkpKsNvtPTZ5SEhIYOzYscDpiz9OFA6HGThwIHfccUePeJ1JksTevXtZuXIly5cvV4bD161bR3NzM/Pnz6e9vZ3t27fzr3/9i46ODu6++25mzJjByJEjMZvNPfZvIgh9gdw6Kjk5mfHjxzNo0KBoh9RjiMROOKPY2FjsdjterxeXy4Xf78dkMkU7rD5JHrIsLi5Gq9XS3t7OqlWrWL16NYcPH6axsZGampqTVo82NTVRVlbGunXrmDt3bo+pcJ3KZDKRlpaG1WolGAyetfprs9nIzMxk6NChUU+I5Dlzy5YtY+/evcouGZIk4XQ6OXjwIG+99RbNzc0cP34cv9/PDTfcwPTp0xk6dGi/aLosCNEi7+RSXl7O3r17GT16NMnJyeL8dIKeeTYQos5utxMfH084HKalpYXU1FTxxrnEEhISlL1mq6ur2b59O++//z6BQOC0eY6hUIiKigreffddpk+f3mOX+BsMBhISEkhKSsLr9dLV1XXabeRt7PLy8sjPz49ClCcLhUI4nU4WLlzI4cOHUalUJ20N1tHRwTPPPENHRwfx8fFMnz6d73//+zgcjj6/m4gg9AQul4sjR45w+PBhfvzjHxMbG9sjKv09hXgmhDNKSkoiPT0dlUpFVVUV7e3t0Q6pX5B3qnj88ceZPn26crA6dRgzHA4rid369etpaWmJRrjnRa1WM3XqVNLS0s6YfKpUKr7yla8wefLkKER3urq6Op588kkOHz6M0+k8aVW4JEkEAgFqampIT0/ny1/+Mv/4xz9ITEzssVVTQehrPvjgA/bt24fJZOK2227r9ztNnEokdsIZ6fV6ZReAhoYGXC5XtEPqF+TE4fHHH2fLli1nrNadKBgM8tRTT7F///4eu8hFrVYzfPhw7Hb7aa1z5J055DkyKpUqqkOxzc3N7N27l/nz59PZ2XnWVj/hcJjKykq2b9/OqlWrCIVCUR9CFoS+TpIkIpEIS5cuVRaPxcbG9sjRimgSiZ1wRnK/tfj4eFpbW3G73dEOqV/o7Oxk586drFu3Ttn141yLDnbv3s3+/fupqqq6jJGeP7VaTWFhITExMac9FoPBwMCBA8nMzIzqThPyUGtZWRlbt26lurr6nImy2+2moqKCRYsW0dzcfF4NjAVBuHiBQIC6ujoOHz6M1WplzJgxaLVacVF1CpHYCWel1+tJT0+nublZmUAuXDrhcJiqqir+85//UFpaSltb2zmbQ0ciEVpaWli/fj3r1q0jEol8biIYDWq1miFDhuBwOE46AKvVamJiYrjmmmuIj4+P+lBmMBjkk08+YeHChef9M1VVVfz73/9m3759tLe397jnXhD6ks7OTjZv3kxDQwOZmZlMmTIl2iH1SCKxE87KZDKRl5dHbW2tmGN3Gaxbt47XX3+dV199Fbfbfd5JQiQSYdGiRbz66qtUVVX1yIbSDoeD7Ozsk7bVkiQJm83GvffeG/WGxOFwmNdff51Vq1Zx+PDh8x7WliQJv9/Pgw8+yOLFi8UFkCBcQo2NjcybN48RI0YwatQocnJyoh1SjyQSO+GsRMXu8tLr9cTHxzNkyBD0er2yY8H5DDMEg0GqqqqYN2/eSW1RegL5MeTk5FBYWIhGo0Gj0ZCdnc3IkSNJTk6O6mpSv9+v9KY7evToeSXG8mNSqVSo1Wri4uIIBAK0trZehogFof9pbW2lsrKSffv2MXnyZAoLC8WuLmchlnEJZ6XT6UhOTsbpdNLV1UUoFEKj0Yg30iUSFxdHYWEhM2fOJDY2lo6ODjo7O2lvb8fpdBIOh5XWG6eKRCK0trbyySefcPfdd6PVajGbzVF4FGeXnp6u7OUoSRLZ2dmMGjUKk8kU1deU0+lU9nl1Op3nvL1KpcJmsxEbG6ssMBozZgzJycmXIVpB6J9qa2s5cuQIXV1dDB8+nPT09GiH1GOJxE44K4PBQFZWFl1dXTidTjweDzabLdph9VlFRUUUFRVxww030NnZyZ49e9i1axdLlixh06ZNeDweVCrVGRdUSJKEy+Vi586drFq1iqlTp1JUVNQtCVN3zRvLzc2lsLBQGeYcMmQIs2fPvqj76K5EUJIkDh06xJNPPonH4znrnEa5MgefLSwqLi5mypQpjB49milTphAfHy/6aAnCJSAfGzZv3sz69evJzc1lxIgRpKSkRDmynkskdsJZyXPstFotbW1tVFdXM3jw4GiH1S9YrVbGjh3L8OHDuf3226mtreXQoUPs3r2bJUuWcPToUTweD1qtlmAwqPycJEn8+c9/JhQKkZmZidVq/cJJkLwTg8fjwev1EggECAQCeL1eIpEIoVDojMO/gUDgpN0mvF4vnZ2dqNVqsrKyMJlMtLS0sGHDBuVnVCoVFosFtVqNVqvFaDRiMBiUf00mU7e2Nti6dStLly5lzZo1+Hy+k76n1WqJRCJoNBry8/P50pe+xPDhwykpKSEtLQ2j0YhOp8NgMIgqtiBcQo2NjWzatIkjR47wwAMPYLVaox1SjyYSO+GsdDodiYmJ6PV6PB4Pzc3N0Q6pX5DnbhkMBgwGA1arFbPZjMPhICsri0GDBnHkyBGOHz9OZWUlu3fvxu12K73UGhsb2b59OwMGDGDq1Km43W68Xq/y4ff7lQ+v14vP5yMQCODz+fB6vUoy5vf7CYVChEIhwuEwgUBA+b/8dXkVbiAQOO1xhMNhIpGIMmdNHi6WJImOjg7Wr19PTU3NaY9dr9crFTKtVqt8aDQadDodOp0OjUaDVqvFYDCg0+nQ6/VK4mc0GtHr9RiNRkwmEwaDQfncarUqt1Gr1Sxfvpx169bh8/lQq9VEIhF0Oh0JCQkMHTqUjIwM0tLSlPmBqamppKamir1gBeEyCYfDrFu3jqamJux2OxMmTBAXU+cgEjvhrDQaDbGxsZjNZrxer0jsokSuYpnNZrKyshg9ejQVFRUcPHiQTZs20dzcTFNTE263m0gkQiAQYO/evVitVqUPYWdnJy6Xi87OTtxut5LEdXV1KYmfx+PB7XafVJGTGyRHIpGTFnOcODQJnHEY8sTbysLhMCkpKZjNZlpbW8+4Y4Y8HCr3lpObksr/l5M6nU6nJG4GgwGz2awkwUajEYvFgtVqVRI8+fmIiYnBZrOh1WpZtWoVu3btQqVSYTQa0Wg02Gw28vPzmTFjBiNHjqS4uJjU1FTlMQmCcHnI2/itWrUKn89HdnY2xcXFYuu+cxCJnfC51Go16enphMPh06orwuUXDAapr6+nubmZSCRCVlYW119/PeXl5dTU1FBeXk5LSwt79uxhz549PP/88yf9vMFgICYmBqvVis1mIy4uTkl00tPTcTgcGI1GjEYjNpsNk8mEXq9Hr9ef9LnBYMBisaBSqdBoNMTExJwWq1xdMxgMyteam5tZuXIl06dPR6/Xnza3LhKJ4HK5kCSJUCikVBTlD7fbfVLFUU5UPR4PnZ2dtLW10dTURFdXFy6Xi7a2Nrq6uvB4PCcNWcNnFy7hcBiNRqNMOygsLCQnJ4eCggJycnJISEgAPjvBiKROEC6vYDBIW1sb77//PrNnz2b27NkiqTsPIrETzkpevZiRkUFnZye1tbXRDqlPCwQCdHZ2UldXx7Fjx2hubqatrY36+nrq6+tpbW2lvb1dqaSpVCp0Oh1xcXGYzWYSEhLIzs7GZrOdNLR6/fXXk56eTkxMDGazGY1Gg1qtVoY61Wq18jX5XzlhO7E6J3+cWrE7tXonO1PFLi4ujquvvhqr1YparT7jookTF+hEIpGTqnWnfi5XE+WPcDh80hCw/H85SWxtbaWxsZGamhrWrVuHw+FAp9MhSRJOp5OmpiaqqqpYtGgRwWBQqQ6azWZSUlKIi4sjISGB9PR0kpOTSUxMJCMjg5ycHAwGg9jaSBC6UXV1NR999BEAo0ePZvLkyeIC6zyIxE74XCqVisTERFwuV4/eaL63CAaD+Hw+WltblTYyLpeL9vZ2Ojo6cLlcyhBlMBgkHA4TDAYJBoMYDAaSkpKUIUZ56NFut5/0udVqJRQKKUOq48aNIz4+HrPZrMxfixatVnvOZsSXYgeKSCRCMBjE5XLR0dFBc3MzaWlpWK1WtFot4XAYl8ulDE27XC5lJbg8TA2ftUZxu93U19crizscDgcpKSlKJdRut+NwOJSqaEJCAnq9XiR9gnABQqEQ9fX1rF+/nuLiYnJycoiLi4t2WL2CSOyEc0pOTubYsWM0NTUBYljqXOQq1KkVo1AopAwXlpaWUllZSUNDA7W1tcrz6/V6lT5pSUlJJCUlkZ6eTkZGBomJiSQlJZGVlUVaWprSR004N7VajcFgIDExkcTERAoKChg/fvxZby9JEu3t7bS2ttLc3Ex1dTW1tbW0tLTQ1NSkfO5yufB6vUiSpFTzsrOzycvLUxZaDB48GIfDoVT05IUg8geIuXuCcCq3201VVRWbNm3iwQcfJDs7G71eH+2wegWR2AnnlJ2dzcaNG6mpqVHmJAmfLxQKcfToUSoqKqiqqmLv3r3s2LGD48eP09zcjCRJxMfHk5KSQk5ODjNnziQzM5P09HTy8/MpKChAp9MpJ/yz/StcOg6HA4fDQV5eHuPGjVMS9hP/lauv+/bto6KiguPHj1NRUcHSpUupr69Xqtzx8fGkp6dTVFTE6NGjyc/PV1Y4izlDgnC6FStWsGbNGkKhEHfeeaeygEk4N5HYCeeUmpqKxWLB5/PR1NREYmKiOBmdQJ6btXfvXg4fPszx48eprq6mvb2dUCiEVqslPj6ekpISpk2bRkJCAnl5ecqcN5PJhMViURYtyKs6z3c7MaH7nfi8f97fQK1Wo9PpsFgslJSUKPMa5cUc7e3tHD16VNlvuaWlhVdffRWNRoPBYCA+Pp5BgwaRk5OjrPhLS0sTlQmh35Lnzq5bt46qqipmzZqlTGcQzo9I7IRzSkxMxGw2EwwGaWlpUSac9zfySk25dUhbW5syH66uro7Dhw/T0tKifN9sNpOYmIjD4SA7O5sBAwaQmpqqVOm6u9mucPnJw6lGo/G07wUCAdxuNwUFBVRXV5807N7R0YHH46GpqYlQKERtbS0HDx7k6NGjZGdnY7fbiY2NJSEhQRnGvRRzDwWhpwmHw1RXV3PkyBGCwSCTJ09WWhEJ50ccKYRzSk1NJSYmhnA4zPHjx8nOzsZkMkU7rEtOHnKTV2FGIhE6OjooKyujtLSULVu2sG3bNqWHXFxcHCNGjGDw4MEMHTqU0aNHk5GRgd1uF9tN9UNymxiHw8HIkSMBlNfR8ePHqampobS0lM2bN1NaWsry5cvp6OggISGB3NxcBg8ezIQJExgzZgwpKSnKjhwnrkwWhL5Ebna+atUqjh8/Tl5eHnPmzBEXNRdIPFvCOdlsNhwOBxaLhWPHjjF69Ohoh3TZOJ1Odu/ezb59+1i/fj2bNm3C7Xaj0WgYMGAAkydPprCwkKKiIq644gr0ev1Jk+PFCVg4lVqtVuZTjhkzhrvuukvZlu3gwYPs2LGDI0eOcOjQIf773/+i0WhITEykqKiIadOmceWVV5Kfn4/dbo/2QxGEbud2u/nHP/6h7JyTnp4ujqEXSCR2wueS31AxMTHEx8dTW1t7WqPXvkIeaj148KCys0NpaSktLS34/X5MJhOzZ88mMzOT1NRUpY+Z3N7C4XCIRE74XCcufpH7CMJnrz2z2cyQIUNITk6mo6OD9vZ2qqurOXr0KE1NTbS0tLBgwQJWrlxJSkoKAwcOZNSoUeTm5pKamqpcSAhCb9XQ0MDu3buprq7mxhtvZOzYsWK04yKIxE44L1arFYfDQWNjY59K7CRJwu/343a7cTqdNDQ0sH37do4cOcLhw4epqanBYrGQmJjIoEGDGD9+PIMGDVI2sRcnUqE7qFQqtFotCQkJym4XkUgEt9vNvn37OHz4MHv37uXTTz+loqKCyspKampqaGtro7i4mIKCAhITE4mLi8NoNPbLObBC7yZJErW1tXz66adotVqKiorIz8+Pdli9kkjshPNit9tJSkqisrLyjBu+9zYn7nggH0zWrFnDu+++S1dXFykpKYwaNYqf/exnjB07lqysLMxmcxQjFvobtVqNzWZj/PjxjB8/HkmS6OzsZNeuXezevZtVq1bx1FNPAZ8tcPryl7/MDTfcQF5eHsnJycrvERcfQm8QiUTYv38/7733HjNmzKCoqIjExMRoh9UricROOC9xcXGkpKSwevVq/H5/tMP5wsrLy9m3bx8LFixg+/bthMNh7HY79913H9OmTSM7O5vExERlk3mxIkvoCaxWK1dccQUjRozg9ttvp6amhj179rBjxw4+/vhj/vvf/5KcnMzQoUO5//77KS4uPudOH4LQE6xdu5ZNmzbR2NjIc889R0ZGRrRD6rVEYiecF5vNRnx8PG1tbXi9XoLBYK8b7nG73dTW1rJp0yZKS0uprq6mpaWFMWPGkJGRQW5uLkOGDKGgoAC73X7GFhaCEC3y/E2536HNZsNsNmOz2cjKyqKgoICysjJaWlqoqanhhRdeYOjQoRQUFDBs2DBycnLEBYrQ48j7Pq9fv57GxkYGDhxIXl5ev+i8cKmIxE44L1arlfj4eLq6uujq6sLv9/eKxE5ePu9yuairq2PHjh3Mnz+f48ePI0kSY8eO5eabb2bw4MHk5uaKxQ9Cr6FSqbBarRQWFlJQUMC0adPYsWMH27dvZ+3ataxevZqysjIGDRqkrOR2OByYTCbR7FXoMeS5pJs3byYcDjNu3DgSEhLERcgXIBI74bzExcWRmZkJQF1dHa2trb1in9JwOExpaSkvvPAC69ev5+DBg0yfPp3vfOc7TJkyhaFDh550e5HUCb2VWq1mzJgxjBkzhm984xts27aN+fPns2nTJt58803Gjh3LXXfdxZe+9CUKCwujHa4gANDV1cWSJUsoKytj+vTp3HfffWIl7BckEjvhvMTExJCcnIxWq6WhoYG2tjays7OjHdZZ+Xw+9u/fz/Lly3nrrbcwmUwMHz6cn//854waNYqEhASsVqtI5IQ+4dTXsVarpaSkhMzMTOrr69m3bx9vvvkmL730Eu+++y433HADt956K/Hx8b2i8i70TZIk0dbWxosvvkhRURFjxowhKysr2mH1eiKxE86LVqvFbDYTExNDR0cHLpcr2iGdUSgUoqmpiU2bNimTyuPj4xk1ahQlJSVMmjSJjIwMUeYX+jR5mNZqtZKQkEB8fLyyora2tpYVK1agVqsZNmyYsvpQXOQIl1tLSwtHjx6ltLSUu+++m6KiIjG3uRuIxE44L/Jm53LzVKfTGe2QTiPv47pjxw6eeeYZqqurMZlM/PSnP2X69OlkZGRc9pOXPDE4EAgQDAaJRCLKFmXy1lDyc6vVapUto76oYDBIMBjE5/MB/78hbkxMzHk9B5IkEQwG8Xq9hMNh5XdYLJbznp8ViUQIBAJ4PJ4zfl+v1yt7oMoxSZKEz+cjEAgo9yvTaDRYrVbleRPOj9lsJi8vj+9///ts3bqVtWvX8sILL1BaWsqsWbO4+eabGT9+PHq9XgyBCZeF3G6qoqKC7du309LSwsSJEykuLo5yZH2DeBcL502n05Gfn09TUxPNzc3RDuc0O3fu5LnnnuPWW29Fp9Px3e9+l5UrV3LXXXdFbem8JEk0Njby6quv8pWvfIVx48aRn59PRkYGgwcPZurUqdx7773MmzePHTt2dNvzumXLFh599FFSU1NJSUkhLy+P6dOnEwqFzuvnPR4P77//PuPGjSMlJYXU1FSysrJYtmzZecfQ2trKyy+/TEpKyhk/HnroIdatW3fSz3i9Xv7yl78o93vix7Rp0zh69GifapB9OalUKq644gp+8IMfsH79eqZNm8bGjRu5++67ef3113vke1rouyRJYs2aNbz//vtcf/31DBs2jKSkpGiH1SeIip1w3rRaLRkZGRw5coT29vZohwP8/03VX3vtNdasWcOBAwe45ZZbuPXWWxk0aBDx8fFRrfAsXbqUNWvWsGzZMiZMmMD06dNJT0/HZDLh8XhoaGjgyJEj/Pvf/+aDDz5g4sSJ/PrXv/7C91tcXExCQgITJkzgT3/6EzU1NReUEBkMBiZMmMBzzz3H+vXreeeddzh69OhJjZ3PJSYmhquvvpq3336bLVu28MEHH9DU1MS0adO45ZZbKCgoOC3h1uv13HbbbUyaNIkdO3bw+uuvM378eEpKSigqKiI9Pb1bNgSXK4PPPvssx48fJzk5mR//+McYDIYv/Lt7ohO3MtPr9SQlJfHQQw+xceNG1q5dyzPPPMPx48eZNGkS06ZNE6vDhUtu69at7N+/n66uLu644w7sdrt4zXUTkdgJ502r1ZKcnMyOHTtwuVzKST5ab0ZJknC73Rw6dIhPPvmE+vp6HA4Hc+fOZdy4ccrerdGKzeVysX37dtatW4fT6WTkyJGMGDGC3NxcrFYrTqeTqqoqbDYbK1asYN++fd3WTDY+Pp74+HiKiop49dVXL7gao9VqSU9PJz09Hb/fz+rVqzl69OgF/Q6DwUBOTg4ZGRkEAgHWr19PZ2cn+fn5zJo1i9jY2DPe78CBAxk4cCAGg4ElS5YwdOhQpk6dyqBBgy7o/j+PvC/wjh07OHToELm5uacN/QIcOnSIyspKrFYrJSUlvWIl+Lmo1Wr0ej3Dhg1Do9Gg0WgoLy9n69atRCIRkpKSGDx4sDjJCpeEfDG+adMmmpubiY+PZ+TIkWJuXTcSiZ1w3rRaLZmZmcoG5ZFIJKpzciKRCLW1tTz55JOsWrWKuXPncueddyoVh2iSJImjR48qe3vecMMN3HjjjcTHxyu3SUxMJDExkeLiYsrKyli1alUUI+6fNBoNWq32rItp5s+fz9NPP01RUREvvfRSn2kTIr8/hgwZQl5eHklJSTz66KMsXLiQtrY2/vrXv2I0GqP+PhL6Hnl/7nfffRer1crEiROjMv+5LxOJnXDe5Dl2AE6nk4aGBtLS0qIWz4oVK1i2bBnvvfcejz/+OOPHj2fgwIFRi+dEkiRx5MgRnE4nBoOBvLy8sw4h6nQ6rrnmGkpLSy9zlP2XvGr0n//8J+FwGI1G02873RuNRubOnYvFYmHRokW89tprFBYWMmfOHLEJu9DtGhsbWbZsGUePHuXrX/869913X7RD6nNEYiecN41GQ1paGjqdDrfbTWNjI6mpqVFZaVpXV8eaNWvYuXMn11xzDePGjSMrK6tH9eQym81otVp8Ph+HDx8+68IFjUZDcXEx3//+93tU/H2Z/JrtC0OrX5Q8727YsGF0dnZSU1PDO++8Q0ZGBg6H46QqsyB8EaFQiPr6ej744AOGDh1KcXFxVM4hfZ1I7ITzptFoSEhIwGAw4Pf7aWpqikockUiE/fv3s3//fjo6OvjGN75BXl4eFoslKvGciUqlIiEhAYvFQjAYpLS0lMOHD5OTk4PNZlNilduQpKamkpqaesbfJUkS4XCYrq4unE4nLpdLWQihVqsxm83Y7XasVitms/mCY5XbmjQ1NdHV1UUkEkGlUmEymUhKSsLv91/8E9HN5LmLra2ttLW1KV93OBykpaVRX1+Px+MhGAyiUqkwm83ExcURExOjtGnx+Xx0dXVRWVmp/LxarWbo0KFotVqCwSBtbW3U19dTV1dHOBzG7Xazf/9+pX+jSqUiPz+f2NhYpaWN0+mks7PzrH8fs9ms/H164oksJSWF4cOH09zczCOPPMK+ffvIysoSiZ3QbTo6OqioqGDbtm3cc889FBQUiIurS0AkdsJ502g02O12YmNjCYVC1NTUXNAqye4gT3r/xz/+QUdHB6NHj+b222/vcSdKlUrFsGHDyM7OZvPmzWzevJmf//znXHXVVcyYMYOxY8eiVqtPe/5OfRzy9zs7O9mwYQOLFy/mk08+oampiXA4jMlkYtSoUVx//fVceeWVjBgx4oy/50zk393S0sK+fft47rnn2LJlCx6PB61Wy5AhQ/jOd77T49pgbN26lZdffpn58+crX7v55pt59NFHefTRR9m+fTtNTU1oNBpGjhzJbbfdxlVXXaV0tK+pqWHDhg18/etfJxKJAGAymaiqqiIuLo7m5mbefPNNHnvsMaVf4549e7jpppuU+9NoNHz44YfMmTMH+Cw5XrNmDStXrmTFihXU1dUBYLFYGDVqFNdddx2jRo1i+PDhPbpXXG5uLnfeeSevvfYa69evx+/3M2rUKKBnJqNC7yAfazZv3syqVatwOp3ceuut5OXlRTmyvkkkdsIFS0tLU1Z0Xu7Ezu12U11dzebNm/nKV77Cdddd12NPOGazmXvvvZfc3Fz+/ve/s337dvbu3cu//vUvkpOTGT58OIWFhQwZMoTJkydjNBpPm8QfDodpb2/nu9/9LocPHwbgkUceYeDAgeh0Otrb25k3bx7//Oc/WbBgAd/97ne59tprz3u+WE1NDU888QRLly4lEAjwyCOPUFRURFJSEq2trSxcuJCysrKoJPFnM3HiREpKSvjhD3/IL37xC7Zt28aePXt48sknueOOO/jhD39IOBzm8OHDPPLIIzz33HPs2LGD5557DoCcnBxSUlKYMmUKjz/+OOvWrTtpxW9ycjLf+MY3uPnmm3nyySd55ZVXyM/P589//jM5OTnAZ0lOSkoK8Nnf6E9/+hNr167F6/Xy61//mqKiIlQqFR0dHbz33ns8/fTTDB06lB/96EeMGjWqx75mVSoVRqORb3/72/zrX/9iy5Yt1NTUkJ6eLnZrEb4Qj8fDkiVL2LlzJ/fccw9ZWVk9apSlLxGJnXDBEhIS8Hg8NDY2Xvb77uzs5ODBg0QiEXJzcxk0aFCPPEnKfcByc3PRaDQEg0F27txJbW0tjY2NHDt2jK6uLg4dOsTOnTvZtWsXV1xxBfn5+SftwdvZ2cny5cvZs2cPNpuNsWPHcuWVV5KamopGo8HtdnPjjTfy73//m2PHjvHOO+8wfvx4kpKSzrlDRCQSYdWqVezevZvOzk5mzZrFlVdeSXZ2NlarleTkZGVoc/fu3Zf4GTs/8vCq2WxW/q9Wq7FYLGRlZVFcXKwMHTocDrKysqivr6e0tBSPx4PRaESn06HT6bDZbNjt9tN612m1Wux2O3a7HYfDgVqtxmAwkJmZecYKgyRJ7N69m/b2dlJSUhgzZgzp6enAZyczn8/H1q1b8fv9+Hy+HpMgn4lKpUKj0TB69Gj++9//0tDQwMGDB0lOThaJnXDRwuEwW7dupbKyEp1Ox6xZs5T3rtD9xLMqXLCEhAS0Wi2NjY2X/STlcrkoKyvDarWSkZERtR0lzldCQgLDhw/nhz/8IV/96le59tprlcTM5XJx4MABPvroI/7617+ycOFCdu3aRSAQUJ5Xl8vFBx98QG1tLZmZmVx33XUMHDiQ2NhYrFYrSUlJ3HTTTQwZMoRwOMzHH39MXV0dXq/3nLGFw2GWLl3KsWPHsFgsfPnLX6a4uJjExERMJhPJyclcffXVjBgxQtma7GKdmHxfitdMUlKSsoAmJiaGmJgY8vLyKC4uRq1W09DQgMvlUoZeu1tDQwOSJBEbG6v0ItTpdMTFxfGlL32JUaNGkZOT0ysaIKtUKgoKCkhKSkKSJEpLS897xxJBOJW8peInn3yiXPxMnjz5vLcmFC6cqNgJFyw9PZ1Dhw5x6NChy57YdXV1UVFRwaBBg6LagPhCyKsOZ8+ezezZs5V5grt27WL79u2sWrWK999/nxdeeIG9e/eSk5PDkCFD0Gq1dHZ2smzZMjweD1lZWUyYMOG036/T6Rg0aBDFxcWsWLGCzZs3Yzabz9gA+ESRSISNGzfS2NjIsGHDlOHgEzkcDtLT08nOzqa8vPyinwN59w+5Oen5kBeNnM/OIQ6HgyFDhpxWAUhOTsZisdDV1UVTUxN2u71bdq44kUqlYvz48SxdupSVK1fy85//nJtvvpmBAweSkZGBWq3mmWee6db7vNTUajWZmZnU19dz5MgRkdgJF01eaPfSSy8xa9YsZs2ahd1uj3ZYfZpI7IQLlpqaitFopKmpCa/Xq3SvvxzC4TBerxej0XjS5vE92Zli1Ol0DB48mJycHCZPnkxMTAwrV66kubmZ1atXK/34/H4/XV1dSp+1U+fOndi2IyYmBoD29vZzVuwikQjBYBCPx4MkSRgMBmV489Tfr9frv/CB2GQyodFolBWm53NBIK8EltvGfB6dTnfG+TparVZZpHKpqnUajYaHHnqIkSNHsnv3bkpLS/nlL3+JWq3GarVSXFzMlClTKCoqoqio6JLE0J3k14DBYECn0+H1env08LHQsx07dowPP/wQlUrFxIkTmTJlSq84bvdmIrETLlhcXBwWiwWfz6c04L1ciZ1arUan0xEIBM64BVRPIUkSzc3NlJaWEggE+NKXvqR878RkzGq1YrfbGTlyJNu3b6erq4uGhgal5YjcDgU+S8bOtttHOBxWng85mTmXE3+3nPic7ee+yIldpVLhcDjQ6/WEQiFaW1vPmWRFIhH8fj8dHR3ExMSccwhTnht2pq9/0ZPI+fx8dnY24XCYxMREkpKSqKiooKOjA5fLxcGDB3G73dTW1gIwcODAXjG3KBgMEgqF0Ol04kQsXBSv10tVVRUbNmxg6NChyi4nwqUlEjvhgiUmJmK324lEItTX1xMbG3vZ9vnT6XTExsZy+PBh/H4/kiT1yJOOJEmUl5fzwgsv0N7ezvTp09FoNGeMVa1Wk5+fj8ViweVyKRPsNRoNBoOBmJgY2tvb8fl8uN3uk4ZY5YSrq6uLzs5OJYk616pYOUG2Wq24XC6lMhgTE3NSgiTPj+ns7Lzo50JeQSr39KuuriYQCHxuIun3++ns7KSlpYXk5OSL6s/XXc7UgqarqwudTqdsSdbZ2Ul6ejq5ubnMnDlTaQe0f/9+XnrpJd577z127dpFKBQiPz+/Ryd2cl++zs5OPB6PsoBEEC5US0sLZWVlbN68md/+9rfk5uaKuXWXgXi3ChcsPT2dxMREVCoV5eXluN3uy3bfdrudkpISSktLqauru6z3faHkk2NdXR07duwgEAic8XbhcJi9e/ficrmwWq3K/DoAm83GnDlzsFqtVFZWsn79+tN+PhAIUFpayv79+zEajUyYMEHp2fZ5VCoVkydPJi0tDZfLxerVq09bJOF0Oqmvr6e8vPyiq3YqlYqsrCzy8vKIjY1lx44dHDt2TOkRdyZbtmxh165dJCQkMHDgwKjNydHpdBgMBoLB4EkLWqZMmcLvf/97Vq9eTTAYZMyYMTzzzDNUV1cDn1VNc3JymDNnDi+//DIFBQXU19ezZs2aSzYk3J2cTifl5eW0trYycuTIbp+XKPQPr732GitXriQ9PZ0777zzvI5Lwhcn3q3CBdNqtVitVuLj4zl+/PgXXjF5IeLi4hg1ahQ6nY6ysjK2b9/O1KlTL9v9XyhJkqitreUvf/kL9913H/n5+SQnJyuVsrq6Og4cOMCCBQsIh8MMHz6cadOmKVuLORwO7r33Xvbu3cuxY8d4+eWXSU5OZsCAAeh0Ojo6Ovjvf//L9u3bMZvNfOUrXyEtLe28KqgajYbbbruN1tZWdu7cyb///W8cDgcFBQU4HA7a2tpYuHAh69atw2Aw4PF4Luo5kIdDp06ditfr5d///jdPPPEE06ZN48orr6SgoACTyUQwGMTlcrFr1y7eeustnE4n9957LyaTKWpV2bi4ONLS0qitraW5uRmLxUJdXR3V1dXEx8crbU38fj+rVq0iGAxyxx13kJycrOySsWrVKtra2oiNje2x7XlOFAqFWLlyJa2trcTFxTFy5Eix1Z1wQeSK9caNG/H7/dx+++1YLBbRMucyEYmdcMHUajUmkwmHw0FTU9Nl3XLKZDKRkZFBVlYWNTU17Nixg/Hjx/fIeUDyibylpYWamhq2bt1KfX09CQkJ6PV6urq6lJ52gUCAYcOGMWHCBLKzs5WhL5PJxIgRI5g6dSqlpaXU19ezdu1aysvL0Wq1uFwutmzZouxAMXv2bGJiYnC5XLS0tFBRUUFDQwNut5tAIMDy5cvJyMggMTGRlJQURowYwfjx45W5MGvWrKGyspL4+Hh8Ph81NTX4/X5iY2Pp6upi165dxMbGKpW0C6nkFBcX43a72bNnD5WVlWzbtg2v10tlZSUmk0lZGHP48GG6urpITk5m+vTpJ/1t5SpoTU0NBw4coKmpiWAwSF1dHatWrSI/P5+0tDT8fj+ffvopR48exeVy4fV62bx5M263m8TERCwWCwcOHKC8vByn00k4HGb16tVkZmaSlJSk9KvLzMykpKSE+vp6tm7dyrFjx2hvbyc9PZ2srCySkpJQqVSMGDECr9dLaWkp69evx+FwKEO2mzdvJiEhgaysLEaPHt2jhzXlv8HatWuBz+YOpqWliROycEHk919jYyPZ2dlMmTKl1yx26wtUkljuJFyExYsX8/zzz2O1WvnJT37CyJEjL9t9B4NBfve737FixQosFgtvvvkm8fHx59UW43KR5ykdO3aMw4cPs27dOj799FOam5tpb2+ntbUVg8GAw+EgIyODOXPmcO2115Kfn39atU3eh3TDhg0sWbKE5cuX09zcTCgUwmQyMXLkSK677rqTthRbt24dixcv5u9///tpsd16663MnTuX22+/HYDGxkYOHDigbCnmdrvRaDQUFhZy//3309HRwdKlS9mwYQORSIT09HRmz57NX/7yF2w22wU9L3JT5hdffJEDBw5QVVVFc3MzkiQpFwsjR47khhtuYMSIEQwaNOi052LPnj3885//ZN68eSd9z2Aw8L3vfY8777yT+vp6Zs+efdr9T548mXHjxlFYWMg3v/nN04ZFr7zySr70pS/xyCOPANDc3MyhQ4f45S9/qTTGjo+P58EHH+Saa64hOzubSCRCaWkpa9asYdu2bezatUvZUsxoNJKbm8vcuXMZP348kydPBnrm9lySJOH1eqmrq2Py5MlMmTKFmTNncv/990c7NKEXkSSJxsZG7rrrLnw+H7NmzVLeTz3xdd8XicROuCgbN27kzTffZMeOHfztb39j4sSJl+2+JUmiqamJX//616xbt46SkhKeeuopZd5fTyC/reTVqvIKw0gkoqxAPXHVq8FgQK/Xn3GBhZwkBoNBAoEAwWDwpIREq9ViMBjQarXKkFkgECAQCJyx7Yl8X3ICGYlECIVCeL1eQqGQErtGo8FoNCoLKE7c2F6v12OxWC64+iT38PP5fMpzIz8W+bnQarXo9Xq0Wu1pFUG5t53X6z3jFACTyYTRaCQSiZxxDp+864RGoznjghD5+3LrlEgkorRoCYfDSJKkVKzlv5ccUzAYPOnvfOJjOvXv01NepycKh8OsW7eOp556ir179/L73/+e2bNnKw2XBeF8HDt2jM2bN/O1r32NP//5z8yYMYMhQ4YAPfN13xeJoVjhopjNZpKSkmhoaFBWcV7ON63D4WDmzJloNBo++eQTFi5cyMSJExk8eDAQ/QOIfP9ycvJFdhyQE0CDwXDev0ev16PX67Faree8rZyofd5qte5alapSqZTk6WJ/XqvVYrPZzlktTExM/Nzvn888RLVajVqt/tzFG3JMWq32vPfo7UnkRH7FihWsWLGCsrIy7r33XoYOHXrBFVmh/5IvQEtLS/nkk08oLi5m6NChZGZmRv143N+IxE64KGazmcTERKUZbjgcvmwr5+SmuWPGjCESibB8+XKWLVuGXq/H4XCQmpqq3E4QhLOTK8F1dXV88skn7N69G7PZzHXXXUdWVpZYDStckPb2dsrKyti1axdTp04lNzdX7DIRBeJdK1wUm81GZmYmHo+HtrY2nE6nsvn65ZKVlYXVakWr1fKd73yHyspK9u7dyx//+EelsiWSO0E4M3lYvKmpiZ/+9KesW7eOoUOH8qc//Ylhw4aJBRPCeZOrvkuXLmXTpk24XC6++93vKhfZwuUlEjvhothsNrKzs1GpVDQ2NtLY2HjZEzv4bOXpVVddxT/+8Q8+/PBDFi9eTHl5OT//+c8pKioSV4uCcAaSJNHa2srHH3/M22+/zb59+3jwwQeZOHEiY8aM6dErd4WeJxKJ4PP5ePXVV4lEItx8883k5OSINjlRIhI74aIYDAbsdjsGgwGn00l7e3tU4tBoNFitVkaNGoXP58NoNLJx40befvttRowYwZgxYygsLBQnKkHgs4TO5/Nx7Ngxli9fzo4dO2htbeXmm29m6tSpFBQUnHHPXUH4PG63m61bt1JbW8vIkSOZPn06er1ejJhEiUjshIui1+uVCeydnZ20tbVFLRZ5ZwObzUZeXh5lZWUsWrSIo0eP4vV6cTgcxMTEKKsYBaG/kVfuut1uGhoaWLlyJS+//DIqlYqCggK+//3vk5KS8oUW+Qj9UyQSoa2tjY8//phwOExhYeFl7ZIgnE6UMYSLplarycnJwel00tDQEO1wsNvtjBo1ivfff5+77roLl8vF9773Pe666y6WLl1KY2NjtEMUhKipqqriySef5N577+XHP/4xw4YN45FHHuHFF18kKytL7OEpXJSOjg5KS0v5z3/+w/XXX8+UKVOIiYmJdlj9mqjYCRdNrVaTmZlJV1cXzc3N0Q5H6RlmtVr56le/ypQpU9i9ezevv/46f/7zn0lPT2f69OnceOONxMXFnVe7C0HozZxOJ5WVlaxcuZIPPvgAn89HXFwcL7zwAuPGjSMlJUVpZSOGzYSLsWbNGpYsWUJcXBzXXHMNgwcPFq+lKBOJnXDR1Go1ycnJHDlyhI6OjmiHA/z/nmKZmZnExMQQFxdHY2Mje/fupa2tjdWrVxMOh8nPzycrK4sBAwZgNBrFHDyhzwgGg8rWbIcOHeLYsWPs3LmTcDhMUVERQ4cOZfr06WRmZorJ7cJFkxfg7N27l8OHD3PllVeSk5MjqnU9gEjshIumVqtJS0tjz549tLa2Kkvee8rVWmxsLDExMfz2t7/lk08+Ye3atSxcuJBVq1YxevRoJkyYwO23305qaioGgwGNRtOjtiUThPMlz6ELhUJ0dnZSV1fHSy+9xOrVq2ltbSU+Pp5vfetbTJ8+nUGDBon+dMIXIjcjlnvWyS1zHA6HmMfcA4gtxYSL5vf7+eijj/jzn/9McnIy77zzDgaDoUclRvLLOxKJEAwGcbvdvPXWWyxdupTdu3fT1dXFrFmzmDBhAhMmTGDEiBE9Kn5BOB9er5edO3fy4Ycfsm3bNnbv3o3dbuf6669n0qRJzJgxA4vFomxZJ17jwhchbzN43XXX4fV6KSoq4h//+Ie4MO4hxGWbcNHUajXp6ekYDAa8Xi8NDQ1kZmb2qCs2+SAjV+O0Wi2zZ89m6NChVFZWsmXLFo4dO8bChQv5+OOPGTx4sNIiZdCgQZjNZnGgEnoceb/crVu3cuDAAQ4ePEhZWRkqlYq4uDjuvfdepkyZQm5uLklJSdhsNnHSFbpNS0sL27dv59ChQ8yZM4drrrmmRx33+zuR2AkXTa1Wk5SUhF6vJxAI0NzcTEZGRrTDOit5/t2AAQPIyspi8ODBxMfHs2rVKo4dO0ZdXR1bt27F5XJRXV1NQ0MD6enp2O12rFYrdrtdqXgIwuUUiUQIh8O0trbidDqVlegbN27k6NGj1NXVKXPoiouLGTVqFJMmTUKn04n5o0K3CofDNDY2smbNGvR6PYMGDaKkpCTaYQknEImdcNHkip3ZbKapqYmamppe8wbXarXEx8czd+5c5s6dS0NDA4cPH2b+/PmsWLGCBQsWADBp0iSuvPJKhg8fzoQJE4iJiVFOlHKCJxI9obvJUwjkuUzBYBCXy8WKFSvYvHkze/fuZceOHej1eoYNG8bkyZO59dZblSqzIFwqPp+PI0eO8Prrr3P99dczevRo0tPTox2WcAKR2AlfiMFgIDExEZfLRXl5OeFwONohXZTExETi4uIYPnw4HR0dVFZWsn37dtasWcMbb7zBs88+i16vZ/z48ZSUlDBkyBCuuOIK4uLixMpCodt5PB5aW1vZuHEjW7ZsoaysjNLSUgKBAHl5eRQUFHDbbbcxbdo04uLisFgsGI1GsShCuOTef/99li1bRjgc5tvf/jY5OTnRDkk4hTgKCBdNrlQ5HA5MJhP19fVEIpEoR3VxNBoNGo0GvV6PyWTCbDYTFxdHfn4+tbW1NDQ0UFFRgdPpZNOmTWzdupWPPvqIrKwsUlJSSE1NJS8vj7S0NCwWizjBCudFrsa1tLTQ1NREZWUllZWVNDY20tTURH19PWq1GpvNxsyZMxk4cCDp6ekkJyeTmZlJdna2sqJbEC4leYeJzZs3c/z4ca6++mrS0tIwmUzRDk04hTj7CF+Yw+HAbDbT2NhIX1hkrdPpSEhIICEhgSFDhuD1emlubmbXrl2sXbuWQ4cOUVFRwe7du0lMTCQ1NZXs7GyGDh3KwIEDSUhIwGq1YjKZ0Ov16PV65eQrhm37L3menM/nw+/3EwgE8Pv9tLe3U1VVRWVlJWVlZRw+fJi2tjY6OzuJjY1l6NChDBo0iBEjRjB+/HgsFouoEguXXTgc5tChQ5SVlREIBJg9ezZWq1VcxPZAot2J8IX961//YsOGDZSXl7No0SJiY2OjHdIlFQgEcLlcbN++nU2bNnHgwAEOHDjAoUOHlKRw0KBBjB07lsLCQgYMGMDQoUOx2+1nncguEr6+42yHVDmJ27FjBwcPHqS8vJz9+/eza9cuvF4vWq2WwsJCxo8fT3FxMUOGDGHcuHGiIif0CC6Xi7vvvpv6+nrGjh3Lk08+KS5WeyiRagtfWHJyMjabjcrKyl47FHshdDoddrudiRMnMnLkSPx+P16vl7q6OkpLS6mpqaGqqoo1a9bw/vvv4/f7MRgMZGdnk5GRQVZWFoMGDSInJ4ekpCSSk5MxmUziANkHyBW5yspKGhoaqKuro6ysjCNHjtDQ0EBjYyN+vx+73Y7D4SA7O5svfelL5Ofnk5mZqWzxpdfrMRqNGI1G8boQoq62tpbt27ezbt06vv3tbzNr1ixxsdGDicRO+MLsdjs2m43Ozk48Hg82m61Pl+fltilWqxWr1Qp8NsyWnJxMYmIizc3N1NfXc/z4cVpaWmhvb6elpYVQKKS0qzh06BB2u52YmBjsdjsJCQkntVWJi4vDZrNhNpuxWq2iB1kPIe/w4HK5cLlcuN1unE4nra2tuFwuOjs7aWtro7m5mc7OTtxuN4FAgFAoRFxcHMnJycrrJC4ujpSUFLKzs0lJSSE+Ph6LxSL+zkKPEgqFKC8v55NPPiEtLY1hw4ZRWFgoXqc9WN89+wqXTVxcHLGxsXi9Xjo6OrDb7X06sTsTtVpNbGzsacPQbrebtrY2Dh48yL59+6ioqKCqqoo9e/bQ1dVFMBhEpVKRkpKiTIpPT08nLy+P1NRUEhMTSUlJwWAwoFarlUUe8v9P/FfsKHDxTtyhRP4Ih8OEw+GT/h8Oh/H7/Rw/fpzjx4/T1NTE8ePHKS8vp76+nubmZpqbm9FoNJhMJmJiYigqKqKkpISsrCzy8vIoKSnBarWi1+uj/KgF4dw6Ozs5cOAAn3zyCdOmTWPw4MGkpqZGOyzhc/Svs69wSaSmppKamookSVRWVirtFwQwm82YzWYyMjKYMWMG8P97k9XV1VFfX091dTVlZWVUVFRQW1vLxo0bqampUVrHaDQa0tPTlWHb3NxcMjMzlYUbGRkZJCQkYLPZMBqN0Xy4vZrX66WlpYW2tjYaGxuV4dTGxkaqqqqorq5Wvh8KhVCpVFgsFlJTUykqKmLUqFGkp6dTUFDAwIEDSUpKwuFwAKLnodB7LViwgGXLluH3+/nZz34mkrpeQCR2whcWExODw+HAaDRSW1tLYWFhtEPqMU48kcv/l6tDycnJ2O12cnNzGTNmDF6vV5mv53Q6aWtro729naamJtrb23G73XR1dVFWVsbWrVsJBAKEw2EMBgM6nQ69Xo/VaiUuLo6YmBhsNhsOhwOHw6EMG8fExCg9zywWizLcq9fre/0OBZIkEYlE8Hg8+Hw+3G63Mj3A6/XS2dlJR0cHXV1duFwu2tvbaW9vp7OzE6fTqVRQg8EggUBAaX1jsVhwOBxMnDgRi8VCTEwMKSkpJCYmYrPZlOfQaDRiMBiUn9HpdGIektBrhUIhGhsb+eijj/D5fNxxxx3K6IHQs4nETvjCdDodZrOZ2NhYmpub8Xg80Q6pR5MTPIPBgMFgICYm5qTvS5KE3+9Xto5qaWmhpaUFp9NJR0cHzc3NtLa20tXVpSQufr+fUCiEy+XC7/fT2tqKTqdTkg2dTqd8bjKZlH+tVqtyG/l28nCvvB2VPNx74udqtRqdTqc8Fvk2p5J/16mCweBpq0flxExegBOJRAiFQspwaCQSIRgMnvR5KBQ66cPn8ykJssfjwe12K5/LH3LiJq9SbWpqorq6mpSUFFJSUkhKSsJoNOJwOIiJiSE2NpakpCTi4+OJjY3FbreTlJREXFyc0tJGEPoar9fLxo0bqaurIz8/n2nTpmE0Gnv9BWB/IBI74QtTqVQYjUbS0tJobGwUid0XJD+fRqOR5OTks1ZAA4EAXV1d1NXV0dLSoiR9TU1NJ1X7mpub6ejooKOjA5fLRSAQQJIkVCqV0g9No9EoSYx831ar9aRqYGxsLHq9XvncZrMp8/rkpPHUg75OpzttWF6SJDo7O09bQR0KhZTES/68q6tL6fkmP95TP/d6vXg8Hrq6unA6nSc9j3LiqdFosFqtJCYmKitS5dXcADU1NUyZMoWZM2cydOhQ0tPTT9o+ThD6k0gkQnt7Oy+++CI6nY6SkhKmT58uphL0EiKxE7qFxWJhwIABVFVVnXRyFS4dnU6Hw+FQFmzIc/fO9hEMBtm3bx+/+tWvOHbsGAMHDuQ73/kOoVAIj8dDR0cHTqdTaaArJ1Fyhau6ulr5XP6+XGWTv35iFS4SiRAIBPD5fKfFbrFYlEUfMo1Gg1arVeYJypVgg8GgNHq22WwnJZhWqxWz2axUH+XVxPIwszwMbTKZTqowykmfJEkcOnSIhQsX8uSTT1JeXs706dP54Q9/eCn/dILQox08eJB169axcuVKnnrqKaZOndrvFsT1ZuIvJXQLo9FIamoqmzdvxu12RzucfuHEatS5yHuPvvDCCwQCASZNmsQ999zDkCFDAJS5ZX6/XxnqPHXYMxAInLRiNBQKKUmj/LUTne3rAFqt9rRqmEqlOmlIV61Wo9VqlQRQHv49cTWwVqtVbqPT6ZQKo1arVSqLJ/7MqSRJIjc3l5tvvhm1Ws22bdtYvnw57e3tfPOb3yQ9PV2p6glCXydfAG7fvp0lS5Ywbtw4Ro4cSUZGhqjW9SIisRO6hcFgICkpidbWVrxeL5FIRAxj9RCBQICamhq2bdvG5s2bGTt2LJMnT2batGknVbH6I5VKRWxsLEOGDCESieDz+ZTkrrCwkJEjR5Kfn4/D4ejXz5PQf1RXV7Nv3z6OHj3KzTffTHZ29mnzgIWeTSR2QrcwGo1kZmYqjVlDoZCYVB5l8rBoW1sbS5Ys4emnnyYpKYkHHniAcePGib/PCdRqNcOHDyczM5Ndu3bxq1/9il/84hdce+213HLLLcyePVu5rUjwhL5Irr6//fbb7NixA51Ox7e//W3i4+OjHZpwgURJRegW8hw7SZJoaWmhoaEh2iH1e/IE6Icffpg333yT5ORkXn75ZcaMGSP63Z2Fw+FgwoQJ/Pe//+WrX/0qhw8f5qGHHuLXv/41x48f7xdb5gn9k8/n4/Dhw7zyyitYrVYeeOABEhMTxdy6Xkj8xYRuYTAYSElJQa1W43K5aG1tJSsrK9ph9VuhUIjm5mZeffVVDh8+TGZmJjNnziQzM1PsP/o51Go1RqORlJQUbrjhBhITE9mxYwcrVqxAr9czbtw4xo0bh9VqFc+h0GfIF+TvvvsuarWaIUOGMHHiRLGVYS8lEjuhW+j1euLj49Hr9XR2dtLS0hLtkPqlE4dfy8rKmD9/PlarlTFjxnDTTTdhNpvFgfoc5L2AJ02aREJCAsnJyfztb39j0aJFdHR0EBMTw+DBgzEajaKaIfQJHo+H6upqPvroI3JycigpKaG4uDjaYQkXSRyVhG6hVqsxmUwkJSXh8Xioq6uLdkj9VjAY5L333uPNN9+kvr6el156idGjR5OQkCCSugs0aNAg8vLyGDduHD/5yU947733WLhwIc8//7zynApCb/fpp5+ydOlSduzYwYcffsiIESNEta4XE4md0C3kvmCZmZn4/X6R2EWBJEmEw2Eee+wx1q9fj9Pp5Pnnn2fMmDFiVedFkqt3ubm5/PWvf2XdunUsXLiQhx56iBtuuIGpU6cye/bsk5ohC0JvIUkSTqeTjz76iI0bN3LHHXdQUlJCfHy8eD33YiKxE7qNSqUiOTlZmWMnXD6SJNHe3s727dtZv349RqORadOmMXbsWGJjY8WQ4Rcgz7sbMGCA0rvv3XffZf/+/bjdbiRJYurUqZhMJrE3rNCrhMNh1q1bx9GjR1Gr1cyZMwe73a7sSCP0TuJoL3SrlJQUWltbaW5ujnYo/YrP56OiooKXX36Z8vJybr31Vu644w7S09PFlXc3MRgMDB06lMLCQqxWK//+979Zvnw5NTU1ZGdnk5GRgc1mE0m00CtEIhG8Xi9vv/02TU1N5OTkMHfuXLFivg8QRyCh26hUKnJzc9m5cyfHjx+Pdjj9ysKFC1m0aBHvv/8+L7zwAuPHjycvL08kdZeAXq/njjvuYNiwYaxcuZK///3v3HzzzTzwwANce+21FBQURDtEQTin1tZWdu3axXvvvcfXv/51brzxxtP2dRZ6J5HYCd1GpVKRnp6ORqOhtbUVl8uF1WoVO1BcQsFgkLfffpsFCxbQ1NTET37yE6ZMmUJiYqJI6i4ReT7dgAEDsFgs5Obm8uyzz/L++++zZcsW7rvvPiZPnixOkkKPFQqFKC0t5fnnn2f48OFMmDCBYcOGiWNGHyESO6FbJSYmotfr8fv9tLW1YTKZRGJ3iXR1dVFTU8OSJUtwuVzk5OQwa9YsUlNTxa4Sl4HVasVgMBAXF0dZWRlbtmzh+PHjfPzxxxgMBgoKCkhLSxOrC4Uep6qqigMHDrBv3z5uvfVWCgsLcTgc0Q5L6CbijCt0G5VKRVpaGhaLhUAgwPHjxwmFQtEOq08Kh8NUV1fz4Ycf8t5771FUVMQ999zD+PHjRVJ3Gel0Oux2Ow8//DDf+MY3GDFiBC+++CJPP/00a9asURZXyP0FBSGaJEkiEomwevVq1q5di8fj4Z577iE/Pz/aoQndSCR2QrdKSUnBbrejUqk4duwYwWAw2iH1SWvXrmXevHn88Y9/5Ic//CEPPPAAM2fOjHZY/ZZarWbWrFn86le/4qWXXuLw4cM89thj3H333VRVVeH3+6MdoiAQCoUoKyvj9ddfp7Kykp/85CdkZ2djNpujHZrQjURiJ3QrjUZDbGwsNpuN2tpaUbHrZoFAgN27d/PGG2+wd+9eZsyYwY033khOTo5oURAl8pw7nU6n7DX7ox/9iPHjx1NXV8cvf/lLli9fTkVFRbRDFfqxSCSC2+1m3rx5+Hw+iouLmTVrFjqdTkwV6GPEHDuh28gHh5iYGGw2G42NjYTD4ShH1XfI8xZXrVrF3r17sdvtzJ49m6FDh4qkrofQ6/VkZGQwd+5cZXu9zZs3k5iYiMfjQavVKi1oxMlUuJy6urqorKxk+fLl5OXlMWLECAYOHCjmQPdB4i8qdLu4uDji4+Oprq4WFbtuIkkSDQ0NbNiwgUceeYTk5GRuuukmvva1r4m+aT2MSqUiNTWVO+64g6effpoZM2bw1ltv8fvf/55///vf+P1+Me9OuKwkSaKsrIy3336b8vJybrrpJr785S+j0WjEBUYfJM4IQrdLSEggISGBjRs3ijl23UCSJI4dO8Zbb73FG2+8wZQpU/jpT39KSUlJtEMTPodGoyEpKYk//vGPTJ48mdWrV/Of//yH0tJSvvnNbzJ27Fjsdnu0wxT6OEmSOH78OMuXL2f+/Pl873vfY9y4caSkpEQ7NOESEYmd0O1iY2Ox2+00NTURDAaJRCKi3H+RQqEQHR0dzJ8/n507d2K327nnnnsoLCzEZrOJq+0eTKVSodFoiI+P58orr8RsNmM2m9m0aZNSOZkzZ47S+1EQuptcGV66dCn79+/HYrEwZ84ckpKSRKW/DxN/WaHbyYldW1sbPp+PcDgsEruLEA6Hcbvd7N+/nw8//BCdTseYMWO48cYb0ev1IqnrRfLy8oiPjyc7O5uDBw+yZcsWqquriY+PZ9KkSdjtdrGVk9DtwuEwLpeLJUuW0NzczODBg7niiivEnNw+TpxthW6XkJBAWloawWCQ+vp6XC5XtEPqlVwuF7t27eKrX/0qALfccgt//OMfMRgMIqnrhWJjYxk5ciTvvvsut99+O16vl7vuuovnnnuOffv2RTs8oQ/q6OjgtddeY+vWreTl5fGLX/xCXBT2A6JiJ3S7+Ph40tLSUKlU1NbWkpOTQ3x8fLTD6jUkScLpdPLqq6+yaNEiLBYLv/rVrygpKVGqOuLA3PuoVCokScJisXD//fczYcIE3n33Xd566y12797NxIkT+c53voPZbBZDs8IX5nQ6OXjwIM899xzTp0/n6quvVhoRi+NH3yYSO6HbGY1GrFYrZrOZ1tZWurq6oh1SrxGJRAgEAixevJgtW7bQ0dHBddddR0lJCSkpKeKE38vJ8+6Sk5PR6XR4vV5aWlpoampi3bp1JCYmMn36dBITE8Ves8JFkVdbHzx4kI0bN+JyuZg4cSJDhgzBZDJFOTrhchCJndDtdDodZrMZh8NBS0uLGIo9T5Ik4ff7aWpq4p///Ccul4sBAwbw/e9/n4SEBDHZuQ9RqVTEx8cza9Ys4uLieO2111i+fDl//etfsVqtjBw5kqysLNE8Vrgofr+fzZs3s3jxYjIzM5k2bRoDBgyIdljCZSLm2AmXhMFgIC8vj/r6etra2qIdTq8QCATYsWMH9913HxUVFdx+++08/vjjJCcni0pdHzZ69Gh+85vfMG/ePBwOB9/73vf42c9+xpIlS0SvO+GCSZLEO++8w+LFi2loaODpp58mIyNDHEP6EVECEC4JnU5HRkYGzc3NOJ3OaIfTo0mSRDgc5u2332bVqlVUVlbys5/9jIkTJ5KcnCwqNn2YvANFTEwMgwcP5i9/+Qvz58/n6NGj/OlPf6KyspLrrruOrKwscWIWzikQCCgLJjQaDbNnz6aoqEgsuOpnRMVOuCR0Oh3Jycl0dHSIOXafQ07q9uzZw9q1azly5AjFxcVMnTqV3Nxc0QKjn9DpdNjtdq688kpmzpzJ4MGDkSSJ5cuXs3btWvbt24fH4xEVPOFzuVwutm/fTnl5OWlpaUyePBm73S4uCvoZkdgJl4RcsZPn2IktlM5MkiTcbjfPPfcc69evR6/X88gjj1BQUIDNZot2eMJlJC+suOGGG3jwwQd58MEH+fTTT/nb3/7GvHnzqKurIxwOi/eRcEaRSITq6mqef/55TCYTEyZMYM6cOdEOS4gCMRQrXBIGg4H8/Hw6Oztpa2ujs7NTJCpnsGPHDt566y3effddHnroIWbNmsXo0aPFFXY/l5OTQ2pqKkVFRfz1r39l5cqVLF++nCeeeIIRI0aQmpoa7RCFHmbTpk0sW7aMlStX8tJLL3HFFVdgMBiiHZYQBaJiJ1wSer2etLQ0NBoNnZ2dNDc3RzukHmfNmjV8+OGHrF69mjvuuIPp06dTVFSEVqsV82H6OY1Gg9FoJD8/n/vvv5877riD5ORknn32WV5//XXWr19PJBIR1TsBSZLo7Oxk1apV7Nixg4kTJzJixAgSExPFcaSfEhU74ZLQarXKfoQej4fm5max3P7/hMNhWlpalANxMBjk+uuvZ+jQoSQkJEQ7PKGHUKvV2O12pkyZQkJCAm63m/feew+NRoPb7SYpKYns7Gz0er3Ysq8fi0QiHD58mF27dtHS0sItt9xCRkYGZrM52qEJUSISO+GS0Ol0pKamYjKZcLvd1NbWRjukHkGSJLq6uliwYAGvvvoqWVlZPPzww8ycOVMMvwpnZDQaGTVqFCUlJQwcOJCXXnqJ559/nu3bt/P444+TkZGByWQS1Zl+SO59+be//Y2KigqKior41re+JRZd9XMisRMuGY1GQ1paGpIkUV1dHe1weoSDBw+yYcMGHn30UW644Qauuuoq5syZIyouwjlpNBpuvvlmBg8ezJYtW3j22We5++67ufHGG7n++usZNGiQSO76mePHj7NlyxaWLl3K17/+dWbPno3FYhGvg35OJHbCJSHvi5mYmIjP56OpqSnaIUWVJEmUl5ezatUqVqxYweDBg5k5cyYjR44Ui0qE86JSqbDZbBQUFKDX6+nq6mLx4sVs2rSJtrY2vvzlLzNkyBCsVmu0QxUuA7fbTVlZGR988AEFBQWMHDmSQYMGiYtEQSR2wqWVmJhIbW0tTU1NSJLUL68kI5EIfr+fbdu2sWbNGnbv3s1DDz3E5MmTSUlJiXZ4Qi/jcDiw2WxkZmbS2NjItm3bOHjwICaTCZvNRlZWllK16Y/vt75OXjBTX1/Pnj17WL16NXfddRfDhg0jLS0tytEJPYFKEsuqhEtEkiT+/ve/s2XLFpxOJ8uWLeuXV5NOp5Pt27dz//33k56ezowZM/jd734HIE68wkWRD9uSJLF+/XoWLVrE3//+d2bPns3111/PnXfeKebd9VHy3/7HP/4x27Ztw+VysWrVKmJjY9HpdFGOTugJRMVOuKTS09NRq9XU1tYSiUT6XRWhtraWHTt28Nvf/paBAwdy/fXXc+211wIiqRMunjzVQaVSKX3t0tLSeOutt3jxxRfZsmULv/zlL0lOTsZkMkU7XKEbeTwelixZwtq1a0lMTOQ73/kOMTExaLXidC58pv+VT4TLKiEhAYPBgMfjobOzk3A4HO2QLgtJkujo6GDTpk2sWLECp9PJVVddxRVXXEFGRka0wxP6gBP3mc3Ozmb69OlMnz6d5ORkSktLWbBgAbt27aKhoSHaoQrdxO/309zczKJFizCZTBQXFzN27FjR+1I4iUjxhUsqOTkZq9WK3++npaUFo9HY568s5f1fjx49ynvvvcfWrVsZNmwYd955JykpKf1yOFq4tAwGA8OGDSMlJYVPPvmEefPm8fe//522tjZmzJhBbGys0gJDJAC9jzz82tHRwZEjR3jvvfe44447mDp1Kvn5+VGOTuhp+vYZVoi6rKws4uLiCIVCHD16lISEhD4/NOT1eqmvr+f+++9Ho9EwadIknnzySWw2m0jqhEsqMTGRr3zlK3zpS1/iscce49133+W9997j+uuv55FHHsFms4nErpcKh8N8/PHHvPjii+Tm5nLfffdRUlIS7bCEHkgkdsIlZbFYiImJwWw2U1tbSyAQiHZIl1RXVxe7du1i3rx5SJLEDTfcwMyZM7FarajVanFSFS4plUqFTqfDbrfzjW98g+zsbHbu3MnixYuJRCLMnTuX0aNHExMTE+1QhQu0ZMkS1q9fT0tLCz//+c/Jysrq86MfwsURrwrhkpFPMhaLBZvNRmNjI8FgMNphXRKSJBEKhdi/fz8bNmxg27ZtXHHFFUycOJHhw4eLA7Bw2ahUKvR6PUOGDCEcDivvvU2bNmEymQgEAowZMwa73S52O+kFwuEwXV1drF69mvr6ejIzM5kyZQp2u12MAAhnJF4VwiUXExNDUlISNTU1+P3+aIfT7SRJQpIkXC4X8+bNY/78+eh0On71q18xfvx4sWejEDUlJSXcfvvtPPHEE6hUKt544w1+/etfs2PHDrxeL5FIRHn9Cj2PJEn4fD4OHz7Me++9h06n48477yQnJweDwRDt8IQeSiR2wiXncDjIyMigvLwcn88X7XC6XSgUoqmpifvuu49NmzZRXFzMG2+8QWZmptizUYg6i8XCoEGDePvtt3nwwQex2Wzcdttt/PWvf2Xz5s3RDk/4HPIirO9973ukpKQwZ84cbrvttmiHJfRwYnxIuOSsVivx8fHs3LnzjHPsIpEI4XC4Vy7ZD4fD7Nu3j08++YRDhw4xa9YsJk2aRE5OTq98PELfo1Kp0Gg0JCYmMmvWLFJTU4mLi2Pt2rU0NDRw7NgxbrrpJoxGoxia7WF27tzJ2rVrqamp4Qc/+AFXXHGFuFgUzkkkdsIlIw/vmM1mYmNjaW5uprm5mfr6ekKhED6fj2AwSDgcJhKJUFxc3Gs6p8uPrba2lq1bt7J48WIcDgczZsxg3LhxYv9XoUc5cd5deno6KpWK5557jl27dtHS0kJhYSH5+fnExMSg1+ujHW6fF4lECAaD+Hw+dDrdadM1JEnC7Xazfft2Nm3ahN1uZ/r06RQUFIiLReGcRGIndIuzzdGRJAm9Xo/FYqG5uZmPP/6YLVu20NjYyKFDhzh+/Dgej4eYmBjWrFlDQkLCZY78zDGf6NQDqfz9SCTCM888w/r166mvr+e///0vRUVFYsWh0GOpVCri4uK4/fbbyc/P56233mLBggXcfffdPPbYY1x55ZVKA22RQFw6oVCI6upqysrKSE9PZ+TIkSd9X5Ikdu7cyaJFizh06BC//vWvKSgoEBeMwnkRe8UK3SISiRAKhXjppZc4fvw4zc3NVFdXU1tbi8vlorOzk9bWVuLi4tBqtUQiEdxuN+FwmLi4OMaPH89//vMf7HZ7tB8KFRUVqFQqEhISlM3UTyRJEs3NzTz99NO8/fbbDB06lDvuuIPZs2djMBjESjWhx5MkCb/fT1NTE0eOHOHRRx+lubmZwYMHc/vtt3PttdeKldyXUEdHB2+99RYvvPACdrudadOm8eMf/xiTyYQkSTidTm6++WZlJOPPf/6z0jJJEM5FvHOFbiEnP+Xl5ezevZuamhra2tpob28nGAwqVa729nZlK6RwOIxarcZgMJCVldVjDlqbN2+moaEBi8XC7bffjtlsPukkd+zYMXbs2MHy5csZPHgwkydPZtSoURiNRlHlEHoFlUqF0WgkJSUFo9HIXXfdxZIlS2hvb2fBggUAjB49mtTU1LMmeHKLnyNHjpCVlYXVar2cD6FX8/l87Ny5k+PHjytTU2w2G9OmTUOv17Nnzx5qamq46qqrmDlzphgFEC6ISOyEbqFSqVCr1QSDQWpqaigrKzvj7U5traBSqTCZTGRlZUV94rYkSQSDQVavXs2OHTuAz05uubm5xMbGolar6erq4tNPP2Xx4sWUl5fzta99jYkTJ5KdnR3V2AXhYuj1ehITE/nqV7+KXq9nxYoVLF++nEAggFqtZuzYsSQmJqLRaE67aPH7/XR0dLBmzRquvvpqDAZDr5kjG02RSASv18uuXbvwer34fD7a29uprq4mEolgs9n45JNPMBgMjBs3junTp0c7ZKG3kQShm0QiEamiokK68847JY1GIwHn9VFSUiItW7ZM8vl8UY3f7/dLu3fvlkaMGCGpVCpJq9VKAwYMkF555RWpsbFRCofD0jPPPCNNnjxZSktLk+bNmye1trZKkUgkqnELwhcRiUSUj6qqKumtt96SkpKSpIEDB0r33HOPVF5eLgWDwdN+bvPmzdJPfvITSavVSk899ZR08ODBKETf+7jdbmnbtm2nHSO1Wq2k1Wolk8kkpaSkSPPnz5cqKyvF8UW4YD1j7EvoM1JTU7nyyiuZMmXKeVfgzGYz+fn5Ua/Yeb1e/vvf/9Le3o5arSYcDtPY2MhTTz3FT37yE15//XX++c9/YrVaeeCBB7jmmmuIiYkRw69CryZPjVCpVCQnJzN9+nReeOEFRo4cyYEDB/j617/O6tWrqaurU35m7969LF68mDfffJNwOMzrr7/OggULCAQCotnxOVRXV7N7924ikchJXw+FQoRCIaUSunDhQvbt24fT6YxSpEJvJYZihW6jUqkwGAwMGTKEuro6NmzYQDgc/tyf0Wq1mM1m4uPjozrHLhQK0dnZycaNG3E6ncqQsdz13el04vf7iUQiDB48mGnTppGUlCSSOqFPMRgMxMfHM378eNrb27FarWzfvp2PPvqI5uZmhg0bRnZ2NitXrmTLli3U1dUhSRJHjx5l9+7d7Nu3j+HDh0f9Iq0na2ho4PDhw6hUqjMmwZFIhEAgoPTHdLvdTJw4keTkZLGgRTgv4lUidLvhw4cTCoV49tlnCYVCp12ZnkjucRcbG3sZIzyd1+uloaGBzZs3n7TYIxwO4/F4qKqqorq6mhtvvJEpU6Zw5ZVXiqRO6JPUajXJycncddddjB07lueff5433niDAwcOMHnyZG666SZee+01Dh48qPyM0+mktLSUhQsXMmjQIEwmU49ZDNVTyMeU48ePs2/fvs89fkQiEY4dO8bx48fZsGEDWq2W6dOnY7PZRHInnJN45wndLiYmhoEDB/LNb34Th8PxuQew7OxsBgwYcBmjO7N9+/bx3//+96xX0HKCumjRIpYtW8b27dvFkJPQp+l0OoqLi3n88cf585//jE6n4/HHH+e6667j0KFDJw27RiIRjhw5wrPPPsuuXbtob2+PcvQ9Uzgc5tChQ2zatOmcoxnBYBC3283evXu58847+d3vfsf69esvU6RCbyYSO6HbqVQqHA4HN998s9K37ky0Wi1paWmkp6df5gj/P+n/WjYcOnSI5cuXEw6Hz5qwRSIRfD4fS5cu5YknnmDbtm14PJ7LHLEgXB7ySne9Xs/MmTOZOHEi+fn5NDY24vf7T0tM5KkL//jHP9i/f/85E5f+6OjRozQ0NFzQcUOr1RIXF0dJSQlpaWmXMDqhrxA1XeGSMBqNFBcXU1BQQGdnJw0NDafdRqVSER8fT2JiYhQi/P+am5uprKzk6NGjnztsLEmSMkTicrkoLi7GarWSmZkp+kwJfZrJZEKv16NSqfD5fGd8n0iSRDgcZuPGjUyYMIG8vDxlFwvhM8eOHaOlpeVzjzMnstvtpKSkMGrUKAYPHtwjduYRej6R2AmXhEajwWq1csMNNxCJRPjkk09OO5gFg0GSk5OjWrGDzxoSl5WV4Xa7z3lbeVFFU1MTjz76KAaDgauuuorRo0dfhkgF4fKSX+/r1q1jzZo1Sn/HswmHwxw/fpzVq1djsVi49957AbE9GXz2XG7fvp36+nq0Wi3BYPCMt5MrpQAlJSVcddVV/PCHP8RoNIp5i8J5EYmdcEndcsstNDc3s23bNtrb208b5szIyIh6c9/58+ezbdu287qtfILS6/Xk5OSQkJCAyWS6lOEJQtR4PB4qKip45JFHqK6uPq+fiUQiLFmyhIaGBq688koGDBggGhf/n1WrVnHkyJGzJnUajQadTkdMTAx/+MMfGDduHDk5OeIYI1wQkdgJl5TNZmPw4MHMmDGDd99996TETp47Eh8fH5XY/H4/NTU1HD16lJaWlnMuhlCpVNhsNoqKihgyZIiylVhqauplilgQLp9AIEBlZSX/+te/qKurw+fznffPyu+tN954gx/+8IfY7fZ+XbULBoO0t7dTX19PV1fXWW+Xk5OjHC8nT55MSkoKFovlMkYq9AUisRMuGZVKhUajoaCggOnTp/PRRx8RCASUIVm51Um09pj0er3s2bOHxsbGz53MrNFoMBgMOBwOBgwYwOTJk5k4cSIzZ85ErVb36xOW0HcFg0FaWlrYtGkTkiQpTbvP1n/tRJIk0d7ezkcffcStt96KwWDAbDZfpsh7HjnRdTqdBAKBk74nHyeTk5MZO3YsU6ZM4bbbbhPNz4WLJhI74ZIbNGgQMTEx/P3vf6e2thafz4dKpSIvLw+bzRaVeSOSJNHa2sorr7yCx+NBrVafcUKzWq3GZrNRUFDAXXfdxd13301MTIxowCr0ecFgkNjYWG666Sbef/99ysvLaWlpQafTEQqFTtv3+USRSITOzk727t3LwoULufrqqxk9enS/TVQ6OjpYvXo1oVDopMRYq9Wi0Wiw2+089thjzJw5U6x8Fb4wkdgJl5xKpSI2NpYf/OAHPP7441RUVKDT6Rg8eHDUGhM7nU7Ky8tZs2YNbrf7pKROrVYrO2Lce++9zJgxg0GDBhEfH09MTIyYwCz0CzabjUGDBpGZmcntt99Oa2srdXV1bNmyhbVr11JRUUF9fT0ajeasbYIikQgvvPACkiSRkZFBSkpKv0zu2tvbWbFiBV6vV/maSqWiqKiImTNncvPNN1NUVCSGXYVuIRI74ZKTtxqbMGEC77zzDi0tLfj9fjIzM6M2PFNRUcHevXvxeDxKUqdWq1Gr1RQXFzNgwACKioqYPn06xcXFJCUliY7vQr+i0WjQaDQYjUbi4+NJSkoiPT0du91OTk4OtbW11NbWUlVVRUVFBa2trco+yydW85qbm9mxYwcDBgzgzjvvRJKkfpXcBYNBnE4nhw8fJhgMolKpsFqtjBs3junTpzNmzBgGDx6MzWbrV8+LcOmIM5VwVid2lZcP1JFIRPmQP5e/d6aPEythSUlJJCYmYrPZ8Pl8WCwWXC4XtbW1J92vvCH5qU6slJ04t03+/4mfn/g1jUajtBCQv1ZWVsbmzZuVz/V6PSaTidjYWGbMmMGUKVOYMWMGFotFHGwFgc/mxJrNZlJSUpg0aRKdnZ00NzezevVqNmzYwOHDhzl27BjBYBCfz0cgEFB2bNm9ezcajYZrrrkGnU6nDEfKx4cTjzWyE5PDU/9/6tdO/JkT/z2b87nd2Y5Dpzq1gi//jPyvx+OhpqaGqqoq4LMV9QkJCcydO5drrrmG+Ph4JEmis7NTuc8TP+T7ONPnZ7p/QVBJYl8k4SzkA2drayudnZ3KgbytrQ2Xy4XL5aK1tRW3243P58Pr9dLZ2YnX68Xn89HV1UVXVxfhcJhwOIzL5SIQCOD3+/H7/dhsNvx+P6FQ6KT71Wg0pzX8ValUWCwWdDodarWamJgYzGYzRqMRs9mM1WrFZDJhNBqx2WzY7XZsNhuxsbEkJSUpn9vtdgwGA//7v//LSy+9hNfrRavVMnLkSGbOnMmDDz5IXFwcer3+pPsWBOFkpyZHbreburo6Fi5cyKZNmygtLaW8vFy5fWxsLPfffz/FxcXo9Xq8Xi8ulwuv10sgECAcDivHi1AopCSHwWBQSRbD4TCBQACfz4ckSXg8npNah/j9fiWh/Ly45fv5vNOffLF3rsTJZrMpF48ABoMBnU6HTqdDr9fj8/no6Ojg2LFjxMbGkp2dTVZWFmlpaSddVKrVaqxWK0ajEZPJpPzfYDBgsViw2WzK5/LxzmQy9etFKcKZicSuH5JXrDmdTtrb22lqaqK1tRWn00lbWxuNjY20t7fT2dmprOIKhUKEw2GCwSCSJKHVapUDjnwQO/FD/r5er1euLuXO9ZWVlezYsYNrrrlG+Xn5oChfxZ+6ciwSiRAMBpVq4YkH/GAwqMQYCoXw+/14vV4lgZTnzGk0GuXfiooKmpubsVqtDBo0iAEDBlBYWMiIESNITk7G4XDgcDiIj48XiZ3Qp0mSRCAQoKurC4/Hg8fjweVy4fF48Hq9eDwe5YLN6/UqF2zyxZycnHk8HpxOJy0tLTidTjwez0ktUjQaDSkpKVitVvR6PTqdDoPBoLwn5R5ucsX91M/l44T8dfgs+TpxIZNcmf+8vnny79BoNGdN2iKRiJJgfh75mHiiYDCoJI3BYJAjR47Q1NREJBIhIyMDk8mETqdTRjtCoZDyO+TjmHx8k29zYrzyc3Pi/+WLWvlC1/b/2DvP8Cius2HfK61WfaVV770LCYGoooiODQYb7Dhxiu3EcYkTJ3GcxH7zpr5Oj2PHiePEdhL3ihtgOqYjUSQkhIR671pJq1XdPt8PfzORQGAwGAk493VxsaudnTkze8pznurtrQh+3t7eeHp6nvV+9LGjBUXB1Y8Q7K5BJEnCbDYzNDTEyMgIQ0NDYybqwcFBenp66O/vx2g00tfXp+yO5cnabDYrQpM8CcsTsTyByJOELMBpNJoxr+Xj5AnI1dUVZ2dnGhsbKSoq4sYbb8TLy2tcwe7MnFkOh0OpTyl/LrdRFuDkyVC+Z3khkjUCstBXV1eHWq1W2icvNrKpSavVKtq+wMBAZVKUd80eHh5j7l2YQgSTBVlIkMfE6HE8MjKivDabzZhMJmVTNDw8PEbzLgtuVqtVOacs6MjnkDXxozd+drtdEUJGC2uyRkveGHp4eODr66uUKpM3XfJr+b38XXn+UKlUynGAIhjKjL7m+ZCvc66xKwtcZ24wz8Rut2M2m8do/mTto7wBraqqwmAwKPOJSqVS5rPR15H/Nvr3kzersgApP2P5t5BfjxaO5ecm/02j0YzZ2MrzuDyXe3p6KnOah4eHMoePnu/l+d/NzW3cz4T/8eRCCHZXKWf6mcgTifxar9fT1NREe3s7TU1N1NbW0tbWRkdHB42NjQwNDSm5qdzc3AgKClIcpCMjIwkKClI0VkFBQXh7e+Pt7Y2fnx++vr6XNJCHhobo7e0lPDz8cxeKHA6Hop0cGBigt7eXF198ER8fH7RaLcPDw7S2ttLT00N3dzctLS3KxAzg6elJWFgYISEhREZGEh8fT1hYGKGhoSQkJODv7z9GqzDavw+EGVdw6ZzPh/XMf/Imrre3l+7ubgwGg7KB6+rqor+/n4GBAXp6ehQXC/lvMNY/TBYKZBcGLy8vPD098fHxwcfHB09PTzw9PfHz81M0PrKQIJsM5Q2Qm5sbarWavr4+LBYLOp3uuqkj29nZic1m+0ylE2UBTtaayhtaeZNuMpkYHh5WtK2yBrWvr0/RrBqNRoxGI0NDQ8p7eZMM/+1fTk5Oiv+fVqtFq9Xi7++Pv78/Xl5eaLVaxa1Fq9Xi5+dHYGAgPj4+eHh4jOsfeC6fQTEvfr4Iwe4qRdbKyYJaSUkJp0+fprGxkdraWlpaWnA4HKjVaiUPW1hYGMHBwcTFxRETE0NQUJDyb7Rz7vkG4+UYmPJEcqHOyZfCeI7WcmSa7MB9ZrCHvOh1dHQoz7Kjo4Pm5maqq6sZGBjAbDajUqkICwsjIiKC+Ph4pk6dSnJyMpGRkcTGxuLm5iYmMMElMzg4SH9/P11dXbS2ttLd3a30z7a2NqW/tre3YzQaFfOhvFi7uroq2iJ5Q+Pn50dAQADe3t5jFmwvLy9lA+fh4aG4TwDnnQfO9f+Zr0dzvWi6RwtOn+W753t9If+P9zfZXWVgYGDMxtdgMKDX6xUf6u7ubnp7exW3nK6urjFJ5kf7CMobXlkYlNcbWQCMiIhAp9OJefEKIAS7qwCHw4HNZqO2tpbm5mba2tqora2loaFB2YnZbDbFTOjl5UVISAiBgYH4+voqO6zRPhajHXPd3NwAsYuSh4Js1pUnPnknLGtD5Emura0NvV6v7Jb7+vqU9BA+Pj7ExcUpTtLx8fHExMQo5miBQDZjdnV10dfXR19fH3q9Hr1er/i/6vV6xUQqa2dkk5qsERvt+iBr0WSBzcvLS3Hml327RpviZAd/2cSmVquVc6vV6utG+LrekE24sil+tK/yaBP9meZ82VQvz43y+iNrD2W3F3n+tFgsivA32tzr6+ur+DLLmw554yEHul3v69GlIAS7SYjsc9HT0zNGnX769GlaWlro7OxUdu6jTakxMTGEhIQQERFBUlISoaGhyiARXD5ks0hTUxN1dXW0t7fT2tpKdXU1vb29DA8PY7FYCAwMVDSisbGxpKSkKFoReQKTfVQE1x7yhkzeGMgL5ejAnsHBQdra2ujt7VWCl0ab0fr6+rDb7UpAgKur6xi3iNFauICAACWJtmwulQMVBILLhdyH5c2HbOqV3Vnk97IrwOj+7nA4cHZ2xsvLC51OpygZfH19CQwMxM/PT6kfPtpfW/b9kzczsr+lYHyEYDcJOFNtPjAwQFtbG9u2baOwsJDq6mrKy8sxmUwEBAQQHh7OrFmzmDVrlqIJioyMFB19EtDW1kZraytVVVXk5+dTUlJCQ0MDra2tuLm5ER0dTXJyMvPmzWPRokVERkYSEBAg/E+uQs6VQ01+bTabGRgYoLy8nJqaGtrb22lubqaurm6MX6fsz+bm5kZ4eDgxMTEEBwcTGhqqbNb8/PwU/1eh9RVcLdjtdoaHh+np6aGlpYWuri66u7tpa2tTNsWdnZ00NjYqyeJVKhXe3t5EREQoSbETEhKIjo5WXoeFhY1JMQNnz5vX8zwqBLtJgOy0v2/fPg4dOqRM/K6urqSmpiranlmzZhEcHIxWq1Uik2SzjChGPzkYHbE2OhqxtbWVgoICamtrqauro6ioSHEij4uLY9myZcycOZPY2NjP5GQtuPJIksTIyAhdXV1UVlbS3NxMe3s79fX1VFdXo9fr6evrw2az4e7ujq+vrxKEI2snQkJCiImJISAgAB8fnzFRoXIqizNTW8D1vWgJrh7OTGwvB/nJ2uzRf5fTbnV1ddHU1ERnZ6eSfqupqYne3l5MJhNqtRqdTkdISIgi6MXFxREaGqq8lwParleEYDcBmM1mjEYjlZWVFBcXKwuCXq9Ho9Hg6emJTqcjOTlZcUYNDg4mPDwcT09PYVq5yrDZbAwNDY3R0jQ3N1NbW6ske7ZYLPj6+hIQEEBsbCzTpk0jNjaW4OBgkVtqgpHT67S3t9Pb24ter6e1tZXOzk7FN66np0dxKJdTSsi+a3LEuez3Jpvj5Rxivr6+iqnpel6MBNc3o9Nz9fX1Kf7NowM85IAOecMkb6IdDodispX99XQ6nbJujvY3vx4Qgt0VQs6kLldraGlp4fDhwxw8eBC9Xo/FYiEkJITZs2eTlpZGRkYGWVlZwoH5GkTOXVVWVkZVVRUlJSUcOHBASWLq7+/PwoULmT59OomJiYqWVvY5EXx+yHnb5GAF2S/OYDBQVVWlmNpra2tpbW1VKhjI9VQDAwOJiYlRNAihoaHExcXh7+8vfjuB4BKQ1085fVd7e7uSuaC5uVlJom232wkKCiIgIIDIyEji4uKIjo4mNDSUsLAwJVBI3njJG6prSQsuBLsrRGdnJ0VFRbz++uvk5+fT2tqKi4sLN910E/PmzWPmzJlkZ2efs+6g4NrizGHncDgoLy/n1KlT7Ny5k02bNjEyMoK3tzdLly7ltttuIzMzk4SEhAlq8fVBa2srzc3NnDx5khMnTlBTU0NDQwP19fVIkoSPjw+hoaFkZ2eTmppKTEwMiYmJJCcn4+Hhcc78jmIcCwSXxvlEFYfDQV9fH21tbZSVlVFeXk59fT3l5eWUlpZisVhwcnLC39+frKws4uPjSUhIIDs7m/T0dHx8fK6pJMtCsPuckJ2njx49yqZNmzh16hQ1NTUEBweTmZlJeno6c+fOJTg4eEy5FxCLwPXG6DQrco3d7u5uKisrqaysJC8vj4aGBnQ6HYmJidx6663k5OTg7+8vtLmfEYfDgdFopKKigvr6eiorKzl27BgdHR0MDg4iSRLh4eEEBAQQGBhIYmIiaWlpii+cHJk3uuLKtbbrFwiuFuTE/LK2XU7RYjKZGBoaoqOjg/b2dqqrq2ltbaWjo4POzk6MRqOSxzEuLo7s7GySk5OJiooiOjoauDrXYyHYXUbkR1lbW6skCi4oKKCtrQ1JktDpdEoS25iYGJKSkkTYtuAsZFOtHEVZUlJCYWEhXV1dDA4OEhoayowZM0hISCAxMZHExEQhVJwHuWanXq+npaVlTJWR1tbWMbkK3dzc8PLyIigoSPFv9fPzIzIykoiICCX/o0AgmPzIgRt9fX0YDAZFqJPzRTY1NdHT04PValVSh8k+enIUbmBgIP7+/kRERKBWq6+KeVYIdpcJOdnj4OAgW7Zs4eDBg+Tl5dHd3U1OTg5z585l9erVpKamilQFgotCkiRFc7d79252796Nn58fqamprFy5kltuuUWpW3stmRM+K3Iknlwb2Gq10t/fT0lJCUePHqWmpkaJTpZLKCUmJjJ37lymTJlCXFycEJYFgmscm81Gc3MzRUVFVFdXU1ZWxtGjRzEajYrbhazBS0pKIicnR8lIIdcrvhLVkz4LQrC7TNTV1XHixAn+8Ic/UFVVRVxcHPPnz+f+++8nOjoaT09PkadM8JmRhRWr1UpNTQ1vvfUWhw4d4siRI0yZMoW7776bRYsWkZ6ePtFNnXDkHXppaSlbtmzh2LFjVFRU0NXVRXR0NImJiUyZMoX58+eTlZVFYGAg7u7uF1UOSyAQXN2MV2pNrvBUU1NDYWEhhw4doqamho6ODjQaDfPnz2f69OnMnDmTpUuX4u7uPik300KwuwRkTcDLL7/MsWPHqK+vx93dnVtuuYWUlBRiY2OJiIjAzc1NaOkElwWHw8HIyAgdHR20trZSXl7Ohg0bGBoaQqfTkZuby1e/+lX8/f2vK5Oh0WikpaWF/fv3U1paqqQQcnNzU/JdZWZmEh8fr1RkkKuyyPkgBQLB9YssCsluGf39/UoqIzn4UU5RNTg4SGBgIGlpaSQmJjJ16lRmzJgxafLnTT5R8ypBjsApKChg//79GI1GdDod8+bNY9myZURERFw3OXMEVw4nJyc8PT2Jj49Xysf19PRQWFiIXq9n3759+Pv7K0KMn5/fNal1khOctre309LSQktLCzU1NZw6dUrxmQkICCApKUlJYDp16lRCQkJECTeBQHAW8jwp11sOCAgAPkmzYjQaCQ4O5uTJkzQ2NtLU1IRer1csAY2NjXR2dio+eWFhYRPqPy80dheB/KhsNhvFxcXs3buXp59+Gm9vb26++WZWrVpFbm7uBLdScD1SUFDAgQMHeO655xgZGWHdunWsX7+enJwcxVRwtQt4o8ef1Wqlr6+P7du3s2nTJsrLy2lubiY5OZmcnBymTZvGggULiI+Pn5SmEoFAcHUyMjJCb28vO3fu5ODBg5SVlVFWVoafnx8LFiwgJyeH1atXExQUhEajUXx1r+T8KwS7i0A2gz311FO89957GAwGZs+eza9//WtCQkImrb1dcO1jt9uVRLo/+9nPOH78OEajkXvvvZdvfvObBAcHTwoTwaVgtVrp7e3ljTfe4NChQxQUFChjcObMmSxevJg5c+Yo5hC5FNfVLtAKBILJw+jgLLvdzuDgIG1tbbz55pscPHiQ2tpaLBYLK1asYPHixcybN4/09HQh2E1GHA4HFRUV7NixgzfeeIOoqCjS0tJYsWIF2dnZojC3YMJxOBxYrVZOnDhBfn4+hYWFVFRUsH79ehYsWMC8efOuOkFHkiQsFgulpaWUlJSwZ88eWlpacHd3JygoiOzsbBISEpSs8oGBgVfV/QkEgqsbm83G8PAwdXV1tLS0KAnO6+vrAdBqteTm5rJs2TLCwsLQarWfe5uEeulTkKXz+vp6jh49yrZt23A4HMyaNYuFCxcyd+7cK9qe2tpaOjs7lbqU58LJyQmNRoO7uzt+fn4EBQVddYu64OJwcnLC1dWVuXPnKjVJKysr2bdvH5IkERgYSFJS0lXRD+QE3waDgdraWvLz8ykqKqKoqIioqChSU1PJzs5m+fLl+Pr6Tnq/ueHhYQYHB+nr66O/v1+pbynnzpLrxgYGBuLs7Dzpfx/B58fIyAg9PT00NTUpfcTFxYXMzExcXV0/83ktFgsjIyNKPky5fJ6TkxNqtRq1Wo2rqytarVZJmi+n9BCcG7VajVarJSsriylTpmAwGIiOjmb79u1UVFRQVVXFyMgIarWa5ORkYmNjCQsL+1zLhQqN3acgawx+9rOfsX//furr63nppZeYMWMGQUFBV7w9Dz30EP/5z38YGRkZU2LlzMHn6upKaGgoycnJ3HTTTdx1111jUq4Irn2Gh4cpKCjgoYcewmKxMH36dJ577jnc3d0nrXZZ7tN2u52mpiZ27NjBE088gdFoJDAwkLvuuou7774bf3//SS/MwX/vp7y8nMLCQnbs2EF+fj69vb1YLBY0Gg2xsbFkZGQwY8YMvvKVr6DVaoVwd4GcmbJisuYVuxhqamp49913efzxxxkeHsbFxYWgoCDy8vKIioq6qHONXiM6OjqoqKjglVdeobi4mM7OTvr6+nBzc0On0+Hn50dUVBTz5s1jypQpJCUlXdXVFyYDFRUVHD16lD/+8Y+0tLQQFxfHypUr+f73v49Op/vcBGch2H0Kvb29/Pa3v2Xbtm0kJSVxzz33sGTJkgkzvRoMBnp6eqipqeHrX/86Hh4ezJo1iz/+8Y+oVCocDgcWi4X6+nrefvttjh8/TnNzMw888AC33HILs2bNuuJtFkwMsmn20KFDbNy4kQ8++IA77riDe+65h+Tk5Ilu3rjY7Xba29v55z//yaFDh6ioqGDx4sWsWrWKmTNnEhYWhqen51WhdQTo7+/n2Wef5Z133sHV1ZWcnBxWrVpFUFAQbm5uWCwWysrK2LdvH4WFhQwPD/P4448zffr0i17Er0dMJhNFRUX8+Mc/Jjo6mi9/+cusWrVqopt1ScimvYGBAZ588kk++ugjhoaGPrNgJ0kSzz33HB9//DFFRUXcfPPNLFiwgMjISPz8/BgcHKS9vZ2qqio2b97MyZMncTgcJCYmcuDAAbHJuASsVisWiwWj0cju3bs5ePAge/bswcPDg3vuuYfc3FymTZt22a8rTLHnoauri7KyMj7++GMyMzOZN28e06ZNG5PM9Eqj0+lwdXVlYGAAZ2dn1Go1np6eREREKIKdw+HA29ubwcFB3N3d+fvf/86uXbtIS0sjMzPzuspvdj0jm2bT09Pp6+tjYGCAjz/+mKlTp+Lq6kpMTMxEN3EMRqOR5uZmXn75ZUpKSnBzc+MrX/kKubm5pKWlER4ertRTvhpoaGigqKiITZs2ERYWxpQpU1ixYgXp6elK/jybzYa7uzteXl6EhYXx8ssv884772AwGFizZg3+/v7X5aLa3t7Oc889R2BgIDNmzGD27NnjHidJEiaTifb2djw8PBgZGbnCLb38yKY9+d+lBOQNDg6Sl5fHli1bMJlMSsRmQkICOp0ODw8PzGYzgYGBhISEoNPpePrpp6mrq0Ov1zPRep+ysjI2bNhAcHAwS5cuJSkpaULbc7G4uLigVqvx8PAgJyeHgIAAYmJi2L59O3v27KG5uZmenh4WLlyIi4vLZRvrQrAbB7mgcH19PYcPH6axsZH77ruP3NxcwsPDJ7p558XJyQknJyeCg4NZtGgRNpuN5557jqKiIurr6xkYGBCC3XVGSEgIs2fPRq1Ws3nzZo4cOYJWqyUiImLS7MaHh4dpbGzk8OHDvPXWW8TGxjJ37ly++c1vEhcXN2lNx+MhR8xVVFSwdetWTpw4wf/+7/+yfPly5syZM+ZYFxcX4uLiiIyMJCEhgUOHDrF7926cnJxITU3F399/gu5iYtHr9TzxxBOkpKTg5OR0TsFOpVLh7u5OdHS0os0VfILNZqO3t5cPP/yQ48ePk5OTw2233caSJUvGjHlZORASEkJ6ejpHjx5lZGQEs9k8ga3/hKqqKv785z8zZcoUYmNjrzrBDv5rxk5ISCA6OpqZM2cyODjIrl27aGpqwmq1Eh8fT3Bw8GVTGgnB7hz09vby0Ucf8eKLL3LHHXdwww03TDoNx6fh6+tLSEgIgYGBdHR00NHRQX19PYGBgRPdNMEVJjw8HB8fH770pS/x8ccf09HRwcyZMwkKCpoUgt3Ro0fZsGEDr776KmvWrOHhhx9m6tSpV4Uf3Xi0traye/duXn/9ddLS0li7di2ZmZnnPF6tVhMeHs5PfvIT7rrrLg4ePIjVauWNN964qoTaK42rqyuzZ89m9+7dE92USUdPTw8lJSU8//zzpKamsnDhQtauXXve8e7s7Mzdd99Nf38/eXl5V7C11wdqtZqAgAB++9vfsnjxYrZt28Y//vEPjEYjX//61y9bHlwh2J2Dd999l9LSUoKCgnj44YcJDQ2dFAvgxSC3V1anq9Xqq3ahFFwaKpUKDw8Pvv/979Pc3ExbWxuvvfYa3/nOdyZUg2s2m6muruaPf/wj/f393Hbbbfz0pz8lNDT0spomrjTbt2/n1KlTuLi4KJG757sXWfM0ffp0wsLCKC8v59ixY7S0tBAcHCy07Ofgau0fV4KTJ0+ya9cuHA4Hc+fOJSkp6VP7oCRJpKam8n//938MDw+LvKyXmdHPf+7cuYSGhhIdHc1f/vIXXFxc6O3tZf369Zd8HfGrnYFshj127Bhms5nMzEyioqKuyg4+MjLC4OCgMkC9vb3PKnMm+6hUVlbS0dFBV1eXcrybmxsBAQFkZmbi6+uLu7s7FouFDz/8kJ6enjHn8fX1JTk5malTp+Ls7ExXVxfvvfee8rlarSYqKorZs2ej0+mU69bW1tLe3k5HRwfDw8OKX1hgYCBTpkxBp9Ph5eUFfOJ3I/styQQEBHDDDTdw4sQJurq66O/vR6VS4e/vT2RkJImJiXh7e6NSqTAajVRVVVFUVITNZsPFxQV/f39WrFiBl5cXNpuNtrY28vLy6O3tBT7RCCxevJi4uLgx9+twODCbzZSVldHZ2UlPT4/y3Dw8PPD39ycrKwsfH59Jsyg7OTkp+ReLioo4dOgQ991334SVvnE4HAwMDLBlyxZ6enqIj4/npptuIiYm5qoJjjgXFRUVdHR04OzsTHx8/AX1AScnJ7y8vAgKCqK+vh6j0UhjYyN6vZ6+vj6qqqqAT/pkQEAAc+bMoaioiN7eXoaGhpAkCT8/P8LCwoiPjx9XG2uxWBgYGOD06dO0tLQwMjKC1WpVxkJoaOiYUnQjIyO0t7ezd+/eMaa5VatWYTKZqK+vp729HYvFgsPhwNnZmTvuuAN3d3esViutra3U1NTQ39/P8PAwFosFLy8v/P39iYiIIDU1VfmtJUmitLSU6upqCgsLsVqt6PV6Dhw4MCbqNTExkSVLltDU1ERRURHt7e1Ku2bPnk1WVtaY/iOnzqmurqa9vZ2uri6GhoaUlFD+/v6kpaUREBCg5BhrbW2ltraW0tJS5dyhoaEsWbJEKd83MDCASqUiICCA6Oho4uLilLlGvq4kSXR1ddHZ2UljYyMGgwGr1YokSXh7exMXF0dYWJji4nM5+3xbWxvV1dUAREVFKSWyzoe8AZSjYUcjP8eamhra29vp7Oz81OcI/y3/d/LkSdrb2zEajYyMjODs7IxWq0Wn0xEfH09YWBgajQabzaZUc9i/fz9Wq5XOzk4++ugj6urqlHbOmjWL7Ozsy/S0rjze3t7ExMSQm5vLzp07aW1tZe/eveTm5uLr63tJmvqrT1r5nJGrS5w4cYK4uDgWLVp0VWkORqeL6Orqoq2tjf7+foKCgggJCRmTokWSJKxWKy0tLWzZsoUTJ05QXl6OwWBAo9Hg4+NDfHw83/jGN5gyZQrh4eGYTCb+8Y9/cOLECUwmE1arFS8vL1JTU/nyl79MWloaKpWK1tZWfv7znzMwMICTkxNarZYbb7yR+Ph4fH19sdvttLa2smPHDo4dO0ZpaSkGg0ERQBMTE/na175GRkYGsbGxqNVqmpqa2LlzJ3/7298YGBjAZrORkZFBWloa7733HiUlJTQ1NSFJEtHR0SxcuJDbb79dyfrd29vLvn37+Otf/0p3dzdqtZqpU6cyc+ZMRbCrqanhmWee4dSpUwwPD6PVavnXv/41RrCTI49bWlr44IMPKCkpUYpDu7m54efnR1xcHPfddx/p6ekEBwdPio2BSqVCo9Ewc+ZMent72bhxI0NDQ3h4eExI+2w2G3q9npdeeonY2Fhyc3O55ZZbrnqhDqC+vh69Xo+zszORkZFoNJoL/m5ISAi+vr60trZSVVWF1Wrl1KlTvPPOOxiNRry9vcnMzOQXv/gFr7zyCtXV1XR2dmK1WomKimLOnDmsXr2a3NxcpQqHLGQYDAaqq6t57bXXKCwspLe3l5GRETw8PIiPj2f69OmsXr2aWbNm4eLiwvDwMKdPn+Z3v/sdnZ2dmM1mrFYrb731FoODg+zevZuCggL6+voYHh7G4XCwfPlyAgIC6OvrY9++fezYsYPW1lZ6enqU4umJiYnMnTuXiIgIvLy8UKvVOBwOjh07xrvvvsuRI0ewWCy0tbWxefNmdu3aBXwi/N52220sWrSIyspK/v3vf3P48GFFaPztb3/LlClTlOct+zu2traybds2CgsLOX36tDL+vby8iI2N5Y477mDatGkkJiai0WhoaGhg69atvPDCC0rOt7lz5xIfH8+GDRs4deoUra2tSJJETEwMy5cv5+abbyY9PX3Mb2kymSgtLVVqijc3NzM0NITdbicoKIglS5awYMECfH19L7t/oF6vp6GhQelTPj4+n/lco5/j9u3bKSgooKysTHmOnp6eY55jUlKSkspDrhjz/vvvU1RURGtrq7LGhIWFERcXx4oVK1ixYgU6nQ6r1UpeXh4vvvgilZWVmM1mmpubefXVVxWLk7OzM//zP/9zVQt2gDKWv/jFL/Laa6+xb98+ampqFAXJZ0YSjMFoNEp79+6VQkNDpe9+97tSc3Oz5HA4JrpZYxgaGpJOnDghhYeHS0lJSdI999yjtNFut0sjIyPSyZMnpYceekiaOnWq5ObmJj399NNSaWnpmHsZHByUTp06JSUmJkrh4eHS6tWrpY8++kjS6/VSdXW19P7770u5ubmSTqeTvvOd70hFRUWSw+GQLBaL9OGHH0qLFy+WnJ2dpX/+859SU1OTZLPZJIfDITkcDslut0tms1m69dZbpW9/+9vSu+++K1mtVsnhcEjDw8NSTU2NlJaWJkVEREiLFi2SNm3aJHV0dEi1tbXS9u3bpdzcXCkgIEC68847pYKCAuWcNptNGhwclG6//XYpIiJC8vPzk9avXy9t3bpVqqqqkjo6OqRt27ZJGRkZUkREhLR8+XLJbDZLkiRJDodDstlsksVikR566CEpICBAmjdvntTU1KR8brfbJYvFIm3cuFHKysqSAgICpPfff3/M8zcajVJ+fr4UHx8vhYWFSV/5ylekbdu2ST09PVJFRYX08ssvSwsWLJC0Wq30y1/+Ujp16tQV6hkXRltbm/SPf/xD0mg00tatW6X29vYJacepU6ekZ555RnJ2dpY2btwodXZ2Kv3nasbhcEhz586VvL29paCgICk/P1/q7++/4O8/+uijUnZ2tuTl5SX9+te/lmprayWr1Sr19/dLc+bMkUJCQqSgoCBp/vz50ubNm6Wqqiqps7NTys/Pl9asWSNFR0dLQUFB0u7du6Xu7m6lTR0dHdIPfvADKTY2VgoJCZH+9a9/SSdOnJC6urqk4uJi6etf/7o0ZcoUKSAgQNq7d6/U3d09Zky88MIL0uLFiyVAmjVrlvToo49K+/fvlwwGg1RcXCz99re/lTw8PKTa2lrp6NGj0p///GfJ2dlZ+uEPfyht27ZN6uzslNra2qSXX35ZuuOOOyQvLy/pkUcekUpLS5U22mw2yWq1SoWFhZKHh4c0ffp06ZlnnpHMZrNkNpsli8WizCN2u12yWq1SUVGRdNNNN0murq7Sb3/7W8lkMil9aHBwUCovL5dSUlKkiIgI6YYbbpA2btyozDU7duyQ1q1bJ2m1WumWW26R3n777TFzjcFgkNavX68881tvvVXauXOnVFNTI7W2tkrbtm2TEhMTpejoaOmmm26S7Ha78jtarVZp27Zt0pIlS6S4uDjpN7/5jVRfXy91dHRIlZWV0l/+8hcpKipKmjFjhvSrX/1KstlsZ/WFX/7yl1JaWpoUHh4uNTY2XlQ//OlPfyoFBgZKgPTWW29d9PdHMzQ0JFVWVkqpqalSRESEtGLFCuU51tXVSTt37pTWrVsn+fj4SGvXrpXeeust5TfIy8uTvvGNb0gBAQHSr371KykvL08yGo1SS0uL9Nprr0l333235OzsLL388stSTU3NmHn6nXfekby8vKTZs2dLmzdvHtMPxnteVyMOh0OyWq3Sr3/9a2nu3LnSXXfddclz8sSrESYZZrOZ2tpa3N3dCQgIuCD19UTS0tLC9u3bufHGG5W/ORwOhoaG8PT0ZPr06Tz88MMsXLjwrHJLpaWlbNy4kdbWVm677TaWLl3KrFmz8PHxwdPTEw8PDx599FHuvfde8vPzcXV1VRzak5KSuPnmmzl48CBHjx4lICCAW2+9VTm3yWRSQuYzMjLIyclRIjDlBJzNzc3ceOONLF26lDlz5uDr64tWq8XLy4vHHnuM7373u5w4cYL//Oc/ZGVlKTsYWQuhUqnw8fFh+fLlZGZmotPpUKvVzJgxgxkzZnDs2DEqKysxGo1KdQJnZ2fl35nIyU3lTOzn0hodO3aMrVu30trayt13382yZcuYMWMGWq0WNzc3JeHnXXfdxe7du5EkiSlTplyun/ySkSuReHt709DQQEJCAiEhIVe8HU1NTZw+fZrMzEyio6M/1Q/takL6/xoyuHTzmtwf5X5vtVoJCAjgW9/6FtnZ2fj4+ODi4kJqair33XcfH3zwAa+//jrPPfccjz32GP7+/jgcDl5//XWOHj2Kw+Hg3nvvZeHChYSEhCh9Vq4p/Le//Y3nn3+e733ve8yePVsZE6MjqJctW8aiRYvIyMjAy8uLuLg4vvjFLzJjxgxCQkLo6+vD3d2d9PR0br31VhISEvDx8UGSJJYtW4ZKpaK9vZ2NGzeyePFixVwtj0t5/MnXHk/jOXqsniuDf3l5OR9++CHNzc3ceuutLFmyhLlz5ypzjVar5dFHH6WiooKSkhLMZjPLli1T0ozIzxzA39+f5cuXk5GRgY+PD05OTspcU1RUpMw13t7eigZckiTCw8MJCgriS1/6EiEhITg5OeHt7c0tt9xCcXExZWVlbN68me9973t4eXld1mCZy9UHKyoq+OCDD2hqamLdunUsXbqUuXPn4uPjg4+PD97e3jz22GNUV1dTWlrKSy+9xPLly9FqtfT09HDy5Ekl5U9aWhqenp64urqyZMkSAgIC2LVrF87OzkiShEqlUuZo+TmqVCrUavVFab6vJpydnUlPT6elpYXdu3dfctoeIdidgRwi7u7ujoeHx6TxjzoX8iCQS83IxeCbmpqUBTwyMpLw8PCzBkVLSwt5eXmMjIyQkZHBtGnTlIhZFxcXXFxcmD17Nn5+fnR2dirmV1dXV4KCgpg+fTo+Pj5UVlYSFRXFmjVrFLP18PAwx48fx9fXl7CwsDEm4M7OTg4dOsTw8DApKSnMmDFjzHU1Gg2zZ88mICCAxsZGjh8/zsjIyLgVEzw8PJgyZQr+/v7KbxUQEEBERATl5eW0tLQwMDCAl5fXZQscqaur49ixY5hMJrKzs8nIyFA2AGq1mtDQUFxdXfHx8aGxsZGSkhJGRkZwdXX93ErIXAyurq54eHjg6emJwWDAZDJNSDsMBgOdnZ1ERUXh7e19TU3arq6uSn+TfaouFKvVit1uVwIqxuszWq2WOXPmEBgYqCx+Pj4+ZGdnc/r0aZycnDh27BhdXV1YLBacnJzIz8+nqakJV1dXJXWTh4cH8Mm4y8jIoKqqCnd3d44ePUp7eztms3ncMlZpaWkkJiai0+mAT0xKss8YoPhNrVmzhuTkZOU4QPEBjI+PJy8vTylx9XnMta2treTl5TE8PEx6ejrTp08fM9fIJbSio6MpKSlRfA8TEhLOck/w8vJS5hr5t9VoNERGRlJZWUlrayv9/f2Ka4NKpSIoKIicnBzUavUYdw6NRkN0dDRJSUnU19dTVlaGwWC4rInv5fuD//apz0pbW5ti8k5LSxvzHAHlOUZFRVFcXMzp06dpbm4mISFBWaOGhobQ6/W0t7cTFBSEj48PISEhSJLEmjVriIyMvC7T1chCd3BwMOHh4bS2tmIymbDb7Z+5LwjB7gxkbZeHh8cl1eW7UoSHh7NgwQJeeOEFRaDq6Ojg8ccfZ8+ePRw6dIjQ0FBmzJgxxldQkiTa29s5ceIEKpWKuLg4IiIizlrk3d3dCQsLo6urS/EbCgkJwd/fn8zMTJKSkqipqcHPz4++vj5FwDEYDLz//vvk5OSQnp6udFBJktDr9Rw/flzxhYuJiRn3uqGhoTQ1NSmav9DQ0LM6upubG4mJiWcJbb6+vnh5eSFJEr29vQQEBFxyclt5cW5sbOTkyZOKI3dQUNBZ7ffw8CA8PJzy8nIaGhro7u4mNDR0Ugh28N8kqENDQ1it1glpw9DQEP39/SQkJFxz0dqjk78ajcaLWlQHBwcxmUxKENCZz8bFxWWMEDWa0NBQwsLClE1FV1cXAwMDeHt7U1BQQFdXF8nJyWRkZACM6bcuLi7KRuzUqVO0tbVhNBrHLZ0YHR193rRJMTExREdHs2TJEiXQaLRwq9FoCA8PV3yB9Xr9ZbeOSJJER0cHBQUFSJJEUlISCQkJY46R67CmpqbS1tZGW1sbJ06cIDQ0VBF6ZTw8PEhISDhrDtLpdHh6euJwOOjt7cXf31/ZxE2fPp2srCzlGcjtkpEFHKvVSlNT02UNtvL09FR8NQcGBj7zBk6SJDo7O5XnmJiYSGJi4phjVCqV8hxbW1tpbm6mqKiI0NBQAgICmDZtGi+//DLvvvsuLS0trFixQql9GxgYyDPPPHNN+NZeCr6+vgQEBChBj2az+aw+eKEIwe4MnJyccHd3V5yErzbkZJ1//vOf+fGPf6zkybnhhhvIyMggKChICZoYGRlhYGAAgDvvvPOcDvSys29YWBj19fVKjTs3Nzfuu+8+/vKXv1BbW8srr7zCt7/9bQYGBqisrOTIkSP88pe/JCUlRTnXmdd96KGH+MEPfjDudYeHh7HZbPj4+FBfX4+vr+9Zk54cWXXmhCCbXeETYf1iNCbnw2q1KgXdAW666aZz7qoGBwdxOBwEBwdTV1c3Rrsy0djtdoaHh3Fzc5uwNrm7u+Pp6Ulvb+8laRMmI9OnT6ezs5PTp09TVlZGVlbWGK3V+aitraWjowONRkNOTs5ZkexypYpz4ebmhr+/P52dnfT399PX16dUqzGbzZSWlpKYmDjuImqz2ZQ61F1dXYp2ZbxrnE/DajKZaG5uZsOGDezZs4fOzk6MRqMy7u12OzabDbvdjsVi+VyS4Y4eq66urnh6ep5TaPL398fT0xO73Y5er8dms511jLwZOvO5qdXqMRvX0QwMDLBz507y8/PJy8tTIknl+7VYLFitVqVqhsPhuBy3DnwiXGdlZVFWVkZtbS0pKSmkpqZe9HnkOftCnqOfn58i5HZ1dWG1WsnOziY5OZkZM2Zw+PBhdu/ezXPPPYezszOxsbGkpqaybt06cnNz8fb2vtTbvmoZGhrCaDTi7OyMl5fXJSmWJscqM4mQCy4PDAwoE+HnVaj380D2O/Hx8WHhwoUMDAzw7rvv8uGHHyomVFk1rtFoFCH2gQceICUl5bz36enpSWJiojKo1Wo18+bN4/3336ekpITt27fzjW98g1OnTlFcXKyUUBndQc+87p133kl2dvZ5r+vq6kpKSso5dy+XUvjbZrONK/SNXuBGI5u95TqfjzzyiFLO7Vz4+vqSlJQ0aYQ6s9nM0NAQBoOBwMDACSvTFRAQQFhYGPv27VOinCfLM7pUcnJyqK6upqSkhKNHj7J27dpP/Y7sQtHV1YWHhwfJyckEBQWdpbGzWCxj+uaZfc9qtSrpT9zc3BTToJubGy4uLoSFhfG///u/n6o9njZtGhEREeN+9mnjbdeuXezdu5d9+/aRnZ3NwoULCQ4OVu6lra2N4uJiPvzwwzH+iJcTea5xdXXFarUqm/XxBFJZ2JL93861WRvvvs/1LKxWK88++yx5eXkYjUamT59OcnLyGF/CI0eOcOLECWpqai77M0hKSmLOnDm8+eabnDp1StHSng9JktiyZQsnT57EaDTy8MMP4+fnd1HP0WKxoFKplOfo7OyMt7c3ixcvJjk5md7eXqXeeVNTE83NzTz11FP09/crQuBorpa191Jpbm6mrq6OhIQE3NzcLsm6c23MopcRFxcXwsPDMZvN9PX10dvbOyGO5ZeCLLhlZWWh1+vZsmULhw8fZvr06UydOlUZcF5eXgQGBtLa2srChQtZsmTJWZoAeeduNBqx2WyEhoYqnzk5OREbG0tcXBy1tbWcOnVKUcFXVVWxcOHCsyZJZ2dnPDw8CAoKorW1lTlz5rB+/fpxd2p6vR6j0YjJZCIkJOSyDnB5l20ymc6aUG02G2azWdHKycjPVavVEhAQQHt7O8uXL2fatGlnCZ2y+aKvrw8nJ6dJ1YeMRiO9vb0MDw8TGho6YX4tQUFBxMbG8vrrr9Pc3KxUSbkWSE1NJSkpiYCAAMrKymhrayMsLGxMfq/RyC4gx44do7+/n+DgYGbPno23t/dZE7ws2A0ODuLp6TlmXJhMJgYHBxkYGMDDwwNvb288PT1xdnYmKCiIwcFBdDodt912G1qt9iwBZnh4GIPBgF6vVwJaLgZ5LBUWFrJt2zbq6ur45je/yezZs0lMTFTuv7S0FJVKxcaNG895rvGEq5qaGhwOBzqdjoCAgE+touDp6UlgYCDNzc309fXR398/xuQrSRIOhwO9Xq/koQwJCblk1wDp/6cH2bRpE9XV1YSEhLBw4UJyc3PHmFs1Go0i5Mjfu1xEREQwZcoUIiIiaGhooL6+np6eHiVH4Zk4HA5MJhO7d+9W0s08+OCDwCdmaPk5Go1GjEbjmLEqC+fd3d0MDQ3h4uJCaGgoGo2G3t5empqaSE5OJiYmBpVKhd1u59SpU+Tn53P48GHefPNNkpKS0Ol0YwQ7WVExWviXJImKigrFj/lc93O1IEkSw8PD1NfX09jYyJQpUy45t+jkcPiZRHh4eChF0ltaWigsLJzoJn1mMjIymDdvHomJiZw6dYpjx45x6tQpZZBERkYyb948HA4HNTU1NDY2Kp+N/vfee+/x6KOP8sADD4wxUciRSrm5ucyZM4eenh42bNjApk2bqKqq4otf/OK4Aps8yUmSRH19PbW1teNed+PGjfz85z/na1/72kU7oH8aXl5eeHl5odfrsdvtY65rNBrR6/V0dnaOe82EhARmzZqF3W5XnKbPbLvNZuPNN9/ku9/9Lj/84Q8nlanx9OnTVFRUoFarmTJlCn5+fhPSjuTkZKUfbNy4kfz8/M9Ne3MlUalUhIWFsWjRIr7whS9QVVWl5P4ar5/LrhFtbW08+eSTmM1m5s2bxwMPPHBOc6nRaOT06dPKuJD/NTc3K0JMQkICoaGhivlwwYIFhIaGMjg4yMmTJxW/t9H/6urq+M9//sP69es5cODAZzINOhwOmpqaqK+vx9/fn7Vr15KdnT1GqJWDvM6Fk5MTbm5u2O32MePz5z//OT/60Y/YvHnzuObSM4mIiGDevHmoVCrKysooLy8/654tFgsnT56ks7NTCUo5n6n7QpDngNLSUmw2G/Hx8dx+++1nVRGxWCwXdB+fBZ1OR1paGvfeey+9vb3k5eWxY8eOc/ZBk8lEQ0MDW7ZswW63k5ubS3R0NK6uroSHhzN//nycnJzO+RzNZjMlJSV0dHTg7e3N7Nmz8fLyYseOHaxevZqqqiqGhoaUdWPatGnce++9PPLII6hUKoqKipS8ezJyP7DZbIpLjSRJPPDAA/z6179W8htercj3U15eTl5eHlVVVXzhC1+4ZJO00NidgVqtxt/fnzlz5tDd3c3777/P6tWrJ7pZn5no6GgefvhhHnroIT7++GMsFgtz584FYMaMGXh7e5Ofn88rr7zC6dOnufPOO8nMzESlUtHT00N+fj7PPPMM06ZN4/bbbx/XVLZw4UKGh4fZsGEDzz77LCkpKSxYsIDg4OBxd93p6ek8/PDDHDlyhA8++ICqqiruu+8+MjMzcXZ2ZmBggP379/Pss88SHR3N97///cueJDo+Pp6MjAw2b97Mu+++y8KFC0lPT6e3t5fXXnuN/Px8/Pz8xp10Fy1ahE6no6CggGeeeYbS0lLWr19PZmYmDoeDjo4Ojh49yt/+9jcl8eZkqve5e/duSktLyc3NJTAwcMKChDw8PIiNjeXRRx/llVdewWg04uPjw4IFC67qHbjMjBkziIyMZHh4mM2bN3PkyBGOHTvGzTffrKQZkYWKXbt2kZeXR3d3N48//jhz5sxRqhGciZubG319fTzxxBM8+OCDJCQkoNVqaWxs5Mknn+TYsWN4e3vzv//7v4qTu7OzM9/5znew2Wzs2LGDH//4x/z4xz9mypQphIWF4XA4yM/PZ/fu3bz33ns88sgjpKenfyZzkJOTEwkJCaSkpFBWVsZrr73GggULSE9Px93dncrKSnbu3Mm77757TsHRzc2N9PR0KisraW5uprW1la6uLurq6ggODiY7O/uC2jZ9+nS8vLzIz8/nvffeo76+HpvNxvTp0xkeHqaurk5J8jxz5kzWrVtHQEDAJY9XOZhg5syZlJaWUlFRwQsvvMDq1avRarXY7XbKy8t59913KS4uvqRrnQ9/f3++853vKEl/f/azn9HY2MiSJUuIjo7Gz88Pg8FAeXk5hYWFvPbaa+h0Or785S/zhS98QTlPVlYW3/ve98jPz+fDDz+koaFBeY4jIyPU19fz8ssvU1lZSXZ2NuvWrSMwMFB5jmazmV/+8pd89atfZcaMGcoG4/jx4+zduxdJksjKyjqrHruPjw+pqalKtHJTUxPd3d00NDSQlZV13vrLVwMWi4Xu7m5+9KMfYTabyc3N5aabbrpk9xgh2J2BbG5buHAhH3/8MaWlpZw6dYqEhITPHKFyOdmyZQtHjx5VHKNNJhMnTpzgF7/4BfHx8aSmpjJr1iwAJc/brFmziImJUQS1X/ziF4SGhpKRkUFycjIPPvggxcXF9Pf3884777Bt2zZcXFyUkk/z5s0jJyeHGTNmKOcdjRzmPmPGDA4dOkRcXByzZ88+p7+Up6cnMTExPPDAA5SUlCh+gNu3b1dMIP39/cyYMYOsrCzmzJmDSqWiu7ub8vJydu7cyalTpzAajTgcDh5//HFycnJIS0vDx8eHp59+mmPHjlFTU4PJZOL5558nMzOTKVOmsGTJEuATbebAwABNTU3k5+crC4aXlxeenp5ER0dTVVVFa2srb775JvX19WRkZLBw4UJ8fX1JTU3l29/+NgUFBbS3t/P222+zZcsWXFxcsNvtDA4OsnTpUpYsWUJWVtakEFRsNhvl5eWUlZVhNptZu3bthPqPyv5MK1as4MSJExgMBl577TWl4snV7kjt5uZGcHAwt956Kzqdjp6eHhobG3n55ZeV3GuSJDEyMkJ/fz8xMTEsW7aMnJwcwsPDzzl+XFxc8PPzY+7cuRw5coRDhw4pC0RjY6NSCUHOcScTFhbGqlWr8Pf3p7CwUBEm5UAIOXJy1apVSt5Ls9lMV1cXb7zxhjJOAP75z38SHByMt7c3P/rRjxQhS+5LOTk52O12NBoNR48epa6ujoCAALy9vXF1dUWv1xMVFUVVVRV79uyhu7ub48eP87WvfQ13d3d8fHy4+eabeeONNygrK+Ppp5/GZrMRFRVFVlYWoaGhVFdXc/jwYQoKCqioqMBms7F7926sVitpaWksX74cLy8v4uPjefDBByksLMRkMrFhwwZ27dqFzWZjaGiItrY21qxZw9y5c1mwYIFSEvHUqVN8/PHHlJWVMTg4SF1dHb/+9a+ZN28eU6ZMwcXFhWeffZYjR45QX1/P0NAQ//znP5k6dSpTp05l9uzZfPnLX2bXrl20tLSwc+dOKisr8fLyUnIH+vr64u/vj8Fg4NVXX6W4uJioqCgiIyPZt28fe/fuRa/XYzKZePLJJ8nIyCAlJYWcnJwLGrfOzs74+Phwww03EBISQklJCc3Nzbz33ntoNBqcnJwU0/7w8DBJSUksXLiQOXPmjAmaGe85vvvuu+zatUsJxGpra+Omm25izpw5ynNUqVQkJCTwta99jdbWVg4ePMjJkydxdnbGbrfT09NDX18fa9euZeXKlWf514WFhbFmzRr0er1SlcFms5GdnU1WVtakcnG5WAYHB6mpqWHz5s10dXWRm5vL8uXLL4trjBDszsH8+fOpqKjg+PHj7N+/Xwlhn2jNy/Hjx3n33Xcxm82Kj4PRaOT1119XkgDLgh18ohVJTExk1qxZlJaW0tXVxeuvv05aWhoeHh7k5ORw7733snHjRgoLCxWThJubGz4+PkqZmLS0NCIjI8dtk+xPsWzZMjo6OsjOzj5vqRe1Wo2vry/33HMP27dv59ixYxQWFtLZ2YlGo8Hb25v4+Hhuu+020tPTlbQORqORsrIy3nzzTQDFV+att95SahU6Ozvz2muvKdeKiIhg7969dHd343A4FMEuOTkZDw8PmpqaOHr0KI2NjdjtdpKTk7nrrrvw9/enqakJh8NBYWEh7e3t2O125s6di5eXF6Ghodx3331otVqKioqU5+bh4YFOpyMmJoavfe1rpKWlERwcfAm/+OXB4XAwPDzMvn37FH+2xYsXT3iwgpubGzNmzGDZsmXs3r2bnTt3kpqairOzMzExMYpJbDIIxp8FNzc3RTty6tQpDhw4QH5+Pn19fVgsFlxcXIiOjiY1NZWpU6eyZs0axSfuXKjVaoKCglizZg3PPPMM5eXlSv+UHeZvuOEGYmNjxwhcnp6eLF68mKSkJFxdXTl8+LCSQ06uWzl16lSWLl3KlClTFO25Xq9nw4YNir9oXFwce/bsAT6JgnzkkUfO0p7NmjWLoKAgLBYL+fn5NDY2YjKZ8Pb2Jjs7m8DAQBYsWEBdXR3Nzc0YDAZaWlr40pe+hLu7O1qtljVr1lBdXU15eTk7duxAq9Vy8803M3fuXPz8/Dh16hQ7d+6koKAA+MQ6IacWMhgM5OTk4OPjg7+/P/feey/BwcEUFhZSXFxMV1eXEuWalJTEl7/8ZVJTU5U5rq+vj5MnT/LOO+8AKELO22+/jbu7u6Jxff3114FPBKiQkBA+/vhjenp6cHFxYd68eaxfvx5XV1eOHDmiJCPWaDTodDpmzZpFZmYmGo2GkZERjhw5Qnt7OwkJCUyfPp0333wTs9ms5AjcvHkz7e3tOBwOcnJyLrgPqlQq5s6dq5x3w4YNlJaWotfr6e/vx9XVlZCQEKKjo1m1ahU333zzWb7R8mbim9/8JiEhIRQUFCjPUQ6OSE5O5ktf+hJpaWlERUUp301KSuIb3/gG7733HpWVlRQXF9Pd3Y2rq6tS0/vmm29myZIlZ/mghoaGsnr1aoqLi6mrq6O8vBwvLy9F8zdRbiSXguxTV1dXR35+Pm+++SbBwcGKYHc5UElXu0PL54QkSWzfvp133nmHDz74gFdeeYW5c+dOuHP3hfxcZy6Cl+snPt/iOt41Lvb4T7vu5biPy3EuuWD5xV5zIjEajdTU1LBy5UoWLVrEjTfeyDe+8Q1gcrTP4XBw6NAhXnvtNV588UXWr1/PjTfeyFe+8pXzVgG5WrjY/jbe/ZpMJpYuXUpHRwdTpkzhww8/vOjzXGy/vdDjL+ecc7Hj63Ke63LONZ8nn2U8XGof/Kxz3qVc99O+ezXOCxaLhe3bt/P0009TXV1NeHg4r7zyCuHh4bi7u1+WexIau3OgUqmYPXs2Wq2Wo0eP8vzzz1NfX883vvENvLy8JqxDfZbrXom2Xuw1Jvo+LvVcV9OEMjQ0xJ49e3j11VcJDg7mi1/84qTzY1OpVEybNo2QkBBCQ0P5+OOP+etf/8ru3bv51re+RVJS0oRvqi6Fz+NZX4kx9FnbPZnG10TPNZOFifpNLuW619LvIJdW27FjBx988AExMTHccccd3HHHHURGRl5Wtxgh2J0HHx8fEhMTWbVqFXl5eRw5coTQ0FBuuummSWGWFQjOh8PhwGKxsHfvXg4cOEB9fT033ngj6enpk05IkvNeubm5sXz5cqxWK5WVlTQ0NLBhwwalykl6evq4aToEAoFgsmG32xkZGeH06dMUFRVRXl7O6dOniY2NJTc3l5kzZ5KWlnbZgwOFYHcenJ2d0el0PPjgg9TW1lJaWkpzczPTpk1TSs5MlhJRAsFoHA4HVquVjo4O/v3vf1NXV4enpyff+ta3CAsLm7SCkYuLC/PnzycuLo7CwkJefvllXnnlFWJjY5k+fTr33HOP4h8pByBcS7v68bBYLFgsFiWJs/zb9vb2KkmHr7WSbALB1croNDpDQ0NKcN3mzZsxm81ERkby6KOPMmvWrM/N/1r42H0K8uNpampi06ZNPPXUU+h0On784x+zaNGiSeEYLxCcSW9vL6dPn+a73/0uAwMD3HTTTTz00ENKgtDJLgzJiWNtNhsHDhxgw4YNHDx4kMbGRtatW8fixYtZsGABSUlJk/5eLpV3332XrVu38vrrryt561QqFRqNhm9961usW7eOhQsXTnQzBQIBn1Qc6e/vZ8OGDezcuZMjR45gs9m48847Wbx4MYsWLVIqS3xec5cQ7C4QOXnj0aNHefrpp/Hz8yM9PZ3bb7+dWbNmiR2zYFIgSRLHjh1j165dHDp0iPb2diXzf0ZGBm5ubleVICRJEr29vbS0tFBfX8/+/fspLi5W6mtOnz6defPmkZSURHx8vJJi4Vqira2Nzs5OWltbz/osJiaG0NBQ/P39J6BlAoEAPrGQ1NXVKZHPx48fR6/Xo9PpiIyM5IYbbiA5OZng4GD8/f0/9821MMVeIG5ubkRHR+Pp6UlBQQElJSWcPHkSLy8vNBoNUVFR+Pn5CQFPMCHYbDYGBwdpbGxkx44dFBYW0t3dzbx581i0aBGxsbETVhP2UlCpVPj7++Pn56eMP5VKRW1tLZ2dnRQUFDA0NERdXZ2Skkcu6D4Z8k5eDsLCwggLC2PatGkT3RSBQACKqdVgMNDX14der6e4uJjTp0/T1NRER0cHERERpKenk5WVxYoVK/Dw8LhiLjBCY3eRyKVTXn75ZbZt28bWrVv54he/yK233sqCBQvw9fU9K1mnQPB5IA9dOZF0eXk5zz33HB988IEymTzyyCNKItJrAfme6+vrOXnyJK+//jqHDx9mZGQEPz8/br/9dubPnz+mLqVs8hDjUSAQfBZGz7WSJGG32+nu7iYvL4+CggL27t1LSUkJ4eHhpKSksG7dOtauXYuvry8ajeaKt1cIdheJ/LjMZjNtbW3s37+f3//+91gsFkJDQ7n//vtZs2bNGAFPIPg8kDcZ77zzDu+++y6lpaXY7XYeffRRcnJySE5OVupSXitCzegJVq432traSllZGfv372fbtm0YjUZcXFyIiYlhxYoVZGdnk5aWpiS6FggEgovBZrNhMBg4cOAAxcXFFBUVcfToUSRJIjg4mKysLL7whS+Qnp5OcHAwGo1GSV8yEXOvEOwugeHhYdrb2zlw4AAnTpygtraWkZER5syZw9SpU8nKylKcu6+VhVUwscjDta6ujqqqKqWklEajITg4mHnz5rFw4UJCQkLGlJO6lhkeHqanp4eWlhZqa2tpaGigo6OD9vZ2+vv7lRJVcsm9sLAwwsPDiYqKuiZ98gQCwaVhNpvp6emhurqa5uZmmpubqayspLOzE0mSlHKB8fHxhIaGEhERQVJSEn5+fpPC5UUIdpeIJEkMDQ2Rl5fHwYMH2bRpE66urqSnpzNnzhzmz5+Pv7+/UoMUrh3tieDKIA9Rk8nE4OAger2egoICioqKOHToEFarlYULFzJv3jxWr1593abhkdMMNDY2Ul9fT2FhoRJAIpfNSk9PJyoqipiYGNLT0/H29sbd3R13d3e8vb3RaDSTNhWMQCC4/NhsNqVe7uDgIMPDwxiNRpqbmzl58iQNDQ20trbS3NyMTqdTSgAuWLCAjIwM/P39J8Tcej6EYHcZkSNn//SnP3HkyBGqq6tJS0vjy1/+MvPmzWP27NljFg0h4AnOx+ih6XA4KC8vJy8vj2effZba2lp8fX2ZM2cOP//5z4mOjj6rzuL1jizo1dfXU1lZyc6dO9m3bx8tLS309vbi6enJ1KlTSU5OJisri9zcXKKios6r6RRjViC4OjmXqNPb26sIcbKptampie7ubrRaLSkpKWRkZLBy5UoWLlyITqebdILcmQjB7jIiSRJWq5W+vj7a2tqoqqri7bffpqysDICoqChuvfVW5syZQ2xsrFiIBedlaGiIzs5O9uzZw+bNm2loaMBoNJKdnc2qVatITU0lLi4OnU6Hi4vLdamlOx/y1DZ6Rz4yMoLBYKCjo4OCggIqKytpbm6msbERq9WKn58fISEhpKWlMWXKFKKjo4mMjCQuLk6YbQWCqxiLxUJnZ6cSudrY2MjJkyepq6tjcHAQh8NBYmIicXFxREdHk5aWRlpaGl5eXri6uuLh4YGbm9tVMQ8Iwe5zYnBwkJ6eHo4ePUpJSQktLS20t7fj7u5OREQE4eHhJCYmMmXKFIKDg/H19QWERuB6RpIkhoeHaWtro7S0lIaGBsVvzGQy4evrS0REBLNmzWLq1Klj+o3gwjGZTAwMDCgmFr1eT0dHB42NjRiNRiwWC05OTjg5OeHl5YWPjw8hISHK8/b39yc0NJSAgAA8PT2VABWBQDCxSJKkBDr09fXR3d1Ne3s7PT09GAwGOjs76ezsxGKxYLfbsdvtuLq6otVqCQ4OVvJCBgYGEhYWRkhICGq1+qpbl4VgdwXo6OigrKyMXbt2KVF7rq6uTJkyhZUrV5Kenk50dDRubm64u7uPKRF0tXUowYUjV1ewWq2KNkmv11NUVMTWrVuprKyku7sbHx8fVqxYwbx585g7dy7h4eGiX1xG5MXg9OnTlJeXU1dXx+nTpykrK6O/vx+TyYSTkxMxMTGEhYURGRlJSkoKCQkJBAYG4uvri1qtxtnZGbVajVqtVnz1ROojgeDyIrtYyIKZxWLBZrNhs9mw2+0MDg5SX19Pa2srtbW1VFRU0NzcjF6vZ3BwEA8PD4KCgoiMjCQjI4PMzEzi4+NJSEjAw8PjmhirQrC7AsiPWE5PceLECQoLC9m0aRN5eXk4Ozvj7+/PwoULWb58ORkZGZ9LYWDB5MJut9PZ2UllZSWbNm3i0KFD1NfX09vbS2ZmphIQsWrVqrMCIkS/uHycawqUJAmDwUB7ezuFhYWK0FdVVUVpaSkOhwNXV1f8/f1JSkpSNPGpqalMmTKFsLAwAgMDr4t6tgLBlUKSJIxGIx0dHbS2tlJaWkp5eTmtra20tLRQWVmJxWJBrVbj5+fH1KlTiY2NJSYmhmnTpjFt2jQlUGo8roWxKgS7K4i80xgaGmJwcBCDwUBFRQWVlZVUV1dTWVmJ0WjEzc2NoKAg5syZQ3p6OjExMSQkJKDVaq+JTnc9IkkSg4ODSmmswsJCiouL6ezsxGg0KtFWMTExZGRkkJiYiK+vL1qtVsmJKH77K4/snzc0NMTw8DBms5mRkRG6urpobGykq6uLzs5OOjo66O3tpb+/n76+PsUnx8vLS/HTCwwMJDg4mIiICEXTp9PpRDokgWAUDoeD/v5+Ojs7MRgMdHV10dzcTHt7O3q9nubmZnp7ezGZTNjtdtzd3QkMDMTHxwc/Pz9CQ0OV8RYYGIinpyeurq64ubnh4eGBp6fnGG36tYgQ7CYQSZLQ6/VKeoaysjJqa2vp6+tjaGgInU6Hv78//v7+REZGEh4ejp+fHz4+PgQEBBAQECCc5ichcvJcvV6PwWDAaDTS3d1NW1sb7e3tdHZ20tXVRX9/P2q1Gh8fH5KTk0lISCA6Oprk5GSCgoLE7zpJkSSJkZER2tvb6e3tpauri9bWVsWPp6uri76+PkwmE1arVUmnIi8uPj4+Y4R2rVarHOPl5YWXlxfu7u6KYHgtVQ4RXN/Y7XasVivDw8MMDAwoLiiDg4MMDg4yMjLC8PAwBoOB3t5eBgYGMBqNjIyMYDKZlI2VWq1WUhQFBAQQEhKirJfh4eGEhobi4+Nz3eTyPBMh2E0yWltbqaqqoqCggN27d1NZWUlfXx8Oh4Pk5GRFAMjKyiIrKwutVqtM/PIuRP4H14ZaeTLjcDiUf3a7HYfDgc1mY2hoiIKCAsrLy6mpqaG4uJiamhrsdjseHh5kZmYyf/58MjMzmTZtGlFRUeK3ugZwOBxYLBZqa2vHmIbq6upob2+nra2Nnp4eHA4HAC4uLkRFRREUFERgYKBizg0KCiIkJERJYyO7ZYwuj3bm+9FaXdGXBFeK0a5GcsmtM1/L781mM4ODg7S1tdHQ0EBnZyfd3d20tLTQ1NSEXq+nu7ubgYEBVCoVLi4uuLm5ER8fT1RUFGFhYcTFxZGUlERkZCQhISHCkjUOQrCbZIweEHa7nZGREbq7u6moqCAvL0/R6lVXV6NWq5UIvfT0dJKTk4mNjSU5OZn09PSrIiz7aqelpYXGxkaqq6spLS2lurqapqYm6urqMJlM+Pn5ERYWxvTp05kzZw5paWnEx8fj4+MjhPBrkNGL3Hj/ZDNTR0cHPT09tLe309LSQltbGx0dHdTX19PY2MjQ0BBWqxWVSoVGo8HT0xM/Pz9FCPTz88Pf35+goCBFey8vdK6urkLDJ7hiWK1WTCYT3d3dNDc3093drZhMu7u76e/vx2Aw0NTURGdnJ0NDQ8oa5+3tjU6nIzY2lvDwcPz9/QkMDCQhIYGYmBjFnCqvZeP9AzF3nokQ7CY5cj3MgYEBZZD09fUpaTBkU5/s36NSqfDw8CA4OJjg4GD8/PwICgoiLCwMf39/RdCQ/QwE50aSJAYGBtDr9fT29qLX6xWTm7woGwwGRkZGsFgs+Pr64uPjo6TIiI+PH+P74e/vj7e3N15eXldlCL3g0pEjcGWzkslkYnh4GJPJxMjIiOJ/OzAwQF9fn2Kyl01WIyMjmM1mzGYzQ0NDmEwmnJ2d0Wg0imnK09MTb29vpS96enoqfdLT03PM5x4eHorJV/TH6xtZ2yy7AskVGAYGBhgeHmZwcPCs9/L8J/fJkZERAJydnRV3Ajc3N8W/Tc78oNPpCA4Oxt3dHTc3N8X9QKPRKO89PDxwdXXF1dUVEMLbxSAEu6sQOQlydXW14rdVV1dHU1MTRqORwcFBbDYbWq0WLy8vtFotfn5+6HQ6dDod4eHh6HQ63NzclIHk6uqKi4uL4gek0WhwcXGZ0ELGnwey5kROMdLa2orFYlHuVV5w5V1ob28vnZ2d9Pb20tPTo/h9yJOa7Ouh1WqJi4sjMjKS4OBgIiMjSU5OxsfHR+Q5E1wUDodDWVRlB3JZ0NPr9RiNRvr7++nt7VWcyOX+6nA4UKvVykI62rdPXkTd3Nzw9vbG19dXcSb38fHBxcVFSdni4uJyVgqX0a/P9X60aVhwZZA3C6NdQaxWK3a7XUkDIn9+rvdWq1WZEw0GA0NDQwwNDSn+baM3IfJGVi5xKLsVjA5Q8PLyUhQJcl8LCAhQfErlIAe5zwkuL0Kwu4aw2Wz09PTQ2NhIcXExDQ0Nii9DZWUlAwMDmEwmAMXRVM7nExISoryPiooiMDAQPz8/AgICFOFO5sxJ+1yT+MVM7vKxF9Mdz5em4lzvJUlSdqX19fW89NJL6PV6RbPZ0tJCR0cHer2ezs5Oenp6gE98obRarZLWIiwsjKSkJNLT04mMjCQsLExo4QRXFHlBl01dra2tSnRuT08P3d3dY0xhnZ2dijbGbDaPOZeLiws6nQ4vLy/FPCZr/+QgD1n7Jzuly5pAnU43JgDkfOPgfONDpVIhSdIFjaHLNc7OdZ7LtSxejvnsfJ/JG9X+/n5F6JJ/74GBAUU4MxqNija4r69P+UzeMPT19SnBC6PNm87Ozsrv6+vrS3BwMDqdTrFCBAcHKwF+oaGhhIeHK5o3wcQhBLtriNGJG2022xiHfrvdrphsZT8eWQMlh5H39fXR09OjZN6XHbJ9fHzw9vZWJnzZpCjvvGSVuZubG56enmPU6x4eHspu3tXV9awIPzc3tzG7NrmtQ0NDY+7LYrFgNpuVe5NNBfIuUn4tO+fKE1V/f7+i5ZD/bjQaleciazm8vLzw8/MjJSWFoKAgdDodAQEBxMXFER4eTkBAAP7+/qjVasUvTtZYCD85wURwpj+f3KdH++me6cwua3QGBgbo6elRtH96vV4RDoaGhhQBUI5YHC04DAwMYLVaAcb1eZLNZ6NTvsiaQln4kzU7snlOo9Gg0WiU4CJ5XvDw8FA0gV5eXgBoNBo8PDyA/84fo8efk5MT3t7e5312sgB6Lm2RbJaUN8LnOkbWlo5eRoeHh5X5d3h4WJmz5DlM1pLJ6TpGz21y9Ofo9DqytkzemMtm+NGas/H8OeETk6g8d8saXFkod3d3VwR12UVE1qz5+Pig1WrR6XTKHDc6QGf0/+O9FvPgxCIEu+sIeYKQd3CjJ2r59fDwsCIUjZ5cRgd0yAvI6H/ywgEophm5goY82EdPEDLj/U2uxjCaM69rtVrHmBHgv4uMLHTJO87REcMARUVFtLa2YjQalfO7uLjg5+fHPffcw6xZswgKCsLDwwNfX1+8vb2VBUkguNqRhRZZMDCbzco4t1qtirAgZ/SXj5Hfy/PI6O/KAo5cqulc0ZGj5wt5Phn9WV9f3xhTMPx3XMvVeJydnce8PjNITA44OR/jzTtnIgtk50LWmMrznozValXuUf5c3rDa7XblOGdnZ0VD6eTkhCRJY4IEzox4PpdwpVKpFC2ZHEUqb7Y1Go3yvzwfj34vfy7/TRa+R78XgtrVh3qiGyC4csgD+Hy5feRs+729vYoA2NPTo6jxjUajIvjJ70c7gstC4JlCHzDmtcx4fwPO2kmfqRUYHVGqUqmUXb+sJZB9h2RNnFarxcnJicHBQRobG+nu7h5zftm/xNXVlaSkJGJiYoQgJ7gmcXJyUjRonxWTyaRoy/v7+5VADjkIRBYE5YCQ0e9Hzxey5slgMNDY2EhLSwtOTk54eHgQExNzllAIjHl95vxxpsbqXIwWsM7FmZGXF3rMmdrD0abN0dp+d3f3MSk9nJycFMFrdO7D0QEF8ntZ6zn6M/k3lc3n8vcF1x9CYye4bMg7cHlylyd8eecrmyhGd7nBwcGztHOy+Xc0cpCCbLaV/X1kc8KFpHYpLi7md7/7HR9++CEWi2XcY9RqNb/73e9YtmwZU6dOFTtVgeBzRK7Ec/jwYR577DFOnTqFr68vM2bM4KWXXiIgIACVSqXMH6O1jQBDQ0NjxrKsxZc/P9c1BwYGzrmplHFxccHd3f2cmj1ZKJOPkRk9T8mv5frBsgAnEHyeCMFOcNk4M1Hl6N02jK+dO9fu+szJ78yd8ZmmCfmY89HZ2cnBgwd54IEHMBgMZ5lQZMLCwli+fDnf/e53mTp1qojaEgg+ByRJwmQy8de//pXt27eTn5+Pt7c39913H+vWrSMrK2vM2BtPY3fm/DF6rjkfn/a5zKcJYaPnn9F/Gz1PjdbYCbOm4EogTLGCy8aZJofJhre3N5mZmaxZs4ZDhw5RW1s7rlDZ2dlJQUEBb775ppIAVqQsEQguH3IVgo0bN7J//36qqqpQq9Xcdttt5ObmkpiYqPjYyYgNlkBwYQjBTnDd4OHhQUJCAnfeeaeSKb2vr++s4+x2O5WVlXR1dbFo0SKmTZtGcHCwWFgEgsuArKlrb2/nP//5D8XFxYyMjBAdHc0999xDQkLCdVvjUyC4HEw+tYpA8DmiUqlYtGgR3/3ud3n44YfPeZzNZqO7u5v77ruPrVu30tXVdQVbKRBcuzgcDvLy8vjRj37Exx9/zODgILNnz+all15i6tSpQqgTCC4RobETXFfI5uL09HTc3d1pbW3lrbfeor+//6xjJUlCr9fzwgsvUFNTw49+9CN0Ot2kNDMLBJMd2e3h1VdfZdeuXRw4cAAXFxfuvvtuVq5cSUpKikjyLRBcBsQKJbgu0Wq1xMTEsGrVKjIzMwkMDBz3OKvVSkVFBQcPHmTnzp0MDg5eUKoEgUAwFovFwokTJ/j4448pLCxkYGCA+fPnK+4O3t7eQqgTCC4DIipWcF0jSRJPPfUUW7Zs4cCBA+dMSOrp6UlcXBxvvvkm0dHReHp6ikVIILhAbDYbXV1d/O53v+Odd97BYDDg5+fHCy+8wKxZswgODp7oJgoE1wxCsBNc10iShNVqZevWrbz22mu899575zzWycmJhQsXcv/997Nu3bqzaugKBILx2bt3Lxs3buTvf/87kiSxYMEC7r//ftavX4+Li4sYRwLBZUT42Amue1xcXJg1axbu7u60tLRQVlbG4ODgWcc5HA5OnTrFu+++S39/P/fcc8+nliUSCK5nJEli3759fPjhh2zduhWbzca6detYvnw58+fPF0KdQPA5IAQ7wXWNvKiEhITg7u7O8uXL6e/vp6GhYdzs9T09PRQUFCBJEjk5OcTHxyv1FAUCwX+xWCz09PSwe/du8vPzaWhoID4+nmXLljFv3jwiIiImuokCwTWJEOwEAj4xs/r6+vKTn/yE3t5e9uzZQ0VFxbjHNjY2YjAY8PDw4PHHHycqKkoIdgLBKOSa01u3buW5556jt7cXf39/vvWtb7F+/XpCQkImuokCwTWL8LETCP4/8lDo6Ohg+/btPPHEE5SXl49bnUKlUuHq6sp9993H6tWrWbFixZVurkAwaTl58iT79u3jV7/6FUajkTlz5rBq1Sq+973v4e7uLpJ9CwSfIyLdiUDw/5HrOPr7+zN79mzuu+8+QkNDcXV1PetYOXv+xx9/zLZt2zh8+PAF158UCK5VHA4H1dXVbN68mffff5++vj6ys7O54YYbWLVqFZ6enkKoEwg+Z4QpViA4A41GQ3x8PD4+PuzcuZOioiI6OzvHFdzKyspwdXVFq9WSkpKCVqvFxcVlAlotEEwsNpuN4eFh8vLy2LVrF0ePHsXHx4fFixcrueoEAsHnjzDFCgTnwOFw0NDQwM9//nO2bt2KwWA457FarZYnnniCVatWER4efgVbKRBMDvR6PYWFhXzlK1/BaDSi0+lYvXo1f/rTnwgICBB+qALBFUIIdgLBOZBz3JWUlLBnzx5+85vfMDAwMK7PnZOTE6Ghofzwhz9k8eLFTJ06dQJaLBBMDPX19ezZs4cnnniC6upqkpOTmTdvHj/96U8JCwvD2dlZCHYCwRVCmGIFgnOgUqkUs6zZbGb9+vW89957DA0NnWWWdTgctLW1sXPnTux2OwEBAYSGhoq6soJrGofDQW9vLzt27GD37t00NDQQFhbGkiVLWLZsGeHh4SLXo0BwhRGrjkDwKeh0OjIyMvjWt75FVFQU7u7u4x4nSRK7d+9m48aNnDhxArPZLAIqBNcsDocDs9lMeXk5b7/9Njt37gRgxowZrF27llWrVglNnUAwAQhTrEBwAUiShCRJ7Ny5k+eff55NmzZht9vHPdbFxQWdTsd7771Hamoq/v7+V7i1AsHnj9FopKamhvXr19PZ2YmnpycZGRm89dZb+Pn5iaoSAsEEIUyxAsEFIC9Qs2bNYnh4mKCgIJ577rlxj7XZbBgMBn72s59x3333sWTJElHkXHBNYTAY2LlzJ6+88gqdnZ0EBAQwb948HnroIXQ6HWq1Wgh1AsEEIQQ7geACUalU+Pn5kZWVhcPh4NixY5SXl2MymcYcJwddFBYWsm/fPtzd3Vm5ciWurq7C505wVeNwOLBYLBw+fJi9e/dy/PhxXFxcmDNnDkuXLmXatGloNBoh1AkEE4gQ7ASCiyQuLg6dToder+f3v/89LS0t4/rSDQwM8P7779Pc3MzUqVOJiIhQkiALBFcbkiRhs9nQ6/U888wzFBUVYTAYSElJ4Z577mHu3Ll4enpOdDMFguse4WMnEHwGZK3cU089xbZt29i/f/+4x6lUKjw9PUlLS+OVV14hOjoaNze3K9xageDSMZlM1NTU8MADD1BcXIyTkxPR0dG8/fbbREVF4enpKTYtAsEkQGjsBILPgJwKZe3atbi5uWG1WsnPzz8rx50kSYyMjFBVVcUzzzzDmjVryM3NFeYqwVWDvImRy+eVl5ej0WjIycnhC1/4ApGRkbi7u4v+LBBMEoRgJxBcAikpKQwODtLT00N1dTV9fX1YrdYxx9jtdvr6+ti5cye+vr6EhoaSnp4OIBZDwaRGjgYvLy9n//797N69m/7+fmbPns2iRYtYuXIlXl5eoh8LBJMIYYoVCC4Rh8NBX18fDz74IPv27aOzs/OcxyYlJTFv3jz++c9/KpGDYlEUTEZkoc5kMnHPPfdw5MgRmpub8fPz48UXXyQ7O5uQkJCJbqZAIDgDEaInEFwiKpUKHx8fnnrqKW655RaSkpLOeWxdXR0fffQRjzzyCM3NzVewlQLBxSFJEpWVlTz88MNs376drq4uEhISeOWVV8jJySEwMHCimygQCMZBCHYCwSWiUqlwcnIiMDCQW265hbVr1xIWFjauJs5ms9HX18eePXv46KOPKC4uvvINFgjOg6ypO3r0KJs2bWL//v2YTCZmzJjBHXfcwdSpU/Hy8sLZ2XmimyoQCMZB+NgJBJcBlUqFWq0mNzcXV1dXysvLMRgM45YVs1qtnD59mi1btqBSqYiJicHX11eYZAWTAkmS6O7uZt++fWzfvp2qqiqioqJYtGgR69evJyQkRPRVgWASI3zsBILLiCRJWCwW9Ho9a9eupba2lv7+/nMeP3PmTL7yla/w7W9/G7Va7LMEE4/ZbOYXv/gFH3zwAdXV1bi4uPDvf/+bnJwcYmNjhVAnEExyhGAnEFxmHA4HNpuNvLw8nnnmGXbv3o3RaBz3WA8PD0JCQvjd737H/PnzCQsLu8KtFQj+S2VlJVu2bOGJJ57AYDAQERHB/fffz1e/+lV0Oh2urq4T3USBQPApCBWBQHCZcXJywsXFhYyMDFauXAnARx99hNlsPuvY4eFhWlpaeP/99/Hw8AAQwp1gQqiuriYvL4+tW7fS1dVFamoqOTk5LFmyBH9/f1xcXCa6iQKB4AIQgp1A8DmgUqnw9/dn9erVBAQEUFBQQGtrKzab7axjLRYLGzZsIDg4GBcXFwICAnBxcbmsJi+bzYbFYmFoaOiC2+/i4oKnpydqtVpJvGy327Hb7VgsFqxWq+I/KAeQqNVqNBoNGo1G+fuFIl/DZrNhtVqx2WzYbLYx13B2dsbV1RUXFxfFef/Ma0iShMPhwGw2Kz6O8rmdnZ2V9olC9Z8gSRJms5nDhw+zdetWPv74Yzw9PVm6dCmrVq1i+vTpynFyrViLxYLdbj/r93d2dsbd3R1nZ+fz1kWWfw+TyTTmXKPPI//OnzUlkFwCzWq1jukHcj/SaDS4uLgoLhCiLwiuFYQpViD4HJEkiYGBAUpKSrjzzjtpaWk5K4GxjEajYcaMGfzf//0fCxYsUISjy0FpaSk7duzgJz/5ybh1bc/E3d2dBQsW8OSTT5KcnKwsxBUVFZw+fZq9e/dy8OBBOjs7GRoawsvLi5iYGGbMmMHixYu55ZZbLnpBliscFBYWsnfvXkpKSiguLqa1tRVXV1e8vb2JioriS1/6Ejk5OWRmZgJnL8gOh4OmpiY+/PBDNmzYQHNzM319fajVatLS0sjNzeWGG25g/vz5YjEHhoaGeO+99/jtb39LdXU1arWan/3sZ6xfv56UlBRFQBseHkav17N9+3Z27txJbW0tra2tmEwmAgICCAkJISUlhQcffJD4+Hj8/PzOeU2Hw8Hw8DAvvvgie/fupbKykra2Ntzc3IiKiiIlJYUvfOEL5ObmfuYEyFarlZKSEnbv3s37779PS0sLJpMJNzc3MjMzWbp0KXPmzGHevHmAEOwE1w5CsBMIPmdsNhuDg4O88sorbNq0ib17944rXKlUKnQ6HZmZmTz55JPExsbi6+t7Wdpw6tQptm/fzmOPPXZBgp1Wq+Wee+7h+9//PlFRUTgcDlpbW/nDH/7AyZMnFW2kn58fGo2GgYEBtm/fTnNzMyMjI3z9619nzZo1F2VWHhgYoKmpiXvvvRetVktMTAzz5s1Dp9NhMpno7Oxk9+7dNDY2kpWVxbJly7jjjjuUBVnWKL3zzjvs27ePvLw8li1bRlJSEkFBQZhMJvbt20dzczMWi4Wf/vSnTJs2DZ1O95mf69VOc3MzxcXF/OxnP6O6uprg4GCWLFnCo48+SlhYGJ6ensqxe/fuZffu3WzatIlFixYRHx9PZGQkrq6u6PV6qqqq2Lt3L2q1mi984QssXrxYEb5HYzabOXbsGBs2bKCmpobZs2cTGxtLQEAAg4ODFBQUUFZWRnt7O3/7299ITU09r5B4JrJW8emnn1ai01etWkVgYCBqtZqhoSE++OADOjo6CAkJ4Q9/+AMRERHC1Cy4ZhCmWIHgc0atVuPj48OiRYswGAz09PSMm79OkiT6+vooKChgx44dLFmyhPT09DGL66Xg5OREaGgoSUlJ5xQYh4eHlUjerKwsxe9PkiSGh4cVzcqiRYtYsGABwcHBuLm5YTQaGRoa4qOPPqKwsJBt27Yxd+5cQkJCzmuSG43NZqO/v5+CggLWr1/P9OnTWbhwIcHBwYyMjNDW1sbIyAhHjhzBbDajVqu5/fbbcXZ2RqVSIUkSzc3NHD58mGPHjmGz2Zg7dy6ZmZmEhoZiMpmw2+1s2bKF/fv3s337dsLDw/H29r7uIpIlSVL64fbt2zl16hShoaFMnz6dG2+8kdjY2LOeiV6vp66ujpGRETIzM8nOziYuLg53d3fa2trQ6XS0tLQobgXe3t7jCnbV1dUcPXqUQ4cOMWvWLLKzs5kyZQqhoaEMDAzg6uqK2Wxm+/btGAwGLBbLRd2b2Wzm5MmT7Nu3D4fDwZQpU5g3bx7h4eHKJqS9vZ233nqLyspKhoaGzqrxLBBczVxfs5lAMEGoVCoyMzNRqVT4+fnxwx/+EKvVetaC4nA4GBwc5E9/+hNWqxWdTkdCQoJyjku5vkajYfbs2Yqm6kwkSaK2tpa///3vbNmyheXLl+Pj4zPmHG5ubqSkpPCDH/wAHx8fRWhzc3Pj7rvvZnh4mEOHDrFt2za+853vYLfbL1iwg0+ETw8PD26//XZWrFiBl5eXcn4fHx8SEhJ48803qa6uZu/evVitVpycnFCpVNhsNvbv38+RI0fo7e3l1ltv5cYbb8Tb2xv4RAv5ta99DbvdTklJCS+88AK5ubmEhYUpx1wPyJrNkpIS3n//fV5//XUA5s+fz5o1a7j11lvH/Z6zszO+vr6sXLmS1atXj9HGxsbG4ufnR3h4OB9++CGHDh1CpVLx9a9/HUARvAFFa93T08P//u//EhQUpETburq6csMNNxAaGspLL72kCO0Xc299fX385z//oby8nDVr1vDYY48REBCgnMfDw4P777+fqqoqTp48KUywgmsOIdgJBFeQtLQ0QkNDMZvNPP3007S0tIx7nMFg4O9//zv5+fk8//zzSmDFZ0Wr1RIfH8/IyMgYYe1M3nnnHfr7+1myZAnBwcHKoufk5ER8fDxvvfUWgKLJk5ETNPv4+BAcHExvby9msxmr1XrB7fb19SU7O5umpibc3d3P0hjJwmlISAitra1KcISLiwtOTk7YbDZ27NhBR0cHQUFBLF++fIyfoixcxMbGkpubywsvvMDBgwfx9PRk0aJFF9TGa4GhoSFOnDjBQw89RH19PS4uLtx222089thjpKSknPN7a9euZdWqVQDjpj3RarWkpaXh4eGBwWCgvb19zOcOh4Oenh42bdrE0NAQN99887j9WqPRMH36dJqbm5VAjIu5t4aGBl599VVWr17N3Llz8ff3P+s4Dw8PnnrqKex2O+7u7he1+RAIJjuiNwsEVxBnZ2e0Wi2rV69m9erVZGRkjHucJEkYDAZKS0v585//TFtb27jpUi4Uf39/MjMzFV+jM3E4HJhMJvLy8nB2dmbhwoVjgh/kSEJPT088PT3PCoyQi8UbjUb6+vqIiopCq9VeVACILBxqtdpxo4Jlh/vu7m7UajURERFjFmVJkujt7cVisaBWq/Hz8ztrwVapVLi7u+Pn54ckSTQ0NNDa2nrBbbza6e7upqioiCeffJLm5mZ8fX2ZPXs29957L9HR0ecVwl1cXHB3dz+nIORwOLBarVitVrRaLQEBAWM+N5vNnDhxAoPBgJeXlxKYcebvfGY/uBihq7W1lbKyMsxmM3FxcYSGhp51Dbnvuru7K6XRhNZOcC0hNHYCwRVGo9GQmprKsmXLsNvtdHR0oNfrzzrOYrHQ2dnJpk2bmD9/PtOnTycqKmrcxfDTkAWyiIiIcT+3WCy0trbS3NxMUlISU6dOvaDzOhwO7HY7IyMj1NbW0tzcjMPhYNasWfj5+V2y79ro1CWDg4PU1dXR399PUFAQWVlZaDSaMc9CTsEiC6LjIecZBOjq6qK7u/uS2ng1IEkSIyMjVFRUsH//fnbt2oVarSY5OZmlS5cyf/78S07/Yjab6erqwmq1EhsbS1xc3Fm/TUVFBSMjI7i7uxMcHExzczODg4OYzWZsNpuSYsfLy4uQkJCLFrq6urqora1FkiQCAwNxcnKivr6e3t5ebDabIjT6+vri7++vpPMRCK4lRI8WCCaIW2+9leDgYKxWK6+99hp2u/2sYywWC3V1dTz++OPcdddd3HfffXh4eCj5uC4Hsl/Sli1b8PT0JCEhgfT09E/9DnyymBuNRmpqanjyySdpaGggKiqKn/3sZ8TExFxyuwAGBwdpaWnh1KlT/P3vf0eSJG644Qbuuuuus77j6+uLi4sLVquV3t7ecSOA5TYD9PT0YDAYLqmdkx1JkhTt5H/+8x/ef/99hoeHmTdvHl/+8pe58847L1mokyQJvV7Pvn37sNvtzJ8/n9WrV485xmKxUFlZiclkUvLH/f3vf+fQoUPU19fT19enCOxz5szhgQcewNfX96I2Ml1dXdTX1wOfmFsrKirYsGEDmzdvpre3V8kvuXLlSm677TZmzpyJVqsFRLoTwbWDEOwEgglkxowZREVF0dfXx5EjR+js7Bz3uNOnT/Pyyy/T2NjIH//4x8ueXFev1/PKK69w0003XZC2TpIk/v3vfytRsDabjbi4OG644QZWrVpFSkrKJWtC6uvr2bhxI3//+98ZHh5Go9EQExPDb37zGzIzM88SHNVqNfPnz6e6upre3l727NnD3Llzz/IHa25uJi8vT0nMe7FRl1cbIyMjtLe3c//991NRUYHNZiM7O5s//elPpKSkXJZ8iXI081NPPcVNN93ELbfcwvz588ccY7fbFY3esWPHaG9vZ+rUqXzve98jISEBNzc3Dh06xObNm3nuuefYsWMHzz33HLGxsbi5uV1QO4aGhujt7QXgySefJCoqiqSkJN544w08PT0xGAwUFxfz1FNPkZ+fz8yZM3nyySc/c648gWAyIgQ7gWCCkKNMQ0JC+OpXv4rdbqewsPAsp3P4xIzV2NjIwYMHef/991myZAmBgYGXZTHq7Oykrq6O9vZ2pk2bRlRU1AW1PSMjAycnJzIzM+nv76erq4uWlhbee+89rFYr06ZNu6Q8fD4+PkyfPp27776b/v5+BgYG6O7uZseOHbS1tTFjxgzmzJmjHO/s7ExOTg6HDh2iuLiYAwcOKHns/P39sVqtnDhxgsLCQsX8+mkVEq52hoeHqaqq4p133qGiogJJkkhMTOSee+4hMTERrVZ7yZq6kZERtm7dytGjR/H39+f2228nJSXlLGFMFqTlihAmk4k1a9Ywbdo0goKC0Gg0ODk5odfrGRoaorS0lLy8PFQq1XmDOkYjV1iBT4S8sLAwbrrpJlJTU3F1dVWChw4ePMjp06cpKSnh0KFDLFq06KyAIIHgakUIdgLBBCJHeq5du5ampibMZjM9PT3japH6+vo4ffo0b7zxBhEREbi6up43wvVCaWxspKKiApVKRWpqKsHBwRfU7jlz5jBz5kxMJhMdHR188MEH7N27l507dyJJEkFBQbi7u3/mwvH+/v4sXLiQnJwcent7aWho4KOPPuKll16ioqKC3t5epk6diqurK87Ozjg7O5OVlcX06dPR6/VUVFSwd+9ejEYjUVFRWK1Wdu/eTW1trbKIy2WlrjVk38SWlhaOHDnCK6+8Qn9/P8nJycydO5evfOUreHl5XZJQK1cKqaurY+fOnbS3tzN79mxuvPHGcwpJsruBi4sLfn5+3HTTTbi5uSntSE9Pp7Ozk/7+fg4cOMDhw4eVihYXgsPhUMzvnp6epKamcuONNyq+ep6envj6+rJs2TJaW1tpampiz549zJkzRwh2gmsGIdgJBJMAFxcXHnjgAaZOnUp3dzclJSXj+tyZTCY2b96MWq1mzZo1Sp6wS+Hw4cPk5eWxbt26iy72Luedi4uL45FHHiEhIQGHw8Ff//pXIiMjWbFixTkjfy8UtVpNUFAQQUFBzJo1i+7ubvbv38+zzz5LTk4OM2fOVDSDGo2Ghx9+mNWrV/Pqq6/y3nvv8Y9//IORkRFlQV+8eDF3330369evx8fHR/GxupaQI4T/8Ic/sGvXLlpbW4mPj+fb3/42t91222XJ22ez2ejq6uKee+4hNDSUFStW8N3vfvecQSsqlQoPDw9UKhXBwcHMnj173OjnpKQkRaNaUFBAdnb2BbdJo9EomsKsrCzi4+PPCsBwcnJi4cKF7Nq1i7KyMg4ePIjJZLrY2xcIJi1CsBMIJhh50XFzc2PatGn89a9/5cEHH6ShoYGBgYFxv7Nnzx4GBgZQq9V86Utf+kxaJ0mSaGlpoaSkhLa2Nn7wgx8oqUwutu0yERERzJkzhx07dlBSUkJUVNQlCXZnnl+SJGbOnEljYyNtbW0cPHiQlJQUfH19xzzHpKQkHn74Yb7xjW8oiaCdnZ3x9vbGaDRSWVkJQFRU1EWVPbsakANHHnnkEQ4dOoTBYCA8PJwnnniCadOm4ePjc8kmfLvdzsGDB3nnnXfw9/fnm9/8JtnZ2YpQN9751Wo1YWFhaDQaNBqN0o4zj/X09FQ00f39/YyMjFxwu7RarZLOx9vbG3d393HTqeh0OlxdXbHZbBgMhgsqsycQXC0IwU4gmCQ4OTmh1WrJyMjg5ptvZteuXZSUlIy7sBmNRioqKvjoo4+UQIKLNcs6HA6KiooYGBhAp9ORmJh4yWZJNzc3RXvW19enRJ9eTnx9fXF3dx/jjD8aWYvo4eExbnqXsrIyOjo6kCSJmJgYIiMjL3sbJwqbzUZ9fb2ihTUajYSFhbFy5UpmzJhBQEDAZUnvUVBQQEFBAa2trdxwww1MmTJlTELrI0eO0NPTo1zT2dkZtVpNVFQUGo0Gu91+zqAVu92OzWYDPhEGLyZBsa+vryKoW61W5TxnYrPZlLQ4Z6bMEQiudoRgJxBMIuTErA8++CA2m43u7m7q6urGrWXZ0tLCxo0byc7OZvXq1Xh4eFywYCZJEna7nd27dwOQkpJyXs2V7CTvcDhwdnbG3d39U69hsVjOubCOh9VqxWKxIEkSbm5u5xRA5PQdsjO+rG2R/ybX/pQjHc9MpNzd3U1VVRUajYaUlBTi4uIuuI2TFbl/9Pf3c+TIEZ599lkaGxsJCwtj9uzZPPLII4SGhl6UkDQeciLrjRs3UldXh6+vL9/85jfH+MkBfPDBBxQXF/OrX/0KX19fnJ2dcXFxITU1FQ8PDywWCz09PTgcjrNS9wwNDSkbAp1Od1G+b0FBQcTHx6NSqejr6xtTB1a+hmymlquW+Pv7X9MBNILrDyHYCQSTkJCQEL797W8zY8YM7r33XoxG4zlzsv3sZz+js7OTtWvXkpube0Hnt9lsGI1GPvzwQ26++WbWrFlz3uNNJhO/+c1v6O7uJiEhgR/96EfjHtfU1MShQ4eQJImEhASio6MvqD3wiRbo/SCjlr8AADRvSURBVPffR6/X84Mf/GDcAvIA+fn51NfXo9FoyMrKwtPTE/hvtQ75eb366qsEBwcrwows9BUXF7NlyxbWrl1LZmbmBQWLXA1YrVb+7//+j71791JaWopOp+OnP/0py5cvv6BI5wuht7eXZ599luPHj7N06VK+9a1vXZCQD+Du7s7SpUuJjY2lpKRE8W2TA19kKioqyM/PB2DBggUkJSVdcPsSExORJInQ0FBOnDhBRkYGZrN5TACPw+Fgz549tLa24uPjww033HDB9yAQXA0IwU4gmGTImoWAgABmzJjBr371K/70pz/R3t4+rgbMYrGwceNGOjs78fb2ZsqUKZ+am6yrq4uDBw8qqSQuJHed3W6nuLiY6upqIiIimD9/Pr6+vqjVavr7+9m/fz87duwgPz+fhIQEcnNzx5zXaDRy6tQpfv/73yNJEmvWrOHGG28kKioKlUqlaIM+/vhjoqOj6ejoYOrUqfj6+jIyMkJnZyeHDx9mx44dDAwMkJqaysqVK8ekVJEjNWtra3n66ae59957CQoKwtnZmZaWFt566y1OnDhBcHAwDz74IBEREVe9Gc7hcGAwGHjuuefYvXs3ra2t6HQ6fvGLX5Cbm0toaOhluceqqiqOHTvGa6+9Rm9vL83Nzezbt29cLWBFRQU6nW7M32Sz55e//GV8fX3ZuXMnf/rTn7jxxhtJSEjA3d2doqIiNm7cyMGDB0lOTmbVqlVjkmX39vZy8OBBXnjhBSRJ4q677mLevHmEh4cDn6SvCQ4O5nvf+x7/+Mc/OHz4ME8//bQSBTwwMMDp06d55513GB4eJisri1tuuUVExAquKYRgJxBMUtzc3AgODmbFihUcPnyYgoKCc5pl6+vrUalU7Nixg5CQEPz8/M6Z1FWSJHp6esjLyyMiIoKIiIhxC6WPxsnJiYSEBCorK+nr6+PYsWNYrVY8PDxwcnJiYGCA48eP09raqqQpSUtLG1OX1mKx0N7eztatW5Ekifj4eBYuXKh87uvrS0JCAoGBgdTX1+Pk5ERPTw/u7u6MjIzQ09PDiRMncHZ2Ji0tjRkzZhAbG6uYn2XBITk5mb6+Pk6dOsWePXvQ6XQ4OzvT09NDdXU13t7epKamMm3aNEXbdzUi94OOjg7KysrYtm0bzc3NaLVapk2bxrJly4iMjLxs2qjm5mYKCwuprq4GPqnaIQehjMeZ1UvkMm8zZ85kYGCA9vZ2iouLcXNzo6amBldXVyorK2ltbUWr1TJz5kxSU1Px8/NTzmE2m2loaFD60IIFC5g2bZryuZOTE15eXixdupQTJ07Q09PD8ePHCQgIwMPDg6GhIRoaGrDZbGRkZDBr1izi4uKuyZQ3gusXIdgJBJMYOcLze9/7Hq+++iovvvjiuKkZHA4H9fX1/P73vyczM5OsrCzCwsLOqanp6upi79693HzzzURFRX2q75VGo+H2228nMjKSoqIiDh06xPbt2zEYDAwPD+Pi4kJMTAzp6elKGhY5v9xonJyccHV1RZIkNBrNGD+6xMREgoKC8PX15eDBg+zbt4/XXnuNzs5OJSAiOjqaW265hdzcXObOnTvG8V2lUqHVarnrrrvIyMhg48aN/OEPf1AiiyMiIli5ciWLFi0iNzcXV1fXq15bZ7PZOHz4MG+//TaHDh3Cw8ODuXPn8v3vf5/k5OTL6jvW1NTEqVOnLrhSxXipTABSU1Px9/cnPT2dv/zlL7z88stK7saoqChmzpzJqlWruPPOO3F1dR1zD7JweK4+BJ+MmezsbH7yk5+wf/9+tmzZwv/8z/9gNptxc3MjNDSUm2++mZtuuonMzMzPnGdRIJisqKTxtv8CgWDSIAc6lJSU8PHHH/M///M/4+a4g08WvoiICO677z6++tWvEh0dPW7KEJvNpghk4y2O47UBPhEk5H+jk8HCJ0KbHP0oL5ajr+1wOLDZbAwODgKfLMDywq1SqZTgBzma0W63K8718rmcnJyUOqNym8+8P/m7FosFu92ufF/+rvz98b57NSFJEk888QQfffQRR44cweFw8Pjjj7NkyRKmT59+Vv62S8VsNmM2my84IMbZ2RlPT89x2+FwOLDb7ZhMprN+o0/rQxaLheHhYQAlYGi8jYncT8+MjnVyclISU1/uZyQQTAaEYCcQXCUYDAbq6+t58skn2bVrF11dXeMe5+rqSnZ2NosXL+bhhx/Gx8fnsqS4EEweenp6OHDgAE8++SRVVVWYzWa+9KUv8fWvf53ExMQx5kuBQHB9IWZ7geAqQafT4ebmxi233EJjYyMWi4W+vr6zjjObzZw8eZLh4WEWL15MdnY23t7el5zqQjA5MBqN1NbWsnnzZoqKihS/wvXr15OSknJZyswJBIKrF6GxEwiuQv71r3+xZcsWPvzww3Meo9FoSEhI4MUXXyQ9PV2J/BOmp6sTearetWsXW7Zs4a9//SsqlYqbb76ZL33pS9x+++3itxUIBEKwEwiuRvr7+zl27Bgvvvgi77zzzjn9nlxcXJg/fz633XYbd99997gllgRXB3a7nY8++oh//etfHDx4EKPRyIMPPsiaNWuYP3/+RZeDEwgE1ybCFCsQXIVotVrS0tK47bbbOH36NHV1dfT39591nNVqpaysDJ1Oh4+PD1/4whfOGa0omLz09/fT0NDA22+/TUlJCZIkkZubq+R58/LymugmCgSCSYIQ7ASCq5TQ0FBuuOEGDhw4gMVioaamZtz6m11dXeTn5zMyMsL8+fMJDg6+JlJ9XC+YzWZaW1vZt28fGzduRJIk4uLiWLduHQsWLECr1U50EwUCwSRCmGIFgqsUeehaLBaeeOIJNm7cyPHjx895vKurK7fccguPPfYYmZmZoj7mVUJBQQGbN2/m97//PRaLhWXLlnHjjTfy8MMPA8JnUiAQjEVo7ASCqxQ595uLiwtf/epXiY+P5ze/+Q0VFRXnLD22a9cufH19WblyJevWrZuAVgsuFKvVSklJCU888QT5+fnY7XZWrFjB17/+dXJzc4VAJxAIxkVs2QWCqxg5aW9YWBjZ2dnccsstBAUFjZtNX5Ikent7yc/PZ+/evZw8efKciY4FE8vw8DBtbW188MEHFBQUYDAYiImJYe3atUyfPp2QkJCJbqJAIJikCI2dQHANIJf0+u53v8vx48cpKSmho6Nj3LqyJSUlmEwmdDodMTExeHl5iRx3kwi73Y5er6ewsJC//e1vmEwmwsPDWbhwIXfeeaeIfhUIBOdF+NgJBNcI8lBuamri2Wef5dVXX6W9vX3cY1UqFa6urjz++OOsWrWKtLS0K9lUwXmoqanhjTfe4O9//ztdXV1MmzaNm266iR//+Md4enoCwq9OIBCcG6GxEwiuEWSfu+DgYNavX4+/vz+//e1vGRgYGFPTFT4RAs1mMy+99JJSn3Xq1KkT1HIBfOJT193dzV/+8hcOHz5Mf38/ycnJ3HvvvcyfPx93d3dACHUCgeD8CMFOILiGUKlUuLm5kZKSglqtZu/eveTn59Pf33+WWVaSJE6fPs2BAwdwc3MjPDwcnU4nzLITgNVqxWAwsH//fvbt20draytarZZly5Yxf/58kpOTxe8iEAguCGGKFQiuUUZGRigtLeX++++nvLwck8k07nHOzs4kJyfzxz/+kUWLFuHh4SG0QlcIefo1GAyUlJTw9a9/nba2NnQ6HRkZGbz11ltotVpcXFwmuKUCgeBqQQh2AsE1iiRJ2Gw2Dhw4wDvvvMOLL76I1Wod91gXFxf8/f15/vnnmT59OuHh4Ve4tdcnkiRhMBh4/vnnefPNNyktLSU8PJzbb7+dBx98kJiYGFQqlRC0BQLBBSNMsQLBNYpKpcLFxYUpU6ZgNBoZHh7mjTfeOMvfDv7r3/Xiiy/S19fHDTfcQEBAwDkFCkmScDgcmM1m7HY73t7en/ftXHPY7XbMZjP/+te/2LlzJ83Nzfj4+HD33XezePFiQkNDhVAnEAguGiHYCQTXOMHBwWRnZ+NwODh06BAdHR3jmmVtNhsff/wxvr6+hIaGsmDBAlxcXM6qUCFJEpIk0dTUxNDQEM7OzqSmpl6p27kmcDgcDA0NUVNTwwcffEBtbS0Oh4Pp06ezZs0akpOTlWAJgUAguBiEKVYguE4YGRnh5Zdf5m9/+xunT58+53He3t4kJyezYcMGQkND0Wg0itZIFupMJhPf+c53sFgsxMXF8atf/QoQEZufxugycIWFhTz22GMcOXIEd3d3MjIyeOWVV4iIiECj0UxwSwUCwdWKEOwEgusEh8NBf38/r7zyCjt27GDr1q3jHufk5IS7uzszZ87kd7/7HVOmTMHLywsAk8lEaWkpTz/9NFu3bkWSJNLT09m0aRNarVZEbn4Kst/j888/z7Zt29izZw+urq7ceeed3H777cyaNQu1Wi0EZIFA8JkRpliB4DrByckJX19fFixYAEBnZyfFxcVnlRVzOByMjIxw6tQpNm7cyMDAAAsXLsRms7Fv3z4OHjxIfn4+fX19ynmKioqYPXu2kkD3euLMvfH5/BKtVitbtmxhz549nDp1CkmSWLduHUuWLFFS1AihTiAQXApCsBMIrjOmTZuGm5sber2e2tpaBgYGxhXuenp6eO+997BYLCQkJDA4OMi7777L3r17aWxsVI41Go0cOHCAjIyM6zZVysjICGazGQBfX19grIAnJ4TW6/W89tprHDlyBIPBQHh4OHfddRdpaWn4+/tPRNMFAsE1hjDFCgTXIbKg8eCDD7Jv3z7q6+vPeWxQUBDx8fF0d3fT1tbG0NDQmM9dXFwICwtj586dJCQknBVsca3jcDjYvn07+fn52O12fvWrX52leZMkifz8fJ577jneeOMNnJycmD59Or/73e+YO3fuGD9GgUAguBSExk4guE7RaDT86Ec/ws/Pj+3bt1NWVjbucQaDgdLSUqxWKxaL5azPbTYbHR0dnDhxAo1GQ0xMzOfc8smFw+Hgww8/5Pjx44yMjGC32/n+979PYGAgavUnU+x7773Hzp072bZtGwB33HEHK1euZNq0abi4uAihTiAQXDaEYCcQXIfIgkRSUhKLFi3CarXS2dlJb2/vWXnurFbrORMbw3+1f0VFRYSGhl5Xgp3FYsFgMFBcXExDQwNms5ldu3YxZcoUZsyYQXR0NJWVlezatYujR49iMBjIyclh8eLFzJw5Ex8fn4m+BYFAcI1xfdlMBAKBgkqlwtnZmWXLlnHXXXeRnp6Om5vbZ9YeHTp0iMrKShwOx1kBBdcqQ0NDVFVVUVFRQV9fHyMjIxQVFfHMM8+wc+dO2traePPNN/noo48oKyvDx8eH+++/n6VLl5KQkDDRzRcIBNcgwsdOILjOkaM19Xo9X/3qVyktLaW7u/uiz+Ps7Mw3v/lNHnvsMaKjo68L82JxcTFPPvkkb7/99hgztZOTE35+fvj4+NDY2Ijdbmf27Nl885vf5Ktf/eq4iZ8FAoHgciBmFoHgOkelUqFWqwkICGDx4sWfWZNkt9upra1l3759/6+9ew+uur7zP/48l5x7Tu4nCbknBBIuJgQUBWGjAioFpLaoO1unnUp11ul2bO1l9p9qd2fdndFpXevardW61bFb1gLqFikMEZCIEAIJ5EISSMj9xjm5nFvO/fv7g985C3KRWiC392Mmownfc87nfJkcX74/n/fnMysqdi6Xi+7ubqqrqwmFQpf8WSQSYXx8nP7+fhRFobKykk2bNrFmzRoJdUKIm0o+XYQQBINBBgcH8fv9VzxL9nr19vZy9OhRwuHwjA93/f39nD17lp6enquevzsxMQFcOM0jJSUFm802KyqZQojJI80TQsxS0eClKAp2u51t27bxn//5n4yMjHzp5zx37hyKojAxMYHZbJ6RJ1FE79vhw4c5fPjwZdW6zwuHwxw7dgybzcZ9991HQUEBIMevCSFuDqnYCTHL7dmzhxdffJF//ud/ZnR09K96Lr/fz/DwMDt37uT8+fM3aIRTj8/no6qqigMHDlzX9UNDQ2zfvp1HH32UxsZGXC7XzR2gEGLWkmAnxCwUPbN0+/bt/PGPf2Tfvn243e4bMn0aDT3RI8dmmlAoRE1NDd3d3Tidzut6TCQSweVy0d7ezquvvkpNTc2XalARQogvIsFOiFkoHA7j8XjYtWsX+/fvv+rmxF9GIBDg6NGjDA8Px47ZmimiHcTV1dUMDg5ec3+/zwuFQoyPj7Nz507q6+sZGhq6iSMVQsxWEuyEmIXcbjdtbW2cOHHihleOwuEwbW1tnDx58ppHlU1XPp+Pd999l76+vi/1eEVR6OrqmpH3Rggx+aR5QohZKD4+nsWLF/Pee+9x5MgRjhw5wvbt27Hb7X9VV+zFqqqqMJlMlJSU3JDnmwr6+vqora2ls7MTn8933Y/T6/XMnTuX1atX861vfYs5c+bIqRNCiJtCgp0Qs5BGo8FgMFBUVIRarcZms5GWlsaJEyc4c+YM3d3dsa06vqzTp09TXFyM2+3GbDbPiC7Q/v5+Pv30U/x+/xeuR1Sr1ej1ekpKSigvL2f+/PmUlZXFTviYiR3DQojJJ8FOiFkqeqTY3LlzKSws5N5772XXrl3s2bOHgwcP0tvbGzvU/stob2+ntbWVwcFBCgsLp32wC4VCdHd3X9cGzAaDAYvFgs1mY/369TzyyCMUFBQQHx9/i0YrhJit5EgxIcQlQcXn8zE8PMybb77Jf//3f9PT0/OlmyAWLVrE3/3d3/GDH/wAnU53o4Y7KVpaWnj77bf513/912teFxcXx8MPP8z69etZs2YNGRkZsVA73cOtEGLqk2AnhLhEJBIhEAgwODhIR0cHjY2NHDp0iF27dv3FJ1MkJSVRWlrKn//8Z8xmM2q1GkVR8Hq9OJ1OnE4n58+fx+Fw4HQ6cblcuFwuvF4vExMTeL1eXC4XwWCQUCgUmx4Oh8OEQqHY+awajYa4uDi0Wm2sEmkwGNDpdBgMBoxGI2azGYvFgtFoxGQykZKSQkpKClarlaSkJGw2GwaDAa32yhMZv/zlL2MVzc9Tq9UsXLiQsrIyNm7cSGlpKTabjaSkJOLi4iTQCSFuGZmKFUJcQq1WYzAYyM/Px2azkZ6eTkJCAmq1mrNnzzIwMMDg4OB17Xnndrtpb2/n0KFDGAwGQqEQbrf7khA3MjKC1+slEAgQCARiIS76FQwGiUQiRCKR2CkPiqLEfgbErouewapWq/F6vajVajQaTSz4xcXFodFo0Gq1GAwGTCYTZrOZhIQEUlJSMJvNGI1GLBYLVqsVk8mE0WhEr9dTW1tLe3v7Je/PaDRis9lYsGAB5eXllJWVcc8995CcnCxr6IQQk0IqdkKILxSJRBgcHGT79u0cOHCAvXv34vP5rutMWLVaTWVlJXAh6HV1deF0OlEUBZVKhUqlIiUlhcTERJKSkkhOTiY+Ph6z2Ux8fDxJSUno9Xp0Oh0mkwkgFs6MRiNwIdj5/f7YvnLhcDhW9fN4PHg8HsbHxxkbG8PtduNyuRgcHGR0dJRAIBALimazmcTERPLy8iguLiYrK4uMjAzS0tL4yU9+Qnt7O5FIBJVKRVxcHNnZ2VRWVvLMM8+Qm5srna5CiEknwU4I8YUURYmdVuFyuejs7OTll1/ms88+u6yKdSVWq5U777yT+fPnk5+fT05OTmwqdM6cOZd0iUbD3uf/Pfr9xaLfX+lj7OKzcC9+Dxf/u9/vx+VyMTw8TG9vL0NDQ5w/f56uri7a2tro7+/Hbrfj9/tjnbBqtZr8/Hw2bNjAihUrLqnQyZSrEGKyyVSsEOILRQOWx+NhcHCQlpYWnE4noVAIlUr1hVU7r9fLggULuP/++yksLMRsNqPX69Hr9RiNxkkLRdHp2ISEBLKysvD5fPh8PtxuN+Pj4zidTjo7O/npT39Kamoq8fHxWK1WJiYmOHjwIHV1dezcuZPS0lLy8/PJzc2ltLSU1NTUq67VE0KIm0k+eYQQVxUIBPB6vYyPj3P+/Hn6+vro6+ujpaWFYDBIeno6VqsVi8WCz+djbGws1ghxsVAohE6nIykpiXnz5k3Su7lcdP2dwWAgMTHxsj93u92cPXuWHTt2kJmZSUpKCgkJCQwMDDAwMMD4+DhNTU04HA46OjrIyspiYGCAgoICEhMTsVqtpKamEhcXF1v/J4QQN5NMxQohYi7+OFAUhcHBQZqbmzl06BA7duygu7sbv99PQkIC99xzDxUVFSxatIjly5fT2dlJVVUVH374IZ999tll3bP33HMPDz74ID/84Q+B6bH1R3d3N319ffj9fpYuXXrJPnTj4+P09/dTX1/Pnj17qK+vp7OzE6fTydy5c6moqGDFihU89NBD2Gw29Hr9NaeVhRDiRpBgJ4QALgS5cDhMa2srdXV1fPDBB5w8eRKv14vBYKCyspKlS5cyb948SkpKMBqNsU5TnU4Xa2BwOp1UV1fzzjvvcOrUKXp6egCwWCysXLmSP/zhD1it1mlRwQqFQkQiERRFuazqFolECIfDBIPB2Bq88fFxGhoa+OSTT2hqauLMmTOEQiHKysooLy9n1apV3HfffVLBE0LcNBLshJjlgsFgbEqxpqaGjo4O+vr6CIfDJCcnk56eTn5+PvPnzyc7O5vU1FRSUlIua2yICoVC9PX1UV9fT2trK/X19Xz88ce4XC6Ki4t56aWXuPvuuzEYDJPwbm+e6P5/w8PDnDt3jr6+Pnp7e2lubmZ0dJRwOIxOp2PJkiUsXLiQoqIiSkpK0Ol0Ur0TQtwwssZOiFkour3H+Pg4drud7u5uqqqq2LdvH263G51Ox+rVq6msrKSkpIT58+dfd4ODVqslLy+P3NxcBgYGKC0txeVy0dHRgVarpaamhjvuuOOyqcnpLrr/X25uLrm5ubEtV44cOcLBgwdpamqisbGRtrY2br/9dsrLy1EUhTlz5hAfH49er5cqnhDiryYVOyFmoVAohNfr5fXXX+eDDz7g1KlTKIrCI488wpo1a1i5ciXZ2dmXPOYvDWGfX69XU1NDTU0Nu3bt4p133iEtLW1GBbvP+/xHq9vtpre3l9dff519+/bR1dWFVqvlu9/9Lg888AAVFRUzLuwKIW49CXZCzBLRKt2nn37KkSNH+Oijj+jt7WXRokWUl5fzwAMPkJWVFdscOHo81414XSC2WfDIyAg5OTno9fpZdTpD9Bi0kZERzp8/T0dHB3v37mX//v2YzWYKCgr427/9W+69914SEhIk4AkhvhSZihViFvD5fIyPj3PgwAE+++wzurq6UBSFdevWsWTJEhYsWMBtt92GwWC44dOB0YBiMpku2VZktgWX6NYqmZmZJCcnk5KSQlxcHAaDgc7OTkZGRtixYwejo6MsWrSIkpISrFbrrLtPQoi/jlTshJihor/aPp+P4eFhWltb+ad/+if6+vpIS0vj/vvv56mnniI1NXXGNTJMF4qi4HA4+Pjjj6mqquL9998nLy+PNWvW8PDDD1NaWhoL2xLwhBDXQ4KdEDNU9Ff7d7/7Hbt27WLv3r0UFxfz/e9/nxUrVpCbmxurzklomBwXH3E2MTFBS0sLzz33HM3Nzfh8Pn70ox/xta99jZycHPk7EkJcFwl2QsxAgUCAoaEh3njjDfbu3YvJZGLBggU89thjFBYWkpSUJFW6KSbaRdvW1sbx48eprq7m+PHjVFZWsnr1ah566CFprhBCfCFZYyfEDBKt/Jw5c4a6ujr279+PyWTitttu47777uOOO+64YU0R4sbSaDTEx8dTXl6OxWLBYDDQ399Pc3MzoVAIq9XKypUrMZlMcg6tEOKqpGInxAyhKAqKotDR0cFbb73Fzp078Xg8/Nu//RvLly+nsLBwsoco/gKBQICjR4/yL//yL5w5c4aUlBTeeOMN8vLypKlCCHFVEuyEmCF8Ph+Dg4N84xvfYHx8nNzcXH7+85+Tk5NzU7pdxc0VPeKtp6eHjz/+mH//939HURSeffZZ1q9fj81mm+whCiGmIKnnCzEDuN1uWltb+c1vfoPb7eaee+5h3bp1ZGdnS6ibplQqFVqtloyMDFatWoVWq+UXv/gFH374IUNDQzz55JMkJCTI360Q4hLyiSDENBbddPjcuXMcPXqUqqoqiouLY8eBmUwm+Q//NGc0GsnPz2f9+vWUlZXR39/PgQMHqK+vx+/3X3bChRBidpOpWCGmMUVRCAQCPP/88+zdu5dAIMB7771Hbm4uJpPppr3mX/qxEV0PNhXWhV28xci1qFSqKTHeKEVROH/+PC+88AJVVVVYrVa2bdtGZmbmrDrBQwhxbRLshJjGAoEAL730Eh9++CE6nY4XXniBpUuX3tQD5aurq3niiSeYmJi4YjiKhqH4+HhsNhvz589n69atFBUVkZSUdFPG9JcYHR2lq6uL3/zmNzQ1NWG323G73UQiERRFQavVYjab2bdvHxkZGZM93Jhodba9vZ1Dhw7x4x//mKeeeooNGzawYsWKyR6eEGKKkDV2QkxTHo+H3t5e/vznP5Oens6SJUsoLS29qaEOIDs7m61bt9LX18f+/ftpbW2lsLCQTZs2YbVaAfD7/TgcDgYHB6mtrcXv97N582aWLl1Kdnb2TRvbtSiKgtPp5PDhw+zatYuWlhbmzZvHnXfeiclkIhKJMDAwQHd3N0ePHiUcDk/KOK9GpVKh0WiYM2cOZWVlrFu3jurqajIzMyksLCQ9PX1KVRiFEJNDgp0Q09TY2BinTp2ioaGBJ598krVr15KSknLTXzc/P58f/vCHNDY2MjQ0RGdnJ/PmzeO73/0u2dnZRCIRPB4PTU1NHDx4kKamJn7/+99jsVgwm82TFuwABgcHqa6u5g9/+APl5eWsWbOGiooKsrKyCIfDtLa2cuzYMex2+xX3ihsfH8dut6PT6UhNTcVoNN7y92CxWCgoKODhhx/m2Wef5dSpU5SVlZGenn7LxyKEmHpkVbUQ01RLSwuvvfYaixcvZu3atVNmOk6lUmGxWFi+fDmPPvooP/rRj1CpVOzcuZO33357Use2e/duampqUKlU/PSnP2XdunUUFRVhMBgwm81UVFTw1FNPUVNTc8XtRN577z0qKip47LHHqKurm4R3cEFycjJf+9rXWL58eWzfQiGEAAl2QkxLra2tHD9+PFatmzdv3pSZhos2HahUKpKTk6moqECtVjM8PEx3dzcul4tIJDIpYxsYGGBsbAyNRkNxcTFGo/GS8X7+62ome2lydHzf+MY3SEtL48iRI5w8eRKv1zup4xJCTD4JdkJMQ2fOnKGnpwez2Ux5efmUaEq4Eq1Wi8ViQaVSEQwG8fv9k7p2zefzEQgEUKvVmM3mad9NunjxYrKysohEIhLshBCArLETYlqJVoqampoYGBigqKiIoqKiSVnrdT0URSEYDAKgVqvRaDSo1WqCwSCBQIBAIBC7VqfTYTQamZiYIBQKXVLVM5lM6PV6VCoViqIQCoUIBAIEg8HYddENfXU6HVqtNrZGLhQKEQwG8Xq9eDye2HM7nU6CweBVG010Oh16vR6tVovf78fn8+F2u2Ov73Q6cTgcsevNZjMGg+GG38OrUalUFBYWUlRURFpaGp999hmVlZWkpKRMmeqtEOLWk2AnxDRUW1uLw+Hg/vvvn9JVJ7vdzpEjRwiHw2RmZlJQUEB8fDx79+7lzTff5P33349d++ijj/KDH/yAl156ibq6OgYHB3G73QC8+OKLfPOb3yQhIYHx8XEOHTrEnj172LdvH8PDw0QiEYxGIxUVFWzevJnly5dTVlYGwOnTpzlw4ADPPvss4XA4FgSLioquGYAefvhhHn/8cVasWMFbb73FK6+8Qm9vL+FwmNraWjZs2BALhWq1mldffZWtW7fepDt5dfPmzWPZsmW8//77PP3007f89YUQU4sEOyGmkUgkgt1uZ3h4mLi4OMrKyqbcyRKKojAxMcHZs2c5fPgw7777LoqisGbNGjZs2ABARUUFaWlpbNq0if/4j//g1KlT1NfX81//9V+sXbuWr3/96wQCAbq7u/nZz35GJBIhFAoxOjrKz372M1paWvB4PDzxxBNkZ2ej0WhwOp3s3r2bd999lwMHDvDNb36TyspK8vPz2bhxI3PnzuVXv/oVJ0+exOl08tvf/hadTnfJ/fN6vTQ2NvL888/HQqDFYmHz5s2Ul5fzwQcf8MYbb5CXl8fWrVspLS2NPXbhwoW3/F4DZGZmUlJSwtDQEA6HA4/Hg8VimZSxCCEmnwQ7IaaRSCTC6OgoXq+X1NRUsrKyJn3aLRgMMjAwwN69e0lJSUFRFHw+H11dXbS0tGC321mxYgWrV6+mvLwclUpFWloaaWlpZGRk8Mc//hGtVhsLUkuWLCEnJweVSsXAwAB1dXUUFRURDAZpbm5m//79KIrC/PnzqayspLCwEK1Wy9jYGCMjI2zbto1jx46Rnp7O0qVLSUxMJD8/n7y8PHbt2kVHRwd+v5+1a9diNBovqXi6XK7LplPj4uIoLCyksLCQjo4OtFotiYmJ3HnnnVOiEzkhIYH09HR8Ph9Op5OJiQkJdkLMYhLshJhGIpEIDoeDYDCIXq8nNTV10oPdxMQEtbW11NfXA/938oTVaiUjI4OysjK+973vMX/+/Gvus1dUVMRXv/pVFi9eHKuiJSUl8fvf/x6Ajo4Ofve733Hu3DnWrVvHt771LW6//fbY6yUkJPDMM89w5swZ/vd//5c33niDb3/72xiNxivuSTdTmM3mWPPM2NgYbrebtLS0SR6VEGKyzNxPOyFmoGiwC4VC6PX6KbFQPj4+nlWrVvHiiy8yZ86c2M+jW3JoNJrrOg0jNTWVxYsXX/X9jI+P89FHH+H1eikoKODuu+++7BqdTsfChQvp6Ojgk08+obq6Gp1Ox/z58/+6NzmFmc3mWGAeHR2NrUsUQsxOEuyEmEZUKhUGgwGVSkU4HMbv96PT6SZ9THFxcVitVhITE7/082i12qt294bDYQKBAE6nE41Gg9FoxGw2XzYOuBA04+PjURSF0dFRJiYmvvSYpoNQKITf7wdAr9cTFxc3ySMSQkymqbXqWghxTSqVKrb/WjgcxuPxTPpmuTeKWq2+6pSpoihEIpFYR6tarb5qBTC6rQpcCD0z5f5cTSAQiO1fZzQa0ev1kzwiIcRkkmAnxDSiVqtJTU0lLi4Ov99/yT5qM5lWq0Wv12OxWAiHw0xMTFxWiVMUBUVRcLvdOJ1O4MLRWzd6b7nPTxVHXzf6dat5PB5GRkaAC2sSpXFCiNlNgp0Q04hGoyErK4v4+Hg8Hg8NDQ2TdjzXrWa1Wlm3bh1Go5Guri4OHz582TXBYJDTp0/T0tKC0WjkrrvuIisr64aNQavVYjAYLqkEhkIhNm/ezHPPPceePXtu2Gtdr8HBQdra2jCZTKSlpZGQkHDLxyCEmDok2AkxjUTX2OXk5KDX62lsbJw1wS4pKYlNmzaRmZlJZ2cnf/rTn2hra8PpdOJ2uxkeHmbHjh00Nzej1Wp54IEHyMzMvKGncpjNZtLT03E4HIyNjeFwOOjp6aGxsRG/34/Var1hr3W9BgcHaW9vZ968ecTHx8/oDmAhxBeTTwAhppHosVmFhYW4XC5OnTpFIBBAq9Xeso2KJyYmGBwcpK+vD7fbTSQSwePx0NPTQzgcJi4ujszMzKt2tyqKEmuE6O7uxuPxEIlEcLlcdHZ2otPpyMjIwGQyXfK4aMVu586dtLe3U11dTUVFBSUlJWi1WhwOB9u3b8fhcJCfn8+WLVtITk5GURTGx8dxOBw4nU4CgQDhcJiuri6SkpIwm81YrVaGh4cZGRlhaGgIuDDFOTQ0RHd3NzabDZ1OR1JSEvPnz+fgwYN0dHRgNptxOBwoioLNZiMnJ+em3/+L72M4HKavr4+zZ89y2223YbFYptyG1UKIW0wRQkw7H330kfL0008rycnJytmzZxWv13vLXnvfvn2K2WxWVCqVAlzylZycrNx9991KKBS66uMjkYhy/Phx5fHHH7/s8dGvjz766IqPi0QiisPhUHbu3Kk8+eSTSkFBgWI2mxWj0agkJycra9euVV577TXlxIkTsetPnjypvPzyy1d8nTVr1iivvPKK4vP5lM2bNytz5sy57Jrs7Gzlk08+UVwulzIyMqLU1tYqlZWVypw5c5TExEQlPz9feemll5TGxkYlEonczFt/2f3o6+tTnnnmGSUrK0t55513FLvdfsteXwgxNakUZYa3jAkxA9ntdj788EOef/55vv3tb7Nly5ZbdqSV0+nkzJkzV5wCjm5FUlJScs2KndfrjR2BdSXFxcVX3DpF+f9VKpfLxfj4OE6nk2AwCPxfx3C0gSBa8fN6vbhcLrq7uy97vugWLWlpabS3t+PxeGLPFxUXF0dRUREmkwlFUfD7/fT09ODz+YhEImg0GtLT00lISLih075fRFEUfvGLX1BVVYXdbuf999+PNdYIIWYvCXZCTEOhUIhPPvmEl19+Ga/XyzPPPMPf/M3fEB8fP9lDE7dAMBjE6XTy1FNP4fF4yM/P55VXXkGr1U76htVCiMklizGEmIa0Wi35+fls2LCB2tpaGhsb6evrm7QtN8StoygKExMTtLa2cuzYMZKSkli/fr2EOiEEIBU7Iaat6JTk5s2b8Xg8lJWV8ctf/jJ2MoWYmfx+P3V1dTz55JNYLBa2bt3K448/LsFOCAFIxU6IaUutVmM2m/n+979PZmYmDQ0N/PrXv8btdkvVbgaKVmOrqqrYtm0bDoeD73znO6xcuVK2OBFCxEiwE2KaUqlUaDQa7rrrLhYvXozZbGbPnj00NTXNmhMpZotoqGtpaaG6upqGhgZKS0u56667yMnJQaVSSbVOCAFIsBNiWlOr1dhsNrZs2cKDDz7IwYMHefPNN6mrqyMcDsuauxkg+nfo8/l4/fXX+dOf/sTIyAg//vGPycvLu2y/PyHE7Cb1eyFmgEWLFpGSkkJSUhLPPfccvb29NDc38w//8A9SyZnmFEWhoaGBd955h7fffpvHHnuMr3zlK1RWVsrWJkKIy0jzhBAzxMTEBL29vbz22mu0trbi8/nYsmULDz30EGlpaRICpplolW737t18+umnHDhwgIqKCjZu3MiyZcuYM2fOZA9RCDEFyVSsEDOE0Whk7ty5bNq0icLCQhwOBzt27OD48eOxDXXl/+Omh1AohNvtprGxkd27d3PkyBEAHnroIQl1QohrkoqdEDNQZ2cnNTU1fO973yMxMZGvfOUrbN26lZKSktg1MkU7tVz8UWy32zl9+jR///d/j8vlYvny5fzkJz9hyZIlaDSaSRylEGKqk2AnxAwUDAbxeDycPn2aF198kfb2dhRF4Tvf+Q4bNmwgNzdXAsIUoygKwWCQbdu28fHHH1NdXU1aWhpPP/00t99+O3l5eej1egnkQohrkmAnxAwVDodxu93s3r2b48eP09DQgEqlYsWKFSxevJiKigqys7NRq2VFxmQbGRmhr6+PTz/9lKqqKlwuFwaDgQcffJB7772X7OzsW3oOrRBi+pKuWCFmKI1GQ0JCAlu2bGHhwoVUVVXx6quv0t/fT3NzM6FQCK1Wi9VqRa/XS3PFLRaJRAiFQoyPj9Pa2kpdXR1vvfUWbrebZcuWsXHjRrZs2YJGo5EqnRDiuknFTogZLvorHg6HaW9v51e/+hUHDx6koaGBNWvW8Nhjj7Fq1SqKioomeaSzi9vt5uzZs7zwwgvU1tZit9tZtWoV//iP/0hJSQkpKSmxayXYCSGulwQ7IWaJ6PYZQ0NDdHV1ceLECXbu3InX68VisXDXXXexceNGioqKSE9Pn+zhzkjhcJjGxkaqq6upr6+ntrYWg8HA0qVLWbZsGcuXLycrKwuj0SgVVCHElyJTsULMEiqVCqPRSH5+PsnJySQlJeHxeGhqamJ4eJj6+npCoRBFRUUUFhZSVFREZmYmer1eGi2+JEVRCIVCDA0NMTw8TE9PD8eOHaO9vZ2RkRGSk5O58847ueOOO1i8eDEFBQVSnRNC/FWkYifELBX91a+vr+fYsWP8z//8D3V1dSQkJFBUVMQjjzzC6tWrsdlsGAwG4uLiUKvVci7pNVw87R0OhwmFQrhcLg4dOkRNTQ379++ntbWVefPmUV5ezle/+lXWrFkj3a5CiBtGgp0Qs1T0V19RFCKRCOFwmOPHj3Po0CEOHjzIwYMHMRqNFBYWsnz5cr7+9a+zYMECUlJSpJP2KqL3sra2lmPHjnHkyBH27NmD3+8nMzOTZcuW8cQTT1BaWkpKSgoajSZ2LyXYCSFuBAl2QgjgQigZGxvDbrczNDREZ2cnTU1N9Pf3Mzg4iM/nw2azkZGRQXFxMWVlZeTn55Oeno7BYJjs4U+KcDhMMBikubmZjo4Ourq6aG5uprOzE7/fj1arpaCggCVLlpCbm0tWVhaFhYXEx8ej0+kme/hCiBlIgp0Q4jKRSASPx0NdXR2tra2cOnWKEydOEAgE0Ov1ZGRkMG/ePAoLC8nKyiItLQ2r1YrJZMJkMmG1WmfcNh3hcBifz4fX62V8fBy3243H42F0dJSGhgZ6e3sZGhqit7cXtVpNamoqubm53H333SxfvhybzYbZbJ7styGEmOEk2AkhvlA4HKatrY2jR4/S0NDA0aNHqaurIxKJYDabWbRoEUuXLmXu3LkUFxezdOlSTCZTLNxFA97FQW8qhr6Lp6c//0+Px0N/fz+tra0cO3aMpqYmOjo6OHPmDJFIhIKCAoqLi1m1ahX33nsveXl5pKWlTdp7EULMThLshBBfSFGU2LRjKBQiGAwyPDxMa2srbW1tNDQ00NzczPnz53E6nRgMBnJzc8nIyCA3N5fi4mJyc3PJzMyMBZ6p2Gk7NjaGw+Ggq6uLtrY2+vv76e/v5+zZs5w7dw6Px0MkEiErK4u5c+eSl5dHSUkJK1euJCUlBZPJRFxcHDqd7pL1c0IIcatIsBNC/MWie+KNjIzgcDgYHh5meHg49n13dzc+n49gMEgwGMTv96PRaNDr9VitVhISEjCbzVgsFhITE0lISMBkMmE2mzGZTBiNRnQ6HQaDAZPJhFarRavVotfrgQvVPo1GQ1xcHCqVKtaBGg6HgQsVxkAgEHvtQCDAxMQEExMT+Hw+fD4fTqcTp9OJx+PB5XIxMjKCx+PB4/HgdDoJhUJoNBq0Wi06nQ6TyURCQgI2m42srCxSUlJITEwkNTWV7Oxs9Ho9Wq3sICWEmFwS7IQQN4zX62VsbIyGhgY6OzsZGhqir6+P9vZ2RkdH8Xg8BAIB1Go1ZrOZ+Pj4WECyWCyxn5lMJgwGA2azmYSEBHQ6HXFxcZhMJuDCcWkajSbWtHFxkIt+7/V68fv9TExM4PV6cblceDyeWMAbGxtjbGwMt9uN0+nEbrcTiURQqVTo9XpsNhvp6emkp6dTUFBAYWEhOTk55ObmkpycPCWnkoUQQoKdEOKWiFbF2tvb6enpwW63Y7fb6evro6+vj5GREUZGRhgdHcXr9caqbzeSyWTCYrEQHx9PRkYGmZmZpKamkp6eTk5ODunp6dhsNoqKikhMTJyS08VCCHEtEuyEELdEOBwmEokQCARi06YXr9uL/nn0Z36/H7fbjcvlIhgMEggEcLvdwIWu3VAohM/nAy5U8KJVPQCtVovZbI5NoRqNxtgWI2q1GrVaHZvejU63Xvx99DqpygkhphsJdkKIKSe6Zi46lXrx93Ah2EUikdjUq1qtjgW06PfRNW86nS62Xm+mbcEihBCfJ8FOCCGEEGKGkF58IYQQQogZQoKdEEIIIcQMIcFOCCGEEGKGkGAnhBBCCDFDSLATQgghhJghJNgJIYQQQswQEuyEEEIIIWYICXZCCCGEEDOEBDshhBBCiBlCgp0QQgghxAwhwU4IIYQQYoaQYCeEEEIIMUNIsBNCCCGEmCEk2AkhhBBCzBD/Dzmb7qATTwGmAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1500x1000 with 1 Axes>"
       ]
@@ -1593,10 +1601,10 @@
    "id": "43e49972-b137-4cae-9ee7-5c5879ff01ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:09:39.309994Z",
-     "iopub.status.busy": "2024-10-25T15:09:39.309486Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.622690Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.622044Z"
+     "iopub.execute_input": "2024-10-25T15:11:05.424990Z",
+     "iopub.status.busy": "2024-10-25T15:11:05.424561Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.155719Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.154891Z"
     }
    },
    "outputs": [
@@ -1605,15 +1613,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   0%|          | 0/121 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Evaluating set functions...:   7%|▋         | 8/121 [00:05<01:21,  1.38it/s]"
+      "Evaluating set functions...:   0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     {
@@ -1621,7 +1621,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  10%|▉         | 12/121 [00:11<01:51,  1.02s/it]"
+      "Evaluating set functions...:   7%|▋         | 8/119 [00:05<01:20,  1.37it/s]"
      ]
     },
     {
@@ -1629,7 +1629,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  13%|█▎        | 16/121 [00:17<02:03,  1.17s/it]"
+      "Evaluating set functions...:  10%|█         | 12/119 [00:11<01:49,  1.02s/it]"
      ]
     },
     {
@@ -1637,7 +1637,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  17%|█▋        | 20/121 [00:23<02:07,  1.27s/it]"
+      "Evaluating set functions...:  13%|█▎        | 16/119 [00:17<02:02,  1.19s/it]"
      ]
     },
     {
@@ -1645,7 +1645,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  20%|█▉        | 24/121 [00:28<02:07,  1.32s/it]"
+      "Evaluating set functions...:  17%|█▋        | 20/119 [00:23<02:06,  1.27s/it]"
      ]
     },
     {
@@ -1653,7 +1653,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  23%|██▎       | 28/121 [00:34<02:06,  1.35s/it]"
+      "Evaluating set functions...:  20%|██        | 24/119 [00:29<02:07,  1.34s/it]"
      ]
     },
     {
@@ -1661,7 +1661,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  26%|██▋       | 32/121 [00:40<02:03,  1.39s/it]"
+      "Evaluating set functions...:  24%|██▎       | 28/119 [00:34<02:05,  1.38s/it]"
      ]
     },
     {
@@ -1669,7 +1669,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  30%|██▉       | 36/121 [00:46<01:59,  1.41s/it]"
+      "Evaluating set functions...:  27%|██▋       | 32/119 [00:40<02:02,  1.41s/it]"
      ]
     },
     {
@@ -1677,7 +1677,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  33%|███▎      | 40/121 [00:51<01:55,  1.42s/it]"
+      "Evaluating set functions...:  30%|███       | 36/119 [00:46<01:57,  1.42s/it]"
      ]
     },
     {
@@ -1685,7 +1685,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  36%|███▋      | 44/121 [00:57<01:49,  1.43s/it]"
+      "Evaluating set functions...:  34%|███▎      | 40/119 [00:52<01:53,  1.44s/it]"
      ]
     },
     {
@@ -1693,7 +1693,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  40%|███▉      | 48/121 [01:03<01:44,  1.43s/it]"
+      "Evaluating set functions...:  37%|███▋      | 44/119 [00:58<01:48,  1.45s/it]"
      ]
     },
     {
@@ -1701,7 +1701,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  43%|████▎     | 52/121 [01:09<01:38,  1.43s/it]"
+      "Evaluating set functions...:  40%|████      | 48/119 [01:04<01:43,  1.45s/it]"
      ]
     },
     {
@@ -1709,7 +1709,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  46%|████▋     | 56/121 [01:14<01:33,  1.43s/it]"
+      "Evaluating set functions...:  44%|████▎     | 52/119 [01:10<01:37,  1.45s/it]"
      ]
     },
     {
@@ -1717,7 +1717,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  50%|████▉     | 60/121 [01:20<01:27,  1.43s/it]"
+      "Evaluating set functions...:  47%|████▋     | 56/119 [01:15<01:31,  1.46s/it]"
      ]
     },
     {
@@ -1725,7 +1725,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  53%|█████▎    | 64/121 [01:26<01:21,  1.43s/it]"
+      "Evaluating set functions...:  50%|█████     | 60/119 [01:21<01:25,  1.46s/it]"
      ]
     },
     {
@@ -1733,7 +1733,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  56%|█████▌    | 68/121 [01:32<01:15,  1.43s/it]"
+      "Evaluating set functions...:  54%|█████▍    | 64/119 [01:27<01:20,  1.46s/it]"
      ]
     },
     {
@@ -1741,7 +1741,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  60%|█████▉    | 72/121 [01:37<01:10,  1.43s/it]"
+      "Evaluating set functions...:  57%|█████▋    | 68/119 [01:33<01:14,  1.46s/it]"
      ]
     },
     {
@@ -1749,7 +1749,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  63%|██████▎   | 76/121 [01:43<01:04,  1.43s/it]"
+      "Evaluating set functions...:  61%|██████    | 72/119 [01:39<01:08,  1.46s/it]"
      ]
     },
     {
@@ -1757,7 +1757,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  66%|██████▌   | 80/121 [01:49<00:58,  1.43s/it]"
+      "Evaluating set functions...:  64%|██████▍   | 76/119 [01:45<01:02,  1.45s/it]"
      ]
     },
     {
@@ -1765,7 +1765,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  69%|██████▉   | 84/121 [01:54<00:52,  1.43s/it]"
+      "Evaluating set functions...:  67%|██████▋   | 80/119 [01:50<00:57,  1.46s/it]"
      ]
     },
     {
@@ -1773,7 +1773,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  73%|███████▎  | 88/121 [02:00<00:47,  1.43s/it]"
+      "Evaluating set functions...:  71%|███████   | 84/119 [01:56<00:51,  1.46s/it]"
      ]
     },
     {
@@ -1781,7 +1781,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  76%|███████▌  | 92/121 [02:06<00:41,  1.43s/it]"
+      "Evaluating set functions...:  74%|███████▍  | 88/119 [02:02<00:45,  1.46s/it]"
      ]
     },
     {
@@ -1789,7 +1789,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  79%|███████▉  | 96/121 [02:12<00:35,  1.43s/it]"
+      "Evaluating set functions...:  77%|███████▋  | 92/119 [02:08<00:39,  1.46s/it]"
      ]
     },
     {
@@ -1797,7 +1797,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  83%|████████▎ | 100/121 [02:17<00:29,  1.43s/it]"
+      "Evaluating set functions...:  81%|████████  | 96/119 [02:14<00:33,  1.46s/it]"
      ]
     },
     {
@@ -1805,7 +1805,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  86%|████████▌ | 104/121 [02:23<00:24,  1.44s/it]"
+      "Evaluating set functions...:  84%|████████▍ | 100/119 [02:20<00:27,  1.45s/it]"
      ]
     },
     {
@@ -1813,7 +1813,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  89%|████████▉ | 108/121 [02:29<00:18,  1.44s/it]"
+      "Evaluating set functions...:  87%|████████▋ | 104/119 [02:25<00:21,  1.45s/it]"
      ]
     },
     {
@@ -1821,7 +1821,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  93%|█████████▎| 112/121 [02:35<00:13,  1.45s/it]"
+      "Evaluating set functions...:  91%|█████████ | 108/119 [02:31<00:15,  1.45s/it]"
      ]
     },
     {
@@ -1829,7 +1829,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  96%|█████████▌| 116/121 [02:41<00:07,  1.45s/it]"
+      "Evaluating set functions...:  94%|█████████▍| 112/119 [02:37<00:10,  1.45s/it]"
      ]
     },
     {
@@ -1837,7 +1837,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  99%|█████████▉| 120/121 [02:46<00:01,  1.45s/it]"
+      "Evaluating set functions...:  97%|█████████▋| 116/119 [02:43<00:04,  1.45s/it]"
      ]
     },
     {
@@ -1845,7 +1845,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 121/121 [02:46<00:00,  1.38s/it]"
+      "Evaluating set functions...: 100%|██████████| 119/119 [02:43<00:00,  1.37s/it]"
      ]
     },
     {
@@ -1866,16 +1866,16 @@
    "id": "29424e87-5b18-49bc-9690-f09cd111bcd7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.624891Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.624701Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.759501Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.758875Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.158576Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.158113Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.300498Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.299096Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1904,17 +1904,17 @@
    "id": "a3853007-fb0c-4a1d-bcca-f7bdd4dcef13",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.762670Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.761635Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.948646Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.948076Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.305710Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.304673Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.508823Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.508084Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x7fe1be8d5af0>"
+       "<matplotlib.collections.LineCollection at 0x7f43edba2e20>"
       ]
      },
      "execution_count": 15,
@@ -1969,10 +1969,10 @@
    "id": "c9be4254-9ac2-4abc-a3f3-906925dac53d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.950811Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.950605Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.969445Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.968946Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.511683Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.511434Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.533488Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.532745Z"
     }
    },
    "outputs": [
@@ -2010,10 +2010,10 @@
    "id": "16215ca0-00c9-411c-b293-675a4cb595c2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.971359Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.971006Z",
-     "iopub.status.idle": "2024-10-25T15:12:40.985718Z",
-     "shell.execute_reply": "2024-10-25T15:12:40.985216Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.535691Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.535471Z",
+     "iopub.status.idle": "2024-10-25T15:14:04.553818Z",
+     "shell.execute_reply": "2024-10-25T15:14:04.553131Z"
     }
    },
    "outputs": [
@@ -2052,10 +2052,10 @@
    "id": "00c6b5bf-e6b7-456f-84f6-91e2cbda62d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:40.987616Z",
-     "iopub.status.busy": "2024-10-25T15:12:40.987237Z",
-     "iopub.status.idle": "2024-10-25T15:12:44.791572Z",
-     "shell.execute_reply": "2024-10-25T15:12:44.790935Z"
+     "iopub.execute_input": "2024-10-25T15:14:04.556092Z",
+     "iopub.status.busy": "2024-10-25T15:14:04.555864Z",
+     "iopub.status.idle": "2024-10-25T15:14:09.052806Z",
+     "shell.execute_reply": "2024-10-25T15:14:09.052128Z"
     }
    },
    "outputs": [
@@ -2064,7 +2064,15 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   0%|          | 0/116 [00:00<?, ?it/s]"
+      "Evaluating set functions...:   0%|          | 0/121 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "Evaluating set functions...:   7%|▋         | 8/121 [00:00<00:01, 61.75it/s]"
      ]
     },
     {
@@ -2072,7 +2080,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:   7%|▋         | 8/116 [00:00<00:01, 65.35it/s]"
+      "Evaluating set functions...:  13%|█▎        | 16/121 [00:00<00:01, 60.13it/s]"
      ]
     },
     {
@@ -2080,7 +2088,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  14%|█▍        | 16/116 [00:00<00:01, 67.75it/s]"
+      "Evaluating set functions...:  20%|█▉        | 24/121 [00:00<00:02, 39.21it/s]"
      ]
     },
     {
@@ -2088,7 +2096,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  21%|██        | 24/116 [00:00<00:01, 49.35it/s]"
+      "Evaluating set functions...:  26%|██▋       | 32/121 [00:00<00:02, 35.01it/s]"
      ]
     },
     {
@@ -2096,7 +2104,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  28%|██▊       | 32/116 [00:00<00:02, 41.36it/s]"
+      "Evaluating set functions...:  33%|███▎      | 40/121 [00:01<00:02, 34.06it/s]"
      ]
     },
     {
@@ -2104,7 +2112,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  34%|███▍      | 40/116 [00:00<00:01, 39.08it/s]"
+      "Evaluating set functions...:  40%|███▉      | 48/121 [00:01<00:02, 33.08it/s]"
      ]
     },
     {
@@ -2112,7 +2120,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  41%|████▏     | 48/116 [00:01<00:01, 38.75it/s]"
+      "Evaluating set functions...:  46%|████▋     | 56/121 [00:01<00:02, 32.05it/s]"
      ]
     },
     {
@@ -2120,7 +2128,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  48%|████▊     | 56/116 [00:01<00:01, 37.44it/s]"
+      "Evaluating set functions...:  53%|█████▎    | 64/121 [00:01<00:01, 31.34it/s]"
      ]
     },
     {
@@ -2128,7 +2136,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  55%|█████▌    | 64/116 [00:01<00:01, 36.73it/s]"
+      "Evaluating set functions...:  60%|█████▉    | 72/121 [00:02<00:01, 31.18it/s]"
      ]
     },
     {
@@ -2136,7 +2144,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  62%|██████▏   | 72/116 [00:01<00:01, 36.13it/s]"
+      "Evaluating set functions...:  66%|██████▌   | 80/121 [00:02<00:01, 30.83it/s]"
      ]
     },
     {
@@ -2144,7 +2152,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  69%|██████▉   | 80/116 [00:02<00:01, 35.87it/s]"
+      "Evaluating set functions...:  73%|███████▎  | 88/121 [00:02<00:01, 30.46it/s]"
      ]
     },
     {
@@ -2152,7 +2160,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  76%|███████▌  | 88/116 [00:02<00:00, 35.72it/s]"
+      "Evaluating set functions...:  79%|███████▉  | 96/121 [00:02<00:00, 29.80it/s]"
      ]
     },
     {
@@ -2160,7 +2168,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  83%|████████▎ | 96/116 [00:02<00:00, 36.00it/s]"
+      "Evaluating set functions...:  86%|████████▌ | 104/121 [00:03<00:00, 30.25it/s]"
      ]
     },
     {
@@ -2168,7 +2176,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  90%|████████▉ | 104/116 [00:02<00:00, 36.10it/s]"
+      "Evaluating set functions...:  93%|█████████▎| 112/121 [00:03<00:00, 30.56it/s]"
      ]
     },
     {
@@ -2176,7 +2184,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  97%|█████████▋| 112/116 [00:02<00:00, 36.24it/s]"
+      "Evaluating set functions...:  99%|█████████▉| 120/121 [00:03<00:00, 30.49it/s]"
      ]
     },
     {
@@ -2184,7 +2192,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 116/116 [00:02<00:00, 39.68it/s]"
+      "Evaluating set functions...: 100%|██████████| 121/121 [00:03<00:00, 32.56it/s]"
      ]
     },
     {
@@ -2196,7 +2204,7 @@
     },
     {
      "data": {
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",
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wYcNtC40k+fj4yMfHx+WZAACA+RwuNQULFsywpsYwDCUlJcnf31/ffvutU8P9k2EYev3117V48WKtX79eISEhLvuzAACA53G41EyYMCHD+15eXipcuLAee+wxFSxY0Fm5MunTp4/mzJmjJUuWKH/+/IqLi5MkBQYGys/Pz2V/LgAA8AxOW1Pjard6jHz69Onq2rVrlj4Gj3QDN2BNDQALydaamvPnz2vq1Kk6cOCAJKly5crq1q3bbR+vzikP6V4AAMAkDu8ovGHDBpUpU0YTJ07U+fPndf78eU2cOFEhISHasGGDKzICAADckcO3n6pWrarQ0FB9+eWX8vb2lnRtF+HXXntNmzdvVmRkpEuCOgO3n4AbcPsJgIU4PFMTFRWlQYMG2QuNJHl7e2vgwIGKiopyajgAAICscrjU1KpVy76W5p8OHDig6tWrOyUUAACAo7K0UDgiIsL+6379+ql///6KiopSnTp1JElbtmzRF198oQ8//NA1KQEAAO4gS2tqrp/MfaeX2my2u3ZKd3awpga4AWtqAFhIlmZqoqOjXZ0DAAAgR7JUakqXLu3qHAAAADmSpVKzdOlSNWvWTLlz59bSpUtv+9pWrVo5JRgAAIAjsrymJi4uTkWKFJGX160fmGJNDeBhWFMDwEKyNFOTnp5+018DAAC4C4f2qbly5YoaN26sI0eOuCoPAABAtjhUanLnzp1hzxoAAAB34fCOwp06ddLUqVNdkQUAACDbsrSm5p+uXr2qadOmafXq1Xr44YeVN2/eDNfHjRvntHAAAABZ5XCp2bdvn2rVqiVJOnz4sNMDAQAAZIfDpWbdunWuyAEAAJAjDq+p6d69u5KSkjKNX7hwQd27d3dKKAAAAEc5XGpmzpypS5cuZRq/dOmSZs2a5ZRQAAAAjsry7afExEQZhiHDMJSUlCRfX1/7tbS0NP38888qUqSIS0ICAADcSZZLTYECBWSz2WSz2fTggw9mum6z2TRy5EinhgMAAMiqLJeadevWyTAMNWrUSD/88IOCgoLs1/LkyaPSpUsrODjYJSEBAADuJMulpkGDBpKk6OholSpVSjabLdNrYmJiVKpUKeelAwAAyCKHFwqXLVtWf/31V6bxs2fPKiQkxCmhAAAAHOVwqTEM46bjycnJGRYPAwAA3E1Zvv00cOBASdcWBA8fPlz+/v72a2lpadq6datq1Kjh9IAAAABZkeVSs3v3bknXZmoiIyOVJ08e+7U8efKoevXqevPNN52fEAAAIAscevpJkrp166bPPvtMAQEBLgsFAADgKIfPfpo+fborcgAAAOSIw6VGknbs2KEFCxYoJiZGqampGa4tWrTIKcEAAAAc4fDTT/PmzVPdunV14MABLV68WFeuXNHvv/+utWvXKjAw0BUZAQAA7sjhUjNmzBiNHz9ey5YtU548efTZZ5/p4MGDateuHRvvAQAA0zhcao4ePaoWLVpIuvbU04ULF2Sz2TRgwAB99dVXTg8IAACQFQ6XmoIFCyopKUmSVLx4ce3bt0+SFB8fr4sXLzo3HQAAQBY5vFC4fv36WrVqlapWrap//etf6t+/v9auXatVq1apcePGrsgIAABwRw6XmkmTJuny5cuSpLffflu5c+fW5s2b9fzzz2vYsGFODwgAAJAVNuNWhzlZUGJiogIDA5WQkMDmgYAkBbQ3O0H2JM4zOwEAN+TwmhoAAAB3RKkBAACWQKkBAACWQKkBAACW4HCpmT59OvvRAAAAt+NwqRkyZIjuv/9+9ejRQ5s3b3ZFJgAAAIc5XGpOnjypmTNn6u+//1bDhg1VsWJFjR07VnFxca7IBwAAkCUOl5pcuXKpTZs2WrJkiWJjY9WrVy999913KlWqlFq1aqUlS5YoPT3dFVkBAABuKUcLhYsWLarHH39coaGh8vLyUmRkpLp06aJy5cpp/fr1TooIAABwZ9kqNadPn9Ynn3yihx56SA0bNlRiYqKWL1+u6OhonTx5Uu3atVOXLl2cnRUAAOCWHD4moWXLllqxYoUefPBB9ezZU507d1ZQUFCG15w5c0b333+/292G4pgE4AYckwDAQhw+0LJIkSIKCwtTaGjoLV9TuHBhRUdH5ygYAACAIxy+/TR16tTbFhpJstlsKl26dLZD3cqGDRvUsmVLBQcHy2az6ccff3T6nwEAADxTlmZqJk6cmOUP2K9fv2yHuZMLFy6oevXq6t69u9q2beuyPwcAAHieLK2pCQkJydoHs9l07NixHIfK6p+1ePFitW7dOsu/hzU1wA1YUwPAQrI0U+Op62NSUlKUkpJifz8xMdHENAAAwJUsfaDlBx98oMDAQPtbyZIlzY4EAABcxOGnnyTpxIkTWrp0qWJiYpSamprh2rhx45wSzBmGDh2qgQMH2t9PTEyk2AAAYFEOl5o1a9aoVatWKlu2rA4ePKgqVaro+PHjMgxDtWrVckXGbPPx8ZGPj4/ZMQAAwF3g8O2noUOH6s0331RkZKR8fX31ww8/KDY2Vg0aNNC//vUvV2QEAAC4I4dLzYEDB9S5c2dJ1w63vHTpkvLly6dRo0Zp7NixTg/4T8nJydqzZ4/27Nkj6doC5j179igmJsalfy4AAHB/DpeavHnz2tfRFCtWTEePHrVf+/vvv52X7CZ27NihmjVrqmbNmpKkgQMHqmbNmho+fLhL/1wAAOD+HF5TU6dOHYWHh6tSpUpq3ry5Bg0apMjISC1atEh16tRxRUa7hg0bysGjqgAAwD3C4VIzbtw4JScnS5JGjhyp5ORkzZ8/Xw888IBbPfkEAADuLQ6f0u3J2FEYuAE7CgOwkGztU3NdcnKy0tPTM4xRFgAAgBkcXigcHR2tFi1aKG/evAoMDFTBggVVsGBBFShQQAULFnRFRgAAgDtyeKamU6dOMgxD06ZNU9GiRWWz2VyRCwAAwCEOl5q9e/dq586dqlChgivyAAAAZIvDt58eeeQRxcbGuiILAABAtjk8U/PNN9/o3//+t06ePKkqVaood+7cGa5Xq1bNaeEAAACyyuFS89dff+no0aPq1q2bfcxms8kwDNlsNqWlpTk1IAAAQFY4XGq6d++umjVrau7cuSwUBgAAbsPhUvPHH39o6dKlKl++vCvyAAAAZIvDC4UbNWqkvXv3uiILAABAtjk8U9OyZUsNGDBAkZGRqlq1aqaFwq1atXJaOAAAgKxy+OwnL69bT+64+0Jhzn4CbsDZTwAsxOGZmhvPegIAAHAHDq+pAQAAcEfZKjVhYWFq2bKlypcvr/Lly6tVq1bauHGjs7MBAABkmcOl5ttvv1WTJk3k7++vfv36qV+/fvLz81Pjxo01Z84cV2QEAAC4I4cXCleqVEmvvPKKBgwYkGF83Lhx+vrrr3XgwAGnBnQmFgoDN2ChMAALcXim5tixY2rZsmWm8VatWik6OtopoQAAABzlcKkpWbKk1qxZk2l89erVKlmypFNCAQAAOMrhR7oHDRqkfv36ac+ePapbt64kadOmTZoxY4Y+++wzpwcEAADICodLTe/evXX//ffr008/1YIFCyRdW2czf/58Pffcc04PCAAAkBUOLxT2ZCwUBm7AQmEAFuLwTM11qampOnPmTKYdhkuVKpXjUAAAAI5yuNQcOXJE3bt31+bNmzOMG4bh9mc/AQAA63K41HTt2lW5cuXS8uXLVaxYMdlsNlfkAgAAcIjDpWbPnj3auXOnKlas6Io8AAAA2eLwPjWVK1fW33//7YosAAAA2eZwqRk7dqzeeustrV+/XmfPnlViYmKGNwAAADM4/Ei3l9e1HnTjWhpPWCjMI93ADXikG4CFOLymZt26da7IAQAAkCMOl5oGDRrc8tq+fftyFAYAACC7HF5Tc6OkpCR99dVXevTRR1W9enVnZAIAAHBYtkvNhg0b1KVLFxUrVkyffPKJGjVqpC1btjgzGwAAQJY5dPspLi5OM2bM0NSpU5WYmKh27dopJSVFP/74oypXruyqjAAAAHeU5Zmali1bqkKFCoqIiNCECRP0559/6vPPP3dlNgAAgCzL8kzNL7/8on79+ql379564IEHXJkJAADAYVmeqQkPD1dSUpIefvhhPfbYY5o0aRI7CwMAALeR5VJTp04dff311zp16pReffVVzZs3T8HBwUpPT9eqVauUlJTkypwAAAC35fCOwv906NAhTZ06VbNnz1Z8fLyaNm2qpUuXOjOfU7GjMHADdhQGYCE52qemQoUK+uijj3TixAnNnTvXWZkAAAAclqOZGk/DTA1wA2ZqAFhIjncUBgAAcAeUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAkeV2q++OILlSlTRr6+vnrssce0bds2syMBAAA34FGlZv78+Ro4cKBGjBihXbt2qXr16nr66ad15swZs6MBAACTeVSpGTdunHr16qVu3bqpcuXKmjx5svz9/TVt2jSzowEAAJPlMjtAVqWmpmrnzp0aOnSofczLy0tNmjTRb7/9dtPfk5KSopSUFPv7iYmJLs8Ji2CnXQDwOB5Tav7++2+lpaWpaNGiGcaLFi2qgwcP3vT3fPDBBxo5cuTdiOe53wQlx74Reurn6eg3+3ulHNwrn+e98u8WuMd51O0nRw0dOlQJCQn2t9jYWLMjAQAAF/GYmZpChQrJ29tbp0+fzjB++vRp3X///Tf9PT4+PvLx8bkb8QAAgMk8ZqYmT548evjhh7VmzRr7WHp6utasWaPQ0FATkwEAAHfgMTM1kjRw4EB16dJFtWvX1qOPPqoJEybowoUL6tatm9nRAACAyTyq1Lz44ov666+/NHz4cMXFxalGjRr69ddfMy0eBgAA9x6PKjWS1LdvX/Xt29fsGAAAwM14zJoaAACA26HUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS/C4U7oBwGGJ88xOAOAuYKYGAABYAqUGAABYAqUGAABYAqUGAABYAguFnYWFiAAAmIqZGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAmUGgAAYAkeU2pGjx6tunXryt/fXwUKFDA7DgAAcDMeU2pSU1P1r3/9S7179zY7CgAAcEO5zA6QVSNHjpQkzZgxw9wgAADALXlMqcmOlJQUpaSk2N9PTEw0MQ0AAHAlj7n9lB0ffPCBAgMD7W8lS5Y0OxIAAHARU0vNkCFDZLPZbvt28ODBbH/8oUOHKiEhwf4WGxvrxPQAAMCdmHr7adCgQeratettX1O2bNlsf3wfHx/5+Phk+/cDAADPYWqpKVy4sAoXLmxmBAAAYBEes1A4JiZG586dU0xMjNLS0rRnzx5JUvny5ZUvXz5zwwEAANPZDMMwzA6RFV27dtXMmTMzja9bt04NGzbM0sdITExUYGCgEhISFBAQ4OSEAADATB5TapyBUgMAgHVZ+pFuAABw76DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS6DUAAAAS8hldoC7yTAMSVJiYqLJSQAAgKPy588vm812y+v3VKlJSkqSJJUsWdLkJAAAwFEJCQkKCAi45XWbcX364h6Qnp6uP//8845Nz50kJiaqZMmSio2Nve1fpKfj87QWPk/ruBc+R4nP01MwU/MPXl5eKlGihNkxsiUgIMAj/wE6is/TWvg8reNe+BwlPk9Px0JhAABgCZQaAABgCZQaN+fj46MRI0bIx8fH7CguxedpLXye1nEvfI4Sn6dV3FMLhQEAgHUxUwMAACyBUgMAACyBUgMAACyBUgMAACyBUgMAORAfH292BGRD9+7d7Ufn/NOFCxfUvXt3ExLBGSg1biI1NVXvvfeeXnnlFW3atMnsOHfFxo0b1alTJ4WGhurkyZOSpNmzZys8PNzkZHBUbGysTpw4YX9/27ZteuONN/TVV1+ZmMr5xo4dq/nz59vfb9eune677z4VL15ce/fuNTEZHDVz5kxdunQp0/ilS5c0a9YsExLBGSg1bqJPnz6aNGmSTp8+raeffloLFy40O5JL/fDDD3r66afl5+en3bt3KyUlRdK1w8rGjBljcrqcKViwoIKCgrL0ZhUdOnTQunXrJElxcXFq2rSptm3bprffflujRo0yOZ3zTJ482X4g7qpVq7Rq1Sr98ssvatasmQYPHmxyOmRFYmKiEhISZBiGkpKSlJiYaH87f/68fv75ZxUpUsTsmE4VExOjm+3eYhiGYmJiTEjkOvfU2U/u7Mcff9T333+vhg0bauXKlXr++ee1Z88ePfjgg2rZsqU2bNighIQEde7c2eyoTvH+++9r8uTJ6ty5s+bNm2cfr1evnt5//30Tk+XchAkT7L8+e/as3n//fT399NMKDQ2VJP32229asWKF3nnnHZMSOt++ffv06KOPSpIWLFigKlWqaNOmTVq5cqX+/e9/a/jw4SYndI64uDh7qVm+fLnatWunp556SmXKlNFjjz1mcjrni4+P18KFC3X06FENHjxYQUFB2rVrl4oWLarixYubHS9bChQoIJvNJpvNpgcffDDTdZvNppEjR5qQzHVCQkJ06tSpTGXt3LlzCgkJUVpamknJnI9S4ybS09PtXySeeuopLVq0SAMGDNCpU6dUo0YNDRkyRIcPH7ZMqTl06JDq16+faTwwMNDj1yh06dLF/uvnn39eo0aNUt++fe1j/fr106RJk7R69WoNGDDAjIhOd+XKFfsOpatXr1arVq0kSRUrVtSpU6fMjOZUBQsWVGxsrEqWLKlff/3VXsANw7DUNwZJioiIUJMmTRQYGKjjx4+rV69eCgoK0qJFixQTE+Oxt2jWrVsnwzDUqFEj/fDDDxlmTPPkyaPSpUsrODjYxITOZxjGTU+2Tk5Olq+vrwmJXIdS4yZq166tsLAwPfDAA5Kkpk2bat++ffbrBw4cMCuaS9x///2KiopSmTJlMoyHh4erbNmy5oRygRUrVmjs2LGZxp955hkNGTLEhESu8dBDD2ny5Mlq0aKFVq1apffee0+S9Oeff+q+++4zOZ3ztG3bVh06dNADDzygs2fPqlmzZpKk3bt3q3z58ianc66BAweqa9eu+uijj5Q/f377ePPmzdWhQwcTk+VMgwYNJEnR0dEqVarUTb/ZW8XAgQMlXZt9euedd+Tv72+/lpaWpq1bt6pGjRompXMN1tS4iWHDhtkXy94LevXqpf79+2vr1q2y2Wz6888/9d133+nNN99U7969zY7nNPfdd5+WLFmSaXzJkiWW+mY/duxYTZkyRQ0bNtRLL72k6tWrS5KWLl1qvy1lBePHj1ffvn1VuXJlrVq1Svny5ZMknTp1Sq+99prJ6Zxr+/btevXVVzONFy9eXHFxcSYkcq4DBw5keCjjiy++UI0aNdShQwedP3/exGTOs3v3bu3evVuGYSgyMtL+/u7du3Xw4EFVr15dM2bMMDumcxmACdLT043333/fyJs3r2Gz2QybzWb4+voaw4YNMzuaU02fPt3w9vY2nn32WeO9994z3nvvPePZZ581cuXKZUyfPt3seE519epV49y5cxnGoqOjjdOnT5uUyPnCwsKMK1euZBq/cuWKERYWZkIi1ylcuLCxa9cuwzAMI1++fMbRo0cNwzCMlStXGiVKlDAzmlNUqVLF+OmnnwzDMIyIiAgjT548xtChQ406deoYXbt2NTmdc3Xt2tVISEgwO8ZdwYGWbsbb2/umC7rOnj2rIkWKWO6+fWpqqqKiopScnKzKlSvbf/K1kq1bt2rixIn2W4iVKlVSv379LLWwdNq0aXryyScVEhJidhSXupf+f/bs2VNnz57VggULFBQUpIiICHl7e6t169aqX79+hgXxnihfvnzat2+fypQpo3fffVf79u3TwoULtWvXLjVv3twSs1H3IkqNm/Hy8lJcXFymL5p//vmnypUrd9N9FTxRQkKC0tLSMj3WfO7cOeXKlUsBAQEmJUN2PPDAAzp27JiKFy+uBg0aqEGDBmrYsKHl1pl4eXnp9OnTKly4cIbxw4cPq3bt2kpMTDQpmfMlJCTohRde0I4dO5SUlKTg4GDFxcUpNDRUP//8s/LmzWt2xBwJCgpSeHi4KleurMcff1ydO3fWK6+8ouPHj6ty5cq6ePGi2RFzpG3btpoxY4YCAgLUtm3b27520aJFdymV67FQ2E1MnDhR0rUFXd98802GGYu0tDRt2LBBFStWNCue07Vv314tW7bMtA5hwYIFWrp0qX7++WeTkjlfenq6oqKidObMGaWnp2e4drMnwDzRkSNHdPLkSa1fv14bNmzQJ598oldffVXFihVTw4YN9e2335odMUeuf1Ow2Wzq2rWr/Ukv6dr/z4iICNWtW9eseC4RGBioVatWKTw8XBEREUpOTlatWrXUpEkTs6M5xeOPP66BAweqXr162rZtm31TxcOHD6tEiRImp8u5wMBA+yLogIAASy+I/idmatzE9Wn7P/74QyVKlJC3t7f9Wp48eVSmTBmNGjXKMrcsgoKCtGnTJlWqVCnD+MGDB1WvXj2dPXvWpGTOtWXLFnXo0EF//PFHps2vbDabpW5XXHfx4kVt3LhRc+fO1XfffSfDMHT16lWzY+VIt27dJF3bhbZdu3by8/OzX7v+/7NXr14qVKiQWRHhoJiYGL322muKjY1Vv3791KNHD0nSgAEDlJaWZv9B01MtXbpUzZo1U+7cuc2OcldRatzMk08+qUWLFqlgwYJmR3GpvHnzasuWLapatWqG8cjISD322GMeP/V7XY0aNfTggw9q5MiRKlasWKaflgIDA01K5lwrV67U+vXrtX79eu3evVuVKlWy34KqX7++Zf49jxw5Um+++abH33rJijvtBG2VDRWtytvbW3FxcSpcuPAt14JZEaUGpnjyySdVpUoVff755xnG+/Tpo4iICG3cuNGkZM6VN29e7d2713JrS27k5eWlwoULa9CgQXrllVdUoEABsyMhh2rWrJnh/StXrig6Olq5cuVSuXLltGvXLpOSOU9aWpp+/PFH+yL+hx56SK1atcowU+6p7r//fn399ddq2bLlLdeCWRGlxs2kpaVpxowZWrNmzU3XYKxdu9akZM61adMmNWnSRI888ogaN24sSVqzZo22b9+ulStX6oknnjA5oXM0atRIb731lp555hmzo7jUhAkTtGHDBm3YsEE+Pj72WZqGDRvedCt6T1KrVi2tWbNGBQsWVM2aNW+7NsEK3+hvJzExUV27dlWbNm308ssvmx0nR6KiotS8eXOdPHlSFSpUkHRtp/OSJUvqp59+Urly5UxOmDPvvvuuRo0alaW1NFa6DU6pcTN9+/bVjBkz1KJFi5verhg/frxJyZxvz549+vjjj7Vnzx75+fmpWrVqGjp0qH1XZStYvHixhg0bpsGDB6tq1aqZ7m9Xq1bNpGSuExkZqbCwMK1du1bLly9XkSJFMpzg7WlGjhypwYMHy9/f/45nAo0YMeIupTJPZGSkWrZsqePHj5sdJUeaN28uwzD03Xff2Z/CPHv2rDp16iQvLy/99NNPJifMuYMHDyoqKkqtWrXS9OnTbzmD+txzz93dYC5EqXEzhQoV0qxZs9S8eXOzo8AJvLwyb9pts9nsZ7FY6SckwzC0e/durV+/XuvWrVN4eLiSkpJUtWpV7d692+x4cJLw8HC1bNnS43fdvdW6vr1796pevXpKTk42KZnz/bOYWx2PdLuZPHnyWHb9RWJion3/mTvt52GVfWqio6PNjnBXtGzZUps2bVJiYqKqV6+uhg0bqlevXqpfv74l19ekpqbe9PZwqVKlTErkfDc+/WMYhk6dOqXZs2fbz7zyZD4+PkpKSso0npycrDx58piQyHWuzyD+9ddfOnTokCSpQoUKllxjw0yNm/n000917NgxTZo0yXL7CvxzBb6Xl9dNPz8rzmDcCwYPHqwGDRroiSeesMwTXTdz+PBh9ejRQ5s3b84wbsV/tzfuDn19MXijRo00dOjQDIdceqLOnTtr165dmjp1qv18sq1bt6pXr156+OGHLXUm0sWLF9W3b1/Nnj3b/m/U29tbnTt31ueff26pGRxKjZtp06aN1q1bp6CgID300EOZ1mB48s6PYWFhqlevnnLlyqWwsLDbvvb6SbpWMHv2bE2ePFnR0dH67bffVLp0aU2YMEEhISGWupd93eXLl+Xr62t2DJe4/u93yJAhN13zdv0gT7i/+Ph4denSRcuWLbN/nb169apatWqlGTNmWKqcv/rqq1q9erUmTZqkevXqSbp2G7Ffv35q2rSpvvzyS5MTOg+lxs1c3+TrVqZPn36XkrjO1atXNWbMGHXv3t0SO3fezpdffqnhw4frjTfe0OjRo7Vv3z6VLVtWM2bM0MyZM7Vu3TqzIzpFenq6Ro8ercmTJ+v06dM6fPiwypYtq3feeUdlypSxb2zm6fLmzaudO3daanfve11UVFSGc9msePu/UKFCWrhwoRo2bJhhfN26dWrXrp3++usvc4K5wt04NRO4Ub58+Yzo6GizY7hcpUqVjMWLFxuGkfGk48jISOO+++4zMZlzjRw50ihbtqzx7bffGn5+fvbPc968eUadOnVMTuc8tWvXNjZu3Gh2jLsiOTnZGDZsmBEaGmqUK1fOCAkJyfDmyRISEoy0tLRM42lpaZY8zdrPz8/Yv39/pvF9+/YZ/v7+JiRyHRYKu6GrV69q/fr1Onr0qDp06KD8+fPrzz//VEBAgGVOsW7UqJHCwsJUpkwZs6O4VHR0dKZNzKRrixQvXLhgQiLXmDVrlr766is1btxY//73v+3j1atX18GDB01M5lxjx47VW2+9pTFjxtz0EX2rLHCXrp3SHRYWppdffvmmt9o81eLFi/Wf//xHe/bsybSW5NKlS3rkkUf0ySefqGXLliYldL7Q0FCNGDFCs2bNst8avnTpkkaOHKnQ0FCT0zkXpcbN/PHHH3rmmWcUExOjlJQUNW3aVPnz59fYsWOVkpKiyZMnmx3RKZo1a6YhQ4YoMjJSDz/8cKZt51u1amVSMucKCQnRnj17VLp06Qzjv/76a6ZzrzzZyZMnbzptn56eritXrpiQyDWuH+Z4fcPI6wwLLhT+5Zdf9NNPP9nXYFjFl19+qbfeeuumi2Pz5s2r//znP5o0aZKlSs2ECRP0zDPPqESJEvZ1X3v37pWvr69WrFhhcjrnotS4mf79+6t27drau3ev7rvvPvt4mzZt1KtXLxOTOdf107nHjRuX6ZqVvjkMHDhQffr00eXLl2UYhrZt26a5c+fqgw8+0DfffGN2PKepXLmyNm7cmKm8LVy48KYzVZ7KKmugsqJgwYL2TemsZN++ffrf//53y+v169fXsGHD7mIi16tataqOHDmi7777zj5z+tJLL6ljx44ZDme1AkqNm9m4caM2b96caZ+EMmXK6OTJkyalcr4b9/ewqp49e8rPz0/Dhg3TxYsX1aFDBwUHB+uzzz5T+/btzY7nNMOHD1eXLl108uRJpaena9GiRTp06JBmzZql5cuXmx3Paaz0VN6dvPfeexo+fLhmzpxpqUd+z58/f9tT469cueLxGwv+05UrV1SxYkUtX77cUj8Y3wqlxs2kp6ffdJbixIkTHr8vxHXHjx/XqlWrdOXKFTVo0EAPPfSQ2ZFcqmPHjurYsaMuXryo5ORkS56U+9xzz2nZsmUaNWqU8ubNq+HDh6tWrVpatmyZmjZtana8HFu6dOlNxwMDA/Xggw+qWLFidzmR63366ac6evSoihYtqjJlymRaP+Sp51yVKVNGO3bsuOUTbDt27Mg04+jJcufOrcuXL5sd467hkW438+KLLyowMFBfffWV8ufPr4iICBUuXFjPPfecSpUq5fGPdK9bt07PPvusLl26JEnKlSuXpk2bpk6dOpmczLXOnDlj38mzYsWKltzJ08pudtzFdTabTe3bt9fXX39tqRkNq55z9fbbb+vbb7/Vtm3bVLRo0QzX4uLi9Nhjj6lTp04aPXq0SQmdb8yYMTp8+LC++eYb5cpl7bkMSo2bOXHihJ5++mkZhqEjR46odu3aOnLkiAoVKqQNGzZ4/E/5jz/+uAoVKqQvv/xSvr6+GjZsmBYvXqw///zT7GgukZSUpNdee01z586133Lz9vbWiy++qC+++MJSG3zdixISErRz50716dNHbdq00ZgxY8yOhDtISkpSaGioYmJi1KlTJ/sJ3QcPHtR3332nkiVLasuWLZaZGZeurclcs2aN8uXLp6pVq2Z6MMOTN3W9EaXGDV29elXz5s1TRESEkpOTVatWLcss6CpQoIA2b96sypUrS7q2fXdAQIBOnz6dYWG0Vbz44ovavXu3Pv/8c/ujk7/99pv69++vGjVqaN68eSYnzL6goCAdPnxYhQoVUsGCBW/7yO+5c+fuYrK779dff9Ubb7xhqcfXpWu77i5cuFBHjx7V4MGDFRQUpF27dqlo0aIqXry42fGyLSEhQUOHDtX8+fPt62cKFCig9u3ba/To0SpYsKDJCZ3rXtjU9TpKjZux8hbz0rVp/Li4uAwzTvnz59fevXtVtmxZE5O5Rt68ebVixQo9/vjjGcY3btyoZ555xqP3qpk5c6bat28vHx8fzZgx47alpkuXLncx2d13/PhxValSxVInO0dERKhJkyYKDAzU8ePHdejQIZUtW1bDhg1TTEyMZs2aZXbEHDMMQ3///bcMw1DhwoUtsxfPdenp6fr444+1dOlSpaamqlGjRnr33Xct8QPyrVj75poHKlKkiNq0aaNOnTqpcePGt72X76lWrFiR4bZLenq61qxZo3379tnHrLJPzX333XfTW0yBgYEe/9Ngly5dtHz5cjVv3lxdu3Y1O46pjh07puDgYLNjONXAgQPVtWtXffTRRxluxTRv3lwdOnQwMZnz2Gw2S69vGz16tN599101adJEfn5+mjhxov766y9NmzbN7GiuY8o+xrilRYsWGS+88ILh5+dn3H///Ub//v2N7du3mx3LaWw22x3fvLy8zI7pNFOmTDGaNGlinDp1yj526tQp46mnnjImT55sYjLn8Pb2NoKDg43//ve/RlRUlNlxTLF7926jZs2axhtvvGF2FKcKCAiw/53+84iP48ePGz4+PmZGQxaVL18+w9eZVatWGXny5LnpERFWwe0nN5WUlKSFCxdq7ty5Wrt2rcqWLatOnTpp+PDhZkeDA2rWrKmoqCilpKSoVKlSkqSYmBj5+PjogQceyPBaT3xENjY2VtOnT9fMmTN1/PhxPf744+rZs6deeOEFS01x32rN0IULF3T16lU1bdpUCxYssNQxCUWKFNGKFStUs2bNDLeIV61ape7duys2NtbsiLgDHx8fRUVFqWTJkvYxX19fRUVFWfYwYUqNB9i/f786duyoiIgIy+y0e6+402Ox/+Spj8het27dOs2YMUM//PCDcuXKpfbt26tHjx565JFHzI6WYzNnzrzpeEBAgCpUqGBf+G4lPXv21NmzZ7VgwQIFBQUpIiJC3t7eat26terXr68JEyaYHRF34O3trbi4uAy32K5vFRISEmJiMteh1Lipy5cva+nSpZozZ45+/fVXFS1aVC+99JI+/PBDs6MBt5WUlKR58+ZpxowZ2rJli6pUqaK9e/eaHQsOSkhI0AsvvKAdO3YoKSlJwcHBiouLU2hoqH7++edMjwXD/Xh5ealZs2by8fGxjy1btkyNGjXK8PdnpUe6WSjsZlasWKE5c+boxx9/VK5cufTCCy9o5cqVql+/vtnRkEOXL1/W/PnzdeHCBTVt2jTT7SeryJ8/vxo3bqw//vhDBw8e1P79+82OhGwIDAzUqlWrFB4enmF7ieuHenqiiRMnZvm1/fr1c2GSu+NmTx1afaNTZmrcjL+/v5599ll17NhRzZs3z7Q1OTzDwIEDdeXKFX3++eeSpNTUVD366KPav3+//P39dfXqVa1cuVJ169Y1OanzXLp0Sd9//72mTZumjRs3KiQkRN26dVPXrl09ek+Te1VsbGyGtRhWkNVbLjabTceOHXNxGrgCMzVu5vTp05bayfJetXLlygy7y3733XeKiYnRkSNHVKpUKXXv3l2jR4/WTz/9ZGJK59iyZYumTZumBQsWKDU1VW3bttXq1av15JNPmh0NOVCmTBk9/vjj6tSpk1544QWP34JAkqKjo82OABez3iYoHur6N4TrhebEiRMZTrK+ePGiPvroI7PiOV3ZsmV19uzZTOPx8fGW2IQvJiYmw+LRlStX6oUXXlDp0qVls9nUv39/7d6928SEzlG5cmXVq1dPu3bt0gcffKBTp07p22+/pdBYwI4dO/Too49q1KhRKlasmFq3bq2FCxcqJSXF7GjALXH7yU14e3vr1KlT9p12AwICtGfPHvs3+NOnTys4ONgyTz/dbGdh6drnWapUKY//wlmgQAFt377dvm4mJCRE77zzjrp37y7p2g60lSpVsh/s6an69eunHj16qHr16mZHgYsYhqH169drzpw5+uGHH5Senq62bdtaYgO3EydOaOnSpYqJiVFqamqGa+PGjTMpFXKC209u4sZuadWuuXTpUvuvb9xZOC0tTWvWrFGZMmVMSOZclSpV0rJlyzRw4ED9/vvviomJyTB78ccff2Q6IdgTObLw0lO1bds2y6+10lMk19lsNj355JN68skn1bt3b/Xo0UMzZ870+FKzZs0atWrVSmXLltXBgwdVpUoVHT9+XIZhqFatWmbHQzZRanBXtW7dWtK1L5Q3rszPnTu3ypQpo08//dSEZM711ltvqX379vrpp5/0+++/q3nz5hkWKf7888969NFHTUyIrPpn8TYMQ4sXL1ZgYKBq164tSdq5c6fi4+MdKj+e5MSJE5ozZ47mzJmjffv2KTQ0VF988YXZsXJs6NChevPNNzVy5Ejlz59fP/zwg4oUKaKOHTvqmWeeMTsesolSg7vq+jqhkJAQbd++XYUKFTI5kWu0adNGP//8s5YvX66nnnpKr7/+eobr/v7+eu2110xKB0f88wTj//znP2rXrp0mT54sb29vSddmGF977TVL7SYsSVOmTNGcOXO0adMmVaxYUR07dtSSJUtUunRps6M5xYEDBzR37lxJUq5cuXTp0iXly5dPo0aN0nPPPafevXubnBDZwZoaN+Hl5aWZM2fafyp86aWXNGHCBPstivj4eHXr1s0ya2oAT1S4cGGFh4erQoUKGcYPHTqkunXr3nTxu6cqWbKkXnrpJXXs2NGSa6buv/9+rVu3TpUqVVLlypX14YcfqlWrVtq7d6/q1atnqRPX7yXM1LiRG2/HvPrqqxnev9nZM55k4sSJeuWVV+Tr63vHtRhW2PjqXnX58mX5+vqaHcMlrl69qoMHD2YqNQcPHszwtKIVxMTEePzXnNupU6eOwsPDValSJTVv3lyDBg1SZGSkFi1apDp16pgdD9nETA3umpCQEO3YsUP33XffbTfBYuMrz5Oenq7Ro0dr8uTJOn36tA4fPqyyZcvqnXfeUZkyZdSjRw+zIzrFwIEDNWvWLP33v/+1r4naunWrPvzwQ7388suWe2Jm48aNmjJlio4ePaqFCxeqePHimj17tkJCQvT444+bHS9Hjh07puTkZFWrVk0XLlzQoEGDtHnzZj3wwAMaN26cZW6z3XPu8qngACxo5MiRRtmyZY1vv/3W8PPzM44ePWoYhmHMmzfPqFOnjsnpnCctLc0YO3asERwcbNhsNsNmsxnBwcHG2LFjjatXr5odz6kWLlxo+Pn5GT179jR8fHzsf6eff/650axZM5PTATfHTA2AHCtfvrymTJmixo0bK3/+/Nq7d6/9UdnQ0FCdP3/e7IhOl5iYKEmWWyB8Xc2aNTVgwAB17tw5w9/p7t271axZM8XFxZkd0SlSU1N15syZTLcPS5UqZVIi5ARramCKtLQ0zZgxQ2vWrLnpF5S1a9ealAzZcfLkSZUvXz7TeHp6uq5cuWJCItezapm57tChQzc9SDcwMFDx8fF3P5CTHT58WD169NDmzZszjBuGIZvNxkMZHopSA1P0799fM2bMUIsWLVSlShVLLUisWbNmlj+fXbt2uTjN3VG5cmVt3Lgx0zqEhQsXqmbNmialco578e9TuvZ0UFRUVKbNMMPDwy1xlEm3bt2UK1cuLV++XMWKFbPU16B7GaUGppg3b54WLFig5s2bmx3F6a5vMChdexLof//7nypXrqzQ0FBJ1w6A/P333y21T83w4cPVpUsXnTx5Uunp6Vq0aJEOHTqkWbNmafny5WbHy5F//n3eS3r16qX+/ftr2rRpstls+vPPP/Xbb79p0KBBGj58uNnxcmzPnj3auXOnKlasaHYUOBFramCK4OBgrV+/Xg8++KDZUVyqZ8+eKlasmN57770M4yNGjFBsbKzHbzX/Txs3btSoUaO0d+9eJScnq1atWho+fLieeuops6MhGwzD0JgxY/TBBx/o4sWLkiQfHx8NHjxYQ4cOlZ+fn8kJc+aRRx7R+PHjPf4pLmREqXEzBQsWvOk0qM1mk6+vr8qXL6+uXbuqW7duJqRznk8//VTHjh3TpEmTLD3tGxgYqB07dtgPtrzuyJEjql27thISEkxKhpzYuXOnDhw4IEl66KGHPP4W2+2kpqYqKipKycnJqly5sqZMmaKPP/7Y4xcKr127VsOGDdOYMWNUtWpV5c6dO8N1q6+ZsipuP7mZ4cOHa/To0WrWrJl9H4xt27bp119/VZ8+fRQdHa3evXvr6tWr6tWrl8lpsy88PFzr1q3TL7/8ooceeijTFxSrHAzo5+enTZs2ZSo1mzZtsuwGdVZ25swZtW/fXuvXr1eBAgUkXdvt+8knn9S8efNUuHBhcwM6QUpKit59912tWrXKPjPTunVrTZ8+XW3atJG3t7cGDBhgdswca9KkiSSpcePGGcZZKOzZKDVuJjw8XO+//77+/e9/ZxifMmWKVq5cqR9++EHVqlXTxIkTPbrUFChQQG3atDE7hsu98cYb6t27t3bt2pVhs7Zp06bpnXfeMTmd89wrM4yvv/66kpKS9Pvvv6tSpUqSpP3796tLly7q16+f/SwhTzZ8+HBNmTJFTZo00ebNm/Wvf/1L3bp105YtW/Tpp5/qX//6l/3cK0+2bt06syPAFUzbIQc3lTdvXuPIkSOZxo8cOWLkzZvXMAzDiIqKMvz9/e92NGTT/Pnzjbp16xoFCxY0ChYsaNStW9eYP3++2bGcaty4ccZ9991ndOrUyZg4caIxceJEo1OnTkahQoWM0aNH2zdw++qrr8yOmiMBAQHGtm3bMo1v3brVCAwMvPuBXCAkJMRYsmSJYRiGERkZadhsNqNbt25Genq6ycmAO2Omxs0EBQVp2bJlmaZ3ly1bpqCgIEnShQsXlD9/fjPiIRvatWundu3aZRrft2+fqlSpYkIi57tXZhjT09Mz3SqVpNy5c1vm7KcTJ07o4YcfliRVqVJFPj4+GjBggCXXvsXHx2vq1KkZ1kd1797dfrAwPA8Lhd3M119/rd69e6t58+b22xXbt2/Xzz//rMmTJ6tHjx769NNPtW3bNs2fP9/ktI671W2KwMBAPfjgg3rzzTfVtGlTE5LdHUlJSZo7d66++eYb7dy50zL37fPly6c9e/Zk2oAvKipKNWrUUHJyso4ePWo/Z8dTPffcc4qPj9fcuXMVHBws6drGgx07dlTBggW1ePFikxPmnLe3t+Li4uzrg/Lnz6+IiIjbntfmiXbs2KGnn35afn5+Gb7WXrp0SStXrlStWrVMTojsoNS4oU2bNmnSpEk6dOiQJKlChQp6/fXXVbduXZOT5dzMmTNvOh4fH6+dO3dq/vz5WrhwoVq2bHmXk7nWhg0b9M0332jRokUKDg5W27Zt9fzzz+uRRx4xO5pTlCpVSgMGDMg0wzh+/HiNHz9eMTExioiI0FNPPeXRT83ExsaqVatW+v3331WyZEn7WJUqVbR06VKVKFHC5IQ55+XlpWbNmsnHx0fStVniRo0aKW/evBle5+mL+Z944gmVL19eX3/9tXLlunbT4urVq+rZs6eOHTumDRs2mJwQ2UGpgVsZN26cFi5cmGnrck8UFxenGTNmaOrUqUpMTFS7du00efJk7d27V5UrVzY7nlNZfYbxnwzD0OrVq3Xw4EFJUqVKlexP0lhBVhdzT58+3cVJXMvPz0+7d+/OtPne/v37Vbt2bfvePPAslBo3lJ6erqioqJueiXSzs1is5PDhw6pTp47OnTtndpQcadmypTZs2KAWLVqoY8eOeuaZZ+Tt7a3cuXNbstRI1p5hhPUULVpUs2fPzrQ55IoVK9S5c2edPn3apGTICRYKu5ktW7aoQ4cO+uOPP3Rj37wX9k5ISUlRnjx5zI6RY7/88ov69eun3r17Z9qjxqrq1aunevXqmR3DJX777TedPXtWzz77rH1s1qxZGjFihC5cuKDWrVvr888/t9+ygft78cUX1aNHD33yySf24r1p0yYNHjxYL730ksnpkF2UGjfz73//W7Vr19ZPP/10Tx6yNnXqVNWoUcPsGDkWHh6uqVOn6uGHH1alSpX08ssvq3379mbHuisuX76s1NTUDGOevjvrqFGj1LBhQ3upiYyMVI8ePdS1a1dVqlRJH3/8sYKDg/Xuu++aGxRZ9sknn8hms6lz5866evWqpGtPsfXu3VsffvihyemQXdx+cjN58+bV3r17Mz1FYhUDBw686XhCQoJ27dqlw4cPa8OGDfZHSj3dhQsXNH/+fE2bNk3btm1TWlqaxo0bp+7du1vqsfyLFy/qrbfe0oIFC3T27NlM1z19hrFYsWJatmyZateuLUl6++23FRYWpvDwcEnS999/rxEjRmj//v1mxkQ2XLx4UUePHpUklStXTv7+/iYnQk54mR0AGT322GOKiooyO4bL7N69+6Zvf//9t5o2bap9+/ZZptBI10pq9+7dFR4ersjISA0aNEgffvihihQpolatWpkdz2kGDx6stWvX6ssvv5SPj4+++eYbjRw5UsHBwZo1a5bZ8XLs/PnzKlq0qP39sLAwNWvWzP7+I488otjYWDOiIYf8/f1VtWpVVa1alUJjAczUuJnFixdr2LBhGjx48E0PWatWrZpJyeAsaWlpWrZsmaZNm6alS5eaHccpSpUqpVmzZqlhw4YKCAjQrl27VL58ec2ePVtz587Vzz//bHbEHCldurRmz56t+vXrKzU1VQUKFNCyZcvs5wZFRkaqQYMGHr/A3eratm2rGTNmKCAgQG3btr3taz39kfV7FWtq3Mzzzz8vSerevbt9zGazcciahXh7e6t169Zq3bq12VGc5ty5cypbtqyka+tnrn9zf/zxx9W7d28zozlF8+bNNWTIEI0dO1Y//vij/P399cQTT9ivR0REqFy5ciYmRFYEBgba1ykGBATcc2sW7wWUGjcTHR1tdgTAYWXLllV0dLRKlSqlihUrasGCBXr00Ue1bNky+2nWnuy9995T27Zt1aBBA+XLl08zZ87M8JTetGnTMj0aDPfzz711ZsyYYV4QuAy3nwDk2Pjx4+Xt7a1+/fpp9erVatmypQzD0JUrVzRu3Dj179/f7IhOkZCQoHz58mU6pfrcuXPKly+fJbYjuFc0atRIixYtylS6ExMT1bp1a61du9acYMgRSo0bWLp0qZo1a6bcuXPfcY2FlRaXwrr++OMP7dy5U+XLl2cdGNySl5eX4uLiVKRIkQzjZ86cUfHixXXlyhWTkiEnKDVu4J//uby8bv1AGmtq4G7S09P18ccfa+nSpUpNTVXjxo01YsQI+fn5mR0NuKmIiAhJUo0aNbR27VoFBQXZr6WlpenXX3/VlClTdPz4cZMSIidYU+MG/nkUwo3HIgDubPTo0Xr33XfVpEkT+fn56bPPPtOZM2c0bdo0s6MBN1WjRg3ZbDbZbDY1atQo03U/Pz99/vnnJiSDMzBTAyDbHnjgAb355pt69dVXJUmrV69WixYtdOnSpdvOOgJmuX4ETdmyZbVt2zYVLlzYfi1PnjwqUqRIpjVT8ByUGje0Zs0ajR8/XgcOHJB07RTgN954w1InAcMafHx8FBUVpZIlS9rHfH19FRUVpRIlSpiYDMC9iNtPbuZ///uf+vfvrxdeeMH+xMiWLVvUvHlzjR8/Xn369DE5IfB/rl69Kl9f3wxjuXPnZpElPMb+/fsVExOT6bwyHsrwTMzUuJkSJUpoyJAh6tu3b4bxL774QmPGjNHJkydNSgZk5uXlpWbNmmU4nXrZsmVq1KiR8ubNax9jd1a4m2PHjqlNmzaKjIy0b3Aqyb4hHw9leCZueruZ+Ph4PfPMM5nGn3rqKSUkJJiQCLi1Ll26qEiRIgoMDLS/derUScHBwRnGAHfTv39/hYSE6MyZM/L399fvv/+uDRs2qHbt2lq/fr3Z8ZBNzNS4mQ4dOqhmzZoaPHhwhvFPPvlEO3bs0Lx580xKBgDWUahQIa1du1bVqlVTYGCgtm3bpgoVKmjt2rUaNGiQdu/ebXZEZANratxM5cqVNXr0aK1fv16hoaGSrq2p2bRpkwYNGqSJEyfaX9uvXz+zYgKAR0tLS1P+/PklXSs4f/75pypUqKDSpUvr0KFDJqdDdjFT42ZCQkKy9DqbzaZjx465OA0AWNMTTzyhQYMGqXXr1urQoYPOnz+vYcOG6auvvtLOnTu1b98+syMiGyg1AIB7zooVK3ThwgW1bdtWUVFRevbZZ3X48GHdd999mj9//k035oP7o9S4sRtX4wMAXOfcuXMqWLAgX3M9GE8/uaGpU6eqSpUq8vX1la+vr6pUqaJvvvnG7FgAYAlXrlxRrly5Mt1iCgoKotB4OBYKu5nhw4dr3Lhxev311+0LhX/77TcNGDBAMTExGjVqlMkJAcCz5c6dW6VKlWIvGgvi9pObKVy4sCZOnKiXXnopw/jcuXP1+uuv6++//zYpGQBYx9SpU7Vo0SLNnj07w0nd8GzM1LiZK1euqHbt2pnGH374YV29etWERABgPZMmTVJUVJSCg4NVunTpDDtgS9KuXbtMSoacoNS4mZdffllffvmlxo0bl2H8q6++UseOHU1KBQDW0rp1a7MjwAW4/eRmXn/9dc2aNUslS5ZUnTp1JElbt25VTEyMOnfurNy5c9tfe2PxAQDgXkapcTNPPvlkll5ns9m0du1aF6cBAOuKj4/XwoULdfToUQ0ePFhBQUHatWuXihYtquLFi5sdD9lAqQEA3HMiIiLUpEkTBQYG6vjx4zp06JDKli2rYcOGKSYmRrNmzTI7IrKBfWrc2IkTJ3TixAmzYwCA5QwcOFBdu3bVkSNH5Ovrax9v3ry5NmzYYGIy5ASlxs2kp6dr1KhRCgwMVOnSpVW6dGkVKFBA7733ntLT082OBwCWsH37dr366quZxosXL664uDgTEsEZePrJzbz99tuaOnWqPvzwQ9WrV0+SFB4ernfffVeXL1/W6NGjTU4IAJ7Px8dHiYmJmcYPHz6swoULm5AIzsCaGjcTHBysyZMnq1WrVhnGlyxZotdee00nT540KRkAWEfPnj119uxZLViwQEFBQYqIiJC3t7dat26t+vXra8KECWZHRDZw+8nNnDt3ThUrVsw0XrFiRZ07d86ERABgPZ9++qmSk5NVpEgRXbp0SQ0aNFD58uWVP39+ZsQ9GDM1buaxxx7TY489pokTJ2YYf/3117V9+3Zt2bLFpGQAYD3h4eGKiIhQcnKyatWqpSZNmpgdCTlAqXEzYWFhatGihUqVKpXhQMvY2Fj9/PPPeuKJJ0xOCACAe+L2k5tp0KCBDh8+rDZt2ig+Pl7x8fFq27atDh06RKEBACdas2aNnn32WZUrV07lypXTs88+q9WrV5sdCznATA0A4J7zv//9T/3799cLL7xgnxXfsmWLFi5cqPHjx6tPnz4mJ0R2UGrcUHx8vLZt26YzZ85k2pumc+fOJqUCAOsoUaKEhgwZor59+2YY/+KLLzRmzBieNPVQlBo3s2zZMnXs2FHJyckKCAiQzWazX7PZbDwBBQBOkC9fPu3Zs0fly5fPMH7kyBHVrFlTycnJJiVDTrCmxs0MGjRI3bt3V3JysuLj43X+/Hn7G4UGAJyjVatWWrx4cabxJUuW6NlnnzUhEZyBmRo3kzdvXkVGRqps2bJmRwEAy3r//ff1ySefqF69ehnW1GzatEmDBg1SQECA/bX9+vUzKyYcRKlxM23btlX79u3Vrl07s6MAgGWFhIRk6XU2m03Hjh1zcRo4C6XGDSxdutT+67/++kujRo1St27dVLVqVeXOnTvDa288PgEAAFxDqXEDXl5ZW9pks9mUlpbm4jQAcO/4+++/JUmFChUyOQmcgYXCbiA9PT1LbxQaAMi5+Ph49enTR4UKFVLRokVVtGhRFSpUSH379lV8fLzZ8ZADzNQAAO4Z586dU2hoqE6ePKmOHTuqUqVKkqT9+/drzpw5KlmypDZv3qyCBQuanBTZQalxE7/99pvOnj2b4VHCWbNmacSIEbpw4YJat26tzz//XD4+PiamBADP9sYbb2jNmjVavXq1ihYtmuFaXFycnnrqKTVu3Fjjx483KSFygttPbmLUqFH6/fff7e9HRkaqR48eatKkiYYMGaJly5bpgw8+MDEhAHi+H3/8UZ988kmmQiNJ999/vz766KOb7l8Dz8BMjZsoVqyYli1bptq1a0uS3n77bYWFhSk8PFyS9P3332vEiBHav3+/mTEBwKP5+Pjo6NGjKlGixE2vnzhxQuXLl9fly5fvcjI4AzM1buL8+fMZfnIICwtTs2bN7O8/8sgjio2NNSMaAFhGoUKFdPz48Vtej46OVlBQ0N0LBKei1LiJokWLKjo6WpKUmpqqXbt2qU6dOvbrSUlJmfasAQA45umnn9bbb7+t1NTUTNdSUlL0zjvv6JlnnjEhGZwhl9kBcE3z5s01ZMgQjR07Vj/++KP8/f31xBNP2K9HRESoXLlyJiYEAM83atQo1a5dWw888ID69OmjihUryjAMHThwQP/73/+UkpKi2bNnmx0T2cSaGjfx999/q23btgoPD1e+fPk0c+ZMtWnTxn69cePGqlOnjkaPHm1iSgDwfNHR0Xrttde0cuVKXf8WaLPZ1LRpU02aNCnTyd3wHJQaN5OQkKB8+fLJ29s7w/i5c+eUL18+5cmTx6RkAGAt58+f15EjRyRJ5cuXZy2NBVBqAACAJbBQGAAAWAKlBgAAWAKlBgAAWAKlBgAAWAKlBgAAWAKlBgAAWAKlBgAAWAKlBgAAWML/A/5GmJ5xZ3XwAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2227,10 +2235,10 @@
    "id": "e48b4b77-7ee6-4669-be82-05d0d15f4065",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:12:44.793809Z",
-     "iopub.status.busy": "2024-10-25T15:12:44.793588Z",
-     "iopub.status.idle": "2024-10-25T15:13:20.983011Z",
-     "shell.execute_reply": "2024-10-25T15:13:20.982440Z"
+     "iopub.execute_input": "2024-10-25T15:14:09.055495Z",
+     "iopub.status.busy": "2024-10-25T15:14:09.055017Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.641772Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.641149Z"
     }
    },
    "outputs": [],
@@ -2252,16 +2260,16 @@
    "id": "4c7b083f-ef7f-47e0-b72f-2d0d1b9501e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:20.985305Z",
-     "iopub.status.busy": "2024-10-25T15:13:20.984934Z",
-     "iopub.status.idle": "2024-10-25T15:13:21.098863Z",
-     "shell.execute_reply": "2024-10-25T15:13:21.098260Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.644066Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.643683Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.784663Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.783984Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2312,10 +2320,10 @@
    "id": "d70a7edb-9374-4fce-b8d3-e9ef9f53f328",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:21.102602Z",
-     "iopub.status.busy": "2024-10-25T15:13:21.102073Z",
-     "iopub.status.idle": "2024-10-25T15:13:21.123269Z",
-     "shell.execute_reply": "2024-10-25T15:13:21.122814Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.787408Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.786979Z",
+     "iopub.status.idle": "2024-10-25T15:14:50.808902Z",
+     "shell.execute_reply": "2024-10-25T15:14:50.808340Z"
     }
    },
    "outputs": [
@@ -2355,10 +2363,10 @@
    "id": "7f4a5065-e436-46cd-be5e-e6c1274fc3b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:21.125274Z",
-     "iopub.status.busy": "2024-10-25T15:13:21.125090Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.423624Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.423055Z"
+     "iopub.execute_input": "2024-10-25T15:14:50.811015Z",
+     "iopub.status.busy": "2024-10-25T15:14:50.810637Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.349481Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.348867Z"
     }
    },
    "outputs": [],
@@ -2379,16 +2387,16 @@
    "id": "fc2467b0-eb84-4f72-b895-954f5a8634e6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.425728Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.425518Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.529699Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.529067Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.352032Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.351562Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.457883Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.457373Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2415,10 +2423,10 @@
    "id": "34b11c19-1256-4ae0-9067-2eb374ce5147",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.532188Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.531831Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.551436Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.550860Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.459947Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.459745Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.476870Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.476442Z"
     }
    },
    "outputs": [
@@ -2471,10 +2479,10 @@
    "id": "1c4785f9-5ec1-4340-8837-6d0cdaf4c406",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:36.553775Z",
-     "iopub.status.busy": "2024-10-25T15:13:36.553444Z",
-     "iopub.status.idle": "2024-10-25T15:13:36.648181Z",
-     "shell.execute_reply": "2024-10-25T15:13:36.647639Z"
+     "iopub.execute_input": "2024-10-25T15:15:07.479022Z",
+     "iopub.status.busy": "2024-10-25T15:15:07.478841Z",
+     "iopub.status.idle": "2024-10-25T15:15:07.574657Z",
+     "shell.execute_reply": "2024-10-25T15:15:07.574020Z"
     }
    },
    "outputs": [],
diff --git a/main/example_notebooks/gcm_rca_microservice_architecture.html b/main/example_notebooks/gcm_rca_microservice_architecture.html
index c4582c4ac..fe47f9dcc 100644
--- a/main/example_notebooks/gcm_rca_microservice_architecture.html
+++ b/main/example_notebooks/gcm_rca_microservice_architecture.html
@@ -566,7 +566,7 @@
   <section id="Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture">
 <h1>Finding the Root Cause of Elevated Latencies in a Microservice Architecture<a class="headerlink" href="#Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture" title="Permalink to this heading">#</a></h1>
 <p>In this case study, we identify the root causes of “unexpected” observed latencies in cloud services that empower an online shop. We focus on the process of placing an order, which involves different services to make sure that the placed order is valid, the customer is authenticated, the shipping costs are calculated correctly, and the shipping process is initiated accordingly. The dependencies of the services is shown in the graph below.</p>
-<p><img alt="c07303a1af6440d79eb4fefc69fb148b" class="no-scaled-link" src="../_images/microservice-architecture-dependencies.png" style="width: 800px;" /></p>
+<p><img alt="5cccc5b7e28b43ce85652d83eb41a91f" class="no-scaled-link" src="../_images/microservice-architecture-dependencies.png" style="width: 800px;" /></p>
 <p>This kind of dependency graph could be obtained from services like <a class="reference external" href="https://aws.amazon.com/xray/">Amazon X-Ray</a> or defined manually based on the trace structure of requests.</p>
 <p>We assume that the dependency graph above is correct and that we are able to measure the latency (in seconds) of each node for an order request. In case of <code class="docutils literal notranslate"><span class="pre">Website</span></code>, the latency would represent the time until a confirmation of the order is shown. For simplicity, let us assume that the services are synchronized, i.e., a service has to wait for downstream services in order to proceed. Further, we assume that two nodes are not impacted by unobserved factors (hidden confounders) at the same time
 (i.e., causal sufficiency). Seeing that, for instance, network traffic affects multiple services, this assumption might be typically violated in a real-world scenario. However, weak confounders can be neglected, while stronger ones (like network traffic) could falsely render multiple nodes as root causes. Generally, we can only identify causes that are part of the data.</p>
@@ -799,9 +799,9 @@ <h2>Setting up the causal model<a class="headerlink" href="#Setting-up-the-causa
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00&lt;00:00, 76.56it/s]
-Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02&lt;00:00,  4.68it/s]
-Test permutations of given graph: 100%|██████████| 50/50 [01:12&lt;00:00,  1.45s/it]
+Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00&lt;00:00, 71.49it/s]
+Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02&lt;00:00,  4.17it/s]
+Test permutations of given graph: 100%|██████████| 50/50 [01:15&lt;00:00,  1.51s/it]
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -830,82 +830,82 @@ <h2>Setting up the causal model<a class="headerlink" href="#Setting-up-the-causa
 We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.
 
 --- Node Customer DB
-- The KL divergence between generated and observed distribution is 0.06851018535777569.
+- The KL divergence between generated and observed distribution is 0.05633729962188637.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Shipping Cost Service
-- The KL divergence between generated and observed distribution is 0.03806638487810255.
+- The KL divergence between generated and observed distribution is 0.01345661046898411.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Product DB
-- The KL divergence between generated and observed distribution is 0.0739495966691543.
+- The KL divergence between generated and observed distribution is 0.04731204756715253.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Order DB
-- The KL divergence between generated and observed distribution is 0.05761158379067712.
+- The KL divergence between generated and observed distribution is 0.03163058332984496.
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 --- Node Auth Service
-- The MSE is 0.01441171705596729.
-- The NMSE is 0.5464573760061932.
-- The R2 coefficient is 0.7013207387303906.
-- The normalized CRPS is 0.3025250310394936.
+- The MSE is 0.014408940211606136.
+- The NMSE is 0.5467885527285155.
+- The R2 coefficient is 0.7008264081937587.
+- The normalized CRPS is 0.30319879712127784.
 The estimated CRPS indicates a good model performance.
 
 --- Node Caching Service
-- The MSE is 0.023177407671490263.
-- The NMSE is 0.8450133333760175.
-- The R2 coefficient is 0.2854163022252113.
-- The normalized CRPS is 0.464166240739747.
+- The MSE is 0.023142169283019896.
+- The NMSE is 0.8424237004319363.
+- The R2 coefficient is 0.2902352340841851.
+- The normalized CRPS is 0.4626635608354152.
 The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.
 
 --- Node Order Service
-- The MSE is 0.0143980612006466.
-- The NMSE is 0.5485423887933651.
-- The R2 coefficient is 0.6989222187579888.
-- The normalized CRPS is 0.3033900342657954.
+- The MSE is 0.014406030212396392.
+- The NMSE is 0.5492949148510787.
+- The R2 coefficient is 0.6979095952300487.
+- The normalized CRPS is 0.30391606590620085.
 The estimated CRPS indicates a good model performance.
 
 --- Node Product Service
-- The MSE is 0.02043925270114256.
-- The NMSE is 0.6494394163446002.
-- The R2 coefficient is 0.5781324594465047.
-- The normalized CRPS is 0.36367607924553963.
+- The MSE is 0.020462217678676974.
+- The NMSE is 0.6499756128867937.
+- The R2 coefficient is 0.5773982772794385.
+- The normalized CRPS is 0.3635695389665327.
 The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.
 
 --- Node API
-- The MSE is 0.014485507834794353.
-- The NMSE is 0.21002966758855104.
-- The R2 coefficient is 0.9558356948931678.
-- The normalized CRPS is 0.11595431985006795.
+- The MSE is 0.014484769133253233.
+- The NMSE is 0.20977705037425515.
+- The R2 coefficient is 0.9559816510988256.
+- The normalized CRPS is 0.11572746053441199.
 The estimated CRPS indicates a very good model performance.
 
 --- Node www
-- The MSE is 0.011041595331444064.
-- The NMSE is 0.13763913442477363.
-- The R2 coefficient is 0.9810370890224425.
-- The normalized CRPS is 0.07783774485363718.
+- The MSE is 0.011040241699128718.
+- The NMSE is 0.13762343752718348.
+- The R2 coefficient is 0.981050435883116.
+- The normalized CRPS is 0.07801098634259836.
 The estimated CRPS indicates a very good model performance.
 
 --- Node Website
-- The MSE is 0.02077984255867196.
-- The NMSE is 0.1852622726654006.
-- The R2 coefficient is 0.9656512057924049.
-- The normalized CRPS is 0.10196150976628031.
+- The MSE is 0.02077833539868886.
+- The NMSE is 0.1850812826494141.
+- The R2 coefficient is 0.9657338637126042.
+- The normalized CRPS is 0.10221678960177454.
 The estimated CRPS indicates a very good model performance.
 
 ==== Evaluation of Invertible Functional Causal Model Assumption ====
 
---- The model assumption for node www is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node www is not rejected with a p-value of 0.3838477086692199 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
---- The model assumption for node Website is not rejected with a p-value of 0.1451092778434615 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node Website is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
---- The model assumption for node Auth Service is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node Auth Service is not rejected with a p-value of 0.4465020720536922 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
---- The model assumption for node API is not rejected with a p-value of 0.742191149095118 (after potential adjustment) and a significance level of 0.05.
+--- The model assumption for node API is not rejected with a p-value of 0.7556572625812968 (after potential adjustment) and a significance level of 0.05.
 This implies that the model assumption might be valid.
 
 --- The model assumption for node Product Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.
@@ -920,7 +920,7 @@ <h2>Setting up the causal model<a class="headerlink" href="#Setting-up-the-causa
 Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.
 
 ==== Evaluation of Generated Distribution ====
-The overall average KL divergence between the generated and observed distribution is 0.10675450625416824
+The overall average KL divergence between the generated and observed distribution is 0.1063974083835325
 The estimated KL divergence indicates an overall very good representation of the data distribution.
 
 ==== Evaluation of the Causal Graph Structure ====
@@ -929,7 +929,7 @@ <h2>Setting up the causal model<a class="headerlink" href="#Setting-up-the-causa
 +-------------------------------------------------------------------------------------------------------+
 | The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |
 | equivalence class of the given DAG (p-value: 0.00).                                                   |
-| The given DAG violates 5/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |
+| The given DAG violates 4/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |
 | Based on the provided significance level (0.2) and because the DAG is informative,                    |
 | we do not reject the DAG.                                                                             |
 +-------------------------------------------------------------------------------------------------------+
@@ -1251,7 +1251,7 @@ <h3>Attributing permanent degradation of latencies at a target service to other
 <h2>Scenario 3: Simulating the intervention of shifting resources<a class="headerlink" href="#Scenario-3:-Simulating-the-intervention-of-shifting-resources" title="Permalink to this heading">#</a></h2>
 <p>Next, let us imagine a scenario where permanent degradation has happened as in scenario 2 and we’ve successfully identified <code class="docutils literal notranslate"><span class="pre">Caching</span> <span class="pre">Service</span></code> as the root cause. Furthermore, we figured out that a recent deployment of the <code class="docutils literal notranslate"><span class="pre">Caching</span> <span class="pre">Service</span></code> contained a bug that is causing the overloaded hosts. A proper fix must be deployed, or the previous deployment must be rolled back. But, in the meantime, could we mitigate the situation by shifting over some resources from <code class="docutils literal notranslate"><span class="pre">Shipping</span> <span class="pre">Service</span></code> to
 <code class="docutils literal notranslate"><span class="pre">Caching</span> <span class="pre">Service</span></code>? And would that help? Before doing it in reality, let us simulate it first and see whether it improves the situation.</p>
-<p><img alt="15e931ee67ad4f86a25d52cf4127110f" class="no-scaled-link" src="../_images/shifting-resources.png" style="width: 600px;" /></p>
+<p><img alt="bcd8d2c996394b0f80b17d981a472da3" class="no-scaled-link" src="../_images/shifting-resources.png" style="width: 600px;" /></p>
 <p>Let’s perform an intervention where we say we can reduce the average time of <code class="docutils literal notranslate"><span class="pre">Caching</span> <span class="pre">Service</span></code> by 1s. But at the same time we buy this speed-up by an average slow-down of 2s in <code class="docutils literal notranslate"><span class="pre">Shipping</span> <span class="pre">Cost</span> <span class="pre">Service</span></code>.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
diff --git a/main/example_notebooks/gcm_rca_microservice_architecture.ipynb b/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
index 76571ae1b..6157159dc 100644
--- a/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
+++ b/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
@@ -38,10 +38,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:43.516178Z",
-     "iopub.status.busy": "2024-10-25T15:13:43.515760Z",
-     "iopub.status.idle": "2024-10-25T15:13:43.788256Z",
-     "shell.execute_reply": "2024-10-25T15:13:43.787635Z"
+     "iopub.execute_input": "2024-10-25T15:15:14.568864Z",
+     "iopub.status.busy": "2024-10-25T15:15:14.568629Z",
+     "iopub.status.idle": "2024-10-25T15:15:14.839339Z",
+     "shell.execute_reply": "2024-10-25T15:15:14.838576Z"
     }
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    "outputs": [
@@ -194,10 +194,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:43.790254Z",
-     "iopub.status.busy": "2024-10-25T15:13:43.790069Z",
-     "iopub.status.idle": "2024-10-25T15:13:54.249879Z",
-     "shell.execute_reply": "2024-10-25T15:13:54.249211Z"
+     "iopub.execute_input": "2024-10-25T15:15:14.841745Z",
+     "iopub.status.busy": "2024-10-25T15:15:14.841241Z",
+     "iopub.status.idle": "2024-10-25T15:15:25.645002Z",
+     "shell.execute_reply": "2024-10-25T15:15:25.644360Z"
     }
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@@ -244,10 +244,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:54.253062Z",
-     "iopub.status.busy": "2024-10-25T15:13:54.252705Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.227050Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.226322Z"
+     "iopub.execute_input": "2024-10-25T15:15:25.648905Z",
+     "iopub.status.busy": "2024-10-25T15:15:25.648409Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.728132Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.727483Z"
     }
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    "outputs": [],
@@ -276,10 +276,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.229825Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.229506Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.477463Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.476809Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.730732Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.730178Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.946082Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.945355Z"
     }
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    "outputs": [
@@ -317,10 +317,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.479724Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.479332Z",
-     "iopub.status.idle": "2024-10-25T15:13:55.482946Z",
-     "shell.execute_reply": "2024-10-25T15:13:55.482441Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.948425Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.948064Z",
+     "iopub.status.idle": "2024-10-25T15:15:26.952028Z",
+     "shell.execute_reply": "2024-10-25T15:15:26.951436Z"
     }
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    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:13:55.484679Z",
-     "iopub.status.busy": "2024-10-25T15:13:55.484488Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.690708Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.689999Z"
+     "iopub.execute_input": "2024-10-25T15:15:26.953870Z",
+     "iopub.status.busy": "2024-10-25T15:15:26.953680Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.313545Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.312773Z"
     }
    },
    "outputs": [
@@ -458,7 +458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 85.79it/s]"
+      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 79.64it/s]"
      ]
     },
     {
@@ -466,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 85.79it/s]  "
+      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 79.64it/s]  "
      ]
     },
     {
@@ -474,7 +474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 76.56it/s]"
+      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 71.49it/s]"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.41it/s]"
+      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.03it/s]"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.68it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]"
      ]
     },
     {
@@ -528,7 +528,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:09,  2.64s/it]"
+      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:03,  2.53s/it]"
      ]
     },
     {
@@ -536,7 +536,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 2/50 [00:05<01:59,  2.48s/it]"
+      "Test permutations of given graph:   4%|▍         | 2/50 [00:05<02:02,  2.56s/it]"
      ]
     },
     {
@@ -544,7 +544,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 3/50 [00:07<01:49,  2.32s/it]"
+      "Test permutations of given graph:   6%|▌         | 3/50 [00:07<01:58,  2.52s/it]"
      ]
     },
     {
@@ -552,7 +552,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 4/50 [00:08<01:36,  2.10s/it]"
+      "Test permutations of given graph:   8%|▊         | 4/50 [00:09<01:47,  2.34s/it]"
      ]
     },
     {
@@ -560,7 +560,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 5/50 [00:11<01:35,  2.12s/it]"
+      "Test permutations of given graph:  10%|█         | 5/50 [00:12<01:46,  2.37s/it]"
      ]
     },
     {
@@ -568,7 +568,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 6/50 [00:13<01:37,  2.22s/it]"
+      "Test permutations of given graph:  12%|█▏        | 6/50 [00:14<01:42,  2.33s/it]"
      ]
     },
     {
@@ -576,7 +576,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 7/50 [00:15<01:32,  2.14s/it]"
+      "Test permutations of given graph:  14%|█▍        | 7/50 [00:16<01:37,  2.27s/it]"
      ]
     },
     {
@@ -584,7 +584,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 8/50 [00:17<01:25,  2.03s/it]"
+      "Test permutations of given graph:  16%|█▌        | 8/50 [00:18<01:32,  2.21s/it]"
      ]
     },
     {
@@ -592,7 +592,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 9/50 [00:19<01:22,  2.02s/it]"
+      "Test permutations of given graph:  18%|█▊        | 9/50 [00:20<01:25,  2.10s/it]"
      ]
     },
     {
@@ -600,7 +600,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 10/50 [00:21<01:19,  1.98s/it]"
+      "Test permutations of given graph:  20%|██        | 10/50 [00:22<01:25,  2.14s/it]"
      ]
     },
     {
@@ -608,7 +608,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 11/50 [00:22<01:13,  1.88s/it]"
+      "Test permutations of given graph:  22%|██▏       | 11/50 [00:24<01:21,  2.08s/it]"
      ]
     },
     {
@@ -616,7 +616,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 12/50 [00:24<01:09,  1.83s/it]"
+      "Test permutations of given graph:  24%|██▍       | 12/50 [00:26<01:18,  2.06s/it]"
      ]
     },
     {
@@ -624,7 +624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 13/50 [00:26<01:07,  1.83s/it]"
+      "Test permutations of given graph:  26%|██▌       | 13/50 [00:28<01:12,  1.96s/it]"
      ]
     },
     {
@@ -632,7 +632,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 14/50 [00:28<01:07,  1.86s/it]"
+      "Test permutations of given graph:  28%|██▊       | 14/50 [00:29<01:07,  1.87s/it]"
      ]
     },
     {
@@ -640,7 +640,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 15/50 [00:29<01:02,  1.79s/it]"
+      "Test permutations of given graph:  30%|███       | 15/50 [00:31<01:00,  1.74s/it]"
      ]
     },
     {
@@ -648,7 +648,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 16/50 [00:31<00:57,  1.69s/it]"
+      "Test permutations of given graph:  32%|███▏      | 16/50 [00:33<00:58,  1.72s/it]"
      ]
     },
     {
@@ -656,7 +656,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 17/50 [00:32<00:54,  1.65s/it]"
+      "Test permutations of given graph:  34%|███▍      | 17/50 [00:34<00:57,  1.74s/it]"
      ]
     },
     {
@@ -664,7 +664,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 18/50 [00:34<00:51,  1.61s/it]"
+      "Test permutations of given graph:  36%|███▌      | 18/50 [00:36<00:52,  1.65s/it]"
      ]
     },
     {
@@ -672,7 +672,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 19/50 [00:35<00:41,  1.33s/it]"
+      "Test permutations of given graph:  38%|███▊      | 19/50 [00:37<00:49,  1.59s/it]"
      ]
     },
     {
@@ -680,7 +680,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 20/50 [00:36<00:40,  1.35s/it]"
+      "Test permutations of given graph:  40%|████      | 20/50 [00:39<00:46,  1.56s/it]"
      ]
     },
     {
@@ -688,7 +688,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 21/50 [00:38<00:43,  1.51s/it]"
+      "Test permutations of given graph:  42%|████▏     | 21/50 [00:41<00:46,  1.62s/it]"
      ]
     },
     {
@@ -696,7 +696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 22/50 [00:40<00:43,  1.57s/it]"
+      "Test permutations of given graph:  44%|████▍     | 22/50 [00:42<00:45,  1.63s/it]"
      ]
     },
     {
@@ -704,7 +704,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 23/50 [00:41<00:41,  1.55s/it]"
+      "Test permutations of given graph:  46%|████▌     | 23/50 [00:43<00:40,  1.51s/it]"
      ]
     },
     {
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 24/50 [00:42<00:37,  1.44s/it]"
+      "Test permutations of given graph:  48%|████▊     | 24/50 [00:44<00:31,  1.21s/it]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 25/50 [00:44<00:35,  1.43s/it]"
+      "Test permutations of given graph:  50%|█████     | 25/50 [00:45<00:30,  1.23s/it]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:45<00:32,  1.35s/it]"
+      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:46<00:29,  1.24s/it]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:46<00:28,  1.24s/it]"
+      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:48<00:28,  1.22s/it]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:47<00:28,  1.30s/it]"
+      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:49<00:30,  1.40s/it]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:49<00:27,  1.29s/it]"
+      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:51<00:28,  1.34s/it]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 30/50 [00:50<00:25,  1.28s/it]"
+      "Test permutations of given graph:  60%|██████    | 30/50 [00:52<00:27,  1.36s/it]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:51<00:24,  1.28s/it]"
+      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:53<00:25,  1.32s/it]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:52<00:22,  1.27s/it]"
+      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:55<00:24,  1.37s/it]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:54<00:21,  1.26s/it]"
+      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:56<00:22,  1.32s/it]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:55<00:22,  1.39s/it]"
+      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:57<00:21,  1.36s/it]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 35/50 [00:56<00:19,  1.28s/it]"
+      "Test permutations of given graph:  70%|███████   | 35/50 [00:59<00:19,  1.30s/it]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:57<00:17,  1.25s/it]"
+      "Test permutations of given graph:  72%|███████▏  | 36/50 [01:00<00:17,  1.29s/it]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 37/50 [00:59<00:16,  1.25s/it]"
+      "Test permutations of given graph:  74%|███████▍  | 37/50 [01:01<00:15,  1.19s/it]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:00<00:15,  1.27s/it]"
+      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:02<00:14,  1.21s/it]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:01<00:12,  1.12s/it]"
+      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:03<00:13,  1.20s/it]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 40/50 [01:02<00:10,  1.02s/it]"
+      "Test permutations of given graph:  80%|████████  | 40/50 [01:04<00:11,  1.19s/it]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:03<00:09,  1.10s/it]"
+      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:06<00:10,  1.18s/it]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:04<00:08,  1.05s/it]"
+      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:07<00:09,  1.17s/it]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:05<00:07,  1.05s/it]"
+      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:08<00:07,  1.13s/it]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:06<00:06,  1.06s/it]"
+      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:09<00:06,  1.07s/it]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 45/50 [01:07<00:05,  1.08s/it]"
+      "Test permutations of given graph:  90%|█████████ | 45/50 [01:09<00:04,  1.01it/s]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:08<00:04,  1.11s/it]"
+      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:10<00:03,  1.02it/s]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:09<00:03,  1.08s/it]"
+      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:12<00:03,  1.11s/it]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:10<00:02,  1.03s/it]"
+      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:13<00:02,  1.15s/it]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:11<00:01,  1.03s/it]"
+      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:14<00:01,  1.12s/it]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:12<00:00,  1.03s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.07s/it]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:12<00:00,  1.45s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.51s/it]"
      ]
     },
     {
@@ -940,7 +940,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 800x300 with 1 Axes>"
       ]
@@ -966,82 +966,82 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node Customer DB\n",
-      "- The KL divergence between generated and observed distribution is 0.06851018535777569.\n",
+      "- The KL divergence between generated and observed distribution is 0.05633729962188637.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Shipping Cost Service\n",
-      "- The KL divergence between generated and observed distribution is 0.03806638487810255.\n",
+      "- The KL divergence between generated and observed distribution is 0.01345661046898411.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Product DB\n",
-      "- The KL divergence between generated and observed distribution is 0.0739495966691543.\n",
+      "- The KL divergence between generated and observed distribution is 0.04731204756715253.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Order DB\n",
-      "- The KL divergence between generated and observed distribution is 0.05761158379067712.\n",
+      "- The KL divergence between generated and observed distribution is 0.03163058332984496.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Auth Service\n",
-      "- The MSE is 0.01441171705596729.\n",
-      "- The NMSE is 0.5464573760061932.\n",
-      "- The R2 coefficient is 0.7013207387303906.\n",
-      "- The normalized CRPS is 0.3025250310394936.\n",
+      "- The MSE is 0.014408940211606136.\n",
+      "- The NMSE is 0.5467885527285155.\n",
+      "- The R2 coefficient is 0.7008264081937587.\n",
+      "- The normalized CRPS is 0.30319879712127784.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Caching Service\n",
-      "- The MSE is 0.023177407671490263.\n",
-      "- The NMSE is 0.8450133333760175.\n",
-      "- The R2 coefficient is 0.2854163022252113.\n",
-      "- The normalized CRPS is 0.464166240739747.\n",
+      "- The MSE is 0.023142169283019896.\n",
+      "- The NMSE is 0.8424237004319363.\n",
+      "- The R2 coefficient is 0.2902352340841851.\n",
+      "- The normalized CRPS is 0.4626635608354152.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node Order Service\n",
-      "- The MSE is 0.0143980612006466.\n",
-      "- The NMSE is 0.5485423887933651.\n",
-      "- The R2 coefficient is 0.6989222187579888.\n",
-      "- The normalized CRPS is 0.3033900342657954.\n",
+      "- The MSE is 0.014406030212396392.\n",
+      "- The NMSE is 0.5492949148510787.\n",
+      "- The R2 coefficient is 0.6979095952300487.\n",
+      "- The normalized CRPS is 0.30391606590620085.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Product Service\n",
-      "- The MSE is 0.02043925270114256.\n",
-      "- The NMSE is 0.6494394163446002.\n",
-      "- The R2 coefficient is 0.5781324594465047.\n",
-      "- The normalized CRPS is 0.36367607924553963.\n",
+      "- The MSE is 0.020462217678676974.\n",
+      "- The NMSE is 0.6499756128867937.\n",
+      "- The R2 coefficient is 0.5773982772794385.\n",
+      "- The normalized CRPS is 0.3635695389665327.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node API\n",
-      "- The MSE is 0.014485507834794353.\n",
-      "- The NMSE is 0.21002966758855104.\n",
-      "- The R2 coefficient is 0.9558356948931678.\n",
-      "- The normalized CRPS is 0.11595431985006795.\n",
+      "- The MSE is 0.014484769133253233.\n",
+      "- The NMSE is 0.20977705037425515.\n",
+      "- The R2 coefficient is 0.9559816510988256.\n",
+      "- The normalized CRPS is 0.11572746053441199.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node www\n",
-      "- The MSE is 0.011041595331444064.\n",
-      "- The NMSE is 0.13763913442477363.\n",
-      "- The R2 coefficient is 0.9810370890224425.\n",
-      "- The normalized CRPS is 0.07783774485363718.\n",
+      "- The MSE is 0.011040241699128718.\n",
+      "- The NMSE is 0.13762343752718348.\n",
+      "- The R2 coefficient is 0.981050435883116.\n",
+      "- The normalized CRPS is 0.07801098634259836.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node Website\n",
-      "- The MSE is 0.02077984255867196.\n",
-      "- The NMSE is 0.1852622726654006.\n",
-      "- The R2 coefficient is 0.9656512057924049.\n",
-      "- The normalized CRPS is 0.10196150976628031.\n",
+      "- The MSE is 0.02077833539868886.\n",
+      "- The NMSE is 0.1850812826494141.\n",
+      "- The R2 coefficient is 0.9657338637126042.\n",
+      "- The normalized CRPS is 0.10221678960177454.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
       "\n",
-      "--- The model assumption for node www is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node www is not rejected with a p-value of 0.3838477086692199 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Website is not rejected with a p-value of 0.1451092778434615 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Website is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Auth Service is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Auth Service is not rejected with a p-value of 0.4465020720536922 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node API is not rejected with a p-value of 0.742191149095118 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node API is not rejected with a p-value of 0.7556572625812968 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "--- The model assumption for node Product Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n",
@@ -1056,7 +1056,7 @@
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.10675450625416824\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.1063974083835325\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -1065,7 +1065,7 @@
       "+-------------------------------------------------------------------------------------------------------+\n",
       "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |\n",
       "| equivalence class of the given DAG (p-value: 0.00).                                                   |\n",
-      "| The given DAG violates 5/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
+      "| The given DAG violates 4/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
       "| Based on the provided significance level (0.2) and because the DAG is informative,                    |\n",
       "| we do not reject the DAG.                                                                             |\n",
       "+-------------------------------------------------------------------------------------------------------+\n",
@@ -1113,10 +1113,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.693116Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.692666Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.704079Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.703611Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.316127Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.315664Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.329926Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.329237Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1207,10 +1207,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.705942Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.705595Z",
-     "iopub.status.idle": "2024-10-25T15:15:24.754291Z",
-     "shell.execute_reply": "2024-10-25T15:15:24.753730Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.332110Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.331883Z",
+     "iopub.status.idle": "2024-10-25T15:17:00.386998Z",
+     "shell.execute_reply": "2024-10-25T15:17:00.386438Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1258,10 +1258,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:15:24.756436Z",
-     "iopub.status.busy": "2024-10-25T15:15:24.756062Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.403122Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.402534Z"
+     "iopub.execute_input": "2024-10-25T15:17:00.389116Z",
+     "iopub.status.busy": "2024-10-25T15:17:00.388740Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.274521Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.273763Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1301,10 +1301,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.405328Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.405129Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.560911Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.560362Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.277280Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.276806Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.695234Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.694487Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1313,7 +1313,7 @@
    "outputs": [
     {
      "data": {
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",
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bNtmRiIiIyEAYVVGTnJyM2rVrY/HixbKjEBERkYHRe5XuBw8eYMyYMQgNDcXDhw/xz5unMjMziyzcP3Xo0AEdOnQott9PRERExkvvoqZ///64desWJk6ciAoVKkClUhVHriKRlpaGtLQ0zbZarZaYhoiIiIqT3kVNREQEwsPDUadOnWKIU7RmzZqFKVOmyI5BREREJUDvMTWurq46l5wM1YQJE5CQkKB53L59W3YkIiIiKiZ6FzXBwcEICgrCjRs3iiFO0bK0tISDg4PWg4iIiJRJ78tP77zzDlJSUlCtWjXY2NjA3Nxca//Tp0+LLBwRkZKkpGfB59s4AMCVz8rDxsKobkAlMnh6FzXBwcHFEKNgkpKSEB0drdmOjY3FuXPnUKpUKVSpUkVaLiIiIpJP76KmX79+xZGjQE6dOoWWLVtqtkePHg0gO9Pq1aslpSIiIiJDoHdRA2TPRbNt2zZcuXIFAPDaa6+ha9euMDU1LdJw/+Tv7280g5SJiIioZOld1ERHR6Njx464e/cuatSoASD71mlXV1fs2rUL1apVK/KQRERERPnRe5RaYGAgqlWrhtu3b+PMmTM4c+YMbt26BXd3dwQGBhZHRiIiIqJ86d1TExYWhmPHjqFUqVKattKlS2P27Nlo2rRpkYYjIiIiKii9ixpLS0skJibqtCclJcHCwqJIQhERGSyHPoX/WXMLYNzs7OcV+gMZ6YX7PeqNhc9ApGB6X37q3LkzBg8ejOPHj0MIASEEjh07hqFDh6Jr167FkZGIiIgoX3oXNQsXLkS1atXQuHFjWFlZwcrKCk2bNoWnpye+++674shIRERElC+9Lz85OTlh+/btuHbtGqKiogAAPj4+8PT0LPJwRERERAVVqHlqAMDLywteXl5FmYWIiIio0ApU1IwePRrTpk2Dra2tZhbf3CxYsKBIghERERHpo0BFzdmzZ5GRkaF5TkRERGRoClTUHDhwIMfnRERERIZC77ufBgwYkOM8NcnJyRgwYECRhCIiIiLSl95FzZo1a5CamqrTnpqairVr1xZJKKJ/IyU9C25z7sFtzj2kpGfJjkNERCWkwHc/qdVqzWR7iYmJsLKy0uzLzMzEH3/8gbJlyxZLSCIiIqL8FLiocXJygkqlgkqlQvXq1XX2q1QqTJkypUjDERERERVUgYuaAwcOQAiBgIAAbNmyRWtBSwsLC7i5uaFixYrFEpKISAlsMtJxc0be02IQUeEVuKhp0aIFACA2NhZVqlSBSqUqtlBERERE+tJ7RuGbN2/i5s2bue5v3rz5vwpEREREVBh6FzX+/v46ba/22mRmZv6rQERERESFoXdR8+zZM63tjIwMnD17FhMnTsSMGTOKLBj9xzn0KfzPmlsA42ZnP6/QH8hIL9zvUW8sfAYiIipxehc1jo6OOm1t2rSBhYUFRo8ejdOnTxdJMCIiIiJ96D35Xm7KlSuHq1evFtWvIyIiItKL3j0158+f19oWQuD+/fuYPXs26tSpU1S5iIiIiPSid1FTp04dqFQqCCG02hs1aoSVK1cWWTAiIiIifehd1MTGxmptm5iYwMXFRWvZBCKZOMEZEdF/k95FjZubW3HkICIiIvpXCjVQODQ0FJ07d0a1atVQrVo1dO7cGfv27SvqbEREREQFpndR87///Q/t27eHvb09Ro0ahVGjRsHBwQEdO3bE4sWLiyMjEUmSkp4Ftzn34DbnHlLSs2THISLKk96Xn2bOnIlvv/0WI0eO1LQFBgaiadOmmDlzJkaMGFGkAYmIiIgKQu+emvj4eLRv316nvW3btkhISCiSUERERET60ruo6dq1K3777Ted9u3bt6Nz585FEoqIiIhIXwW6/LRw4ULNc19fX8yYMQMHDx5E48aNAQDHjh3D4cOH8fnnnxdPSiIiIqJ8FKio+fbbb7W2nZ2dcfnyZVy+fFnT5uTkhJUrV+Krr74q2oT/sHjxYsybNw9xcXGoXbs2Fi1ahAYNGhTrf5OIiIgMX4GKmn9OuCfLpk2bMHr0aCxZsgQNGzZEcHAw2rVrh6tXr6Js2bKy4xEREZFEet/9JNOCBQswaNAgfPTRRwCAJUuWYNeuXVi5ciWCgoIkpyMyUA59Cv+z5hbAuNnZzyv0BzLSC/+71BsL/7NERAVQoKJm9OjRmDZtGmxtbTF6dN7Tzy9YsKBIgv1Teno6Tp8+jQkTJmjaTExM0Lp1axw9ejTHn0lLS0NaWppmW61WF0s2IiKinKSkZ8Hn2zgAwJXPysPGolBz3lIBFaioOXv2LDIyMgAAZ86cgUqlyvF1ubUXhcePHyMzMxPlypXTai9XrhyioqJy/JlZs2ZhypQpxZZJy7/5NlyU8vs2bAg5C/KN3Vi+1RvD8fw3xzI9C/j/D2TcXw0U5weysbznzFl0DOH8AYr3c7OoejsL8n4awvGU/O+uQEXNgQMHNM8PHjxYXFmK3IQJE7R6ltRqNVxdXSUmIiIiouKi19eujIwMmJmZ4eLFi8WVJ1dlypSBqakpHjx4oNX+4MEDlC9fPsefsbS0hIODg9aDCi/F3AJuXy6A25cLkGJuITsOERGRFr0GCpubm6NKlSrIzMwsrjy5srCwQL169RAaGoru3bsDALKyshAaGqq1ZAPlw1guRShcirkFfP6/S/rK3CDY/JsBuEREBKAQdz99+eWX+OKLL7Bu3TqUKlWqODLlavTo0ejXrx/q16+PBg0aIDg4GMnJyZq7oYiIiIocvwwaDb2Lmu+//x7R0dGoWLEi3NzcYGtrq7X/zJkzRRbun9555x08evQIkyZNQlxcHOrUqYPdu3frDB4mIiKi/x69i5pu3boV611O+Rk5ciQvNxEREZEOvYuar7/+uhhikDGwsTDBzfEVZcegEsT3nIiMid4X9zw8PPDkyROd9vj4eHh4eBRJKCIiIio5Srm7Ve+emhs3buR491NaWhru3LlTJKGIjEZhBxBy8CARUZErcFHz+++/a56HhITA0dFRs52ZmYnQ0FC4u7sXbToiIiIjxku4JavARc3LuWEAoF+/flr7zM3NUbVqVXzzzTdFFoyIiCg3nOuJclLgoiYrKwsA4O7ujpMnT6JMmTLFFoqIiIhIX3pfyJ8yZQrs7e112tPT07F27doiCUWkdC+7pG+Or8hVe4mIiojen6YfffQREhISdNoTExM5sy8RERFJo3dRI4TIcfK9O3fuaA0eJiIiIipJBR5TU7duXahUKqhUKrRq1QpmZn//aGZmJmJjY9G+fftiCUlERESUH73vfjp37hzatWsHOzs7zT4LCwtUrVoVvXr1KvKARESkUFwosmjxeBa8qJk8eTIAoGrVqnjnnXdgZWWl85qLFy+iZs2aRZeOiIiIqID0LsX69eunVdAkJiZi2bJlaNCgAWrXrl2k4YiIiIgKqtD9S4cOHUK/fv1QoUIFzJ8/HwEBATh27FhRZiMiIiIqML3WfoqLi8Pq1auxYsUKqNVq9O7dG2lpadi2bRt8fX2LKyMREZEWLj9AOSlwT02XLl1Qo0YNnD9/HsHBwbh37x4WLVpUnNmIiIioBChlQtAC99T8+eefCAwMxLBhw+Dl5VWcmYiIiIj0VuByLCIiAomJiahXrx4aNmyI77//Ho8fPy7ObEREREQFVuCiplGjRvjxxx9x//59DBkyBBs3bkTFihWRlZWFvXv3IjExsThzEhEREeVJJYQQhf3hq1evYsWKFVi3bh3i4+PRpk0b/P7770WZr0ip1Wo4OjoiISEBDg4ORfvLHfoU7e8rrH8z+RIREZER+1ejgWrUqIG5c+fizp072LBhQ1FlIiIiItLbv+qpMTbsqSEiIlIu471vi4iIiOgVLGqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGqIiIhIEYymqJkxYwaaNGkCGxsbODk5yY5DREREBsZoipr09HS8/fbbGDZsmOwoREREZIDMZAcoqClTpgAAVq9eLTcIERERGSSj6akhIiIiyovR9NQURlpaGtLS0jTbarVaYhoiIiIqTlJ7aoKCgqBSqfJ8REVFFfr3z5o1C46OjpqHq6trEaYnIiIiQ6ISQghZ//FHjx7hyZMneb7Gw8MDFhYWmu3Vq1fj008/RXx8fL6/P6eeGldXVyQkJMDBwaHQuXPk0Kdof19hqTfKTkBERCSF1MtPLi4ucHFxKbbfb2lpCUtLy2L7/URERGQ4jGZMza1bt/D06VPcunULmZmZOHfuHADA09MTdnZ2csMRERGRdEZT1EyaNAlr1qzRbNetWxcAcODAAfj7+0tKRURERIZC6piakqZWq+Ho6MgxNURERArEeWqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQIRlHU3LhxAwMHDoS7uzusra1RrVo1TJ48Genp6bKjERERkYEwkx2gIKKiopCVlYWlS5fC09MTFy9exKBBg5CcnIz58+fLjkdEREQGQCWEELJDFMa8efPwww8/4Pr16wX+GbVaDUdHRyQkJMDBwaFoAzn0KdrfV1jqjbITEBERSWEUl59ykpCQgFKlSsmOQURERAbCKC4//VN0dDQWLVqU76WntLQ0pKWlabbVanVxRyMiIiJJpPbUBAUFQaVS5fmIiorS+pm7d++iffv2ePvttzFo0KA8f/+sWbPg6Oioebi6uhbn/x0iIiKSSOqYmkePHuHJkyd5vsbDwwMWFhYAgHv37sHf3x+NGjXC6tWrYWKSd02WU0+Nq6srx9QQEREpkNTLTy4uLnBxcSnQa+/evYuWLVuiXr16WLVqVb4FDQBYWlrC0tLy38YkIiIiI2AUY2ru3r0Lf39/uLm5Yf78+Xj06JFmX/ny5SUmIyIiIkNhFEXN3r17ER0djejoaFSuXFlrn5HekU5ERERFzChu6e7fvz+EEDk+iIiIiAAjKWqIiIiI8sOihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGoMQIq5Bdy+XAC3LxcgxdxCdhwiIiKjxKKGiIiIFIFFDRERESkCixoiIiJSBBY1REREpAgsaoiIiEgRWNQQERGRIrCoISIiIkVgUUNERESKwKKGiIiIFIFFDRERESkCixoiIiJSBBY1REREpAhmsgMohnpj4X82PQv4Ni77+f3VgAVrTSIiIn3xrycREREpAosaIiIiUgQWNURERKQILGqIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCihoiIiBSBRQ0REREpAosaIiIiUgQWNURERKQILGqIiIhIEVjUEBERkSIYTVHTtWtXVKlSBVZWVqhQoQI++OAD3Lt3T3YsIiIiMhBGU9S0bNkSv/zyC65evYotW7YgJiYGb731luxYREREZCDMZAcoqM8++0zz3M3NDUFBQejevTsyMjJgbm4uMRkREREZAqPpqXnV06dP8fPPP6NJkyYsaIiIiAiAkRU148ePh62tLUqXLo1bt25h+/bteb4+LS0NarVa60FERETKJLWoCQoKgkqlyvMRFRWlef3YsWNx9uxZ7NmzB6ampvjwww8hhMj198+aNQuOjo6ah6ura0n83yIiIiIJVCKvqqCYPXr0CE+ePMnzNR4eHrCwsNBpv3PnDlxdXXHkyBE0btw4x59NS0tDWlqaZlutVsPV1RUJCQlwcHD4d+GLUEp6Fny+jQMAXPmsPGwsjKoDjYiIyCBIHSjs4uICFxeXQv1sVlYWAGgVLf9kaWkJS0vLQv1+IiIiMi5GcffT8ePHcfLkSbz55ptwdnZGTEwMJk6ciGrVquXaS0NERET/LUZxncPGxgZbt25Fq1atUKNGDQwcOBB+fn4ICwtjTwwREREBMJKemlq1amH//v2yYxAREZEBkzpQuKSp1Wo4Ojoa3EBhIiIi+veM4vITERERUX5Y1BAREZEisKghIiIiRWBRQ0RERIrAooaIiIgUgUUNERERKQKLGiIiIlIEFjVERESkCCxqiIiISBFY1BAREZEisKghIiIiRWBRQ0RERIrAooaIiIgUgUUNERERKQKLGiIiIlIEM9kBSpIQAgCgVqslJyEiIiJ92dvbQ6VS5br/P1XUJCYmAgBcXV0lJyEiIiJ9JSQkwMHBIdf9KvGy++I/ICsrC/fu3cu30pNBrVbD1dUVt2/fzvMNk405i44xZASYs6gZQ05jyAgwZ1EzhpzsqXmFiYkJKleuLDtGnhwcHAz2H9OrmLPoGENGgDmLmjHkNIaMAHMWNWPJmRMOFCYiIiJFYFFDREREisCixkBYWlpi8uTJsLS0lB0lT8xZdIwhI8CcRc0YchpDRoA5i5qx5MzLf2qgMBERESkXe2qIiIhIEVjUEBERkSKwqCEiIiJFYFFDREREisCixkCkp6cjKSlJdowchYeH4/3330fjxo1x9+5dAMC6desQEREhOdnfnjx5onl++/ZtTJo0CWPHjkV4eLjEVHm7fv06Ll26hKysLNlRdLx48QL79u3D0qVLNcuL3Lt3zyD/jaanp+Pq1at48eKF7Ci5MobjaQznOVG+BJW4lStXipEjR4qffvpJCCFEUFCQsLCwECYmJqJ169bi8ePHkhP+bfPmzcLa2lp8/PHHwtLSUsTExAghhFi0aJHo0KGD5HRCnD9/Xri5uQkTExNRo0YNcfbsWVGuXDlhZ2cnHBwchKmpqfjtt9+kZkxPTxeTJk0SnTt3FtOnTxcvXrwQffr0ESYmJsLExET4+PiI2NhYqRlfdePGDeHt7S1sbGyEqamp5j0PDAwUQ4YMkZzub8nJyWLAgAHC1NRUK+fIkSPFrFmzJKf7mzEcT0M/z43NihUrxPXr12XHKJDo6Gjx5Zdfij59+ogHDx4IIYT4448/xMWLFyUnKxwWNSVs+vTpwtraWrRu3VqUKlVKDB06VJQvX17Mnj1bzJ07V1SuXFkMHTpUdkyNOnXqiDVr1gghhLCzs9N82J05c0aUK1dOZjQhhBDt27cXnTt3FhEREWLIkCGiUqVKYsCAASIzM1NkZmaK4cOHi4YNG0rNOHr0aOHi4iI+/vhj4eHhIbp27Spq1KghNm7cKH755RdRq1Yt8d5770nN+Kpu3bqJ999/X6SlpWm95wcOHBCenp6S0/0tMDBQ1KtXT4SHhwtbW1tNzm3btok6depITvc3Yziehn6ef/bZZwV6GApPT09hYmIiXF1dxfvvvy9+/PFHce3aNdmxdBw8eFDz98jCwkLzvs+aNUv06tVLcrrCYVFTwjw9PcX69euFEEKcPHlSmJiYiM2bN2v2//HHH6JKlSqy4umwtrbW9CK8+mEXExMjLC0tJSbLVrp0aREZGSmEECIxMVGoVCpx6tQpzf4rV64IR0dHSemyValSRezatUsIIcTVq1eFSqUSf/zxh2b/wYMHRaVKlWTF01GqVCkRFRUlhNB+z2NjY4W1tbXMaFqqVKkijh49KoTQznnt2jVhb28vM5oWYziehn6e+/v75/to2bKl7Jha7ty5I3766ScxePBgUaNGDWFiYiIqVaok+vbtKzuaRqNGjcQ333wjhNB+348fP25Qn0n6+E8taGkIbt26hTfffBMAUL9+fZiZmaFmzZqa/X5+frh//76seDrKly+P6OhoVK1aVas9IiICHh4eckK94unTpyhfvjwAwM7ODra2tnB2dtbsd3Z21oxhkOXevXuoXbs2AKB69eqwtLSEp6enZn/16tURFxcnK56OrKwsZGZm6rTfuXMH9vb2EhLl7NGjRyhbtqxOe3Jycp6r+JY0Yziehn6eHzhwQHYEvVWqVAl9+/ZFjx49EB4ejg0bNuDnn3/Gxo0b8dNPP8mOBwC4cOEC1q9fr9NetmxZPH78WEKif48DhUtYRkaG1hTUFhYWMDc312ybmZnl+AEoy6BBgzBq1CgcP34cKpUK9+7dw88//4wxY8Zg2LBhsuMBgM4fMEP6gwYAmZmZOu+xqampZtvExATCgCb2btu2LYKDgzXbKpUKSUlJmDx5Mjp27Cgv2D/Ur18fu3bt0my/fN+XL1+Oxo0by4qlwxiOpzGc52q1Gnv37sWuXbvw6NEj2XHytGfPHnzxxRdo0qQJSpcujQkTJsDZ2RmbN282qOxOTk45fok+e/YsKlWqJCHRv8eeGgkuX76s+WYuhEBUVJTmLghDq46DgoKQlZWFVq1aISUlBc2bN4elpSXGjBmDTz75RHY8AED//v01heLz588xdOhQ2NraAgDS0tJkRtMICQmBo6MjgOxv7qGhobh48SIAID4+XmIyXd988w3atWsHX19fPH/+HO+99x6uXbuGMmXKYMOGDbLjacycORMdOnTA5cuX8eLFC3z33Xe4fPkyjhw5grCwMNnxNIzheBr6eX7u3Dl07NhR87lpb2+PX375Be3atZOcLGft27eHi4sLPv/8c/zxxx9wcnKSHSlHffr0wfjx4/Hrr79CpVIhKysLhw8fxpgxY/Dhhx/KjlcoXPuphJmYmEClUuX4zfxlu0qlMqjeGiD7ttno6GgkJSXB19cXdnZ2siMBAD766KMCvW7VqlXFnCR3Jib5d4ga2nv+4sULbNq0CZGRkUhKSsLrr7+Ovn37wtraWnY0LTExMZg9e7ZWzvHjx6NWrVqyo2kxluNpqOd5u3btkJSUhPnz58PKygrTpk3DhQsXcO3aNdnRchQcHIxDhw7h0KFDsLS0RIsWLeDv7w9/f39Ur15ddjyN9PR0jBgxAqtXr0ZmZqbmSsF7772H1atXa/UoGwsWNSXs5s2bBXqdm5tbMScpmISEBGRmZqJUqVJa7U+fPoWZmRkcHBwkJSOiomLo53mZMmWwZ88evP766wCyezdLlSqF+Ph46dnyc+HCBYSFhWH//v3YuXMnypYtizt37siOpeX27du4cOECkpKSULduXXh5ecmOVGgcU1PC3NzcCvQwFH369MHGjRt12n/55Rf06dNHQqLcCSHw+PFjrYn4SH+zZs3CypUrddpXrlyJOXPmSEiUsz/++AMhISE67SEhIfjzzz8lJMqZMRxPQz/Pnz59isqVK2u2nZycYGtra9DnuhACZ86cwd69exESEoIDBw4gKysLLi4usqNpTJ06FSkpKXB1dUXHjh3Ru3dveHl5ITU1FVOnTpUdr1DYUyPJtWvXsH37dty4cQMqlQru7u7o3r27Qdxp8KpSpUrh8OHD8PHx0WqPiopC06ZNDeJDJS4uDuPGjcPvv/+uudPJwcEBPXr0wKxZs1CuXDnJCbPt378fW7du1XrP33rrLTRv3lx2NC1Vq1bF+vXr0aRJE63248ePo0+fPoiNjZWUTJufnx9mz56tM9h29+7dGD9+PCIjIyUl02YMx9PQz3MTExPs379fqyepSZMm+OWXX7SKHT8/PxnxdHTp0gWHDx+GWq1G7dq14e/vjxYtWqB58+YGNb7G1NQU9+/f17mL8MmTJyhbtqxBXRIvKA4UlmDWrFmYNGkSsrKyULZsWQgh8OjRIwQFBWHmzJkYM2aM7IgaaWlpOU4/n5GRgdTUVAmJtKnVajRp0gRJSUn46KOP4O3tDSEELl++jA0bNiAiIgJnzpyRPjZg6NChWLZsGZydnVG9enUIIXDkyBEsXrwYw4cPx6JFi6Tme1VcXBwqVKig0+7i4mJQ0w1cu3YNvr6+Ou3e3t6Ijo6WkChnxnA8Df08B4BWrVrpjEXs3Lmz5rkhjUvz9vbGkCFD0KxZM80NAobo5RjOf4qMjNS5FGk0SnxmnP+4/fv3CxMTEzF58mTx9OlTTfuTJ0/ExIkThampqQgLC5OYUJu/v78YOXKkTvvw4cPFm2++KSGRtqlTpwpPT0/x8OFDnX0PHjwQnp6eYsaMGRKS/W3r1q3CwsJCrFq1SmRlZWnaMzMzxYoVK4SFhYXYvn27xITaPD09xbp163Ta165dK9zd3SUkylm5cuVEaGioTvvevXuFi4uLhEQ5M4bjaejn+Y0bN/J9XLhwQXZMjdTUVNkR8uTk5CScnZ2FiYmJ5vnLh4ODgzAxMRHDhw+XHbNQWNSUsN69e4vBgwfnun/QoEGiT58+JZgobxEREcLKyko0a9ZMfP311+Lrr78WzZo1E1ZWVuLQoUOy44mGDRuKlStX5rp/xYoVolGjRiWYSFeXLl1EUFBQrvvHjRsnunbtWoKJ8jZnzhxRunRpsXLlSs0fjBUrVojSpUuLmTNnyo6nMXjwYFGrVi0RHR2tabt27Zrw8/MTAwcOlJhMmzEcT0M/z3OjVqvF0qVLRYMGDYSJiYnsOBqWlpaiWbNm4quvvhL79u0TKSkpsiNpWb16tVi1apVQqVTiu+++E6tXr9Y81q9fL44cOSI7YqGxqClhVatWFeHh4bnuP3TokKhatWoJJsrf2bNnxXvvvSd8fX1FvXr1xEcffST++usv2bGEEEI4OztrpqDPyZUrV4Szs3MJJtJVqVIlcfz48Vz3Hzt2zKCmJM/KyhLjxo0TVlZWmkU3bWxsxJQpU2RH0xIfHy8aNWokzMzMRNWqVUXVqlWFmZmZaNmypXj27JnseBrGcjwN+Tz/p7CwMPHhhx8KW1tb4eXlJcaPHy9OnDghO5ZGeHi4mDFjhmjTpo2wtbUVlpaWomnTpuKLL74Qe/bskR1P4+DBgyI9PV12jCLFgcIlzMbGBn/99ZfW4LZX3blzRzP6nPJnZmaGu3fv5joYOC4uDpUrV85xvEBJsbKywvXr11GxYsUc99+9exeenp4G954nJSXhypUrsLa2hpeXl9ZM2IZCCIG9e/ciMjIS1tbW8PPzM7iB1y8Zw/E0ZHFxcVi9ejVWrFgBtVqN3r17Y8mSJYiMjMxxbJWhePHiBU6ePImlS5fi559/znXZjJKiVqs1t8Gr1eo8X2vot8vnhAOFS9jz589hYWGR635zc3Okp6eXYCJdxvSPXgiR5+R2uU10WJLS09O1lkn4JzMzM+nveU7s7OzwxhtvyI6RJ5VKhbZt26Jt27ayo+TL0I6nMZ3nXbp0waFDh9CpUycEBwejffv2MDU1xZIlS6Tmystff/2FgwcPah5paWno3Lkz/P39peZydnbW3PHk5OSU40BhYaCTwBYEixoJli9fnuvdOLIXXwSM6x+9EALVq1fPdb0n2QXNSxMnToSNjU2O+1JSUko4ja6ePXti9erVcHBwQM+ePfN87datW0sola6FCxdi8ODBsLKywsKFC/N8bWBgYAml0mUMx9OYzvM///wTgYGBGDZsmFFMDFepUiWkpqZqZhEeP348/Pz8DGJduldvjTfGhULzw6KmhFWpUgU//vhjvq+R6dV/9Pv37zeIEzE3Mpc/KKjmzZvj6tWr+b5GJkdHR837bMi3oH777bfo27cvrKys8O233+b6OpVKJbWoMYbjaUzneUREBFasWIF69erBx8cHH3zwgUFMCpgbFxcXREVFIS4uDnFxcXjw4AFSU1Nz/WJTklq0aJHjc6XgmBoiIjIKycnJ2LRpE1auXIkTJ04gMzMTCxYswIABA2Bvby87npb4+HgcOnQIYWFhCAsLw+XLl1GnTh20bNkSM2bMkB0PQPZElXZ2dnjzzTcBAIsXL8aPP/4IX19fLF68GM7OzpITFoKc8clkLDw9PcXkyZMN9i4IKnrTpk0T169flx0jX3ndRWhIjOF4GuN5HhUVJcaOHSvKly8vrKysRJcuXWRHytHjx4/F5s2bxQcffCDMzMwM6tbzmjVril27dgkhhDh//rywsLAQEyZMEI0aNRL9+/eXnK5wuPYT5Wn48OHYtWsXvL298cYbb+C7775DXFyc7FhUjH799Vd4enqiSZMm+N///ofHjx/LjpSjgIAAuLu744svvsClS5dkx8mVMRxPYzzPa9Sogblz5+LOnTvYsGGD7Dhatm7disDAQPj5+aFcuXIYNmwYkpKS8M033+DMmTOy42nExsZq7hzbsmULunTpgpkzZ2Lx4sUGtX6aXmRXVWQcrl69KiZNmiS8vLyEmZmZaNOmjVizZo3sWFRMLl68KCZMmCDc3d2Fubm56Nixo/j5559FcnKy7Ggajx49EosWLRJNmjQRKpVK1K5dW8ydO1fcvn1bdjQdxnA8heB5XlRcXFzEW2+9JRYtWiTOnz8vO06unJ2dxaVLl4QQQjRt2lQsXbpUCCFEbGyssLa2lhmt0DimhvR27NgxDBs2DOfPn5d+VwQVv8OHD2P9+vX49ddf8fz583xv/5UhNjYW69evx4YNGxAVFYXmzZtj//79smPlyBiOJ8Dz/N94//33ERAQgBYtWqBatWqy4+Sqa9euSE9PR9OmTTFt2jTExsaiUqVK2LNnD0aOHIm//vpLdkS98fKTBC9evMDatWvx4MED2VH0cuLECXz66afo0aMH/vrrL7z99tuyI2lMnTo1x1ujU1NTMXXqVAmJtL148QJTp07FnTt3ZEfRm62tLaytrWFhYYGMjAzZcXLk7u6OoKAgzJ49G7Vq1UJYWJjsSLky9ONpyOe5sbC2tsbs2bNRvXp1uLq64v3338fy5ctx7do12dG0fP/99zAzM8PmzZvxww8/oFKlSgCyb6Fv37695HSFJLur6L/K2tpa3LhxQ3aMfP2zO7pt27ZizZo1IjExUXY0LSYmJuLBgwc67Y8fPzaYgXl2dnYiNjZWdowCuX79upg+fbrw9fUVpqamIiAgQCxfvlzEx8fLjqYjIiJCDBs2TLi4uAh7e3vx/vvviz///FN2LC2GfjyN5Tw3Nnfu3BHr168XQ4YMEd7e3sLExMSglkRRIs5TI0mDBg1w7tw5uLm5yY6Sp5cDB0eMGIE+ffrkuhyBbOL/Jwn7p8jISM1cHLIFBAQgLCwMVatWlR0lT40aNcLJkyfh5+eHjz76CO+++67mG5whCQoKwqZNm3Dv3j20adMG3333Hbp162YQc4G8yhiOp7Gc58bG2dkZpUuXhrOzM5ycnGBmZgYXFxfZsbRkZmbit99+w5UrVwAAPj4+6N69O8zMjLM8MM7UCjB8+HCMHj0at2/fRr169WBra6u138/PT1Kyv2VmZmLp0qV46623DHa+AmdnZ6hUKqhUKp2ZhTMzM5GUlIShQ4dKTPi3Dh06ICgoCBcuXMjxPe/ataukZNpatWqFlStXGvR6OgAQHh6OsWPHonfv3ihTpozsOLky9ONpDOe5sfniiy9w8OBBnD17Fj4+PmjRogWCgoLQvHlzgzrGly5dQpcuXfDgwQPUqFEDADBnzhy4uLhgx44dqFmzpuSE+uNAYUlyWq/o5TpFhjAt+UtWVla4cuUK3N3dZUfJ0Zo1ayCEwIABAxAcHKw1e6uFhQWqVq2Kxo0bS0z4t/zWqDKE9zwjIwPe3t7YuXMnfHx8ZMfJVUZGBoYMGYKJEyca7L9NwHiOp6Gf58bGxMQELi4u+Oyzz9CzZ09Ur15ddqQcNW7cGC4uLlizZo2m2Hr27Bn69++PR48e4ciRI5IT6o89NZLExsbKjlAgNWvWxPXr1w32w65fv34AsgeKNm3a1KC7TLOysmRHyJe5uTmeP38uO0a+zM3NsWXLFkycOFF2lDwZy/E09PPc2Jw9exZhYWE4ePAgvvnmG1hYWKBFixaataAMpcg5d+4cTp06pdV75OzsjBkzZhjU4qv64N1Pkri5ueX5MBTTp0/HmDFjsHPnTty/fx9qtVrrYSiSk5MRGhqq0x4SEmKQk0gZ8h+6ESNGYM6cOXjx4oXsKHnq3r07tm3bJjtGvozheBrLeW4sateujcDAQGzduhWPHj3CH3/8AQsLC4wYMcKgeuyqV6+e4124Dx8+hKenp4RE/x4vP0m0bt06LFmyBLGxsTh69Cjc3NwQHBwMd3d3dOvWTXY8ANqXTF4dr2Jol8n8/Pwwe/ZsdOzYUat99+7dGD9+PCIjIyUl+1tmZiZmzpyJJUuW4MGDB/jrr7/g4eGBiRMnomrVqhg4cKDsiACAHj16IDQ0FHZ2dqhVq5bO2B+Zq3S/avr06fjmm2/QqlWrHMcoyVzQ8lXGcDyN5Tw3FkIInD17FgcPHsTBgwcREREBtVoNPz8/tGjRIs/FWIvbq0VqREQExo0bh6+//hqNGjUCkD0/0dSpU3P8PDUGhttXr3A//PADJk2ahE8//RQzZszQfGg4OTkhODjYYIoaY1ma/tq1azkOxPT29kZ0dLSERLpmzJiBNWvWYO7cuRg0aJCmvWbNmggODjaYosbJyQm9evWSHSNfK1asgJOTE06fPo3Tp09r7ZO9SverjOF4Gst5bixKlSqFpKQk1K5dGy1atMCgQYPQrFkzODk5yY4GJycnncK1d+/emraX/RxdunQxymKWPTWS+Pr6YubMmejevTvs7e0RGRkJDw8PXLx4Ef7+/ga5PowhK1++PNavX4+AgACt9n379uG9997Dw4cPJSX7m6enJ5YuXYpWrVppvedRUVFo3Lgxnj17JjsiERWBXbt2oVmzZnBwcJAdRYc+E1O2aNGiGJMUD/bUSBIbG4u6devqtFtaWiI5OVlCotyFh4dj6dKluH79On799VdUqlQJ69atg7u7u2bJetm6deuGTz/9FL/99ptmWvLo6Gh8/vnnBnOr9N27d3O8Tp2VlWVwM8u+ePECBw8eRExMDN577z3Y29vj3r17cHBwgJ2dnex4WtLT0xEbG4tq1aoZ7EBxYziexnCeG4tOnTrJjpArYyxU9MGBwpK4u7vj3LlzOu27d+82qIFkW7ZsQbt27WBtbY0zZ84gLS0NAJCQkICZM2dKTve3uXPnwtbWFt7e3nB3d4e7uzt8fHxQunRpzJ8/X3Y8ANm9c+Hh4TrtmzdvzrHAleXmzZuoVasWunXrhhEjRuDRo0cAsuevGDNmjOR0f0tJScHAgQNhY2OD1157Dbdu3QIAfPLJJ5g9e7bkdH8zhuNpLOc5Fb3w8HC8//77aNKkCe7evQsge7xnRESE5GSFJGMaYxLixx9/FJUqVRIbN24Utra2YsOGDWL69Oma54aiTp06mlV67ezsRExMjBBCiDNnzohy5crJjKYjKytLhISEiLlz54pFixaJsLAw2ZG0bNu2TTg6OorZs2cLGxsbMW/ePPHxxx8LCwsLsWfPHtnxNLp16ybef/99kZaWpvWeHzhwQHh6ekpO97fAwEBRr149ER4eLmxtbTU5t23bJurUqSM53d+M4Xga03lORWfz5s3C2tpafPzxx8LS0lLzvi9atEh06NBBcrrCYVEj0U8//SQ8PT2FSqUSKpVKVKpUSSxfvlx2LC3W1taa9Ype/bCLiYkRlpaWEpMZp0OHDonWrVsLFxcXYW1tLZo2bSpCQkJkx9JSqlQpERUVJYTQfs9jY2OFtbW1zGhaqlSpIo4ePSqE0M557do1YW9vLzOaFmM4njzP/5uUWMwa5gXo/4i+ffuib9++SElJQVJSEsqWLSs7ko7y5csjOjpaZ72iiIgIeHh4yAmVg/xW4p40aVIJJclbs2bNsHfvXtkx8pSVlZXjXQ937tyBvb29hEQ5e/ToUY7nTHJyco7rgMliDMfTWM5zKlpXr15F8+bNddodHR0RHx9f8oGKAIsaA2BjY2Nwi/C9NGjQIIwaNQorV66ESqXCvXv3cPToUYwZM8agZnP97bfftLYzMjIQGxsLMzMzVKtWzWCKGmPQtm1bBAcHY9myZQCyb49OSkrC5MmTDWreivr162PXrl345JNPAPw9v8ry5csNZmkMwDiOp7Gc51S0FFnMyu4q+q96/PixGD58uPDx8RGlS5cWzs7OWg9DkZWVpRnr8/IymZWVlfjqq69kR8tXQkKC6NGjh1i7dq20DE5OTjrvbW4PQ3H79m3h6+srfHx8hJmZmWjUqJEoXbq0qFGjhnjw4IHseBrh4eHCzs5ODB06VFhZWYlRo0aJNm3aCFtbW3Hq1CnZ8TSM4Xga83lOhTdz5kzh6+srjh07Juzt7UV4eLj46aefhIuLi1i4cKHseIXCeWok6dixI6KjozFw4ECUK1dOp7v85ZpGhiI9PR3R0dFISkqCr6+vwdyGmp8LFy6gS5cuuHHjhpT//po1azTPnzx5gunTp6Ndu3aanoSjR48iJCQEEydOxGeffSYlY05evHiBTZs2ITIyEklJSXj99dfRt29fWFtby46mJSYmBrNnz9bKOX78eNSqVUt2NC3GcjyN9Twn/cTGxsLd3R1CCMycOROzZs1CSkoKgOxpRcaMGYNp06ZJTlk4LGoksbe3R0REBGrXri07il5u3ryJ5ORkeHt757nqtKGIiIhAly5dDGJiu169eqFly5YYOXKkVvv333+Pffv2GcU6RvTfYGznOenHxMQEbm5uaNmyJVq2bAl/f38kJiYqopjlmBpJvL29kZqaKjtGrlauXIn4+HiMHj1a0zZ48GCsWLECAFCjRg2EhITA1dVVVkQtCxcu1NoWQuD+/ftYt24dOnToICmVtpCQEMyZM0envX379ggKCpKQSNtff/2F+Ph4NGjQQNMWGhqK6dOnIzk5Gd27d8cXX3whMWG2Fy9eIDMzE5aWlpq2Bw8eYMmSJUhOTkbXrl0NYrI4YziexnaeU9HYv3+/Zl2qDRs2ID09HR4eHggICEBAQAD8/f1Rrlw52TELR+a1r/+yEydOiICAAHHw4EHx+PFjkZCQoPWQrWHDhmLlypWa7T///FOYmZmJn376SZw+fVo0btxYDBw4UGJCbVWrVtV6eHh4iIYNG4oJEyYItVotO54QIvsW5Pnz5+u0z58/X1SpUkVCIm3du3cXEydO1Gxfv35dWFtbi7Zt24rAwEBhZ2cnvv32W3kB/1///v3F4MGDNdtqtVq4uroKFxcX4efnJ8zMzMSuXbskJsxmDMfT2M5zKnqpqakiNDRUTJw4UTRr1kxYWloKExMT4evrKztaobCokeSvv/4S9evXFyYmJloPlUolTExMZMcTpUqVEufPn9dsDx06VPTq1UuzfeDAAVG1alUZ0YzWqlWrhKmpqejcubOYNm2amDZtmujcubMwMzMTq1atkh1PVK5cWRw5ckSzPW3aNFG7dm3N9vLly7W2ZfHy8tKa2+f7778XFStWFPHx8UIIIcaNGyf8/f1lxdMwhuPJ85xeSktLE/v37xdjx44VDg4OBvF3qDB4+UmSvn37wtzcHOvXr89xoLBsqampWouxHTlyRGsVaQ8PD8TFxcmIZrT69+8PHx8fLFy4EFu3bgUA+Pj4ICIiAg0bNpScDnj8+DEqV66s2T5w4AC6dOmi2fb398fnn38uI5qWu3fvwsvLS7MdGhqKXr16wdHREUD2IPtVq1bJiqdhDMeT5/l/V3p6Oo4dO4YDBw7g4MGDOH78OFxdXdG8eXN8//33RrtGFIsaSS5evIizZ8+iRo0asqPkyM3NDadPn4abmxseP36MS5cuoWnTppr9cXFxmj8isvTs2bPAr31ZRMiSkZGBIUOGYOLEifj555+lZslNqVKlcP/+fbi6uiIrKwunTp3SGmuRnp4OYQD3FVhZWWmNRzt27BjmzZuntT8pKUlGNC3GcDyN4TynohcQEIDjx4/D3d0dLVq0wJAhQ7B+/XpUqFBBdrR/jcPaJalfvz5u374tO0au+vXrhxEjRmDatGl4++234e3tjXr16mn2HzlyBDVr1pSYMHvWy5cPBwcHhIaG4tSpU5r9p0+fRmhoqEF8KJubm2PLli2yY+TJ398f06ZNw+3btxEcHIysrCz4+/tr9l++fFlnki4Z6tSpg3Xr1gHIXozvwYMHCAgI0OyPiYlBxYoVZcXTMIbjaQznORW98PBwlC5dGgEBAWjVqhXatGmjiIIGYE+NNJ988glGjRqFsWPHolatWjA3N9fa7+fnJylZtnHjxiElJQVbt25F+fLl8euvv2rtP3z4MN59911J6bK9eolh/Pjx6N27N5YsWQJTU1MAQGZmJoYPH67VvS5T9+7dsW3bNoOaj+ZVM2bMQJs2beDm5gZTU1MsXLgQtra2mv3r1q3TKh5kmTRpEjp06IBffvkF9+/fR//+/bU+kH/77Tet3gZZjOF4GsN5TkUvPj4e4eHhOHjwIObMmYN3330X1atXR4sWLeDv748WLVrAxcVFdsxC4Tw1kuQ094NKpYIQAiqVKse1Yih3Li4uiIiI0Lmcd/XqVTRp0gRPnjyRlOxv06dPxzfffINWrVqhXr16Wn/gACAwMFBSsr+9ePECly5dgouLi05vR2RkJCpXrozSpUtLSve3K1euYM+ePShfvjzefvttrfNp2bJlaNCgAerUqSMv4P8zluNJ/22JiYmIiIjQjK+JjIyEl5cXLl68KDua3ljUSHLz5s0897u5uZVQEmVwdnbG6tWr0a1bN6327du3o3///gYx+Z67u3uu+1QqFa5fv16CaYiIsmVlZeHkyZM4cOAADhw4gIiICDx//twov1zz8pMkLFqK1kcffYSBAwciJiZGM9nZ8ePHMXv2bHz00UeS02WLjY2VHYGISDNw/eDBgzhw4AAOHz6M5ORkVKpUCS1btsTixYvRsmVL2TELhT01EsXExCA4OBhXrlwBAPj6+mLUqFGoVq2a5GTGJysrC/Pnz8d3332H+/fvAwAqVKiAUaNG4fPPP9eMszEEjx8/BgCUKVNGchIi+i9ycHBAcnIyypcvr7VUghL+9rCokSQkJARdu3ZFnTp1NIMaDx8+jMjISOzYsQNt2rSRnNB4qdVqADCYAcJA9sC8L7/8Eps2bdJcCnN2dkafPn0wffp0ODk5yQ1IRP8ZS5cuRcuWLVG9enXZUYocixpJ6tati3bt2mH27Nla7UFBQdizZw/OnDkjKZm2qVOnYsyYMbCxsdFqT01Nxbx58zBp0iRJyXL26NEjXL16FUD2+lqG0Bvy9OlTNG7cGHfv3kXfvn3h4+MDIPuW3vXr18PV1RVHjhyBs7Oz5KRU1F68eIGZM2diwIABWhPxGRpjO8+JcsOiRhIrKytcuHBBa2ZUIHsRPD8/Pzx//lxSMm2mpqa4f/8+ypYtq9X+5MkTlC1b1mAGkiUnJ+OTTz7B2rVrkZWVBSA7+4cffohFixbpfFiXpE8//RShoaHYt2+fziJxcXFxaNu2LVq1aoVvv/1WUkJd8fHxOHHiBB4+fKg5ni99+OGHklIZJ3t7e1y4cEH6nDR5MZbznCg/HCgsiYuLC86dO6dT1Jw7d07ng0Wml7eY/1NkZCRKlSolIVHORo8ejbCwMOzYsUNzOS8iIgKBgYH4/PPP8cMPP0jLtm3bNixdujTHVW/Lly+PuXPnYujQoQZT1OzYsQN9+/ZFUlISHBwctN5/lUplMEWNs7Nzjv82VSoVrKys4Onpif79+0sfKB4QEICwsDCDLmqM5Twnyg+LGkkGDRqEwYMH4/r162jSpAmA7DE1c+bM0ZpKXZaXfzBUKhWqV6+u9YGXmZmJpKQkDB06VGJCbVu2bMHmzZu1Zmzt2LEjrK2t0bt3b6lFzf379/Haa6/lur9mzZoGtb7O559/jgEDBmDmzJlSe7jyM2nSJMyYMQMdOnTQ3PF24sQJ7N69GyNGjEBsbCyGDRuGFy9eYNCgQdJydujQAUFBQbhw4UKO8xN17dpVUjLjO8+J8sPLT5IIIRAcHIxvvvkG9+7dAwBUrFgRY8eORWBgoPQFLtesWQMhBAYMGIDg4GCtpQYsLCxQtWpVNG7cWGJCbTY2Njh9+rRmvMpLly5dQoMGDZCcnCwpGVCpUiVs2rQJb775Zo77w8PD8c4772j+Hchma2uLCxcuwMPDQ3aUPPXq1Qtt2rTR+aO7dOlS7NmzB1u2bMGiRYuwbNkyXLhwQVLKnCfafEn2RJvGdp4T5YdFjQFITEwEkH3t3dCEhYWhadOmMDMz7E69Vq1aoXTp0li7di2srKwAZA9y7NevH54+fYp9+/ZJyzZgwADExMRg7969sLCw0NqXlpaGdu3awcPDAytXrpSUUFvPnj3Rp08f9O7dW3aUPNnZ2eHcuXPw9PTUao+OjkadOnWQlJSEmJgY+Pn5SS1qjYGxnOdE+eG/YEkCAgKwdetWODk5aRUzarUa3bt3x/79+yWm+5u9vT2uXLmCWrVqAcieoXfVqlXw9fXF119/rfNHWpbg4GC0b98elStXRu3atQFkjwewsrJCSEiI1GxTp05F/fr14eXlhREjRsDb2xtCCFy5cgX/+9//kJaWplmgUZbff/9d87xTp04YO3YsLl++nOO6ZDIvl7yqVKlS2LFjh85aWjt27NCMA0lOTjaoLwvPnz/XFN2GJDk5GaGhoWjXrp1We0hICLKystChQwdJyYj0JEgKlUolHjx4oNP+4MEDYWZmJiFRzurXry82b94shBAiJiZGWFpainfffVd4enqKUaNGyQ33D8nJyWLZsmVi9OjRYvTo0eLHH38UKSkpsmMJIYS4fv26aN++vTAxMREqlUqoVCphYmIi2rVrJ65duyY7niZTfg8TExPZUTWWLVsmTE1NRZcuXcS0adPEtGnTRNeuXYWZmZlYvny5EEKI+fPni969e0vN+eLFCzF16lRRsWJFYWpqKmJiYoQQQnz11VeanLLVqlVL7Nq1S6f9zz//FH5+fhISERUOLz+VsPPnzwMA6tSpg/3792vdWZCZmYndu3dj6dKluHHjhqSE2hwdHXHmzBlUq1YNc+bMwf79+xESEoLDhw+jT58+uH37tuyIyMjIgLe3N3bu3KkzpsbQPHv2DNeuXQMAeHp68s6Sf+nw4cP4/vvvNXMT1ahRA5988olm8L0hmDp1KtasWYOpU6di0KBBuHjxIjw8PLBp0yYEBwfj6NGjsiPC2toaV65c0blD68aNG3jttdd4+Y6MBi8/lbA6depo7jYICAjQ2W9tbY1FixZJSJYzIYRmnpJ9+/ahc+fOAABXV1fNdP+ymZubG8y8PvlxdnbW3KljqNauXYt33nkHlpaWWu3p6enYuHGjwdzSDQBNmzbV3MJvqNauXYtly5ahVatWWoOaa9eujaioKInJ/ubo6Ijr16/rFDXR0dE6d2sRGTL21JSwmzdvQggBDw8PnDhxAi4uLpp9FhYWKFu2rEGtUxQQEABXV1e0bt0aAwcOxOXLl+Hp6YmwsDD069fPYHqUZs6cib/++gvLly/nYMd/yZgmYsvKykJ0dHSOkwQ2b95cUipt1tbWiIqKgpubG+zt7REZGQkPDw9cvnwZDRo0QFJSkuyIGDJkCI4ePYrffvtNs/5PdHQ0evXqhTfeeAPLly+XnJCoYPjpX8Jers79zw9gQxUcHIy+ffti27Zt+PLLLzV3mmzevNmguvhPnjyJ0NBQ7NmzB7Vq1dL5drl161ZJyYyPyGUitjt37mjd8ivbsWPH8N5772m+KLxK9q3Sr/L19UV4eLjm3H9p8+bNqFu3rqRU2ubOnYv27dvD29tbs5zDnTt30KxZM8yfP19yOqKCY1EjyZo1a1CmTBl06tQJADBu3DgsW7YMvr6+2LBhg84HoCx+fn45zvExb948g+pRcnJyQq9evWTHMGp169bVXBpt1aqVVo9XZmYmYmNj0b59e4kJtQ0dOhT169fHrl27UKFCBelzO+Vm0qRJ6NevH+7evYusrCxs3boVV69exdq1a7Fz507Z8QBkX346cuQI9u7di8jISFhbW8PPz89geruICoqXnySpUaMGfvjhBwQEBODo0aNo1aoVgoODsXPnTpiZmRlcz8Lp06dx5coVANnfPF9//XXJiaioTZkyRfO/n3/+Oezs7DT7Xk7E1qtXL4O5jd/W1haRkZE689QYovDwcEydOhWRkZFISkrC66+/jkmTJqFt27ayoxEpCosaSWxsbBAVFYUqVapg/PjxuH//PtauXYtLly7B398fjx49kh0RAPDw4UO88847CAsLg5OTE4DsxQ5btmyJjRs3ao0JkiErKwvz5s3D77//jvT0dLRq1QqTJ0+GtbW11FzGbM2aNXjnnXcMcj6VVwUEBGDcuHEG1XtkrKZOnZrnfq7STcaCl58ksbOzw5MnT1ClShXs2bNHs96TlZUVUlNTJaf72yeffIKkpCRcunRJc7v05cuX0a9fPwQGBmLDhg1S882YMQNff/01WrduDWtra3z33Xd4+PChwczOa4z69esnO0KBfPLJJ/j8888RFxeX4ySBfn5+kpIZn99++01rOyMjA7GxsTAzM0O1atVY1JDRYE+NJH379kVUVBTq1q2LDRs24NatWyhdujR+//13fPHFF7h48aLsiACyr7Xv27cPb7zxhlb7iRMn0LZtW8THx8sJ9v+8vLwwZswYDBkyBED2beedOnVCampqnmvuUO5MTEzyHJ9iKANwc3p/VSqVZqCzzJy5rSCek6dPnxZzmsJRq9Xo378/evTogQ8++EB2HKICYU+NJIsXL8ZXX32F27dvY8uWLShdujSA7LEr7777ruR0f8vKytL5Bgxkzw1jCHdw3bp1Cx07dtRst27dGiqVCvfu3dPcxUH62bp1q9Yf5IyMDJw9exZr1qzRjLsxBLGxsbIj5Co4OFjz/MmTJ5g+fTratWunWRzy6NGjCAkJwcSJEyUlzJ+DgwOmTJmCLl26sKgho8GeGspTt27dEB8fjw0bNqBixYoAgLt376Jv375wdnbW6bYuaaampoiLi9Ma22Nvb4/z58/D3d1dYjLlWb9+PTZt2oTt27fLjmJUevXqhZYtW2LkyJFa7d9//z327duHbdu2yQlWABEREejSpQuePXsmOwpRgbCokeTQoUN57jeUWylv376Nrl274tKlS3B1ddW01axZE7///rv03hATExN06NBBa/bbHTt2ICAgQGuuGkO7m8wYXb9+HX5+flIni/v999/RoUMHmJubay3CmRNDWXizIKuJy7Zw4UKtbSEE7t+/j3Xr1qFFixZYv369pGRE+mFRI0lu4wFeMpRxC0D2B9y+ffs0U7r7+PigdevWklNl++ijjwr0ulWrVhVzEmVLTU3FhAkT8Oeff2rWWZLBxMQEcXFxKFu2bJ5jpmSPqXmVm5sbAgMD8fnnn2u1f/PNN1i4cCFu3rwpKdnf/tmraWJiAhcXFwQEBGDChAkGtdI5UV5Y1EiSkJCgtf1y3MLEiRMxY8YMtGrVSlIy+q/75yBXIQQSExNhY2ODn376yWB6QIzF6tWr8fHHH6NDhw5o2LAhAOD48ePYvXs3fvzxR/Tv319uQCIFYVFjYMLCwjB69GicPn1aao79+/dj5MiROHbsGBwcHLT2JSQkoEmTJliyZAmaNWsmKSEVlzVr1mhtv/zW3rBhQzg7O0tKZdyOHz+OhQsXaiaw9PHxQWBgoKbIIaKiwaLGwERFRaF+/frSr7N37doVLVu2xGeffZbj/oULF+LAgQPSBwpTybp48SJq1qwpO4ZGaGgoQkNDc1zQ0hDmKsrIyMCQIUMwceJEgxu43rNnzwK/lmPSyFhwIg9Jzp8/r/WIjIzE7t27MXToUNSpU0d2PERGRuY5U2vbtm2l9yZRyUhMTMSyZcvQoEED1K5dW3YcjSlTpqBt27YIDQ3F48eP8ezZM62HITA3N8eWLVtkx8iRo6Oj5uHg4IDQ0FCcOnVKs//06dMIDQ01qEVMifLDeWokqVOnjmaisFc1atTIIL5hPnjwIMf5aV4yMzMzmKUcqHgcOnQIK1aswJYtW1CxYkX07NkTixcvlh1LY8mSJVi9erXBz6HSvXt3bNu2LddeT1leHTw/fvx49O7dG0uWLNEsVJuZmYnhw4frXH4mMmQsaiT558RhL8ctGMp6O5UqVcLFixdzXSzw/PnzqFChQgmnouIWFxeH1atXY8WKFVCr1ejduzfS0tKwbds2+Pr6yo6nJT09HU2aNJEdI19eXl6YOnUqDh8+jHr16mlNNQAAgYGBkpL9beXKlYiIiNAUNED2HFCjR49GkyZNMG/ePInpiAqOY2ooR5988gkOHjyIkydP6hRaqampaNCgAVq2bKkzvwUZry5duuDQoUPo1KkT+vbti/bt28PU1BTm5uaIjIw0uKJm/PjxsLOzM+hZeQHd26VfpVKpcP369RJMkzNnZ2esXr0a3bp102rfvn07+vfvbzCX84jyw6KmhBnLXUUPHjzA66+/DlNTU4wcORI1atQAkD2QefHixcjMzMSZM2dQrlw5qTmp6JiZmSEwMBDDhg2Dl5eXpt2QipqXC78C2Ut4rFmzBn5+fvDz89O5XLpgwYKSjme0Ro8ejbVr1+KLL75AgwYNAGTfsTV79mx88MEHPJZkNHj5qYQFBwdj0KBBOV6ndnR0xJAhQ7BgwQLpRU25cuVw5MgRDBs2DBMmTNCM/VGpVGjXrh0WL17MgkZhIiIisGLFCtSrVw8+Pj744IMP0KdPH9mxtJw9e1Zr++Wg+n8uAFvQxSRL0uPHjwEAZcqUkZxE1/z581G+fHl88803uH//PgCgQoUKGDt2rM6kgUSGjD01JczNzQ27d++Gj49PjvujoqLQtm1b3Lp1q4ST5e7Zs2eIjo6GEAJeXl6cq0ThkpOTsWnTJqxcuRInTpxAZmYmFixYgAEDBnBmWT3Fx8fjyy+/xKZNmzSXcJydndGnTx9Mnz4dTk5OcgPmQK1WAwAHCJNRYlFTwqysrPIcgBsdHY1atWohNTW1hJMR6bp69SpWrFiBdevWIT4+Hm3atMl3zaWSkpCQgMzMTJQqVUqr/enTpzAzM5P+R/np06do3LixZgHYl19kLl++jPXr18PV1RVHjhwxqC8Jjx490iyD4e3tbZC9SkR54Tw1JezlXUW54V1FZEhq1KiBuXPn4s6dO9iwYYPsOFr69OmDjRs36rT/8ssvBnHZbOrUqbCwsEBMTAyWLl2KTz/9FJ9++imWLVuG6OhomJubY+rUqbJjAsjunRswYAAqVKiA5s2bo3nz5qhQoQIGDhyIlJQU2fGICoxFTQnr2LEjJk6ciOfPn+vsS01NxeTJk9G5c2cJyYhyZ2pqiu7duxtMLw2QPZC1ZcuWOu3+/v44fvy4hETatm3bhvnz5+c49qx8+fKYO3euwczIPXr0aISFhWHHjh2Ij49HfHw8tm/fjrCwMI6pIaPCy08ljHcVERUNW1tbHDt2DLVq1dJqv3DhAho2bCi9h8HS0hIxMTGoXLlyjvvv3LkDT0/PHL/glLQyZcpg8+bN8Pf312o/cOAAevfuzYk2yWjw7qcSxruKiIpGgwYNsGzZMixatEirfcmSJahXr56kVH8rU6YMbty4kWtRExsbqzMeSJaUlJQcP3fKli0rvTgk0gd7aiQyhruKcrvcoFKpYGVlBU9PT4NbqI/+Gw4fPozWrVvjjTfeQKtWrQBkL3B58uRJ7NmzR/q0CAMGDEBMTAz27t0LCwsLrX1paWlo164dPDw8DGJZlFatWqF06dJYu3atZrLN1NRU9OvXD0+fPsW+ffskJyQqGBY1lCcTE5Mc16h62aZSqfDmm29i27ZtBlmUkbKdO3cO8+bNw7lz52BtbQ0/Pz9MmDBBa/JAWe7cuYP69evD0tISI0aMgLe3N4QQuHLlCv73v/8hLS0Np06dgqurq+youHDhAtq3b4+0tDTNoqWRkZGwsrJCSEgIXnvtNckJiQqGRQ3lKTQ0FF9++SVmzJihmWn0xIkTmDhxIr766ivNhIENGzbEihUrJKclMiyxsbEYPnw49uzZo3WpuU2bNvj+++9zndpBhpSUFPz888+IiooCAPj4+KBv376wtraWnIyo4FjUUJ5q1qyJZcuW6SwcePjwYQwePBiXLl3Cvn37MGDAAIOaMJD+W54/f4709HStNtnz1Lzq2bNnuHbtGgDA09PTYMbSAEBGRga8vb2xc+fOXCcFJTIWHChMeYqJicnxj4ODg4NmIT4vLy/NFPBEJSUlJQXjxo3DL7/8gidPnujsz8zMlJAqZ87OzpqeTkNjbm5uEHdgERUFzlNDeapXrx7Gjh2rdUvno0ePMG7cOLzxxhsAgGvXrhnEuAD6bxk7diz279+PH374AZaWlli+fDmmTJmCihUrYu3atbLjGZURI0Zgzpw5ePHihewoRP8KLz9Rnq5evYpu3bohNjZWU7jcvn0bHh4e2L59O6pXr45t27YhMTERH3zwgeS09F9SpUoVrF27Fv7+/nBwcMCZM2fg6emJdevWYcOGDfjjjz9kRzQaPXr0QGhoKOzs7FCrVi3Y2tpq7d+6daukZET64eUnylONGjVw+fJl7NmzB3/99ZemrU2bNjAxye7o6969u8SE9F/19OlTeHh4AMi+HPr06VMAwJtvvolhw4bJjGZ0nJyc0KtXL9kxiP41FjWULxMTE7Rv3x7t27eXHYVIw8PDA7GxsahSpQq8vb3xyy+/oEGDBtixY4dBrn5tyFatWiU7AlGR4OUnyldoaChCQ0Px8OFDZGVlae0zhInD6L/p22+/hampKQIDA7Fv3z506dIFQghkZGRgwYIFGDVqlOyIBi8rKwvz5s3D77//jvT0dLRq1QqTJ0/mbdxktFjUUJ6mTJmCqVOnon79+qhQoQJUKpXWfkNZkI/o5s2bOH36NDw9PeHn5yc7jlGYNm0avv76a7Ru3RrW1tYICQnBu+++yy8rZLRY1FCeKlSogLlz53IQMJECeXl5YcyYMRgyZAgAYN++fejUqRNSU1M1Y+aIjAn/1VKe0tPTdSbeI5Jp//798PX1hVqt1tmXkJCA1157DeHh4RKSGZ9bt26hY8eOmu3WrVtDpVLh3r17ElMRFR6LGsrTxx9/jPXr18uOQaQRHByMQYMG5Tgp5MtlOxYsWCAhmfF58eKFZgHLl8zNzZGRkSEpEdG/w7ufKE/Pnz/HsmXLsG/fPvj5+cHc3FxrP/94UEmLjIzEnDlzct3ftm1bzJ8/vwQTGS8hBPr37w9LS0tN2/PnzzF06FCtuWo4Tw0ZCxY1lKfz58+jTp06AICLFy9q7fvnoGGikvDgwQOd4vpVZmZmWjNgU+769eun0/b+++9LSEJUNFjUUJ4OHDggOwKRlkqVKuHixYu5rnB9/vx5VKhQoYRTGSfOT0NKwzE1RGRUOnbsiIkTJ+a4CGNqaiomT56Mzp07S0hGRLLxlm7S0bNnT6xevRoODg7o2bNnnq/ltXYqaQ8ePMDrr78OU1NTjBw5EjVq1AAAREVFYfHixcjMzMSZM2dQrlw5yUmJqKTx8hPpcHR01IyXcXR0lJyGSFu5cuVw5MgRDBs2DBMmTMDL72UqlQrt2rXD4sWLWdAQ/Uexp4aIjNazZ88QHR0NIQS8vLzg7OwsOxIRScSihgrk4cOHuHr1KoDsVbrLli0rOREREZE2DhSmPKnVanzwwQeoVKkSWrRogRYtWqBSpUp4//33kZCQIDseERGRBosaytOgQYNw/Phx7Ny5E/Hx8YiPj8fOnTtx6tQpzXoxREREhoCXnyhPtra2CAkJwZtvvqnVHh4ejvbt2yM5OVlSMiIiIm3sqaE8lS5dOsc7oBwdHTkok4iIDAqLGsrTV199hdGjRyMuLk7TFhcXh7Fjx2LixIkSkxEREWnj5SfKU926dREdHY20tDRUqVIFAHDr1i1YWlrCy8tL67VnzpyREZGIiAgAJ9+jfHTv3l12BCIiogJhTw0REREpAntqqEBOnTqFK1euAAB8fX1Rr149yYmIiIi0saihPN25cwfvvvsuDh8+DCcnJwBAfHw8mjRpgo0bN6Jy5cpyAxIREf0/3v1Eefr444+RkZGBK1eu4OnTp3j69CmuXLmCrKwsfPzxx7LjERERaXBMDeXJ2toaR44cQd26dbXaT58+jWbNmiElJUVSMiIiIm3sqaE8ubq6IiMjQ6c9MzMTFStWlJCIiIgoZyxqKE/z5s3DJ598glOnTmnaTp06hVGjRmH+/PkSkxEREWnj5SfKk7OzM1JSUvDixQuYmWWPK3/53NbWVuu1T58+lRGRiIgIAO9+onwEBwfLjkBERFQg7KkhIiIiRWBPDelQq9VwcHDQPM/Ly9cRERHJxp4a0mFqaor79++jbNmyMDExgUql0nmNEAIqlQqZmZkSEhIREeliTw3p2L9/P0qVKgUAOHDggOQ0REREBcOeGiIiIlIE9tRQvuLj43HixAk8fPgQWVlZWvs+/PBDSamIiIi0saeG8rRjxw707dsXSUlJcHBw0Bpfo1KpODcNEREZDBY1lKfq1aujY8eOmDlzJmxsbGTHISIiyhWLGsqTra0tLly4AA8PD9lRiIiI8sS1nyhP7dq101r3iYiIyFBxoDDp+P333zXPO3XqhLFjx+Ly5cuoVasWzM3NtV7btWvXko5HRESUI15+Ih0mJgXrwOPke0REZEhY1BAREZEicEwNERERKQKLGsrR0aNHsXPnTq22tWvXwt3dHWXLlsXgwYORlpYmKR0REZEuFjWUo6lTp+LSpUua7QsXLmDgwIFo3bo1goKCsGPHDsyaNUtiQiIiIm0cU0M5qlChAnbs2IH69esDAL788kuEhYUhIiICAPDrr79i8uTJuHz5ssyYREREGuypoRw9e/YM5cqV02yHhYWhQ4cOmu033ngDt2/flhGNiIgoRyxqKEflypVDbGwsACA9PR1nzpxBo0aNNPsTExN15qwhIiKSiUUN5ahjx44ICgpCeHg4JkyYABsbGzRr1kyz//z586hWrZrEhERERNo4ozDlaNq0aejZsydatGgBOzs7rFmzBhYWFpr9K1euRNu2bSUmJCIi0saBwpSnhIQE2NnZwdTUVKv96dOnsLOz0yp0iIiIZGJRQ0RERIrAMTVERESkCCxqiIiISBFY1BAREZEisKghIiIiRWBRQ0RERIrAooaIiIgUgUUNERERKQKLGiIiIlKE/wMODWt0c/p6lAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1349,10 +1349,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.562833Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.562644Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.575914Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.575309Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.697653Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.697207Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.711068Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.710652Z"
     }
    },
    "outputs": [
@@ -1503,10 +1503,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.577740Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.577435Z",
-     "iopub.status.idle": "2024-10-25T15:16:18.593281Z",
-     "shell.execute_reply": "2024-10-25T15:16:18.592800Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.713088Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.712895Z",
+     "iopub.status.idle": "2024-10-25T15:18:00.729802Z",
+     "shell.execute_reply": "2024-10-25T15:18:00.729274Z"
     }
    },
    "outputs": [
@@ -1550,16 +1550,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:16:18.595127Z",
-     "iopub.status.busy": "2024-10-25T15:16:18.594943Z",
-     "iopub.status.idle": "2024-10-25T15:18:11.683715Z",
-     "shell.execute_reply": "2024-10-25T15:18:11.683089Z"
+     "iopub.execute_input": "2024-10-25T15:18:00.731769Z",
+     "iopub.status.busy": "2024-10-25T15:18:00.731406Z",
+     "iopub.status.idle": "2024-10-25T15:19:55.834645Z",
+     "shell.execute_reply": "2024-10-25T15:19:55.833944Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1607,10 +1607,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:11.685860Z",
-     "iopub.status.busy": "2024-10-25T15:18:11.685492Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.241741Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.241031Z"
+     "iopub.execute_input": "2024-10-25T15:19:55.836917Z",
+     "iopub.status.busy": "2024-10-25T15:19:55.836716Z",
+     "iopub.status.idle": "2024-10-25T15:19:56.379230Z",
+     "shell.execute_reply": "2024-10-25T15:19:56.378498Z"
     }
    },
    "outputs": [],
@@ -1639,16 +1639,16 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:12.244160Z",
-     "iopub.status.busy": "2024-10-25T15:18:12.243785Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.315222Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.314622Z"
+     "iopub.execute_input": "2024-10-25T15:19:56.381826Z",
+     "iopub.status.busy": "2024-10-25T15:19:56.381599Z",
+     "iopub.status.idle": "2024-10-25T15:19:56.442961Z",
+     "shell.execute_reply": "2024-10-25T15:19:56.442320Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x200 with 1 Axes>"
       ]
@@ -1689,10 +1689,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:18:12.317811Z",
-     "iopub.status.busy": "2024-10-25T15:18:12.317445Z",
-     "iopub.status.idle": "2024-10-25T15:18:12.333491Z",
-     "shell.execute_reply": "2024-10-25T15:18:12.332926Z"
+     "iopub.execute_input": "2024-10-25T15:19:56.445516Z",
+     "iopub.status.busy": "2024-10-25T15:19:56.445234Z",
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diff --git a/main/example_notebooks/gcm_supply_chain_dist_change.html b/main/example_notebooks/gcm_supply_chain_dist_change.html
index f1872f0e0..9f8892bdf 100644
--- a/main/example_notebooks/gcm_supply_chain_dist_change.html
+++ b/main/example_notebooks/gcm_supply_chain_dist_change.html
@@ -567,7 +567,7 @@
 <h1>Finding Root Causes of Changes in a Supply Chain<a class="headerlink" href="#Finding-Root-Causes-of-Changes-in-a-Supply-Chain" title="Permalink to this heading">#</a></h1>
 <p>In a supply chain, the number of units of each product in the inventory that is available for shipment is crucial to fulfill customers’ demand faster. For this reason, retailers continuously buy products anticipating customers’ demand in the future.</p>
 <p>Suppose that each week a retailer submits purchase orders (POs) to vendors taking into account future demands for products and capacity constraints to consider for demands. The vendors will then confirm whether they can fulfill some or all of the retailer’s purchase orders. Once confirmed by the vendors and agreed by the retailer, products are then sent to the retailer. All of the confirmed POs, however, may not arrive at once.</p>
-<p><img alt="0db7577eeef445a1a083762be1cce4bd" class="no-scaled-link" src="../_images/supply-chain.png" style="width: 800px;" /></p>
+<p><img alt="9c0ab7f4a6a945b098685a8b8696fb07" class="no-scaled-link" src="../_images/supply-chain.png" style="width: 800px;" /></p>
 <section id="Week-over-week-changes">
 <h2>Week-over-week changes<a class="headerlink" href="#Week-over-week-changes" title="Permalink to this heading">#</a></h2>
 <p>For this case study, we consider synthetic data inspired from a real-world use case in supply chain. Let us look at data over two weeks, <em>w1</em> and <em>w2</em> in particular.</p>
@@ -1007,12 +1007,9 @@ <h4>Attributing change<a class="headerlink" href="#Attributing-change" title="Pe
 </div>
 <div class="output_area stderr docutils container">
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-  warnings.warn(
-
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 </pre></div></div>
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diff --git a/main/example_notebooks/gcm_supply_chain_dist_change.ipynb b/main/example_notebooks/gcm_supply_chain_dist_change.ipynb
index f218f3293..e899f5159 100644
--- a/main/example_notebooks/gcm_supply_chain_dist_change.ipynb
+++ b/main/example_notebooks/gcm_supply_chain_dist_change.ipynb
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",
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AAFAkxfpYas6cOXm+drlcSk1N1YcffqiePXt6JBgAAEBxFKvcvPXWW3m+9vHxUfXq1TVw4ECNGzfOI8EAAACKg3VuAACAUW55zk1KSopSUlI8kQUAAOCWFavcXL16VRMmTFBAQIBCQ0MVGhqqgIAAjR8/XleuXPF0RgAAgEIr1sdSzz//vJYsWaLXX39dkZGRkqTExERNnjxZP/74o9555x2PhgQAACisYp25WbhwoebPn69nnnlGLVq0UIsWLfTMM8/o//7v/7Rw4cJCv8+mTZv04IMPqlatWrLZbFq2bNlNt9+wYYNsNlu+R1paWnG+DQAAYKBilRu73a7Q0NB842FhYfL19S30+2RlZSkiIkJz584t0t9/+PBhpaamuh81atQo0usBAIC5ivWx1PDhwzVt2jR98MEHstvtkqTs7Gy9+uqrGj58eKHfp2fPnsVaF6dGjRq68847i/w6AABgvkKXm379+uX5eu3atapTp44iIiIkSXv27NHly5d1//33ezZhAVq2bKns7Gw1a9ZMkydPVseOHW+4bXZ2trKzs91fO53OEs8HAACsU+hyExAQkOfrhx9+OM/XISEhnkl0E8HBwZo3b57atm2r7Oxsvf/+++rcubO2bdum1q1bF/ia2NhYTZkypcSzAQAA72BzuVwuq0NIks1m09KlSxUdHV2k13Xq1El33XWXPvzwwwKfL+jMTUhIiDIyMuRwOG4lcsEcj3n+PU3lXGR1AtwqjvfC43gHSk2x5tx4k3bt2mnz5s03fN5ut7vnBQEAAPMVuty0bt1aCQkJCgwMVKtWrWSz2W64bVJSkkfCFcbu3bsVHBxcan8fAADwboUuN3379nWfASnqR0c3kpmZqaNHj7q/PnHihHbv3q0qVarorrvu0rhx4/Tdd99pwYIFkqRZs2YpLCxMTZs21aVLl/T+++9r3bp1+vLLLz2SBwAA3P4KXW4mTZokScrJyVGXLl3UokWLW74ce8eOHerSpYv76zFjxkiSBg4cqPnz5ys1NVWnTp1yP3/58mW98MIL+u6771SpUiW1aNFCa9euzfMeAACgbCvWhGI/Pz8dPHhQYWFhJZGpRDmdTgUEBDCh2BswwfL2x/FeeBzvQKkp1grFzZo10/Hjxz2dBQAA4JYVq9xMnz5dY8eO1YoVK5Samiqn05nnAQAAYJViXQreq1cvSVKfPn3yXDXlcrlks9mUk5PjmXQAAABFVKxys379ek/nAAAA8IhilZuwsDCFhITkW+vG5XIpJSXFI8EAAACKo1hzbsLCwnT27Nl84+fOnbstr6ACAADmKFa5uT635tcyMzPl5+d3y6EAAACKq0gfS11fZM9ms2nChAmqVKmS+7mcnBxt27ZNLVu29GhAAACAoihSudm1a5eka2du9u3bJ19fX/dzvr6+ioiI0NixYz2bEAAAoAiKVG6uXyU1aNAgzZ49u2RW+AUAALgFxbpa6oMPPvB0DgAAAI8oVrnJysrSa6+9poSEBJ05c0a5ubl5nufWDAAAwCrFKjdPPfWUNm7cqP/3//6fgoODC7xyCgAAwArFKjcrV67U559/ro4dO3o6DwAAwC0p1jo3gYGBqlKliqezAAAA3LJilZtp06Zp4sSJunjxoqfzAAAA3JJifSwVFxenY8eOKSgoSKGhoapQoUKe55OSkjwSDgAAoKiKVW6io6M9HAMAAMAzilVuJk2a5OkcAAAAHlGscnPdzp07dfDgQUlS06ZN1apVK4+EAgAAKK5ilZszZ87oscce04YNG3TnnXdKks6fP68uXbpo0aJFql69uiczAgAAFFqxrpZ6/vnndeHCBR04cEDnzp3TuXPntH//fjmdTo0YMcLTGQEAAAqtWGduVq1apbVr16pJkybusfDwcM2dO1fdunXzWDgAAICiKtaZm9zc3HyXf0tShQoV8t1nCgAAoDQVq9xERUVp5MiR+v77791j3333nUaPHq3777/fY+EAAACKqljl5u2335bT6VRoaKjq16+v+vXrKywsTE6nU3/+8589nREAAKDQijXnJiQkRElJSVq7dq0OHTokSWrSpIm6du3q0XAAAABFVaQzN+vWrVN4eLicTqdsNpseeOABPf/883r++ed19913q2nTpvrqq69KKisAAMB/VaRyM2vWLA0dOlQOhyPfcwEBAXrmmWf05ptveiwcAABAURWp3OzZs0c9evS44fPdunXTzp07bzkUAABAcRWp3KSnpxd4Cfh15cuX19mzZ285FAAAQHEVqdzUrl1b+/fvv+Hze/fuVXBw8C2HAgAAKK4ilZtevXppwoQJunTpUr7nfv75Z02aNEm/+93vPBYOAACgqGwul8tV2I3T09PVunVrlStXTsOHD1ejRo0kSYcOHdLcuXOVk5OjpKQkBQUFlVjgW+V0OhUQEKCMjIwCJ0bfMsdjnn9PUzkXWZ0At4rjvfA43oFSU6R1boKCgrR161Y999xzGjdunK73IpvNpu7du2vu3LleXWwAAID5iryIX926dfXFF1/op59+0tGjR+VyudSwYUMFBgaWRD4AAIAiKdYKxZIUGBiou+++25NZAAAAblmx7i0FAADgrSg3AADAKJQbAABgFMoNAAAwiqXlZtOmTXrwwQdVq1Yt2Ww2LVu27L++ZsOGDWrdurXsdrsaNGig+fPnl3hOAABw+7C03GRlZSkiIkJz584t1PYnTpxQ79691aVLF+3evVujRo3SU089pdWrV5dwUgAAcLso9qXgntCzZ0/17Nmz0NvPmzdPYWFhiouLkyQ1adJEmzdv1ltvvaXu3buXVEwAAHAbua3m3CQmJqpr1655xrp3767ExMQbviY7O1tOpzPPAwAAmOu2KjdpaWn5bu8QFBQkp9Opn3/+ucDXxMbGKiAgwP0ICQkpjagAAMAit1W5KY5x48YpIyPD/UhJSbE6EgAAKEGWzrkpqpo1ayo9PT3PWHp6uhwOhypWrFjga+x2u+x2e2nEAwAAXuC2OnMTGRmphISEPGNr1qxRZGSkRYkAAIC3sbTcZGZmavfu3dq9e7eka5d67969W6dOnZJ07SOlmJgY9/bPPvusjh8/rpdeekmHDh3SX/7yF/3973/X6NGjrYgPAAC8kKXlZseOHWrVqpVatWolSRozZoxatWqliRMnSpJSU1PdRUeSwsLC9Pnnn2vNmjWKiIhQXFyc3n//fS4DBwAAbjaXy+WyOkRpcjqdCggIUEZGhhwOh+f/Asdjnn9PUzkXWZ0At4rjvfA43oFSc1vNuQEAAPhvKDcAAMAolBsAAGAUyg0AADAK5QYAABiFcgMAAIxCuQEAAEah3AAAAKNQbgAAgFEoNwAAwCiUGwAAYBTKDQAAMArlBgAAGIVyAwAAjEK5AQAARqHcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwCuUGAAAYhXIDAACMQrkBAABGodwAAACjUG4AAIBRKDcAAMAolBsAAGAUyg0AADAK5QYAABiFcgMAAIxCuQEAAEah3AAAAKNQbgAAgFEoNwAAwCiUGwAAYBTKDQAAMArlBgAAGIVyAwAAjEK5AQAARqHcAAAAo1BuAACAUbyi3MydO1ehoaHy8/PTPffco+3bt99w2/nz58tms+V5+Pn5lWJaAADgzSwvN5988onGjBmjSZMmKSkpSREREerevbvOnDlzw9c4HA6lpqa6H8nJyaWYGAAAeDPLy82bb76poUOHatCgQQoPD9e8efNUqVIlxcfH3/A1NptNNWvWdD+CgoJKMTEAAPBmlpaby5cva+fOneratat7zMfHR127dlViYuINX5eZmam6desqJCREffv21YEDB264bXZ2tpxOZ54HAAAwl6Xl5ocfflBOTk6+My9BQUFKS0sr8DWNGjVSfHy8li9fro8++ki5ubnq0KGDTp8+XeD2sbGxCggIcD9CQkI8/n0AAADvYfnHUkUVGRmpmJgYtWzZUp06ddKSJUtUvXp1vfvuuwVuP27cOGVkZLgfKSkppZwYAACUpvJW/uXVqlVTuXLllJ6enmc8PT1dNWvWLNR7VKhQQa1atdLRo0cLfN5ut8tut99yVgAAcHuw9MyNr6+v2rRpo4SEBPdYbm6uEhISFBkZWaj3yMnJ0b59+xQcHFxSMQEAwG3E0jM3kjRmzBgNHDhQbdu2Vbt27TRr1ixlZWVp0KBBkqSYmBjVrl1bsbGxkqSpU6eqffv2atCggc6fP6833nhDycnJeuqpp6z8NgAAgJewvNw8+uijOnv2rCZOnKi0tDS1bNlSq1atck8yPnXqlHx8/nOC6aefftLQoUOVlpamwMBAtWnTRlu3blV4eLhV3wIAAPAiNpfL5bI6RGlyOp0KCAhQRkaGHA6H5/8Cx2Oef09TORdZnQC3iuO98DjegVJz210tBQAAcDOUGwAAYBTKDQAAMArlBgAAGIVyAwAAjEK5AQAARqHcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwCuUGAAAYhXIDAACMQrkBAABGodwAAACjUG4AAIBRKDcAAMAolBsAAGAUyg0AADAK5QYAABiFcgMAAIxCuQEAAEah3AAAAKNQbgAAgFEoNwAAwCiUGwAAYBTKDQAAMArlBgAAGIVyAwAAjEK5AQAARqHcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwCuUGAIDbzMXLuao783vVnfm9Ll7OtTqO16HcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwileUm7lz5yo0NFR+fn665557tH379ptu/+mnn6px48by8/NT8+bN9cUXX5RSUuC/4yoGlCUc7/BGlpebTz75RGPGjNGkSZOUlJSkiIgIde/eXWfOnClw+61bt+rxxx/XkCFDtGvXLkVHRys6Olr79+8v5eQAAMAbWV5u3nzzTQ0dOlSDBg1SeHi45s2bp0qVKik+Pr7A7WfPnq0ePXroxRdfVJMmTTRt2jS1bt1ab7/9diknBwAA3sjScnP58mXt3LlTXbt2dY/5+Pioa9euSkxMLPA1iYmJebaXpO7du99w++zsbDmdzjwPAABgrvJW/uU//PCDcnJyFBQUlGc8KChIhw4dKvA1aWlpBW6flpZW4PaxsbGaMmWKZwIXhnNR6f1dgNU43gF4Ics/lipp48aNU0ZGhvuRkpJidSQAAFCCLD1zU61aNZUrV07p6el5xtPT01WzZs0CX1OzZs0ibW+322W32z0TGAAAeD1Lz9z4+vqqTZs2SkhIcI/l5uYqISFBkZGRBb4mMjIyz/aStGbNmhtuD5S2Sr4+Sn65lpJfrqVKvsafHEUZx/EOb2TpmRtJGjNmjAYOHKi2bduqXbt2mjVrlrKysjRo0CBJUkxMjGrXrq3Y2FhJ0siRI9WpUyfFxcWpd+/eWrRokXbs2KH33nvPym8DAAB4CcvLzaOPPqqzZ89q4sSJSktLU8uWLbVq1Sr3pOFTp07Jx+c//xro0KGDFi5cqPHjx+uPf/yjGjZsqGXLlqlZs2ZWfQsAAMCL2Fwul8vqEKXJ6XQqICBAGRkZcjgcVscBAKDILl7OVZO3rl0lfHB0TT4S/BX2BgAAMArlBgAAGIVyAwAAjMKcGwAAYBTO3AAAAKNQbgAAgFEoNwAAwCiUGwAAYBTKDQAAMArlBgAAGIVyAwAAjEK5AQAARqHcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwCuUGAAAYhXIDAACMUt7qAKXN5XJJkpxOp8VJAABAUfn7+8tms910mzJXbi5cuCBJCgkJsTgJAAAoqoyMDDkcjptuY3NdP5VRRuTm5ur7778vVPMzgdPpVEhIiFJSUv7rwQDPYb9bg/1uDfa7NcrqfufMTQF8fHxUp04dq2OUOofDUaYOfm/BfrcG+90a7HdrsN/zY0IxAAAwCuUGAAAYhXJjOLvdrkmTJslut1sdpUxhv1uD/W4N9rs12O83VuYmFAMAALNx5gYAABiFcgMAAIxCuQEAAEah3AAAAKNQbgAAgFEoN4CHDB482H3vsl/KysrS4MGDLUgEAGUTl4IbolWrVoW+V1ZSUlIJpymbypUrp9TUVNWoUSPP+A8//KCaNWvq6tWrFiUzD8c7ypK9e/cWetsWLVqUYJLbR5m7t5SpoqOj3X++dOmS/vKXvyg8PFyRkZGSpK+//loHDhzQH/7wB4sSmsvpdMrlcsnlcunChQvy8/NzP5eTk6MvvvgiX+HBreF4t8Znn31W6G379OlTgknKlpYtW8pms8nlcv3XUp+Tk1NKqbwbZ24M9NRTTyk4OFjTpk3LMz5p0iSlpKQoPj7eomRm8vHxuekPHJvNpilTpuhPf/pTKaYqOzjeS4+PT96ZDNd/4f7y6+v4Jes5ycnJ7j/v2rVLY8eO1Ysvvugu84mJiYqLi9Prr7+ep/iXZZQbAwUEBGjHjh1q2LBhnvEjR46obdu2ysjIsCiZmTZu3CiXy6WoqCj94x//UJUqVdzP+fr6qm7duqpVq5aFCc3G8W6NtWvX6uWXX9aMGTPy/JIdP368ZsyYoQceeMDihGZq166dJk+erF69euUZ/+KLLzRhwgTt3LnTomTehY+lDFSxYkVt2bIl3w/7LVu25PnIBJ7RqVMnSdKJEycUEhKS71+3KFkc79YYNWqU5s2bp9/+9rfuse7du6tSpUp6+umndfDgQQvTmWvfvn0KCwvLNx4WFqZvvvnGgkTeiXJjoFGjRum5555TUlKS2rVrJ0natm2b4uPjNWHCBIvTmatu3bo6f/68tm/frjNnzig3NzfP8zExMRYlMxvHuzWOHTumO++8M994QECATp48Wep5yoomTZooNjZW77//vnx9fSVJly9fVmxsrJo0aWJxOu/Bx1KG+vvf/67Zs2e7//XUpEkTjRw5Uo888ojFycz1z3/+UwMGDFBmZqYcDkee+Qc2m03nzp2zMJ3ZON5L33333Sc/Pz99+OGHCgoKkiSlp6crJiZGly5d0saNGy1OaKbt27frwQcflMvlcl8ZtXfvXtlsNv3zn/90F/yyjnIDeMhvfvMb9erVSzNmzFClSpWsjgOUqKNHj+qhhx7St99+q5CQEElSSkqKGjZsqGXLlqlBgwYWJzRXVlaWPv74Yx06dEjStTLfv39/Va5c2eJk3oNyY6jz589r8eLFOn78uMaOHasqVaooKSlJQUFBql27ttXxjFS5cmXt27dP9erVszpKmXT58uUCPw686667LEpkPpfLpTVr1uT5Jdu1a9dCr0EElBTKjYH27t2rrl27uj/7Pnz4sOrVq6fx48fr1KlTWrBggdURjdSvXz899thjfBRSyo4cOaLBgwdr69atecavrwnCJckl79KlS7Lb7ZSaUvLhhx/q3Xff1fHjx5WYmKi6devqrbfeUr169dS3b1+r43kFJhQbaMyYMXryySf1+uuvy9/f3z3eq1cv9e/f38JkZuvdu7defPFFffPNN2revLkqVKiQ53kWNSsZTz75pMqXL68VK1YoODiYX7ClJDc3V6+++qrmzZun9PR0ffvtt6pXr54mTJig0NBQDRkyxOqIRnrnnXc0ceJEjRo1StOnT3eX98DAQM2aNYtyc50LxnE4HK6jR4+6XC6X64477nAdO3bM5XK5XCdPnnTZ7XYroxnNZrPd8OHj42N1PGNVqlTJdfDgQatjlDlTpkxx1atXz/XRRx+5Klas6P45s2jRIlf79u0tTmeuJk2auJYuXepyufL+fN+3b5+ratWqFibzLizIYSC73S6n05lv/Ntvv1X16tUtSFQ25Obm3vDBRyMlJzw8XD/88IPVMcqcBQsW6L333tOAAQNUrlw593hERIR7Dg4878SJE2rVqlW+cbvdrqysLAsSeSfKjYH69OmjqVOn6sqVK5KuXYZ86tQpvfzyy3r44YctTgd41syZM/XSSy9pw4YN+vHHH+V0OvM8UDK+++67Aq+Iys3Ndf/sgeeFhYVp9+7d+cZXrVrFOje/wJwbA8XFxen3v/+9atSooZ9//lmdOnVSWlqaIiMj9eqrr1odzyhz5szR008/LT8/P82ZM+em244YMaKUUpUtXbt2lSTdf//9ecZdTCguUeHh4frqq69Ut27dPOOLFy8u8MwCPGPMmDEaNmyYLl26JJfLpe3bt+tvf/ube2E/XMPVUgbbvHmz9u7dq8zMTLVu3dr9SwCeExYWph07dqhq1aoFLol+nc1m0/Hjx0sxWdnx3xaLu357DHjW8uXLNXDgQI0bN05Tp07VlClTdPjwYS1YsEArVqzg3lIl6OOPP9bkyZN17NgxSVKtWrU0ZcoUJnH/AuUGAFAsX331laZOnao9e/a4/xE1ceJEdevWzepoZcLFixeVmZmpGjVqWB3F61BuDPWvf/1L69evL3BRszfffNOiVEDJuXjxok6dOqXLly/nGb++RD1ggqioKC1ZsiTffb2cTqeio6O1bt06a4J5GebcGGjGjBkaP368GjVqpKCgoHz3OELJOX36tD777LMCf8lSKkvG2bNnNWjQIK1cubLA55lzUzKeeuopPfHEE+rcubPVUcqUDRs25PvZIl1bSPGrr76yIJF3otwYaPbs2YqPj9eTTz5pdZQyJSEhQX369FG9evV06NAhNWvWTCdPnpTL5VLr1q2tjmesUaNG6fz589q2bZs6d+6spUuXKj09XdOnT1dcXJzV8Yx19uxZ9ejRQ9WrV9djjz2mAQMGqGXLllbHMtbevXvdf/7mm2+Ulpbm/jonJ0erVq3i1jq/wMdSBgoODtamTZvUsGFDq6OUKe3atVPPnj01ZcoU+fv7a8+ePapRo4YGDBigHj166LnnnrM6opGCg4O1fPlytWvXTg6HQzt27NBvfvMbffbZZ3r99de1efNmqyMa66efftKnn36qhQsX6quvvlLjxo01YMAA9e/fX6GhoVbHM4qPj4/7zHtBv7YrVqyoP//5zxo8eHBpR/NKlBsDvf766/r+++81a9Ysq6OUKf7+/tq9e7fq16+vwMBAbd68WU2bNtWePXvUt29fnTx50uqIRnI4HNq7d69CQ0NVt25dLVy4UB07dtSJEyfUtGlTXbx40eqIZcLp06f1t7/9TfHx8Tpy5IiuXr1qdSSjJCcny+VyqV69etq+fXueBVl9fX1Vo0aNPIsplnV8LGWgsWPHqnfv3qpfv77Cw8Pz3eNoyZIlFiUzW+XKld2fhQcHB+vYsWNq2rSpJLGCbglq1KiRDh8+rNDQUEVEROjdd99VaGio5s2bp+DgYKvjlQlXrlzRjh07tG3bNp08eVJBQUFWRzLO9fWE1q9fr5YtW6p8+by/vnNycrRp0ybdd999VsTzOpQbA40YMULr169Xly5dVLVqVSYRl5L27dtr8+bNatKkiXr16qUXXnhB+/bt05IlS9S+fXur4xlr5MiRSk1NlSRNmjRJPXr00McffyxfX1/Nnz/f2nCGW79+vRYuXKh//OMfys3NVb9+/bRixQpFRUVZHc1YUVFRSk1NzXf59/nz59WlSxcm0P8bH0sZyN/fX4sWLVLv3r2tjlKmHD9+XJmZmWrRooWysrL0wgsvaOvWrWrYsKHefPPNfCu5omRcvHhRhw4d0l133aVq1apZHcdYtWvX1rlz59SjRw8NGDBADz74oOx2u9WxjOfj46P09PR89wn89ttv1bZtW2458m+cuTFQlSpVVL9+fatjlCk5OTk6ffq0e02VypUra968eRanKpsqVarE1WmlYPLkyfqf//mffOutoGT069dP0rXlPJ588sk8RTInJ0d79+5Vhw4drIrndSg3Bpo8ebImTZqkDz74QJUqVbI6TplQrlw5devWTQcPHuSHfSlzuVxavHjxDRetZI5ZyRg6dKjVEcqUgIAASdeOd39/f1WsWNH9nK+vr9q3b8//J79AuTHQnDlzdOzYMQUFBSk0NDTfhOKkpCSLkpmtWbNmOn78+E3vMQXPGzVqlN5991116dIl36KV8Kx+/fpp/vz5cjgc7jMJN0Kp9KwPPvhAkhQaGqqxY8eqcuXKFifybpQbA0VHR1sdoUyaPn26xo4dq2nTpqlNmzb5fvg4HA6Lkpntww8/1JIlS9SrVy+roxgvICDAXR6vn0lA6Zo0aZLVEW4LTCg2zNWrVzVjxgwNHjxYderUsTpOmeLj4+P+8y/PHrhcLtlsNq5iKCFhYWFauXKlGjdubHUUoES0bt1aCQkJCgwMVKtWrW56dpIz89dw5sYw5cuX1xtvvKGYmBiro5Q569evtzpCmTR58mRNmTJF8fHxeeYhoORdvXpVGzZs0LFjx9S/f3/5+/vr+++/l8Ph0B133GF1PGP07dvXPYGYM/OFw5kbA/Xt21f9+vXTwIEDrY5Sppw6dUohISH5/lXlcrmUkpKiu+66y6JkZvv555/10EMPacuWLcwxK0XJycnq0aOHTp06pezsbH377beqV6+eRo4cqezsbK4WhKU4c2Ognj176pVXXtG+ffsKnPvRp08fi5KZLSwsrMDFtc6dO6ewsDA+liohAwcO1M6dO/XEE08wobgUjRw5Um3bttWePXtUtWpV9/hDDz3EVTulJDMzM9/Vgcztu4YzNwb65dyPX2PuR8m50eJaycnJCg8PV1ZWlkXJzFa5cmWtXr1av/3tb62OUqZUrVpVW7duVaNGjdw3iq1Xr55Onjyp8PBw7ulVQk6cOKHhw4drw4YNunTpknucuX15cebGQL9u8ihZY8aMkXStOE6YMCHP2kI5OTnatm2bWrZsaVE684WEhPCvVQvk5uYW+Iv09OnT8vf3tyBR2fDEE0/I5XIpPj6eM5U3QbkBbtGuXbskXfuX0759++Tr6+t+ztfXVxERERo7dqxV8YwXFxenl156SfPmzVNoaKjVccqMbt26adasWXrvvfckXSv3mZmZmjRpEpfll6A9e/Zo586datSokdVRvBofSxlizpw5evrpp+Xn56c5c+bcdNsRI0aUUqqyZdCgQZo9ezZnEUpZYGCgLl68qKtXr6pSpUr5JhSfO3fOomRmO336tLp37y6Xy6UjR46obdu2OnLkiKpVq6ZNmzblm3sGz+jSpYv+9Kc/qWvXrlZH8WqUG0OEhYVpx44dqlq16k1XyLXZbDp+/HgpJiu7nE6n1q1bp8aNG7MGSwn661//etPnuWqw5Fy9elWffPKJ9uzZo8zMTLVu3VoDBgzgkvwSdOzYMT377LN64okn1KxZs3xl/vr97co6yo0hMjIyWDHUYo888ojuu+8+DR8+XD///LMiIiJ08uRJuVwuLVq0SA8//LDVEQHc5r7++mv1799fJ0+edI/ZbDYmFP/KjS+rwW2lSpUqOnPmjCQpKipK58+ftzZQGbRp0ybde++9kqSlS5fK5XLp/PnzmjNnjqZPn25xOrMdO3ZM48eP1+OPP+7+72DlypU6cOCAxcnMFRsbq/j4+Hzj8fHxmjlzpgWJyobBgwerVatWSkxM1PHjx3XixIk8/4trKDeGuOOOO/Tjjz9KkjZs2KArV65YnKjsycjIUJUqVSRJq1at0sMPP6xKlSqpd+/eOnLkiMXpzLVx40Y1b95c27Zt05IlS5SZmSnp2sRL7sNTct59990CP25t2rQpC/iVoOTkZM2cOVP33HOPQkNDVbdu3TwPXMPVUobo2rWrunTpoiZNmki6tpDWL6/a+aV169aVZrQyIyQkRImJiapSpYpWrVqlRYsWSZJ++ukn+fn5WZzOXK+88oqmT5+uMWPG5LkEOSoqSm+//baFycyWlpam4ODgfOPVq1dXamqqBYnKhqioKO3Zs0cNGjSwOopXo9wY4qOPPtJf//pXHTt2TBs3blTTpk3zrLeCkjdq1CgNGDBAd9xxh+rWravOnTtLuvZxVfPmza0NZ7B9+/Zp4cKF+cZr1KihH374wYJEZUNISIi2bNmS7wKGLVu2qFatWhalMt+DDz6o0aNHa9++fWrevHm+CcWsQH8N5cYQFStW1LPPPitJ2rFjh2bOnKk777zT2lBlzB/+8Ae1a9dOKSkpeuCBB9wrRderV485NyXozjvvVGpqar5fsrt27VLt2rUtSmW+oUOHatSoUbpy5YqioqIkSQkJCXrppZf0wgsvWJzOXNd/zk+dOjXfc0wo/g+ulgJwWxs7dqy2bdumTz/9VL/5zW+UlJSk9PR0xcTEKCYmhnk3JcTlcumVV17RnDlzdPnyZUmSn5+fXn75ZU2cONHidCjrKDcGysnJ0fz585WQkKAzZ87kux0Dc25KBvvdGpcvX9awYcM0f/585eTkqHz58rp69aoGDBig+fPnq1y5clZHNFpmZqYOHjyoihUrqmHDhrLb7VZHAvhYykQjR47U/Pnz1bt3bzVr1ox7j5QS9rs1fH199b//+7+aOHGi9u3bp8zMTLVq1UoNGza0OlqZkJaWpnPnzum+++6T3W53r7eCkpOQkKC33npLBw8elCQ1adJEo0aNYtXiX+DMjYGqVaumBQsWcH+XUsZ+Lz3Xb1ZaGG+++WYJJim7fvzxRz3yyCNav369bDabjhw5onr16mnw4MEKDAxUXFyc1RGN9Je//EUjR47U73//e0VGRkq6trDf4sWL9dZbb2nYsGEWJ/QOnLkxkK+vL5cJWoD9Xnqu36z0uqSkJF29etV9M8Fvv/1W5cqVU5s2bayIVyaMHj1aFSpU0KlTp9xLUEjSo48+qjFjxlBuSsiMGTP01ltvafjw4e6xESNGqGPHjpoxYwbl5t9YxM9AL7zwgmbPni1OypUu9nvpWb9+vfvx4IMPqlOnTjp9+rSSkpKUlJSklJQUdenSRb1797Y6qrG+/PJLzZw5U3Xq1Mkz3rBhQyUnJ1uUynznz59Xjx498o1369ZNGRkZFiTyTpy5MdDmzZu1fv16rVy5Uk2bNs23DsKSJUssSmY29rs14uLi9OWXXyowMNA9FhgYqOnTp6tbt25cllxCsrKyClxL69y5c0wqLkF9+vTR0qVL9eKLL+YZX758uX73u99ZlMr7UG4MdOedd+qhhx6yOkaZw363htPp1NmzZ/ONnz17VhcuXLAgUdlw7733asGCBZo2bZqka2us5Obm6vXXX1eXLl0sTmeWOXPmuP8cHh6uV199VRs2bMgz52bLli0U+V9gQjGA21pMTIy++uorxcXFqV27dpKkbdu26cUXX9S9996rv/71rxYnNNOBAwcUFRWl1q1ba926derTp48OHDigc+fOacuWLapfv77VEY3x6wUqb8Rms3HzzH+j3Bjs7NmzOnz4sCSpUaNGql69usWJygb2e+m6ePGixo4dq/j4ePcNY8uXL68hQ4bojTfeUOXKlS1OaJ4rV66oR48eio2N1Zo1a7Rnzx5lZmaqdevWGjZsWIH3nAJKE+XGQFlZWXr++ee1YMEC90Jy5cqVU0xMjP785z9zz6kSwn63VlZWlo4dOyZJql+/PqWmhFWvXl1bt25lPSF4JcqNgZ555hmtXbtWb7/9tjp27Cjp2mTXESNG6IEHHtA777xjcUIzsd9RlowePVp2u12vvfaa1VHKlMGDB9/0+fj4+FJK4t0oNwaqVq2aFi9e7L4r9XXr16/XI488UuDkS9w69jvKkutnKRs2bKg2bdrkO1PG4okl49cXLVy5ckX79+/X+fPnFRUVxVWZ/8bVUga6ePGigoKC8o3XqFFDFy9etCBR2cB+R1myf/9+tW7dWtK1RRN/idsvlJylS5fmG8vNzdVzzz3HJO5f4MyNge6//35VrVpVCxYskJ+fnyTp559/1sCBA3Xu3DmtXbvW4oRmYr8DsMrhw4fVuXNnpaamWh3FK3DmxkCzZs1Sjx49VKdOHUVEREiS9uzZI7vdri+//NLidOZivwOwyrFjx3T16lWrY3gNztwY6uLFi/r444916NAhSdfuGjtgwABVrFjR4mRmY78DKEm/vmmsy+VSamqqPv/8cw0cOFBvv/22Rcm8C+XGQLGxsQoKCso3qz4+Pl5nz57Vyy+/bFEys7HfAZS0X6/+7OPjo+rVqysqKkqDBw9W+fJ8ICNRbowUGhqqhQsXqkOHDnnGt23bpscee0wnTpywKJnZ2O8AStrFixflcrncV6edPHlSy5YtU5MmTdS9e3eL03kP7gpuoLS0tAJXCK1evTqTzUoQ+x1ASYuOjtaHH34o6dodwtu3b6+4uDhFR0ezltYvUG4MFBISoi1btuQb37Jli2rVqmVBorKB/Q6gpCUlJenee++VJC1evFhBQUFKTk7WggUL8txgs6zjwzkDDR06VKNGjdKVK1cUFRUlSUpISNBLL73EXWNLEPsdQEm7ePGi/P39JUlffvml+vXrJx8fH7Vv317JyckWp/MelBsDvfjii/rxxx/1hz/8QZcvX5Yk+fn56eWXX9a4ceMsTmcu9juAktagQQMtW7ZMDz30kFavXq3Ro0dLks6cOSOHw2FxOu/BhGKDZWZm6uDBg6pYsaIaNmwou91udaQygf0OoKQsXrxY/fv3V05Oju6//373GlqxsbHatGmTVq5caXFC70C5AQDgNpKWlqbU1FRFRETIx+fa1Nnt27fL4XCocePGFqfzDpQbAABgFK6WAgAARqHcAAAAo1BuAACAUSg3AADAKJQbAABgFMoNAAAwCuUGAAAY5f8DuhufnUP2zX0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -795,10 +788,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:19:55.863266Z",
-     "iopub.status.busy": "2024-10-25T15:19:55.863051Z",
-     "iopub.status.idle": "2024-10-25T15:19:55.875697Z",
-     "shell.execute_reply": "2024-10-25T15:19:55.875126Z"
+     "iopub.execute_input": "2024-10-25T15:21:51.336014Z",
+     "iopub.status.busy": "2024-10-25T15:21:51.335617Z",
+     "iopub.status.idle": "2024-10-25T15:21:51.349033Z",
+     "shell.execute_reply": "2024-10-25T15:21:51.348321Z"
     },
     "jupyter": {
      "outputs_hidden": false
diff --git a/main/example_notebooks/graph_conditional_independence_refuter.ipynb b/main/example_notebooks/graph_conditional_independence_refuter.ipynb
index a0c3a7708..d648a013a 100644
--- a/main/example_notebooks/graph_conditional_independence_refuter.ipynb
+++ b/main/example_notebooks/graph_conditional_independence_refuter.ipynb
@@ -16,10 +16,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:00.675813Z",
-     "iopub.status.busy": "2024-10-25T15:20:00.675438Z",
-     "iopub.status.idle": "2024-10-25T15:20:00.691627Z",
-     "shell.execute_reply": "2024-10-25T15:20:00.691004Z"
+     "iopub.execute_input": "2024-10-25T15:21:56.384962Z",
+     "iopub.status.busy": "2024-10-25T15:21:56.384768Z",
+     "iopub.status.idle": "2024-10-25T15:21:56.401963Z",
+     "shell.execute_reply": "2024-10-25T15:21:56.401431Z"
     }
    },
    "outputs": [],
@@ -33,10 +33,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:00.693619Z",
-     "iopub.status.busy": "2024-10-25T15:20:00.693281Z",
-     "iopub.status.idle": "2024-10-25T15:20:02.109181Z",
-     "shell.execute_reply": "2024-10-25T15:20:02.108495Z"
+     "iopub.execute_input": "2024-10-25T15:21:56.404073Z",
+     "iopub.status.busy": "2024-10-25T15:21:56.403886Z",
+     "iopub.status.idle": "2024-10-25T15:21:58.051991Z",
+     "shell.execute_reply": "2024-10-25T15:21:58.051335Z"
     }
    },
    "outputs": [],
@@ -69,10 +69,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:02.111603Z",
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@@ -128,10 +128,10 @@
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diff --git a/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb b/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb
index 226575e8d..33ffb5282 100644
--- a/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb
+++ b/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb
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     <tr>
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       <td>1.0</td>
       <td>0.0</td>
       <td>0.0</td>
-      <td>1.0</td>
-      <td>2636.353</td>
-      <td>2937.2640</td>
-      <td>7535.942</td>
-      <td>0.0</td>
       <td>0.0</td>
-      <td>0.388545</td>
-      <td>2.573708</td>
+      <td>0.0000</td>
+      <td>0.00000</td>
+      <td>10747.3500</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>0.540418</td>
+      <td>1.850419</td>
     </tr>
   </tbody>
 </table>
@@ -920,7 +920,7 @@ <h2>Treatment Effect Estimation<a class="headerlink" href="#Treatment-Effect-Est
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 2090.93439377732$</div></div>
+$\displaystyle 1852.43749904146$</div></div>
 </div>
 <p>We could get some rough error bars on the outcome using the normal approximation for a 95% confidence interval, like</p>
 <div class="nbinput docutils container">
@@ -939,7 +939,7 @@ <h2>Treatment Effect Estimation<a class="headerlink" href="#Treatment-Effect-Est
 </div>
 <div class="output_area docutils container">
 <div class="math notranslate nohighlight">
-$\displaystyle 1150.04804650325$</div></div>
+$\displaystyle 1251.83212213323$</div></div>
 </div>
 <p>but note that these DO NOT contain propensity score estimation error. For that, a bootstrapping procedure might be more appropriate.</p>
 <p>This is just one statistic we can compute from the interventional distribution of <code class="docutils literal notranslate"><span class="pre">'re78'</span></code>. We can get all of the interventional moments as well, including functions of <code class="docutils literal notranslate"><span class="pre">'re78'</span></code>. We can leverage the full power of pandas, like</p>
@@ -958,12 +958,12 @@ <h2>Treatment Effect Estimation<a class="headerlink" href="#Treatment-Effect-Est
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 count      445.000000
-mean      5122.485177
-std       6370.769334
+mean      5203.569643
+std       6687.889826
 min          0.000000
 25%          0.000000
-50%       3786.628000
-75%       7565.273000
+50%       3515.929000
+75%       7952.540000
 max      60307.930000
 Name: re78, dtype: float64
 </pre></div></div>
@@ -1120,87 +1120,87 @@ <h2>Specifying Interventions<a class="headerlink" href="#Specifying-Intervention
     <tr>
       <th>0</th>
       <td>True</td>
-      <td>45.0</td>
-      <td>5.0</td>
+      <td>25.0</td>
+      <td>10.0</td>
       <td>1.0</td>
       <td>0.0</td>
+      <td>0.0</td>
       <td>1.0</td>
-      <td>1.0</td>
+      <td>0.0</td>
       <td>0.000</td>
       <td>0.000</td>
-      <td>8546.715</td>
       <td>1.0</td>
       <td>1.0</td>
-      <td>0.520700</td>
-      <td>1.920493</td>
+      <td>0.374159</td>
+      <td>2.672663</td>
     </tr>
     <tr>
       <th>1</th>
       <td>True</td>
-      <td>23.0</td>
-      <td>8.0</td>
-      <td>0.0</td>
+      <td>26.0</td>
+      <td>10.0</td>
       <td>1.0</td>
       <td>0.0</td>
+      <td>0.0</td>
       <td>1.0</td>
+      <td>0.0</td>
       <td>0.000</td>
-      <td>0.000</td>
-      <td>3881.284</td>
+      <td>9265.788</td>
       <td>1.0</td>
       <td>1.0</td>
-      <td>0.286244</td>
-      <td>3.493526</td>
+      <td>0.375730</td>
+      <td>2.661485</td>
     </tr>
     <tr>
       <th>2</th>
       <td>True</td>
-      <td>42.0</td>
-      <td>9.0</td>
+      <td>40.0</td>
+      <td>12.0</td>
       <td>1.0</td>
       <td>0.0</td>
-      <td>1.0</td>
-      <td>1.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0</td>
       <td>0.000</td>
-      <td>3058.531</td>
-      <td>1294.409</td>
+      <td>10804.320</td>
       <td>1.0</td>
-      <td>0.0</td>
-      <td>0.465132</td>
-      <td>2.149926</td>
+      <td>1.0</td>
+      <td>0.563628</td>
+      <td>1.774220</td>
     </tr>
     <tr>
       <th>3</th>
       <td>True</td>
-      <td>20.0</td>
-      <td>9.0</td>
+      <td>19.0</td>
+      <td>11.0</td>
       <td>1.0</td>
       <td>0.0</td>
       <td>0.0</td>
       <td>1.0</td>
-      <td>6083.994</td>
-      <td>0.000</td>
-      <td>8881.665</td>
       <td>0.0</td>
+      <td>0.000</td>
+      <td>7458.105</td>
+      <td>1.0</td>
       <td>1.0</td>
-      <td>0.378167</td>
-      <td>2.644335</td>
+      <td>0.353143</td>
+      <td>2.831718</td>
     </tr>
     <tr>
       <th>4</th>
       <td>True</td>
-      <td>23.0</td>
-      <td>12.0</td>
+      <td>28.0</td>
+      <td>11.0</td>
       <td>1.0</td>
       <td>0.0</td>
       <td>0.0</td>
+      <td>1.0</td>
       <td>0.0</td>
-      <td>6269.341</td>
-      <td>3039.960</td>
-      <td>8484.239</td>
-      <td>0.0</td>
+      <td>1284.079</td>
+      <td>60307.930</td>
+      <td>1.0</td>
       <td>0.0</td>
-      <td>0.535418</td>
-      <td>1.867699</td>
+      <td>0.367047</td>
+      <td>2.724449</td>
     </tr>
   </tbody>
 </table>
diff --git a/main/example_notebooks/lalonde_pandas_api.ipynb b/main/example_notebooks/lalonde_pandas_api.ipynb
index 12338d6e9..64104150c 100644
--- a/main/example_notebooks/lalonde_pandas_api.ipynb
+++ b/main/example_notebooks/lalonde_pandas_api.ipynb
@@ -22,10 +22,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:10.657695Z",
-     "iopub.status.busy": "2024-10-25T15:20:10.657249Z",
-     "iopub.status.idle": "2024-10-25T15:20:10.663107Z",
-     "shell.execute_reply": "2024-10-25T15:20:10.662672Z"
+     "iopub.execute_input": "2024-10-25T15:22:07.904205Z",
+     "iopub.status.busy": "2024-10-25T15:22:07.904015Z",
+     "iopub.status.idle": "2024-10-25T15:22:07.910065Z",
+     "shell.execute_reply": "2024-10-25T15:22:07.909573Z"
     }
    },
    "outputs": [],
@@ -48,10 +48,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:10.664949Z",
-     "iopub.status.busy": "2024-10-25T15:20:10.664619Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.327763Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.327130Z"
+     "iopub.execute_input": "2024-10-25T15:22:07.912159Z",
+     "iopub.status.busy": "2024-10-25T15:22:07.911698Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.601886Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.601097Z"
     }
    },
    "outputs": [],
@@ -80,10 +80,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.329848Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.329673Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.333634Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.333025Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.604465Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.604229Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.608581Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.608091Z"
     }
    },
    "outputs": [],
@@ -107,10 +107,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.335677Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.335322Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.374265Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.373732Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.610406Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.610208Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.653084Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.652480Z"
     },
     "scrolled": true
    },
@@ -138,10 +138,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.375967Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.375787Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.388547Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.388040Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.655733Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.655294Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.669279Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.668648Z"
     }
    },
    "outputs": [
@@ -290,10 +290,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.390388Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.389972Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.402061Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.401437Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.671470Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.671045Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.684068Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.683422Z"
     },
     "scrolled": true
    },
@@ -339,106 +339,106 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>True</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.000</td>\n",
        "      <td>0.0000</td>\n",
-       "      <td>8061.485</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>7458.1050</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.385363</td>\n",
-       "      <td>2.594954</td>\n",
+       "      <td>0.353143</td>\n",
+       "      <td>2.831718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>True</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>11.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5424.485</td>\n",
-       "      <td>5463.8030</td>\n",
-       "      <td>6788.463</td>\n",
+       "      <td>9594.3080</td>\n",
+       "      <td>16341.16000</td>\n",
+       "      <td>16900.3000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.292719</td>\n",
-       "      <td>3.416244</td>\n",
+       "      <td>0.715871</td>\n",
+       "      <td>1.396900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>True</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>2611.2180</td>\n",
-       "      <td>2484.549</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>506.40760</td>\n",
+       "      <td>604.1987</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.588747</td>\n",
-       "      <td>1.698522</td>\n",
+       "      <td>0.618675</td>\n",
+       "      <td>1.616359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>False</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>9.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5214.306</td>\n",
-       "      <td>474.5018</td>\n",
-       "      <td>4812.576</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>445.1704</td>\n",
+       "      <td>74.34345</td>\n",
+       "      <td>6210.6700</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.430120</td>\n",
-       "      <td>2.324932</td>\n",
+       "      <td>0.268051</td>\n",
+       "      <td>3.730636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>True</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2636.353</td>\n",
-       "      <td>2937.2640</td>\n",
-       "      <td>7535.942</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.388545</td>\n",
-       "      <td>2.573708</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>10747.3500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.540418</td>\n",
+       "      <td>1.850419</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   treat   age  educ  black  hisp  married  nodegr      re74       re75  \\\n",
-       "0   True  17.0   8.0    1.0   0.0      0.0     1.0     0.000     0.0000   \n",
-       "1   True  19.0  11.0    0.0   1.0      1.0     1.0  5424.485  5463.8030   \n",
-       "2   True  31.0  12.0    0.0   0.0      0.0     0.0     0.000  2611.2180   \n",
-       "3  False  27.0  13.0    0.0   0.0      0.0     0.0  5214.306   474.5018   \n",
-       "4   True  19.0   8.0    1.0   0.0      0.0     1.0  2636.353  2937.2640   \n",
+       "   treat   age  educ  black  hisp  married  nodegr       re74         re75  \\\n",
+       "0   True  19.0  11.0    1.0   0.0      0.0     1.0     0.0000      0.00000   \n",
+       "1  False  29.0   9.0    0.0   1.0      0.0     1.0  9594.3080  16341.16000   \n",
+       "2  False  22.0   9.0    1.0   0.0      0.0     1.0     0.0000    506.40760   \n",
+       "3   True  17.0   9.0    0.0   1.0      0.0     1.0   445.1704     74.34345   \n",
+       "4   True  26.0  12.0    1.0   0.0      0.0     0.0     0.0000      0.00000   \n",
        "\n",
-       "       re78  u74  u75  propensity_score    weight  \n",
-       "0  8061.485  1.0  1.0          0.385363  2.594954  \n",
-       "1  6788.463  0.0  0.0          0.292719  3.416244  \n",
-       "2  2484.549  1.0  0.0          0.588747  1.698522  \n",
-       "3  4812.576  0.0  0.0          0.430120  2.324932  \n",
-       "4  7535.942  0.0  0.0          0.388545  2.573708  "
+       "         re78  u74  u75  propensity_score    weight  \n",
+       "0   7458.1050  1.0  1.0          0.353143  2.831718  \n",
+       "1  16900.3000  0.0  0.0          0.715871  1.396900  \n",
+       "2    604.1987  1.0  0.0          0.618675  1.616359  \n",
+       "3   6210.6700  0.0  0.0          0.268051  3.730636  \n",
+       "4  10747.3500  1.0  1.0          0.540418  1.850419  "
       ]
      },
      "execution_count": 6,
@@ -464,10 +464,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.403961Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.403521Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.456088Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.455589Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.686337Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.685971Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.745203Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.744548Z"
     }
    },
    "outputs": [
@@ -502,21 +502,21 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.457994Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.457669Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.474035Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.473541Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.747482Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.747053Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.766960Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.766398Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 2090.93439377732$"
+       "$\\displaystyle 1852.43749904146$"
       ],
       "text/plain": [
-       "2090.934393777318"
+       "1852.4374990414572"
       ]
      },
      "execution_count": 8,
@@ -540,21 +540,21 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.475734Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.475538Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.493528Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.492932Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.769128Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.768736Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.790005Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.789340Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 1150.04804650325$"
+       "$\\displaystyle 1251.83212213323$"
       ],
       "text/plain": [
-       "1150.0480465032542"
+       "1251.8321221332321"
       ]
      },
      "execution_count": 9,
@@ -587,10 +587,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.495494Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.495163Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.501434Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.500860Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.792425Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.791976Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.799189Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.798685Z"
     }
    },
    "outputs": [
@@ -598,12 +598,12 @@
      "data": {
       "text/plain": [
        "count      445.000000\n",
-       "mean      5122.485177\n",
-       "std       6370.769334\n",
+       "mean      5203.569643\n",
+       "std       6687.889826\n",
        "min          0.000000\n",
        "25%          0.000000\n",
-       "50%       3786.628000\n",
-       "75%       7565.273000\n",
+       "50%       3515.929000\n",
+       "75%       7952.540000\n",
        "max      60307.930000\n",
        "Name: re78, dtype: float64"
       ]
@@ -622,10 +622,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.503193Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.503027Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.508818Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.508320Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.801248Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.800806Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.807841Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.807208Z"
     }
    },
    "outputs": [
@@ -664,10 +664,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.510619Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.510253Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.514883Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.514405Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.809835Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.809481Z",
+     "iopub.status.idle": "2024-10-25T15:22:10.815070Z",
+     "shell.execute_reply": "2024-10-25T15:22:10.814470Z"
     }
    },
    "outputs": [],
@@ -680,10 +680,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.516638Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.516272Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.756215Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.755501Z"
+     "iopub.execute_input": "2024-10-25T15:22:10.817059Z",
+     "iopub.status.busy": "2024-10-25T15:22:10.816714Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.103505Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.102956Z"
     }
    },
    "outputs": [
@@ -699,7 +699,7 @@
     },
     {
      "data": {
-      "image/png": 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7Ww6HQ1FRUVbNuXPU1tTOAQAA4NNANGnSJOXk5Oibb77Rrl27dOedd8rf31/33XefnE6nEhISlJKSog8//FB5eXl66KGH5HK51LdvX0nSoEGDFBUVpQceeECffvqpsrKyNG3aNCUmJsput0uSJkyYoK+//lpTpkzRgQMHtGjRIq1evVrJycm+XDoAAGhEfHoP0f/+7//qvvvu07Fjx9S6dWv169dPH330kVq3bi1Jmjdvnvz8/BQfH6/KykrFxsZq0aJF1uP9/f21fv16Pfroo3K5XAoODtbYsWM1c+ZMqyYyMlKZmZlKTk7W/Pnz1a5dO73++ut85B4AAFhsHo/H4+smGju32y2n06ny8nLuJ7rCnTx5UsOGDbP2165dq5CQEB92hCtF9OTlvm4BaJTyZo+pt7nr8v7dqO4hAgAA8AUCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjBfi6AfxT9OTlvm7BeLbqKjnP2R/w5NvyBAT6rB/8Q97sMb5uAcAVjjNEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYLyAuj7g008/VV5engYMGKCOHTuqoKBACxcuVE1Nje68807FxsbWR58AAAD1pk5niN59911FR0drypQp6tmzpzZv3qx+/frp4MGD+uabbxQXF6eVK1fWV68AAAD1ok6B6K9//auefvpp/f3vf9drr72mu+++WykpKcrOztbGjRv1wgsvaPbs2fXVKwAAQL2oUyAqLCzUqFGjJEn33nuvKioqNHz4cGv8zjvv1KFDh35TI88//7xsNpuSkpKsY6dPn1ZiYqJatWqlkJAQxcfHq7S01OtxRUVFiouLU7NmzRQaGqrJkyerurraq2bbtm3q1auX7Ha7OnXqpIyMjN/UIwAAuDLVKRA1b95cx44dkySVlZWpurra2pekY8eOKSQkpM5N7NmzR6+88op69OjhdTw5OVnr1q3TmjVrlJOTo+LiYo0YMcIaP3v2rOLi4lRVVaVdu3Zp2bJlysjI0PTp062aw4cPKy4uTgMHDlR+fr6SkpI0btw4ZWVl1blPAABwZapTIIqJiVFiYqJWrFihsWPHatCgQUpNTdWBAwdUWFioyZMnq1+/fnVq4OTJkxo1apRee+01XXXVVdbx8vJyvfHGG5o7d65uvfVWRUdHa+nSpdq1a5c++ugjSdKmTZv0+eef66233tINN9ygIUOG6JlnntHChQtVVVUlSUpPT1dkZKTmzJmjrl27auLEibrrrrs0b968OvUJAACuXHUKRC+++KIcDocmTJigqqoqrVq1Sr1791ZUVJSioqJUXFys559/vk4NJCYmKi4uTjExMV7H8/LydObMGa/jXbp0Ufv27ZWbmytJys3NVffu3RUWFmbVxMbGyu12q6CgwKr56dyxsbHWHBdSWVkpt9vttQEAgCtXnT52HxYWpk2bNnkde/nll5WcnKwff/xRXbp0UUDAxU/59ttv65NPPtGePXvOGyspKVFgYKBatGhxXg8lJSVWzblhqHa8duyXatxut06dOqWgoKDznjstLU1PP/30Ra8DAABc3i7JFzN27NhR3bp1q1MYOnLkiP7jP/5DK1asUNOmTS9FG5dMamqqysvLre3IkSO+bgkAANSjOgWixx57TP/93/99SZ44Ly9PR48eVa9evRQQEKCAgADl5ORowYIFCggIUFhYmKqqqlRWVub1uNLSUoWHh0uSwsPDz/vUWe3+r9U4HI4Lnh2SJLvdLofD4bUBAIArV50C0cKFCzVgwAB17txZL7zwgnVZ6re47bbbtG/fPuXn51tb7969NWrUKOvvJk2aaMuWLdZjCgsLVVRUJJfLJUlyuVzat2+fjh49atVkZ2fL4XAoKirKqjl3jtqa2jkAAADqfMls06ZNuuOOO/Tiiy+qffv2GjZsmNavX6+ampo6zdO8eXN169bNawsODlarVq3UrVs3OZ1OJSQkKCUlRR9++KHy8vL00EMPyeVyqW/fvpKkQYMGKSoqSg888IA+/fRTZWVladq0aUpMTJTdbpckTZgwQV9//bWmTJmiAwcOaNGiRVq9erWSk5PrunQAAHCFqnMg6t69u1566SUVFxfrrbfeUmVlpYYPH66IiAj95S9/+c1fzHgh8+bN09ChQxUfH6+bb75Z4eHhevfdd61xf39/rV+/Xv7+/nK5XBo9erTGjBmjmTNnWjWRkZHKzMxUdna2evbsqTlz5uj111/nN9cAAIDF5vF4PBdb7Ofnp5KSEoWGhnodLyoq0pIlS5SRkaEjR47o7Nmzl7xRX3K73XI6nSovL6/X+4miJy+vt7lxkTwe2c6e+eeufxPJZvNhQ5CkvNljfN3C78brG7iw+nx91+X9+5J8yqx9+/aaMWOGDh8+rI0bN16KKQHfsNnkCQi0NsIQAJihToGoQ4cO8vf3/9lxm82m22+//Xc3BQAA0JDq9MWMhw8frq8+AAAAfOZ3XTI7dOiQsrKydOrUKUlSHW5HAgAAaDR+UyA6duyYYmJi1LlzZ91xxx367rvvJEkJCQn6z//8z0vaIAAAQH37TYEoOTlZAQEBKioqUrNmzazj9957LzdVAwCAy06d7iGqtWnTJmVlZaldu3Zex6+99lp9++23l6QxAACAhvKbzhBVVFR4nRmqdfz4cesbogEAAC4XvykQ9e/fX8uX//NLxmw2m2pqajRr1iwNHDjwkjUHAADQEH7TJbPZs2fr1ltv1d69e1VVVaUpU6aooKBAx48f186dOy91jwAAAPWqzoHozJkzevzxx7Vu3TplZ2erefPmOnnypEaMGKHExES1adOmPvoEAACoN3UORE2aNNFnn32mq666Sn/5y1/qoycAAIAG9ZvuIRo9erTeeOONS90LAACAT/yme4iqq6u1ZMkSbd68WdHR0QoODvYanzt37iVpDgAAoCH8pkC0f/9+9erVS5L05Zdfeo3Z+HVwAABwmflNgejDDz+81H0AAAD4zO/6cVcAAIArAYEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4/k0EC1evFg9evSQw+GQw+GQy+XShg0brPHTp08rMTFRrVq1UkhIiOLj41VaWuo1R1FRkeLi4tSsWTOFhoZq8uTJqq6u9qrZtm2bevXqJbvdrk6dOikjI6MhlgcAAC4TPg1E7dq10/PPP6+8vDzt3btXt956q4YNG6aCggJJUnJystatW6c1a9YoJydHxcXFGjFihPX4s2fPKi4uTlVVVdq1a5eWLVumjIwMTZ8+3ao5fPiw4uLiNHDgQOXn5yspKUnjxo1TVlZWg68XAAA0TjaPx+PxdRPnatmypWbPnq277rpLrVu31sqVK3XXXXdJkg4cOKCuXbsqNzdXffv21YYNGzR06FAVFxcrLCxMkpSenq6pU6fq+++/V2BgoKZOnarMzEzt37/feo6RI0eqrKxMGzduvGAPlZWVqqystPbdbrciIiJUXl4uh8NRb2uPnry83uYGLmd5s8f4uoXfjdc3cGH1+fp2u91yOp0X9f7daO4hOnv2rN5++21VVFTI5XIpLy9PZ86cUUxMjFXTpUsXtW/fXrm5uZKk3Nxcde/e3QpDkhQbGyu3222dZcrNzfWao7amdo4LSUtLk9PptLaIiIhLuVQAANDI+DwQ7du3TyEhIbLb7ZowYYLee+89RUVFqaSkRIGBgWrRooVXfVhYmEpKSiRJJSUlXmGodrx27Jdq3G63Tp06dcGeUlNTVV5ebm1Hjhy5FEsFAACNVICvG7juuuuUn5+v8vJyvfPOOxo7dqxycnJ82pPdbpfdbvdpDwAAoOH4PBAFBgaqU6dOkqTo6Gjt2bNH8+fP17333quqqiqVlZV5nSUqLS1VeHi4JCk8PFwff/yx13y1n0I7t+ann0wrLS2Vw+FQUFBQfS0LAABcRnx+yeynampqVFlZqejoaDVp0kRbtmyxxgoLC1VUVCSXyyVJcrlc2rdvn44ePWrVZGdny+FwKCoqyqo5d47amto5AAAAfHqGKDU1VUOGDFH79u114sQJrVy5Utu2bVNWVpacTqcSEhKUkpKili1byuFw6LHHHpPL5VLfvn0lSYMGDVJUVJQeeOABzZo1SyUlJZo2bZoSExOtS14TJkzQ3/72N02ZMkUPP/ywtm7dqtWrVyszM9OXSwcAAI2ITwPR0aNHNWbMGH333XdyOp3q0aOHsrKydPvtt0uS5s2bJz8/P8XHx6uyslKxsbFatGiR9Xh/f3+tX79ejz76qFwul4KDgzV27FjNnDnTqomMjFRmZqaSk5M1f/58tWvXTq+//rpiY2MbfL0AAKBxanTfQ9QY1eV7DH4PvqcEuDC+hwi4cvE9RAAAAI0EgQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxvNpIEpLS9NNN92k5s2bKzQ0VMOHD1dhYaFXzenTp5WYmKhWrVopJCRE8fHxKi0t9aopKipSXFycmjVrptDQUE2ePFnV1dVeNdu2bVOvXr1kt9vVqVMnZWRk1PfyAADAZcKngSgnJ0eJiYn66KOPlJ2drTNnzmjQoEGqqKiwapKTk7Vu3TqtWbNGOTk5Ki4u1ogRI6zxs2fPKi4uTlVVVdq1a5eWLVumjIwMTZ8+3ao5fPiw4uLiNHDgQOXn5yspKUnjxo1TVlZWg64XAAA0TjaPx+PxdRO1vv/+e4WGhionJ0c333yzysvL1bp1a61cuVJ33XWXJOnAgQPq2rWrcnNz1bdvX23YsEFDhw5VcXGxwsLCJEnp6emaOnWqvv/+ewUGBmrq1KnKzMzU/v37recaOXKkysrKtHHjxvP6qKysVGVlpbXvdrsVERGh8vJyORyOelt/9OTl9TY3cDnLmz3G1y38bry+gQurz9e32+2W0+m8qPfvRnUPUXl5uSSpZcuWkqS8vDydOXNGMTExVk2XLl3Uvn175ebmSpJyc3PVvXt3KwxJUmxsrNxutwoKCqyac+eoramd46fS0tLkdDqtLSIi4tItEgAANDqNJhDV1NQoKSlJf/zjH9WtWzdJUklJiQIDA9WiRQuv2rCwMJWUlFg154ah2vHasV+qcbvdOnXq1Hm9pKamqry83NqOHDlySdYIAAAapwBfN1ArMTFR+/fv144dO3zdiux2u+x2u6/bAAAADaRRnCGaOHGi1q9frw8//FDt2rWzjoeHh6uqqkplZWVe9aWlpQoPD7dqfvqps9r9X6txOBwKCgq61MsBAACXGZ8GIo/Ho4kTJ+q9997T1q1bFRkZ6TUeHR2tJk2aaMuWLdaxwsJCFRUVyeVySZJcLpf27duno0ePWjXZ2dlyOByKioqyas6do7amdg4AAGA2n14yS0xM1MqVK7V27Vo1b97cuufH6XQqKChITqdTCQkJSklJUcuWLeVwOPTYY4/J5XKpb9++kqRBgwYpKipKDzzwgGbNmqWSkhJNmzZNiYmJ1mWvCRMm6G9/+5umTJmihx9+WFu3btXq1auVmZnps7UDAIDGw6dniBYvXqzy8nINGDBAbdq0sbZVq1ZZNfPmzdPQoUMVHx+vm2++WeHh4Xr33XetcX9/f61fv17+/v5yuVwaPXq0xowZo5kzZ1o1kZGRyszMVHZ2tnr27Kk5c+bo9ddfV2xsbIOuFwAANE4+PUN0MV+B1LRpUy1cuFALFy782ZoOHTrov/7rv35xngEDBuh//ud/6twjAAC48jWKm6oBAAB8iUAEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOP5NBBt375d//7v/662bdvKZrPp/fff9xr3eDyaPn262rRpo6CgIMXExOjgwYNeNcePH9eoUaPkcDjUokULJSQk6OTJk141n332mfr376+mTZsqIiJCs2bNqu+lAQCAy4hPA1FFRYV69uyphQsXXnB81qxZWrBggdLT07V7924FBwcrNjZWp0+ftmpGjRqlgoICZWdna/369dq+fbvGjx9vjbvdbg0aNEgdOnRQXl6eZs+erRkzZujVV1+t9/UBAIDLQ4Avn3zIkCEaMmTIBcc8Ho9eeuklTZs2TcOGDZMkLV++XGFhYXr//fc1cuRIffHFF9q4caP27Nmj3r17S5Jefvll3XHHHXrxxRfVtm1brVixQlVVVVqyZIkCAwN1/fXXKz8/X3PnzvUKTgAAwFyN9h6iw4cPq6SkRDExMdYxp9OpPn36KDc3V5KUm5urFi1aWGFIkmJiYuTn56fdu3dbNTfffLMCAwOtmtjYWBUWFuqHH3644HNXVlbK7XZ7bQAA4MrVaANRSUmJJCksLMzreFhYmDVWUlKi0NBQr/GAgAC1bNnSq+ZCc5z7HD+VlpYmp9NpbREREb9/QQAAoNFqtIHIl1JTU1VeXm5tR44c8XVLAACgHjXaQBQeHi5JKi0t9TpeWlpqjYWHh+vo0aNe49XV1Tp+/LhXzYXmOPc5fsput8vhcHhtAADgytVoA1FkZKTCw8O1ZcsW65jb7dbu3bvlcrkkSS6XS2VlZcrLy7Nqtm7dqpqaGvXp08eq2b59u86cOWPVZGdn67rrrtNVV13VQKsBAACNmU8D0cmTJ5Wfn6/8/HxJ/7iROj8/X0VFRbLZbEpKStKzzz6rDz74QPv27dOYMWPUtm1bDR8+XJLUtWtXDR48WI888og+/vhj7dy5UxMnTtTIkSPVtm1bSdL999+vwMBAJSQkqKCgQKtWrdL8+fOVkpLio1UDAIDGxqcfu9+7d68GDhxo7deGlLFjxyojI0NTpkxRRUWFxo8fr7KyMvXr108bN25U06ZNrcesWLFCEydO1G233SY/Pz/Fx8drwYIF1rjT6dSmTZuUmJio6OhoXX311Zo+fTofuQcAABabx+Px+LqJxs7tdsvpdKq8vLxe7yeKnry83uYGLmd5s8f4uoXfjdc3cGH1+fquy/t3o72HCAAAoKEQiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPKMC0cKFC3XNNdeoadOm6tOnjz7++GNftwQAABoBYwLRqlWrlJKSoqeeekqffPKJevbsqdjYWB09etTXrQEAAB8zJhDNnTtXjzzyiB566CFFRUUpPT1dzZo105IlS3zdGgAA8LEAXzfQEKqqqpSXl6fU1FTrmJ+fn2JiYpSbm3tefWVlpSorK6398vJySZLb7a7XPs9WnqrX+YHLVX2/9hoCr2/gwurz9V07t8fj+dVaIwLR3//+d509e1ZhYWFex8PCwnTgwIHz6tPS0vT000+fdzwiIqLeegTw85wvT/B1CwDqSUO8vk+cOCGn0/mLNUYEorpKTU1VSkqKtV9TU6Pjx4+rVatWstlsPuwMDcHtdisiIkJHjhyRw+HwdTsALiFe32bxeDw6ceKE2rZt+6u1RgSiq6++Wv7+/iotLfU6XlpaqvDw8PPq7Xa77Ha717EWLVrUZ4tohBwOB/9hAlcoXt/m+LUzQ7WMuKk6MDBQ0dHR2rJli3WspqZGW7Zskcvl8mFnAACgMTDiDJEkpaSkaOzYserdu7f+9V//VS+99JIqKir00EMP+bo1AADgY8YEonvvvVfff/+9pk+frpKSEt1www3auHHjeTdaA3a7XU899dR5l00BXP54fePn2DwX81k0AACAK5gR9xABAAD8EgIRAAAwHoEIAAAYj0AEnCMjI4PvnAIAAxGIcEV68MEHZbPZztsOHTrk69YAXCIXeo2fu82YMcPXLeIyYszH7mGewYMHa+nSpV7HWrdu7aNuAFxq3333nfX3qlWrNH36dBUWFlrHQkJCrL89Ho/Onj2rgADe9nBhnCHCFctutys8PNxrmz9/vrp3767g4GBFREToz3/+s06ePPmzc3z66acaOHCgmjdvLofDoejoaO3du9ca37Fjh/r376+goCBFRETo8ccfV0VFRUMsDzDeua9tp9Mpm81m7R84cEDNmzfXhg0bFB0dLbvdrh07dujBBx/U8OHDveZJSkrSgAEDrP2amhqlpaUpMjJSQUFB6tmzp955552GXRwaHIEIRvHz89OCBQtUUFCgZcuWaevWrZoyZcrP1o8aNUrt2rXTnj17lJeXpyeeeEJNmjSRJH311VcaPHiw4uPj9dlnn2nVqlXasWOHJk6c2FDLAfArnnjiCT3//PP64osv1KNHj4t6TFpampYvX6709HQVFBQoOTlZo0ePVk5OTj13C1/i3CGuWOvXr/c6ZT5kyBCtWbPG2r/mmmv07LPPasKECVq0aNEF5ygqKtLkyZPVpUsXSdK1115rjaWlpWnUqFFKSkqyxhYsWKBbbrlFixcvVtOmTethVQDqYubMmbr99tsvur6yslLPPfecNm/ebP3WZceOHbVjxw698soruuWWW+qrVfgYgQhXrIEDB2rx4sXWfnBwsDZv3qy0tDQdOHBAbrdb1dXVOn36tH788Uc1a9bsvDlSUlI0btw4vfnmm4qJidHdd9+tf/mXf5H0j8tpn332mVasWGHVezwe1dTU6PDhw+ratWv9LxLAL+rdu3ed6g8dOqQff/zxvBBVVVWlG2+88VK2hkaGQIQrVnBwsDp16mTtf/PNNxo6dKgeffRR/fWvf1XLli21Y8cOJSQkqKqq6oKBaMaMGbr//vuVmZmpDRs26KmnntLbb7+tO++8UydPntSf/vQnPf744+c9rn379vW6NgAXJzg42Gvfz89PP/3FqjNnzlh/195TmJmZqT/84Q9edfz+2ZWNQARj5OXlqaamRnPmzJGf3z9un1u9evWvPq5z587q3LmzkpOTdd9992np0qW688471atXL33++edeoQtA49a6dWvt37/f61h+fr51b2BUVJTsdruKioq4PGYYbqqGMTp16qQzZ87o5Zdf1tdff60333xT6enpP1t/6tQpTZw4Udu2bdO3336rnTt3as+ePdalsKlTp2rXrl2aOHGi8vPzdfDgQa1du5abqoFG7NZbb9XevXu1fPlyHTx4UE899ZRXQGrevLkmTZqk5ORkLVu2TF999ZU++eQTvfzyy1q2bJkPO0d9IxDBGD179tTcuXP1wgsvqFu3blqxYoXS0tJ+tt7f31/Hjh3TmDFj1LlzZ91zzz0aMmSInn76aUlSjx49lJOToy+//FL9+/fXjTfeqOnTp6tt27YNtSQAdRQbG6snn3xSU6ZM0U033aQTJ05ozJgxXjXPPPOMnnzySaWlpalr164aPHiwMjMzFRkZ6aOu0RBsnp9eTAUAADAMZ4gAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRACuCAMGDFBSUlKDPNeDDz6o4cOHN8hzAWgYBCIARvB4PKqurvZ1GwAaKQIRgMvegw8+qJycHM2fP182m002m00ZGRmy2WzasGGDoqOjZbfbtWPHDtXU1CgtLU2RkZEKCgpSz5499c4771hznT17VgkJCdb4ddddp/nz51vjM2bM0LJly7R27VrrubZt2+aDVQO4lAJ83QAA/F7z58/Xl19+qW7dumnmzJmSpIKCAknSE088oRdffFEdO3bUVVddpbS0NL311ltKT0/Xtddeq+3bt2v06NFq3bq1brnlFtXU1Khdu3Zas2aNWrVqpV27dmn8+PFq06aN7rnnHk2aNElffPGF3G63li5dKklq2bKlz9YO4NIgEAG47DmdTgUGBqpZs2YKDw+XJB04cECSNHPmTN1+++2SpMrKSj333HPavHmzXC6XJKljx47asWOHXnnlFd1yyy1q0qSJnn76aWvuyMhI5ebmavXq1brnnnsUEhKioKAgVVZWWs8F4PJHIAJwRevdu7f196FDh/Tjjz9aAalWVVWVbrzxRmt/4cKFWrJkiYqKinTq1ClVVVXphhtuaKiWAfgAgQjAFS04ONj6++TJk5KkzMxM/eEPf/Cqs9vtkqS3335bkyZN0pw5c+RyudS8eXPNnj1bu3fvbrimATQ4AhGAK0JgYKDOnj37izVRUVGy2+0qKirSLbfccsGanTt36t/+7d/05z//2Tr21Vdf1fm5AFxeCEQArgjXXHONdu/erW+++UYhISGqqak5r6Z58+aaNGmSkpOTVVNTo379+qm8vFw7d+6Uw+HQ2LFjde2112r58uXKyspSZGSk3nzzTe3Zs0eRkZFez5WVlaXCwkK1atVKTqdTTZo0acjlArjE+Ng9gCvCpEmT5O/vr6ioKLVu3VpFRUUXrHvmmWf05JNPKi0tTV27dtXgwYOVmZlpBZ4//elPGjFihO6991716dNHx44d8zpbJEmPPPKIrrvuOvXu3VutW7fWzp076319AOqXzePxeHzdBAAAgC9xhggAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxvv/1iFBPnWP+rkAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -719,10 +719,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.758261Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.758069Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.941689Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.941065Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.105726Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.105267Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.279425Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.278591Z"
     }
    },
    "outputs": [
@@ -738,7 +738,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -765,10 +765,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.944221Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.943854Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.982783Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.982238Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.282051Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.281516Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.466557Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.465945Z"
     }
    },
    "outputs": [],
@@ -786,10 +786,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.984678Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.984461Z",
-     "iopub.status.idle": "2024-10-25T15:20:13.997109Z",
-     "shell.execute_reply": "2024-10-25T15:20:13.996623Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.469038Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.468641Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.481827Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.481228Z"
     }
    },
    "outputs": [
@@ -834,106 +834,106 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>True</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>10.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>8546.715</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.520700</td>\n",
-       "      <td>1.920493</td>\n",
+       "      <td>0.374159</td>\n",
+       "      <td>2.672663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>True</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>10.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>3881.284</td>\n",
+       "      <td>9265.788</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.286244</td>\n",
-       "      <td>3.493526</td>\n",
+       "      <td>0.375730</td>\n",
+       "      <td>2.661485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>True</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>3058.531</td>\n",
-       "      <td>1294.409</td>\n",
+       "      <td>10804.320</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.465132</td>\n",
-       "      <td>2.149926</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.563628</td>\n",
+       "      <td>1.774220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>True</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>6083.994</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>8881.665</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7458.105</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.378167</td>\n",
-       "      <td>2.644335</td>\n",
+       "      <td>0.353143</td>\n",
+       "      <td>2.831718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>True</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>12.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6269.341</td>\n",
-       "      <td>3039.960</td>\n",
-       "      <td>8484.239</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1284.079</td>\n",
+       "      <td>60307.930</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.535418</td>\n",
-       "      <td>1.867699</td>\n",
+       "      <td>0.367047</td>\n",
+       "      <td>2.724449</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   treat   age  educ  black  hisp  married  nodegr      re74      re75  \\\n",
-       "0   True  45.0   5.0    1.0   0.0      1.0     1.0     0.000     0.000   \n",
-       "1   True  23.0   8.0    0.0   1.0      0.0     1.0     0.000     0.000   \n",
-       "2   True  42.0   9.0    1.0   0.0      1.0     1.0     0.000  3058.531   \n",
-       "3   True  20.0   9.0    1.0   0.0      0.0     1.0  6083.994     0.000   \n",
-       "4   True  23.0  12.0    1.0   0.0      0.0     0.0  6269.341  3039.960   \n",
+       "   treat   age  educ  black  hisp  married  nodegr  re74      re75       re78  \\\n",
+       "0   True  25.0  10.0    1.0   0.0      0.0     1.0   0.0     0.000      0.000   \n",
+       "1   True  26.0  10.0    1.0   0.0      0.0     1.0   0.0     0.000   9265.788   \n",
+       "2   True  40.0  12.0    1.0   0.0      0.0     0.0   0.0     0.000  10804.320   \n",
+       "3   True  19.0  11.0    1.0   0.0      0.0     1.0   0.0     0.000   7458.105   \n",
+       "4   True  28.0  11.0    1.0   0.0      0.0     1.0   0.0  1284.079  60307.930   \n",
        "\n",
-       "       re78  u74  u75  propensity_score    weight  \n",
-       "0  8546.715  1.0  1.0          0.520700  1.920493  \n",
-       "1  3881.284  1.0  1.0          0.286244  3.493526  \n",
-       "2  1294.409  1.0  0.0          0.465132  2.149926  \n",
-       "3  8881.665  0.0  1.0          0.378167  2.644335  \n",
-       "4  8484.239  0.0  0.0          0.535418  1.867699  "
+       "   u74  u75  propensity_score    weight  \n",
+       "0  1.0  1.0          0.374159  2.672663  \n",
+       "1  1.0  1.0          0.375730  2.661485  \n",
+       "2  1.0  1.0          0.563628  1.774220  \n",
+       "3  1.0  1.0          0.353143  2.831718  \n",
+       "4  1.0  0.0          0.367047  2.724449  "
       ]
      },
      "execution_count": 16,
@@ -964,10 +964,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:13.998855Z",
-     "iopub.status.busy": "2024-10-25T15:20:13.998670Z",
-     "iopub.status.idle": "2024-10-25T15:20:14.002382Z",
-     "shell.execute_reply": "2024-10-25T15:20:14.001896Z"
+     "iopub.execute_input": "2024-10-25T15:22:11.484032Z",
+     "iopub.status.busy": "2024-10-25T15:22:11.483636Z",
+     "iopub.status.idle": "2024-10-25T15:22:11.487338Z",
+     "shell.execute_reply": "2024-10-25T15:22:11.486795Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/load_graph_example.html b/main/example_notebooks/load_graph_example.html
index 2693d47f8..3fcde9463 100644
--- a/main/example_notebooks/load_graph_example.html
+++ b/main/example_notebooks/load_graph_example.html
@@ -642,37 +642,37 @@ <h2>I. Generating dummy data<a class="headerlink" href="#I.-Generating-dummy-dat
     </tr>
     <tr>
       <th>1</th>
-      <td>7</td>
+      <td>9</td>
       <td>1</td>
       <td>10</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>4</td>
+      <td>7</td>
       <td>2</td>
       <td>20</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>9</td>
+      <td>6</td>
       <td>3</td>
       <td>30</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>6</td>
+      <td>8</td>
       <td>4</td>
       <td>40</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>5</td>
+      <td>3</td>
       <td>5</td>
       <td>50</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>3</td>
+      <td>4</td>
       <td>6</td>
       <td>60</td>
     </tr>
@@ -690,7 +690,7 @@ <h2>I. Generating dummy data<a class="headerlink" href="#I.-Generating-dummy-dat
     </tr>
     <tr>
       <th>9</th>
-      <td>8</td>
+      <td>5</td>
       <td>9</td>
       <td>90</td>
     </tr>
diff --git a/main/example_notebooks/load_graph_example.ipynb b/main/example_notebooks/load_graph_example.ipynb
index f4a91b57b..85d3551f5 100644
--- a/main/example_notebooks/load_graph_example.ipynb
+++ b/main/example_notebooks/load_graph_example.ipynb
@@ -18,10 +18,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:16.668178Z",
-     "iopub.status.busy": "2024-10-25T15:20:16.668001Z",
-     "iopub.status.idle": "2024-10-25T15:20:16.674020Z",
-     "shell.execute_reply": "2024-10-25T15:20:16.673422Z"
+     "iopub.execute_input": "2024-10-25T15:22:14.364374Z",
+     "iopub.status.busy": "2024-10-25T15:22:14.364159Z",
+     "iopub.status.idle": "2024-10-25T15:22:14.370401Z",
+     "shell.execute_reply": "2024-10-25T15:22:14.369911Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:16.675665Z",
-     "iopub.status.busy": "2024-10-25T15:20:16.675444Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.080346Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.079672Z"
+     "iopub.execute_input": "2024-10-25T15:22:14.372118Z",
+     "iopub.status.busy": "2024-10-25T15:22:14.371929Z",
+     "iopub.status.idle": "2024-10-25T15:22:15.967732Z",
+     "shell.execute_reply": "2024-10-25T15:22:15.967061Z"
     }
    },
    "outputs": [],
@@ -65,10 +65,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.082757Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.082425Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.091124Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.090593Z"
+     "iopub.execute_input": "2024-10-25T15:22:15.970563Z",
+     "iopub.status.busy": "2024-10-25T15:22:15.970165Z",
+     "iopub.status.idle": "2024-10-25T15:22:15.979563Z",
+     "shell.execute_reply": "2024-10-25T15:22:15.978919Z"
     }
    },
    "outputs": [
@@ -107,37 +107,37 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
+       "      <td>9</td>\n",
        "      <td>1</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>4</td>\n",
+       "      <td>7</td>\n",
        "      <td>2</td>\n",
        "      <td>20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>9</td>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>6</td>\n",
+       "      <td>8</td>\n",
        "      <td>4</td>\n",
        "      <td>40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>5</td>\n",
+       "      <td>3</td>\n",
        "      <td>5</td>\n",
        "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>60</td>\n",
        "    </tr>\n",
@@ -155,7 +155,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>8</td>\n",
+       "      <td>5</td>\n",
        "      <td>9</td>\n",
        "      <td>90</td>\n",
        "    </tr>\n",
@@ -166,15 +166,15 @@
       "text/plain": [
        "   Z  X   Y\n",
        "0  1  0   0\n",
-       "1  7  1  10\n",
-       "2  4  2  20\n",
-       "3  9  3  30\n",
-       "4  6  4  40\n",
-       "5  5  5  50\n",
-       "6  3  6  60\n",
+       "1  9  1  10\n",
+       "2  7  2  20\n",
+       "3  6  3  30\n",
+       "4  8  4  40\n",
+       "5  3  5  50\n",
+       "6  4  6  60\n",
        "7  2  7  70\n",
        "8  0  8  80\n",
-       "9  8  9  90"
+       "9  5  9  90"
       ]
      },
      "execution_count": 3,
@@ -202,10 +202,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.092957Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.092618Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.197730Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.197172Z"
+     "iopub.execute_input": "2024-10-25T15:22:15.981447Z",
+     "iopub.status.busy": "2024-10-25T15:22:15.981246Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.084396Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.083700Z"
     }
    },
    "outputs": [
@@ -255,10 +255,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.200013Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.199600Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.308258Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.307646Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.086689Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.086424Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.183660Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.183089Z"
     }
    },
    "outputs": [
@@ -309,10 +309,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.310580Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.310346Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.419639Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.418975Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.185886Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.185670Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.274783Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.274116Z"
     }
    },
    "outputs": [
@@ -356,10 +356,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:20:18.421820Z",
-     "iopub.status.busy": "2024-10-25T15:20:18.421609Z",
-     "iopub.status.idle": "2024-10-25T15:20:18.533029Z",
-     "shell.execute_reply": "2024-10-25T15:20:18.532430Z"
+     "iopub.execute_input": "2024-10-25T15:22:16.276944Z",
+     "iopub.status.busy": "2024-10-25T15:22:16.276731Z",
+     "iopub.status.idle": "2024-10-25T15:22:16.368549Z",
+     "shell.execute_reply": "2024-10-25T15:22:16.367853Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html
index 72e2992ca..303b1925a 100644
--- a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html
+++ b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html
@@ -625,7 +625,7 @@ <h2>Initialize dataset<a class="headerlink" href="#Initialize-dataset" title="Pe
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "1d2bbd56e61840999f012ea910a5d5de"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "cebd2d4393354d96a237e787ab998e9c"}</script></div>
 </div>
 <div class="nboutput docutils container">
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@@ -646,7 +646,7 @@ <h2>Initialize dataset<a class="headerlink" href="#Initialize-dataset" title="Pe
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@@ -1144,7 +1144,7 @@ <h2>References<a class="headerlink" href="#References" title="Permalink to this
 <p>[2] Ghifary, M., Kleijn, W., Zhang, M., &amp; Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.</p>
 <p>[3] Arjovsky, M., Bottou, L., Gulrajani, I., &amp; Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.</p>
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diff --git a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
index 472b38f4a..9cc8912f8 100644
--- a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
+++ b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb
@@ -90,10 +90,10 @@
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diff --git a/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.html b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.html
index b5b0450a1..ad54f3e86 100644
--- a/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.html
+++ b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.html
@@ -927,9 +927,9 @@ <h3>Obtaining a causal estimate using Model, Identify, Estimate steps<a class="h
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W6,W1,W5,W3,W4])
+─────(E[y|W3,W1,W5,W2,W4,W6])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)
 
 ### Estimand : 2
 Estimand name: iv
@@ -984,17 +984,17 @@ <h3>Obtaining a causal estimate using Model, Identify, Estimate steps<a class="h
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W6,W1,W5,W3,W4])
+─────(E[y|W3,W1,W5,W2,W4,W6])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)
 
 ## Realized estimand
-b: y~v0+W2+W6+W1+W5+W3+W4 |
+b: y~v0+W3+W1+W5+W2+W4+W6 |
 Target units: ate
 
 ## Estimate
-Mean value: 11.722958585421619
-Effect estimates: [[11.72295859]]
+Mean value: 11.71497437833765
+Effect estimates: [[11.71497438]]
 
 </pre></div></div>
 </div>
@@ -1037,7 +1037,7 @@ <h3>Sensitivity Analysis using the Refute step<a class="headerlink" href="#Sensi
 <div class="highlight"><pre>
 Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization
 
-Original Effect Estimate : 11.722958585421619
+Original Effect Estimate : 11.71497437833765
 Robustness Value : 0.45
 
 Robustness Value (alpha=0.05) : 0.41000000000000003
@@ -1164,14 +1164,14 @@ <h3>Sensitivity Analysis using the Refute step<a class="headerlink" href="#Sensi
   <tbody>
     <tr>
       <th>0</th>
-      <td>0.048408</td>
-      <td>0.053452</td>
-      <td>11.722959</td>
-      <td>1.038205</td>
-      <td>10.684754</td>
-      <td>12.761164</td>
-      <td>9.535324</td>
-      <td>13.883763</td>
+      <td>0.048655</td>
+      <td>0.052962</td>
+      <td>11.714974</td>
+      <td>1.036116</td>
+      <td>10.678858</td>
+      <td>12.751091</td>
+      <td>9.530065</td>
+      <td>13.873046</td>
     </tr>
   </tbody>
 </table>
@@ -1222,17 +1222,17 @@ <h2>II. Sensitivity Analysis for general non-parametric models<a class="headerli
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W2,W6,W1,W5,W3,W4])
+─────(E[y|W3,W1,W5,W2,W4,W6])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)
 
 ## Realized estimand
-b: y~v0+W2+W6+W1+W5+W3+W4 |
+b: y~v0+W3+W1+W5+W2+W4+W6 |
 Target units: ate
 
 ## Estimate
-Mean value: 11.831637730148332
-Effect estimates: [[11.83163773]]
+Mean value: 11.816420420308324
+Effect estimates: [[11.81642042]]
 
 </pre></div></div>
 </div>
@@ -1267,7 +1267,7 @@ <h2>II. Sensitivity Analysis for general non-parametric models<a class="headerli
 <div class="highlight"><pre>
 Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization
 
-Original Effect Estimate : 11.831637730148332
+Original Effect Estimate : 11.816420420308324
 Robustness Value : 0.66
 
 Robustness Value (alpha=0.05) : 0.64
diff --git a/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
index cb5cafd84..ea49720f0 100644
--- a/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
+++ b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
@@ -57,10 +57,10 @@
    "id": "bbbab4ea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:51.220272Z",
-     "iopub.status.busy": "2024-10-25T15:21:51.219858Z",
-     "iopub.status.idle": "2024-10-25T15:21:51.236331Z",
-     "shell.execute_reply": "2024-10-25T15:21:51.235746Z"
+     "iopub.execute_input": "2024-10-25T15:23:57.125804Z",
+     "iopub.status.busy": "2024-10-25T15:23:57.125624Z",
+     "iopub.status.idle": "2024-10-25T15:23:57.141329Z",
+     "shell.execute_reply": "2024-10-25T15:23:57.140785Z"
     }
    },
    "outputs": [],
@@ -75,10 +75,10 @@
    "id": "ba6f68b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:51.238307Z",
-     "iopub.status.busy": "2024-10-25T15:21:51.237993Z",
-     "iopub.status.idle": "2024-10-25T15:21:52.657925Z",
-     "shell.execute_reply": "2024-10-25T15:21:52.657315Z"
+     "iopub.execute_input": "2024-10-25T15:23:57.143407Z",
+     "iopub.status.busy": "2024-10-25T15:23:57.143052Z",
+     "iopub.status.idle": "2024-10-25T15:23:58.749971Z",
+     "shell.execute_reply": "2024-10-25T15:23:58.749341Z"
     }
    },
    "outputs": [],
@@ -106,10 +106,10 @@
    "id": "41c60ca7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:52.660279Z",
-     "iopub.status.busy": "2024-10-25T15:21:52.659822Z",
-     "iopub.status.idle": "2024-10-25T15:21:52.755002Z",
-     "shell.execute_reply": "2024-10-25T15:21:52.754509Z"
+     "iopub.execute_input": "2024-10-25T15:23:58.752654Z",
+     "iopub.status.busy": "2024-10-25T15:23:58.752102Z",
+     "iopub.status.idle": "2024-10-25T15:23:58.852569Z",
+     "shell.execute_reply": "2024-10-25T15:23:58.851930Z"
     }
    },
    "outputs": [
@@ -183,10 +183,10 @@
    "id": "75882711",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:52.757745Z",
-     "iopub.status.busy": "2024-10-25T15:21:52.757220Z",
-     "iopub.status.idle": "2024-10-25T15:21:52.861655Z",
-     "shell.execute_reply": "2024-10-25T15:21:52.861124Z"
+     "iopub.execute_input": "2024-10-25T15:23:58.855477Z",
+     "iopub.status.busy": "2024-10-25T15:23:58.855153Z",
+     "iopub.status.idle": "2024-10-25T15:23:58.964758Z",
+     "shell.execute_reply": "2024-10-25T15:23:58.964152Z"
     }
    },
    "outputs": [
@@ -223,10 +223,10 @@
    "id": "636b6a25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:52.863694Z",
-     "iopub.status.busy": "2024-10-25T15:21:52.863239Z",
-     "iopub.status.idle": "2024-10-25T15:21:52.902586Z",
-     "shell.execute_reply": "2024-10-25T15:21:52.902082Z"
+     "iopub.execute_input": "2024-10-25T15:23:58.967056Z",
+     "iopub.status.busy": "2024-10-25T15:23:58.966608Z",
+     "iopub.status.idle": "2024-10-25T15:23:59.010192Z",
+     "shell.execute_reply": "2024-10-25T15:23:59.009549Z"
     },
     "scrolled": true
    },
@@ -451,10 +451,10 @@
    "id": "207034e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:52.904470Z",
-     "iopub.status.busy": "2024-10-25T15:21:52.904114Z",
-     "iopub.status.idle": "2024-10-25T15:21:53.073422Z",
-     "shell.execute_reply": "2024-10-25T15:21:53.072875Z"
+     "iopub.execute_input": "2024-10-25T15:23:59.012606Z",
+     "iopub.status.busy": "2024-10-25T15:23:59.012122Z",
+     "iopub.status.idle": "2024-10-25T15:23:59.190987Z",
+     "shell.execute_reply": "2024-10-25T15:23:59.190344Z"
     }
    },
    "outputs": [
@@ -498,10 +498,10 @@
    "id": "4eaec5dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:53.075924Z",
-     "iopub.status.busy": "2024-10-25T15:21:53.075496Z",
-     "iopub.status.idle": "2024-10-25T15:21:53.130837Z",
-     "shell.execute_reply": "2024-10-25T15:21:53.130278Z"
+     "iopub.execute_input": "2024-10-25T15:23:59.193191Z",
+     "iopub.status.busy": "2024-10-25T15:23:59.192942Z",
+     "iopub.status.idle": "2024-10-25T15:23:59.252595Z",
+     "shell.execute_reply": "2024-10-25T15:23:59.251940Z"
     },
     "scrolled": true
    },
@@ -516,9 +516,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -543,10 +543,10 @@
    "id": "56889b39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:53.132938Z",
-     "iopub.status.busy": "2024-10-25T15:21:53.132529Z",
-     "iopub.status.idle": "2024-10-25T15:21:54.717873Z",
-     "shell.execute_reply": "2024-10-25T15:21:54.717191Z"
+     "iopub.execute_input": "2024-10-25T15:23:59.254776Z",
+     "iopub.status.busy": "2024-10-25T15:23:59.254399Z",
+     "iopub.status.idle": "2024-10-25T15:24:01.015521Z",
+     "shell.execute_reply": "2024-10-25T15:24:01.014896Z"
     }
    },
    "outputs": [
@@ -577,17 +577,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W6+W1+W5+W3+W4 | \n",
+      "b: y~v0+W3+W1+W5+W2+W4+W6 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.722958585421619\n",
-      "Effect estimates: [[11.72295859]]\n",
+      "Mean value: 11.71497437833765\n",
+      "Effect estimates: [[11.71497438]]\n",
       "\n"
      ]
     }
@@ -631,16 +631,16 @@
    "id": "2488ccbb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:54.719857Z",
-     "iopub.status.busy": "2024-10-25T15:21:54.719663Z",
-     "iopub.status.idle": "2024-10-25T15:21:55.742509Z",
-     "shell.execute_reply": "2024-10-25T15:21:55.741924Z"
+     "iopub.execute_input": "2024-10-25T15:24:01.017733Z",
+     "iopub.status.busy": "2024-10-25T15:24:01.017251Z",
+     "iopub.status.idle": "2024-10-25T15:24:02.335229Z",
+     "shell.execute_reply": "2024-10-25T15:24:02.334574Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -654,7 +654,7 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.722958585421619\n",
+      "Original Effect Estimate : 11.71497437833765\n",
       "Robustness Value : 0.45\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.41000000000000003\n",
@@ -694,10 +694,10 @@
    "id": "52b2e904",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:55.744623Z",
-     "iopub.status.busy": "2024-10-25T15:21:55.744247Z",
-     "iopub.status.idle": "2024-10-25T15:21:55.789430Z",
-     "shell.execute_reply": "2024-10-25T15:21:55.788952Z"
+     "iopub.execute_input": "2024-10-25T15:24:02.337549Z",
+     "iopub.status.busy": "2024-10-25T15:24:02.337030Z",
+     "iopub.status.idle": "2024-10-25T15:24:02.388262Z",
+     "shell.execute_reply": "2024-10-25T15:24:02.387571Z"
     }
    },
    "outputs": [
@@ -754,16 +754,16 @@
    "id": "85eefe08",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:21:55.791427Z",
-     "iopub.status.busy": "2024-10-25T15:21:55.791077Z",
-     "iopub.status.idle": "2024-10-25T15:22:10.228231Z",
-     "shell.execute_reply": "2024-10-25T15:22:10.227470Z"
+     "iopub.execute_input": "2024-10-25T15:24:02.390594Z",
+     "iopub.status.busy": "2024-10-25T15:24:02.390102Z",
+     "iopub.status.idle": "2024-10-25T15:24:18.999707Z",
+     "shell.execute_reply": "2024-10-25T15:24:18.998961Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -808,16 +808,16 @@
    "id": "df23a49b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:10.230520Z",
-     "iopub.status.busy": "2024-10-25T15:22:10.230258Z",
-     "iopub.status.idle": "2024-10-25T15:22:23.591281Z",
-     "shell.execute_reply": "2024-10-25T15:22:23.590610Z"
+     "iopub.execute_input": "2024-10-25T15:24:19.001952Z",
+     "iopub.status.busy": "2024-10-25T15:24:19.001622Z",
+     "iopub.status.idle": "2024-10-25T15:24:34.949014Z",
+     "shell.execute_reply": "2024-10-25T15:24:34.948429Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -827,7 +827,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -855,10 +855,10 @@
    "id": "934eef00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:23.593400Z",
-     "iopub.status.busy": "2024-10-25T15:22:23.593058Z",
-     "iopub.status.idle": "2024-10-25T15:22:23.638177Z",
-     "shell.execute_reply": "2024-10-25T15:22:23.637536Z"
+     "iopub.execute_input": "2024-10-25T15:24:34.951155Z",
+     "iopub.status.busy": "2024-10-25T15:24:34.950748Z",
+     "iopub.status.idle": "2024-10-25T15:24:34.995967Z",
+     "shell.execute_reply": "2024-10-25T15:24:34.995468Z"
     }
    },
    "outputs": [
@@ -896,14 +896,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.048408</td>\n",
-       "      <td>0.053452</td>\n",
-       "      <td>11.722959</td>\n",
-       "      <td>1.038205</td>\n",
-       "      <td>10.684754</td>\n",
-       "      <td>12.761164</td>\n",
-       "      <td>9.535324</td>\n",
-       "      <td>13.883763</td>\n",
+       "      <td>0.048655</td>\n",
+       "      <td>0.052962</td>\n",
+       "      <td>11.714974</td>\n",
+       "      <td>1.036116</td>\n",
+       "      <td>10.678858</td>\n",
+       "      <td>12.751091</td>\n",
+       "      <td>9.530065</td>\n",
+       "      <td>13.873046</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -911,10 +911,10 @@
       ],
       "text/plain": [
        "     r2tu_w   r2yu_tw  short estimate      bias  lower_ate_bound  \\\n",
-       "0  0.048408  0.053452       11.722959  1.038205        10.684754   \n",
+       "0  0.048655  0.052962       11.714974  1.036116        10.678858   \n",
        "\n",
        "   upper_ate_bound  lower_confidence_bound  upper_confidence_bound  \n",
-       "0        12.761164                9.535324               13.883763  "
+       "0        12.751091                9.530065               13.873046  "
       ]
      },
      "execution_count": 13,
@@ -941,10 +941,10 @@
    "id": "7cff3837",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:23.640125Z",
-     "iopub.status.busy": "2024-10-25T15:22:23.639792Z",
-     "iopub.status.idle": "2024-10-25T15:22:24.227690Z",
-     "shell.execute_reply": "2024-10-25T15:22:24.227009Z"
+     "iopub.execute_input": "2024-10-25T15:24:34.997944Z",
+     "iopub.status.busy": "2024-10-25T15:24:34.997579Z",
+     "iopub.status.idle": "2024-10-25T15:24:35.590885Z",
+     "shell.execute_reply": "2024-10-25T15:24:35.590288Z"
     }
    },
    "outputs": [
@@ -975,17 +975,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W2,W6,W1,W5,W3,W4])\n",
+      "─────(E[y|W3,W1,W5,W2,W4,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W6,W1,W5,W3,W4,U) = P(y|v0,W2,W6,W1,W5,W3,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W5,W2,W4,W6,U) = P(y|v0,W3,W1,W5,W2,W4,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W2+W6+W1+W5+W3+W4 | \n",
+      "b: y~v0+W3+W1+W5+W2+W4+W6 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.831637730148332\n",
-      "Effect estimates: [[11.83163773]]\n",
+      "Mean value: 11.816420420308324\n",
+      "Effect estimates: [[11.81642042]]\n",
       "\n"
      ]
     }
@@ -1017,17 +1017,17 @@
    "id": "946e1237",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:24.229804Z",
-     "iopub.status.busy": "2024-10-25T15:22:24.229412Z",
-     "iopub.status.idle": "2024-10-25T15:22:39.334625Z",
-     "shell.execute_reply": "2024-10-25T15:22:39.333974Z"
+     "iopub.execute_input": "2024-10-25T15:24:35.593069Z",
+     "iopub.status.busy": "2024-10-25T15:24:35.592669Z",
+     "iopub.status.idle": "2024-10-25T15:24:50.873580Z",
+     "shell.execute_reply": "2024-10-25T15:24:50.872836Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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rVq14+PChKeG8d+8eq1atMs0hetXHdHZ2ZuHChQBER0ezcuVK2rVr98LHjD9JiT+JTszNmzd58OABhQoVSnBbkSJFMBqNCeaWPJ/wxZ8gPXusk3L+/Pkk9xV/+6vIkSOHRQUc7OzszBItgIIFCwIkWTb/VY5VcsQ/56QeN/7E/XW9aD+FCxc2O/bVqlXj6tWrnDp1im3btmEwGKhUqZJZ8rZlyxaqVKliSgYs/cxa+pqB9hrExMS88D0d/1wteZ+9zns6sX3nz58/wWfz+XhOnjwJQHh4eILjNWvWLB4/fkx0dLRFcZ4+fRo7O7sXJh/x+61Vq1aC/a5du/aVCjgVKFDA7LrBYCB//vymz5Il7z1LveyYxD/28zEmFY8QQlgLqe4odOPk5ET58uUpX748BQsWpFOnTixZsoRhw4ZhNBoxGAysXr060WqLmTJlMrueVEVG9UwhiCJFinD8+HFWrlzJmjVrWLp0KVOnTmXo0KGMGDEiRZ5TxYoVCQwMZPHixbRt25bff/+dhw8fJiiuYInMmTPzxhtvsHDhQoYOHcovv/zC48ePzXoO01JyjvXrSir5jC+88jxXV9cU27fA1AO1efNmzpw5Q5kyZXB3d6datWp8++233Lt3j7179zJy5EjTfSz9zFrTa5YW7+nnxfeSjRs3LsllN17ley65+50/fz5+fn4Jbndw0Pe0wGAwJPp8kvrs6/HaCSFEWpAkTViF+KFLV69eBSBfvnwopQgKCjL1cKQEd3d3WrVqRatWrYiNjaV58+aMHDmSQYMGJVlO39IesJYtWzJx4kRiYmJYtGgRgYGBVKxY8YX3edk+OnToQNOmTdm1axcLFy6kdOnSFCtW7IX3ie8pOnToUJJtsmXLhpubG8ePH09w27Fjx7CzsyNXrlwv3E9ikno+efLkSXJf8bfD//8aHhUVZdbudX5xf5bRaOTMmTNm760TJ04AJLmumSXHypL3TPxzTupxs2bNiru7e7IfLzn7qVWrltltx48fN1sUO3fu3OTOnZstW7Zw5swZ0xCy6tWrM2DAAJYsWUJcXBzVq1c33Se1PrPPypYtG56eni98T0Py32epIU+ePBw6dAillNn74Pl48uXLB4Cnpyd16tRJkX3ny5cPo9HIkSNHkkz84vfr6+ubYvuN752Lp5Ti1KlTlCxZErDsvZc5c+ZEh5m+6mc//rGfjzF+30IIYa1kuKNIUxs3bkz0F874uQvxw0+aN2+Ovb09I0aMSNBeKcXt27ct3vfz93FycqJo0aIopXjy5EmS93N3d0+QLLxIq1atePz4MfPmzWPNmjW0bNnypfeJPwlPaj8NGzYka9asjBkzhr///jtZvWjZsmWjevXqzJ49mwsXLpjdFn9M7e3tqVevHr/++qvZML/r16/z448/UrVqVTw9PV+6r8SeT2LPpVGjRuzcuZOIiAjTtvv37zNjxgwCAwNNw7TiTyQ3b95sahcXF8eMGTMsjiUpkydPNv1fKcXkyZNxdHSkdu3aiba35Fi97PV8lr+/P8HBwcybN8+s/aFDh1i7di2NGjWy/Mkloly5cvj6+jJ9+nSzcvCrV6/m6NGjNG7c2Kx9tWrV2LBhAzt37jQlacHBwXh4eDB69GhcXV0pW7asqX1qfGafZ2dnR1hYGL///jv//vtvgtvj95vc91lqaNSoEVeuXOGXX34xbXvw4EGC927ZsmXJly8fX331ldkc1niJLbvxMmFhYdjZ2fHZZ58lmM8Wf2zq16+Pp6cnX375ZaLfe6+y3x9++IG7d++arv/yyy9cvXqVhg0bApa99/Lly8exY8fM4ti/f/8rD01/9vP17PDRdevWvfIcWCGESAvSkybSVO/evXnw4AHNmjWjcOHCxMbGsm3bNlOPU3whgXz58vHFF18waNAgzp07R1hYGB4eHpw9e5bly5fTvXt3/ve//1m073r16uHn50eVKlXInj07R48eZfLkyTRu3Nhs0vvzypYty7Rp0/jiiy/Inz8/vr6+CX4NflaZMmXInz8/n3zyCY8fP07WUMd8+fLh7e3N9OnT8fDwwN3dnZCQEIKCggBwdHSkdevWTJ48GXt7e9q0aZOs5/ztt99StWpVypQpQ/fu3QkKCuLcuXP88ccf7Nu3D4AvvviCdevWUbVqVd5//30cHBz47rvvePz4caJrzSVHUsds4MCB/PTTTzRs2JA+ffrg4+PDvHnzOHv2LEuXLjXNbypWrBgVK1Zk0KBBREZG4uPjw88//8zTp09fKZ7nubi4sGbNGsLDwwkJCWH16tX88ccfDB48mGzZsiV5v+Qeq+DgYOzt7RkzZgzR0dE4OztTq1atRNcCBG3IW8OGDalUqRJdunTh4cOHTJo0CS8vL9N6XK/L0dGRMWPG0KlTJ0JDQ2nTpg3Xr19n4sSJBAYG0r9/f7P21apVY+HChRgMBtPwR3t7eypXrsyff/5JjRo1zOaUpcZnNjFffvkla9euJTQ0lO7du1OkSBGuXr3KkiVL+Oeff/D29k72+yw1dOvWjcmTJ9OhQwd2796Nv78/8+fPT7BQt52dHbNmzaJhw4YUK1aMTp06kSNHDi5fvszGjRvx9PTk999/t2jf8d87n3/+OdWqVaN58+Y4Ozuza9cuAgICGDVqFJ6enkybNo327dtTpkwZWrduTbZs2bhw4QJ//PEHVapUMfsBIzl8fHyoWrUqnTp14vr160yYMIH8+fPTrVs3wLL3XufOnRk/fjz169enS5cu3Lhxg+nTp1OsWLFXKswDMGrUKBo3bkzVqlXp3LkzkZGRpjUzE0uQhRDCKqRlKUkhVq9erTp37qwKFy6sMmXKpJycnFT+/PlV79691fXr1xO0X7p0qapatapyd3dX7u7uqnDhwqpnz57q+PHjpjahoaGJltZ/vmTzd999p6pXr66yZMminJ2dVb58+dSHH36ooqOjTW0SK/1+7do11bhxY+Xh4aEAU3noxMrBx/vkk08UoPLnz5/ocUiszPSvv/6qihYtqhwcHBItx79z504FqHr16iX6mEk5dOiQatasmfL29lYuLi6qUKFCCdYD27Nnj6pfv77KlCmTcnNzUzVr1lTbtm0zaxN/bJ4vfZ7YcUjqmCmlrSP29ttvm+KpUKGCWrlyZYK4T58+rerUqaOcnZ1V9uzZ1eDBg9W6desSLcH/sqUVnhUeHq7c3d3V6dOnVb169ZSbm5vKnj27GjZsWIKy5TxXgj+5x0oppWbOnKny5s2r7O3tk1WO/6+//lJVqlRRrq6uytPTUzVp0kQdOXLErM3rlOCPt2jRIlW6dGnl7OysfHx8VLt27dSlS5cS3P/w4cOm8vTP+uKLLxJdUy7e63xmk+v8+fOqQ4cOKlu2bMrZ2VnlzZtX9ezZ06yMfnLeZ0kdz/jy7s9+BpNbgj8+vjfffFO5ubmprFmzqr59+6o1a9Yk+nrs3btXNW/e3PS9lCdPHtWyZUu1fv36l+47qaUqZs+ebXqNM2fOrEJDQ9W6desSPPf69esrLy8v5eLiovLly6c6duyo/v333wTHOynxx++nn35SgwYNUr6+vsrV1VU1btw4wbIfSiX/vbdgwQKVN29e5eTkpIKDg9Wff/6ZZAn+cePGJbh/Yp/bpUuXqiJFiihnZ2dVtGhRtWzZsgSPKYQQ1sSglMyuFcIW7N+/n+DgYH744Qfat2+vdzg2q2PHjvzyyy/yC7oQQgghrJbMSRPCRsycOZNMmTLRvHlzvUMRQgghhBCpSOakCWHlfv/9d44cOcKMGTPo1atXilT6E8La3Lt376W9m9myZUuy5LpIObGxsURGRr6wjZeXl1UtoyCEEOmNJGlCWLnevXtz/fp1GjVqlGLruQlhbb766quXvr/Pnj2b5BIJIuVs27aNmjVrvrDNnDlz6NixY9oEJIQQGZDMSRNCCKG7M2fOJLo+1rOqVq2a5HqGIuXcuXOH3bt3v7BNsWLF8Pf3T6OIhBAi45EkTQghhBBCCCGsiBQOEUIIIYQQQggrInPSdGQ0Grly5QoeHh4YDAa9wxFCCCFEOqeU4u7duwQEBKTqwu5CiNcjSZqOrly5Qq5cufQOQwghhBAZzMWLF8mZM6feYQghkiBJmo48PDwA7YvS09NT52iEEEIIkd7FxMSQK1cu0zmIEMI6SZKmo/ghjp6enpKkCSGEECLNyDQLIaybDEYWQgghhBBCCCsiSZoQQgghhBBCWBFJ0oQQQgghhBDCisicNCGEEEIIoQulFE+fPiUuLk7vUIRIdfb29jg4OCRrTqgkaUIIIYQQIs3FxsZy9epVHjx4oHcoQqQZNzc3/P39cXJyemE7SdKEEEIIIUSaMhqNnD17Fnt7ewICAnBycpKKkyJdU0oRGxvLzZs3OXv2LAUKFHjhgvKSpAkhhBBCiDQVGxuL0WgkV65cuLm56R2OEGnC1dUVR0dHzp8/T2xsLC4uLkm2lcIhQgghhBBCFy/qSRAiPUrue14+GUIIIYQQQghhRSRJE0IIIYQQQggrIkmaEEIIIYSwaU+ePNE7hDSxadMmDAYDUVFRiV63JsOHDyc4ONhsW2xsLPnz52fbtm36BGWhNWvWEBwcjNFoTPN9S5ImhBBCCCFs1uzZs8mUKROzZ89Ok/3VqFGDfv36Jdg+d+5cvL290ySGeJUrV+bq1at4eXmlyOMlllilpOnTpxMUFETlypVN20aOHEnlypVxc3NL8vj16dOHsmXL4uzsnOz4rl27Rvv27fHz88Pd3Z0yZcqwdOlS0+3nzp2jS5cuBAUF4erqSr58+Rg2bBixsbGmNg0aNMDR0ZGFCxe+0vN9HZKkCSGEEEIImzR79my6du1KbGwsXbt2TbNEzVo4OTnh5+dnE8sXKKWYPHkyXbp0MdseGxtLixYteO+99154/86dO9OqVatk769Dhw4cP36c3377jYMHD9K8eXNatmzJ3r17ATh27BhGo5HvvvuOw4cP88033zB9+nQGDx5s9jgdO3bk22+/TfZ+U4okaSJRt27BlSv67PvSJbhzR599p5XLl+H2bb2jsG3Hj8PDh3pHIZLy77/w9KneUYi09vgx2MgoJpEOxCdoSilASwKsJVHr2LEjYWFhfPXVV/j7+5MlSxZ69uxpNixz/vz5lCtXDg8PD/z8/Gjbti03btwwe5xVq1ZRsGBBXF1dqVmzJufOnTO7/fnhjon1hE2YMIHAwECz+1SoUAF3d3e8vb2pUqUK58+fZ+7cuYwYMYL9+/djMBgwGAzMnTsXgKioKLp27Uq2bNnw9PSkVq1a7N+/32w/o0ePJnv27Hh4eNClSxcePXpkdvvu3bs5ffo0jRs3Nts+YsQI+vfvT4kSJZI8nt9++y09e/Ykb968SbZ53rZt2+jduzcVKlQgb968fPrpp3h7e7N7925A6yWbM2cO9erVI2/evLz55pv873//Y9myZWaP06RJE/79919Onz6d7H2nBEnSRKLmzIHPP9dn3//7HzzTG50uDRoEOvScpytdusDy5XpHIRKjFPTsCTNm6B2JSGvHj0PjxrBzp96RiPTu+QQtnjUlahs3buT06dNs3LiRefPmMXfuXFPSA9o8us8//5z9+/ezYsUKzp07R8eOHU23X7x4kebNm9OkSRP27dtH165dGThw4GvF9PTpU8LCwggNDeXAgQNERETQvXt3DAYDrVq14oMPPqBYsWJcvXqVq1evmnquWrRowY0bN1i9ejW7d++mTJky1K5dm8jISAAWL17M8OHD+fLLL/n333/x9/dn6tSpZvvesmULBQsWxMPD47WeQ3JVrlyZRYsWERkZidFo5Oeff+bRo0fUqFEjyftER0fj4+Njti137txkz56dLVu2pHLE5mQxa5EoBweIi9Nn39mzw/Xr+uw7rZQtq/U0iFfXsSPMmwdt2+odiXiewQATJ8Ibb0Dr1vDc3zuRjpUsCVOmQLNmsGMH5Mypd0QiPUoqQYsXn6iBNkROL5kzZ2by5MnY29tTuHBhGjduzPr16+nWrVuC2PLmzcu3335L+fLluXfvHpkyZWLatGnky5ePr7/+GoBChQpx8OBBxowZ88oxxcTEEB0dzRtvvEG+fPkAKFKkiOn2TJky4eDggJ+fn2nbP//8w86dO7lx4wbOzs4AfPXVV6xYsYJffvmF7t27M2HCBLp06WIayvjFF1/w119/mfWmnT9/noCAgFeO3VKLFy+mVatWZMmSBQcHB9zc3Fi+fDn58+dPtP2pU6eYNGkSX331VYLbAgICOH/+fGqHbEZ60kSi7O31G6qUUZK0/3rbxStq0QK2btWGjgrrU7EiNGoEQ4fqHYlIa23bQufOWpJ+757e0Yj05mUJWjxr6FErVqwY9vb2puv+/v5mwxl3795NkyZNyJ07Nx4eHoSGhgJw4cIFAI4ePUpISIjZY1aqVOm1YvLx8aFjx47Ur1+fJk2aMHHiRK5evfrC++zfv5979+6RJUsWMmXKZLqcPXvWNAQwObE+fPgQFxeX14rfEkOGDCEqKoq//vqLf//9lwEDBtCyZUsOHjyYoO3ly5dp0KABLVq0MCXRz3J1deXBgwdpEbaJJGkiUXr2pPn6wnNDstOd4GA4eRLu39c7Etvl5QVvvgnz5+sdiUjK6NGwYAEk8vdQpHMjRkDhwtCmjX5/S0T68+TJE957772XJmjxlFK89957KV6e39PTk+jo6ATbo6KizKosOjo6mt1uMBhMpdzv379P/fr18fT0ZOHChezatYvl/43hf7a6oKXs7OwSHJ/nn/+cOXOIiIgwDQcsWLAg27dvT/Ix7927h7+/P/v27TO7HD9+nA8//DDZsWXNmpU7aVR04PTp00yePJnZs2dTu3ZtSpUqxbBhwyhXrhxTpkwxa3vlyhVq1qxJ5cqVmZHEOP3IyEiyZcuWFqGbSJImEiU9aakrUyYoUAD27dM7EtsWP+QxmX+vRRoLCICPP4Z+/eQ1ymjs7LS5zbduafOMhUgJjo6OTJs2LdmVDA0GA9OmTUuQLL2uQoUKsWfPngTb9+zZQ8GCBZP1GMeOHeP27duMHj2aatWqUbhw4QRFQ4oUKcLO5yZ4viiZAsiWLRvXrl0zS9T2JXKyUbp0aQYNGsS2bdsoXrw4P/74I6BVi4x77peVMmXKcO3aNRwcHMifP7/ZJWvWrKZYd+zY8cJYS5cuzbFjx5KdZL+O+F4vOzvzVMfe3t5szbPLly9To0YNypYty5w5cxK0B3j06BGnT5+mdOnSqRv0cyRJE4mSOWmpT+alvb7ateHuXdi1S+9IRFL694dz56TIS0bk6gorVmiX5+oHCPHKOnfuzKxZs16aqBkMBmbNmpUqc9Lee+89Tpw4QZ8+fThw4ADHjx9n/Pjx/PTTT3zwwQfJeozcuXPj5OTEpEmTOHPmDL/99hufP1exrUePHpw8eZIPP/yQ48eP8+OPP5oVHklMjRo1uHnzJmPHjuX06dNMmTKF1atXm24/e/YsgwYNIiIigvPnz7N27VpOnjxpmpcWGBjI2bNn2bdvH7du3eLx48fUqVOHSpUqERYWxtq1azl37hzbtm3jk08+4d//TmT69u3L7NmzmTNnDidOnGDYsGEcPnzYLLaaNWty7969BNsvXLjAvn37uHDhAnFxcaaeunvPjJc+deoU+/bt49q1azx8+NDUJr7X8fLlyxQuXNiU1BYuXJj8+fPz7rvvsnPnTk6fPs3XX3/NunXrCAsLM92nRo0a5M6dm6+++oqbN29y7do1rl27Zhbf9u3bcXZ2fu2hphZTQjfR0dEKUNHR0XqHksCcOUq9/bY++z53TqksWfTZd1qaOFGp9u31jsL2DRyo1Hvv6R2FeJEVK5QKDFTq4UO9IxF6OHxYKR8fpVav1jsSoZT1nHs8fPhQHTlyRD18xS+G77//XhkMBgUkuBgMBvX999+ncMTmdu7cqerWrauyZcumvLy8VEhIiFq+fLnp9vDwcNW0aVOz+/Tt21eFhoaarv/4448qMDBQOTs7q0qVKqnffvtNAWrv3r2mNr///rvKnz+/cnZ2VtWqVVOzZ89WgLpz545SSqmNGzeaXVdKqWnTpqlcuXIpd3d31aFDBzVy5EiVJ08epZRS165dU2FhYcrf3185OTmpPHnyqKFDh6q4uDillFKPHj1Sb731lvL29laAmjNnjlJKqZiYGNW7d28VEBCgHB0dVa5cuVS7du3UhQsXTPsdOXKkypo1q8qUKZMKDw9XH330kSpVqpTZMWjZsqUaOHCg2bbw8PBEX8eNGzea2oSGhiba5uzZs0oppc6ePZvgPidOnFDNmzdXvr6+ys3NTZUsWVL98MMPptvnzJmT6GM+nx51795dvfvuuyqlJPe9r3uSNnnyZJUnTx7l7OysKlSooHbs2PHC9osXL1aFChVSzs7Oqnjx4uqPP/4wu91oNKohQ4YoPz8/5eLiomrXrq1OnDhh1ub27duqbdu2ysPDQ3l5eanOnTuru3fvmm5/+PChCg8PV8WLF1f29vYJPmTP++eff5S9vX2CN+LLWMsXZWLmz1cqLEyffT94oBQoFRurz/7Tyj//KFW0qN5R2L6jR5XKnFmpR4/0jkQkxWhUqm5dpb74Qu9IhF7WrtUStQMH9I5EWMu5x+smaUolnqilRYJmTdasWaMMBoN6/Pix3qEky/79+5Wvr6/Zebc1u3nzpvLx8VFnzpxJscdM7ntf1+GOixYtYsCAAQwbNow9e/ZQqlQp6tevn2BMbrxt27bRpk0bunTpwt69ewkLCyMsLIxDhw6Z2owdO5Zvv/2W6dOns2PHDtzd3alfv75ZCdB27dpx+PBh1q1bx8qVK9m8eTPdu3c33R4XF4erqyt9+vShTp06L3wOUVFRdOjQgdq1a7/m0bAu9vb6DXd0dQUPD7h5U5/9p5X44iFS/ez1FC6sze/7/Xe9IxFJMRhgwgQYN05brF5kPHXrwqhRWsXH50YSCfHKnh/6mJpDHK3R9evX+fXXXylQoABOTk56h5MsJUuWZMyYMZw9e1bvUJLl3LlzTJ06laCgoLTfeYqlha+gQoUKqmfPnqbrcXFxKiAgQI0aNSrR9i1btlSNGzc22xYSEmLqgjQajcrPz0+NGzfOdHtUVJRydnZWP/30k1JKqSNHjihA7dq1y9Rm9erVymAwqMuXLyfYZ2Ld1c9q1aqV+vTTT9WwYcPSVU/a4sVKNWyo3/7z51dqzx799p9WihZVassWvaOwfVOnKvXcV4OwQn36KNW2rd5RCD198IFSFSoodf++3pFkXNZy7pESPWnxvv/+e+Xk5JShetCUUqpMmTKqQIECat26dXqHIixg9T1psbGx7N6926ynys7Ojjp16hAREZHofSIiIhL0bNWvX9/U/uzZs1y7ds2sjZeXFyEhIaY2EREReHt7U65cOVObOnXqYGdnl6AqzcvMmTOHM2fOMGzYsGS1f/z4MTExMWYXa6VnTxpoxUPSexl+gHLlZL20lNCqFWzalDEKztiy4cNh7VptfTuRMY0ZA/7+EB4OzxRYE+K1dO7cmXv37mWYHrR4u3fv5sSJEy8d9SVsk25J2q1bt4iLiyN79uxm27Nnz56gqkq8a9euvbB9/L8va+Pr62t2u4ODAz4+PknuNzEnT55k4MCBLFiwAAcHh2TdZ9SoUXh5eZkuuXLlSvb+0preSZqvb8Y44ZYKjynDxwcaNoSFC/WORLxI5szwxRfQt6+coGdU9vba5/T0afj0U72jEelJSpfZF0JvUoL/FcTFxdG2bVtGjBiR7PUwAAYNGkR0dLTpcvHixVSM8vU4OOi3ThpkrDL80pOWMjp2hLlzZT0ua9e1q/bdMnu23pEIvbi7a3NI58/X1lITQgiRkG5JWtasWbG3t+f6c2fi169fx8/PL9H7+Pn5vbB9/L8va/N8YZKnT58SGRmZ5H6fd/fuXf7991969eqFg4MDDg4OfPbZZ+zfvx8HBwc2bNiQ6P2cnZ3x9PQ0u1grvXvSMkqSFhwMp05BdLTekdi++vW1IbKyQLh1s7eHyZNh0CBtoWORMeXIoSVqH3wAGzfqHY0QQlgf3ZI0JycnypYty/r1603bjEYj69evT3KxuEqVKpm1B1i3bp2pfVBQEH5+fmZtYmJi2LFjh6lNpUqViIqKYvcz3RcbNmzAaDQSEhKSrNg9PT05ePCgaSG9ffv20aNHDwoVKsS+ffuS/TjWTO+eNF/fjDEnzd0diheXxZhTgoMDtGun9aYJ61a1KjRpoiVqIuMKDoYffoAWLeD4cb2jEUII65K8yVSpZMCAAYSHh1OuXDkqVKjAhAkTuH//Pp06dQKgQ4cO5MiRg1GjRgHaauahoaF8/fXXNG7cmJ9//pl///2XGTNmAFrp1X79+vHFF19QoEABgoKCGDJkCAEBAabVxYsUKUKDBg3o1q0b06dP58mTJ/Tq1YvWrVsTEBBgiu3IkSPExsYSGRnJ3bt32fffz/PBwcHY2dlRvHhxs+fi6+uLi4tLgu22SnrS0k6lShARATLv9/WFh0Pt2lqpdxupRpxhjRkDRYpA587aZ0BkTG+8AUOGQOPGsH07ZM2qd0RCCGEddE3SWrVqxc2bNxk6dCjXrl0jODiYNWvWmAp/XLhwATu7/+/sq1y5Mj/++COffvopgwcPpkCBAqxYscIsMfroo4+4f/8+3bt3JyoqiqpVq7JmzRpcXFxMbRYuXEivXr2oXbs2dnZ2vPXWW3z77bdmsTVq1Ijz58+brpcuXRoAlUEmvOjdk5bRkrQff9Q7ivShZEnIlQtWrYL/fpcRVipbNhg5Et57Tyuek8z6SyId6tMHTpyAZs1g3Tp45s+1EEJkWAaVUbIOKxQTE4OXlxfR0dFWNz9t2zbt5Gn/fn32f+oUVKsGV6/qs/+0dOaMVkDk9m2wk1I+r23SJO1E77ff9I5EvIzRCFWqwFtvwf/+p3c0Qk9Pn0KvXtoQ2Dx59I4mfbOWc49Hjx5x9uxZgoKCzH5IF6+nY8eOzJs3D4Dly5ebRpLZQjzHjx8nNDSUkydP4uHhkUYRvrrWrVtTvnx5PvjgA4vul9z3vpwSikQ5OOg73NHPT5uTpmcMaSUoSPvl+MgRvSNJH9q1gw0bMkaCb+vs7GDGDK0s/9mzekcj9OTgANOnS4ImbMOUKVMIDAzExcWFkJAQdu7c+cL2M2fOpFq1amTOnJnMmTNTp06dl97n448/JjAwkLt375ptb9KkCdWrV8f4gnVMGjRowNWrV2nYsKFpm8Fg4Ny5cwD4+/szevRos/sMHDgQg8HApk2bzLbXqFGD9u3bA3D16lXatm1LwYIFsbOzo1+/fgn2PXz4cDp27Gi6PnHiRK4m8w/yoEGD6N27tylBe/ToER07dqREiRI4ODgkmuAlJ6bn7d+/nzZt2pArVy5cXV0pUqQIEydONGuzadMmDAZDgsuzy3V9+umnjBw5kuhUqv4mSZpIlN7DHTNlAlfXjFH9zWDQehNkgd+U4eOjFaX474c7YeVKlICePbWeexnXIV7m/v37rFu3Tu8whLVRSqvAlQZfIosWLWLAgAEMGzaMPXv2UKpUKerXr5+gcvizNm3aRJs2bdi4cSMRERHkypWLevXqcfny5STv89lnn5EpUyYGDBhg2jZ79mw2btzInDlzzKYDPc/Z2Rk/Pz+cnZ0Tvb1GjRoJkrGNGzeSK1cus+2PHj1i+/bt1KpVC4DHjx+TLVs2Pv30U0qVKpXk/p/l5eWVrOrpFy5cYOXKlWYJXlxcHK6urvTp0yfJBbtfJabdu3fj6+vLggULOHz4MJ988gmDBg1i8uTJCdoeP36cq1evmi7PrrVcvHhx8uXLx4IFC5K1X0tJkiYSZW+vb5IGWm+aBeuL2zRJ0lJWly7w/fdy0m8rhgzRhv3+9JPekQhrZjQaWbt2LRMnTqRnz556hyOsyYIFUKGCtlJ6Khs/fjzdunWjU6dOFC1alOnTp+Pm5sbsFyz+uHDhQt5//32Cg4MpXLgws2bNMlU0T4qzszPz5s1j3rx5rFmzhgsXLtC/f3/Gjh1Lvnz5Xus51KxZk61bt/L0vxO9u3fvsnfvXj7++GOzJC0iIoLHjx9Ts2ZNAAIDA5k4cSIdOnTAy8vrtWJ43uLFiylVqhQ5cuQwbXN3d2fatGl069YtyUTvVWLq3LkzEydOJDQ0lLx58/LOO+/QqVMnli1blqCtr68vfn5+psvzyXGTJk34+eefLXimySdJmkiU3sMdQSseIkmaeBW1amnv3+d+KBRWysUFvvsO+vfX5mYKkRg7OzvefPNNGjVqxIwZM5gyZYreIQlr8PQpDBum/X/YsFT9hTk2Npbdu3eb9erY2dlRp04dIiIikv04Dx484MmTJ/j4+LywXdmyZRk0aBBdu3alffv2VKhQgffee++V449Xs2ZN7t27x67/1v/ZsmULBQsW5K233mLHjh08evQI0HrXAgMDCQwMfO19vsyWLVsoV65cqu8nKdHR0Ym+HsHBwfj7+1O3bl22JnKiVqFCBXbu3Mnjx49TPCZJ0kSipCctbZUurc2hyijPN7XZ2UGPHjB1qt6RiOSqWVMrw/7hh3pHIqzZ7t27OXLkCLVr16Zw4cJ6hyOswU8//f+k1jNnIJV6NQBu3bpFXFycqQp5vOzZs5vNVXqZjz/+mICAgCSH8D3r008/xc7Ojh07dvD9999jMBgsjhu06uTxyVaBAgXIkSOHqdds06ZNhIaG4ufnR+7cuU0J56ZNm0y9aMk1fPhw5r7CgqXnz583WworLW3bto1FixbRvXt30zZ/f3+mT5/O0qVLWbp0Kbly5aJGjRrs2bPH7L4BAQHExsZa9PonlyRpIlHW0JOWkZI0R0dtpIb0pqWczp1h9Wq4ckXvSERyjRsHf/yhFX4R4nn//PMPM2bM4OzZswwaNIjatWvrHZLQW3wvWnziYmeX6r1pr2v06NH8/PPPLF++PFlVLdetW8e1a9cwGo2mnq+U8Oy8tE2bNlGjRg0AQkND2bRpEw8fPmTHjh0WJ2mv6uHDh7pU+Tx06BBNmzZl2LBh1KtXz7S9UKFCvPvuu5QtW5bKlSsze/ZsKleuzDfffGN2f1dXV0DrHU1pkqSJROldOAQyVpIGMuQxpWXNqq27NGuW3pGI5MqSBb75Bt59Fx4+1DsaobdnT3o2bdrE999/z/Xr1xk8eDChoaGm23bv3s3atWvTNrg0LFQhXiC+Fy3+dTAaU7U3LWvWrNjb23P9uYVcr1+/nqziGF999RWjR49m7dq1lCxZ8qXt79y5Q7du3fj000/55JNPeP/997mVQhXV4uel3b59m71795o+U6GhoWzcuJFt27YRGxtrKhqS2rJmzcqdO3fSZF/x4nvlu3fvzqeffvrS9hUqVODUqVNm2yIjIwHIli1biscnSZpIlAx3THuSpKW899/X5jo9eaJ3JCK52rSB/Pm1svwi43r69Cn9+vVjxowZHDp0iNmzZ3P79m0+/fRTqlSpAmjDt6Kioti+fTvDhg1LUFI8VaVhoQqRhOd70eKlYm+ak5MTZcuWNSv4EV8ApFKlSi+879ixY/n8889Zs2ZNsude9e7dGz8/PwYPHswnn3xCjhw5UqxoTs2aNbl//z7jx4+nQIECpqqF1atXZ+fOnaxevdo0LDItlC5dmiNpuBbR4cOHqVmzJuHh4YwcOTJZ99m3bx/+/v5m2w4dOkTOnDnJmjVriscoSZpIlAx3THuVKsG+fZAKPeYZVsWK4OsLv/+udyQiuQwGbS7hlClw8KDe0Qi9ODg40LFjR3r06EHbtm1RSjF06FBCQkJMbZRSeHt70717dypUqMDw4cMTlBVPFWlYqEK8wPO9aPFSuTdtwIABzJw5k3nz5nH06FHee+897t+/T6dOnUxtOnTowKBBg0zXx4wZw5AhQ5g9ezaBgYFcu3aNa9euce/evST3s3z5cpYsWcK8efNwcHDAwcGBefPmsWLFCpYuXfrazyNv3rzkzp2bSZMmmfVM58qVi4CAAGbMmJHoUMd9+/axb98+7t27x82bN9m3b1+KJFf169cnIiKCuOdOPo8cOcK+ffuIjIwkOjratH9LYlq+fLnZHNZDhw5Rs2ZN6tWrx4ABA0yvx82bN01tJkyYwK+//sqpU6c4dOgQ/fr1Y8OGDQmS5C1btpgNk0xRSugmOjpaASo6OlrvUBK4cUMpNzd9Y9i5U6nChfWNIa0VK6bU33/rHUX68t13StWpo3cUwlJffaVUSIhST5/qHYnQU0REhHJyclLDhg1TSikVFxdn9q9SSi1YsEC99dZb6r333lMnTpxI/aB++EEpLTXQLvPnp/4+U5C1nHs8fPhQHTlyRD18+NDyOz95olRQkFIGg/lrEX+xs1Mqb16tXSqYNGmSyp07t3JyclIVKlRQ27dvN7s9NDRUhYeHm67nyZNHAQku8e/r5928eVP5+vqqkSNHJrht5MiRytfXV928eTPR+4aHh6umTZsm63mEh4crQP38889m2zt27KgA9dNPPyW4T2LPI0+ePC/dF6CWL1+e5O1PnjxRAQEBas2aNWbbkzp2lsQ0Z84cs/sMGzbspfcZM2aMypcvn3JxcVE+Pj6qRo0aasOGDWb7ffjwofLy8lIREREvff7P3y85733Df09O6CAmJgYvLy+io6Px9PTUOxwzkZHg7w+pUFE02S5ehJIlIY2HKOvq3XchMBCe+QFOvKZ79yBnTtixAwoV0jsakVxPn2qjybp00Ra7FhnXn3/+SVhYGOvXr6dy5coYjUbTWkVz5sxh6dKlFChQgF69er32+lEv9fQpFCwI585p6YCdnfalffy4NgTFBljLucejR484e/YsQUFBlheM2LRJKwn7Mhs3wn8FMTKKjh07EhUVxYoVK/QOxYzBYGD58uWEhYUl2WbKlCn89ttv/Pnnn2kX2GuYNm0ay5cvt3hObHLf+7bxjSLSnDUMd/T1hagoePRIW0cpI6hSBRYv1juK9CVTJujQAaZP14pSCNvg4AAzZ0KdOtC0qZZoi4ypfv36bN26lcKFC3Pr1i3T3I+ZM2fy66+/UrhwYXr16pUmazmZlXsH86F177yT+vsXmkqVtD+WL/ol2dlZa5cBrVy5kkyZMvHzzz/zxhtv6BpLjx49WLBgQbLavvvuu0RFRXH37l08PDxSObLX5+joyKRJk1Lt8aUnTUfW8mtWYu7f105ujcaEc3LTUpYssGcP5MmjXwxp6fRpKF8ebt3SfqAVKePIEahaFS5dAjc3vaMRlvjf/+DkSVixQt/vIqG/mzdv0rRpU4YPH86dO3eYN28exYsXp3fv3uTKlSv1A3i+Fy2ejfWmWcu5x2v1pIkk3bhxg5iYGEBb68vd3V3isTLJfe/LaaBIVPzfGaNR3zj8/OC5SrfpWt684OQEx47pHUn6UrQolCqVqmucilQyYgTs3w/Ll+sdidBbtmzZePfdd2nQoAGjRo0iNDTUlKClye/NOhWqEMISvr6+5M+fn/z581tFQmRt8dgSSdJEouKTNL2LVmW0Co8Gg5TiTy3vv69VDJSxA7bF3R2mTYNevSA6Wu9ohN7Cw8P58ccfOXDgAIUKFSJXrlzExcVheEE3a1RUFNde9w9JUuXe49nAIspCCNsiSZpIVPxQO73/3mS0JA0kSUstYWFw5Yq2/qywLQ0banP/Bw7UOxJhDVq3bs2ECRP4+OOPuXTpEvb29km2vXnzJj///DPt2rVjw4YNr77Tf/5JvBctXnxv2j//vPo+MiiZdSMymuS+561/8LTQhcFgPQtaX7mibwxprVo1rcdHpCxHR+jeHSZNgvnz9Y5GWGrCBChWDFq1ynDF2kQi+vTpQ4UKFciSJYtpW1xcXIKELVu2bISGhmIwGHjrrbdYsWKF2ZpQySaFKlKco6MjAA8ePMDV1VXnaIRIOw/+WxA3/jOQFEnSRJKsocKjv79WNCAjKV0abtzQilxIRbuU9d57kD8/XL2qvbeE7fD1hW+/1UryHzigDYMUGVvFihUBWLt2LcWKFSNHjhyJtitSpAhFihTBzs6OoUOH8scff5ApUybLdubsDC1avG7I4hn29vZ4e3tz48YNANzc3F44bFUIW6eU4sGDB9y4cQNvb+8XjgIASdLECzg46N+TFhAAmzfrG0Nac3CAypW15922rd7RpC9+ftCsmVaOf8QIvaMRlmrdGhYtgsGDYeJEvaMR1uLw4cNs2bKFYcOG4eDggPG/ild2dnbExcVhNBpxdHSkbNmyfP/998Tp/eujMPHz8wMwJWpCZATe3t6m9/6LSJImkmQNwx39/bVej4wmNFSStNTSrx80aqQtGC5Vn22LwaAVESlRAt5+WxsaLET//v25e/cuDg4OxMXFcfv2bRwcHPDx8eHx48dER0fj4OBA//79cXNzw8vLy2xBbKEfg8GAv78/vr6+PHnyRO9whEh1jo6OL+1BiydJmkiSNfSk+ftnvDlpANWrQ7duekeRPpUtCwUKaNWyO3bUOxphKX9/bVHyzp210vyy7p0A8PDw4MiRI7Rt25bbt28TGBhIZGQkBoOB+/fv4+zsTM6cOZkwYQKg9bIdOXIEd3d38mSUhTitmL29fbJPXIXIKGQxax1Zy4KSScmeHSIitLW79BITAz4+2lztjPT9/fgxZM6srZnq66t3NOnPkiXw5ZfaQukyBcL2KAVvvqnNL/zmG72jEdbkww8/ZPLkyezYsYPs2bNz8+ZNnJycMBgMFChQwNTu77//pmbNmjg7O3PkyBGCgoJ0jDptWfu5hxBCI339IknWUDjEw0Obr33rlr5xpDVnZwgJgS1b9I4kfWrWDG7fluNrqwwG+O47rUrnxo16RyOsybhx4+jQoQNdunQhc+bMFC9enIIFC5olaOvXr6d27dpMmjSJjz76iCpVqnDx4kUdoxZCiIQkSRNJsobhjgZDxh7y+PffekeRPjk4QM+eUnzClgUEaPPTOnaURa6Fue+++45ChQpRokSJBLfFxMTw9ddf4+bmRs+ePRkxYgQdOnQgJCSEqKiotA9WCCGSIEmaSJI1FA4B7WQsIxcPEamjWzdYt04bUipsU4sWWvGQPn30jkRYmwULFtC5c2cOHDgAwNP//phlypSJX375hWbNmlGnTh0ARo8ezWeffSbl34UQVkWSNJEka+hJg4xb4bFiRTh2DO7c0TuS9MnHR6ueKQuH27bJk2HDBli6VO9IhLX5+OOPKVmyJJcvX+bgwYOAtk6Rm5sbc+bM4cmTJ6xevRqArl274uXlJeX5hRBWQ5I0kSRJ0vTl5gZlysA//+gdSfrVpw/MmgX37ukdiXhV3t4wdy706JExvyfEy23fvp0BAwbw5MkTUwVBOzs7IiMjE6zPJRUGhRDWQpI0kSRrStIy4pw0kCGPqa1oUShfHn74Qe9IxOuoXRvatYOuXbXKj0I866233sLe3p6wsDDiC1rPmTOHp0+fUqFCBQBu377Nxo0bqV69Oq1bt6ZNmzamRbGFEEIPkqSJJFlLkpZR56SBFA9JC/36wbffgpyP2bZRo+DsWZg5U+9IhDX666+/MBqN1K5dmyJFijBx4kQ6dOhAoUKFOH36NJMnT6Znz54EBgbSunVrHj58SEhIiN5hCyEyMFnMWiTJ3l7/EvyQcYc7AlSpoi3Ye/euthyBSHkNGkD//rB2rfZ/YZtcXbWS/HXqaD1r+fLpHZGwNqtXr2bDhg2cPXuWSpUqkStXLh49esS8efP4559/6NOnDz169AAgLCyMGjVqcPToUYoUKaJz5EKIjEh60kSSrKUnLSMPd/T0hBIltEXFReqws4PevaUcf3pQtix88AG0b28d313C+tSqVYsuXbpQtGhRPDw8WLduHYsXL+add94xJWigzWPbsWMHT5480TFaIURGJkmaSJK1JGkBAXDtWsadayJDHlNfeDhs365V0xS2beBA7bti7Fi9IxHWTClFbGwsn3zyCY0bN6Zz586m2y5dusTChQtp1aoVxYoVM7UXQoi0JEmaSJK1JGne3lpvx+3bekeiDykekvo8PKBTJ21umrBtDg5aIZhx42DvXr2jEdbKYDBgZ2dHYGAgLVq0MG2/ePEiCxcu5MCBAzRq1MhU7fH06dNcu3ZNr3CFEBmQJGkiSQ4OYA0jPQwGrTft8mW9I9FHtWqwaxc8eKB3JOlb796wYEHG/TEgPSlQAEaP1tbBu39f72iEtTIYDDx48IC5c+cCsHnzZsaNG8cff/xBu3btaNmyJVu3buWNN96gevXqNGvWjLZt2+obtBAiw5AkTSTJwcE6CoeAlqRl1HlpPj5aqfitW/WOJH0LCoKGDWHqVL0jESmhe3coUkSr3ilEYuzt7fnll19Yv349tWvXpmHDhsTExNC3b1+6d+/O3LlzGTp0KEop1q9fz08//cTRo0f58MMP9Q5dCJEBSHVHkSRrGe4IkCNHxu1JA6hVCzZsgLp19Y4kffvwQ2jUCP73P61aoLBdBoO2UHmpUrBkCTwzok0IEx8fH/bu3cv58+cByJs3L66urvz+++/Mnz+f4OBgRo0ahZOTEwCDBw9m9erVKKUwGAx6hi6ESOekJ00kSZI06xGfpInUVa6c1ms5f77ekYiU4OMDCxdCjx7w3zm4EAlkypSJYsWKUaxYMVxdXVFK8cUXX5ArVy7GjBljStAA5s6dy+PHjyVBE0KkOknSRJIkSbMe1app66VFRekdSfr34Yfw9deyuHV6Ub069OqlzU+zlu8zYd0WL17MpUuXmD17Ng4O2oCjJ0+eMHv2bOLi4ggPD9c5QiFERiBJmkiSJGnWw8NDWwNKqjymvoYNtff+b7/pHYlIKUOGaMMfP/tM70iELfDy8qJ06dLY2WmnSLGxsSxatIilS5eSP39+qlatqnOEQoiMQJI0kSRJ0qyLDHlMG3Z22py0ceP0jkSkFAcHbdjjlCmy5qB4uUKFChEREcGkSZPYunUr/fv3Z8GCBeTMmZMxY8bg5uYm66YJIVKdJGkiSZKkWRdJ0tJO27Zw9ixs26Z3JCKl5MkD06fDO+/IMgvixYKCgli1ahVLlizh/fff5+bNm7z11ltMmjQJd3d3KRoihEgTBiU/B+kmJiYGLy8voqOj8fT01DucBLp1g2LFrKOE9aNH4OYGDx+Cs7Pe0ejj0SOtEMK5c+Drq3c06d/o0bBjByxfrnckIiV17w43b8KyZdoQSCGS8uDBA2JjY/H29jZtSw8JmrWfewghNNKTJpJkTT1pLi5agnL1qt6R6MfFBSpVgk2b9I4kY+jRQ+u5PHFC70hESpowAY4f13rVhHgRNzc3swQNsPkETQhhOyRJE0mypiQNtAWtZcijDHlMK97eWm/y6NF6RyJSkpsb/PQTDBoEBw/qHY2wVbdvw88/6x2FECI9kyRNJMnakjSZlyZJWlr74AP45RdtiKlIP0qVgs8/hzZttCHUQljqxg3o3x/GjtU7EiFEeiVJmkiSJGnWp1w5bcjnxYt6R5Ix+PtDeLiciKVHvXpBUBAMGKB3JMIWFSkCW7bAtGkweDDI7H4hREqTJE0kSZI06+PoqC3OK71paefDD2H+fLhyRe9IREoyGGDOHPj9d1i0SO9ohC3Knx/++QdWrICePcFo1DsiIUR6IkmaSJIkadZJhjymrdy5oXVr+OorvSMRKS1rVi1Be+89rZiIEJbKkQM2b4Zdu6B9e3jyRO+IhBDphSRpIkmSpFmn+CRNhteknYEDYdYsrXS7SF+qVNGGq7VoAQ8e6B2NsEVZs8L69Vpve/PmMs9RCJEyJEkTSZIkzTqVKgX378OpU3pHknHkywdNm8I33+gdiUgNH3wAefNq89SEeBWenrBqlTaMtmFDiInROyIhhK2TJE0kycHBuoZuxCdpGb0Hyc5O60376y+9I8lYBg+GqVO10tsifYmfn7ZpE8yYoXc0wla5usLSpZArF9SoAdeu6R2REMKWSZImkuToaF09aVmzav/KSTLUqwfr1ukdRcZSpAi88YbMTUuvMmeG5cvh449h+3a9oxG2ytER5s3TfkirXBlOntQ7IiGErZIkTSTJ2oY7GgyQMydcuqR3JPqrW1ebA2FNr09GMHSo1pt244bekYjUUKqU9vq+9Zb0gohXZ2en/ZjTsydUraoVFRFCCEu9cpJ26tQp/vzzTx7+N0NWZfQxaOmQtQ13BEnS4gUFQfbssHOn3pFkLAULQrNmsm5aetamDbRqBS1bWt/3n7AtH3ygzWOtXx/WrNE7GiGErbE4Sbt9+zZ16tShYMGCNGrUiKtXrwLQpUsXPvjggxQPUOjH2oY7gpakyULOmrp1Ye1avaPIeIYO1eYt/ffVJ9KhsWPB3l47yRbidbRtqy3z0LYt/PCD3tEIIWyJxUla//79cXBw4MKFC7i5uZm2t2rVijXyU1G6Ym3DHUGbkC09aZp69SRJ00PevFpPy5gxekciUouDg3ZivWKFnFiL11e3rjaH+OOP4fPPpfiVECJ5LE7S1q5dy5gxY8iZM6fZ9gIFCnD+/PkUC0zoT4Y7WreaNWHPHoiK0juSjOeTT2D2bHkvpme+vrBsGfTtq33OhHgdZctCRAT89BN07gyxsXpHJISwdhYnaffv3zfrQYsXGRmJs7NzigQlrIO1DneUE2ONpyeUL68tbC3SVmAgtGsHo0bpHYlITeXKwfjx2jzEW7f0jkbYusBA2LYNLlyARo3kBzYhxItZnKRVq1aNH54Z/2EwGDAajYwdO5aaNWumaHBCX9Y63FHmpP0/GfKon08+gfnztRMukX516gSNG0Pr1tb3fShsj7c3rF6t/eBYtSrIACQhRFIsTtLGjh3LjBkzaNiwIbGxsXz00UcUL16czZs3M+YVJmlMmTKFwMBAXFxcCAkJYedLytUtWbKEwoUL4+LiQokSJVi1apXZ7Uophg4dir+/P66urtSpU4eTzy1UEhkZSbt27fD09MTb25suXbpw79490+2PHj2iY8eOlChRAgcHB8LCwhLEsWzZMurWrUu2bNnw9PSkUqVK/PnnnxY/f2tmzcMdZUy/pl49+PNPOR56yJkTOnaEL77QOxKR2iZMgIcPYdAgvSMR6YGTk7Z4esuWULEi/Puv3hEJIayRxUla8eLFOXHiBFWrVqVp06bcv3+f5s2bs3fvXvLly2fRYy1atIgBAwYwbNgw9uzZQ6lSpahfvz43kliEaNu2bbRp04YuXbqwd+9ewsLCCAsL49ChQ6Y2Y8eO5dtvv2X69Ons2LEDd3d36tevz6NHj0xt2rVrx+HDh1m3bh0rV65k8+bNdO/e3XR7XFwcrq6u9OnThzp16iQay+bNm6lbty6rVq1i9+7d1KxZkyZNmrB3716LjoE1s8bhjlmzQlwc3LmjdyTWoVw5bcjM6dN6R5IxDRoEP/8MZ87oHYlITU5O8MsvsHChVlBEiNdlMGiVYseO1QqL/P673hEJIayO0lGFChVUz549Tdfj4uJUQECAGjVqVKLtW7ZsqRo3bmy2LSQkRL377rtKKaWMRqPy8/NT48aNM90eFRWlnJ2d1U8//aSUUurIkSMKULt27TK1Wb16tTIYDOry5csJ9hkeHq6aNm2arOdTtGhRNWLEiGS1VUqp6OhoBajo6Ohk3yctrVmjVJUqekeRUFCQUvv36x2F9Xj7baWmTNE7ioxrwAClOnXSOwqRFrZuVcrbW6kDB/SORKQnGzYolSWLUpMmpc3+rP3cQwiheaXFrB89esTOnTtZuXIlv/32m9kluWJjY9m9e7dZT5WdnR116tQhIiIi0ftEREQk6NmqX7++qf3Zs2e5du2aWRsvLy9CQkJMbSIiIvD29qZcuXKmNnXq1MHOzo4dO3YkO/7nGY1G7t69i4+PT5JtHj9+TExMjNnFmlnjcEfQ5qXJPKD/J/PS9PXRR7B0KTw3qlqkQ5Urw5dfaoVEpDdfpJSaNWHLFvj6axgwQBstIoQQDpbeYc2aNXTo0IFbiZS6MhgMxCXz2+XWrVvExcWRPXt2s+3Zs2fn2LFjid7n2rVriba/du2a6fb4bS9q4+vra3a7g4MDPj4+pjav4quvvuLevXu0bNkyyTajRo1ixIgRr7yPtGaNhUMAgoLg7Fm9o7Aedetqi+4+eaINURVpK3t26NEDPvtMKyQi0rcePbQ5RO3aaUPU7O31jkikB0WKwPbt0KQJtGgBCxZAIoW0hRAZiMU9ab1796ZFixZcvXoVo9Fodklugpbe/Pjjj4wYMYLFixcnSACfNWjQIKKjo02Xi1ZeptAa56QB5Msnc7CeFRgI/v7aGjxCHx9+qJ2wHz2qdyQitRkMMGWKVpJ/+HC9oxHpSfbssGmTVgiqZk24fl3viIQQerI4Sbt+/ToDBgxI0FtlqaxZs2Jvb8/1576Frl+/jp+fX6L38fPze2H7+H9f1ub5wiRPnz4lMjIyyf2+yM8//0zXrl1ZvHhxkkVG4jk7O+Pp6Wl2sWbWOtwxb14p1PC8N96AlSv1jiLjypoVevUCG+ooF6/BxUUb4jpjBqxYoXc0Ij1xc9OK1FSuDJUqQRIDi4QQGYDFSdrbb7/Npk2bXnvHTk5OlC1blvXr15u2GY1G1q9fT6VKlRK9T6VKlczaA6xbt87UPigoCD8/P7M2MTEx7Nixw9SmUqVKREVFsXv3blObDRs2YDQaCQkJseg5/PTTT3Tq1ImffvqJxo0bW3RfW2Ctwx3z5pWetOdJkqa/AQO05RAOHtQ7EpEWcuXSKj127iwn0iJl2dvDN99A//7aWmopcMolhLBBFs9Jmzx5Mi1atGDLli2UKFECx+cmwfTp0yfZjzVgwADCw8MpV64cFSpUYMKECdy/f59OnToB0KFDB3LkyMGoUaMA6Nu3L6GhoXz99dc0btyYn3/+mX///ZcZM2YA2py4fv368cUXX1CgQAGCgoIYMmQIAQEBprXOihQpQoMGDejWrRvTp0/nyZMn9OrVi9atWxMQEGCK7ciRI8TGxhIZGcndu3fZt28fAMHBwYA2xDE8PJyJEycSEhJims/m6uqKl5eXpYfVKjk6WmdPWr58Wk+a0Qh2r1T6Jv2pWhWuXtWKVxQooHc0GZOPD/Trp5XVXr5c72hEWqhRQ3u9mzbVhhu/oG6UEBbr3Rvy5NEK1Xz9tfaDgBAiA7G0HOSsWbOUg4ODypQpk8qTJ48KDAw0XYKCgiwuLzlp0iSVO3du5eTkpCpUqKC2b99uui00NFSFh4ebtV+8eLEqWLCgcnJyUsWKFVN//PGH2e1Go1ENGTJEZc+eXTk7O6vatWur48ePm7W5ffu2atOmjcqUKZPy9PRUnTp1Unfv3jVrkydPHgUkuDwbW2K3Px/vi1h7GdwjR5TKmVPvKBIyGpXy8FDq0iW9I7Eu7dop9czqE0IH0dFKZcum1LZtekci0orRqFTXrkqFhir16JHe0Yj0aM8e7W/xBx8o9fTp6z+etZ97CCE0BqWUsiSp8/Pzo0+fPgwcOBA76cZ4LTExMXh5eREdHW2V89NOnYJq1bQeGmtTpow2HCQ0VO9IrMcvv8CECfDPP3pHkrF9+632Wvz9t1ZkQqR/T55oQ46zZdMqfMrrLlLa1ataj1rJktpcyNdh7eceQgiNxVlWbGwsrVq1kgQtA7DWwiEA+fNrSaT4fw0awN69UhFMb+++CxcvwqpVekci0oqjIyxZAgcOwLBhekcj0iN/f9i4UaskK4TIGCzOtMLDw1m0aFFqxCKsjLXOSQNJ0hKTKRPUqgUWrCkvUoGzM3zxBQwcKIvSZiSenvDHH/D99zBnjt7RiPTI1VXmHAuRkVhcOCQuLo6xY8fy559/UrJkyQSFQ8aPH59iwQl9WWt1R9CStNWr9Y7C+oSFwbJl0K2b3pFkbG3awLhxsHAhdOigdzQireTKpVVZrV1b+/9LVmURQgghkmRxknbw4EFKly4NwKFDh8xuM8hA/HTFmnvS8uWTnrTEvPmmVhHs7l3w8NA7mozLzg5GjYIePaBlS21dLZExlC4NCxZAq1bavMTixfWOSAghhC2yOEnbuHFjasQhrJCjo3X3pJ06BUrJJP1nZcsG5crBmjXQooXe0WRsDRpoa/pNm6atdyQyjkaNYORIaNwYtm/X5hMJIYQQlnit6h+XLl3i0qVLKRWLsDIODloSZI3zavz9tbhu3NA7EusTFgYrVugdhTAYYPRo+PJLiI7WOxqR1nr00HrT3ngD7t3TOxohhBC2xuIkzWg08tlnn+Hl5UWePHnIkycP3t7efP755xiNxtSIUegkfrqhNfam2dnJkMekhIVpBQxiY/WORISEaMtEjB2rdyRCD6NHa72pbdpY549dQgghrJfFSdonn3zC5MmTGT16NHv37mXv3r18+eWXTJo0iSFDhqRGjEIn9vbav9Y6L00qPCYub17Ikwc2bdI7EgHa3LTJk+H8eb0jEWnNzg5++AFu34a+fbWRCUIIIURyWJykzZs3j1mzZvHee+9RsmRJSpYsyfvvv8/MmTOZO3duKoQo9GIwWH+FR0nSEidDHq1HgQJatc2BA/WOROjB1RV+/VWrRiur1wghhEgui5O0yMhIChcunGB74cKFiYyMTJGghPWw9gWtT5/WOwrrFBamnRjKCGTr8OmnsH49bNumdyRCD9myaQsRN2umdyRCCCFshcVJWqlSpZg8eXKC7ZMnT6ZUqVIpEpSwHtZchl960pIWHKy9drt26R2JAPD2hhEjtCqPkjhnTLlzawudv0hUVBQrV65Mm4CEEEJYNYuTtLFjxzJ79myKFi1Kly5d6NKlC0WLFmXu3LmMGzcuNWIUOrKFMvwiIYNBhjxam27d4P59+PFHvSMR1iguLo7169czefJkuslq9EIIkeFZnKSFhoZy/PhxmjVrRlRUFFFRUTRv3pzjx49TrVq11IhR6MiahzvmzKmd9Moo28RJkmZdHBzgm2+0uWn37+sdjbA29vb2hIWF8cYbbzBv3jymTp2qd0hCCCF0ZPFi1gA5cuRg5MiRKR2LsELW3JNmb69VMjx1CipU0Dsa61O1Kty8CceOQSLTSIUO6tbVhqJ+9RUMG6Z3NMLabNu2jf3799OgQQPKly+vdzhCCCF0ZHFP2pw5c1iyZEmC7UuWLGHevHkpEpSwHtbckwbakMeTJ/WOwjo5OECTJrB8ud6RiGd9/bXWo3bhgt6RCGuyceNGZs2axY0bNxg2bJgkaUIIkcFZnKSNGjWKrFmzJtju6+vLl19+mSJBCethzYVDAAoWhBMn9I7Cer39NiTym4rQUaFC0L27VkREZFz3nxnz+tdffzF79myio6MZPnw4ZcuWNd32999/J/rDqBBCiPTN4iTtwoULBAUFJdieJ08eLshPw+mOJGm2rW5dOHtWCqxYmyFDYMcO+PNPvSMRenj69Cn9+vVjypQp7N+/n7lz53Lv3j1GjBhB6dKlAVBKERUVxalTpxg/fjwffPCBzlELIYRISxYnab6+vhw4cCDB9v3795MlS5YUCUpYD2tP0goVguPH9Y7Cejk5QdOm0ptmbTw8YPx46N0bHj/WOxqR1hwcHOjWrRu9e/emdevW2NvbM2LECEqVKoVSCtCSNG9vb8LDw6lUqRIzZsxg7dq1OkcuhBAirVicpLVp04Y+ffqwceNG4uLiiIuLY8OGDfTt25fWrVunRoxCR9aepMX3pP13XiMS0bIlLF6sdxTieS1aQK5c2hw1kfFUqFCBXbt2cf78eQICAihZsiRGoxGDwYDRaMTOTvvzPGfOHM6ePcu7775L0aJFdY5aCCFEWrE4Sfv8888JCQmhdu3auLq64urqSr169ahVq5ZUfEyHrD1J8/fX1gS7elXvSKxXnTpw7pwUWLE2BgNMngxjx8L583pHI/RQtmxZ/vzzTyZOnMiGDRuws7MzS9CmTZvGihUrKFSoEP379ydnzpymnjYhhBDpm8VJmpOTE4sWLeL48eMsXLiQZcuWcfr0aWbPno2zs3NqxCh05OBgvSX4QTvRLVhQhjy+iJOTtmaaDHm0PkWKwLvvShGRjKxatWrs3r2bSpUqceXKFVOCNnnyZFauXEnJkiXp27cvOXLkQCmFwWDQOWIhhBBpweIk7bPPPuPBgwcUKFCAFi1a8MYbb5AnTx4ePnzIZ599lhoxCh1Ze08aSPGQ5JAhj9ZryBDYuRPWrNE7EqGXIkWK8PDhQ5o3b87ChQtZuHAhq1atolSpUvTt2xd/f39J0IQQIoOxOEkbMWIE9+7dS7D9wYMHjBgxIkWCEtbDFpK0QoUkSXuZ2rW1dbnkOFmfTJmkiIgAHx8fhgwZQvv27fnss8+oXbs2ffr0wc/PTxI0IYTIgCxO0pL6Y7F//358fHxSJChhPWwhSZPhji8nQx6tW4sWkCcPfPWV3pEIPTVu3Jh169Zx8uRJMmXKhJ+fn6mYSGKMRiNXr17l8OHDaRypEEKI1JbsJC1z5sz4+PhgMBgoWLAgPj4+pouXlxd169alZcuWqRmr0IGtJGnSQ/RyMuTResUXERk3TivyIjKu2rVrs3jxYsaNG8fRo0dNc9QSEx0dze+//06XLl2YOHFiGkYphBAitTkkt+GECRNQStG5c2dGjBiBl5eX6TYnJycCAwOpVKlSqgQp9GMrSdrZs1qcjo56R2O9ateGixe1XsdChfSORjyvcGHo0UMrIrJ8ud7RCD29/fbbFCtWjLx585q2PVv1MV7mzJlp0qQJxYoVo0WLFhgMBvr06ZPW4QohhEgFyU7SwsPDAQgKCqJy5co4ytlwhmALSZqnJ2TNCmfOSPLxIo6O0KyZNuTx00/1jkYk5tNPtYqPq1dDw4Z6RyP0VKRIEQBWrlxJ1qxZqVixYqLt/P398ff3Z/HixXz88ce0adOGbNmypWWoQgghUoHFc9KCgoK4evUqFy5cSPQi0hdbSNJAhjwmlwx5tG7PFhF59EjvaIQ1ePLkCStWrODxf1Vlnl0nzWg0mv7v5eXFtWvXTO2EEELYtmT3pMULDAx8YZWpuLi41wpIWBdbSdKkwmPy1KoFly/DsWPa8Dphfd5+G2bM0IqISI+naNasGQ0aNMDZ2ZmoqCguXbqEi4sLHh4e3Lt3j3v37vH06VO6dOlC4cKFTQteSzVIIYSwbRYnaXv37jW7/uTJE/bu3cv48eMZOXJkigUmrIOtJGlS4TF5nh3yOGSI3tGIxBgMMGkSVKwI77wDgYF6RyT05urqyoULFwgLC+Pw4cPUqFGDS5cuAfD06VM8PT0pWrQon3/+OQAGg4F169YRFRVFixYt9AxdCCHEK7I4SStVqlSCbeXKlSMgIIBx48bRvHnzFAlMWAdbSdIKFYKVK/WOwja0bAkffCBJmjUrXBjee0+KiIj/lzt3bvr160e3bt348MMPqVOnDpGRkdjZ2WE0Gs2WwFm2bBlvv/02AA8fPqRDhw56hS2EEOIVWZykJaVQoULs2rUrpR5OWAkHB9tI0qQnLflq1oSrV+HIEShaVO9oRFLii4isXAlvvKF3NMIadOjQgXv37vHOO++watUqypQpA5jPU/v99995++23Wbp0KVmyZKFJkybY2dnxzjvv6BW2EEKIV2BxkhYTE2N2XSnF1atXGT58OAUKFEixwIR1cHKyjSQtb164fRuiosDbW+9orJujo7Z48sKFICOUrZe7uzbssWdPqFFDKyoixPvvv8/jx4+pWrUqx44dI3fu3Kb5Z/fv32fDhg04ODiQL18+SpYsyYoVK2jSpAlBQUFUqVJF5+iFEEIkl8XVHb29vcmcObPp4uPjQ9GiRYmIiGDatGmpEaPQka0Md3R0hPz54ehRvSOxDe3awY8/wjM/wAsr1LQplC0LQ4fqHYmwJv3792fhwoVcv34dgAcPHgDg7u7ON998w9SpU2nSpAlnz56lZs2abN++nfLly+sZshBCCAtZ3JO2ceNGs+t2dnZky5aN/Pnz4+CQYqMnhZVwdIS7d/WOInmKFNGSNFlT/eUqV9b+3bYN5Md16zZpEpQoAW3bQrlyekcjrEWzZs0AuHLlCocPH6Z27dqmYY9du3Zl06ZN/P333wQFBVG8eHEA7t69i1IKT09P3eIWQgiRPBZnVaGhoakRh7BStjLcEf4/SRMvZ2ennfQvWCBJmrXLkQM+/xy6doWdO7XPpBDxLly4QK9evVi8eLFZYa9jx46RJ08e0/W7d++yZs0aVq9eTfv27alZs6Ye4QohhEgmi4c7Apw+fZrevXtTp04d6tSpQ58+fTh9+nRKxyasgK0MdwStCIYkacnXrp22sHVsrN6RiJd57z3w8oIvv9Q7EmFtKlasSLt27WjcuDEHDx4kKiqKyZMnYzAYzKote3h4UKRIEYoWLcpbb73F6tWrdYxaCCHEy1jck/bnn3/y5ptvEhwcbJqEvHXrVooVK8bvv/9O3bp1UzxIoR9HR9s5iZeeNMsULQp58sCff0KTJnpHI17Ezg5mz9bmp4WFQXCw3hEJazJ06FDs7e3p2LEjMTExODk50aVLF/LmzWvWrnjx4hQvXpyCBQvSv39/ihcvTq5cuXSKWgghxIsYlLKsdEDp0qWpX78+o0ePNts+cOBA1q5dy549e1I0wPQsJiYGLy8voqOjrXaOwPTp8M8/2rA4a/fgAXh6anPoXF31jsY2fP21NoRu0SK9IxHJMXkyzJwJu3bJsEeR0IkTJ7h16xb58uXD09MT1/++CJVSPH36FEdHR+Li4njw4AFNmjRh9uzZCRI5kf7ZwrmHEOIVhjsePXqULl26JNjeuXNnjhw5kiJBCethS3PS3NwgVy44cULvSGxH69bwxx/w3Moawkq9/z74+MAXX+gdibBGBQsWpHLlymTPnh1XV1eePn0KgMFgwNHREdAStsWLF7N161aMRqOe4QohhHgBi4c7ZsuWjX379iVYE23fvn34+vqmWGDCOtjSnDTQhjweOQLPzJ8XL5AjB1SsCMuWQceOekcjXiZ+2GOZMtqwx//WMhbCzNWrVxk1ahTr16+nRIkSXLx4kYcPH+Lu7s6jR484ePAg3333Hfnz5we0Ev63b9+WoY9CCGFFLE7SunXrRvfu3Tlz5gyV/6vjvXXrVsaMGcOAAQNSPEChL1tM0mRemmXatdMWtpYkzTYEBWmLkHfsqA17dHbWOyJhbfz9/fH19eXo0aMMHDiQAgUKcOHCBVxcXMiSJQsBAQEEBQUB8PDhQ9544w02bdrE9u3bqVChgs7RCyGEgFeYk6aUYsKECXz99ddcuXIFgICAAD788EP69OmDwWBIlUDTI1sYF750KcyYoRWXsAWzZmmxLlmidyS2Izpa61E7cQICAvSORiSH0Qh162prAsrQR5GUAQMGsG7dOvbv34+dXcLZDTExMYSFhfHw4UOaNm3K+PHjWb16NWXLltUhWpFWbOHcQwjxCj1pBoOB/v37079/f+7+t8qxh4dHigcmrIMtzUkDrSdtwgS9o7AtXl7QqBH8/DNIZ7htsLOD77+H0qW1YY+yyLVIzPjx4+nevTu5c+fm9OnTODs7o5TCYDDw5MkTZs+ezaZNm7h48SI5cuTA2dmZevXqsXXrVgoXLqx3+EIIkaG90jpp8Tw8PCRBS+dscbjjyZPw33x5kUzxQx6F7QgMhNGjtWGPjx/rHY2wVjNmzKBJkyb89ttvZtsdHR1555136NWrF2+99RYPHz6kf//+TJgwgePHj+sUrRBCiHgWD3cUKccWhhysXw+ffALbt+sdSfJlzw5btkDBgnpHYjtiY8HfX1tuoUgRvaMRyaUU1KsH5cvLQtfixeLi4njy5AmHDx+mVKlSODj8/0Capk2bUq5cOYYMGaJjhCKt2MK5hxDiNXvSRPpnaz1pIMVDXoWTE7RsCT/8oHckwhIGgzYPc9o0rYiIEEmxt7fnxIkTfPXVV1y7dg3ANGUhb9683LlzR8/whBBCPEeSNPFCTk5aL4stkSTt1XTpAvPmyVBRW5MnD4wdqw17fPRI72iENStZsiT29vaEhYUB2pQFo9HI77//LlMXhBDCykiSJl7IFnvSihbV1koTlilbFrJlg1Wr9I5EWKprV8iZE4YO1TsSYe0WLFhA3rx5CQkJoWPHjuTOnZsSJUrQrVs3vUMTQgjxDIurOwKsX7+e9evXc+PGDYxGo9lts2fPTpHAhHWwteqOAMWKwdy5ekdhewwG6N4dpk+HN9/UOxphCYNBq/YYHKxV6qxRQ++IhDVbvHgx8+bN4/r165QoUYL27dvj6+urd1hCCCGeYXGSNmLECD777DPKlSuHv7+/rIuWzjk62t5wx+LFtZ60uDiwt9c7GtvSvj0MGgRnz2qLJgvbkTOnlmC3bw8HDkDmzHpHJKxZeHi43iEIIYR4AYurO/r7+zN27Fjat2+fWjFlGLZQYenMGahcGf6bZ24zfH1h61YoUEDvSGzPu++Cjw+MGqV3JOJVdO4M9+7BokVaD5sQQjzLFs49hBCvMCctNjaWypUrp0YswgrZYk8aaL1phw7pHYVt6tFDGzona2/ZpokTYc8eqdQpXs/ff9veUHchhEhPLE7Sunbtyo8//pgasQgrZIvVHQFKlJAk7VWVLg1588KyZXpHIl6Fh4e2MHn//nD6tN7RCFtkNMLIkdC4McTE6B2NEEJkTMmakzZgwADT/41GIzNmzOCvv/6iZMmSODo6mrUdP358ykYodGWLhUNA60n76y+9o7Bd772nzW9q00bvSMSrCAnRkrR33tEWdnd4pRJRIqOys4OVK7VlOapXhz/+gBw59I5KCCEylmTNSatZs2ayH3Djxo2vFVBGYgvjwu/d036ZNxpta35LRIR2giGl+F/Nw4daIYrNm7VqmcL2PH2qVXmsUweGD9c7GmGLlIIhQ7T1E1et0kYoCNtnC+ceQohXKBwiUo4tfFHGxoKzs9abZku/xsfEQJYsWpLp7Kx3NLbpgw+013/SJL0jEa/q3DkoU0brFZGpxOJVzZihVX1dsgRq1dI7GvG6bOHcQwjxCnPSOnfuzN27dxNsv3//Pp07d06RoIT1iB/Namvz0jw9ISAAjh/XOxLb9e67MH++lugK2xQYCN9+qw17lLlF4lV17659F7z9tvavEEKI1GdxkjZv3jwePnyYYPvDhw/54RXKiU2ZMoXAwEBcXFwICQlh586dL2y/ZMkSChcujIuLCyVKlGDVqlVmtyulGDp0KP7+/ri6ulKnTh1Onjxp1iYyMpJ27drh6emJt7c3Xbp04d4zZ6KPHj2iY8eOlChRAgcHB8LCwhKNZdOmTZQpUwZnZ2fy58/P3HS4grLBoPWg2VqSBlLh8XUVLAjlysHPP+sdiXgd7dpBxYrQu7fekQhb1qiRNs/344+1oiIyBkcIIVJXspO0mJgYoqOjUUpx9+5dYmJiTJc7d+6watUqfH19Ldr5okWLGDBgAMOGDWPPnj2UKlWK+vXrc+PGjUTbb9u2jTZt2tClSxf27t1LWFgYYWFhHHrmTHzs2LF8++23TJ8+nR07duDu7k79+vV59OiRqU27du04fPgw69atY+XKlWzevJnu3bubbo+Li8PV1ZU+ffpQp06dRGM5e/YsjRs3pmbNmuzbt49+/frRtWtX/vzzT4uOgS2w1eIhUuHx9b33HkybJidktsxggKlTYdMmbe00IV5VmTKwbZtWPbR7d9v8uyCEEDZDJZPBYFB2dnZJXuzt7dUXX3yR3IdTSilVoUIF1bNnT9P1uLg4FRAQoEaNGpVo+5YtW6rGjRubbQsJCVHvvvuuUkopo9Go/Pz81Lhx40y3R0VFKWdnZ/XTTz8ppZQ6cuSIAtSuXbtMbVavXq0MBoO6fPlygn2Gh4erpk2bJtj+0UcfqWLFiplta9Wqlapfv/5LnvX/i46OVoCKjo5O9n304OWl1KVLekdhufnzlWrSRO8obFtsrFL+/krt2KF3JOJ1bd6slI+PUmfO6B2JsHWRkUpVr65UgwZKxcToHY2wlK2cewiR0SW7J23jxo2sX78epRS//PILGzZsMF3++ecfLly4wCeffJLs5DA2Npbdu3eb9VTZ2dlRp04dIiIiEr1PREREgp6t+vXrm9qfPXuWa9eumbXx8vIiJCTE1CYiIgJvb2/KlStnalOnTh3s7OzYsWNHsuN/WSyJefz4sVkPZIyNTBKx1Z40Ge74+hwdoWtXrRy/sG3VqkHfvtCqlW0OXxbWI3NmWLsWvL0hNBSuXNE7IiGESH+SXa8vNDQU0BKh3LlzY3jNeuy3bt0iLi6O7Nmzm23Pnj07x44dS/Q+165dS7T9tWvXTLfHb3tRm+eHZTo4OODj42NqkxxJxRITE8PDhw9xdXVNcJ9Ro0YxYsSIZO/DWtjqgtaFC8PFi1rhi0yZ9I7GdnXrBkWLwtdfaydnwnZ98om2btpHH8GECXpHI2yZs7M27HHwYKhUSSvRL8t1CCFEyklWT9qBAwcwGo0AREdHc/DgQQ4cOJDoRSRt0KBBREdHmy4XL17UO6RkcXS0zSTNxQXy5YPDh/WOxLblygW1a2trJQnbZm8PCxbA4sWwfLne0QhbZ2cHo0drxUSqVwdZJlUIIVJOsnrSgoODTT1QwcHBGAwGVCKVBAwGA3FxccnacdasWbG3t+f69etm269fv46fn1+i9/Hz83th+/h/r1+/jr+/v1mb4OBgU5vnC5M8ffqUyMjIJPdrSSyenp6J9qIBODs742yDi3bZ6nBH+P8hjyEhekdi23r0gH79tOFytrSouUgoe3atB+TttyE4GIKC9I5I2Lr339d+zGneHKZMgbZt9Y5ICCFsX7J60s6ePUu2bNlM/z9z5gxnz55NcDlz5kyyd+zk5ETZsmVZv369aZvRaGT9+vVUqlQp0ftUqlTJrD3AunXrTO2DgoLw8/MzaxMTE8OOHTtMbSpVqkRUVBS7d+82tdmwYQNGo5EQC87kXxZLeuLoCI8f6x3FqylRAg4e1DsK21evnpaoyy/l6UPNmlrS3bIlPFP4VohX1qSJNk9twAAp0S+EECkhWT1pefLkSfT/r2vAgAGEh4dTrlw5KlSowIQJE7h//z6dOnUCoEOHDuTIkYNRo0YB0LdvX0JDQ/n6669p3LgxP//8M//++y8zZswAtJ68fv368cUXX1CgQAGCgoIYMmQIAQEBprXOihQpQoMGDejWrRvTp0/nyZMn9OrVi9atWxMQEGCK7ciRI8TGxhIZGcndu3fZt28fgKlHrkePHkyePJmPPvqIzp07s2HDBhYvXswff/yRYsfHWthyT1rJkjBpkt5R2D47O+jZEyZOhFq19I5GpITBg7Vy6j17wqxZ0kMqXl/58hARAW+8AceOwYwZkMTAEiGEEC9jaTnIXLlyqfbt26tZs2apU6dOvXZ5yUmTJqncuXMrJycnVaFCBbV9+3bTbaGhoSo8PNys/eLFi1XBggWVk5OTKlasmPrjjz/MbjcajWrIkCEqe/bsytnZWdWuXVsdP37crM3t27dVmzZtVKZMmZSnp6fq1KmTunv3rlmbPHnyKCDB5VkbN25UwcHBysnJSeXNm1fNmTPHouduK2VwK1ZU6q+/9I7i1Zw+rZUdNxr1jsT2RUUp5emp1MmTekciUkpkpFJ58yo1bZrekYj0JDpaqcaNlSpXTqkLF/SORjzPVs49hMjoDEpZNihhwYIFbN68mU2bNnHq1Cly5MhBaGgooaGh1KhRgwIFCqR4IplexcTE4OXlRXR0NJ6ennqHk6TQUBg4EBo21DsSyxmNWkXCQ4e0ORPi9fTtqx1T6Z1MPw4e1Io+rFwJVaroHY1IL4xGGDYMZs7UCtVUr653RCKerZx7CJHRJXudtHjvvPMOM2bM4MSJE1y+fJlx48YB8P7771O4cOEUD1Doz1ZL8IM2TK9kSfhvtKp4TX36wA8/QFSU3pGIlFKiBHz3HbRoIetdiZRjZweffw5Tp0JYGEyeLPPUhBDCEsleJ+1ZDx484J9//mHTpk1s3LiRvXv3Urx4cWrUqJHC4QlrYMtJGmgV7Pbv1ya2i9eTL582J23mTPjwQ72jESmlZUvYvVur+Lhpk/aZFyIlNG8OhQppidqePVrS5uKid1RCCGH9LO5Jq1y5MlmyZGHgwIE8evSIgQMHcvXqVfbu3cs333yTGjEKndl6klaqlPSkpaT+/bXhjk+f6h2JSEkjR4K7uzakVYiUVKwY7NwJ165pw+cvXdI7IiGEsH4WJ2nHjh3D3d2dwoULU7hwYYoUKULmzJlTIzZhJWw9SYvvSRMpo1o1yJYNli7VOxKRkhwc4OefYc0ardqjECkpc2b4/XeoXVurAvnPP3pHJIQQ1s3iJO327dts2LCBihUr8ueff1KlShVy5MhB27ZtmTlzZmrEKHRm60lasWJw7hzcvat3JOmDwaD1pknHefqTJQssX64NZd2xQ+9oRHpjbw9ffqn1xDdpAtOmyTw1IYRIisVJmsFgoGTJkvTp04dffvmF1atXU7duXZYsWUKPHj1SI0ahM1tP0lxdoWBBWdQ6JbVsCRcuaGsiifQlOBimTIG33oLr1/WORqRHb78NW7bAV19Bt27w+LHeEQkhhPWxOEnbs2cP48eP58033yRLlixUqlSJAwcO0Lt3b5YtW5YaMQqd2XqSBtqJp8xLSzlOTtoiyNKblj61basl4i1a2O5C9sK6FS8Ou3bBxYtQo4ZUFhVCiOdZnKRVqFCBn376iYIFCzJv3jxu3bplStyaNm2aGjEKnaWHJE2Kh6S8d9+F1avh/Hm9IxGpYexYbXjaBx/oHYlIr3x8YNUqrZhIuXKwbZveEQkhhPWwuAR/ZGSkLH6YwTg52f5wlOBgKXSR0rJm1XpcJk+G/5ZLFOmIg4O2CHHZstolPFzviER6ZG8Po0dD6dLQuLH2/3ff1TsqIYTQn8U9aZKgZTzppSft4EGIi9M7kvSlXz+tEuC9e3pHIlJDtmywbBn8/bfekYj0rlUrbY2++CTN1n8YFEKI12VxkiYynvSQpGXPDl5ecPKk3pGkL0WKQMWKMGeO3pGI1FKuHMyerXcUIiMoVQr+/RfOnIGaNeHqVb0jEkII/UiSJl7K2dn2kzSQeWmppX9/mDhReikzsidPnhAhpT5FCsiSRZvrWqWK9gPB1q16RySEEPqQJE28VHroSQOp8Jha6tYFFxdYuVLvSIQelFKsWbOGsWPH8r///U/vcEQ64OCgzXMdPx7eeEOrIivrqQkhMhqLkrQnT56QL18+jh49mlrxCCuUHgqHgPSkpRaDQZubJuX4MyaDwUCjRo1o0KABEyZMYOrUqXqHJNKJVq20io+zZmlrq0VF6R2REEKkHYuSNEdHRx49epRasQgrlV560sqUgb175RfZ1NCuHRw9Cjt36h2J0MOxY8c4ffo01atXJ2vWrHqHI9KRIkW075VMmbQKkNu36x2REEKkDYuHO/bs2ZMxY8bw9OnT1IhHWKH0kqQVKAAPH8Lly3pHkv64umq9aWPG6B2JSGu7d+9m2rRp7N+/n759+9KyZUu9QxLpjLs7zJsHn30GjRpp3zNGo95RCSFE6rJ4nbRdu3axfv161q5dS4kSJXB3dze7fdmyZSkWnLAOzs7pY7ijnZ32S+y//0LOnHpHk/689x4EBsKxY1C4sN7RiNTy8OFDXF1dAYiIiGDu3LlcuHCB/v3706BBA1O7U6dOER0dTdmyZfUKVaQz7dtDSAi0bg3r18P8+VrlXiGESI8s7knz9vbmrbfeon79+gQEBODl5WV2EelPeulJA21R3t279Y4iffL21tY3kt609Ovp06f079+fOXPmcPz4cWbOnMmlS5f46KOPTAma0WgkOjqa3377jX79+vHjjz/qHLVITwoWhIgIKFZMKwa1bp3eEQkhROqwuCdtjiyIlOGkl5400Eo6L1igdxTp14AB2knUJ59A/vx6RyNSmoODA+3atSM0NJTSpUtTqFAhBg8eTJUqVUxtDAYDXl5e9OjRg+PHj9OpUycKFy5MmTJldIxcpCfOzlqholq1tPmwnTvD55+Do6PekQkhRMp5pRL8T58+5a+//uK7777j7t27AFy5coV79+6laHDCOqSXddLg/3vSpHhI6sieXetN+/xzvSMRqaVatWqsX7+evXv3UrJkSapUqYLxvwlCSikMBgMA69evJzo6mrfeesu0TYiU1KQJ7Nmj9axVrw5nz+odkRBCpByLk7Tz589TokQJmjZtSs+ePbl58yYAY8aMkTVy0qn0UoIfpHhIWvjwQ/j1Vzh+XO9IRGqpWbMmixYtYsSIEezfvx87OzuMRqMpGVu8eDGzZs3C29ubjz76iNKlS+scsUivcuaEDRugQQNtpMSSJXpHJIQQKcPiJK1v376UK1eOO3fumCaPAzRr1oz169enaHDCOqSnnrRni4eI1JEtG7z/vlaJTaRfLVq0YM2aNeTLl4+YmBjs7LQ/J/Pnz2f+/PnkypWL3r17ExwcrG+gIt2zt4dhw2DZMujfH3r00H6ME0IIW2ZxkrZlyxY+/fRTnJyczLYHBgZyWbon0qX01JMGUjwkLXzwAaxapa2dJtKv0NBQoqOjadq0KUePHmXp0qX8/PPP5M+fn759+1KsWDG9QxQZSGgo7NunjZQoXx4OH9Y7IiGEeHUWJ2lGo5G4uLgE2y9duoSHh0eKBCWsS3rqSQNtSIwkaakrSxbo1QtGjNA7EpHacuTIQa1atShWrBgTJkwgJCSEXr16UaBAAb1DExlQ1qzw22/QvTtUrQozZ8ocZCGEbbI4SatXrx4TJkwwXTcYDNy7d49hw4bRqFGjlIxNWIn02pMmf7hT14AB8OefcPCg3pGI1DZkyBBGjRrF1q1badSoEfny5Uv0x7xnPX78mCdPnqRRhCIjMRigTx9tLbVx46BFC7h9W++ohBDCMhYnaV9//TVbt26laNGiPHr0iLZt25qGOo6RBZLSpfTWkybFQ9JG5szQr5/0pmUUH3/8Me+++y69evXiwYMH2NvbJ9n26tWrLFiwgJ49e0pVYJFqypTRqj/6+ECJEtqPRkIIYSssTtJy5szJ/v37GTx4MP3796d06dKMHj2avXv34uvrmxoxCp2lt540KR6Sdvr1g40btXkiIv2bNm0agwYNMiu5n1iPWvbs2cmdOzePHj2iYsWKxKanX4GEVcmUCWbMgOnToUMHrYdNiooIIWyBQSkZ9KWXmJgYvLy8iI6OxtPTU+9wknTnjrb+VXo6j/rgA3B1hS++0DuS9G/kSNi1C1as0DsSkZa2b99OxYoVX9quffv2PH36lJ9++ikNohIZ2fXr0KULnDkDCxdqP9ZlRLZy7iFERueQnEa//fZbsh/wzTfffOVghHVycoInT7Q5XOllTdrKlWHKFL2jyBh694a8ebV5gGXL6h2NSCuzZs0iIiKC/v37A1rRKaPRiIODA3FxccTGxuLq6kqXLl2YMmUKT58+xcEhWX+ShHgl2bPD77/Dd99BrVowcCD8739aCX8hhLA2yepJi1//xnQng4Hn7xY/vOVlk8XF/7OVX7OePgVHR3j0SJuflh5cuwb58kFUlPbcROoaPRq2btVOkETGcevWLbJmzWq6rpQy/a2IT8oaNWpETEwM//zzD0ajMcHfGyFSw/Hj8M472oiKH36AwEC9I0o7tnLuIURGl6y/hvG/gBqNRtauXUtwcDCrV68mKiqKqKgoVq9eTZkyZVizZk1qxyt0YG+v9aClp+GOfn7g7w979+odScbQqxfs2KFdRMaRNWtWtm3bRtWqVQkNDaVatWpUr16d2rVrU65cOQoWLMilS5eYOHEioP0gePfuXZ2jFhlBoUKwbRvUqKH18P/wg1T8FUJYF4vnpBUvXpzp06dTtWpVs+1btmyhe/fuHJXVa5PNln7NcnWFixe1NWjSi06doFgxbbiLSH3jx2s9aRs2pJ9hsyJ5QkJCuHjxIitXruTKlSs8ePAAFxcXHB0dadCgAUop7OzsOHHiBMWLFycsLIzFixfrHbbIICIitKIiRYpoQyH9/fWOKHXZ0rmHEBmZxeNKTp8+jbe3d4LtXl5enDt3LgVCEtbI2Tl9VXgECA2Fv//WO4qMo2dPOHcOVq/WOxKR1rZu3YqPjw9Lly7ljTfeoGXLlrz55ps0bNgQg8GAnZ0de/fuJSQkhDZt2nDkyBG6deumd9gig6hUCfbv14bAlygBCxZIr5oQQn8W96RVr14dFxcX5s+fT/bs2QG4fv06HTp04NGjR/wtZ73JZku/ZmXPrv3amDev3pGknHPnoFQpiIyUieNp5ccf4csvtZL8UiMiY4mJiaFkyZI0adKESZMmmd12+/ZtypYti4eHBwcPHuTBgweUKlWKBg0aJGgrRGraskUbZVG8uFa2389P74hSni2dewiRkVnckzZ79myuXr1K7ty5yZ8/P/nz5yd37txcvnyZ77//PjViFFbAxUUrHJKeBAaCt7f2C6pIG61ba++lefP0jkSkNU9PT7Zv305UVBQXL140u83Z2ZnvvvuOR48eMWvWLNzc3IiIiODcuXPcvn1bp4hFRlStmvY3IU8erVftxx+lV00IoY9XWidNKcW6des4duwYAEWKFKFOnTpmC5iKl7OlX7MKFoRFi9LfujLh4Vpv2oABekeScWzcqFVVO3EC3N31jkaktdjYWJycnLh06RIAOXPmNFV93LhxI71792bZsmUULFhQqj0KXW3erPWqlSwJ06aln141Wzr3ECIje6W/fgaDgXr16tGnTx/69OlD3bp1JUFL59LjnDSQeWl6qFkTgoPhm2/0jkTowcnJCaPRyJIlS5g5cyagleMHKFmyJK6urhiNRrPtoC2OLURaql4dDhyAXLm04Y9z5kivmhAi7bzSrJD169ezfv16bty4YfpjGm/27NkpEpiwLuk1SatRAz74AOLiZF5aWhozBqpWhe7dwddX72hEWrOzs6N27dpUrVqV4sWL06JFCwB+/fVXLl++jJubG0ajEScnJwBCQ0PZsmULc+fOpUOHDnqGLjIYd3f49lto1Qq6dYOFC7UKkPny6R2ZECK9s7gnbcSIEdSrV4/169dz69Yt7ty5Y3YR6VN6TdKCgiBTJjh4UO9IMpbixeHtt+Gzz/SOROilZMmS/Prrr/Tu3Zv//e9/NGrUiJkzZ9KnTx9y5MhhGuYYEhKCnZ0dP/zwA/369WPJkiU6Ry4yoipVtHU1q1bV1lUbNw6e6egVQogUZ3FP2vTp05k7dy7t27dPjXiElUqvSZrBoPWmbdqkDcETaWfECChaFPr00eY8ioynZs2arFixgq1bt3Lp0iV69+5NqVKlsP+vW3vatGns2rWLNWvWUK9ePXx8fGjRogWZM2emTp06OkcvMhpnZxg+HFq00HrVfvoJZs2CMmX0jkwIkR5ZnKTFxsZSuXLl1IhFWLH0mqSBNi/tjz+gXz+9I8lYcuTQErRBg2DpUr2jEXqpWLEiFStWNF1/dgh9x44dMRgMdO/enb///ptGjRqxaNEiYmJi9AhVCACKFYN//tGKidSurSVsw4eDm5vekQkh0hOLhzt27dqVH3/8MTViEVYsPSdpNWpoVbyem14p0sCHH2rrEm3bpnckwhpER0ezfft2Hjx4AICrqys9evSgfv36TJkyBaPRyBtvvEHz5s11jlRkdHZ20LOnVljk6FGtXP/69XpHJYRITyzuSXv06BEzZszgr7/+omTJkjg6OprdPn78+BQLTliP9LhOWrx8+bTnd+iQVmpZpB1PTxg2DP73P9i6VRt+KjKuyMhIBg0axMiRI6latSqPHj3CxcUFDw8PLl26JFWEhdXJlQt++w0WL4Z27aBhQ/jqK8iSRe/IhBC2zuKetAMHDhAcHIydnR2HDh1i7969psu+fftSIURhDdJzT9qz89JE2uveHW7dguXL9Y5E6C0oKIj69evTqlUrLl++jIuLC8ePH2fjxo2ULFkSg8GA0Wjk3LlzDBs2jA4dOtCzZ08uX76sd+giAzMYtOqPR45o/y9aFH7+Wcr1CyFej8U9aRs3bkyNOISVS89JGmjz0v78U5sjJdKWoyOMHg0DB0KTJtp1kXENHjyY+/fvU79+ffLkycOBAweoV68eLVu2JDY2liVLlrBw4UJOnjxJ586dOXHiBOXLl+fQoUP4+PjoHb7IwHx8YPZsbdhj9+6wYAFMnQq5c+sdmRDCFhmUkt969BITE4OXlxfR0dF4enrqHc4L9e6tFXoYOFDvSFLHiRNQuTLcuKHNNRBpSymttHW7dvD++3pHI6zBhg0buHTpEh4eHlSoUIEcOXKwaNEi5s2bR0BAABMnTsTd3R2AFi1a0LhxYzp27Khv0EL858EDrYLtjBnaUiPvv289a3Ha0rmHEBmZxT1pNWvWfOG8gA0bNrxWQMI6pfeetAIFtB6cI0e0NbxE2jIYtHWHmjWDd97R5qqJjK1WrVpm169fv86kSZMoWrQoEydOxNXV1XTbkSNHqFq1alqHKESS3NxgzBho3Rq6doUff4SZM+XvixAi+SzuMwgODqZUqVKmS9GiRYmNjWXPnj2UKFEiNWIUViC9J2kyL01/lStrvWljx+odibBGo0aN4s6dO8yYMcOUoBmNRmbNmkXWrFl58803dY5QiIRKl4YdO+Ctt7Tvt4ED4f59vaMSQtgCi3vSvvnmm0S3Dx8+nHv37r12QMI6ubjAnTt6R5G6ataENWugVy+9I8m4Ro2C8uW1dYfy5NE7GmFNHBwc6NSpk+n606dPWblyJb///julS5fG19dXx+iESJqDg1bB9u23tXnPhQtrFSBbtpSKtkKIpKXY7Jt33nmH2bNnp9TDCSuT3nvSAOrWhQ0b4OlTvSPJuAoW1Cbc/+9/ekcirI3RaOSPP/4AtFL9kyZN4vvvv8fd3Z1PP/0Ud3d3ZIq1sGaBgVq5/hkzYOhQ7YfBAwf0jkoIYa1SLEmLiIjAxcUlpR5OWJn0vE5avKAgyJoVdu3SO5KMbcgQbXHr1av1jkRYk/Hjx3Pv3j1q1apFgQIFWLt2LSVLlmT69OlkzZoVo9Eo66gJm9CwIRw8CI0ba5WFe/WCyEi9oxJCWBuLhzs2b97c7LpSiqtXr/Lvv/8yZMiQFAtMWJeMkKSB1pu2bh1UqqR3JBmXpydMngzvvQeHD8N/BfyEYNeuXWzbto3IyEiqVKmCh4cHDg4OKKWwk7KswoY4OcGHH2qFkj7+GAoVgi++0IqMWEsVSCGEviz+q+bl5WV28fHxoUaNGqxatYphw4alRozCCmSE4Y7w/0ma0FezZhAcDPKVIp5XuXJl3njjDTJnzmxK0KQHTdgqf3/44Qf49VdtGGT58rB1q95RCSGsgayTpiNbWqtk0SKYPx9WrtQ7ktQVFQV+ftp6aVb+kqR7ly5BiRLawrBlyugdjbAVcXHSEyFsU1ycthj2J59A/fpaCf+AgJTfjy2dewiRkb3y+JDdu3ezYMECFixYwN69e1MyJmGFMspwR29vrWSylOLXX86c2iKw3btLMReRPEePQqlScPas3pEIYTl7e62y7fHjkDkzFC2qJWoZYRSLECIhi5O0GzduUKtWLcqXL0+fPn3o06cPZcuWpXbt2ty8eTM1YhRWwNk5YyRpIEMercn772snLpMn6x2JsAWFC0O7dlClCshvh8JWZc4M334LW7Zoy8KUKCGFlITIiCxO0nr37s3du3c5fPgwkZGRREZGcujQIWJiYujTp09qxCisQEbpSQOoV0+SNGthbw8zZ8KIEXDhgt7RCGtnMMCgQdp6e7Vry+dY2LYSJbRlYb74QhtR0KQJnDqld1RCiLRicZK2Zs0apk6dSpEiRUzbihYtypQpU1gtP/WkWy4uGWfIRUgIXLkiSYG1KFkS3n1X61WTGbQiOcLD4ccfoVUrWLhQ72iEeHUGg7bo9bFj2lD8mTP1jkgIkVYsTtKMRiOOjo4Jtjs6OmI0Gi0OYMqUKQQGBuLi4kJISAg7d+58YfslS5ZQuHBhXFxcKFGiBKtWrTK7XSnF0KFD8ff3x9XVlTp16nDy5EmzNpGRkbRr1w5PT0+8vb3p0qUL9+7dM2tz4MABqlWrhouLC7ly5WLs2LEJYpkwYQKFChXC1dWVXLly0b9/fx6l0+4mV1d48EDvKNKGoyPUqCG/wluToUPhyBH45Re9IxG2okEDWLsWPvgAxo2TBF/YNnd3bY7umDF6RyKESCsWJ2m1atWib9++XLlyxbTt8uXL9O/fn9q1a1v0WIsWLWLAgAEMGzaMPXv2UKpUKerXr8+NGzcSbb9t2zbatGlDly5d2Lt3L2FhYYSFhXHo0CFTm7Fjx/Ltt98yffp0duzYgbu7O/Xr1zdLntq1a8fhw4dZt24dK1euZPPmzXTv3t10e0xMDPXq1SNPnjzs3r2bcePGMXz4cGbMmGFq8+OPPzJw4ECGDRvG0aNH+f7771m0aBGDBw+26BjYisyZM9ZimzLk0bq4ucH06dCnj1aBU4jkKFcO/vkHvvsO+veHV/gdUQghhNCHstCFCxdUcHCwcnR0VHnz5lV58+ZVjo6OqnTp0urixYsWPVaFChVUz549Tdfj4uJUQECAGjVqVKLtW7ZsqRo3bmy2LSQkRL377rtKKaWMRqPy8/NT48aNM90eFRWlnJ2d1U8//aSUUurIkSMKULt27TK1Wb16tTIYDOry5ctKKaWmTp2qMmfOrB4/fmxq8/HHH6tChQqZrvfs2VPVqlXLLJYBAwaoKlWqJPv5R0dHK0BFR0cn+z56uX9fKVDqyRO9I0kbx44plSWLUnFxekcinvXOO0r993EXItmuX1eqXDmlWrRQ6uFDvaMRQl+2dO4hREZmcU9arly52LNnD3/88Qf9+vWjX79+rFq1ij179pAzZ85kP05sbCy7d++mTp06pm12dnbUqVOHiIiIRO8TERFh1h6gfv36pvZnz57l2rVrZm28vLwICQkxtYmIiMDb25ty5cqZ2tSpUwc7Ozt27NhhalO9enWcnJzM9nP8+HHu3LkDaAuq7t692zQ888yZM6xatYpGjRol+ZwfP35MTEyM2cVWuLlp89L+e/rpXsGC2nOWCnHWZfx4WLpU6x0RIrl8fWHjRrh7VxsGKb2xQgghrF2ykjQfHx9u3boFQOfOnbl37x5169ald+/e9O7dO0HilBy3bt0iLi6O7Nmzm23Pnj07165dS/Q+165de2H7+H9f1sbX19fsdgcHB3x8fMzaJPYYz+6jbdu2fPbZZ1StWhVHR0fy5ctHjRo1XjjccdSoUXh5eZkuuXLlSrKtNcqSBf57G6R7BoMMebRG2bJp84u6d884hWxEysiUCX77DQIDoVo1uHxZ74iEEEKIpCUrSYuNjTX1+sybNy/dFsewxKZNm/jyyy+ZOnUqe/bsYdmyZfzxxx98/vnnSd5n0KBBREdHmy4XL15Mw4hfX5YscPu23lGkHVkvzTqFh4OfHyRSy0eIF3J0hDlz4M03oVIlOHxY74iEEEKIxDkkp1GlSpUICwujbNmyKKXo06cPrq6uibadPXt2snacNWtW7O3tuX79utn269ev4+fnl+h9/Pz8Xtg+/t/r16/j7+9v1iY4ONjU5vnCJE+fPiUyMtLscRLbz7P7GDJkCO3bt6dr164AlChRgv9j777DojraNoDfS1t6EaUpAvYSe0EssWE30WgSRWPsGDUxRo3lM1Fjjz0aa4waYyLWaGJX7IqoiF2xYResFCtl5/tjXlZWQVkFzi57/67rXMDZYfcZhON5dmaeefLkCYKDgzF8+HCYmb2e/6rVaqjV6kx+IobP1JK0hg2BLl1kVUtbW6WjoTQqlSwiUr26LE1dsqTSEZExUamAceOAggWBunWBf/6RI2tERESGJEsjacuWLUPz5s3x+PFjqFQqxMfH49GjRxkeWWVlZYUqVaogNDRUe06j0SA0NBQBAQEZfk9AQIBOewDYvn27tr2fnx88PDx02iQkJCA8PFzbJiAgAHFxcYiIiNC22blzJzQaDfz9/bVt9u7di+TkZJ3XKVmyJFxcXAAAT58+fS0RMzc3ByC3AciL8uc3nemOgOzvBx/ItSxkWEqUAAYPBrp3B1JTlY6GjFGfPsCCBXJUbeFCpaMhIiJ6hb6VRnx9fcX9+/ezpWpJSEiIUKvVYsmSJeLs2bMiODhYODs7i5iYGCGEEJ06dRJDhw7Vtj9w4ICwsLAQU6ZMEefOnRMjR44UlpaW4tSpU9o2EydOFM7OzmL9+vXi5MmTolWrVsLPz088S1fSq2nTpqJSpUoiPDxc7N+/XxQvXlwEBQVpH4+LixPu7u6iU6dO4vTp0yIkJETY2tqK+fPna9uMHDlSODg4iOXLl4srV66Ibdu2iaJFi4rPP/88y/03tgpLffoIMXGi0lHkrlGjhOjdW+koKCPJyUJUqybEzz8rHQkZs8hIIXx9hfjqKyHSFfQlyrOM7d6DyFTpnaRlt1mzZonChQsLKysrUb16dXHo0CHtY3Xr1hWdO3fWab9y5UpRokQJYWVlJcqWLSs2btyo87hGoxE//vijcHd3F2q1WjRs2FBERUXptHnw4IEICgoS9vb2wtHRUXTt2lUkJibqtDlx4oSoXbu2UKvVomDBgmLiK9lJcnKyGDVqlChatKiwtrYW3t7eok+fPuLRo0dZ7ruxXShHjBBi0CClo8hdR48K4e0thEajdCSUkXPnhHByEiLd+zREert3T4iGDYWoVUuIO3eUjoYoZxnbvQeRqVIJof/cvNDQUISGhuLu3bvQvLI7aFbXpJGciunk5IT4+Hg4OjoqHc5b/forcPQosGSJ0pHkHo0GKFQI2LwZqFBB6WgoI9OnA3/+CRw6BKTbNYNILykpwNChQEiI3Obhf7PfifIcY7v3IDJVeu+T9tNPP6Fx48YIDQ3F/fv333lNGhmf/PmBe/eUjiJ3mZkBLVoAGzYoHQll5ttvAUdHYOxYpSMhY2ZhAUyZIquGNm0K/P670hEREZEpy1J1x/TmzZuHJUuWoFOnTjkRDxmwAgVMq3BImpYtgYkTgeHDlY6EMmJmJsuqV64MfPQRUK2a0hGRMevQAShTBmjdGoiIAGbM4AgtERHlPr1H0pKSklCzZs2ciIUMXIECpjeSBgCBgcCJE8ArOzeQAfHzk5tcf/kl8OyZ0tGQsatYUU7tjoqSW3HExCgdERERmRq9k7QePXrg77//zolYyMCZ4nRHALCzA+rVAzZtUjoSepPu3WWy9n//p3QklBfkzw9s3Sr346taFQgPVzoiIiIyJXpPd3z+/DkWLFiAHTt2oHz58rC0tNR5fNq0adkWHBmW/PmBx4+B588Ba2ulo8ldLVvKdWlduigdCWVGpZL7XZUvD7RqJRNrovdhYQFMnQpUqSLXqU2dCnTrpnRURERkCvSu7li/fv3Mn0ylws6dO987KFNhjBWWnJ2B06dlxUNTcu0aUK6cXJPH9SmGLSREVuk7dQpwcFA6GsorIiOBTz6RhYSmT+d1gIyXMd57EJmidyrBT9nDGC+UxYoBq1YBlSopHUnuK18emDZNrlEjw9aunaz4+NtvSkdCecn9+/J3KykJWL0acHdXOiIi/RnjvQeRKdJ7TVp6N2/exM2bN7MrFjICplrhEZBTHv/7T+koKCvmzAE2bgT++UfpSCgvSVunVq2anAJ5+LDSERERUV6ld5Km0WgwevRoODk5wcfHBz4+PnB2dsaYMWNe29ia8h5TLR4CvEzSOPZs+Fxd5QbXPXvKqapE2cXCQo6oT5wINGkit38gIiLKbnoXDhk+fDh+//13TJw4EbVq1QIA7N+/H6NGjcLz588xbty4bA+SDIcpj6T5+wOJicD580Dp0kpHQ2/TsCHQuzcQFATs2QO8UuOI6L188YXcT+2TT+R+atOmcZ0aERFlH71H0v744w8sXLgQvXv3Rvny5VG+fHn06dMHv/32G5YsWZIDIZIhMdW90gDA3Bxo3lxWeSTjMHKkHPkYMULpSCgvqlxZ7qd2/jxQpw5HbYmIKPvonaQ9fPgQpUqVeu18qVKl8PDhw2wJigyXKU93BF6W4ifjYGEB/P23LM2/bZvS0VBeVKCAXKfWrJlcp8Z1q0RElB30TtIqVKiAX3/99bXzv/76KypUqJAtQZHhMuXpjgDQuLEsFsD3I4xHoULAokXAl18CMTFKR0N5kbk5MGoUsHw50KMHMHgwkJysdFRERGTM9F6TNmnSJLRo0QI7duxAQEAAACAsLAw3btzApk2bsj1AMiwFCgB37yodhXKcnIBatWTlwE6dlI6Gsuqjj4AOHYD27YHt27k+jXJGo0ZyP7X27eX0x7/+AooWVToqIiIyRnqPpNWtWxdRUVH45JNPEBcXh7i4OLRp0wZRUVGoU6dOTsRIBsTNzbSTNEAWCmBpd+Pz88+ARiNHOYhyipcXsHOn3PS6WjVg6VJWhCUiIv1xM2sFGeOGktevAxUqAI8eKR2Jcm7fBooXl8mqnZ3S0ZA+YmPluqGJE2V1PqKcdOiQHMGtXh2YNw9wdlY6IiLjvPcgMkV6j6QtXrwYq1ateu38qlWr8Mcff2RLUGS43NyAuDjgxQulI1GOl5dMVLdsUToS0pe7O7B2LfDNN3JaGlFOqlEDOH5cluavUAHYt0/piIiIyFjonaRNmDAB+fPnf+28m5sbxo8fny1BkeGytpbrskx9ymPbtsCaNUpHQe+ienVg6lQ5bdWUi+BQ7nB0lFMeJ04EWrcGfvyRRUWIiOjt9E7Srl+/Dj8/v9fO+/j44Pr169kSFBk2d3cmaW3ayOIhpjyiaMy6dZN73rVvD6SkKB0NmYKgIODYMWD3bllU5PJlpSMiIiJDpneS5ubmhpMnT752/sSJE3B1dc2WoMiwubnJtT2mzM9PVm3bsUPpSOhdzZgBPHsGDBumdCRkKnx8gF27WFSEiIjeTu8kLSgoCP369cOuXbuQmpqK1NRU7Ny5E99++y3at2+fEzGSgXF3Z5IGcMqjsbOyAlavlptdh4QoHQ2ZCgsLOeVx0ya5t1pQkFznS0RElJ7eSdqYMWPg7++Phg0bwsbGBjY2NmjcuDEaNGjANWkmgkma9PnnsgjFs2dKR0LvytNTJmq9ewMnTigdDZmStKIiajWLihAR0ev0TtKsrKywYsUKREVF4a+//sLatWtx+fJlLFq0CFZWVjkRIxkYJmlS8eJAmTLAf/8pHQm9j4AAuYfaJ58ADx8qHQ2ZEkdH4I8/5O9f69Zy6i3XuRIREfAOSVqa4sWL47PPPkPLli3h4+OTnTGRgWPhkJe+/FLeZJFxCw4GAgPl1LPUVKWjIVPTvr3cEiIiQu7jFxGhdERERKS0d07SyHSxcMhL7drJam0xMUpHQu9r1izAzAyIilI6EjJFhQsDW7fKPfwCA4GRI4GkJKWjIiIipTBJI71xuuNLLi6ylPvy5UpHQu9LrQY2b5ZTWImUoFIBvXrJUv179gD+/lwrSURkqpikkd6YpOnq3JlTHk1FTEwM1q5dq3QYlMf5+QE7dwJduwJ16wJjx3IDbCIiU8MkjfTm7g7cv89NgNM0aQLcvs13vPO65ORkHDhwAHPmzEHnzp2VDofyODMzoF8/4MgROcIbEACcOaN0VERElFssstIoo82rM1O+fPl3DoaMg50dYG0NPHggEzZTZ2kJdOwoN6adOlXpaCgnCCFgaWmJVq1a4ebNmxgyZAgCAgLw1VdfKR0a5XHFiwN798rN12vVkhUgBw0CzM2VjoyIiHKSSggh3tbIzMwMKpUKQgioVKo3tk1labQsS0hIgJOTE+Lj4+Ho6Kh0OHrx8wPWrweYk0uRkUCzZsDNm3KzWsqbtm/fjmXLluHp06cYM2YMSpUqpXRIZELOnZPTq83NgSVLgJIllY6IjJEx33sQmZIsTXeMjo7GlStXEB0djTVr1sDPzw9z5sxBZGQkIiMjMWfOHBQtWhRr1qzJ6XjJQLi7s6JhehUryqqXW7YoHQllp/TvYW3ZsgWLFy/GkydPmKCRIkqXBg4eBD7+WBYVmTCBa9WIiPKqLL3nn34ftM8++wwzZ85E8+bNtefKly8Pb29v/Pjjj2jdunW2B0mGx9OTSVp6KhXQsycwfz7QsqXS0VB2ePz4Mezt7QEAmzdvxp9//onk5GSMHTtWJ0HbtGkT7ty5g+7duysVKpkQCws55bF1a3nNCQkBfvsNqF5d6ciIiCg76V045NSpU/Dz83vtvJ+fH86ePZstQZHh8/KSxTLopU6d5J5p168rHQm9r+TkZPTv3x+//PILjh49iqVLlyIpKUk7gpY2whYfH49Hjx5h0aJF+PLLLxWOmkxJ6dJyrVqfPkDTpkD//sDjx0pHRURE2UXvJK106dKYMGECktLtspmUlIQJEyagdOnS2RocGS5PT+DOHaWjMCzOzsDnnwMLFyodCb0vS0tLfPXVVxgwYACCgoJgbW2tHUFLW5ubmpoKJycntGvXDgEBAdi4cSM2btyodOhkQszM5L5qp08DN24AZcsC//2ndFRERJQd9E7S5s2bh61bt6JQoUIIDAxEYGAgChUqhK1bt2LevHk5ESMZII6kZaxXL5mkcZ2I8atatSqOHz+Ou3fvIl++fChVqhQ0Gg1UKhU0Gg3M/1deb/bs2bh48SL69u2LGjVqKBw1mSIvL2DNGmDmTODrr+WatehopaMiIqL3oXeSVr16dVy5cgVjx45F+fLlUb58eYwbNw5XrlxBdU6KNxkcSctYtWqAhwewYYPSkVB2KFeuHHbs2IEFCxZgy5YtMDMzg0ajgZmZvHTOmDED27ZtQ8WKFdGvXz+4uroiCwVziXJEq1bA2bPABx/IYkZjxgDPnysdFRERvYssleCnnGHMZXCPHwfatgUuX1Y6EsMzfz6wdi2wdavSkVB2iY6OhpeXF2JiYrSFlKZNm4bQ0FBUrVpVJ0F72zYlRLkhKkqOql29CsyaJdetEQHGfe9BZEr0HkkDgD///BO1a9eGl5cXrl27BgCYPn061q9fn63BkeFKm+7IFP91HToAhw4BV64oHQllFz8/P7x48QLt27fH9OnTsWTJEuzYsQNVqlRhgkYGqWRJYNs2YPx4oEcPoE0bFjUiIjImeidpc+fOxYABA9CsWTM8evRIu3m1i4sLZsyYkd3xkYHKnx9ISQHi45WOxPA4OABBQcCCBUpHQtnJ0dER06ZNw5AhQzBixAg0atSICRoZNJUK+Owz4Px5oFgxoHx5ubdaurpfRERkoPRO0mbNmoXffvsNw4cPh4XFy23WqlatilOnTmVrcGS4zMzk2iuuS8tYr17AokW8GcprAgICcPjwYdy/fx8JCQnInz+/tphIRlJSUnD58mWEhobmcqREL9nbA5MmAQcOyNG18uWBHTuUjoqIiN5E7yQtOjoalSpVeu28Wq3GkydPsiUoMg6enqzwmJlKlYAiRYB//lE6EspuFStWxI4dO/D333/j8OHD2iIiGXn+/Dn279+PESNGoGfPnrkYJdHrypYFdu4ERo4EvvxSbhly86bSURERUUb0TtL8/Pxw/Pjx185v2bKF+6SZGC8vjqS9Sa9eAHelyJtq1qyJnTt3omLFim9sZ29vj7Zt22Lt2rU4fvw4goODcydAokyoVHI69vnzQKFCshLk5MncNoSIyNDonaQNGDAAffv2xYoVKyCEwOHDhzFu3DgMGzYMgwcPzokYyUBxJO3N2rWTVTDPn1c6EsoJBQsWhJWVFf7991+EhIRk2EYIATs7O7i7u2PdunW4cOECbty4kcuREr3O0RGYNg3Yuxf4919Zsn/XLqWjIiKiNHonaT169MDPP/+MH374AU+fPkWHDh0wd+5c/PLLL2jfvn1OxEgGinulvZmtLdCpEwuI5HWFChXCqVOnMpzurVKptOvV4uLicOLECSQmJuZ2iESZKl9eJmpDhgDt28vqtHzzjYhIee+1T9rTp0/x+PFjuLm5ZWdMJsPY9ypZuFAuPs9kEIEAnDkDfPihXPdhY6N0NJRTkpOTYWFhgevXr+PSpUuwtbWFubk5EhMT8eLFCyQmJmLIkCGoVq0aVq1apXS4RBmKiwN+/BFYtkwmbf37A9bWSkdF2c3Y7z2ITIXF25voGjt2LDp27Ag/Pz/Y2trC1tY2J+IiI8Dpjm9XtixQpgywerUcVaO8ydLSEo8ePcIXX3yBAwcO4JNPPsGNGzeg0WhgZmaGAgUK4LPPPsPAgQO137N48WIcPXoUs2fPVjByopecneXG18HBwHffyVkAkyfLPda4wwQRUe7SeyStQoUKOH36NPz9/fHFF1/g888/R/78+XMqvjzN2N/NioyU1cEuXlQ6EsP211/AnDmy/DXlbUeOHEGzZs0wbtw49OrVC4Asww9AZ8uShQsXIjg4GJaWlujYsSMWLVqkSLxEmRFCrlUbOFAWGJkxQ65bI+Nn7PceRKZC7zVpJ06cwMmTJ1GvXj1MmTIFXl5eaNGiBf7++288ffo0J2IkA5U2kvbuE2ZNQ9u2QFQUcOKE0pFQTqtWrRrWr1+PoUOHYsH/FiOam5vr7KO2Zs0aBAcHY+vWrXjy5AkOHz6Mzp07KxUyUYZUKqBVKzllu2VLoEEDoFs3gHVviIhyh95JGgCULVsW48ePx5UrV7Br1y74+vqif//+8PDwyO74yIC5ucmyzXFxSkdi2Kytga++kpXUKO+rVasW/v33X/Tt2xe7du2CSqWCubk5AODFixd4+PAh8uXLh0uXLsHCwgLHjh1DREQEFi9erHDkRK9Tq4FBg+QbTQ4OsmT/kCHAo0dKR0ZElLe9U5KWnp2dHWxsbGBlZYVkbrRiUszMgIIFuRlqVnz9NbB2LX9WpqJOnTo4fvw47O3tAQCP/ndHq1ar0bNnTxw4cABjx47FmjVrYGVlhZMnT6Jr165Khkz0RgUKAL/8Ahw7JkfTihWT69WeP1c6MiKivOmdkrTo6GiMGzcOZcuWRdWqVREZGYmffvoJMTEx2R0fGbhChTj9JSs8POS+abNmKR0J5ZayZcuiWrVqiI2NxebNm/H8f3ezT58+RcmSJdG1a1dc/N+CTjMzeSmOiYnB5s2bFYuZ6G2KFgX+/hvYvh3Ytg0oUQJYsgRITVU6MiKivEXvJK1GjRooVqwYVq9eja5du+LatWsIDQ1F9+7d4eTklBMxkgHz9uboUFYNGCCrpXGbLNOSnJyMUaNGaaczplXEPXjwIE6dOqXT1s3NDWPGjEFQUFCux0mkj8qVZaL2++/AzJlAhQrAhg1co0xElF30TtIaNmyIU6dOITIyEoMGDULBggVzIi4yEhxJy7oyZYCaNeVNDZmOQoUKYcGCBRg2bBjmz5+Ps2fPYvTo0UhKSkJwcLC2nRACZmZm2LlzJ86cOYNu3bopGDVR1jRqBBw9CgwfDvTrB9SrBxw6pHRURETGT68kLTk5GSEhITqVysi0cSRNP4MGyVLW/6vKTiaiXr162LBhA5YtW4ZOnTph5cqV+PLLL1GuXDltG5VKhefPn8Pa2hpHjhzBsWPH0Lt3bwWjJsoaMzMgKAg4fx749FPg44/l3mqnTysdGRGR8dIrSbO0tNSuqyAC5Egak7Ssq1cPcHUF1qxROhLKbbVr18Z///2HTZs24fDhwwgODka+fPkghEDadpXW1tYAgCtXrqBmzZqYP38+Vq5cqWTYRFlmZQV88w1w6RJQvjxQpw7QoYOsDElERPrRe7pj37598fPPP2s3aCXT5u3N6Y76UKnk5rCTJ3PthilydnaGu7s7bG1ttddQlUoFlUqFO3fuYNeuXahXrx6CgoIQGRmJtWvXonz58gpHTaQfR0dg1Cjg8mXA1xeoXh3o3Fl+TUREWaMSQr9bxU8++QShoaGwt7dHuXLlYGdnp/P42rVrszXAvCwhIQFOTk6Ij4+Ho6Oj0uG8k5gYWYo5MVEmIPR2ycmyQtqffwJ16yodDSntxo0b2LlzJ2bMmAEhBHx8fDBkyBCUKlUK+fLl07ZbsWIF2rVrp2CkRO/m3j1g0iRZOOnzz4EffgB8fJSOynTlhXsPIlNgoe83ODs7o23btjkRCxkhNzcgKUluaO3ionQ0xsHSEujfH5g6lUmaqfvuu+8QGhqK+/fvo0+fPmjQoAFq1qz5WrsmTZpg+/bt2L17N+bOnatApETvrkABOXtgwABg4kSgXDngiy9ksRHWHiMiypje0x0XL178xkNfs2fPhq+vL6ytreHv74/Dhw+/sf2qVatQqlQpWFtbo1y5cti0aZPO40IIjBgxAp6enrCxsUFgYKB2L6I0Dx8+RMeOHeHo6AhnZ2d0794djx8/1mlz8uRJ1KlTB9bW1vD29sakSZNeiyUuLg59+/aFp6cn1Go1SpQo8Vo8eR03tH43PXoAe/fKhfZkujp27IiBAwdi586d+OGHH7QJWmq6TacaNWqER48e4ciRI9izZw969eqlVLhE78XTU26IffasnO5durR8w4pbrBIRve6dNrNOSUnBjh07MH/+fCT+b9On27dvv5bovM2KFSswYMAAjBw5EseOHUOFChXQpEkT3L17N8P2Bw8eRFBQELp3747IyEi0bt0arVu3xul0JaQmTZqEmTNnYt68eQgPD4ednR2aNGmiU/CkY8eOOHPmDLZv344NGzZg7969OqWwExIS0LhxY/j4+CAiIgKTJ0/GqFGjsGDBAm2bpKQkNGrUCFevXsXq1asRFRWF3377zSS3JOC6NP05OgI9ewLTpikdCSmpatWq6Ny5M0qVKoX0M8/Nzc0BAJs3b0ZoaCg+/vhjVKlSBUeOHMHOnTsxYMAApUImem+FCgFz5wInT8qp8iVLAoMHA/fvKx0ZEZEBEXq6evWqKFWqlLC1tRXm5ubi8uXLQggh+vXrJ3r16qXXc1WvXl307dtX+3Vqaqrw8vISEyZMyLD9559/Llq0aKFzzt/fX/u6Go1GeHh4iMmTJ2sfj4uLE2q1WixfvlwIIcTZs2cFAHHkyBFtm82bNwuVSiVu3bolhBBizpw5wsXFRbx48ULbZsiQIaJkyZLar+fOnSuKFCkikpKS9OpzevHx8QKAiI+Pf+fnMARBQULMn690FMbn+nUhHByEiI1VOhIyJCkpKdrPk5KSRHh4uChYsKD4888/hRBCPH78WGzatEmp8Iiy3cWLQnTqJISTkxDDhwvx8KHSEeVteeXegyiv03sk7dtvv0XVqlXx6NEj2NjYaM+nFRTJqqSkJERERCAwMFB7zszMDIGBgQgLC8vwe8LCwnTaA3KtRlr76OhoxMTE6LRxcnKCv7+/tk1YWBicnZ1RtWpVbZvAwECYmZkhPDxc2+bDDz+ElZWVzutERUXh0aNHAIB///0XAQEB6Nu3L9zd3fHBBx9g/PjxOtOUXvXixQskJCToHHkBR9Lejbc30KoVMHu20pGQoUhJSUFkZCTu/29IQaVSoXr16hg/fjzWrFmDxMRE2NnZoVmzZgpHSpR9ihUDli4FwsJk+f4iRYCffgLi45WOjIhIOXonafv27cMPP/ygk8AAgK+vL27dupXl57l//z5SU1Ph7u6uc97d3R0xmUxQj4mJeWP7tI9va+Pm5qbzuIWFBfLly6fTJqPnSP8aV65cwerVq5GamopNmzbhxx9/xNSpUzF27NhM+zxhwgQ4OTlpD29v70zbGhPulfbuBg6USZqeM4Upj3r+/Dl++eUX/PnnnwAAjUYDQO6fdubMmTe+CURk7EqXBkJC5HrdEydksjZmjCxMRURkavRO0jQaTYY3Cjdv3oSDg0O2BGUMNBoN3NzcsGDBAlSpUgXt2rXD8OHDMW/evEy/Z9iwYYiPj9ceN/LI8BNH0t5dxYpAQAAwa5bSkZAhsLe3R5cuXTBixAisW7cOVlZWuH//PpYvX45atWrB2dlZ6RCJcly5csDatcCOHcDx4zJZGzECePhQ6ciIiHKP3kla48aNMWPGDO3XKpUKjx8/xsiRI9G8efMsP0/+/Plhbm6O2NhYnfOxsbHw8PDI8Hs8PDze2D7t49vavFqYJCUlBQ8fPtRpk9FzpH8NT09PlChRQrvAHwBKly6NmJgYJCUlZRi/Wq2Go6OjzpEXFCrEJO19jB4NTJnCd4tJatiwIVatWoX+/fujbdu2KFGiBCwsLNChQwelQyPKVZUqAWvWAHv2ABcuyGTt++85c4OITIPeSdrUqVNx4MABlClTBs+fP0eHDh20Ux1//vnnLD+PlZUVqlSporOOTaPRIDQ0FAEBARl+T0BAwGvr3rZv365t7+fnBw8PD502CQkJCA8P17YJCAhAXFwcIiIitG127twJjUYDf39/bZu9e/ciOTlZ53VKliwJl/9tBlarVi1cunRJOx0JAC5cuABPT8/XpoLmdT4+wPXrsqQy6a9SJaBxY2DcOKUjIUPRtGlTbN68Gb169cKMGTPw66+/olGjRkqHRaSIcuXkNMjwcLlOrXRpoEsXIF1hZyKivOddqo0kJyeLZcuWie+//1707t1b/Pbbb+Lp06d6P09ISIhQq9ViyZIl4uzZsyI4OFg4OzuLmJgYIYQQnTp1EkOHDtW2P3DggLCwsBBTpkwR586dEyNHjhSWlpbi1KlT2jYTJ04Uzs7OYv369eLkyZOiVatWws/PTzx79kzbpmnTpqJSpUoiPDxc7N+/XxQvXlwEBQVpH4+LixPu7u6iU6dO4vTp0yIkJETY2tqK+elKGF6/fl04ODiIr7/+WkRFRYkNGzYINzc3MXbs2Cz3P69UWNJohLCzE+LOHaUjMV7R0UI4Ogrxv2KpRBnSaDRKh0CkuNu3hRg2TAhnZyFatBBi9275/xBlTV659yDK694pSctOs2bNEoULFxZWVlaievXq4tChQ9rH6tatKzp37qzTfuXKlaJEiRLCyspKlC1bVmzcuFHncY1GI3788Ufh7u4u1Gq1aNiwoYiKitJp8+DBAxEUFCTs7e2Fo6Oj6Nq1q0hMTNRpc+LECVG7dm2hVqtFwYIFxcSJE1+L/eDBg8Lf31+o1WpRpEgRMW7cOJ3y2W+Tly6UZcsKERamdBTGbcgQIT77TOkoyJi8w3tjRHlGQoIQU6cKUaiQENWrC7F6tRB6/BdssvLSvQdRXqYSQr9Jan/88Qfy58+PFi1aAAAGDx6MBQsWoEyZMli+fDl8fHyyfbQvr0pISICTkxPi4+ONfn1ay5ZAx45AUJDSkRiv+HigeHFg3TqgZk2loyFj0KED4OQE/PILYGKzrIm0kpPldMhJk4Dnz2XV3M6dgXS7BFE6eenegygv03tN2vjx47X7o4WFheHXX3/FpEmTkD9/fnz33XfZHiAZB19f4OpVpaMwbk5OwKhR8gaD6/soK2bOBC5eBOrXB+7cUToaImVYWgKdOgEnT8pKuStXyrXSY8YADx4oHR0R0bvRO0m7ceMGihUrBgBYt24dPv30UwQHB2PChAnYt29ftgdIxsHPD4iOVjoK4xccLKs8rlypdCRkDPLnB7ZskSOvVavKzYCJTJVKBTRtCuzcCWzaJAuL+PkBX38tq0MSERkTvZM0e3t7PPjfW1Pbtm3TVhyztrbGs2fPsjc6MhocScseFhbA5MnAkCFy2g7R26T9zkydCjRvDixYoHRERMqrWhVYsQKIjJR/I9WqAS1aANu3c6YCERkHvZO0Ro0aoUePHujRowcuXLig3RvtzJkz8PX1ze74yEgwScs+LVoAxYrJqWxEWdW+PbB7NzBxohyRffFC6YiIlFe0KDBjhtzLs3Fj4KuvgA8+kG9mPH2qdHRERJnTO0mbPXs2AgICcO/ePaxZswaurq4AgIiICASxaoTJ8vUFrl0D0m0bR+9IpZKjIhMnAvfuKR0NGZMKFYCjR+XfYr16wO3bSkdEZBgcHYFvv5XTHidMkIVGChcGhg3j5thEZJj0ru5I2ScvVVgSQha+OHcOKFhQ6Wjyhm7dAFtb4NdflY6EjE1qKvDDD8CSJcDq1UCtWkpHRGR4Tp6UlVFXrpQVir/9FqhRQ+mocl5euvcgysv0HkkDgEePHmHKlCno3r07unfvjilTpuDhw4fZHRsZEZWKUx6z29ixwLJlwPnzSkdCxsbcXI4WzJwJfPQRMHcu1+EQvap8eeD334ErV4CyZYFPPpFJWkiILOtPRKQkvZO0vXv3wtfXFzNnzsSjR4/w6NEjzJo1C35+fti7d29OxEhGwteXFR6zk5cX0L8/MHiw0pGQsfrsM2DvXjl9tkcPFqMhykiBAnLk+do1WQly6lRZFXLCBJbwJyLl6J2k9e3bF+3atUN0dDTWrl2LtWvX4sqVK2jfvj369u2bEzGSkeBIWvb7/nsgIkKWlCZ6Fx98ABw5IvdR+/BD/o0SZcbKCvjiC+Dw4ZeVIX19ZSGe06eVjo6ITI3eSdqlS5cwcOBAmJuba8+Zm5tjwIABuHTpUrYGR8bFz483gNnNzk5Oexw4UK4zInoXLi7Af//JyqFVqgBr1igdEZHhUqnkOs6VK4EzZwBnZ6BuXVmMZ+VKToUkotyhd5JWuXJlnDt37rXz586dQ4UKFbIlKDJOnO6YM778Uq4n+vNPpSMhY2ZuDowcCaxdC/TrB/Tty+mPRG9TuDAwaZKsANm5s/zcx0f+Ld26pXR0RJSXWWSl0cmTJ7Wf9+vXD99++y0uXbqEGv8rg3To0CHMnj0bEydOzJkoyShwumPOMDeXayS+/FKuMbKzUzoiMmZ16wLHj8sbzho15LSukiWVjorIsNnYAF27yuPwYWDOHKBUKaBpU6BPHznKplIpHSUR5SVZKsFvZmYGlUqFtzVVqVRI5ZysLMtrZXAfPQLc3OS78+lmw1I2+egjOVVt1CilI6G8QKMBpk0Dxo2TbwJ07cqbTCJ93L8PLFokq6fa2AC9esk1bf/bPtZg5bV7D6K8KktJ2rVr17L8hD4+Pu8VkCnJixfKfPlkoQs/P6UjyXsuXgSqVQPCwznyQdnn8GGgUyf5O7VgAeDhoXRERMYlNRXYuhX47Tdg+3b5hlqPHkD9+oDZO210lLPy4r0HUV6UpemOTLwoq4oXl8kEk7TsV7w4MGgQ8NVXstojRz0oO1SvLqvYDRsGlCsnp3F99pnSUREZD3NzoHlzecTEAH/8Ia/TqalA9+5Aly5AwYJKR0lExuad3uO5fPkyvvnmGwQGBiIwMBD9+vXD5cuXszs2MkJpSRrljMGDgbt3gSVLlI6E8hJbW+CXX+T6tEGDgA4dgIcPlY6KyPh4eABDhgAXLgCLFwPnz8tR6o8+AtatA5KSlI6QiIyF3kna1q1bUaZMGRw+fBjly5dH+fLlER4ejrJly2L79u05ESMZESZpOcvKCpg/X+6fdu+e0tFQXtOgAXDyJGBtLUfVNm9WOiIi46RSySI9f/4J3LghC4yMHi1H1Pr3l8V7iIjeJEtr0tKrVKkSmjRp8lolx6FDh2Lbtm04duxYtgaYl+XFeeHLlwPLlgEbNyodSd7Wqxfw9CnL8lPO+fdfuYlvq1aysIi9vdIRERm/kyfldMhly+SoW5cuQMeOsuhWbsmL9x5EeZHeI2nnzp1D9+7dXzvfrVs3nD17NluCIuPFkbTcMXEisGOHXKROlBM+/hg4fVpOeyxfHti3T+mIiIxf+fLyTY+bN2Vl1QMH5Brujz+WexhyOiQRpdE7SStQoACOZzBOf/z4cbjl5ltBZJCKF5cbWqekKB1J3ubiAsyYAfTuDTx7pnQ0lFflzw+sXAmMHQt88olcr8YNsInen6Ul0LIlsHo1cP060KQJMGEC4OUFfPONrJKs3zwnIspr9E7SevbsieDgYPz888/Yt28f9u3bh4kTJ6JXr17o2bNnTsRIRsTJSSYQ3NQ6533+OVCiBDBmjNKRUF6mUslCIidOyJG1KlWAQ4eUjooo73B1Bfr2BY4cAfbskXuutWwpR92mTJEVI4nI9Oi9Jk0IgRkzZmDq1Km4ffs2AMDLywvff/89+vXrBxXrgmdZXp0XXqsW8MMPQLNmSkeS9129ClSsKKeilSundDSU1wkBLFwIDB0qE7dx44A8dOkiMhgpKcC2bXL92saNsghJly6ySqS19fs9d1699yDKa/RO0tJLTEwEADg4OGRbQKYkr14ou3QBKlcG+vVTOhLTMGUKsGaNXNtgiBunUt5z546sUHfgADB7tiwuQkQ549EjuT3GkiVAtWrArFnv93x59d6DKK95r1s6BwcHJmj0GhYPyV39+8vF5tOmKR0JmQpPT3nTOG+eXD/Tti1w65bSURHlTS4ucnPsQ4d4nScyJXonabGxsejUqRO8vLxgYWEBc3NznYOISVrusrCQ5ZzHjuXeO5S7WrYEzp4FfHzkdNs5cwCNRumoiPIuS0ulIyCi3GKh7zd06dIF169fx48//ghPT0+uQaPXMEnLfaVLA+PHy3VCERFy4TlRbrC3l+/ud+gA9Owp3zBYsAD44AOlIyMiIjJeeq9Jc3BwwL59+1CxYsUcCsl05NV54YmJcnrG06eAlZXS0ZgOIeSicj+/91+zQPQuUlLk1hBjxwJffy0LCL1vkQMiyl559d6DKK/Re7qjt7c33qPWCJkABwegQAHgyhWlIzEtKhWwaBGwahWwaZPS0ZApsrCQe6lFRgJHj8oS4rt2KR0VERGR8dE7SZsxYwaGDh2Kq9wIi96gRAlOeVSCm5tM1Lp1A+7eVToaMlV+fsDmzcCoUUD79kDXrsCDB0pHRUREZDz0TtLatWuH3bt3o2jRonBwcEC+fPl0DiKA69KU1Lw58OmnMlFjEQdSStom2GfPys9Ll5Z7PvF3koiI6O30LhwyY8aMHAiD8ppSpYDz55WOwnRNngzUrAlMmAAMH650NGTKXF3l6O7u3XKd2ty5cs1ktWpKR0ZERGS49E7SOnfunBNxUB5Ttizwzz9KR2G6bGyAtWuB6tWBSpXk6BqRkurVk1tEzJ0LNG0KtGghC4wULqx0ZERERIZH7+mO169ff+NBBMgk7cwZWXGQlOHnB/z9N/Dll8ClS0pHQyQLi3zzDRAVJUfYypUDhgwB4uKUjoyIiMiw6F2C38zM7I17o6Wmpr53UKYiL5fBFQJwcpLrUQoVUjoa0zZ5slwLFBYmK28SGYrLl+V03B07ZLn+3r0BtVrpqIjytrx870GUl+g9khYZGYljx45pj/DwcMybNw8lSpTAqlWrciJGMkIqFVCmjBxNI2UNGiRHNrt25cgmGZaiRYGQELllxD//yOIiISEsLkJERKT3SFpmNm7ciMmTJ2P37t3Z8XQmIa+/m9W9u0wOBgxQOhJ68gQICJDV9oYOVToaotcJAWzYIKc/2tsDkybJdWxElL3y+r0HUV6h90haZkqWLIkjR45k19NRHpC2Lo2UZ2cnRyqmTAG2bFE6GqLXqVTARx8BJ08CPXsCQUHya15DiIjIFOmdpCUkJOgc8fHxOH/+PH744QcUL148J2IkI8UkzbAULQosWwZ88YVcC0RkiCwsZJJ26ZIs01+rlvz69m2lIyMiIso9eidpzs7OcHFx0R758uVDmTJlEBYWhrlz5+ZEjGSkypaVhUO4DspwNG0KDBwIfPKJnAJJZKjs7IARI2QlSEtLuV5txAggMVHpyIiIiHKe3mvS9uzZo/O1mZkZChQogGLFisHCQu9t10xaXp8XLgTg7AycOsW9kAyJEMBnn8kRi+XL5TQzIkN3/jwwbBhw8CAwcqQcXbO0VDoqIuOT1+89iPKKbCscQvozhQtlzZrAjz8CzZopHQmll5gI1KghKz4OGqR0NERZt38/8P33wMOHwPjxclTYLNtWVxPlfaZw70GUF/C/NspRXJdmmBwcZCGRCROA8HCloyHKutq15WjauHFyb7UqVYB//+W0aiIiyluYpFGOYpJmuEqUAPbskTe5RMZEpQI+/RQ4fVqOBA8aJIuMbNrEZI2IiPIGJmmUo5ikGbYPPpBr0zLz/Pnz3AuGSE/m5kDHjrJA0ddfyyMgANi2jckaEREZtywlaTNnztTerF2/fh1cxkZZlVbhUaNROhLS1/PnzzFlyhQM4G7kZOAsLIAuXWQlyB49ZFGRDz8Edu1SOjIiIqJ3k6UkbcCAAUhISAAA+Pn54d69ezkaFOUdnp6yAtv160pHQvpKSkpCzZo1sW3bNtSoUQOPHz9WOiSiN7K0lEnaxYtyhK1TJ6B+fWDfPqUjIyIi0k+WkjQvLy+sWbMG165dgxACN2/exPXr1zM8iNJTqTjl0RgJIeDo6Ag3NzfUrVsXqampuHHjhtJhEWWJlRXw1VdyQ+w2bYDPPwcaNWKyRkRExiNLJfgXLFiAb775BikpKZm2EUJApVIhNTU1WwPMy0ylDG6fPkChQsD//Z/SkVBWpP0tX7t2DXPnzsWxY8cwYMAANG3aVOnQiN7Js2fAvHnAlCmAnx8wdCjQvDlL95NpMpV7DyJjl+V90hITE3Ht2jWUL18eO3bsgKura4btKlSokK0B5mWmcqFcuBDYsgVYvVrpSOht0hK0+/fvY968edi9eze6dOmCL774QtsmLCwMKpUKNWrUUDBSIv29eAH89Rfw889yauSQIUD79twUm0yLqdx7EBk7vTez/uOPP9C+fXuo1eqcislkmMqF8tgxWS77yhWlI6E3iY2Nhbu7O168eIE5c+Zg06ZNaNmyJb799lsAgEajwYMHD7Bo0SKsXLkSbdu2xf9xeJSMUGoqsH49MHEiEBsLfPONXMvm7Kx0ZEQ5z1TuPYiMnd5JWpqIiAicO3cOAFCmTBlUrlw5WwMzBaZyoUxKkpsn37kD5MundDSUkaSkJHTu3BlOTk5o0KABli5dikqVKmHMmDEAZIJm9r+5Yc+fP8eQIUOwcOFCrF69Gs2aNVMydKJ3JgSwdy8wfbqsBNm5M9CvH1CsmNKREeUcU7n3IDJ2es/Iv3v3Lho0aIBq1aqhX79+6NevH6pWrYqGDRuy6iNlyMpK7scVGal0JJQZKysrTJs2DZs3b0bnzp1RsmTJ1xK0tPdz9u7dizt37qBbt24oU6aMkmETvReVCqhbF1i3DoiIkOeqVAFatQJ27+Zea0REpBy9k7RvvvkGiYmJOHPmDB4+fIiHDx/i9OnTSEhIQL9+/XIiRsoDKleW0x7JcHl6eiI6Oho1a9bE9u3btefTEjSVSoXw8HD89ttvcHBwQK9eveDj48N9EylPKFYMmDkTuHYNqFNHjqpVrgwsXSrXshEREeUmvZO0LVu2YM6cOShdurT2XJkyZTB79mxs3rw5W4OjvINJmnEwMzNDaGgoihcvjunTp2vPq1QqnD9/Hr/++iusrKzQrVs3fPDBB9rHiPIKZ2dg0CDg8mVZkXbePMDXFxgzBuBkESIiyi16J2kajQaWGZTCsrS0hEajyZagKO+pUuXldCIyfGvWrMF3332H5cuXIzo6GgAwbdo0PHv2DB07dkStWrUUjpAoZ1lYAJ99Bhw8KKdDnjkDFCkC9OzJfR+JiCjn6Z2kNWjQAN9++y1u376tPXfr1i189913aNiwYbYGR3lHuXLA1atAQoLSkZA+Hj9+jKJFi6JJkyZ48uQJ2rRpg+bNmwPAG6c5Jicn51aIRDnO3x8ICZHJmYsLULs20KSJ3FqEs32JiCgn6J2k/frrr0hISICvry+KFi2KokWLws/PDwkJCZg1a1ZOxEh5gI0NUKoUcPy40pGQPnr27InVq1dj7969SExMRIcOHQDIEfXMpjlevHgRs2bNwp9//pmboRLluMKFgUmTgBs3gI8/lqX7y5YF5s8HnjxROjoiIspL9E7SvL29cezYMWzcuBH9+/dH//79sWnTJhw7dgyFChXKiRgpj+C6NOPUpk0b7NmzB9u2bcPs2bMBQFuOPyNCCKSmpmLs2LH46quvcitMolxjbw/07QtERcmNsVeuBAoWlEkbp0ISEVF2eOd90uj9mdpeJbNmAYcPAxxgMU63bt3Crl278MUXX2jPpaamwtzcPMP2SUlJqFGjBj777DMMGzYst8IkUkRUlBxRW7JETu/+6iugTRtArVY6MiJdpnbvQWSs9B5JywmzZ8+Gr68vrK2t4e/vj8OHD7+x/apVq1CqVClYW1ujXLly2LRpk87jQgiMGDECnp6esLGxQWBgIC5evKjT5uHDh+jYsSMcHR3h7OyM7t274/HjxzptTp48iTp16sDa2hre3t6YNGlSpjGFhIRApVKhdevW+nXehHAkzbgVLFhQm6D99ddfAKCToKV/v+fJkyewsrLCN998g6tXr+ZqnERKKFkSmDYNuHUL6NoV+OUXwNsbGDYM+F/tHSIioixTPElbsWIFBgwYgJEjR+LYsWOoUKECmjRpgrt372bY/uDBgwgKCkL37t0RGRmJ1q1bo3Xr1jh9+rS2zaRJkzBz5kzMmzcP4eHhsLOzQ5MmTfD8+XNtm44dO+LMmTPYvn07NmzYgL179yI4OFj7eEJCAho3bgwfHx9ERERg8uTJGDVqFBYsWPBaTFevXsWgQYNQp06dbPzJ5D0VKgAXLwKv5MJkhLZv346///5bJzFLv0bNzs4OALB06dJM/5aJ8iIbG6BLF+DQIWDrVuDBA3nta94c+O8/IDVV6QiJiMgYKD7d0d/fH9WqVcOvv/4KQBYk8Pb2xjfffIOhQ4e+1r5du3Z48uQJNmzYoD1Xo0YNVKxYEfPmzYMQAl5eXhg4cCAGDRoEAIiPj4e7uzuWLFmC9u3b49y5cyhTpgyOHDmCqlWrApD7vzVv3hw3b96El5cX5s6di+HDhyMmJgZWVlYAgKFDh2LdunU4f/689rVTU1Px4Ycfolu3bti3bx/i4uKwbt26LPXdFKccVKsG/PSTvGEh4/bs2TOYm5vj6tWruHLlCm7cuAGNRoO7d+/i5MmTuHbtGh49eoSVK1eiUqVKSodLpJj4eGDZMmDuXCAxEQgOBrp3Bzw8lI6MTJEp3nsQGSNFR9KSkpIQERGBwMBA7TkzMzMEBgYiLCwsw+8JCwvTaQ8ATZo00baPjo5GTEyMThsnJyf4+/tr24SFhcHZ2VmboAFAYGAgzMzMEB4erm3z4YcfahO0tNeJiorCo0ePtOdGjx4NNzc3dO/e/a39ffHiBRISEnQOU9OoEbB9u9JRUHawsbFBVFQUateujbZt2+LIkSPYvn07bty4gVKlSqFXr144duyYNkE7evQoFixYgEWLFmHfvn0KR0+Ue5ycZKGRU6dksnb2LFCsGPD558CuXSzjT0REr7NQ8sXv37+P1NRUuLu765x3d3fXGa1KLyYmJsP2MTEx2sfTzr2pjZubm87jFhYWyJcvn04bPz+/154j7TEXFxfs378fv//+O45nsa78hAkT8NNPP2WpbV7VuLG8WaG8oVy5cpg9ezZ69OiBJk2aoG3btq+1uXDhAv7991+MHj0azZs3h7W1NYYOHYrBgwdrR7uJTIFKBdSpI4+7d4HFi+WImpWVLDTSsSNQoIDSURIRkSHI8khacnIyBg8ejGLFiqF69epYtGiRzuOxsbGZVnnLixITE9GpUyf89ttvyJ8/f5a+Z9iwYYiPj9ceN27cyOEoDU/NmnKPIdaSyDs+++wzrFu3DsHBwfjxxx91Hrt//z4WL16M+fPn49NPP0VISAiWLFmCPXv2YPLkyQgJCVEoaiJlubkBQ4YAly4BM2YAe/YAvr5Ay5bA8uXA06dKR0hERErKcpI2btw4LF26FF999RUaN26MAQMGoFevXjpt9F3elj9/fpibmyM2NlbnfGxsLDwymazv4eHxxvZpH9/W5tViBikpKXj48KFOm4yeI+2xy5cv4+rVq/joo49gYWEBCwsLLF26FP/++y8sLCxw+fLl12JXq9VwdHTUOUyNlZVcj7Z+vdKRUHaqX78+Dh48iEWLFmHNmjXa87t27cL06dOhVqthaWmJwoULY8+ePShdujQ2bNgAf39/BaMmUp6ZGdC0KfDPP/INrI8+AubMkevVOncGtm0DUlKUjpKIiHJblpO0v/76CwsXLsSgQYMwduxYHD16FDt37kTXrl21yVn66m5ZYWVlhSpVqiA0NFR7TqPRIDQ0FAEBARl+T0BAgE57QFaaS2vv5+cHDw8PnTYJCQkIDw/XtgkICEBcXBwiIiK0bXbu3AmNRqO9aQwICMDevXuRnJys8zolS5aEi4sLSpUqhVOnTuH48ePa4+OPP0b9+vVx/PhxeHt76/WzMCWtWwNZrK1CRqRkyZK4dOkSKlasqD03ceJEtG3bFqdPn8b8+fPx008/Yfz48Xj06BEqVarEvxOidPLlA3r1AvbtA06elGX9+/eXpfwHDAAiIrh+jYjIZIgssrGxEdHR0Trnbt68KUqUKCE6duwobt26JczMzLL6dFohISFCrVaLJUuWiLNnz4rg4GDh7OwsYmJihBBCdOrUSQwdOlTb/sCBA8LCwkJMmTJFnDt3TowcOVJYWlqKU6dOadtMnDhRODs7i/Xr14uTJ0+KVq1aCT8/P/Hs2TNtm6ZNm4pKlSqJ8PBwsX//flG8eHERFBSkfTwuLk64u7uLTp06idOnT4uQkBBha2sr5s+fn2lfOnfuLFq1apXlvsfHxwsAIj4+PsvfkxfExQlhYyPE/ftKR0I56dKlS6JYsWLi4sWL2nO7du0SJUqU0P59P3/+XFy5ckVMnTpVzJw5U9y9e1epcIkMkkYjxLFjQgwYIISHhxClSgkxZowQV64oHRkZK1O99yAyNlkeSUub4pdewYIFsWvXLhw5cgRdunR5pySxXbt2mDJlCkaMGIGKFSvi+PHj2LJli7ZIx/Xr13Hnzh1t+5o1a+Lvv//GggULUKFCBaxevRrr1q3DBx98oG0zePBgfPPNNwgODka1atXw+PFjbNmyBdbW1to2f/31F0qVKoWGDRuiefPmqF27ts4eaE5OTti2bRuio6NRpUoVDBw4ECNGjNDZS43ejZMT8OGHQLpdFCgPcnFxgZubm3ZqcUJCAiIjI1GiRAm4uLggJiYGU6dORYsWLbBy5Urs3bsXZcuW1dnzkMjUqVRApUrA1KnAzZvArFlyHVuFCkCtWrKs/4MHSkdJRETZLcv7pPXo0QNCCPz++++vPXbr1i3Uq1cPV65cQSp36swyU96rZN48udHrP/8oHQnlpHHjxmH69OkYN24cNm7ciBcvXqBly5bo3bs3hg8fjtOnT6Nhw4YYMGAAAPkGy7NnzzBr1iyFIycybE+fys2xly0Ddu4EGjYEvvhCrmmzsVE6OjJkpnzvQWRMslyC/8cff8y0LH7BggWxZ88ebOcGWJRFH38MDBokbzRsbZWOhnLK8OHDUaRIERw4cABFihRBvXr10Lp1a/z55584ceIEWrVqhd69e2vbOzs7IyIiAikpKbCwUHSHECKDZmsLtGsnj/v3gZUrZZXIHj2AFi2Azz4DmjVjwkZEZKyyPJJG2c/U382qUQMYNgxo1UrpSCinvZp0tWrVCoUKFcL06dO1G8bHxsaidevW+PjjjzFs2DClQiUyateuAatXA6tWyU2zW7aUCVvTpkzYSDL1ew8iY5HlNWlpmNNRdmGVR9ORfg/FiIgIbN26Ff3799cmaE+fPsWvv/6KfPnyoXHjxtq2x44dw7Fjx3I9XiJj5eMDDBwIHDoEnDoFVKkCTJwIuLsDHTrIa+7z50pHSUREb6NXkpaUlITPPvssp2IhE9O6tVxTwT2A8r7023MUKFAANWrUgJeXl/bc5MmTceTIEbRs2RJVqlTBv//+i8aNG6NFixbo0aMHGjRooETYREYtLWELD5cl/StXBsaPlxtpd+zIhI2IyJBlOUl7/PgxmjVrhhTeUVM2KVUKyJ8fOHhQ6UgoNxUuXBiWlpYIDAzEypUrUb9+fYSGhqJZs2bo3bs3lixZgjlz5sDT0xNHjx7F0aNHYWlpif79+ysdOpHR8vWV64APHwZOnAAqVtRN2NavZ8JGRGRIspSk3b9/H3Xr1oW5uTlWrVqV0zGRCeGUR9O0fft2VKtWDaGhoXBzc8OcOXPwzTffIDQ0FCtXrkSlSpUwbdo0FCxYEGZmZmjfvr3OVhxE9O78/IDvv9dN2MaOlQnbF1/Ia/LTp0pHSURk2rJUOKRUqVJwc3PDli1bYMtSfNmGi3fluokOHYDLl+V+QGTa+vTpgwcPHmDSpEnw8fHRnm/atCm8vLywaNEiBaMjytuio2XBkTVrgDNngEaNgE8+kdUiXV2Vjo6yC+89iIxDlkbSLl++jKZNmzJBo2xXvTrw7Jlc4E6m7dq1a/jjjz/Qs2dPnQRt7ty5SE5ORvv27QGweBFRTvHzAwYPlmvYoqJkkrZsGVCoENCgATBzJnD9utJREhGZhiwlaStXrsTYsWPx22+/5XQ8ZGLMzGQJfk55pNTUVFSpUgV169bVnlu+fDm2bt2KihUrwt/fH4BuERIiyhkFCwJ9+gDbtgF37sj91/btA8qWlRUjx4wBTp8G+J4JEVHOyFKS9sknn2Djxo0YPHgw/v7775yOiUwM16URADg5OSE2NhbDhw/H3bt3MX78eMydOxeFChXC119/DScnJ46iESnA2VlOS1+1Crh3Dxg9Wu7H1qABULy4LEiyfz+Qmqp0pEREeYdem1lHRETg448/xq1bt3IyJpPBeeHSixdywfrJk7JkNJmuGzduoGXLlnBzc8PZs2cxZswYNGjQAL6+vhBCcBSNyICkpgJhYcA//8g32h4/Bj7+WL7x1qABN882VLz3IDIOeiVpAHDhwgWUKFEip+IxKbxQvtShg6wwNniw0pGQ0jQaDeLj42FmZgYnJyelwyGiLBBCTn9ct04e584BdeoAjRvL44MPWBzKUPDeg8g46J2kUfbhhfKl7duBr78Gzp/nf+T0domJwIQJwJAhAPM4IsMTGwvs2AFs3SrXtalULxO2Ro3k7AlSBu89iIxDljezzorVq1dn59ORCWnYUE573L9f6UjIGCQlyXLhpUsDq1ezeAGRoXF3l5tkL10qC49s3QqUKwf88YfcWLtyZWDoUGDnTnntJyIiXXqNpKWkpOD8+fOwsrLSmfK4fv16jBgxAufPn8cLXm2zjO9m6Ro9GrhyBViyROlIyFhs2SIr0JUtC/z6K9c0EhmDZ89kpci0UbYrV4C6dV+OtJUuzRkVOYn3HkTGIcsjaadPn0axYsVQoUIFlC5dGm3atEFsbCzq1q2Lbt26oVmzZrh8+XJOxkp5XJcuchPVhASlIyFj0bSpXAdTpgxQoQIwbRqQkqJ0VET0JjY2MhmbOlXukXnxItCuHXDkCFCvHuDtDXTrBoSEAHfvKh0tEZEysjyS1qJFC7x48QL9+/fH8uXLsXz5cpQsWRLdu3dH3759YcMyTnrju1mva9oUaNMGCA5WOhIyNidPyt+bpCRgwQKgalWlIyIifWk0wIkTcoRt2zbg4EFZ5r9BA3l8+KHcEoDeHe89iIxDlpM0Nzc3bNu2DRUrVkR8fDxcXFzwxx9/oFOnTjkdY57FC+XrVq0CJk8GDh9WOhIyRqmpwPz5wA8/AJ06AaNGAS4uSkdFRO/q2TNZ5n/nTnkcOwaULy/XMTdoANSqBdjaKh2lceG9B5FxyPJ0x/v378PLywuA3HTWzs4ONWrUyLHAyDR9/LEsCHHqlNKRkDEyN5dr1E6fltOkihcHZs6Uo2tEZHxsbGQyNnasHFW7d0+++ZKcLKu75ssn17P99JNc58a/dSLKK7KcpKlUKiQmJiIhIQHx8fFQqVR49uwZEhISdA6i96FWyxGQ339XOhIyZl5ewPLlwMaNwMqVco+mf/5hFUgiY+fgADRvDkyZIkfVbt0Cvv1WJm+9esmR8yZNgJ9/lmvcUlOVjpiI6N1kebqjmZkZVOnKLQkhMvw6lVfELOOUg4ydOSPfGb11SyZtRO9DCGDtWvmuu5eXLFZQrZrSURFRTrhzB9i16+X0yIcP5Tq2tKNyZcDCQukolcV7DyLjkOUkbc+ePVl6wrp1675XQKaEF8rM1agBfPedrPhFlB2SkoA5c4AxY2SBmvHjWbKfKK+Ljgb27AH27pUf794FataUCVvduvING1N7M5D3HkTGQa990ih78UKZuYUL5TS1bduUjoTymocPgXHjgN9+A3r3Bv7v/wAnJ6WjIqLccPOmXLuWlrhFRwP+/jJh+/BD+QahnZ3SUeYs3nsQGQcmaQrihTJziYlAwYKygAhHOygnXL4MDBsG7N4NjBwpy/dbWiodFRHlprt3ZdKWNtJ2/rycEpk2PbJWrbz3Jg7vPYiMA5M0BfFC+WbdugGFC8tKXkQ55eBBYOBA4NEjYNIk4KOPgHTLbYnIhDx6BOzfL5O2vXuB48dl4aE6dWTCVquWXNtqzHjvQWQcmKQpiBfKNztwAAgKktNRzM2VjobyMiHkHn1Dh8qR2ylTgCpVlI6KiJSWmCjfyNm/X/6fFB4OuLu/TNhq1QLKlgXMslwrW3m89yAyDkzSFMQL5ZsJAZQuDfzyiyypTJTTXrwAfv1Vrllr0UIWF/H2VjoqIjIUyclydO3AgZfH06dAQABQu7ZM2qpXN+wNtnnvQWQcjOi9HzI1KhXQowf3TKPco1bLqY8XLwKurnKa06BBcg8mIiJLS1kRsn9/Ofp+6xYQGQl07AjcuAF8843cq616dVmhePVquS0AEZG+sjSS1qZNmyw/4dq1a98rIFPCd7Pe7u5doGhR4Nw5oFAhpaMhU3PpEjB6NLBunbwJ++47oEQJpaMiIkMWFweEhb0caTt8WCZu1aoBVavKqdRVqgAFCigTH+89iIxDlrZ0dMprpY3IaLi5AR06ANOmyYMoNxUrBixdCly9KqfdVqsG1K8vR9tq12aBESJ6nbMz0KyZPAAgJQU4exY4cgSIiADWrwdOnpRr29KStrSPrq6Khk5EBoRr0hTEd7Oy5vJlWRL58mUgf36loyFTFhcn91f75RdZ4W3QIKBNG8AiS293ERFJSUnAmTMyaYuIAI4elYmbl5du0lalCpAvX/a+Nu89iIwDkzQF8UKZdR07AkWKAGPGKB0JkbzBWrkSmDpVluzu3x/o3h1wcFA6MiIyVklJwOnTL5O2iAi5V2jBgi8Tt8aNgUqV3u91eO9BZBzeKUlbvXo1Vq5cievXryMpKUnnsWPHjmVbcHkdL5RZd/q03Fj06lWAPyoyFEIAO3fKZC0sTG6I/c03XD9JRNnjxQvdxK1KFaBXr/d7Tt57EBkHvas7zpw5E127doW7uzsiIyNRvXp1uLq64sqVK2iWNgGbKJt98IFM0ubOVToSopdUKqBhQ2DTJrmP0oMHctuITp1kmW4iovehVsvELDgYWLDg/RM0IjIeeidpc+bMwYIFCzBr1ixYWVlh8ODB2L59O/r164f4+PiciJEIADBsmCwe8uyZ0pEQva5sWWDhQlm+39cXCAyUx+bNcsSNiIiIKKv0TtKuX7+OmjVrAgBsbGyQmJgIAOjUqROWL1+evdERpePvD5Qrx33TyLB5eMi1k9euyaIi/frJ0bVp0+RIGxEREdHb6J2keXh44OHDhwCAwoUL49ChQwCA6OhosAYJ5bT/+z9g0iS5wJrIkNnZAX36AFFRwIwZwL59gI8P8MUX8nNeLomIiCgzeidpDRo0wL///gsA6Nq1K7777js0atQI7dq1wyeffJLtARKlV7++LMqwYIHSkRBljZkZ0LQp8M8/wIULQMmSslrpBx8AM2fK6pBERERE6eld3VGj0UCj0cDifxsDhYSE4ODBgyhevDh69eoFKyurHAk0L2KFpXcTEQE0aiQrXnl5KR0Nkf5SU4EtW4D584Fdu4C2bWVBgBo1uEE2EeUs3nsQGQfuk6YgXijfXb9+wN27QEiI0pEQvZ8bN+Q6y4UL5aa1vXrJKZFOTkpHRkR5Ee89iIxDlpK0kydP4oMPPoCZmRlOnjz5xrbly5fPtuDyOl4o311CgizGsGgR0KSJ0tEQvb+UFFnKf948uWbt889lwlatGkfXiCj78N6DyDhkKUkzMzNDTEwM3NzcYGZmBpVKlWGREJVKhdTU1BwJNC/ihfL9rF4NDBkipz3a2CgdDVH2uXpVjqz9/rusFtmrl1zH5uCgdGREZOx470FkHLKUpF27dg2FCxeGSqXCtWvX3tjWx8cn24LL63ihfD9CAC1aAJUrA2PHKh0NUfZLTgb++0+uXTt4EGjdGujcWRbQMTdXOjoiMka89yAyDnqvSdu7dy9q1qypLRySJiUlBQcPHsSHH36YrQHmZbxQvr/oaKBSJSAsTE5/JMqrrl8Hli0D/vgDePJErlv78kugTBmlIyMiY8J7DyLjoHcJ/vr162v3SUsvPj4e9evXz5agiLLKz09OefzqK+47RXlb4cJyn8Dz54G1a4HERKBOHblmbdYs4N49pSMkIiKi7KJ3kiaEgCqDVewPHjyAnZ1dtgRFpI+BA4EHD+SeU0R5nUoFVK8OzJ4N3L4tE7edOwFfX6BxY1l4JCZG6SiJiIjofVi8vYnUpk0bALI4SJcuXaBWq7WPpaam4uTJk6hZs2b2R0j0FlZWwMqVQO3acuojZ9ySqVCrgU8+kcfDh8CGDcCaNfKNi8qVgTZt5GO+vkpHSkRERPrIcpLm9L9Ne4QQcHBwgE26cnpWVlaoUaMGevbsmf0REmVBmTLAb7/JsuUREUDBgkpHRJS78uWTa9S+/BJ4/BjYvFkmbCNHAiVKyIStTRugZEmW9CciIjJ0ehUOEUKgW7dumDVrFuzt7XMyLpPAxbvZb+hQYPduYM8eOcpAZOqePwd27JAJ27//ymSuZUtZGfXDD+VINBGZDt57EBkHvZI0jUYDa2trnDlzBsWLF8/JuEwCL5TZLzUVaNYMKFJErs0hopdSUoDwcDktcsMG4No1uY6tZUv5d+PurnSERJTTeO9BZBz0KhxiZmaG4sWL48GDBzkVD9F7MTcHli8Htm6VGwET0UsWFkCtWsCECcCpU8DJk3LPtRUrZKVUf39gzBggMpLVUomIiJSkd3XHiRMn4vvvv8fp06dzIh6i9+bqKkuUDxoEHD6sdDREhsvXF+jbV65fu3cPGD4cuHFDjqwVKgQEB8spkk+eKB0pERGRadF7M2sXFxc8ffoUKSkpsLKy0ikgAiDDPdQoY5xykLP+/FOWJ4+IANzclI6GyHgIARw//nJa5KlTQL16ch1bixasFklkzHjvQWQc9E7S/vjjjzc+3rlz5/cKyJTwQpnz+vWTN5jbt8upXkSkv9hYOdq2YQOwbRvg5QU0aiSPevUAXr6IjAfvPYiMg95JGmUfXihzXnIy0LAhULUqMG2a0tEQGb+kJFl8ZPt2eURGyr+vtKStenW+IUJkyHjvQWQc3itJe/78OZKSknTO8Q8+63ihzB0xMUCVKsCUKUBQkNLREOUtcXHArl0vk7a7d2UxkrSkrXhx7stGZEh470FkHPRO0p48eYIhQ4Zg5cqVGVZ5TE1Nzbbg8jpeKHNPWBjQvLncP618eaWjIcq7rl59mbCFhgL29i8TtoYNgfz5lY6QyLTx3oPIOOhd3XHw4MHYuXMn5s6dC7VajYULF+Knn36Cl5cXli5dmhMxEr23gABg4kTgk08A1rYhyjm+vkDPnsDKlXJUbc0aoGhRYP58wNtbjmoPHSrXtj1+rHS0REREhknvJO2///7DnDlz0LZtW1hYWKBOnTr44YcfMH78ePz111/vFMTs2bPh6+sLa2tr+Pv74/Bb6qavWrUKpUqVgrW1NcqVK4dNmzbpPC6EwIgRI+Dp6QkbGxsEBgbi4sWLOm0ePnyIjh07wtHREc7OzujevTsev3LHcPLkSdSpUwfW1tbw9vbGpEmTdB7/7bffUKdOHbi4uMDFxQWBgYFvjZ2UExwsixx07Cg3vSainGVuLterDRsG7NwJ3L8PjBsn14oOHSpH1QIC5OebNwOJiUpHTEREZBj0TtIePnyIIkWKAJDrz9JK7teuXRt79+7VO4AVK1ZgwIABGDlyJI4dO4YKFSqgSZMmuHv3bobtDx48iKCgIHTv3h2RkZFo3bo1WrdurbNv26RJkzBz5kzMmzcP4eHhsLOzQ5MmTfD8+XNtm44dO+LMmTPYvn07NmzYgL179yI4OFj7eEJCAho3bgwfHx9ERERg8uTJGDVqFBYsWKBts3v3bgQFBWHXrl0ICwuDt7c3GjdujFu3bun9c6Ccp1IBs2cDDx4Ao0YpHQ2R6bGzA5o2BaZOBY4dk+tFhw8HUlKAESNk0la9OvD997KSZFyc0hETEREpROipXLlyYvfu3UIIIRo2bCgGDhwohBDil19+EQULFtT36UT16tVF3759tV+npqYKLy8vMWHChAzbf/7556JFixY65/z9/UWvXr2EEEJoNBrh4eEhJk+erH08Li5OqNVqsXz5ciGEEGfPnhUAxJEjR7RtNm/eLFQqlbh165YQQog5c+YIFxcX8eLFC22bIUOGiJIlS2bal5SUFOHg4CD++OOPLPU9Pj5eABDx8fFZak/Z4/p1IdzchFi1SulIiCi9+HghNm0SYsgQIfz9hbCyEqJyZSG++06IdeuEePBA6QiJjB/vPYiMg94jaV27dsWJEycAAEOHDsXs2bNhbW2N7777Dt9//71ez5WUlISIiAgEBgZqz5mZmSEwMBBhYWEZfk9YWJhOewBo0qSJtn10dDRiYmJ02jg5OcHf31/bJiwsDM7Ozqhataq2TWBgIMzMzBAeHq5t8+GHH8LKykrndaKiovDo0aMMY3v69CmSk5ORL1++DB9/8eIFEhISdA7Kfd7ewNq1cvrj+vVKR0NEaRwdgWbN5PrRQ4fkqPeECYCNDTB5styfrUIF4Ntv5d/wvXtKR0xERJQz9N7N5rvvvtN+HhgYiPPnzyMiIgLFihVDeT3L5t2/fx+pqalwd3fXOe/u7o7z589n+D0xMTEZto+JidE+nnbuTW3c3Nx0HrewsEC+fPl02vj5+b32HGmPubi4vBbbkCFD4OXl9VoSmWbChAn46aefMnyMcletWjJBa9UK+PlnWeiAiAyLvT3QuLE8AODpU1mpdc8e4Jdf5PrSQoVkMZIqVeT6t8qVAScnZeMmIiJ6X1lO0jQaDSZPnox///0XSUlJaNiwIUaOHAkfHx/4+PjkZIxGYeLEiQgJCcHu3bthbW2dYZthw4ZhwIAB2q8TEhLg7e2dWyHSK+rUkfs7ffKJ3Jz311+BTP7piMgA2NrKMv4NG8qvX7wATpwAIiKAo0eBv/4Czp4FfHxeJm5VqsjEzdlZ0dCJiIj0kuUkbdy4cRg1ahQCAwNhY2ODX375BXfv3sWiRYve+cXz588Pc3NzxMbG6pyPjY2Fh4dHht/j4eHxxvZpH2NjY+Hp6anTpmLFito2rxYmSUlJwcOHD3WeJ6PXSf8aaaZMmYKJEydix44dbxxNVKvVUKvVmT5Oua9CBXlz98UXQO3aslw433MgMg5qtSw0Ur36y3PPnwMnT8rELSKCiRsRERmnLK9JW7p0KebMmYOtW7di3bp1+O+///DXX39Bo9G884tbWVmhSpUqCA0N1Z7TaDQIDQ1FQEBAht8TEBCg0x4Atm/frm3v5+cHDw8PnTYJCQkIDw/XtgkICEBcXBwiIiK0bXbu3AmNRgN/f39tm7179yI5OVnndUqWLKkz1XHSpEkYM2YMtmzZorPGjYxHvnyyklyLFkC1anITXiIyTtbWMmnr3RtYuBA4fhxISJDJWt26QFQUMHAg4OYGFCsGtGsn171t2gTcugUIoXQPiIiIkPXqjlZWVuL69es659Rqtbhx48Z7VS4JCQkRarVaLFmyRJw9e1YEBwcLZ2dnERMTI4QQolOnTmLo0KHa9gcOHBAWFhZiypQp4ty5c2LkyJHC0tJSnDp1Sttm4sSJwtnZWaxfv16cPHlStGrVSvj5+Ylnz55p2zRt2lRUqlRJhIeHi/3794vixYuLoKAg7eNxcXHC3d1ddOrUSZw+fVqEhIQIW1tbMX/+fJ3XsbKyEqtXrxZ37tzRHomJiVnqOyssGZ7//hPC1VWI8eOF0GiUjoaIcsqzZ0IcPizE3LlCfPWVEAEBQtjZCZE/vxANGwoxcKAQS5cKceKEEElJSkdLlH1470FkHLKcpJmZmYm7d+/qnLO3txdXrlx57yBmzZolChcuLKysrET16tXFoUOHtI/VrVtXdO7cWaf9ypUrRYkSJYSVlZUoW7as2Lhxo87jGo1G/Pjjj8Ld3V2o1WrRsGFDERUVpdPmwYMHIigoSNjb2wtHR0fRtWvX15KrEydOiNq1awu1Wi0KFiwoJk6cqPO4j4+PAPDaMXLkyCz1mxdKw3TpkhDlywvx/fdKR0JEuSk1VYgLF+T2HMOHC9GypRDe3nIrgIoVhejcWYjp04XYuZPbAZDx4r0HkXFQCZG1yR1mZmZo1qyZzpqq//77Dw0aNICdnZ323Nq1a7NtlC+vS0hIgJOTE+Lj4+Ho6Kh0OJTO06ey/DfruhDRgwdynduJE3L65IkTcp2bmxtQsaJc21quHPDBB0CJEoClpdIRE2WO9x5ExiHLSVrXrl2z9ISLFy9+r4BMCS+UxmvFihV49uwZgoKCWAyGyAQlJQHnz79M3M6cAU6fBu7eBUqWlAlb+sPPDzDTe2dSouzHew8i45DlJI2yHy+UxikhIQFLly7F4sWLceXKFSxcuBBt27ZVOiwiMgCPHr1M2NKOU6dk1ckyZV5P3ry8AJVK6ajJlPDeg8g46L2ZNZGpc3R0RO/eveHo6Ii5c+ciKSlJ+5gQAirecRGZLBcXuZ1H7dovzwkBxMbqJm6rV8uPFhYyWStbViZxpUvLo2BBJm9ERKaMSRrRO3j8+DGOHTsGb29vnVG0devW4cyZM2jbti1Kly6tYIREZChUKsDDQx6BgS/PazTAjRsvR9siIoBly4Bz5+T3lCr1MmlL+7xoUZnYERFR3sZLPdE72Lt3L86fP4/PP/8cVlZWEELgxYsXePDgAaKiolC2bFmMGTMGgwcPhiWrCBBRBszM5CbbPj5yn8Y0Qsg9286de3ls2SLXwD18CBQv/jJ5SztKlgRsbZXrCxERZS8maUR6SkxMxP79+2FtbY1PP/1Ue97a2ho9evSAr68v7t27BwsLCyZoRKQ3lQooVEgejRrpPvbwoUzW0pK3v/6SH69fl+1LlZIJW4kSLw9vbxYtISIyNkzSiPR04MABnDp1Cs2bN4ejoyM0Gg3M0t0B7dixA87Ozvjoo48A4LXHiYjeVb58QM2a8kjv6VPgwgUgKkp+PHQIWLpUfv3ihRx9S5+4pSVyrq7K9IOIiN6MSRpRFjx//hx///036tatizNnzkCtVmvXoqlUKm0iFhERgbNnz6JmzZooU6YMADBBI6IcZ2sr92yrWFH3vBDAvXsycUs71q+XHy9dAuztdZO2tKNoUSDdFqhERJTLmKQRZYEQAgcOHECPHj1gbW2Nr776Cp6engCgU83x33//haWlJRr9b45SZqNo165dw9KlS+Hi4oI+ffowkSOiHKFSyU233dx0K04CQGqqnCaZlrxFRQG7d8vPb9yQ31Os2OtH0aKAs7MSvSEiMh3cJ01B3KvE+MTGxmLChAmYOXMmPv/8cyxcuBD29vYAgNOnT2P48OGoXLkyRo4cmelzpKSkIDw8HFu3bsXBgwdx4sQJDB8+HN9++y3L9xORQXjxArh6VY62pT8uXwaiowEnp4yTt2LFgPz5uX2AIeO9B5FxYJKmIF4ojVdiYiLWrFmDLl26YMqUKahduzaOHj2KgwcPok+fPqhdu/Zb16KlPb5r1y78/PPPmDt3Lvz8/HKxF0RE+ktJkSNwryZwly4BV64AarVM2AoXlkVL0oqgpH1esKBsQ8rgvQeRceB0R6J34ODggC5dugCQVR0bNGiA58+fo2nTpqj9vzlFb5vCqFKpkJycjPr166N9+/bYu3cv/Pz8uCE2ERk0CwugSBF5NG6s+5hGI7cPuHwZuHlTTpu8fh04eFB+fvMmcP8+UKCAbuL26ueFCjGRIyLTxpE0BfHdrLxlyZIl+P777+Hr64vFixfjgw8+0D6WPvFKTU0FAJibmwMAQkND8dlnn2HHjh2oXLkyAODYsWOIiIjAmTNn0LFjR1SrVi2Xe0NElDOeP5eJXFoSl/5j2uf378tpkwULysPL6+Xn6b92deXUSn3x3oPIODBJUxAvlHnTtm3bULJkSfj4+CA8PBz+/v4Zttu1axdmzpyJCxcu4NNPP8VPP/0EAJg/fz6mTJmC6tWro0CBAli2bBmqVq2KZcuWIX/+/LnZFSIiRaQlcrduAbdvv/w8/de3b8vqlZklcGmfe3lxo+/0eO9BZByYpCmIF8q8TaPRoE2bNmjQoAG+/vprmJmZITw8HDt27MA///yDpKQkBAYGokuXLihfvjwAYOXKlfj555/RoUMHDBw4EIAs/z9//nx07doVjo6OiIuLgzNLqxGRiRNCbu6dUTKX/vN79wBHR8DT8+Xh5ZXx1w4OSvcq5/Heg8g4MElTEC+UpuHp06ewtrbGL7/8goEDB+Kjjz7C6NGjUaFCBZ12t2/fxsCBA7F9+3Y0adIEZ86cwbfffouuXbsiOTkZlpaWOHfuHJYtW4awsDB06NABPXr0UKhXRETGITkZiI0F7tyRx+3bLz9P/3VsLGBjk3ES5+kJeHi8PFxdAWPdOYX3HkTGgYVDiHKY7f/m2bRt2xaHDh3C1q1b8ffffyN//vwoWLCgtt21a9ewadMmtGrVCoMHD0Z0dDQmTJgAHx8fNGjQAABgZWWF+vXrw9fXF/Pnz8cff/yBpUuXsiokEVEmLC1fFiN5k9RUOer2ahIXHS0Ln8TGAjEx8khOBtzddRO3V79OOxwcuG6OiPTHkTQF8d0s03T16lUMHz4cy5cvx+jRozFo0CBYW1tjxYoV6NWrF+Li4rRtmzVrhgoVKmDixIkZPlerVq3Qtm1bfPnll7kUPRGRaRMCSEh4mbDFxOgmcK+eV6tfJnDu7nKTcHd33c/TPrq45HxCx3sPIuPAkTSiXObr64u//voLM2fOxKlTp7RVH2/evInSpUtr2z1+/BilS5dGUlKStjpk+vdUVCoVHBwc8M8//2iTtAMHDmDXrl3o3bs3XF1dc7djREQmQKWSm3k7OQElS765bWoq8ODBy6Tt7l2ZuMXGAkePvvz87l15CJFxEpf2eaVKQLrCwUSUhzFJI1KIq6sr6tWrp/26ffv2WLt2LdatW4fWrVtj5syZiIqKQqtWraBSqZCSkgILi5d/ssuXL8fWrVsxb948ADKpmzt3Lv7++29Mnz4dTZo0wfjx4+Hr65vLPSMiIgAwN5fJlZsb8L/6UJkSAnj06GUilz6hu3AB2LcPePyYSRqRqWCSRmQgChYsiK5du6JXr16YOHEinj9/jt69e6NTp04AAAsLC1y/fh0rVqzArl27cPnyZfz4449o27YtAODs2bMIDQ3FgwcPtJttf/TRR/j1119Rt25dJbtGRERvoVIB+fLJo1QppaMhIqUxSSMyID169ECPHj1w9OhReHp6omDBgnjy5AnWrVuHbdu2Yf369ahevTo6deqEunXrwsvLC4AcRduwYQNiY2MRExMDFxcXLFu2DElJSUhOTla4V0RERESkDyZpRAaoatWq2s9DQkIQHBwMd3d37Nu3D0WLFgUg92FLY25ujk8//RSALCZSpUoV/PLLL3Bzc4OVlVXuBk9ERERE78VId/kgMh3du3fH/v37UaJECdSsWRM//fQTkpKSYJZukx4bGxuUL18eo0ePRlhYGO7cuYOIiAgFoyYiIiKid8UkjcgIBAQEYPfu3fjvv/+wc+dOlClTBomJiQCA6OhoAEBqaioeP34MV1dXeHl5YfHixZk+X2KiXKRORERERIaHSRqREalevTr27NmDQ4cOwcHBAdeuXcNvv/2Go0ePwtzcHPb29gCAQ4cOaadMZrQVYq9eQPHiwMCBwN69skw0ERERERkGJmlERih//vwAAE9PT9jZ2aF+/fr49NNPMW/ePDRp0gTJycno0qULAGj3YUvvzz+BJUsAMzOge3e5B0/XrsCqVcDDh7nYESIiIiJ6jUpk9DY75YqEhAQ4OTkhPj4ejo6OSodDRuzx48eYMGECoqKiUK1aNTRo0ADVqlXL0vcKAZw7B6xfD2zdCoSHy314atYEKleWR+nSgAXLDBERGT3eexAZByZpCuKFkgzR48dy09TDh4Fjx+Rx/77ciDUtaatcWSZyarXS0RIRkT5470FkHJikKYgXSjIWd+8CkZEvk7bISOD6daBsWd3ErXx5wM5O6WiJiCgzvPcgMg5M0hTECyUZs7g44Pjxl4nbsWPAxYtA0aIyeStTRn4sWxYoUYKjbkREhoD3HkTGgUmagnihpLzmyRPg9Gng7FngzJmXH2/flslb+sStTBmgZEkmb0REuYn3HkTGgaUAiCjb2NkB/v7ySC8xURYnOXNGHkuXMnkjIiIiygyTNCLKcQ4OQPXq8kgvs+Tt1i3A21vu5Va8OFCs2MvP/fwAKytl+kFERESUG5ikEZFiMkvenjwBLl2Sx8WLcgrlP//Ir+/eBQoXzjiB8/VlAkdERETGj0kaERkcOzugQgV5vOrx45fJ26VLwMmTwJo18uv79wEfH91RN1/fl4eLC5DB3t5EREREBoVJGhEZFXt7oGJFebwqMVE3gTt/HtiyBbh6Fbh2Ta5zS5+0vXowiSMiIiJDwCSNiPIMBwegUiV5vEqjAWJiZMKWdrwtifPxkVMrCxWSa+Q8PAALXjWJiIgoh/F2g4hMgpkZ4OUlj5o1X388syRu+3bg5k15JCYCnp4vk7ZChV7/3MuLiRwRERG9H95KEBHh7UkcIJO0tITt5k3gxg0gKgoIDX35dUKCHHFLn8B5ecnkLu2jpyfg7MyplURERJQxJmlERFnk4ACULi2PzDx+LLcQuHHjZeIWHQ0cPAjcufPysLCQydyrydurR/78MoEkIiIi08EkjYgoG9nby824S5bMvI1GAzx4oJu03b4tPx47pnsuNVUmcx4egLu7PNzcMv48Xz7A3Dz3+kpEREQ5g0kaEVEuMzMDChSQR/nymbcTAoiLe5m03b0rj9hY4NQpOc0yNvbluaQk+ZyZJXJubvLx/PnlRzs7TrkkIiIyREzSiIgMlEoltwVwcQHKlHlzWyHkVMvYWN3E7e5d4Pp14OhR+fX9+8C9e3Ikz8pKN2lL/zGjc66uLIpCRESUG/jfLRFRHqBSyTVzDg5AsWJvb5+aCjx69DJpe/XjhQuvn3/6VCaMrq4vkzZXV1kERa0GrK3lkfZ5RufSPk9/vHqOiSAREZk6/ldIRGSCzM1fjpiVKpW173n69OUoXPojPh548QJ4/lz38+fP3/z5q0dSknwdM7PXE7nMkjorq/f/mHa8+nVGh4UFp4gSEVHOY5JGRERZYmsrN/j28cmZ59doZKKWUQKX2ZHWPrOPiYlZa5v+SE5+/VxaAglknLxZWr7++ZvOZfbYq4e+5992mJkxySQiMgZM0oiIyCCYmb2cEmlohABSUjJO3l68eJnYZfbxbedevJBrCpOTXx5pj2f29avnkpJkjK+2SU7W7cubkjgLi4y/fp+PWf38bY/pc5ibMxklIuPGJI2IiOgtVKqXiYudndLR6EcIuQYxOTnzJC7tSP942uf6fkw7nj17eT7tSP/12x5LTpZxp2/ztiM9c/PXE7fMErqsnE/7Ov35dz1nbp7xubc95u0NFCmizO8REeUuJmlERER5mEr1MlnIy4SQU2ZfTfzSJ3qZJX1vO5/2PBk915vOPX36+uPp22Tl8/TngoKAAQOU/kkTUW7I45dsIiIiMgUq1ctRKLVa6WiIiN6PmdIBEBERERER0UtM0oiIiIiIiAwIkzQiIiIiIiIDwiSNiIiIiIjIgDBJMxHJr26UQ0REREREBolJmglYtGgR7O3tsWjRIqVDISIiIiKit2AJ/jxu0aJF6NGjB4QQ6NGjBwCgW7duCkdFRERERESZMYiRtNmzZ8PX1xfW1tbw9/fH4cOH39h+1apVKFWqFKytrVGuXDls2rRJ53EhBEaMGAFPT0/Y2NggMDAQFy9e1Gnz8OFDdOzYEY6OjnB2dkb37t3x+PFjnTYnT55EnTp1YG1tDW9vb0yaNEnvWJSUPkEDoE3UOKJGRERERGS4FE/SVqxYgQEDBmDkyJE4duwYKlSogCZNmuDu3bsZtj948CCCgoLQvXt3REZGonXr1mjdujVOnz6tbTNp0iTMnDkT8+bNQ3h4OOzs7NCkSRM8f/5c26Zjx444c+YMtm/fjg0bNmDv3r0IDg7WPp6QkIDGjRvDx8cHERERmDx5MkaNGoUFCxboFYtSXk3Q0jBRIyIiIiIycEJh1atXF3379tV+nZqaKry8vMSECRMybP/555+LFi1a6Jzz9/cXvXr1EkIIodFohIeHh5g8ebL28bi4OKFWq8Xy5cuFEEKcPXtWABBHjhzRttm8ebNQqVTi1q1bQggh5syZI1xcXMSLFy+0bYYMGSJKliyZ5VjeJj4+XgAQ8fHxWWqfVb///rtQqVQCQKaHSqUSv//+e7a+LhERERm2nLr3IKLspehIWlJSEiIiIhAYGKg9Z2ZmhsDAQISFhWX4PWFhYTrtAaBJkyba9tHR0YiJidFp4+TkBH9/f22bsLAwODs7o2rVqto2gYGBMDMzQ3h4uLbNhx9+CCsrK53XiYqKwqNHj7IUy6tevHiBhIQEnSO7ZTaC9irBETUiIiIiIoOkaJJ2//59pKamwt3dXee8u7s7YmJiMvyemJiYN7ZP+/i2Nm5ubjqPW1hYIF++fDptMnqO9K/xtlheNWHCBDg5OWkPb2/vDNu9q+TkZPTu3futCVoaIQR69+7N8vxERERERAZE8TVppmTYsGGIj4/XHjdu3MjW57e0tMTcuXOhUqmy1F6lUmHu3LmwtLTM1jiIiIiIiOjdKZqk5c+fH+bm5oiNjdU5HxsbCw8Pjwy/x8PD443t0z6+rc2rhUlSUlLw8OFDnTYZPUf613hbLK9Sq9VwdHTUObJbt27dsHDhwrcmaiqVCgsXLmQ5fiIiIiIiA6NokmZlZYUqVaogNDRUe06j0SA0NBQBAQEZfk9AQIBOewDYvn27tr2fnx88PDx02iQkJCA8PFzbJiAgAHFxcYiIiNC22blzJzQaDfz9/bVt9u7dqzMVcPv27ShZsiRcXFyyFItS3paoMUEjIiIiIjJgytYtESIkJESo1WqxZMkScfbsWREcHCycnZ1FTEyMEEKITp06iaFDh2rbHzhwQFhYWIgpU6aIc+fOiZEjRwpLS0tx6tQpbZuJEycKZ2dnsX79enHy5EnRqlUr4efnJ549e6Zt07RpU1GpUiURHh4u9u/fL4oXLy6CgoK0j8fFxQl3d3fRqVMncfr0aRESEiJsbW3F/Pnz9YrlTXK6wlJGVR5Z1ZGIiMh0sbojkXFQPEkTQohZs2aJwoULCysrK1G9enVx6NAh7WN169YVnTt31mm/cuVKUaJECWFlZSXKli0rNm7cqPO4RqMRP/74o3B3dxdqtVo0bNhQREVF6bR58OCBCAoKEvb29sLR0VF07dpVJCYm6rQ5ceKEqF27tlCr1aJgwYJi4sSJr8X+tljeJDculOkTNSZoREREpo1JGpFxUAmRxVKAlO0SEhLg5OSE+Pj4HFmflmbRokXo3bs35s6dyymOREREJiy37j2I6P0wSVNQbl4ok5OTWcWRiIjIxDFJIzIOLMFvIpigEREREREZByZpREREREREBoRJGhERERERkQFhkkZERERERGRAmKQREREREREZECZpREREREREBoRJGhERERERkQFhkkZERERERGRAmKQREREREREZECZpREREREREBoRJGhERERERkQFhkkZERERERGRAmKQREREREREZECZpREREREREBoRJGhERERERkQFhkkZERERERGRAmKQREREREREZEAulAzBlQggAQEJCgsKREBERkSlIu+dIuwchIsPEJE1BiYmJAABvb2+FIyEiIiJTkpiYCCcnJ6XDIKJMqATfSlGMRqPB7du34eDgAJVKlWOvk5CQAG9vb9y4cQOOjo459jqGiH1n39l308G+s+/s+9sJIZCYmAgvLy+YmXHVC5Gh4kiagszMzFCoUKFcez1HR0eT+w8sDfvOvpsa9p19NzXse9b7zhE0IsPHt1CIiIiIiIgMCJM0IiIiIiIiA8IkzQSo1WqMHDkSarVa6VByHfvOvpsa9p19NzXsu2n2nSivY+EQIiIiIiIiA8KRNCIiIiIiIgPCJI2IiIiIiMiAMEkjIiIiIiIyIEzSiIiIiIiIDAiTNCMwe/Zs+Pr6wtraGv7+/jh8+PAb269atQqlSpWCtbU1ypUrh02bNuk8LoTAiBEj4OnpCRsbGwQGBuLixYs6bR4+fIiOHTvC0dERzs7O6N69Ox4/fpztfXub3O771atX0b17d/j5+cHGxgZFixbFyJEjkZSUlCP9exMl/t3TvHjxAhUrVoRKpcLx48ezq0tZplTfN27cCH9/f9jY2MDFxQWtW7fOzm5liRJ9v3DhAlq1aoX8+fPD0dERtWvXxq5du7K9b2+T3X1fu3YtGjduDFdX10x/l58/f46+ffvC1dUV9vb2aNu2LWJjY7OzW1mS231/+PAhvvnmG5QsWRI2NjYoXLgw+vXrh/j4+Ozu2lsp8e+eRgiBZs2aQaVSYd26ddnQG/0o1fewsDA0aNAAdnZ2cHR0xIcffohnpFnDugAAJmpJREFUz55lV7eIKDsIMmghISHCyspKLFq0SJw5c0b07NlTODs7i9jY2AzbHzhwQJibm4tJkyaJs2fPih9++EFYWlqKU6dOadtMnDhRODk5iXXr1okTJ06Ijz/+WPj5+Ylnz55p2zRt2lRUqFBBHDp0SOzbt08UK1ZMBAUF5Xh/01Oi75s3bxZdunQRW7duFZcvXxbr168Xbm5uYuDAgbnS5zRK/bun6devn2jWrJkAICIjI3OqmxlSqu+rV68WLi4uYu7cuSIqKkqcOXNGrFixIsf7m55SfS9evLho3ry5OHHihLhw4YLo06ePsLW1FXfu3MnxPqfJib4vXbpU/PTTT+K3337L9Hf5q6++Et7e3iI0NFQcPXpU1KhRQ9SsWTOnupkhJfp+6tQp0aZNG/Hvv/+KS5cuidDQUFG8eHHRtm3bnOzqa5T6d08zbdo07bXun3/+yebevZlSfT948KBwdHQUEyZMEKdPnxbnz58XK1asEM+fP8+prhLRO2CSZuCqV68u+vbtq/06NTVVeHl5iQkTJmTY/vPPPxctWrTQOefv7y969eolhBBCo9EIDw8PMXnyZO3jcXFxQq1Wi+XLlwshhDh79qwAII4cOaJts3nzZqFSqcStW7eyrW9vo0TfMzJp0iTh5+f3Pl3Rm5J937RpkyhVqpQ4c+aMIkmaEn1PTk4WBQsWFAsXLszu7uhFib7fu3dPABB79+7VtklISBAAxPbt27Otb2+T3X1PLzo6OsPf5bi4OGFpaSlWrVqlPXfu3DkBQISFhb1Hb/SjRN8zsnLlSmFlZSWSk5P168B7ULLvkZGRomDBguLOnTuKJGlK9d3f31/88MMP7xc8EeU4Tnc0YElJSYiIiEBgYKD2nJmZGQIDAxEWFpbh94SFhem0B4AmTZpo20dHRyMmJkanjZOTE/z9/bVtwsLC4OzsjKpVq2rbBAYGwszMDOHh4dnWvzdRqu8ZiY+PR758+d6nO3pRsu+xsbHo2bMn/vzzT9ja2mZnt7JEqb4fO3YMt27dgpmZGSpVqgRPT080a9YMp0+fzu4uZkqpvru6uqJkyZJYunQpnjx5gpSUFMyfPx9ubm6oUqVKdnczQznR96yIiIhAcnKyzvOUKlUKhQsX1ut53odSfc9IfHw8HB0dYWFh8V7Pk1VK9v3p06fo0KEDZs+eDQ8PD/2Df09K9f3u3bsIDw+Hm5sbatasCXd3d9StWxf79+9/t44QUY5hkmbA7t+/j9TUVLi7u+ucd3d3R0xMTIbfExMT88b2aR/f1sbNzU3ncQsLC+TLly/T181uSvX9VZcuXcKsWbPQq1evd+rHu1Cq70IIdOnSBV999ZVOgp6blOr7lStXAACjRo3CDz/8gA0bNsDFxQX16tXDw4cP379jWaBU31UqFXbs2IHIyEg4ODjA2toa06ZNw5YtW+Di4pItfXubnOh7VsTExMDKygrOzs7v9TzvQ6m+ZxTHmDFjEBwc/M7P8S6vqVTfv/vuO9SsWROtWrXSL+hsolTf01/revbsiS1btqBy5cpo2LBhpmuUiUgZTNKIMnHr1i00bdoUn332GXr27Kl0ODlu1qxZSExMxLBhw5QOJddpNBoAwPDhw9G2bVtUqVIFixcvhkqlwqpVqxSOLmcJIdC3b1+4ublh3759OHz4MFq3bo2PPvoId+7cUTo8ygUJCQlo0aIFypQpg1GjRikdTo77999/sXPnTsyYMUPpUHJd2rWuV69e6Nq1KypVqoTp06ejZMmSWLRokcLREVF6TNIMWP78+WFubv5apbHY2NhMp2d4eHi8sX3ax7e1uXv3rs7jKSkpePjwYa5NC1Gq72lu376N+vXro2bNmliwYMF79UVfSvV9586dCAsLg1qthoWFBYoVKwYAqFq1Kjp37vz+HcsCpfru6ekJAChTpoz2cbVajSJFiuD69evv0aOsU/LffcOGDQgJCUGtWrVQuXJlzJkzBzY2Nvjjjz+ypW9vkxN9zwoPDw8kJSUhLi7uvZ7nfSjV9zSJiYlo2rQpHBwc8M8//8DS0lLv53hXSvV9586duHz5MpydnWFhYaGd3tm2bVvUq1dPv068I6X6ntG1DgBKly6da9c6IsoaJmkGzMrKClWqVEFoaKj2nEajQWhoKAICAjL8noCAAJ32ALB9+3Ztez8/P3h4eOi0SUhIQHh4uLZNQEAA4uLiEBERoW2zc+dOaDQa+Pv7Z1v/3kSpvgNyBK1evXra0RQzs9z9M1Gq7zNnzsSJEydw/PhxHD9+XFvaecWKFRg3bly29jEzSvW9SpUqUKvViIqK0rZJTk7G1atX4ePjk239exOl+v706VMAeO333MzMTPuue07Lib5nRZUqVWBpaanzPFFRUbh+/bpez/M+lOo7IH8XGjduDCsrK/z777+wtrbWvwPvQam+Dx06FCdPntRe69LK1E+fPh2LFy/WvyPvQKm++/r6wsvLS+daB8htOHLrWkdEWaR05RJ6s5CQEKFWq8WSJUvE2bNnRXBwsHB2dhYxMTFCCCE6deokhg4dqm1/4MABYWFhIaZMmSLOnTsnRo4cmWFJbmdnZ7F+/Xpx8uRJ0apVqwxL8FeqVEmEh4eL/fv3i+LFiytSgj+3+37z5k1RrFgx0bBhQ3Hz5k1x584d7ZHX+/4qfarCZSel+v7tt9+KggULiq1bt4rz58+L7t27Czc3N/Hw4cM83fd79+4JV1dX0aZNG3H8+HERFRUlBg0aJCwtLcXx48eNuu8PHjwQkZGRYuPGjQKACAkJEZGRkTp/z1999ZUoXLiw2Llzpzh69KgICAgQAQEBudZvIZTpe3x8vPD39xflypUTly5d0rnWpaSk5Om+ZwQKleBXou/Tp08Xjo6OYtWqVeLixYvihx9+ENbW1uLSpUu513kieismaUZg1qxZonDhwsLKykpUr15dHDp0SPtY3bp1RefOnXXar1y5UpQoUUJYWVmJsmXLio0bN+o8rtFoxI8//ijc3d2FWq0WDRs2FFFRUTptHjx4IIKCgoS9vb1wdHQUXbt2FYmJiTnWx8zkdt8XL14sAGR45DYl/t3TUypJE0KZviclJYmBAwcKNzc34eDgIAIDA8Xp06dzrI+ZUaLvR44cEY0bNxb58uUTDg4OokaNGmLTpk051sfMZHffM/t7HjlypLbNs2fPRJ8+fYSLi4uwtbUVn3zySa6/KSNE7vd9165dmV7roqOjc7i3upT4d3+VEkmaEMr1fcKECaJQoULC1tZWBAQEiH379uVUF4noHamEECLnxumIiIiIiIhIH1yTRkREREREZECYpBERERERERkQJmlEREREREQGhEkaERERERGRAWGSRkREREREZECYpBERERERERkQJmlEREREREQGhEkaERERERGRAWGSRti9ezdUKhXi4uKUDgUHDhxAuXLlYGlpidatW2f5+5YsWQJnZ+cciyu7Xb16FSqVCsePH1c6lDcypJ+rr68vZsyYkSPPff78edSoUQPW1taoWLFijrxGVo0aNUrxGLJLly5d9Po7zs2/C5VKhXXr1mX6eL169dC/f/8cjyMnGNI1PTsYSn8M6TphLP+HENG7Y5KmoC5dukClUr12XLp0KcdeM6Mbj5o1a+LOnTtwcnLKsdfNqgEDBqBixYqIjo7GkiVLMmyTkzfrucXb2xt37tzBBx98oHQob9SuXTtcuHDhvZ4j7QYr7ShQoACaN2+OU6dO6fU8R44cQXBw8HvFkpmRI0fCzs4OUVFRCA0NzZHXyEhGicKgQYNyNQZDYix/F2SalLpOEJFpYpKmsKZNm+LOnTs6h5+f32vtkpKSciwGKysreHh4QKVS5dhrZNXly5fRoEEDFCpUyGBGcLJbUlISzM3N4eHhAQsLC6XDeSMbGxu4ublly3NFRUXhzp072Lp1K168eIEWLVro9XtdoEAB2NraZkssr7p8+TJq164NHx8fuLq65shrZJW9vb3iMSjFWP4u6HU5+X+UoTCk60R2MIV/MyJjxiRNYWq1Gh4eHjqHubk56tWrh6+//hr9+/dH/vz50aRJEwDAtGnTUK5cOdjZ2cHb2xt9+vTB48ePdZ7zwIEDqFevHmxtbeHi4oImTZrg0aNH6NKlC/bs2YNffvlFO6px9erVDKeSrFmzBmXLloVarYavry+mTp2q8xq+vr4YP348unXrBgcHBxQuXBgLFix4Y19fvHiBfv36wc3NDdbW1qhduzaOHDkC4OXUjQcPHqBbt25QqVQZjqTVq1cP165dw3fffaftQ3pbt25F6dKlYW9vr02A01u4cCFKly4Na2trlCpVCnPmzMk03gULFsDLywsajUbnfKtWrdCtWzcA8j/tVq1awd3dHfb29qhWrRp27Njx2s9qzJgx+PLLL+Ho6Ijg4ODXpqqkpqaie/fu8PPzg42NDUqWLIlffvlF53nSpo5NmTIFnp6ecHV1Rd++fZGcnKzzMx4yZAi8vb2hVqtRrFgx/P7779rHT58+jWbNmsHe3h7u7u7o1KkT7t+/n+nP4NXpjmlT8f7880/4+vrCyckJ7du3R2JiYqbPkcbNzQ0eHh6oXLky+vfvjxs3buD8+fPax/fv3486derAxsYG3t7e6NevH548eaLzc0wbQRVCYNSoUShcuDDUajW8vLzQr18/AK+P3KUdXbp0yTAulUqFiIgIjB49GiqVCqNGjcrwb+L48ePav5n0P5u3/c4tWrRI+7fk6emJr7/+WtsfAPjkk0+gUqm0X7863VGj0WD06NEoVKgQ1Go1KlasiC1btmgfT/tdWrt2LerXrw9bW1tUqFABYWFhb/z3iIuLQ48ePVCgQAE4OjqiQYMGOHHiBADg3r178PDwwPjx47XtDx48CCsrK+0IQlqc8+fPh7e3N2xtbfH5558jPj4+09fcsmULateuDWdnZ7i6uqJly5a4fPnya31J+7tI+3cIDQ1F1apVYWtri5o1ayIqKkrnedevX4/KlSvD2toaRYoUwU8//YSUlBTt4xcvXsSHH34Ia2trlClTBtu3b3/jzyZNSkoKvv76azg5OSF//vz48ccfIYQAAIwePTrDEb+KFSvixx9/zPD5Mpo+vG7dOp3rWFb+xt50LU3vwIEDKF++PKytrVGjRg2cPn1a+9i1a9fw0UcfwcXFBXZ2dihbtiw2bdqkffxt14qM/o/q0KED2rVrpxNDcnIy8ufPj6VLlwKQv88TJkzQXusqVKiA1atX63zPpk2bUKJECdjY2KB+/frav7k3iYuLQ69eveDu7g5ra2t88MEH2LBhg/bx9/0/LaPrBACcOnUKDRo0gI2NDVxdXREcHKzzf3JGs1dat26tcz3Kyv+nhw8fRqVKlWBtbY2qVasiMjLytZ/Bu/ybEZEBE6SYzp07i1atWmX4WN26dYW9vb34/vvvxfnz58X58+eFEEJMnz5d7Ny5U0RHR4vQ0FBRsmRJ0bt3b+33RUZGCrVaLXr37i2OHz8uTp8+LWbNmiXu3bsn4uLiREBAgOjZs6e4c+eOuHPnjkhJSRG7du0SAMSjR4+EEEIcPXpUmJmZidGjR4uoqCixePFiYWNjIxYvXqx9HR8fH5EvXz4xe/ZscfHiRTFhwgRhZmamjTMj/fr1E15eXmLTpk3izJkzonPnzsLFxUU8ePBApKSkiDt37ghHR0cxY8YMcefOHfH06dPXnuPBgweiUKFCYvTo0do+CCHE4sWLhaWlpQgMDBRHjhwRERERonTp0qJDhw7a7122bJnw9PQUa9asEVeuXBFr1qwR+fLlE0uWLMkw3ocPHworKyuxY8cOnddPf+748eNi3rx54tSpU+LChQvihx9+ENbW1uLatWs6PytHR0cxZcoUcenSJXHp0iURHR0tAIjIyEghhBBJSUlixIgR4siRI+LKlSti2bJlwtbWVqxYsUL7PJ07dxaOjo7iq6++EufOnRP//fefsLW1FQsWLNC2+fzzz4W3t7dYu3atuHz5stixY4cICQkRQgjx6NEjUaBAATFs2DBx7tw5cezYMdGoUSNRv379TP/NFi9eLJycnLRfjxw5Utjb24s2bdqIU6dOib179woPDw/xf//3f5k+x6u/X3FxcaJDhw4CgDh37pwQQohLly4JOzs7MX36dHHhwgVx4MABUalSJdGlSxedn+P06dOFEEKsWrVKODo6ik2bNolr166J8PBw7c/hxYsX2t+NO3fuiJ07dwpra2vx+++/ZxjfnTt3RNmyZcXAgQPFnTt3RGJi4msxCyH/tgCI6Oho7c/mbb9zc+bMEdbW1mLGjBkiKipKHD58WNuHu3fvCgBi8eLF4s6dO+Lu3bvan3GFChW0zzFt2jTh6Ogoli9fLs6fPy8GDx4sLC0txYULF4QQQvu7VKpUKbFhwwYRFRUlPv30U+Hj4yOSk5Mz/XcJDAwUH330kThy5Ii4cOGCGDhwoHB1dRUPHjwQQgixceNGYWlpKY4cOSISEhJEkSJFxHfffaf9/pEjRwo7OzvRoEEDERkZKfbs2SOKFSum0/9Xr3GrV68Wa9asERcvXhSRkZHio48+EuXKlROpqak6fUn7u0j7d/D39xe7d+8WZ86cEXXq1BE1a9bUPufevXuFo6OjWLJkibh8+bLYtm2b8PX1FaNGjRJCCJGamio++OAD0bBhQ3H8+HGxZ88eUalSJQFA/PPPP5n+fNKuwd9++604f/689m8y7ffsxo0bwszMTBw+fFj7PceOHRMqlUpcvnw5w+d89e9JCCH++ecfkf6/4qz8jb3pWpr+51a6dGmxbds2cfLkSdGyZUvh6+srkpKShBBCtGjRQjRq1EicPHlSXL58Wfz3339iz549QoisXSsy+j9qw4YNwsbGRiQmJmrb/ffff8LGxkYkJCQIIYQYO3asKFWqlNiyZYu4fPmyWLx4sVCr1WL37t1CCCGuX78u1Gq1GDBggPbn7u7u/trfY3qpqamiRo0aomzZsmLbtm3a/mzatEkIkT3/p2V0nXj8+LHw9PTU/luFhoYKPz8/0blzZ52f07fffqsTb6tWrXTavO21ExMTRYECBUSHDh3E6dOnxX///SeKFCmi87fyrv9mRGS4mKQpqHPnzsLc3FzY2dlpj08//VQIIS+mlSpVeutzrFq1Sri6umq/DgoKErVq1cq0fUb/Ybx6Q9qhQwfRqFEjnTbff/+9KFOmjPZrHx8f8cUXX2i/1mg0ws3NTcydOzfD1338+LGwtLQUf/31l/ZcUlKS8PLyEpMmTdKec3Jy0vmPMyPpb9bTLF68WAAQly5d0p6bPXu2cHd3135dtGhR8ffff+t835gxY0RAQECmr9WqVSvRrVs37dfz588XXl5e2pvKjJQtW1bMmjVLJ97WrVvrtHn1ZjQjffv2FW3bttV+3blzZ+Hj4yNSUlK05z777DPRrl07IYQQUVFRAoDYvn17hs83ZswY0bhxY51zN27cEABEVFRUht+TUZJma2urveESQv5u+Pv7Z9qPtN+vtN9xAAKA+Pjjj7VtunfvLoKDg3W+b9++fcLMzEw8e/ZMCKH77z516lRRokQJ7Q1nZu7fvy+KFCki+vTp88Z2FSpUECNHjnwt5rclaW/7nfPy8hLDhw/P9HUzShReTdK8vLzEuHHjdNpUq1ZN26e036WFCxdqHz9z5oxOEvyqffv2CUdHR/H8+XOd80WLFhXz58/Xft2nTx9RokQJ0aFDB1GuXDmd9iNHjhTm5ubi5s2b2nObN28WZmZm2jdP3vRGlBBC3Lt3TwAQp06d0unLq0la+jdKNm7cKABofy8aNmwoxo8fr/O8f/75p/D09BRCCLF161ZhYWEhbt26pRNnVpK00qVLC41Goz03ZMgQUbp0ae3XzZo103mT7JtvvhH16tXL9DmzmqS96W8sK9fStJ9b2hs0Qsg3mGxsbLRv/JQrV06byL4qK9eKjP6PSk5OFvnz5xdLly7VngsKCtJeo54/fy5sbW3FwYMHdb6ve/fuIigoSAghxLBhw3T+rxFC/tzflKRt3bpVmJmZZXody67/0169TixYsEC4uLiIx48fa89t3LhRmJmZiZiYGCFE1pO0N732/Pnzhaurq/Z3Xggh5s6dq/O38q7/ZkRkuDjdUWH169fH8ePHtcfMmTO1j1WpUuW19jt27EDDhg1RsGBBODg4oFOnTnjw4AGePn0KQE7Jatiw4XvFdO7cOdSqVUvnXK1atXDx4kWkpqZqz5UvX177uUqlgoeHB+7evZvhc16+fBnJyck6z2tpaYnq1avj3Llz7xVvGltbWxQtWlT7taenpzaeJ0+e4PLly+jevTvs7e21x9ixY3WmW72qY8eOWLNmDV68eAEA+Ouvv9C+fXuYmck/ncePH2PQoEEoXbo0nJ2dYW9vj3PnzuH69es6z1O1atW3xj979mxUqVIFBQoUgL29PRYsWPDa85QtWxbm5uYZ9vH48eMwNzdH3bp1M3z+EydOYNeuXTr9L1WqFAC88WfwKl9fXzg4OGQYw5vs27cPERERWLJkCUqUKIF58+bpxLZkyRKd2Jo0aQKNRoPo6OjXnuuzzz7Ds2fPUKRIEfTs2RP//POPzvQ2QE6zatu2LXx8fF6bOppd3vQ7d/fuXdy+ffu9/h4TEhJw+/btDP8eX/27Sf/36OnpqY0hIydOnMDjx4/h6uqq8zOPjo7W+V2YMmUKUlJSsGrVKvz1119Qq9U6z1O4cGEULFhQ+3VAQAA0Gs1r0xHTXLx4EUFBQShSpAgc/7+9O42J6moDOP6HKgoGoyil2qK0wiAWME6DCkZJrRXTOElF6gJVXCCyKLig1lIVUIMEQaOmGGnrggraWLeOCojiMgTrEhAKDItQbZXYqmlqrdEg7wdf7sswgDOiLX3z/BK+cO6cOWfuvefe557l9uypDPFseZy31F7diouLSUhIMKhHaGgot2/f5uHDh5SXl+Po6Ej//v0NymmKkSNHGgxF9Pb2NmgHQ0NDyczM5NGjRzx+/Jh9+/YpQ6E7or1zzJy2tHk97ezscHV1VbaJiopi7dq1jBo1itWrV3Pt2jVlW1PbipbXqC5dujBlyhT27t0LPGt3jxw5QlBQEADV1dU8fPiQDz/80CDv3bt3K/mWl5czYsSINuvRmqKiIt566y1UKlWr6a/imtaU79ChQ+nRo4dBvu2dA21p77vLy8uVYatNWv4mL7rPhBCdl8zO/of16NEDZ2fnNtOaq6urY+LEiYSHh7Nu3Trs7Oy4cOECc+fO5fHjx9jY2GBtbf13FBt4dmPQnIWFhdH8rb9Ta+Vp/O/8kaY5Aunp6UY3AM2DnpY0Gg2NjY1otVq8vLw4f/48GzduVNJjYmLIzc1lw4YNODs7Y21tTUBAgNGE7Jb7sqWsrCxiYmJISUnB29sbW1tbkpOTuXjx4nPr2PSbP2/fP3jwAI1GQ1JSklFa042vKV50v7/99tv06tULV1dX7ty5w9SpUzl37pxStnnz5inzypobMGCA0f8cHR3R6/WcOnWK3NxcIiIiSE5O5uzZs0r5wsPDuXnzJj/88IPZC1E0BeFNxw9gMPevSXvH3N95LrYsS1Ng0dZ+efDgAf369SM/P98orfmcqZqaGm7dusXTp0+pq6vDw8OjQ2XUaDQMHDiQ9PR0Zb6nu7v7cxcwaK9uDx48ID4+Hn9/f6PPNb+pfRU0Gg3dunXj0KFDWFlZ8eTJEwICAtrc3tLS0uCYAtOPq5fdtoaEhODn54dWqyUnJ4fExERSUlJYsGCByW1Fa+1aUFAQvr6+3Llzh9zcXKytrZkwYQLwv3ZYq9UaBPeA0QMAc7ysc+1V/O5/1z7vyD4TQnROEqT9i1y5coWnT5+SkpKi3EQeOHDAYBtPT0/y8vKIj49vNQ8rKyuDJ4etcXNzQ6fTGfxPp9OhUqnaDWjaM2jQIKysrNDpdAwcOBB4dqG6dOmS2e8iMqUOLTk4ONC/f3+uX7+uPNU1Rffu3fH392fv3r1UV1fj6uqKWq1W0nU6HbNmzWLSpEnAswulKZPcW9LpdPj4+BAREaH8z5zeLQAPDw+ePn3K2bNnGTdunFG6Wq3m4MGDODk5/eOr50VGRpKYmMihQ4eYNGkSarWasrKyNh9YtMba2hqNRoNGoyEyMpLBgwdTUlKCWq0mNTWVAwcOUFBQ8EKrsNnb2wNw+/ZtevfuDWD2+4hsbW1xcnIiLy+P999/v9Vtunbt2u6x3LNnT/r3749OpzPoIdXpdAwfPtys8jSnVqupr6+nS5cuSm9WS48fP+bTTz9l6tSpuLq6EhISQklJicFqnzdu3ODWrVtKL1VhYSGWlpa4uroa5Xf37l30ej3p6emMHj0aeLZYTEep1Wr0en2bx46bmxs3b97k9u3bys1qYWGhSXm3fEhSWFiIi4uL0g526dKF4OBgduzYgZWVFdOmTWs3YLC3t+ePP/7gzz//VG6WzT2uzGlLCwsLlYcc9+/fp7KyEjc3NyXd0dGRsLAwwsLCWLFiBenp6SxYsKBDbYWPjw+Ojo7s37+fEydO8MknnygByJAhQ+jWrRs3btxos8ffzc2No0ePGtWjPZ6envz8889UVla22pv2Kq5pTfnu3LnTYH/qdDqDc8De3t5gMaGGhgZKS0vbbBPa+p6MjAwePXqkPHho+Zt0pvZdCPFyyHDHfxFnZ2eePHnCli1buH79OhkZGQZDxgBWrFjBpUuXiIiI4Nq1a1RUVJCWlqas8OTk5MTFixepq6vjt99+a/VJ3ZIlS8jLy2PNmjVUVlaya9cutm7dSkxMzAuXvUePHoSHh7N06VJOnjxJWVkZoaGhPHz4kLlz55qVl5OTE+fOneOXX35pd2XCluLj40lMTGTz5s1UVlZSUlLCjh07SE1NbfdzQUFBaLVavvnmG6MAz8XFhe+++46ioiKKi4sJDAx8oSevLi4uXL58mezsbCorK1m5cmWrq7W1x8nJieDgYObMmcPhw4epra0lPz9fCeQjIyO5d+8e06dP59KlS9TU1JCdnc3s2bPNDno7ysbGhtDQUFavXk1jYyPLly+noKCA+fPnU1RURFVVFUeOHFFWQmxp586dfP3115SWlnL9+nX27NmDtbU1AwcO5NSpUyxbtozk5GT69u1LfX099fX17a462JKzszOOjo7ExcVRVVWFVqs1Wg3OFHFxcaSkpLB582aqqqq4evUqW7ZsUdKbgrj6+nru37/fah5Lly4lKSmJ/fv3o9fr+eyzzygqKiI6Otrs8jQZN24c3t7efPzxx+Tk5FBXV0dBQQGxsbFcvnwZgNjYWH7//Xc2b97M8uXLUalURkP5unfvTnBwMMXFxZw/f56oqCimTJnCG2+8YfSdvXv3pk+fPmzfvp3q6mpOnz7N4sWLX7gOTVatWsXu3buJj4/nxx9/pLy8nKysLL744gulriqVyqCcsbGxJuV948YNFi9ejF6vJzMzky1bthj97iEhIZw+fZqTJ08+d6jjiBEjsLGx4fPPP6empoZ9+/a1+T7ItpjTliYkJJCXl0dpaSmzZs2ib9++ysvFFy5cSHZ2NrW1tVy9epUzZ84oAVxH24rAwEC2bdtGbm6uQZtpa2tLTEwMixYtYteuXdTU1CjnxK5duwAICwujqqqKpUuXotfrTfqNfH19GTNmDJMnTyY3N5fa2lpOnDihrIL6Kq5p8Oza0HQOlJaWcubMGRYsWMCMGTNwcHAAYOzYsWi1WrRaLRUVFYSHh5v9Uu7AwEAsLCwIDQ2lrKyM48ePs2HDBoNtOlP7LoR4OSRI+xcZOnQoqampJCUl4e7uzt69e0lMTDTYRqVSkZOTQ3FxMcOHD8fb25sjR44oT9ZiYmJ47bXXGDJkCPb29q3OBVGr1Rw4cICsrCzc3d1ZtWoVCQkJbS5hbqr169czefJkZsyYgVqtprq6muzsbKWnwlQJCQnU1dUxaNAgpcfDFCEhIXz11Vfs2LEDDw8PfH192blzZ6vvpWtu7Nix2NnZodfrCQwMNEhLTU2ld+/e+Pj4oNFo8PPzM+hpM9W8efPw9/dn6tSpjBgxgrt37xr0qpkqLS2NgIAAIiIiGDx4MKGhocoy9k09Mg0NDYwfPx4PDw8WLlxIr169lJ7Zv9P8+fMpLy/n22+/xdPTk7Nnz1JZWcno0aMZNmwYq1atMphH1FyvXr1IT09n1KhReHp6curUKY4dO0afPn24cOECDQ0NhIWF0a9fP+XPnKCma9euZGZmUlFRgaenJ0lJSaxdu9bsOgYHB7Np0ya+/PJL3n33XSZOnEhVVZWSnpKSQm5uLo6OjgwbNqzVPKKioli8eDFLlizBw8ODkydPcvToUVxcXMwuTxMLCwuOHz/OmDFjmD17NiqVimnTpvHTTz/h4OBAfn4+mzZtIiMjg549e2JpaUlGRgbnz58nLS1NycfZ2Rl/f38++ugjxo8fj6enZ5uvtbC0tCQrK4srV67g7u7OokWLSE5OfuE6NPHz8+P7778nJycHLy8vRo4cycaNG5VeJktLSw4dOsRff/3F8OHDCQkJYd26dSblPXPmTOVzkZGRREdHG71Q3cXFBR8fHwYPHmw0lLolOzs79uzZw/Hjx/Hw8CAzM1NZyt0cpral69evJzo6mvfee4/6+nqOHTuGlZUV8KxHJzIyEjc3NyZMmIBKpVL2XUfbiqCgIMrKynjzzTeN5oKtWbOGlStXkpiYqHy3VqtV2uEBAwZw8OBBDh8+zNChQ9m2bZvBqyDacvDgQby8vJg+fTpDhgxh2bJlSnDyqq5pNjY2ZGdnc+/ePby8vAgICOCDDz5g69atyjZz5swhODiYmTNn4uvryzvvvGNWLxo8e3fisWPHKCkpYdiwYcTGxhoNa+xs7bsQouMsGlsOlhZCCCGeIy4ujsOHD5s9XO//TWNjIy4uLkRERLyUnkEhhBACZE6aEEII8UJ+/fVXsrKyqK+vZ/bs2f90cYQQQvwfkSBNCCGEeAGvv/46ffv2Zfv27WYP2xZCCCHaI8MdhRBCCCGEEKITkdmkQgghhBBCCNGJSJAmhBBCCCGEEJ2IBGlCCCGEEEII0YlIkCaEEEIIIYQQnYgEaUIIIYQQQgjRiUiQJoQQQgghhBCdiARpQgghhBBCCNGJSJAmhBBCCCGEEJ3IfwC4BiXvP8z18gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -1041,7 +1041,7 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.831637730148332\n",
+      "Original Effect Estimate : 11.816420420308324\n",
       "Robustness Value : 0.66\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.64\n",
diff --git a/main/example_notebooks/sensitivity_analysis_testing.html b/main/example_notebooks/sensitivity_analysis_testing.html
index 0e38881c4..16e0508d6 100644
--- a/main/example_notebooks/sensitivity_analysis_testing.html
+++ b/main/example_notebooks/sensitivity_analysis_testing.html
@@ -908,9 +908,9 @@ <h2>Step 4: Identification<a class="headerlink" href="#Step-4:-Identification" t
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W3,W5,W6,W2,W0,W1])
+─────(E[y|W1,W0,W2,W3,W5,W6])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)
 
 ### Estimand : 2
 Estimand name: iv
@@ -949,16 +949,16 @@ <h2>Step 5: Estimation<a class="headerlink" href="#Step-5:-Estimation" title="Pe
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W3,W5,W6,W2,W0,W1])
+─────(E[y|W1,W0,W2,W3,W5,W6])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)
 
 ## Realized estimand
-b: y~v0+W3+W5+W6+W2+W0+W1
+b: y~v0+W1+W0+W2+W3+W5+W6
 Target units: ate
 
 ## Estimate
-Mean value: 10.697677486880934
+Mean value: 10.69767748688092
 
 </pre></div></div>
 </div>
@@ -1004,14 +1004,14 @@ <h2>Step 6a: Refutation and Sensitivity Analysis - Method 1<a class="headerlink"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;estimate&#39;: 10.697677486880934,
- &#39;standard_error&#39;: 0.5938735661282953,
+{&#39;estimate&#39;: 10.69767748688092,
+ &#39;standard_error&#39;: 0.5938735661282948,
  &#39;degree of freedom&#39;: 492,
- &#39;t_statistic&#39;: 18.01339223872763,
- &#39;r2yt_w&#39;: 0.39741498372666834,
- &#39;partial_f2&#39;: 0.6595168698094569,
- &#39;robustness_value&#39;: 0.546744557218101,
- &#39;robustness_value_alpha&#39;: 0.5076289101030929}
+ &#39;t_statistic&#39;: 18.013392238727622,
+ &#39;r2yt_w&#39;: 0.39741498372666817,
+ &#39;partial_f2&#39;: 0.6595168698094563,
+ &#39;robustness_value&#39;: 0.5467445572181009,
+ &#39;robustness_value_alpha&#39;: 0.5076289101030926}
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1127,15 +1127,15 @@ <h3>Parameter List for plot function<a class="headerlink" href="#Parameter-List-
 Sensitivity Analysis to Unobserved Confounding using R^2 paramterization
 
 Unadjusted Estimates of Treatment [&#39;v0&#39;] :
-Coefficient Estimate : 10.697677486880934
+Coefficient Estimate : 10.69767748688092
 Degree of Freedom : 492
-Standard Error : 0.5938735661282953
-t-value : 18.01339223872763
-F^2 value : 0.6595168698094569
+Standard Error : 0.5938735661282948
+t-value : 18.013392238727622
+F^2 value : 0.6595168698094563
 
 Sensitivity Statistics :
-Partial R2 of treatment with outcome : 0.39741498372666834
-Robustness Value : 0.546744557218101
+Partial R2 of treatment with outcome : 0.39741498372666817
+Robustness Value : 0.5467445572181009
 
 Interpretation of results :
 Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0
@@ -1195,11 +1195,11 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;converted_estimate&#39;: 2.457447065735426,
- &#39;converted_lower_ci&#39;: 2.2288567270043167,
- &#39;converted_upper_ci&#39;: 2.709481505797999,
- &#39;evalue_estimate&#39;: 4.34995836428987,
- &#39;evalue_lower_ci&#39;: 3.8838327336304186,
+{&#39;converted_estimate&#39;: 2.457447065735423,
+ &#39;converted_lower_ci&#39;: 2.2288567270043145,
+ &#39;converted_upper_ci&#39;: 2.7094815057979953,
+ &#39;evalue_estimate&#39;: 4.349958364289864,
+ &#39;evalue_lower_ci&#39;: 3.883832733630414,
  &#39;evalue_upper_ci&#39;: None}
 </pre></div></div>
 </div>
@@ -1310,15 +1310,15 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 Sensitivity Analysis to Unobserved Confounding using the E-value
 
 Unadjusted Estimates of Treatment: v0
-Estimate (converted to risk ratio scale): 2.457447065735426
-Lower 95% CI (converted to risk ratio scale): 2.2288567270043167
-Upper 95% CI (converted to risk ratio scale): 2.709481505797999
+Estimate (converted to risk ratio scale): 2.457447065735423
+Lower 95% CI (converted to risk ratio scale): 2.2288567270043145
+Upper 95% CI (converted to risk ratio scale): 2.7094815057979953
 
 Sensitivity Statistics:
-E-value for point estimate: 4.34995836428987
-E-value for lower 95% CI: 3.8838327336304186
+E-value for point estimate: 4.349958364289864
+E-value for lower 95% CI: 3.883832733630414
 E-value for upper 95% CI: None
-Largest Observed Covariate E-value: 1.6811401167656743 (W1)
+Largest Observed Covariate E-value: 1.6811401167656792 (W1)
 
 Interpretation of results:
 Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.
@@ -1492,9 +1492,9 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W3,W5,W6,W2,W0,W1])
+─────(E[y|W1,W0,W2,W3,W5,W6,W4])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1529,16 +1529,16 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W4,W3,W5,W6,W2,W0,W1])
+─────(E[y|W1,W0,W2,W3,W5,W6,W4])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)
 
 ## Realized estimand
-b: y~v0+W4+W3+W5+W6+W2+W0+W1
+b: y~v0+W1+W0+W2+W3+W5+W6+W4
 Target units: ate
 
 ## Estimate
-Mean value: 10.08192437558829
+Mean value: 10.081924375588311
 
 </pre></div></div>
 </div>
@@ -1603,11 +1603,11 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 Sensitivity Analysis to Unobserved Confounding using R^2 paramterization
 
 Unadjusted Estimates of Treatment [&#39;v0&#39;] :
-Coefficient Estimate : 10.08192437558829
+Coefficient Estimate : 10.081924375588311
 Degree of Freedom : 491
-Standard Error : 0.10446229543424747
-t-value : 96.51256784735537
-F^2 value : 18.97082637981746
+Standard Error : 0.10446229543424763
+t-value : 96.51256784735543
+F^2 value : 18.970826379817485
 
 Sensitivity Statistics :
 Partial R2 of treatment with outcome : 0.9499269594066172
@@ -1636,12 +1636,12 @@ <h2>Step 6b: Refutation and Sensitivity Analysis - Method 2<a class="headerlink"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;estimate&#39;: 10.08192437558829,
- &#39;standard_error&#39;: 0.10446229543424747,
+{&#39;estimate&#39;: 10.081924375588311,
+ &#39;standard_error&#39;: 0.10446229543424763,
  &#39;degree of freedom&#39;: 491,
- &#39;t_statistic&#39;: 96.51256784735537,
+ &#39;t_statistic&#39;: 96.51256784735543,
  &#39;r2yt_w&#39;: 0.9499269594066172,
- &#39;partial_f2&#39;: 18.97082637981746,
+ &#39;partial_f2&#39;: 18.970826379817485,
  &#39;robustness_value&#39;: 0.9522057801012398,
  &#39;robustness_value_alpha&#39;: 0.950386691319526}
 </pre></div></div>
diff --git a/main/example_notebooks/sensitivity_analysis_testing.ipynb b/main/example_notebooks/sensitivity_analysis_testing.ipynb
index eb9eef341..0b4635837 100644
--- a/main/example_notebooks/sensitivity_analysis_testing.ipynb
+++ b/main/example_notebooks/sensitivity_analysis_testing.ipynb
@@ -34,10 +34,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:44.304292Z",
-     "iopub.status.busy": "2024-10-25T15:22:44.304112Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.714260Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.713585Z"
+     "iopub.execute_input": "2024-10-25T15:24:56.020002Z",
+     "iopub.status.busy": "2024-10-25T15:24:56.019819Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.581981Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.581342Z"
     }
    },
    "outputs": [],
@@ -82,10 +82,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.716665Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.716348Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.732777Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.732175Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.584834Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.584117Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.601515Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.600944Z"
     }
    },
    "outputs": [],
@@ -105,10 +105,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.734790Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.734383Z",
-     "iopub.status.idle": "2024-10-25T15:22:45.889457Z",
-     "shell.execute_reply": "2024-10-25T15:22:45.888848Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.603726Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.603335Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.766716Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.766064Z"
     }
    },
    "outputs": [
@@ -277,10 +277,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:45.891860Z",
-     "iopub.status.busy": "2024-10-25T15:22:45.891450Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.035468Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.034805Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.769145Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.768750Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.934497Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.933837Z"
     }
    },
    "outputs": [
@@ -444,10 +444,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.037549Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.037356Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.052458Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.051872Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.936753Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.936529Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.957833Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.957198Z"
     }
    },
    "outputs": [
@@ -461,9 +461,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -494,10 +494,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.054282Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.054097Z",
-     "iopub.status.idle": "2024-10-25T15:22:46.070377Z",
-     "shell.execute_reply": "2024-10-25T15:22:46.069781Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.960243Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.960027Z",
+     "iopub.status.idle": "2024-10-25T15:24:57.980802Z",
+     "shell.execute_reply": "2024-10-25T15:24:57.980155Z"
     }
    },
    "outputs": [
@@ -514,16 +514,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,U) = P(y|v0,W1,W0,W2,W3,W5,W6)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W3+W5+W6+W2+W0+W1\n",
+      "b: y~v0+W1+W0+W2+W3+W5+W6\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.697677486880934\n",
+      "Mean value: 10.69767748688092\n",
       "\n"
      ]
     }
@@ -557,10 +557,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:46.072158Z",
-     "iopub.status.busy": "2024-10-25T15:22:46.071973Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.365118Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.364439Z"
+     "iopub.execute_input": "2024-10-25T15:24:57.983260Z",
+     "iopub.status.busy": "2024-10-25T15:24:57.983019Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.320714Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.319956Z"
     },
     "scrolled": false
    },
@@ -599,24 +599,24 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.367164Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.366853Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.371057Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.370447Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.323070Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.322657Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.326633Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.326132Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'estimate': 10.697677486880934,\n",
-       " 'standard_error': 0.5938735661282953,\n",
+       "{'estimate': 10.69767748688092,\n",
+       " 'standard_error': 0.5938735661282948,\n",
        " 'degree of freedom': 492,\n",
-       " 't_statistic': 18.01339223872763,\n",
-       " 'r2yt_w': 0.39741498372666834,\n",
-       " 'partial_f2': 0.6595168698094569,\n",
-       " 'robustness_value': 0.546744557218101,\n",
-       " 'robustness_value_alpha': 0.5076289101030929}"
+       " 't_statistic': 18.013392238727622,\n",
+       " 'r2yt_w': 0.39741498372666817,\n",
+       " 'partial_f2': 0.6595168698094563,\n",
+       " 'robustness_value': 0.5467445572181009,\n",
+       " 'robustness_value_alpha': 0.5076289101030926}"
       ]
      },
      "execution_count": 8,
@@ -633,10 +633,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.372919Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.372544Z",
-     "iopub.status.idle": "2024-10-25T15:22:50.380119Z",
-     "shell.execute_reply": "2024-10-25T15:22:50.379496Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.328455Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.328086Z",
+     "iopub.status.idle": "2024-10-25T15:25:02.335819Z",
+     "shell.execute_reply": "2024-10-25T15:25:02.335341Z"
     }
    },
    "outputs": [
@@ -757,10 +757,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:50.381929Z",
-     "iopub.status.busy": "2024-10-25T15:22:50.381601Z",
-     "iopub.status.idle": "2024-10-25T15:22:54.704772Z",
-     "shell.execute_reply": "2024-10-25T15:22:54.704198Z"
+     "iopub.execute_input": "2024-10-25T15:25:02.337636Z",
+     "iopub.status.busy": "2024-10-25T15:25:02.337261Z",
+     "iopub.status.idle": "2024-10-25T15:25:06.676899Z",
+     "shell.execute_reply": "2024-10-25T15:25:06.676220Z"
     }
    },
    "outputs": [
@@ -792,10 +792,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:54.706827Z",
-     "iopub.status.busy": "2024-10-25T15:22:54.706418Z",
-     "iopub.status.idle": "2024-10-25T15:22:54.709805Z",
-     "shell.execute_reply": "2024-10-25T15:22:54.709295Z"
+     "iopub.execute_input": "2024-10-25T15:25:06.679079Z",
+     "iopub.status.busy": "2024-10-25T15:25:06.678662Z",
+     "iopub.status.idle": "2024-10-25T15:25:06.682013Z",
+     "shell.execute_reply": "2024-10-25T15:25:06.681525Z"
     },
     "scrolled": true
    },
@@ -807,15 +807,15 @@
       "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n",
       "\n",
       "Unadjusted Estimates of Treatment ['v0'] :\n",
-      "Coefficient Estimate : 10.697677486880934\n",
+      "Coefficient Estimate : 10.69767748688092\n",
       "Degree of Freedom : 492\n",
-      "Standard Error : 0.5938735661282953\n",
-      "t-value : 18.01339223872763\n",
-      "F^2 value : 0.6595168698094569\n",
+      "Standard Error : 0.5938735661282948\n",
+      "t-value : 18.013392238727622\n",
+      "F^2 value : 0.6595168698094563\n",
       "\n",
       "Sensitivity Statistics : \n",
-      "Partial R2 of treatment with outcome : 0.39741498372666834\n",
-      "Robustness Value : 0.546744557218101\n",
+      "Partial R2 of treatment with outcome : 0.39741498372666817\n",
+      "Robustness Value : 0.5467445572181009\n",
       "\n",
       "Interpretation of results :\n",
       "Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n",
@@ -850,10 +850,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:54.711393Z",
-     "iopub.status.busy": "2024-10-25T15:22:54.711214Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.117137Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.116532Z"
+     "iopub.execute_input": "2024-10-25T15:25:06.684000Z",
+     "iopub.status.busy": "2024-10-25T15:25:06.683634Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.142967Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.142380Z"
     }
    },
    "outputs": [
@@ -890,21 +890,21 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.119102Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.118903Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.123075Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.122449Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.145169Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.144756Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.149016Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.148522Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'converted_estimate': 2.457447065735426,\n",
-       " 'converted_lower_ci': 2.2288567270043167,\n",
-       " 'converted_upper_ci': 2.709481505797999,\n",
-       " 'evalue_estimate': 4.34995836428987,\n",
-       " 'evalue_lower_ci': 3.8838327336304186,\n",
+       "{'converted_estimate': 2.457447065735423,\n",
+       " 'converted_lower_ci': 2.2288567270043145,\n",
+       " 'converted_upper_ci': 2.7094815057979953,\n",
+       " 'evalue_estimate': 4.349958364289864,\n",
+       " 'evalue_lower_ci': 3.883832733630414,\n",
        " 'evalue_upper_ci': None}"
       ]
      },
@@ -922,10 +922,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.124832Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.124523Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.131441Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.130847Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.150820Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.150459Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.157934Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.157294Z"
     }
    },
    "outputs": [
@@ -1044,10 +1044,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.133292Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.132945Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.136206Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.135714Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.159949Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.159557Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.162963Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.162383Z"
     }
    },
    "outputs": [
@@ -1058,15 +1058,15 @@
       "Sensitivity Analysis to Unobserved Confounding using the E-value\n",
       "\n",
       "Unadjusted Estimates of Treatment: v0\n",
-      "Estimate (converted to risk ratio scale): 2.457447065735426\n",
-      "Lower 95% CI (converted to risk ratio scale): 2.2288567270043167\n",
-      "Upper 95% CI (converted to risk ratio scale): 2.709481505797999\n",
+      "Estimate (converted to risk ratio scale): 2.457447065735423\n",
+      "Lower 95% CI (converted to risk ratio scale): 2.2288567270043145\n",
+      "Upper 95% CI (converted to risk ratio scale): 2.7094815057979953\n",
       "\n",
       "Sensitivity Statistics: \n",
-      "E-value for point estimate: 4.34995836428987\n",
-      "E-value for lower 95% CI: 3.8838327336304186\n",
+      "E-value for point estimate: 4.349958364289864\n",
+      "E-value for lower 95% CI: 3.883832733630414\n",
       "E-value for upper 95% CI: None\n",
-      "Largest Observed Covariate E-value: 1.6811401167656743 (W1)\n",
+      "Largest Observed Covariate E-value: 1.6811401167656792 (W1)\n",
       "\n",
       "Interpretation of results:\n",
       "Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.\n",
@@ -1092,10 +1092,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.137947Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.137619Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.153227Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.152622Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.164755Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.164424Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.180759Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.180110Z"
     }
    },
    "outputs": [],
@@ -1115,10 +1115,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.155225Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.154896Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.281156Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.280501Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.182782Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.182483Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.330841Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.330199Z"
     }
    },
    "outputs": [
@@ -1279,10 +1279,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.283245Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.282929Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.306568Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.305920Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.333226Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.332788Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.364721Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.364099Z"
     }
    },
    "outputs": [
@@ -1296,9 +1296,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W4,W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6,W4])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -1306,7 +1306,13 @@
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
-      "No such variable(s) found!\n",
+      "No such variable(s) found!\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n"
      ]
     }
@@ -1321,10 +1327,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.308434Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.308246Z",
-     "iopub.status.idle": "2024-10-25T15:22:55.324287Z",
-     "shell.execute_reply": "2024-10-25T15:22:55.323676Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.367125Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.366599Z",
+     "iopub.status.idle": "2024-10-25T15:25:07.386712Z",
+     "shell.execute_reply": "2024-10-25T15:25:07.386138Z"
     }
    },
    "outputs": [
@@ -1341,16 +1347,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W4,W3,W5,W6,W2,W0,W1])\n",
+      "─────(E[y|W1,W0,W2,W3,W5,W6,W4])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W5,W6,W2,W0,W1,U) = P(y|v0,W4,W3,W5,W6,W2,W0,W1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W5,W6,W4,U) = P(y|v0,W1,W0,W2,W3,W5,W6,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W4+W3+W5+W6+W2+W0+W1\n",
+      "b: y~v0+W1+W0+W2+W3+W5+W6+W4\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.08192437558829\n",
+      "Mean value: 10.081924375588311\n",
       "\n"
      ]
     }
@@ -1365,10 +1371,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:55.326084Z",
-     "iopub.status.busy": "2024-10-25T15:22:55.325902Z",
-     "iopub.status.idle": "2024-10-25T15:22:59.696935Z",
-     "shell.execute_reply": "2024-10-25T15:22:59.696261Z"
+     "iopub.execute_input": "2024-10-25T15:25:07.388841Z",
+     "iopub.status.busy": "2024-10-25T15:25:07.388607Z",
+     "iopub.status.idle": "2024-10-25T15:25:11.761178Z",
+     "shell.execute_reply": "2024-10-25T15:25:11.760460Z"
     }
    },
    "outputs": [
@@ -1406,10 +1412,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:22:59.699066Z",
-     "iopub.status.busy": "2024-10-25T15:22:59.698684Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.064278Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.063654Z"
+     "iopub.execute_input": "2024-10-25T15:25:11.763292Z",
+     "iopub.status.busy": "2024-10-25T15:25:11.763099Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.164415Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.163717Z"
     }
    },
    "outputs": [
@@ -1433,10 +1439,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.066342Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.065969Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.068971Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.068438Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.166769Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.166329Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.170110Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.169585Z"
     },
     "scrolled": true
    },
@@ -1448,11 +1454,11 @@
       "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n",
       "\n",
       "Unadjusted Estimates of Treatment ['v0'] :\n",
-      "Coefficient Estimate : 10.08192437558829\n",
+      "Coefficient Estimate : 10.081924375588311\n",
       "Degree of Freedom : 491\n",
-      "Standard Error : 0.10446229543424747\n",
-      "t-value : 96.51256784735537\n",
-      "F^2 value : 18.97082637981746\n",
+      "Standard Error : 0.10446229543424763\n",
+      "t-value : 96.51256784735543\n",
+      "F^2 value : 18.970826379817485\n",
       "\n",
       "Sensitivity Statistics : \n",
       "Partial R2 of treatment with outcome : 0.9499269594066172\n",
@@ -1477,10 +1483,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.070816Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.070443Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.074448Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.073853Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.172027Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.171646Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.176112Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.175591Z"
     },
     "scrolled": true
    },
@@ -1488,12 +1494,12 @@
     {
      "data": {
       "text/plain": [
-       "{'estimate': 10.08192437558829,\n",
-       " 'standard_error': 0.10446229543424747,\n",
+       "{'estimate': 10.081924375588311,\n",
+       " 'standard_error': 0.10446229543424763,\n",
        " 'degree of freedom': 491,\n",
-       " 't_statistic': 96.51256784735537,\n",
+       " 't_statistic': 96.51256784735543,\n",
        " 'r2yt_w': 0.9499269594066172,\n",
-       " 'partial_f2': 18.97082637981746,\n",
+       " 'partial_f2': 18.970826379817485,\n",
        " 'robustness_value': 0.9522057801012398,\n",
        " 'robustness_value_alpha': 0.950386691319526}"
       ]
@@ -1512,10 +1518,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:04.076256Z",
-     "iopub.status.busy": "2024-10-25T15:23:04.075929Z",
-     "iopub.status.idle": "2024-10-25T15:23:04.083866Z",
-     "shell.execute_reply": "2024-10-25T15:23:04.083244Z"
+     "iopub.execute_input": "2024-10-25T15:25:16.178071Z",
+     "iopub.status.busy": "2024-10-25T15:25:16.177692Z",
+     "iopub.status.idle": "2024-10-25T15:25:16.186536Z",
+     "shell.execute_reply": "2024-10-25T15:25:16.186027Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/timeseries/effect_inference_timeseries_data.html b/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
index c70dafaf7..fa5215394 100644
--- a/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
+++ b/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
@@ -645,8 +645,8 @@ <h2>Dataset Shifting and Filtering<a class="headerlink" href="#Dataset-Shifting-
       <th>V7_-3</th>
       <th>V7_-5</th>
       <th>V4_-3</th>
-      <th>V4_-7</th>
       <th>V4_-9</th>
+      <th>V4_-7</th>
     </tr>
   </thead>
   <tbody>
@@ -726,7 +726,7 @@ <h2>Causal Effect Estimation<a class="headerlink" href="#Causal-Effect-Estimatio
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-[&#39;V5_-1&#39;, &#39;V7_-3&#39;, &#39;V7_-5&#39;, &#39;V4_-3&#39;, &#39;V4_-7&#39;, &#39;V4_-9&#39;]
+[&#39;V5_-1&#39;, &#39;V7_-3&#39;, &#39;V7_-5&#39;, &#39;V4_-3&#39;, &#39;V4_-9&#39;, &#39;V4_-7&#39;]
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -774,23 +774,23 @@ <h2>Causal Effect Estimation<a class="headerlink" href="#Causal-Effect-Estimatio
 Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-7,U) = P(V6_0|V5_-1,V4_-7)
 
 ## Realized estimand
-b: V6_0~V5_-1+V4_-7+V5_-1*V7_-3+V5_-1*V7_-5
+b: V6_0~V5_-1+V4_-7+V5_-1*V7_-5+V5_-1*V7_-3
 Target units:
 
 ## Estimate
-Mean value: -0.12612138021763197
+Mean value: -0.12612138021763286
 p-value: [0.75401158]
 ### Conditional Estimates
-__categorical__V7_-3  __categorical__V7_-5
-(-0.001, 0.6]         (-0.001, 5.4]           0.111915
-(0.6, 5.4]            (7.8, 9.0]             -0.257861
-                      (9.0, 10.0]            -0.314176
-(5.4, 7.8]            (-0.001, 5.4]           0.073537
-                      (7.8, 9.0]             -0.285273
-(7.8, 9.0]            (-0.001, 5.4]          -0.034356
-                      (5.4, 7.8]             -0.216503
+__categorical__V7_-5  __categorical__V7_-3
+(-0.001, 5.4]         (-0.001, 0.6]           0.111915
+                      (5.4, 7.8]              0.073537
+                      (7.8, 9.0]             -0.034356
+(5.4, 7.8]            (7.8, 9.0]             -0.216503
+(7.8, 9.0]            (0.6, 5.4]             -0.257861
+                      (5.4, 7.8]             -0.285273
                       (7.8, 9.0]             -0.296238
-(9.0, 10.0]           (7.8, 9.0]             -0.261853
+                      (9.0, 10.0]            -0.261853
+(9.0, 10.0]           (0.6, 5.4]             -0.314176
 dtype: float64
 </pre></div></div>
 </div>
@@ -823,7 +823,7 @@ <h1>Importing temporal causal graph from Tigramite library<a class="headerlink"
 <div class="highlight"><pre>
 Collecting tigramite
   Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)
-     <span class="ansi-black-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-green-fg">296.8/296.8 kB</span> <span class="ansi-red-fg">26.1 MB/s</span> eta <span class="ansi-cyan-fg">0:00:00</span>
+     <span class="ansi-black-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-green-fg">296.8/296.8 kB</span> <span class="ansi-red-fg">39.5 MB/s</span> eta <span class="ansi-cyan-fg">0:00:00</span>
 Requirement already satisfied: numpy&gt;=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)
 Requirement already satisfied: scipy&gt;=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)
 Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)
diff --git a/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb b/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
index 8f62b6ea7..01e3da649 100644
--- a/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
+++ b/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:06.615600Z",
-     "iopub.status.busy": "2024-10-25T15:23:06.615154Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.049138Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.048510Z"
+     "iopub.execute_input": "2024-10-25T15:25:19.104975Z",
+     "iopub.status.busy": "2024-10-25T15:25:19.104546Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.672525Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.671834Z"
     }
    },
    "outputs": [],
@@ -40,10 +40,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.051703Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.051121Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.056626Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.056003Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.675298Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.674756Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.680705Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.680030Z"
     }
    },
    "outputs": [],
@@ -67,10 +67,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.058728Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.058239Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.171519Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.170957Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.682879Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.682506Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.799389Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.798817Z"
     }
    },
    "outputs": [
@@ -116,10 +116,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.173759Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.173379Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.293392Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.292837Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.802130Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.801724Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.923916Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.923294Z"
     }
    },
    "outputs": [
@@ -159,10 +159,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.295510Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.295295Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.300229Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.299666Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.926479Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.926084Z",
+     "iopub.status.idle": "2024-10-25T15:25:20.930803Z",
+     "shell.execute_reply": "2024-10-25T15:25:20.930278Z"
     }
    },
    "outputs": [],
@@ -175,10 +175,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.302269Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.302060Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.403901Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.403366Z"
+     "iopub.execute_input": "2024-10-25T15:25:20.932862Z",
+     "iopub.status.busy": "2024-10-25T15:25:20.932459Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.035193Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.034525Z"
     }
    },
    "outputs": [
@@ -205,10 +205,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.406129Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.405775Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.419013Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.418476Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.037677Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.037229Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.050881Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.050287Z"
     }
    },
    "outputs": [
@@ -238,8 +238,8 @@
        "      <th>V7_-3</th>\n",
        "      <th>V7_-5</th>\n",
        "      <th>V4_-3</th>\n",
-       "      <th>V4_-7</th>\n",
        "      <th>V4_-9</th>\n",
+       "      <th>V4_-7</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -298,7 +298,7 @@
        "</div>"
       ],
       "text/plain": [
-       "   V6_0  V5_-1  V7_-3  V7_-5  V4_-3  V4_-7  V4_-9\n",
+       "   V6_0  V5_-1  V7_-3  V7_-5  V4_-3  V4_-9  V4_-7\n",
        "0     6      0      0      0      0      0      0\n",
        "1     7      5      0      0      0      0      0\n",
        "2     8      6      0      0      0      0      0\n",
@@ -330,17 +330,17 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.421046Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.420829Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.426887Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.426295Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.053270Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.052848Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.057319Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.056778Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['V5_-1', 'V7_-3', 'V7_-5', 'V4_-3', 'V4_-7', 'V4_-9']"
+       "['V5_-1', 'V7_-3', 'V7_-5', 'V4_-3', 'V4_-9', 'V4_-7']"
       ]
      },
      "execution_count": 8,
@@ -361,10 +361,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.428915Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.428704Z",
-     "iopub.status.idle": "2024-10-25T15:23:08.528196Z",
-     "shell.execute_reply": "2024-10-25T15:23:08.527542Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.059391Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.058978Z",
+     "iopub.status.idle": "2024-10-25T15:25:21.160118Z",
+     "shell.execute_reply": "2024-10-25T15:25:21.159398Z"
     }
    },
    "outputs": [
@@ -386,23 +386,23 @@
       "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-7,U) = P(V6_0|V5_-1,V4_-7)\n",
       "\n",
       "## Realized estimand\n",
-      "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-3+V5_-1*V7_-5\n",
+      "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-5+V5_-1*V7_-3\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: -0.12612138021763197\n",
+      "Mean value: -0.12612138021763286\n",
       "p-value: [0.75401158]\n",
       "### Conditional Estimates\n",
-      "__categorical__V7_-3  __categorical__V7_-5\n",
-      "(-0.001, 0.6]         (-0.001, 5.4]           0.111915\n",
-      "(0.6, 5.4]            (7.8, 9.0]             -0.257861\n",
-      "                      (9.0, 10.0]            -0.314176\n",
-      "(5.4, 7.8]            (-0.001, 5.4]           0.073537\n",
-      "                      (7.8, 9.0]             -0.285273\n",
-      "(7.8, 9.0]            (-0.001, 5.4]          -0.034356\n",
-      "                      (5.4, 7.8]             -0.216503\n",
+      "__categorical__V7_-5  __categorical__V7_-3\n",
+      "(-0.001, 5.4]         (-0.001, 0.6]           0.111915\n",
+      "                      (5.4, 7.8]              0.073537\n",
+      "                      (7.8, 9.0]             -0.034356\n",
+      "(5.4, 7.8]            (7.8, 9.0]             -0.216503\n",
+      "(7.8, 9.0]            (0.6, 5.4]             -0.257861\n",
+      "                      (5.4, 7.8]             -0.285273\n",
       "                      (7.8, 9.0]             -0.296238\n",
-      "(9.0, 10.0]           (7.8, 9.0]             -0.261853\n",
+      "                      (9.0, 10.0]            -0.261853\n",
+      "(9.0, 10.0]           (0.6, 5.4]             -0.314176\n",
       "dtype: float64\n"
      ]
     },
@@ -451,10 +451,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:08.530382Z",
-     "iopub.status.busy": "2024-10-25T15:23:08.529989Z",
-     "iopub.status.idle": "2024-10-25T15:23:11.141113Z",
-     "shell.execute_reply": "2024-10-25T15:23:11.140321Z"
+     "iopub.execute_input": "2024-10-25T15:25:21.162171Z",
+     "iopub.status.busy": "2024-10-25T15:25:21.161832Z",
+     "iopub.status.idle": "2024-10-25T15:25:23.782498Z",
+     "shell.execute_reply": "2024-10-25T15:25:23.781639Z"
     }
    },
    "outputs": [
@@ -462,17 +462,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting tigramite\r\n",
-      "  Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n",
-      "\u001b[?25l"
+      "Collecting tigramite\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
+      "  Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n",
+      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
       "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n",
       "Requirement already satisfied: scipy>=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)\r\n",
       "Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)\r\n"
@@ -511,10 +510,10 @@
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    "metadata": {
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-     "iopub.status.busy": "2024-10-25T15:23:11.143451Z",
-     "iopub.status.idle": "2024-10-25T15:23:11.151852Z",
-     "shell.execute_reply": "2024-10-25T15:23:11.151366Z"
+     "iopub.execute_input": "2024-10-25T15:25:23.785227Z",
+     "iopub.status.busy": "2024-10-25T15:25:23.784755Z",
+     "iopub.status.idle": "2024-10-25T15:25:23.794030Z",
+     "shell.execute_reply": "2024-10-25T15:25:23.793516Z"
     }
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@@ -535,10 +534,10 @@
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    "metadata": {
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-     "iopub.status.busy": "2024-10-25T15:23:11.153313Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.065057Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.064420Z"
+     "iopub.execute_input": "2024-10-25T15:25:23.795869Z",
+     "iopub.status.busy": "2024-10-25T15:25:23.795583Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.743728Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.743118Z"
     }
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@@ -563,10 +562,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.067122Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.066772Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.072173Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.071586Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.745850Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.745398Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.751621Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.750975Z"
     }
    },
    "outputs": [],
@@ -586,10 +585,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.074164Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.073812Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.255141Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.254548Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.753621Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.753178Z",
+     "iopub.status.idle": "2024-10-25T15:25:24.940289Z",
+     "shell.execute_reply": "2024-10-25T15:25:24.939600Z"
     }
    },
    "outputs": [
@@ -620,10 +619,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.257137Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.256786Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.642767Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.642085Z"
+     "iopub.execute_input": "2024-10-25T15:25:24.942687Z",
+     "iopub.status.busy": "2024-10-25T15:25:24.942139Z",
+     "iopub.status.idle": "2024-10-25T15:25:25.333636Z",
+     "shell.execute_reply": "2024-10-25T15:25:25.332948Z"
     }
    },
    "outputs": [
@@ -761,7 +760,13 @@
       "\n",
       "Testing condition sets of dimension 0:\n",
       "\n",
-      "    Link (V1 -1) -?> V2 (1/21):\n",
+      "    Link (V1 -1) -?> V2 (1/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.12317 / val = -0.591\n",
       "    Non-significance detected.\n",
       "\n",
@@ -797,13 +802,7 @@
       "    Subset 0: () gives pval = 0.25844 / val =  0.454\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V4 -1) -?> V2 (10/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V4 -1) -?> V2 (10/21):\n",
       "    Subset 0: () gives pval = 0.08165 / val = -0.649\n",
       "    Non-significance detected.\n",
       "\n",
@@ -1961,7 +1960,13 @@
       "    Subset 0: () gives pval = 0.25198 / val =  0.460\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V2 -2) -?> V3 (5/21):\n",
+      "    Link (V2 -2) -?> V3 (5/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.81987 / val = -0.097\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2846,7 +2851,13 @@
       "    Subset 0: () gives pval = 0.30492 / val = -0.416\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V3 -2) -?> V1 (8/21):\n",
+      "    Link (V3 -2) -?> V1 (8/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.90322 / val = -0.052\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2854,13 +2865,7 @@
       "    Subset 0: () gives pval = 0.59372 / val =  0.224\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V4 -1) -?> V1 (10/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V4 -1) -?> V1 (10/21):\n",
       "    Subset 0: () gives pval = 0.08508 / val = -0.644\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2925,13 +2930,7 @@
       "\n",
       "Testing condition sets of dimension 0:\n",
       "\n",
-      "    Link (V1 -1) -?> V2 (1/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V1 -1) -?> V2 (1/21):\n",
       "    Subset 0: () gives pval = 0.12317 / val = -0.591\n",
       "    Non-significance detected.\n",
       "\n",
@@ -6103,10 +6102,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:12.644964Z",
-     "iopub.status.busy": "2024-10-25T15:23:12.644773Z",
-     "iopub.status.idle": "2024-10-25T15:23:12.722100Z",
-     "shell.execute_reply": "2024-10-25T15:23:12.721456Z"
+     "iopub.execute_input": "2024-10-25T15:25:25.336365Z",
+     "iopub.status.busy": "2024-10-25T15:25:25.335891Z",
+     "iopub.status.idle": "2024-10-25T15:25:25.417540Z",
+     "shell.execute_reply": "2024-10-25T15:25:25.416441Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
index 329454a2e..07457653e 100644
--- a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
+++ b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
@@ -768,9 +768,9 @@ <h2>The four steps of causal inference<a class="headerlink" href="#The-four-step
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W2,W4,W0])
+─────(E[y|W1,W0,W2,W3,W4])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)
 
 ### Estimand : 2
 Estimand name: iv
@@ -778,9 +778,9 @@ <h2>The four steps of causal inference<a class="headerlink" href="#The-four-step
  ⎡                              -1⎤
  ⎢    d        ⎛    d          ⎞  ⎥
 E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥
- ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦
-Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})
-Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)
+ ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦
+Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})
+Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)
 
 ### Estimand : 3
 Estimand name: frontdoor
@@ -817,16 +817,16 @@ <h2>The four steps of causal inference<a class="headerlink" href="#The-four-step
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W2,W4,W0])
+─────(E[y|W1,W0,W2,W3,W4])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)
 
 ## Realized estimand
-b: y~v0+W1+W3+W2+W4+W0
+b: y~v0+W1+W0+W2+W3+W4
 Target units: ate
 
 ## Estimate
-Mean value: 10.167002772144512
+Mean value: 9.895558883688192
 
 </pre></div></div>
 </div>
@@ -864,17 +864,17 @@ <h2>The four steps of causal inference<a class="headerlink" href="#The-four-step
 Estimand name: backdoor
 Estimand expression:
   d
-─────(E[y|W1,W3,W2,W4,W0])
+─────(E[y|W1,W0,W2,W3,W4])
 d[v₀]
-Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)
+Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)
 
 ## Realized estimand
-b: y~v0+W1+W3+W2+W4+W0 |
+b: y~v0+W1+W0+W2+W3+W4 |
 Target units: ate
 
 ## Estimate
-Mean value: 9.898014646310529
-Effect estimates: [[9.89801465]]
+Mean value: 10.019941851969312
+Effect estimates: [[10.01994185]]
 
 </pre></div></div>
 </div>
@@ -898,9 +898,9 @@ <h2>The four steps of causal inference<a class="headerlink" href="#The-four-step
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:10.167002772144512
-New effect:-2.5223045497420395e-05
-p value:1.0
+Estimated effect:9.895558883688192
+New effect:-0.001012646866716932
+p value:0.96
 
 </pre></div></div>
 </div>
@@ -952,11 +952,11 @@ <h3>A mystery dataset: Can you find out if if there is a causal effect?<a class=
 <div class="output_area docutils container">
 <div class="highlight"><pre>
    Treatment    Outcome        w0         s        w1
-0  20.030841  39.674690  3.737967  2.488108 -0.248427
-1  25.694528  50.855084 -4.386344  0.104081  0.221216
-2  13.591056  26.956307  2.681680  9.584895  0.419222
-3  16.291246  32.617104  3.116600  3.939894  0.538060
-4  15.858877  32.098983  3.163008  3.914872 -0.095866
+0  20.489473  40.330239  3.768185  3.481471 -0.176429
+1   8.367748  16.237554  1.425685  1.395533  0.375315
+2   5.651532  11.051624 -0.251996  0.937963 -1.389341
+3   7.079860  13.399747  0.995682  4.733297 -0.657510
+4   7.781453  15.734203 -1.433446  8.602047 -0.114190
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1012,9 +1012,9 @@ <h4>Identify the correct estimand for the target quantity based on the causal mo
 Estimand name: backdoor
 Estimand expression:
      d
-────────────(E[Outcome|w1,w0])
+────────────(E[Outcome|w0,w1])
 d[Treatment]
-Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)
+Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1058,18 +1058,18 @@ <h4>Estimate the target estimand<a class="headerlink" href="#Estimate-the-target
 Estimand name: backdoor
 Estimand expression:
      d
-────────────(E[Outcome|w1,w0])
+────────────(E[Outcome|w0,w1])
 d[Treatment]
-Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)
+Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)
 
 ## Realized estimand
-b: Outcome~Treatment+w1+w0
+b: Outcome~Treatment+w0+w1
 Target units: ate
 
 ## Estimate
-Mean value: 1.99899396995031
+Mean value: 1.9993089624430813
 
-Causal Estimate is 1.99899396995031
+Causal Estimate is 1.9993089624430813
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1116,17 +1116,17 @@ <h4>Estimate the target estimand<a class="headerlink" href="#Estimate-the-target
 Estimand name: backdoor
 Estimand expression:
      d
-────────────(E[Outcome|w1,w0])
+────────────(E[Outcome|w0,w1])
 d[Treatment]
-Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)
+Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)
 
 ## Realized estimand
-b: Outcome~Treatment+w1+w0 |
+b: Outcome~Treatment+w0+w1 |
 Target units: ate
 
 ## Estimate
-Mean value: 1.144759400960652
-Effect estimates: [[1.1447594]]
+Mean value: 1.0590662846792145
+Effect estimates: [[1.05906628]]
 
 </pre></div></div>
 </div>
@@ -1149,9 +1149,9 @@ <h4>Check robustness of the estimate using refutation tests<a class="headerlink"
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Add a random common cause
-Estimated effect:1.144759400960652
-New effect:1.1582026466388928
-p value:0.6599999999999999
+Estimated effect:1.0590662846792145
+New effect:1.0511514897778662
+p value:0.72
 
 </pre></div></div>
 </div>
@@ -1172,9 +1172,9 @@ <h4>Check robustness of the estimate using refutation tests<a class="headerlink"
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Refute: Use a Placebo Treatment
-Estimated effect:1.144759400960652
-New effect:-0.00012178524613168886
-p value:0.44451697404875146
+Estimated effect:1.0590662846792145
+New effect:0.00011819241215427258
+p value:0.38070788967795144
 
 </pre></div></div>
 </div>
diff --git a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
index 62c095e8c..6f4eedbde 100644
--- a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
+++ b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
@@ -104,10 +104,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:14.980114Z",
-     "iopub.status.busy": "2024-10-25T15:23:14.979691Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.683261Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.682624Z"
+     "iopub.execute_input": "2024-10-25T15:25:27.774601Z",
+     "iopub.status.busy": "2024-10-25T15:25:27.774405Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.577042Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.576355Z"
     }
    },
    "outputs": [],
@@ -152,10 +152,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.686484Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.686067Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.690617Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.690073Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.580486Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.580173Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.584440Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.583924Z"
     }
    },
    "outputs": [],
@@ -181,10 +181,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.693229Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.692781Z",
-     "iopub.status.idle": "2024-10-25T15:23:16.865195Z",
-     "shell.execute_reply": "2024-10-25T15:23:16.864583Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.586695Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.586496Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.746928Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.746340Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:16.867535Z",
-     "iopub.status.busy": "2024-10-25T15:23:16.867123Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.021231Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.020584Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.749384Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.749031Z",
+     "iopub.status.idle": "2024-10-25T15:25:29.909850Z",
+     "shell.execute_reply": "2024-10-25T15:25:29.909274Z"
     }
    },
    "outputs": [
@@ -278,10 +278,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.023408Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.023191Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.275964Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.275323Z"
+     "iopub.execute_input": "2024-10-25T15:25:29.912508Z",
+     "iopub.status.busy": "2024-10-25T15:25:29.912241Z",
+     "iopub.status.idle": "2024-10-25T15:25:30.077554Z",
+     "shell.execute_reply": "2024-10-25T15:25:30.076836Z"
     }
    },
    "outputs": [
@@ -295,9 +295,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -305,9 +305,9 @@
       " ⎡                              -1⎤\n",
       " ⎢    d        ⎛    d          ⎞  ⎥\n",
       "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
-      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
-      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
-      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
+      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
+      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
+      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
       "\n",
       "### Estimand : 3\n",
       "Estimand name: frontdoor\n",
@@ -338,10 +338,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.278175Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.277779Z",
-     "iopub.status.idle": "2024-10-25T15:23:17.782907Z",
-     "shell.execute_reply": "2024-10-25T15:23:17.782327Z"
+     "iopub.execute_input": "2024-10-25T15:25:30.080135Z",
+     "iopub.status.busy": "2024-10-25T15:25:30.079495Z",
+     "iopub.status.idle": "2024-10-25T15:25:30.677136Z",
+     "shell.execute_reply": "2024-10-25T15:25:30.676491Z"
     }
    },
    "outputs": [
@@ -358,16 +358,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W2+W4+W0\n",
+      "b: y~v0+W1+W0+W2+W3+W4\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.167002772144512\n",
+      "Mean value: 9.895558883688192\n",
       "\n"
      ]
     }
@@ -385,10 +385,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:17.784797Z",
-     "iopub.status.busy": "2024-10-25T15:23:17.784608Z",
-     "iopub.status.idle": "2024-10-25T15:23:22.810653Z",
-     "shell.execute_reply": "2024-10-25T15:23:22.809699Z"
+     "iopub.execute_input": "2024-10-25T15:25:30.679266Z",
+     "iopub.status.busy": "2024-10-25T15:25:30.678894Z",
+     "iopub.status.idle": "2024-10-25T15:25:35.848903Z",
+     "shell.execute_reply": "2024-10-25T15:25:35.847729Z"
     }
    },
    "outputs": [
@@ -405,17 +405,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W1,W3,W2,W4,W0])\n",
+      "─────(E[y|W1,W0,W2,W3,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W2,W4,W0,U) = P(y|v0,W1,W3,W2,W4,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,W3,W4,U) = P(y|v0,W1,W0,W2,W3,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W3+W2+W4+W0 | \n",
+      "b: y~v0+W1+W0+W2+W3+W4 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.898014646310529\n",
-      "Effect estimates: [[9.89801465]]\n",
+      "Mean value: 10.019941851969312\n",
+      "Effect estimates: [[10.01994185]]\n",
       "\n"
      ]
     }
@@ -450,10 +450,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:22.813421Z",
-     "iopub.status.busy": "2024-10-25T15:23:22.813176Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.174799Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.174119Z"
+     "iopub.execute_input": "2024-10-25T15:25:35.851755Z",
+     "iopub.status.busy": "2024-10-25T15:25:35.851511Z",
+     "iopub.status.idle": "2024-10-25T15:26:10.974933Z",
+     "shell.execute_reply": "2024-10-25T15:26:10.974258Z"
     }
    },
    "outputs": [
@@ -462,9 +462,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:10.167002772144512\n",
-      "New effect:-2.5223045497420395e-05\n",
-      "p value:1.0\n",
+      "Estimated effect:9.895558883688192\n",
+      "New effect:-0.001012646866716932\n",
+      "p value:0.96\n",
       "\n"
      ]
     }
@@ -503,10 +503,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.176926Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.176548Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.180143Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.179539Z"
+     "iopub.execute_input": "2024-10-25T15:26:10.976982Z",
+     "iopub.status.busy": "2024-10-25T15:26:10.976788Z",
+     "iopub.status.idle": "2024-10-25T15:26:10.980247Z",
+     "shell.execute_reply": "2024-10-25T15:26:10.979791Z"
     }
    },
    "outputs": [],
@@ -530,10 +530,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.182127Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.181795Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.506902Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.506267Z"
+     "iopub.execute_input": "2024-10-25T15:26:10.982076Z",
+     "iopub.status.busy": "2024-10-25T15:26:10.981732Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.339143Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.338414Z"
     }
    },
    "outputs": [
@@ -542,16 +542,16 @@
      "output_type": "stream",
      "text": [
       "   Treatment    Outcome        w0         s        w1\n",
-      "0  20.030841  39.674690  3.737967  2.488108 -0.248427\n",
-      "1  25.694528  50.855084 -4.386344  0.104081  0.221216\n",
-      "2  13.591056  26.956307  2.681680  9.584895  0.419222\n",
-      "3  16.291246  32.617104  3.116600  3.939894  0.538060\n",
-      "4  15.858877  32.098983  3.163008  3.914872 -0.095866\n"
+      "0  20.489473  40.330239  3.768185  3.481471 -0.176429\n",
+      "1   8.367748  16.237554  1.425685  1.395533  0.375315\n",
+      "2   5.651532  11.051624 -0.251996  0.937963 -1.389341\n",
+      "3   7.079860  13.399747  0.995682  4.733297 -0.657510\n",
+      "4   7.781453  15.734203 -1.433446  8.602047 -0.114190\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -586,10 +586,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.508995Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.508628Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.613929Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.613293Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.341441Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.340994Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.452544Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.451769Z"
     }
    },
    "outputs": [
@@ -626,10 +626,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.616156Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.615858Z",
-     "iopub.status.idle": "2024-10-25T15:23:57.622232Z",
-     "shell.execute_reply": "2024-10-25T15:23:57.621533Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.454870Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.454644Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.461446Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.460759Z"
     }
    },
    "outputs": [
@@ -643,9 +643,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -683,10 +683,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:57.624102Z",
-     "iopub.status.busy": "2024-10-25T15:23:57.623918Z",
-     "iopub.status.idle": "2024-10-25T15:23:58.011850Z",
-     "shell.execute_reply": "2024-10-25T15:23:58.011273Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.463503Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.463291Z",
+     "iopub.status.idle": "2024-10-25T15:26:11.871330Z",
+     "shell.execute_reply": "2024-10-25T15:26:11.870650Z"
     },
     "scrolled": true
    },
@@ -704,23 +704,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w1+w0\n",
+      "b: Outcome~Treatment+w0+w1\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.99899396995031\n",
+      "Mean value: 1.9993089624430813\n",
       "\n",
-      "Causal Estimate is 1.99899396995031\n"
+      "Causal Estimate is 1.9993089624430813\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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8eNHg5IyIiIioObl8+TL+/e9/IyYmRmtydvfuXaxbt042ObO0tMRTTz2FyZMnV5uc1XeFRl1wBo3o/zt37hxiY2Px4MEDAICjoyOGDBmCfv36GbwOPj8/HzExMUhISEBqairatGkDV1dXeHh4wNzcvMZj5gwaERE1V/y71LL88ccfuHbtmtbXRVHE8ePHsW/fPtkljS4uLvD394etrW2155w6dSqGDx9e5/ed6Xu9xjL7RP/foEGDMGjQoFrt08LCAlOnTq3VPomIiIiak71798omZ7m5udi+fTuuX78u28+YMWMwfvx4nZY0KpXKeknODMEEjYiIiIiIGkRxcTGOHz+u9fU7d+4gKCgIarVaa4yVlRX8/Pz0um3E09OzUSZnAO9BIyKiWrZ582YoFAooFIpqN4GnlqfsZ0OhUCA6OrrBxvHss89K43j22WcbbBxELd2ePXuqfF4QBBw9ehSbNm2STc46d+6MZcuW6ZycNcZ7ziriDBoRUQt1+fJl7NmzB1FRUUhMTMSjR4+Qn58PR0dHtG3bFiNHjoSXlxcmTpzI+z+IiKhWlJXPV6vViI+Px/nz5yvF5OTkYPv27RpbHVVl3LhxcHd3r3ZJo5WVFQYMGIBevXrBxcWl0c6clWGCRkTUwly8eBFvv/02IiIiqnz97t27uHv3Ls6cOYPvvvsOHTt2xOrVqzF//vxG/0eNqKnw8PBATEwMAGDVqlX44IMPGnZARLWkLAHLysqCjY2NRkJ0+fJl7N69GwUFBVrb3759G8HBwcjKytIaY21tDT8/P3Tr1q3a8dRXIZDaxASNiKgF+d///odXXnkFJSUlGs+3b98eHTt2hJWVFR4+fIi4uDgUFRUBKE3YFi5ciF9//RUhISE6VcYiIqKWR6VSITIyUmNJolKphKenJy5cuCBbCEQQBBw5cgQHDx6EXJH5Ll26wM/PDzY2NtWOpzEXApHDBI2IqIV477338O9//1s6NjY2xvPPP4+lS5eif//+GrEZGRkICwvDqlWrcOfOHQDA/v37MWbMGBw4cACOjo71OnYiImrcVCoVtm3bVul5tVpd5fPlZWdnIywsDLdu3dIao1Ao4O7ujnHjxumccDXmQiBymt6IiYhIb9u2bdNIzhwdHXHixAl88803lZIzALCzs8OCBQugUqng7+8vPX/58mXMmzdP9tNNIiJqWQRBQGRkpEFt4+Pj8cMPP8gmZ61atcK8efPg4eFRKeHq1atXpb3FmkIhEDmcQSMiaubu3r2LJUuWSMc2NjY4ePAg3Nzcqm1rbW2NP/74A7NmzUJ4eDgAIDIyEl999RVeffXVuhoyERE1IWVFP/QhCAIOHTqEmJgY2Q/9unXrBl9fX7Rq1Urj+QEDBmDGjBkwMTGRve+tKWq6IyciIp189tlnGn84P/nkE52SszImJibYsGED7OzspOfWrFmD/Px8vcZx48YNrFy5Eo899hgcHBxgZWWF7t27Y+HChThy5IhefV25cgUrV67EmDFj4OTkBHNzc5iZmcHOzg59+/bFjBkzsHr1apw+fVqv8a1evRpjx45Fx44dYWFhATs7O/Tp0wdLly7F/v37derngw8+kMq3e3h4SM9funQJb731FgYNGoS2bdvC2NgYCoUCGRkZWLRokdRmwIABen0tzpw5o1G6vrqvZV5eHjZu3IjAwEB0794dtra2sLS0hIuLC6ZPn47//e9/yM3N1WsMQGniPmfOHHTp0gUWFhZo27Ythg0bhn/961+4f/++3v0ZKiEhAe+88w4GDhwIOzs72NjYoHfv3nj22Wdx6NAhg/rMzc3Fjh078Prrr8PDwwMdOnSApaUlLCws0L59e4wePRpvv/227P01ZWMr+z6VFQgBgA8//FDje1jdVgSCIODw4cP48MMPMX36dHTr1g02NjYwNTWFo6MjBg4ciGXLlun8M0tUU3IFPbTFb926FdHR0VqTM4VCgQkTJmDu3LkayZmlpSVmz54NX19fmJiUzjUZGRnB1dUV/fv3h6ura5NOzgAAIhE1WZmZmSIAMTMzs8Z95eXliVeuXBHz8vJqYWTUWKSnp4utWrUSAYgAxG7duomCIBjU1+rVq6V+AIjr16+vMm7Tpk1STOfOnUVRFMVvv/1WNDMz02hf8b+FCxeK+fn5smMoLi4WX375ZdHIyEi2r/L/7dmzR7bPrKwscenSpaKJiUm1fU2ePFl8+PChbH+rVq2S4t3d3cXi4mLxnXfe0Trm9PR0MSYmRuO5s2fPyp6jvJdfflnj+yvnl19+EZ2dnat9n87OzuLu3bt1On9mZqbo4+Mj259SqRSDg4NFURQ1nj948KDO71MX33//vWhlZSU7lsWLF4t5eXni/Pnzpefmz5+vtc9169ZV22fZf0ZGRuLSpUu1/hzHx8fr/HOr7Wv0999/i+3atdO5/dixY8V79+7V4le5/vDvUtMRHx8vfvDBBzr9N2/ePNHa2lr259bGxkZcsGBBle1v3brV0G9Xb/per3GJIxFRM7Z3715kZ2dLx2UzNYZYuHAhPvzwQ6kCZFhYmMbSSW3Wr1+Pl156CUBpYZL+/fvDzs4Od+/exc2bN6W4jRs34tGjRwgLC5M+Fa1o2bJl+OmnnzSe69KlCzp16gQzMzNkZWXh9u3bePjwofS6IAhax5aUlAQvLy+cO3dOes7IyAi9e/dG27ZtkZeXh8uXL0tfw6ioKIwaNQqHDh1Cx44dq33vALBixQp8+eWXAAAzMzO4ubnBzs4ODx8+xNWrVwEAY8eORZcuXRAfHw8A+PnnnzFo0KBq+y4qKsIff/whHc+bN09r7DvvvIOPP/5Y47l27dqha9euMDU1RUJCAm7fvg0AuH//PmbNmoWNGzfK9pmTkwMvLy8cO3ZM4/k+ffqgXbt2ePToEa5cuQK1Wo3AwECD71HRxZdffonXXntN4zknJyf06tULhYWFiI2NRXZ2Nn766Sfk5eVp/Rmr6Pr16xozim3atEGXLl2gVCpRVFSExMRE6esmCALWr1+PxMREhIeHV/q3ZmlpialTpwIATp06hfT0dAClS7i0bbJrb2+vcfzw4UONn28rKyv06NEDdnZ2MDIyQlJSEq5duyb9Oz18+DBGjBiBs2fPsrgP1RkXFxcolUrZZY6CICAmJkZj9rgq3bt3h6+vL6ytrSu9plQq4erqWtPhNn51nDASUR3iDBpV58UXX9T4VPLcuXM16m/AgAEasyIlJSWVYsrPoLVq1Uq0sLAQAYiLFi0Sk5KSNGIvXLggjho1SmOM//rXv6o89/nz5zXi5s+fL96+fbvK2AcPHogbN24Ux4wZo3UmqKioSBwzZozUn4WFhfivf/1LTE1N1YgrLCwUN2zYICqVSil23LhxVb53UdScQbOxsREBiObm5uJ///tfMSsrSyM2ISFBLCwsrNSubdu2YlFRUZX9l7djxw6pjUKhEOPj46uM+/777zW+dt7e3uKZM2cqxZ05c0bj+2FhYSFevHhR6/lfeukljX49PT3FmzdvasTEx8eL3t7eIgDR0dGxTmbQTp48qTFD2a5dOzE0NFTje5Sbmyt+/vnnorm5eaWxyM2grVixQnR3dxfXrVsnJiYmVhlz69YtcenSpRrv7auvvpIds7u7uxS7atUqnd/rrl27xB49eoj/+c9/xIsXL1b5c5iWlib+97//1Zil8PX11fkcjQX/LjUdJSUlYnR0tNZZs9dff110dXWVnTVTKBTipEmTxPfff19rP1euXGnot2oQfa/XmKARNWFM0Kg6w4YN07jY1uWiX86iRYs0/qBW9ceyfIJW9t9rr72mtc/c3FyNpMDc3Fy8e/dupbiPPvpIihk9erTOYy4uLq7y+TVr1mgkUidPnpTt59y5cxpL3bZt21ZlXPlEC/9/2VtkZGS147x165aoUCikdrosMfT399dIGquSkJAgJckAxLfeeku2z4KCAtHDw0OKnzZtWpVxly5d0kiKvL29tX6tS0pKNMZa2wla+Z/z1q1bi1evXtUaGxYWpvF1ri5Bq5hUy/n444+lPl1cXLR+PUTR8AQtJydH52XKhw8fFo2NjaWL32vXrul8nsaAf5eahitXrohr167VmlQ9/fTT1S4TViqV4sKFC7X2sXbt2iabnImi/tdrTfwOOiKi/yMIAhISEnDp0iUkJCTILm1rKR49eiQ97tixo87LurTp3Lmz1v616datG9asWaP1dUtLS2zcuFEaW0FBATZs2FAprmw/NgAYM2aMrkOGsbFxpecKCgrwxRdfSMefffYZhg8fLtvPY489hrfffls6/uabb3Q6/+LFi6VlbXK6du2K0aNHS8c///yzbHx6ejp2794tHc+fP7/KuC+++EIq6PL4449XWuZYkZmZmcb3IyIiosry199//730b6xVq1ZYv359lV9roHTZ6A8//FAnm5z//fff+Pvvv6Xj1atXo1evXlrjfXx88NRTT+ncf8XKcXLefPNNaelrYmKiXkVqdGVlZaXzMuUxY8bgiSeeAACIooiwsLBaHw+1DFX9fRUEAQcPHsS2bduqXNpYUlKC/fv345dffpEtPNSzZ08sW7YMLi4uGs+bm5vD19cX8+fPx/Lly5tsyXxD8B40ImoWVCoVIiMjNf5IKJVKeHp6tqhf6hWlpaVJj8tXYTRUxT7K96/NsmXLYGZmJhvTu3dvTJ48GREREQCAkJAQvP/++xoxlpaW0uPy94wZIiIiAklJSQBK7ylasGCBTu3mz58vjev48ePIzc2FlZWVbJtly5bpPK758+dLVRh37tyJzMxMrUnNn3/+iYKCAgClF+2BgYGVYgRB0Ej0/vGPf+h0cd+lSxeMHTsWBw8ehCiK2L9/P7p166YRExoaKj0OCAhA+/btZft0cHDAU089he+//77a8+uj/Disra2xcOHCatu88sor+PXXX2t1HEBpIjpixAjcvXsXQOl9ZiNGjKj18+hj1KhR+O2336TxEOmrqr+vlpaWKCkpQWFhYZVtMjMzERISgsTERK39GhkZYdKkSRg1alSVv5dmzZrVYv9+M0EjoiZPpVJh27ZtlZ5Xq9XYtm1bk96ssqbKl8I3NzevcX8WFhYax3l5edW2mTZtmk59T58+XUrQYmNjkZOTo3GTePkL3b1792L58uV47733DCp8UP4mdXd3d5iamurUzsXFBXZ2dsjIyEBxcTHOnz+Pxx9/XGu8UqnEY489pvO4Zs+ejVdeeQV5eXnIz8/Hn3/+iaVLl1YZu2XLFumxr68vbGxsKsVcunRJKkQBAJMmTdJ5LAMHDsTBgwcBAKdPn9YYR2JiokahCi8vL536nD59eq0naCdPnpQejxs3rtqEGQCGDx8OBwcHpKSk6HWuxMREHDhwABcvXkRSUhKysrIqXaBeunRJelyWqNUVtVqNqKgonD9/Hrdv30ZWVhby8/M1ypbfu3ev3sZDzY+2v69yv/tv3LiB0NBQ2RhbW1sEBgZWWWyJH64yQSOiJk4QhGorw0VGRqJXr15Nf18UA7Ru3VpahpiZmVnj/ir2UbHCXEWmpqayy83K69+/v/S4pKQEt27d0tgTzM/PD926dZOW23399df43//+h7Fjx2L8+PEYOXIkRo4cWWWiUtHFixelx6dPn4anp6dOYwQ0k97k5GTZ2C5duuhVNVOpVMLHxwe///47gNJljlUlaDdu3MCJEyekY23LG8u/T2NjY8yePVvnsZSvsFnxfV6/fl3juPz3To6ucfooPxZ9+u/fv7+UgFbn8uXLeP3117Fv3z7ZDXUrysjI0DlWH6mpqXjnnXfw888/67UfYV2Nh5onXf6+lldSUoIDBw7g6NGjsnG9e/fGrFmzNFZFTJ06FdbW1s1ik+nawASNiJq0xMRE2bK+QOmnzImJiS2jNG8F9vb2UoKWmppa4/4q9lFdgmZnZ6f1vqSK2rRpo3FcfuYHKJ0BDA8Px4wZM6SL8uLiYhw8eFC60DY2NsawYcMwe/ZsLFiwQOuyzvLvIzExUXYZjpzqkl6lUql3n/Pnz5cStKNHjyIuLg5du3bViCm/bLFDhw6YOHFilX2Vf58lJSX466+/9B4PUPl9VvzeVPzeaaNrnD7Kj0Wf/nWN3bNnD/z9/aXlpPowpE114uLiMH78eIN+ZutiPNR86fL3tUxGRgZCQkI07hWuyMjICFOmTMGIESMqfXBlbW1dJx/gNFUtOz0loiYvKyurVuOam/L3DT148ECnoh5yzp8/Lz1WKBSVEoeKqrv3rLyKSzCrupjs2bMnLl68iG+//bbKfcJKSkpw4sQJvP7663B1da20Z1qZnJwcncclp7pCNIZ8Cjxp0iSN+7kqFgsRRRG//PKLdPz0009rPU9dvc+Ky/p0/T7XxjLbisqPpSY/b1W5d+8ennjiCY17/ZYtW4awsDCoVCpkZGSgoKAAYmlVbIiiqHU2szYIgoDZs2dLyZlCocCsWbOwefNmnD9/HikpKcjLy9MYz6ZNm+psPNS86ZqcXbt2DevWrZNNzuzs7LBo0SKMHDmyylUFuqx8aEk4g0ZETZquv9Rb6i//sWPHYs+ePdLx8ePHMWvWLIP6EgRBoypdnz594ODgINtGn8S44sWAtuIY5ubmePHFF/Hiiy8iKSkJhw8fxrFjxxATE4Nz585JS9AyMzOxZMkSlJSU4LnnntPoo/zM2gsvvIDvvvtO53HWNWNjYzz99NP49NNPAQBbt27FBx98IL1+6NAhJCQkSMdyCUH592llZVVrCVvFmcGsrCyditDoesGn71jKitXU5OetKl988YX0NbO1tcWxY8fQt29f2TZ1+WFQeHg4zpw5Ix3/8ssv1VakbKkfTlHNlBUGkVNcXIz9+/fj+PHjsnF9+vTBzJkzNZY0lqdUKitVcGzpOINGRE2ai4tLtcvIWvIv/wkTJmgcl5950VdERIRGUYXx48dX20atVutU6REoXbpVXtu2batt07ZtWwQEBGDt2rU4c+YM7t69iw8++ECjmMnbb79d6Wb1du3aSY/Lqjk2JuWTrri4OKmyI6A5ozZs2DDZG+nLv8/c3FxkZ2fXyvgqfm/i4+N1alfxe1zbY9F1HLqOpfwF6vLly6tNzgDIziLUVPnxjBs3TqftAupyPNQ8lRUGkSvykZ6ejk2bNskmZ8bGxpg2bRpmz56tNTkDAE9PzxZ/z1lF/GoQUZNmZGRUbYGHlvzLf9iwYRpLAXfs2KHXRWx5X331lcZxxVkpbcoXs5BTvhpfmzZtDLpn0NnZGatWrdLYoywjI6PSGMpXXtR1fPXJzc0NQ4YMkY7LkrK8vDwEBwdLz1e3nG7UqFEax9V90q2rAQMGaFS+LP+9k6NrnD7Kf5107T8zMxPXrl2rNu727dvS4+r2yQOA7OxsXLhwQacxlP+dpGvhEX3HA0AjuSeSIwgC4uLisGvXLtk4lUqFdevWaVQIrah169ZYtGgRhg8fLi1prFgtV6lUtugqy3Ja5hULETUrffr0wezZsyvNpPGXf6k333xTelxUVKS1bLuc33//HVFRUdKxp6enzjd0l+3BJKe4uFijlPO4ceP0HmN5/v7+GsflS8IDmmXh7927p1elsvpSPvnatm0b8vPzERYWJi3NMzMzw5NPPinbh7OzMwYOHCgdV7UBuCEsLCw0EoSyoibV0eVnQV/u7u7S4ytXrmhUrtTmzz//RHFxcbVxRUVFeo1l69atWveFqqj8Bti6bFdhyHiuXLlSa0k5NW8qlQpfffUVtm7dqvXnsbi4GBEREfjzzz9lq4e6ubnhueeeg7Ozs/ScQqHAihUrMH/+fPj5+bXIzaf1wQSNiJqFPn36YPny5fzlX4XZs2dj8uTJ0vG+ffvw8ssv69z+yJEjGkldq1atKs2myfn9999x9uxZ2ZjvvvtOoyrdokWLKsXoU9684lK+itUmH3vsMY09wV599dVa2YagNs2ZM0f6xDkzMxM7duzQWN44ffp0nSoRvvHGG9LjoKAgjXsSa6L85t7nzp3Dn3/+KRu/Y8cOHDt2rFbOXd7s2bM19st76623ZOOzs7Px0Ucf6dR3+QvMQ4cOycYmJSVV2lxdTvlCMBW3LaiN8QiCgBdffFHn8VDLVbakUe6+zLS0NGzYsEF2ltrY2Bje3t4ICAiotGfmyJEjYWZmBldXV/Tv3x+urq4tdmWLLviVIaJmw8jIiL/8q2BkZIRffvlF44Lw22+/hZ+fn+wSFUEQ8O2338LT01Mj4fn+++/Rs2dPnc8vCAJ8fHy0XoTu2LFDI4kYMmRIlRsfP/nkk1i9ejXu378ve77i4mKNWUMLC4tKS/0A4NNPP5UuIq5duwZ3d3dcvXq12vdz+/ZtvPvuu/jHP/5RbWxNODg4aGzyvXbtWuzbt0861rVa4Jw5czB69GgA/1cFcNOmTdUmvHl5efj11181lhCW99RTT2lU8Vy8eDEOHz5cZezx48frrLqhUqnU+MAhMjIS//jHP1BSUlIpVq1Ww9fXV+cNm8vfw/ndd99pFMkpLzExEZMnT9Zr4+vyX9e9e/dqVEjVZTynTp3Suul3bm4unn76aURHR+s8HmreBEFAQkICLl26hISEBKkya3FxMXbv3i3bNjY2FuvWrcODBw+0xrRp0wZLlizB0KFDK1Vp7NWrF6ZMmVLzN9GCKER9PpIkokZFrVbD1tYWmZmZBu23VF5+fj7i4+PRpUuXSp98UfNw/fp1TJs2TdroGQAsLS0xbdo0TJ06FZ06dYKlpSUePnyIM2fOIDg4WON+NWNjY3z//fdYsmSJ7Hk2b94sza507NgRXbp0weHDh2FpaYlFixZh0qRJaN26Ne7evYuQkBCEhoZKbS0sLHDixAmNZXllPDw8EBMTA4VCgccffxxjxozBwIED4ejoCEtLS6Snp+PChQvYunWrxv1F77zzDv79739XOdbffvsNzzzzjHSxYmRkBG9vb0yePBndu3eHjY0NsrKykJSUhAsXLuDQoUNSFb358+dj8+bNlfr84IMP8OGHHwIoXX5Xk4vksLAw+Pn5VXrewcEB9+/fr3RPhzZJSUkYMWKExj1Mffr0gb+/P4YMGYI2bdqgqKgI6enpUKlU+Pvvv7Fv3z7k5uYC0D57eeDAAUyZMkVKhoyMjDB37lzMmDEDbdu2xaNHj7Bnzx5s3boVJSUleOqppzSWOR48eBAeHh66fjm0ys/Px+DBg6FSqaTnBg0ahEWLFqFPnz4oKirC33//jXXr1uHu3btwdHTEwIEDpYRX2/fy8uXLeOyxx6T3Z2FhgcWLF2Py5MnSHoP79+/H5s2bkZubi06dOqF///4IDw+X7Rco3aPOxcVF+horFAoMHDgQHTp0gInJ/xXZ/te//oV+/foBKN16okePHhqFP3x8fDB79mx07NgRWVlZOHXqFDZu3Ig7d+7A1NQU8+bNk5a2du7cWaMCaGPHv0u1o6wiY/kZMqVSiX79+uH8+fPSz2BFRUVF2Lt3L/7++2/Z/gcMGIDp06dXuXWFhYUF3njjjRb/gam+12tM0IiaMCZopK/k5GQsXboU27dv16tdly5d8P3332Pq1KnVxpZP0Dp37oyjR49i3Lhx1VbNs7CwwPbt27WeoyxB08czzzyDjRs3alzwVhQeHo65c+ciIyNDr77rI0ErLCyEs7NzpQ3CX3nlFb2WmQKlSVpgYKDWWS45cpcKv/76K+bPn1/ljFV5Q4YMQXR0tMaWF7WVoAGl9xK6u7trfABRFWtra2zfvh2//PILtmzZAkA+kfr666+xfPnyas/v6OiIiIgIfPPNNzr1C5Tes7Zo0SLZe8sqfo1OnDiBiRMnar2oLmNqaorvv/8exsbGGv8emaC1LGXLF/WVmpqKoKCgSvfvlmdiYoJp06Zh0KBBVe5tVmb+/PkGFX1qTvS9XmvZ6SwRUQvj6OiIsLAwHD16FL6+vhqFCipSKBQYPHgwvvrqK1y9elWn5KwqHTp0wJkzZ/DMM89o3Rx4woQJOHv2rOw53n77bTzzzDMaSzW1GTZsGEJCQvDzzz/LJmcAMG3aNNy4cQMrV66Ek5OTbKy5uTkmTJiA7777DmvXrq12HDVlZmaGOXPmVHp+3rx5evfVtm1bREdH448//qhyGVJFvXv3xj/+8Y9ql97NnTsXx48fx7Bhw6p8vVWrVnj11Vdx9OhR2Z+3mir7OVu2bJnWn7OxY8fi1KlTGvcfVueVV15BcHCw1gtMMzMzBAYG4uLFi1qXg2rzzDPP4Ny5c1i+fDmGDBmC1q1bV/vzOnLkSJw4cUKjEmlFo0aNwpEjR6q8l5NaDkEQDCqAdOnSJaxbt042OXNwcMCSJUswePDgan+XcC8+/XEGjagJ4wwa1VRhYSFOnjyJxMREPHr0CAUFBXBwcEDbtm0xfPhwnfYi00d6ejqio6Nx9+5d5Obmon379hg3bpzen64mJiYiNjYWt2/fRkZGBkpKSmBjY4POnTtj8ODB6NSpk0HjE0URly5dwsWLF5GSkoLs7GxYW1vD0dERvXr1Qr9+/WT382lKkpOTcfToUTx48ADp6ekwMTGBnZ0dunbtin79+mnsoaYrlUqFEydOICkpSdp/cPz48RpFPOpDZmYm9u/fj9u3b6OkpATOzs4YMWIEunXrZnCfJSUlOHHiBM6fP4+MjAy0bt0aHTp0gLu7u06bdNcFlUqFY8eO4dGjR7C0tET79u0xfPhwdOnSpUHGU9v4d6lmEhISpNlcXRQVFSEyMlJjM/SqDBw4ENOnT4eZmZlO/XIGjUsciVoUJmhERNRc8e9SzVy4cEHn5ewpKSkICgpCUlKS1hhTU1NMnz4djz32mM5jUCqVWL58Oe9B0/N6TX4enYiIiIiImpzyW5fIuXjxInbt2iV7L6SjoyMCAwOrXQZekaenZ4tPzgzBBI2IiIiIqJmRmw0DSpe4R0RE4Ny5c7JxgwYNgpeXl85LGoHSmTNPT0/uRWogJmhERERERM1MYWGh1teSk5MRFBSER48eaY0xNTXFjBkzMGDAAJ3POXLkSPTq1QsuLi6cOasBJmhERERERM2EIAhITEzUWs30/Pnz2LNnj+ySxrZt2yIwMBAODg4az3t4eMDJyanKfdU4Y1Z7mKARERERETVRZQlZVlYW0tLScObMmSpL2xcWFmLPnj24cOGCbH9DhgyBp6cnTE1NNZ5XKpUYO3YsjIyM0KtXL+mcNjY2nDGrZUzQiIiIiIiaIJVKVWk2qypJSUkICgpCSkqK1hgzMzPMmDED/fv3r/L18gU/jIyMWnzp/LrEBI2IiIiIqIlRqVTYtm2bbIwoijh37hzCw8NRXFysNa5du3YIDAxEmzZtKr1mZWUFb29vLl+sR0zQiIiIiIiaEEEQEBkZKRtTUFCA3bt349KlS7Jxw4YNw5QpUyotaQRKk7PXXnsNJiZMGeoTv9pERERERE1IQkKC7LLGhw8fIigoCKmpqVpjzM3NMXPmTLi5uWmN8fb2ZnLWAPgVJyIiIiJqIlQqFcLCwqp8TRRFnDlzBhERESgpKdHaR/v27REYGAh7e/sqX2dVxobFBI2IiIiIqBEqq9CoVquRm5uL9PR0nDp1qsrY/Px87Nq1C7GxsbJ9jhgxApMnT640M+bu7o42bdqwKmMjwASNiIiIiKiR0bVCIwA8ePAAQUFBSEtL0xpjYWGBWbNmVZoV42xZ48MEjYiIiIioEdGlQiNQuqTx77//xl9//SW7pLFDhw4ICAhA69atpefMzMzwxBNPwNXVlbNljQwTNCIiIiKiRkKXCo1A6ZLGnTt34sqVK7Jxo0aNwsSJEystafTx8UHXrl1rNFaqG0zQiIiIiIjqWdn9ZVlZWRr3fZXdcybn3r17CAoKQkZGhtYYS0tL+Pj4oFevXpWenzFjBpc0NmJM0IiIiIiI6lFV95eV3QtWVFSktZ0oijh58iT27t0LQRC0xnXq1An+/v6ws7Or9FpAQABnzho5JmhERERERPVE2/1larUa27Ztg5mZWZXt8vLysGPHDly9elW2/9GjR2PChAkwNjau9JpSqYSrq6tB46b6wwSNiIiIiKge6HJ/WWFhYaXn7t69i6CgIGRmZmptZ2lpCV9fX/Ts2VNrjKenJwuCNAFM0IiIiIiI6lDZ/WZxcXE6lc0vI4oijh8/jn379skuaXRxcYG/vz9sbW21xnh4ePC+syaCCRoRERERUR3RZz+z8nJzc7F9+3Zcv35dNm7s2LHw8PCockljGaVSibFjx+p1fmo4TNCIiIiIiOqArvuZVZSYmIjg4GDZpM7Kygp+fn7o3r17tf1xaWPTwgSNiIiIiKiWCYKAXbt26d3m2LFj2L9/P0RR1Brn6uoKPz8/KJVK2f7KKkNyaWPTwgSNiIiIiKiWHT58GHl5eTrH5+TkICwsDDdv3pSNc3d3h7u7e5UzYrNmzYKdnV2lvdWoaeF3jIiIqJF69tlnoVAooFAo8Oyzzzb0cJocV1dX6eu3efPmhh6Ozvh9b/oEQcDJkyd1jr99+zZ++OEH2eTM2toa8+bNw/jx47UmXXZ2dnB1dUX//v3h6urK5KyJ4gwaEVEL9fDhQ+zduxf79u3DxYsXkZKSgpSUFBgbG6N169bo1KkThg4dinHjxsHb2xuWlpYNPWQioiYhMTFRp9kzQRBw5MgRHDx4UHZJY5cuXeDn5wcbGxutMUqlEi4uLgaNlxoXJmhERC3MnTt38O9//xsbN25EUVFRlTG5ubm4d+8eTpw4gW+//RY2NjZ46qmn8M9//hMdOnSo5xETNU2urq64ffs2AGDTpk2cDWtBqttMGgCys7MRGhqKuLg4rTEKhQIeHh4YO3ZstbNhLATSfDBBIyJqQUJDQzFv3jzk5ORoPG9ubo7OnTvD0dERRkZGSEpKwp07d6RPgLOysrBu3Tr8/PPPOH78OAYOHNgQwyciavRUKlW1yxvj4+MREhKC7OxsrTGtWrWCv78/unTpItsXC4E0P0zQiIhaiM8//xxvvPGGxjKaadOm4eWXX8a4ceNgZWWlEV9YWIiYmBgEBwdj8+bNKCwsRF5eHtLT0+t76EQGSUhIaOghGGTz5s1N6p45+j+CICAyMlL29UOHDiEmJkZ2SWO3bt3g6+uLVq1aaY0ZMWIEevfuzUIgzRATNCKiFmDPnj0ayZmtrS3+/PNPTJ06VWsbMzMzTJ48GZMnT8a7776Lt99+G7///nt9DZmIqMlJTEzUundZVlYWQkNDER8fr7W9QqHAhAkTMHr0aNmky8PDA+7u7jUeLzVOTNCIiJq5hw8f4plnnpGSM2traxw6dAgDBgzQuQ8XFxf89ttvGDduHCwsLOpqqERETVpWVlaVz8fFxSEkJKTS8vLybGxsEBAQgM6dO1d7Hnt7e4PHSI0f50OJiJq5zz//XGNZ4tq1a/VKzspbtmwZRo4cqfX106dP45NPPoGPjw969eoFW1tbmJqawt7eHn379sWCBQsQFhYGQRB0Op8h5cY3b94stXF1da02/tSpU1i+fDmGDx8OBwcHmJmZwdzcHG3atMGAAQPg7++PTz75BCqVSrafwsJC7N27FytXrsTkyZPRuXNnWFtbw8zMDG3btsWwYcPw6quv4u+//9bpfdS2w4cPS18XhUKBM2fO6NW+f//+UtvFixdXGZObm4sdO3bg9ddfh4eHBzp06ABLS0tYWFigffv2GD16NN5++21cu3ZN5/NWVSq/pKQEISEhmD17Nnr27AmlUgmFQgEfH59q22qTkZGBP//8Ey+88AJGjx6Ndu3awcLCApaWlujQoQMmTJiADz/8EHfv3pXtJzo6WjpnWYEQAFiwYIHG17/8fxWXYhryc19YWIgtW7YgMDAQ3bp1g42NDaysrODq6ooZM2bg+++/l73fSZfzX7x4Ea+88grc3Nxga2sLa2tr9OjRA4sXL8a5c+d06rs5EQQBCQkJuHTpEhISEiAIQqUqi4Ig4MCBA/j5559lk7MePXpg2bJlOiVnAGSrOVLTxxk0IqJmLDMzE+vWrZOOu3fvjiVLltT6ee7cuQN3d3etS3fS09ORnp4OlUqFzZs3w83NDUFBQQ16U3tOTg4WLVqEP//8s8rX09LSkJaWhkuXLiE0NBRvv/02YmNj0bdv30qxu3fvxrx587Ten/fo0SM8evQIp0+fxldffQVfX19s2rQJtra2tfqe5IwZMwZdu3aVKsb9/PPPGDJkiE5tz549i8uXL0vH8+fPrxSzfv16vPbaa8jNza2yj4cPH+Lhw4c4duwYPv30UyxevBhff/01zM3N9XofCQkJmDt3Lo4dO6ZXOznvv/8+PvnkExQWFlb5+v3793H//n0cPHgQ//73v7Fy5Up88MEHUCgUtTaGmti7dy+WLVtW5b+/27dv4/bt29i9ezdWr16NL7/8Ek888YRe/ZeUlGDVqlX4+OOPK324cvPmTdy8eRMbN27EqlWrsGrVqhq9l6YiNjYW4eHhGj/vSqUSU6ZMgYWFBfLz86FWqxESEqKRqFekUCgwadIkjBo1Suf7yFhOv/ljgkZE1IxFRUVpLLl57rnn6uSiMjMzU+Pi0NzcHN27d4e9vT1MTU2RkpKCq1evShfAsbGxGDlyJE6dOoVevXrV+nh04evri6ioKOnYyMgI3bt3R/v27WFiYgK1Wo24uDikpqZKMdpm/hISEjSSM6VSie7du8PW1hYlJSV48OABbt68KS0zDQsLQ1xcHI4fP15v+8spFArMmzcPH3zwAQDg999/x+effw4Tk+ovBX7++WfpcdeuXTFmzJhKMdevX9e4WG3Tpg26dOkCpVKJoqIiJCYmSheqgiBg/fr1SExMRHh4uM4/k+np6Zg4caKUZDo5OaF79+5QKBS4deuWTn1U5cqVKxrJWbt27eDi4gIbGxvk5+cjLi4ODx48AAAUFRVh9erVePjwocaHH2Xs7e2leztjYmKQn58PAOjXr5/WLSpq8jPw22+/Yf78+SguLpaes7W1Re/evWFqaopr164hOTkZQGmSPGfOHNy7dw+vv/66zud46aWX8MMPPwAorSzo5uYGS0tLxMfHS99TURTxwQcfoH379li6dKnB76cpiIqKqvIDArVajeDgYBgZGeHmzZsIDQ3V+oEFUPp7IjAwEJ06ddLr/Cyn3/wxQSMiasaio6M1jidPnlxn53J2dsbChQsxY8YMDB48uNKFf25uLn777TesXLkSKSkpUKvVeOqpp/Realcbdu7cqZGcvfHGG3jjjTfg6OhYKTYhIQF79uzBjz/+KNvnoEGDMH/+fEyfPh3du3ev9PrDhw/x9ddf49NPP0VxcTEuXLiAd999F2vXrq35G9LRvHnz8OGHH0IURSQnJyMiIgIzZsyQbVNcXKxRHGbevHlVJlQKhQLu7u546qmn4OXlVeVFZ1xcHD755BOsX78eABAZGYlvvvkGr7zyik7j/+CDD6BWq9G3b198/fXXmDBhgjQWURRl95OSY2xsDG9vbzzxxBOYOnVqlT8Hly9fxqpVqxAaGgqgdMZw+vTpmDlzpkbcgAEDpCp+5fdB+8c//lHr+6BdunQJCxculJIzW1tbrF27Fk8//TTMzMwAlCbDO3bswIsvvogHDx5AFEWsWLECAwcOxMSJE6s9x549e5CSkoI2bdrg888/x5w5c6S+AWD//v146qmn8OjRIwDAm2++iblz58La2rpW32tjERsbKzt7W1JSgn379uHIkSOy/fTs2RM+Pj6VqufKYTn9loMJGhEZZMmSJRpLnqh6/fr1q/Yiv7aVv9/JysoK/fr1q5Pz9OjRAwkJCTA1NdUaY2VlhcWLF2PcuHEYNmwY1Go1zp49i6ioqDpNHKuyc+dO6fFTTz2F//73v1pjXV1d8eKLL+LFF19ESUlJlTHPPvssXnrpJdlztmvXDv/5z38wYMAAzJkzB0DpRf77778POzs7/d+EAbp06YKxY8fi0KFDAEpnxqpL0CIjI6WLb4VCgWeeeabKuFWrVsmWBAdKZ9/WrVuHLl26YOXKlQBK75F88cUXYWxsXO341Wo13NzccOTIkUpfM4VCgW7dulXbR1U2bNhQ7dj79euHkJAQLFu2TJo5++STTyolaPXp+eefR0FBAYDSf19RUVEYNmyYRoyRkRF8fX3Rv39/PP7440hOToYoiliyZAlu3LhR7dc9JSUFdnZ2OHr0aJWz3RMnTkRISAjGjh0LoHQ2PTQ0VOvPSVMmCALCw8O1vp6ZmYmQkBAkJiZqjTEyMsLkyZMxcuRI6cMFhUIhW3IfgM6bVVPzwASNiAxy+fJlnDhxoqGHQdUou7AGSme4dLkINoQ+9xH17NkTL730Ev7zn/8AKN08u74TtDt37kiPyy4sdaHt61fdxX15Tz75JL755hscO3YMOTk5+Ouvv/S+J6gm5s2bJyVou3btQkZGhmyCWH55Y9l9bFXR52vw5ptv4rvvvsPdu3eRmJiI06dPY8SIETq1/fHHH2s9odVn7P/973+xZcsW5Ofn49ixY0hKSkLbtm1rdTy6OHPmDI4ePSodv/vuu5WSs/K6d++Ozz77TLp/MD4+Hjt37oSvr2+15/rvf/8ruxR5zJgxGDVqFI4fPw6gtCBNU0jQBEFAYmIisrKyYGNjU+1+YomJiVqXLF6/fh1hYWHIy8vT2t7W1haBgYHo2LGjxvNPPfUUTExMcO3aNVy8eLHSfW2cNWt5mKARETVj5e+fqq9ZGl2MGjVKenzq1Kl6P3/5e34aovrcqFGjpGVSp06dqtcELTAwEC+//DLy8vJQUFCAP//8E88991yVsRkZGdi1a5d0XFVxEEMYGRlhxIgRUkXEU6dO6ZSgDRgwQONnpyEolUq4ublJS3NPnTpV7SxkXShbagkAFhYWePHFF6ttM3fuXLz11lt4+PCh1Ed1CVqrVq0wb968avt2d3eXErTY2Nhq4xuaSqVCZGSkxp5l1SVDVVVyLSkpwf79+6stWtO7d2/MmjWr0v2GlpaW6Nq1K4yMjODq6orJkyfrlTRS88QEjYioGSsrUADoN8tV03Pu27cP586dw61bt6BWq5GXl6exhCctLU16XF3Z8rowYsQI7NixA0DpjEznzp3x0ksvQalU1rjv5ORkREVF4cKFC7h//z7UarW0DK3MzZs3pcf1/f6VSiV8fX3x22+/AQC2bNmiNUH7888/pZ8hS0tLBAYG6nSOxMREHDhwABcvXkRSUhKysrIqVUi8dOmS9FjXr8G4ceN0iquJa9euISYmBpcvX0ZycjKysrI0CnAA0LjXrSF+fgFIyRBQOgusS0XQsnvtfvrpJwDQqRLm0KFDdfrdUX5WKCMjo9r4hqRSqbBt27ZKz6vVamzbtg2zZ89Gnz59pBk2tVqN7OzsSltkZGRkIDg4WPZnwMjICFOmTMGIESOqvHdzxowZGglYWaJGLRsTNCKiZqx169bSMsfMzMw6PVdOTg4++ugjfP/99xqfSlenIS7mFi9ejM8++wxpaWkQRRHvvvsuPvroI4wfPx4eHh4YOXIkhg0bpld1vdu3b+ONN95AWFhYpQt6OQ3x/ufPny8laMePH8fNmzerLGxSfnmjr69vtQns5cuX8frrr2Pfvn3V3lNTnq5fA0PvMdPFkSNHsGLFCpw8eVKvdg2VjNy4cUN6PHDgQJ3bld8DMT4+HoIgyM7QtGvXTqd+yxcFkatc2NAEQZCKuGize/duJCQk4PLly1rfy7Vr1xAWFqbxIVhFrVu3RkBAQJXVO7l0keQwQSMig9RVsYnmrCG+Zm3atJEStPKzVrUtJSUFkyZNwoULF/Ruq23vqbrk6OiI3bt3w8/PT1rulZ+fj4iICERERAAAzMzMMGbMGDz55JN4+umnZZO1v//+G1OmTDHoYr3i7Fp9mDRpEjp06IB79+4BKE3EVq9erRFz69YtjRmW6pa57dmzB/7+/ga9H13b1MYMZ1XWr1+PZcuW6ZVUlmmI7x8AjW0dqqo6qU35WFEUkZGRAXt7e63xhsy8G/J1rC9lM2JycnNztS69Li4uxv79+zVmMKvSt29fzJw5ExYWFpVeGzt2LDw8PLh0kbRigkZEBqnvaoRkmG7dukn3Tdy/fx8pKSlwcHCo9fMsWbJEIznz8PDAnDlzMGTIEHTq1Ak2NjawsLCQlvhER0dj/PjxtT4OfYwaNQrXrl3Dd999hy1btuDatWsarxcWFuLAgQM4cOAA3n//faxfv77Ke41ycnLg5+cnJWempqZ44okn4O3tDTc3N3To0AFWVlYaF7offPABPvzwwzp9f3KMjIwwd+5cqXrlL7/8gg8//FBjCVb52TNnZ2dMmjRJa3/37t3DE088oVFRcN68eZg6dSp69+6N9u3bw9LSUqM8+7PPPostW7boPe7adu7cOTz//PNSUmFvb48FCxZg4sSJ6NGjB9q2bQsLCwuNCqUeHh6IiYmp9bHoo3xiWP7rWp2KCZfcDFBzVH5fSH2lp6cjODhY+mCjKsbGxpg6dSqGDRumdX+/snvOiLRhgkZE1Iy5u7tj9+7d0vGJEyfg7e1dq+e4fPkytm/fLh3/5z//kUqoa1OTi6TqaCuFXxWlUomVK1di5cqVSExMxOHDh3H06FFER0drFAR4+PAhfHx8sGvXLkybNk2jj02bNkn3oJiamiIqKgru7u6y563L96+r+fPnSwlafHw8Dh8+LN3jJYoifvnlFyn26aeflq0A+sUXXyAnJwdAaaW6Y8eOoW/fvrLnbwxfAwD497//LW1A7urqiqNHj8LZ2Vm2TWMYu52dHVJSUgDoN56Ks0eNqXhQfbCxsTGonUqlwo4dO2QTWnt7ewQGBqJ9+/ZaY5RKJVxcXAwaA7UcTN+JiJqxCRMmaBz/+uuvtX6O8vdzuLq64u233662Tfky93LKzwwUFRXp1Kb80i99uLi4YO7cufjf//6HK1eu4MaNG3j11VelT7oFQcBrr71WqV359z9nzpxqkzNA9/dfl/r27YuhQ4dKx+VnzI4cOaJRCKO66o3lvwbLly+vNjkDGsfXQBRF/PXXX9Lx+++/X21yBjRcYZDynJycpMe3bt3SuV35WGtra702Sm4OXFxc9FoqW1xcjPDwcI2COVXp168fli5dKpucAYCnpydnz6ha/AkhImrGBg8ejOHDh0vHoaGhtX5hfPv2benx0KFDtS7rKe/IkSM69V3+QkrXe+jKVwesie7du+OLL77QmA28fv064uPjNeLKv//yX2ttRFHUqXpefSifeAUFBUl7OJVP1oYOHVptwqXv1yA7O9ug+xVrW1paGrKzs6VjXcZ+/fp1jf0F5ZS/EK/t+7KGDBkiPa7ufqjyyv/slU/QW4Kyqoy9e/fWKT4tLQ0bNmyQ3QqkrDKmv79/lfeblbG0tJSqQxJVhwkaEVEzVz7BKCwsxLPPPmvwxaJarZaKapTRdWarTGpqqsaSSDmdO3eWHl+8eLHa+LJCH7XJ399f47im7z8yMlL2Hpb6NGfOHGmWUq1WY/v27cjPz0dQUJAUo8seWPp+DbZu3dogxWEq0nfcALBhwwadY8tvgC23gbEhPDw8pMfXr1/XqfpkYmIiDh48WGUfzZ1KpcJXX32FLVu26LT3YmxsLNatW4cHDx5ojWnTpg2WLFki+8GUpaUlPDw8sGLFCiZnpDMmaEREzdysWbMwa9Ys6fjAgQNYvHixXqXggdKZqWHDhuHq1asaz5dfEnb8+PFq+33ttdd0vlgt/wn//fv3q515++yzz5CcnFxtv/okqOVnWABUqnhX/v0fOnRItq/c3Nwql0k2lDZt2mjcU/fzzz9j+/bt0pYMpqammDNnTrX96PM1SEpKwvvvv2/giGuXg4ODxjLa6sZ+5coVfP311zr3X3652/Xr1/UfoIwnnnhCY++zN954Q7qXTpvyMSYmJli0aFGtjqmxEQQBcXFx2LZtG7Zt26bT9h9FRUXYs2cPgoKCZCt0DhgwAEuXLtW6DcHw4cMxf/58rFixAu7u7lzWSHrhTwsRUTOnUCiwZcsWjX2uNm7ciIkTJ+L06dPVtr979y6ef/55DBkypMqLzPL3ud27dw/vvfdelf0UFxdjxYoV2Lp1q85jHzFihMYGuC+//LLWi6yNGzfqXBlx/Pjx+PLLL5Gamiobl5OTg3/+85/ScceOHdGjRw+NmPLvPzg4WKMoS3mpqanw9vauVC2yoZVf5hgVFYW1a9dKx9OnT9ep6mf5r8F3332n9ecqMTERkydPlopbNDQTExOMHTtWOl69enWlJaxlLl26hKlTp+pV9bD8MsQ///xTYyloTVlbW2PFihXS8eHDh7F48eIqZyYFQcDKlSs1NmdetGgROnXqVGvjaWxUKhU+++wzbN26VaPgj5zU1FRs2LCh0obU5ZmYmGDmzJnw9fWtcgsCExMTzJ49G15eXnB1dWViRgZhFUciohbA1tYW0dHR8PHxkS6eDx06hGHDhmHEiBGYMmUK3Nzc4ODgACMjIyQlJeHmzZuIjIzEiRMnZCsjjhkzBsOHD5eWDX3yySc4efIk5s+fj65duyIvLw8XLlzApk2bpNm3ZcuW4Ycffqh23EZGRnjrrbfw8ssvAwDOnz+PgQMH4pVXXsHAgQMhiiKuX7+OP/74A4cOHYKJiQmefvppjQqEVUlISMBrr72GN954A+PHj8eoUaPQr18/tGnTBmZmZkhJScHp06exZcsWjYIQ77//fqULrqVLl+KTTz5BdnY2BEHArFmz8Mwzz2DGjBlo27Yt0tPTcfjwYWzcuBGpqalQKpWYPn06fv/992rff30oS8JSUlJQUlKicXFaXXGQMq+++io2b96MkpIS5OTkYOzYsVi8eDEmT54Me3t7PHr0CPv378fmzZuRm5uLTp06oX///ggPD6+rt6Wzf/zjH9i/fz+A0uWrgwcPxrJlyzBu3Di0atUK9+/fR3h4OH777TcUFxdj0KBBMDU11WmZ3Ny5c/HJJ59AEAQ8fPgQPXr0wKBBg+Dk5KRRFXP9+vUaRT90tXLlSkRGRuLo0aMASiuKHj16FIsXL8bAgQNhbGyMq1evYuPGjTh79qzUrlevXvj888/1Pl9ToVKpNJJRXVy6dAm7du2SXXrr4OCA2bNny36vZsyYwaWMVGNM0IiIWogOHTrg0KFDWLFiBX788Ufp/puTJ0/qdP+KhYUFXnrpJY1ZgTK//vorHn/8cWl5YXR0NKKjoyvFKRQKrFq1Cu7u7jolaADwwgsvICoqCjt37gRQmly9/vrrleJMTEzwww8/wNjYuNoErUxxcTGioqIQFRVVbezbb7+NJUuWVHreyckJW7ZswRNPPIHi4mIIgoAtW7ZUuceXtbU1/vjjD52+3vWlbBnjN998o/G8g4MDpk+frlMf/fr1w9q1a7F8+XIApfcCfvvtt/j2228rxTo6OiIsLKzS+RqKl5cXXn/9dWnmMCMjA2vWrMGaNWsqxXbt2hWhoaF49tlnderbzc0N//rXv/Duu+9CFEUUFRVVmdh9+eWXBo3d2NgY4eHh8PHxke4tu379Ot58802tbR577DFERETA2traoHM2VsXFxTh9+jRSU1P1KhRUVFSEyMhInDlzRjbusccew7Rp06rdc66uNlOnloXzrkRELYilpSW+++473LhxAy+99BJcXV2rbdO/f3+sWbMGcXFx+PTTT6vcR6h79+44ffp0pT3CKvazZ88erFq1Sq8xGxkZITg4GO+++y4sLS2rjBk8eDAOHTqk8z01a9asQUBAANq0aVPtucePH4/o6Gh8/PHHWuP8/Pywb98+9OvXr8rXjY2NMWXKFJw9exZeXl46jbE+VTVT9uSTT2pszlydV155BcHBwVp/pszMzBAYGIiLFy9WmeQ3pM8//xz/+9//tM6MWFtbY+nSpTh37pxO/2bKW7lyJY4dO4YlS5agf//+sLW1ld1TTl9KpRJRUVFYt26d7Njatm2L//73vzhx4oTW+6aaqr179+I///kP/vrrL5w+fVr23rHyUlJS8NNPP8kmZ6ampvDx8YGPj49OyRn3OKPaoBBru+4rEdUbtVoNW1tbZGZm1vhTu/z8fMTHx6NLly6ypYKp+bl58yYuXbqElJQUpKamwtjYGHZ2dujcuTOGDh1aqShGdRISEnDo0CE8ePAAJiYmaN++PR577DGd9saqTnZ2Ng4ePIi4uDjk5+ejffv2GDx4sNbESBc3btzA1atXkZiYKBXHUCqV6Nq1K4YOHarX0jNRFHH27Fnpk3wbGxu0b98eY8aMaXYXxdqUlJTgxIkTOH/+PDIyMtC6dWt06NAB7u7ujX5T5IKCAhw5cgSxsbHIzs5GmzZt0KlTJ3h4eDSZ/cIuXbqEc+fO4dGjRxAEAY6Ojujfvz+GDBmi0xYYjUl1f5cEQcDmzZsN2jrkwoUL2L17t2wlTycnJwQGBsLR0VGnPllGn7TR93qNCRpRE8YEjYiImiu5v0sqlQo7d+7Uq2gLULrVSEREBM6dOycbN3jwYHh6elY7awaUfqDj6enJ5Iy00vd6jfegEREREVGTYUgREAB49OgRgoKCZLfiMDMzg7e3NwYMGCDbl4eHB+zt7WFjYwMXFxdWa6RaxQSNiIiIiBotQRCQkJCArKwsWFtbG7QZ/blz5xAeHi67pLFt27YIDAyU3VqCs2VUH5igEREREVGjIYoicnJykJubi+zsbPz0009IT083qK/CwkLs2bMHFy5ckI0bOnQopk6dqrUwjqmpKZ588knubUb1ggkaERERETUKarUa2dnZAEpL5xcXF8vuTSYnKSkJQUFBshujm5mZYebMmdUWGho9ejS6du1q0DiI9MUEjYiIiIgaXPnkrCZEUZSWNBYXF2uNa9euHQIDA6vdbsPS0hJjx46t8biIdMUEjYiIiIgajCiKKCgoqJXkrKCgALt37652s+phw4ZhypQpOu31N2PGDC5rpHrFBI2IiIiIGkReXh7UajVKSkpq3NfDhw8RFBSE1NRUrTHm5uaYOXMm3NzcKr1mYmKiMePGgiDUUJigEREREVG9y8vLM7j4R3miKOLMmTOIiIiQTfScnZ0REBAAe3v7Kl+fMWMGlEolsrKyWD6fGhQTNCIiIiKqV6IoQq1W17if/Px87Nq1C7GxsbJxI0aMwOTJk2Fiov3SV6lUwtXVtcZjIqopJmhEREREVK8KCwtrvKzx/v37CAoKkp2Fs7CwwKxZs6pdpqhUKuHi4lKj8RDVFiZoRERERFSv8vPzDW4riiJOnTqFvXv3yiZ5HTp0QEBAAFq3bl1tn56enlzOSI0GEzQi0iCKYkMPgYiImrG8vDzk5ORUG1fV36O8vDzs3LkTKpVKtu2oUaMwceJE2SWNAAuBUOPEBI2IAED65FAQhAYeCRERNXWiKErLGI2NjWFmZgaFQqFXYRBRFCGKolRZ8d69ewgKCkJGRobWNpaWlvDx8UGvXr2qfL1nz54YNWoUC4FQo8YEjYgAAMbGxgCAoqKiBh4JERE1ZVWVzjcyMoKVlZVOM2dlSkpKUFJSgqKiIpw4cQJ79+6V/RCxU6dOCAgIgK2tbZWvjxo1ClOmTNH9jRA1ECZoRASgNEGzsLBATk4O7OzsGno4RETUBGmbIRMEQe+NqPPz8/Hw4UP89ttvuHr1qmzs6NGjMWHCBOnDxvJMTEzwxhtvwMzMTK/zEzUUJmhEJGnVqhXS0tIgiiIUCkVDD4eIiJqQ2iqdD5QmdFlZWdiyZYtscmZlZQVfX1/06NFDa4yfnx+TM2pSmKARkcTGxgYpKSlITk6Gk5NTQw+HiIiakNoonQ+UJnopKSm4ffs2Dh06pDXOxcUFAQEBUCqVVb5uY2MDLy8vFgChJocJGhFJLCws4OTkhEePHsHExAStW7fmTBoREemkNpKz4uJiJCcnIy4uDh999BGysrKqjBs7diw8PDwqLWk0NjbG0KFD0bt3bxYAoSaLCRoRabC3t0dRURGSkpKQlpYGW1tbWFpawtjYmMkaERFpVVxcLFVc1FVZpcaSkhLk5OQgNTUVjx49wkcffYTY2NhK8VZWVvD390e3bt2q7G/27Nno2bOnQeMnaiyYoBGRBoVCgbZt28LW1hYZGRlIS0tj6X0iIpJVlmTpWwikrG12djYOHTqEY8eO4fTp01XOnLm6usLf3x82NjZa+yooKND7/ESNDRM0IqpEoVDA0tISlpaWaNu2rVTqmIiIqIwgCHjw4AHi4uJw/fp15OXl6d1Hbm4udu/ejYsXL8omV+7u7nB3d692yaJc8kbUVDBBIyJZRkZGMDIygqmpaUMPhYiIGgmVSoXIyMgaVW28ffs2goODtd5nBgDW1tbw9/dH165dq+3P1NQULi4uBo+HqLFggkZEREREOlOpVNi2bZvB7QVBwJEjR3Dw4EGIoqg1rkuXLvDz89N5VmzUqFEsCkLNAhM0IiIiIqqWIAhISEjArl27DO4jOzsboaGhiIuL0xqjUCjg4eGBsWPH6pxwmZmZwd3d3eBxETUmTNCIiIiISFZtLGmMj49HSEiIbCGRVq1aISAgAK6urnr17ePjw9kzajaYoBERERGRVrWxpPHQoUOIiYmRXdLYrVs3+Pr6olWrVjr3bWZmBh8fH25GTc0KEzQiIiIiqpIgCIiMjDS4fVZWFkJDQxEfH681RqFQYMKECRg9erRes2Bubm7w8/PjzBk1O0zQiIiIiKhKCQkJBi9rvHXrFkJDQ5GTk6M1xsbGBgEBAejcubPO/Xbu3BlPP/00TEx4GUvNE3+yiYiIiKiS2NhYhIaG6t2upKQEMTExOHTokGxcjx494OPjA2tra5377tixI5599lm9x0TUlDBBIyIiIiINUVFROHbsmN7t1Go1QkJCcPv2ba0xRkZGmDhxot5l8Y2MjLBgwQK9x0TU1DBBIyIiIiIIgoDExERcvXoVJ0+e1Lv9zZs3ERoaitzcXK0xtra2CAgIQKdOnfTuPyAggPebUYvABI2IiIiohVOpVIiIiEBWVpbebUtKSnDw4EEcOXJENq5Xr16YNWsWrKys9Orf0tISM2bMYKVGajGYoBERERG1YDUpo5+ZmYmQkBAkJiZqjTEyMsLkyZMxcuRIKBQKnftu06YNpk2bBldXV86cUYvCBI2IiIiohSlbzqhWq7Fnzx6D+rh+/TrCwsKQl5enNcbOzg4BAQHo2LGj3v27u7uja9euBo2NqCljgkZERETUgtRkOSNQuqRx//791RYR6d27N2bNmgVLS0uDzmNjY2NQO6KmjgkaERERUQtRk+WMAJCRkYHg4GDcvXtXa4yxsTGmTJmC4cOH67WksTwbGxu4uLgYOkyiJo0JGhEREVELIAgCdu3aZXD7q1evYvv27cjPz9ca07p1awQGBsLZ2dng8wCAl5cX7zujFosJGhEREVELkJCQIHu/mDbFxcXYt28fTpw4IRvXt29fzJw5ExYWFoYOkRUbicAEjUiruXPn4rffftN4Lj4+Hq6urjr3ce/ePfzyyy/YuXMnEhISkJKSAgcHB7i6umLmzJl4+umn0aFDh1oeORERtXRlRUCysrKk5YLx8fF695Oeno7g4GDcu3dPa4yxsTE8PT0xdOhQg5c09unTB0OHDmXFRiIAClEUxYYeBLVc0dHR2LdvH06fPo3k5GRkZmZCFEXcunWrUuydO3dQ9uNa1+vSd+7ciVmzZlV6Xp8E7YcffsCKFSuQk5OjNaZVq1b47LPP8Nxzzxk0TrVaDVtbW2RmZkKpVBrUBxERNS8qlQqRkZFQq9XSc2ZmZigsLNS7n+3bt6OgoEBrjL29PQIDA9G+fXuDxxsQEAA3NzeD2xM1dvper3EGjRrEvn378PrrryM2NlbjeVEUtX76Nn/+fMTExEChUODw4cMYNWpUnYwtPT0dy5Ytq1Efq1evxqpVqzSe69GjB5ydnXH37l0pAc3OzsayZcuQnJyM9957r0bnJCIi0lYERJ/krLi4GHv37sWpU6dk4/r164cZM2bA3Nxc73ECXM5IpA0TNKp3H3/8Mf75z39CFEXoM4H72muvITo6GgDw66+/1lmC9uqrr+LBgwcAgClTpmDv3r16td+xY4dGcta3b19s3boVgwcPlp47ffo05s2bB5VKBQD45z//iQEDBmDmzJm18A6IiKglEgQBkZGRNeojLS0NQUFB0t/BqpiYmMDLywuDBw/Wa0mjpaUlhgwZAoVCAVdXVy5nJNKCSxypXm3atAmLFi2CQqGAKIqws7ODr68vBg4ciJ9++gmXL1+GQqFASUlJpbbFxcVwcnJCZmYmXF1dq1wGWVPh4eGYPn06AGD69OkICAjAggULpNerW+JYVFSEvn374ubNmwCAjh074uLFi2jdunWl2LS0NAwYMEBa19+jRw9cuXIFJia6f27CJY5ERFQmISEBW7ZsMbh9bGwsdu7cKbuksU2bNggMDES7du307n/27NmcLaMWSd/rNX5sQfUmPT0d//jHP6TjuXPn4vbt29iwYQNeeeWVaotlmJiYYMqUKRBFEQkJCbhz506tji8zMxNLly4FULr/yvfff693H3/88YeUnAHA2rVrq0zOgNJ1+2vXrpWOb9y4gT/++EPvcxIREQEweOPpoqIi7N69G0FBQbLJ2YABA7B06VK9kzOlUsnkjEgPTNCo3mzYsAEZGRlQKBTw9vbG1q1bYWNjo1cfQ4YMkR5fuXKlVsf3+uuvS7NZa9asQadOnfTuo/y6f2dnZ/j6+srG+/n5adxYHRQUpPc5iYiIACA1NdWgNhs2bMDp06e1xpiYmGDmzJnw9fXV+36zyZMnY/ny5UzOiPTAe9Co3pRfF19+5kgfPXv2lB4nJCTUdEiSv/76Cxs3bgQAjBkzBs8//7zefeTl5SEqKko69vT0rHa5oomJCTw9PbFp0yYAwN69e5Gfn1+jPWSIiKh5q6qEPgAcOXJEr34uXbqEXbt2yRYQcXBwwOzZs+Hk5KT3OC0tLTFy5EjeZ0akJyZoVG+uXr0KoPReq27duhnUh52dnfQ4MzOzNoYFtVqNJUuWAADMzc3x008/GbSPi0ql0lgaMnr0aJ3ajR49WkrQ8vPzoVKpMGjQIL3PT0REzV9VJfStrKxgZGRU5f3bVSkqKkJERATOnj0rG/fYY49h2rRpMDMzM2isM2bMYHJGZAAmaFRvUlJSoFAo0LFjR4P7KJ841VZ9mzfeeEO6n+39999Hr169DOqn4pYBPXr00KldxbgrV64wQSMiokq0ldDPzc3VuY/k5GQEBQXh0aNHWmNMTU0xffp0PPbYY4YME0qlEp6enlzWSGQgJmhUb6ytrZGRkYG8vDyD+0hOTpYet2nTpsZj2r9/P9avXw8AGDhwIN58802D+6q45FLXzbQ7d+6scRwfH2/wGIiIqHkqLi7G9u3ba9THhQsXsHv3bhQVFWmNcXJyQmBgIBwdHfXqu2PHjhg+fLi05JIzZ0SGY4JG9cbJyQnp6em4fv26wX2cPHlSelyTmTigdJPoxYsXAwCMjY3x008/6VXivqLyy00AzeWYcmxtbTWO5apwFRQUaCyjrHhOIiJqflQqFbZv367XZtPlFRYWIjw8HOfPn5eNGzx4MLy8vGBqaqpX/yNGjICnp6dBYyOiyvjxBtWb4cOHAyjd/+vo0aN6ty8pKZGWdhgbG+t8j5c2b731ljTr9dprr2Ho0KE16i87O1vj2NLSUqd2FePkErSPP/4Ytra20n+GVJokIqKmo2xZo6HJ2aNHj/Djjz/KJmdmZmbw8/PDzJkz9U7ORo0axeSMqJYxQaN6U7YBNACsXLlS73vIPv/8cyQmJkKhUGDs2LF6l+gvLzo6WtrnrFu3bli9erXBfZWpuGRE19m4inFyS09WrlyJzMxM6b/a3guOiIgaj+LiYuzatcvg9ufOncP69es1bg+oqG3btli6dCkGDBigV9/m5uYICAjAlClTDB4fEVWNSxyp3vj7+6NHjx64efMmjh49ivnz5+Onn37SqTrUunXr8O6770rHb731lsHjyM3NxaJFi6QE8ccff9R5tkuOtbW1xnF+fj6srKyqbZefny/bT3nm5uZ670FDRESNX8XS+Tk5Odi5c6dBM2cFBQUIDw/HhQsXZOOGDh2KqVOn6j1rZmJighUrVtTotgAi0o7/sqjeGBsb4+uvv4a3tzcEQcCvv/6KY8eO4eWXX8akSZM0Zo7S0tLw8OFDHD9+HBs3bsSJEycgiiIUCgX8/f1r9Ind22+/jbi4OADA4sWLMX78+Bq/NwBo1aqVxnFubq5OCVrF6ls1mRkkIqKmp6rS+YZKSkpCUFAQUlJStMaYmZlh5syZ6Nevn0Hn8PPzY3JGVIf4r4vq1dSpU/H111/jpZdeAlBasfD111/XiBFFsVL1qLLZrsGDB2Pz5s0Gn//KlSv49ttvAQDt27fHp59+anBfFVUc84MHD+Dg4FBtuwcPHmgc69KGiIiaB22l8/UliiLOnj2LiIgIFBcXa41r3749AgICDKqEbGlpiRkzZrB8PlEdY4JG9e7555+Hq6srnn32WY118QqFQtrnrCwhUygU0uMnn3wSGzZsqNFyxEePHkn9PXjwAK1bt9arfZcuXaTHtra2yMjIkI579+6tEXv79m3079+/2j5v376tccw/fERELYMgCIiMjKxxPwUFBdi9ezcuXbokGzd8+HBMmTLFoNkvKysrvPbaa5w5I6oHLBJCDcLLywtxcXFYu3YtBg4cCCMjI4iiKP1XxsrKCrNmzcKRI0fw22+/1cq9YnXFzc1N4/js2bM6tasY17dv31obExERNV6JiYk1Xtb48OFDrF+/XjY5Mzc3x+zZszFt2jSDEyxvb28mZ0T1hP/SqMFYW1vj1VdfxauvvorMzExcunQJqampyMnJgZ2dHdq1a4cBAwbU6h8EU1NTvZZ1FBQUaJTPb926tbT5ZsX9yzp16oRu3brh1q1bAICYmBidzlE+rnv37jXe342IiJoGuW1VqiOKIk6fPo3IyEiUlJRojXN2dkZAQADs7e0NOo9SqYSnpydXdxDVIyZo1CjY2tpizJgxdX6e0aNHy944XdHmzZuxYMEC6fjs2bNwdXXVGu/n5yfd1xYdHY3ExES4uLhojU9MTNRI0Pz8/HQeGxERNS0VKzXq8/eovPz8fOzatQuxsbGycSNHjsSkSZP0/qCzX79+6NmzJ2xsbODi4iJ9MElE9YMJGlEtWrBgAdauXYuSkhIIgoCPPvoIP/74o9b41atXQxAEAKVVLssng0RE1DwIgoDDhw/jxIkTlbZW0df9+/cRFBSE9PR0rTEWFhbw8fGpdG+0rgYNGoSuXbsaOkQiqiEmaES1qE+fPpg/fz42btwIAPjpp58wYsQILF68uFLsunXrsGHDBun42WefNfiPKRERNU4qlQq7du1CXl5ejfoRRRGnTp3C3r17ZZc0dujQAYGBgbCzszPoPBYWFrIrRYio7jFBI6pln3zyCWJiYqR70ZYsWYJdu3bhySefhLOzM+7du4fff/8du3fvltp0794da9asaaghExFRHaitEvp5eXnYuXMnVCqVbNyoUaMwceLEGt27PXPmTC5pJGpgTNCoweTn5+P8+fO4cuUK0tPTkZubq1HBsTrvv/9+HY7OcA4ODoiIiMDUqVMRHx8PANi5cyd27txZZXyXLl0QERHB/c+IiJqR2iqhf+/ePQQFBWls61KRpaUlfHx80KtXL4PPY2FhgZkzZ7IYCFEjwASN6t3Dhw/xz3/+E3/++SdycnIM7qexJmgA0KNHD1y8eBHvvvsuNm/eXGUZZVtbW8yfPx///ve/0apVqwYYJRER1ZWaltAXRREnTpxAVFSUdK9yVTp16oSAgIBKlYV10atXLzg5OcHV1RWurq6cOSNqJBSiPlMWRDV09OhReHt7Q61W6zVbVpFCoZBdg9+Y5OfnIyYmBgkJCUhNTUWbNm3g6uoKDw8PmJub16hvtVoNW1tbZGZmQqlU1tKIiYiopi5evIiwsDCD2ubm5mLHjh24du2abNzo0aMxYcIEGBsbG3Se+fPn834zonqg7/UaZ9Co3iQlJcHb2xuZmZlQKBTS8x07dkT79u1hZWXVgKOrOxYWFpg6dWpDD4OIiOqJIAi4e/euQW3v3LmD4OBgZGZmao2xsrKCr68vevToYegQoVQqZbeBIaKGwwSN6s2nn34qJWdGRkZ488038cILL6BDhw4NPTQiIqJaoVKpEBkZqffyRkEQcPz4cezfv192SWPnzp3h7+9f41UTnp6eXNJI1EgxQaN6Ex4eLj3+3//+hyVLljTgaIiIiGpP2V5n0dHRerfNzc1FWFgYbty4IRs3btw4uLu7G7ykESidOfP09GQxEKJGjAka1ZvExEQoFAq0bduWyRkRETUbKpUKERERyMrK0rttYmIigoODZWfcrK2t4efnh27duhk0PisrK0ydOlVa1siZM6LGjQka1ZuyoiDcjJmIiJqDmsyaCYKAo0eP4sCBA7JFs1xdXeHv7w8bGxuDx+nt7c0ZM6ImhAka1RsXFxdcv34d+fn5DT0UIiKiGqnJrFlOTg7CwsJw8+ZN2Th3d3e4u7sbPOPF5YxETRMTNKo348ePx7Vr13DlyhWUlJTUaA09ERFRQ1GpVNi2bZtBbRMSEhASEiKb2LVq1Qp+fn7o2rWrTn26ubmhV69esLa2BlCaANrY2HA5I1ETxQSN6s0LL7yA9evXIysrC7/++ivmzZvX0EMiIiLSmSAIiI+PN2h/M0EQcOTIERw8eFB2SWPXrl3h5+eHVq1a6dTv3Llz0b17d73HQ0SNFxM0qjf9+vXDm2++iTVr1uC1117DY489hgEDBjT0sIiIiKoVGxuLnTt3orCwUO+22dnZCA0NRVxcnNYYhUIBDw8PjB07VudZL6VSqfMsGxE1HUzQqF795z//gVqtxv/+9z88/vjjeOedd7BkyRI4Ojo29NCIiIiqFBUVhWPHjhnUNj4+HiEhIcjOztYaY2NjA39/f7i6uurVN/cyI2qeFKLcPDtRHfnjjz/w7LPPoqioCADQpUsXtGvXDmZmZjq1VygU2L9/f10OsUlQq9WwtbVFZmZmjTctJSKiymJjYxEcHKx3O0EQcOjQIcTExMguaezWrRv8/Pyk+8d0YWFhgZkzZ7L4B1EToe/1GmfQqN7t2bMH//nPf1BUVCT90YqLi0N8fLxO7UVRhEKhqMshEhERQRAEbN++Xe92WVlZCA0Nlf27plAoMGHCBIwePVqvWbABAwZg1qxZnDkjasaYoFG9+vHHH/H8889LiZlCoZAeczKXiIgakiAISExMRFZWFqytrXHo0CEUFxfr1cetW7cQGhqKnJwcrTFKpRL+/v7o3LmzXn1bWFgwOSNqAZigUb05ffo0li1bppGI9ejRAyNHjkS7du1gZWXVgKMjIqKWTKVSITIyEmq12qD2JSUliI6OxuHDh2XjevToAR8fH72WNJaZOXMmkzOiFoAJGtWbL774Qlqe6OTkhF9//RUTJkxo6GEREVELJggCYmJicOjQIYP7UKvVCAkJwe3bt7XGGBkZYeLEiRg1apTeSZaZmRl8fHx4zxlRC8EEjepN+T9+oaGhGDVqVAOOhoiIWjqVSoVdu3YhLy/P4D5u3LiBsLAw5Obmao2xtbVFQEAAOnXqpFffJiYmGD16NMaNG8eZM6IWhAka1ZtHjx5BoVCge/fuTM6IiKhBqVQqbNu2zeD2JSUlOHjwII4cOSIb16tXL8yaNUuvZfxGRkYYN26cXnuiEVHzwQSN6k3r1q2RnJys9yeIREREtUkQBERERBjcPjMzE8HBwbhz547WGCMjI0yePBkjR47Uq/Jw37594e/vz8SMqAVjgkb1xtXVFY8ePUJGRkZDD4WIiFqwskqNhrh+/TrCwsJkl0Xa2dkhMDAQHTp00LlfR0dHLF26FCYmvDQjaun4W4DqjY+PD06dOoVLly5BrVZzY2UiImoQ165d07tNSUkJ9u/fj2PHjsnG9enTBzNnzoSlpaVe/Xt6ejI5IyIAAOfPqd4sXLgQ9vb2KC4uxn//+9+GHg4RETVjgiAgISEBly5dQkJCAgRBAFB679mJEyf06isjIwObNm2STc6MjY3h5eWF2bNn652cWVpawtXVVa82RNR88aMaqjdOTk7YtGkTfH19sWbNGri6umLx4sUNPSwiImpmqtrTTKlUYsqUKdi7d69efV29ehXbt29Hfn6+1pjWrVsjMDAQzs7OBo13xIgRvOeMiCQKsfyuwUT1YP/+/XjyySeRlpaGiRMnYsmSJRg1ahTatWvH5R16UqvVsLW1RWZmJpeMEhGh5tUZyxQXF2Pfvn3Vzra5ublhxowZsLCwMPhcfn5+6N+/v8Htiahx0/d6jVfDVG+MjY01jkVRxP79+7F//369+1IoFCguLq6toRERUTMgCAIiIyNr3E96ejqCgoJw//59rTHGxsbw9PTE0KFD9arSWBUbG5satSei5oUJGtUbURShUCik/5f9QeMkLhER1YbExESNZY2GuHLlCnbs2IGCggKtMfb29ggMDET79u1rdC6gdOmli4tLjfshouaDCRrVq7JkjEkZERHVNkNL5wOlSxr37t2LU6dOycb169cPM2bMgLm5ucHnKs/T05P3nxGRBiZoVG8OHjzY0EMgIqJmzNClgmlpaQgKCsKDBw+0xpiYmMDLywuDBw+u8ZJGoHTmzNPTE3369KlxX0TUvDBBo3rj7u7e0EMgIqJmpqycfkJCAh49eqR3+9jYWOzcuVN2SWObNm0QGBiIdu3aGTTG4cOHo1evXgCAnJwc2NjYwMXFhTNnRFQlJmhERETUpAiCgMTERFy7dg1nz55FYWGh3n0UFRXhr7/+wunTp2XjBgwYgOnTp9doSWOfPn24zxkR6YwJGhERETUZKpUKERERNbrfLCUlBUFBQUhKStIaY2JigunTp+Oxxx6r8ZLGmoyViFoeJmhERETUJNTGHmcXL17E7t27ZWfdHB0dERgYCCcnpxqdqwzL6BORPpigUYMrLCzE33//jZs3byI9PR35+fmws7ODk5MThg4dyvLDREQEQRCwa9cug9sXFRUhIiICZ8+elY177LHHMG3aNJiZmRl8rvJYRp+I9MUEjRpMTEwMvvzyS0RERKCoqEhrXOfOnbFs2TI899xzsLW1rccREhFRY5GQkIC8vDyD2iYnJyMoKEi2iIipqSm8vb0xcOBAQ4dYJZbRJyJ9KURuSEX1LCcnBy+99BJ+/vlnAP+3J1pVa/zLv9auXTts2bIFkyZNqr/BNnJqtRq2trbIzMyEUqls6OEQEdWZ/fv348iRI3q3O3/+PPbs2SP7QaCTkxMCAwPh6Oiod/8eHh5wcHDA3r17NTbJZhl9Iiqj7/UaZ9CoXhUUFMDLywtHjx6FKIoaSZkoijA3N4e5uTmys7MhCIJG2wcPHmDatGkICQnBjBkz6nvoRETUQFQqFY4ePapXm8LCQoSHh+P8+fOycYMHD4aXlxdMTU316l+hUMDf3x9ubm4ASis1JiYmIisri2X0iahGmKBRvVq+fDmOHDkiJWb29vZYuHAhZs2ahQEDBqBVq1ZSbHx8PI4dO4YtW7Zg3759UCgUKC4uxpNPPonLly+jS5cuDfU2iIioDgmCgLi4OFy8eBGpqam4f/++Xu0fPXqEoKAgJCcna40xMzODt7c3BgwYYNAYyydnAGBkZMRS+kRUK7jEkeqNSqXCgAEDpJkxPz8//Pjjj7Czs6u2bUREBJ566ilp+ciTTz6JX3/9tS6H2yRwiSMRNTcqlQrbt283aG8zURSlJY3FxcVa49q2bYvAwEA4ODjofQ4uXSQifXGJIzVav/76K0pKSqBQKODl5YWgoCCd23p5eSE8PBzjxo1DSUkJQkNDkZubCysrqzocMRER1aealNEvKCjAnj17cPHiRdm4oUOHYurUqTovaezZsydGjRrFpYtEVG/4G4bqTVRUlPT466+/1rv9qFGj8NRTTwEovbcgJiam1sZGREQNSxAEhIWFGdQ2KSkJP/74o2xyZmZmhoCAAHh7e+t1v9nDhw/h4uKC/v37w9XVlckZEdU5/pahepOYmAiFQoHevXuja9euBvXh7e2t0R8RETUP0dHRspUWqyKKIs6cOYMff/wRKSkpWuPat2+PZcuWoV+/fnqPS61W8+8NEdUrLnGkepOWlgag9A+lodq1ayc9Tk9Pr/GYiIio4cXGxuLw4cN6tSkoKMCuXbtw+fJl2bjhw4djypQpMDEx/JInKyvL4LZERPpigkb1xtbWFqmpqbKfclYnNTVVesyiGERETZMgCFJJ+rS0NERHR+vV/sGDBwgKCpI++KuKubk5Zs2ahb59+9ZwtICNjU2N+yAi0hUTNKo3HTp0QEpKCmJjY5GUlIS2bdvq3ce+ffukx87OzrU5PCIiqgcqlQqRkZEamzrrShRFnD59GpGRkSgpKdEa5+zsjMDAQLRu3bomQwVQ+mGgi4tLjfshItIV70GjejN+/HgApZ+cvvvuu3q3v3nzJjZt2gSgdL8Zd3f3Wh0fERHVrbIqjYYkZ/n5+QgODsaePXtkk7ORI0di4cKFtZKcAYCnpycLgxBRveJvHKo3s2fPlh5v2rQJb7/9trQnWnVUKhWmTJmCvLw8KBQKTJo0qdb++BIRUd0TBAEREREGtb1//z7WrVuH2NhYrTEWFhZ48skn4enpWaP7zcoolUrMnj2b+50RUb3jRtVUr3x8fLBz504oFAoAQN++fbF8+XJ4e3trFAABgOLiYpw+fRpbt27Fhg0bUFRUBFEUYWRkhL///huDBg1qiLfQqHCjaiJqKmJiYvS+10wURZw6dQp79+6VnTXr2LEjAgICYGdnZ/D4lEolBg8eDHt7e+53RkS1St/rNSZoVK9SU1Px+OOP48aNG1AoFBBFUUrWHBwc4OjoCDMzM2RlZeHOnTtSyeXyP6ZffvklXnnllQYZf2PDBI2IGjtBEHDo0CG9967My8vDzp07oVKpZOMef/xxTJw4EcbGxgaP0cPDA2PHjmVCRkR1Qt/rNRYJoXrVpk0bHDhwAHPmzMGRI0ek5EwURSQnJ0sVHssnZGUxlpaWWLt2LZ577rn6HzgREelNpVJh586dyM/P16vd3bt3ERwcjIyMDK0xlpaW8PX1Rc+ePQ0en1KphKenJ5cxElGjwgSN6l2HDh0QExODDRs24Ouvv9bYw6aqCV0LCws89dRTePPNN9GjR4/6HCoRERno8uXLCAkJ0auNKIo4ceIEoqKiZO9R7tSpEwICAmBra6v3uMzMzDB9+nSpOiNnzYioseESR2pwN2/exPHjx3Hjxg2kp6ejoKAAdnZ2cHJywpAhQzBixAhYWVk19DAbJS5xJKLGaO/evTh+/LhebXJzc7Fjxw5cu3ZNNm7MmDEYP368wUsaWfiDiOoblzhSk9O9e3d07969oYdBRES1ICoqSu/k7M6dOwgODkZmZqbWGCsrK/j6+hq8koLLGYmoqWCCRkRERLWiuLgYx44d0zleEAQcP34c+/fvl13S2LlzZ/j7+xu0UmDEiBHo3bs3lzMSUZPBBI2IiIhqxdatW3WOzcnJwfbt23Hjxg3ZuHHjxsHd3V3vJY02Njbw8vLijBkRNTlM0KheJSUloaCgAADg7Oys12aiKSkpyM3NBQC0a9cOZmZmdTJGIiKqXtlelampqVAoFEhMTERSUpJObW/fvo2QkBCo1WqtMdbW1vDz80O3bt30HhvL5hNRU8YEjeqNWq1Gt27dkJeXB2dnZ8THx+vVftu2bXj55ZcBAB9++CHee++9uhgmERFVY+/evThx4kSVlXflCIKAo0eP4sCBA7JtXV1d4e/vDxsbG736531mRNQcMEGjehMWFobc3FwoFAo8//zzes2eAcDChQuxcuVKZGVlYevWrUzQiIgawO+//47r16/r3S4nJwdhYWG4efOmbJyHhwfGjRun8+xX165dMXDgQJbNJ6Jmgwka1Zv9+/dLj2fPnq13ewsLC3h7e+P333/HzZs3cfv2bXTu3Lk2h0hEROUIgoDExERkZWXBxsYGV69eNSg5S0hIQEhICLKysrTGtGrVCv7+/ujSpYvO/VpaWmLu3LlMyoioWWGCRvXmwoULAAAHBweDy+qPHj0av//+OwDg/PnzTNCIiOqISqVCZGSk7H1i1REEAYcPH0Z0dLTsksauXbvCz88PrVq10qv/GTNmMDkjomaHCRrVm9u3b0OhUBh0w3eZ8oldYmJibQyLiIgqUKlU2LZtW436yM7ORmhoKOLi4rTGKBQKjB8/HmPGjNEr0eK9ZkTUnDFBo3pTVoHR2tra4D7Kt5VbKkNERIYRBAGRkZE16iMuLg6hoaHIzs7WGmNjYwN/f3+4urrq1ffUqVMxfPhwzpwRUbPFBI3qjVKpRHp6OtLS0gzuIz09XXpck0SPiIiqlpiYaPCyRkEQEBMTg5iYGNm47t27w9fXV+/f40qlkskZETV7TNCo3jg5OSEtLQ1Xr15Ffn4+LCws9O7jzJkz0mNHR8faHB4RUYsnCILskkQ5WVlZCAkJQUJCgtYYhUKBiRMn4vHHHzcoyfL09GRyRkTNHhM0qjfDhw+XkrOQkBDMnTtXr/aCIEgFQgBg8ODBtT1EIqIWqyZFQW7duoXQ0FDk5ORojVEqlQgICICLi4ve/fOeMyJqSZigUb2ZMmUKfv75ZwDAO++8g6lTp8LBwUHn9p999hmuX78OhUIBFxcX9O7du66GSkTUIgiCgISEBJw+fRoqlUrv9iUlJYiOjsbhw4dl43r06AFfX19YWVnp1f+IESPQu3dv7m9GRC0KEzSqN4GBgVi5ciXu3r2Lu3fvYvLkyQgODtapquPnn3+Od955Rzp+9dVX63CkRETNn0qlwq5du5CXl2dQe7VajeDgYNmKukZGRpg4cSJGjRqld4IVEBAANzc3g8ZGRNSUKUS5jUmIatnvv/+OuXPnQqFQQBRFWFlZ4ZlnnsHs2bMxbNgwjT1wbty4gYMHD+KHH37AhQsXIIoiFAoF+vTpg7Nnz8LMzKwB30njoFarYWtri8zMTCiVyoYeDhE1cmUbT1+7dg0nTpwwuJ8bN24gLCxMqs5bFVtbWwQEBKBTp05698/kjIiaE32v15igUb1744038Pnnn0tJmkKhkF4zNzeHubk5srKyKm1qKooinJyccOLECb3LMjdXTNCISFe1sfF0SUkJDhw4gKNHj8rG9erVC7NmzdJ7SaNCoYC/vz+TMyJqVvS9XuMSR6p3n376KZydnfHWW2+huLgYAKRkLD8/H/n5+VJsWRIHAMOGDcO2bdvQuXPn+h80EVETVhsbT2dmZiI4OBh37tzRGmNkZITJkydj5MiRGh++6YrJGRERwDtuqUG89tpruHLlChYuXCi7D44oihg0aBB+/vlnHDt2jMkZEZGeBEHArl27atTHtWvX8MMPP8gmZ3Z2dli0aBFGjRqld3KmVCoxe/ZsJmdEROASR2oESkpKcPbsWVy5cgVpaWnIz89H69at0b59ezz++OPc70wGlzgSUXViYmIQHR1tUNuSkhLs27cPx48fl43r06cPZs6cCUtLS5377tOnD/r06QMbGxtWaSSiZo1LHKnJMTY2xrBhwzBs2LCGHgoRUbMiCAJOnjxpUNuMjAwEBQXh3r17WmOMjY0xZcoUDB8+XO9Zs+HDh/N+YiKiKjBBIyIiaqYSExMNKqN/9epVbN++XeOe4Ipat26NwMBAODs7692/lZWVQRtWExG1BEzQqN4sXLgQANC/f3+89tprBvXx7bff4uzZs1AoFNiwYUNtDo+IqFkRBAFxcXF6tSkuLkZUVFS1s25ubm6YMWMGLCwsDBrbtGnTuKSRiEgL3oNG9cbIyAgKhQJTp05FeHi4QX34+vpix44dUCgUKCkpqeURNj28B42IqhIbG4vw8HDZfcoqSk9PR1BQEO7fv681xtjYGJ6enhg6dKhBVRoB4PHHH8fkyZMNaktE1BTxHjQiIqIWShAEhIaGIjY2Vq92V65cwY4dO1BQUKA1xt7eHoGBgWjfvr1BY7OyssK0adNYqZGIqBpM0IiIiJoBlUqFXbt26XXPWXFxMfbu3YtTp07JxvXv3x/e3t4wNzevtk+FQoGnnnoKrq6uuHv3LrKyslipkYhID0zQqEkpW66jTylnIqLmLjY2FsHBwXq1SU1NRXBwMB48eKA1xsTEBF5eXhg8eLDOSxrd3d3RvXt3AGCVRiIiAzBBoyalbNmOvb19A4+EiKhxMCQ5u3z5Mnbu3InCwkKtMQ4ODggMDETbtm117tfCwgJjx47VayxERKSJCRo1CWq1Gp9++inu378PhUKBvn37NvSQiIgajCAISEhIwOnTp6FSqXRuV1RUhL/++gunT5+WjRs4cCCmTZum05LG8mbOnMlljERENcQEjepE165dtb4WExMj+3p5oigiLy8PycnJGs9Pnz69RuMjImqqDLnXDABSUlIQFBSEpKQkrTEmJiaYPn06Bg0apFffZmZm8PHxQZ8+ffRqR0RElTFBozqRkJBQ5f0KZQnX7du3de6rbCeIsv66du0q7alGRNSSqFQqbNu2Te92Fy9exO7du2WXNDo6OiIwMBBOTk4692tkZISxY8di3LhxnDkjIqolTNCozshtsWfI9nsmJibw8/PD2rVrYW1tXZOhERE1KWVLGsPCwvRqV1hYiMjISJw9e1Y2btCgQfDy8oKZmZle/c+dO1fnFRFERKQbJmhUJzZt2qRxLIoiFi5cCIVCgX79+uH111/XqR8jIyO0atUK7du3x4ABA2BlZVUXwyUiarQM2XQaAJKTkxEUFIRHjx5pjTE1NYW3tzcGDhyo97iUSiWrNBIR1QEmaFQn5s+fX+m5smWJHTp0qPJ1IiLSFBUVhWPHjund7vz589izZw+Kioq0xjg5OSEwMBCOjo4Gjc3T05PLGomI6gATNKo348aNg0KhwIABAxp6KEREjVpxcTF27dqFixcv6tWusLAQ4eHhOH/+vGzc4MGD4eXlBVNTU73HplQq4enpyYIgRER1hAka1Zvo6OiGHgIRUaO3d+9eHD9+XO92jx49QlBQUKWqt+WZmZlhxowZ6N+/v879WllZYfjw4bC3t4eNjQ1cXFw4c0ZEVIeYoBERETUCgiBg8+bNuHPnjl7tRFHEuXPnEB4ejuLiYq1xbdu2RWBgIBwcHHTu28rKCq+99hpMTHi5QERUX/gbl4iIqIEZurdZQUEB9uzZU+1SyKFDh2Lq1Kl6L2n09vZmckZEVM/4W5eIiKgBxcbGIjg4WO92Dx8+RFBQEFJTU7XGmJubY8aMGejXr59effM+MyKihsMEjeqNsbFxrfWlUChkl/IQETUFhiRnoijizJkziIyMlP092L59ewQGBsLe3l7nvocPH44+ffrwPjMiogbEBI3qjSiKUCgUBm1STUTU1AmCgMTERGRlZcHa2hqJiYmIiYnRq4/8/Hzs3r0bly9flo0bPnw4pkyZovPyRAsLC8ycOZMzZkREjQATNKpXhiRnCoWiRu2JiBqaSqVCZGQk1Gq1wX08ePAAQUFBSEtL0xpjbm6OWbNmoW/fvnr1HRgYiK5duxo8NiIiqj1M0KjeHDx4UOfYkpISpKen49KlSwgLC8OlS5egUCiwYMECzJs3rw5HSURUu1QqFbZt22Zwe1EUcfr0aURGRqKkpERrnLOzMwIDA9G6dWu9+lcqlXB1dTV4fEREVLsUIqckqAn45Zdf8PzzzyM3Nxcffvgh3nvvvYYeUqOgVqtha2uLzMxMKJXKhh4OEVUgCAK++uorg2fO8vPzsXPnTly5ckU2buTIkZg0aZJBFRdnz57NpY1ERHVI3+s1zqBRk/D000/D3t4e3t7eWLVqFQYPHoxp06Y19LCIiGQlJiYanJzdv38fQUFBSE9P1xpjYWEBHx8f9O7dW+/+WamRiKhxYoJGTca0adPg7e2N3bt3Y8WKFUzQiKjRu3r1qt5tRFHEqVOnsHfvXtkljR07dkRAQADs7Ox06tff3x+tWrVCVlYWbGxsWKmRiKiRYoJGTYqfnx92796Na9eu4fTp0xg6dGhDD4mIqEqxsbE4efKkXm3y8vKwc+dOqFQq2bjHH38cEydO1Hn7koCAALi5uek1FiIiahhM0KhJ6dKli/T48uXLTNCIqFFSqVR672929+5dBAcHIyMjQ2uMpaUlfH190bNnT536NDU1ha+vL5cxEhE1IUzQqEkpKiqSHiclJTXgSIioJSu/p1nZckEASEhIQFxcnF4zZ6Io4sSJE4iKioIgCFrjOnXqhICAANja2urUb6dOnfDss89yGSMRURPDBI2alPIXPa1atWrAkRBRS1XVnmZmZmYoKSmRvWesKrm5udi+fTuuX78uGzdmzBiMHz9e5yWN48aNw/jx4/UaCxERNQ5M0KjJuH//Pr7++mvpuFevXg04GiJqibTtaVZYWKh3X3fu3EFQUJBslUcrKyv4+fmhe/fuOvdraWkJd3d3vcdDRESNAxM0avTy8/MRGhqKd955B8nJyQAAOzs7XoAQUb0SBAERERG10s/x48exf/9+2SWNnTt3hr+/v957HA4cOJDLGomImjAmaFRvJkyYoFd8UVER0tLScPPmTRQXF0MURSgUCgDABx98AFNT07oYJhFRlQ4fPoysrKwa9ZGTk4Pt27fjxo0bsnHjxo2Du7u7zksay+PqAiKipo0JGtWb6OhoKcHSR/nETBRFvPzyy3j55Zdre3hERFqpVCpER0fXqI/bt28jODhYNsmztraGn58funXrZtA5lEqlVLCEiIiaJiZoVK9EUTS43eOPP46VK1di+vTptTwqIiLtarq0URAEHD16FAcOHJD9Hejq6gp/f3/Y2NgYfC5PT08ubyQiauKYoFG9WbVqlV7xZmZmUCqV6Ny5M4YMGYL27dvX0ciIiLQ7dOiQwUsbs7OzERYWhlu3bsnGeXh4YNy4cQYnV0qlEp6entzvjIioGVCIhk5pEFGDU6vVsLW1RWZmpt6FBIhIniAICA0NRWxsrEHtExISEBwcjOzsbK0xrVq1gr+/P7p06WLQOUaMGIHevXvDxcWFM2dERI2UvtdrnEEjIiKqQKVSYefOncjPz9e7rSAIOHz4MKKjo2WXNHbt2hV+fn4G7eloYmICPz8/zpgRETVDTNCIiIjK0bbXmS6ys7MREhKC+Ph4rTEKhQLjx4/HmDFjDJ71mjNnDrp27WpQWyIiatyYoBEREf1/giAgMjLSoLZxcXEICQlBTk6O1hgbGxv4+/vD1dXVwBGWkjsHERE1bUzQqMFcu3YNjx49QlpaGvLy8mBvbw97e3t07doV9vb2DT08ImqBEhMToVar9WojCAJiYmIQExMjG9e9e3f4+vrC2tq6JkMEgBpVeiQiosaNCRrVm8LCQmzduhVhYWE4evSo1osghUKBPn36YPz48Xjuuefg5uZWzyMlopbq6tWresVnZWUhJCQECQkJWmMUCgUmTpyIxx9/vFYKeXCvMyKi5o1VHKlefP3111izZg2SkpIA6LYfWtnm1F5eXvjyyy/RvXv3Oh1jU8QqjkS1o7CwENu2bau2HH55t27dQmhoqOxyQ6VSiYCAAJ0TqpEjR6Jjx44IDg7WGjN79mwWByEiakJYxZEaldTUVMybNw+RkZFSUqZQKKBQKGSTtLLkTBRFhIeH4/Dhw/jhhx8wZ86cehk3EbUcv//+O65fv65zfElJCaKjo3H48GHZuJ49e8LHxwdWVlY69Tty5EhMnToVAGBkZITIyEiNlQbc64yIqGVggkZ1Rq1WY/z48YiNjYUoilJSZmRkBDc3NwwbNgydO3eGnZ0dLCwskJmZidTUVJw/fx6nT59GSkoKgNJkLSsrC08//TTy8vKwcOHCBn5nRNSUCYKAxMREpKenIzw8HMXFxTq3VavVCA4ORmJiotYYIyMjTJo0CaNGjZI+bKpO+eQMAPr06YNevXohMTERWVlZsLGx4V5nREQtBBM0qhOiKMLf3x+XL1+WLlBsbW3x+uuvY+HChXB2dq62fXh4OL766ivs27dPSu6WLVsGFxcXTJo0qT7eBhE1MyqVqtLMlK5u3LiBsLAw5Obmao2xtbVFQEAAOnXqpHO/o0aNwpQpUyo9b2RkVONqj0RE1PTwHjSqE5s3b8bChQulxGratGnYsGED2rZtq3dfv//+O5577jnk5ORAFEV069YNKpUKJib8fIH3oBHpztD9zUpKSnDgwAEcPXpUNq5Xr17w8fGBpaWlTv2amZlh5syZLIRERNTM8R40anAFBQV4//33peOAgAD8/vvvMDY2Nqi/OXPmoFOnTvDy8kJubi7i4uKwbt06vPjii7U1ZCJq5gzd3ywjIwMhISG4c+eO1hgjIyNMmTIFI0aM0HlJo7u7O8aNG8cli0REVAn/MlCt279/P+7evQuFQoFOnTphy5YtBidnZcaMGYOPP/5YKiyyefPmWhgpEbUEgiDg1KlTei9rvHbtGtatWyebnNnZ2WHRokUYOXKkXvebeXh4MDkjIqIqcQaNal1ERIT0+MMPP9R5uU91XnzxRXzxxReIj4/HuXPnkJycDEdHx1rpm4iap9jYWISHh8veN1ZRcXEx9u/fj+PHj8vG9enTBzNnztT7d1yPHj30iiciopaFH99RrTtx4gQAwNTUFL6+vrXWr0KhQEBAAIDSIiKnTp2qtb6JqHkRBAHBwcEIDg7WKzlLT0/Hpk2bZJMzY2NjTJs2DbNnz661D6CIiIjKcAaNal3ZZtRdu3at9cIVQ4YMqXQeIqLyVCoVdu7cifz8fL3b7dixQ7Zd69atERgYWG0lWjlyG1sTERExQaNal5ycDIVCgXbt2tV63+X7LNsnzRAFBQU4evQooqOjcfbsWVy5cgXJyckoKCiAra0tOnbsiBEjRsDPzw+TJ0/W+d6S8u7du4dffvkFO3fuREJCAlJSUuDg4ABXV1fMnDkTTz/9NDp06GDweyCiUmX7mmVlZSElJQWHDh3Sq31xcTGioqJw8uRJ2Tg3NzfMmDEDFhYWNRkubGxsatSeiIiaNyZoVOssLCxQWFio17IiXeXl5UmPzc3N9W6flJSEV199FXv27EFWVlaVMSkpKUhJScH58+exbt06uLm5YcOGDRgxYoTO5/nhhx+wYsWKSp+U379/H/fv38exY8fwr3/9C5999hmee+45vd8HEZWqyb5mAJCWlobg4GDcv39fa4yxsTG8vLwwZMgQgz6sKU+pVMLFxaVGfRARUfPGBI1qnZOTEzIzM3H37t1a77t8NTUnJyeD2v/xxx+Vnm/fvj06duwIGxsbPHz4EFevXoUgCABKiwyMGTMGf/75J/z8/Ko9x+rVq7Fq1SqN53r06AFnZ2fcvXsXt27dAgBkZ2dj2bJlSE5Oxnvvvaf3eyFq6Qzd16zMlStXsGPHDhQUFGiNadOmDQIDA2ttRYCnpyerNxIRkSz+laBaV/bp8IMHDxAbG1urff/111+VzmOokSNH4ocffkB8fDzu37+PU6dOYf/+/YiNjcW9e/fw0ksvSZ+WFxcXY86cObh27Zpsnzt27NBIzvr27YszZ87g+vXriI6Oxs2bN/H333+jT58+Usw///lP7Ny5s0bvhailMXRfMwAoKirCnj17sG3bNtnkrH///li6dGmtJGdKpRKzZ8/W+LdPRERUFSZoVOsmT54sPf7xxx9rrd/79+8jPDwcANCqVSu9lhyWMTIywqxZs3DmzBkcP34czz33HFxdXSvFtWvXDt988w2++uor6bnCwkK8++67WvsuKirCihUrpOOOHTviyJEjGDx4sEbc0KFDceTIEY37z1asWIHi4mK93w9RS2TovmYAkJqaig0bNuDvv//WGmNiYoKZM2fCz8/PoKXUZaysrDBy5EjMnz8fy5cvZ3JGREQ6UYhlO/8S1ZLY2Fj0798fQOmFzsmTJzFo0KAa9+vv74+wsDAoFAr4+voiODi4xn3qYsSIEVJJfwsLC6SmpsLKyqpS3NatWzFv3jzpeNu2bQgMDNTa77Zt2/DEE09otH/66af1GptarYatrS0yMzNrvWImUWOkUqkQERGh9R5SOZcvX8bOnTtRWFioNcbBwQGBgYFo27atQeOzsrLC1KlTpXvNuJyRiIj0vV7jXw6qdW5ubvD09ARQujTQy8sLV69erVGfy5cvR1hYmHRcfqaqrs2a9f/au++4pu79f+CvAGEThjgQFVEcQNVq6xZRWxWvIjKkttaitfP21urt9vZWrW1v1+3tuq12OFpvVXBjq9VawbZSB06UOkEUUdkbAuTz+4NfzjeBJCQh7Nfz8fBhTvI+n/PJOCHv81lh0u2Kigqkp6frjNMcC9O9e/cG14CLiIiAl5eXtB0XF9e4ihK1c+oxZ6YmZ1VVVYiPj8eWLVsMJmdDhgzBE088YXZyBgAzZszA4MGD0bt3byZnRERkFv71oCbxzjvvwNraGjKZDHfu3MGoUaPw5ZdfmlzOtWvXMHXqVHz22WcAahernjVrFkaNGmXpKuvl4eGhta2rW1V5eTn2798vbYeEhMDGxvAcPDY2NlIiCwD79u0zed0moo5CpVIhPj7e5P1ycnLw9ddfIzk5WW+MXC5HWFgYwsPDYWtra1b9OMaMiIgshQkaNYnBgwfj448/hhACMpkMRUVFePrpp9G/f3+8++67OHPmjDRLYl05OTmIj4/HAw88gAEDBuDnn3+Wyunbt69ZiV5j1G0x0zV7ZGpqqtZkA2PHjjWqbM24iooKpKammldJojZMpVIhPT0dZ8+eRXp6us7vhvT0dK1lNoxx5swZrF692uCi9p07d8bjjz9udjdsBwcHzJs3j2PMiIjIYjjNPjWZv/71r7h58ybefvttyGQyCCFw+fJlLF26FEuXLoW9vT169OgBNzc32NnZobCwELm5ucjKypLKUCdmQO3EHT/++CM6derUbM9BCKE11s3Lywu+vr714urOVtmvXz+jyq8bd/78eYuM1yNqK3StY6ZQKDBlyhQ4OTmhuLgYTk5O2LNnj9FlKpVK7N27FydOnDAYN3ToUEybNs3sVjMACA0NRZ8+fczen4iIqC4maNSk3nzzTQwePBhPPPEEioqKpGRLCIHy8nJcunRJ6z5NmvdPnjwZGzZsQOfOnZu1/t9//720bhkAzJ07V+dCtXVb2YxdAsDHx0drOy0tzfRKErVR+tYxKyoqMnsSoOzsbMTFxeHOnTt6Y+RyOWbMmIEhQ4aYdQygNokMCQlhqxkREVkcEzRqctHR0Rg7dizef/99rFmzBiUlJdJjmslO3URNCIGhQ4fipZdeQnR0tM7EqCnduHEDzz33nLTt5uaGV199VWds3XFpbm5uRh3D1dVVa7uhyQ8qKyu1ulKaM804UWvQmHXM9Dl16hR++OEHVFVV6Y3p0qULZs+ebfLFnpdffhm3bt1CcXExXFxcOEMjERE1GSZo1Cy8vb3x0UcfYcWKFdi3bx8OHTqEo0eP4s6dO8jLy0NFRQXc3d3h4eGBvn37IigoCJMmTcK9997bIvUtKytDREQEcnNzpftWr15db8IQNc2kE6gdl2KMunENJWj/+te/sGLFCqPKJmrNMjIyLHaBQalU4ocffsDp06cNxt1zzz0ICQmBXC43qfzRo0fD3t5e55qJRERElsYEjZqVq6srZs+ebXB9sJZWXV2NOXPmaC1k+8wzzyA6OlrvPnWv2Dc0g6O+OENX/gHg1Vdfxd///ndpu6ioCD179jTqWEStiTnrmOly+/ZtxMXFIScnR2+Mra0tQkNDpfUZTTFgwABMmTKlMVUkIiIyCRM0Ig0qlQrz5s3Tms47OjoaH3/8scH9nJyctLYrKip0LmZdV91p9euWU5ednR3s7OwaLJeotXNxcWnU/kIInDx5Ej/++COqq6v1xnXr1g2zZ882eXIhuVyOmTNn4q677mpUPYmIiEzFBI3o/1OpVJg/fz42bdok3RcZGYn//e9/sLa2Nrivs7Oz1nZZWZlRCVpZWZnWdmN/tBK1Fb169YKDg4PJU+cDtWMxd+/ejbNnzxqMGz58OKZMmWJyl8bx48cjODiYY8yIiKhFMEEjQm1ytnDhQnz33XfSfeHh4di0aZNR3RXrTjiQlZUFT0/PBvfTXFIAgFH7ELUXmhPeGOvWrVuIi4vTGh9al52dHWbOnInAwECTynZwcEBoaChnZiQiohbFBI06PJVKhcceewzr1q2T7ps1axY2b95s9FiygQMHam1fu3bNqPEu165d09rmD0Nqj1QqFTIyMrRmQNy6davexep1EUIgOTkZe/bsQU1Njd44Ly8vzJ49W++EPro4OjoiMjISvXv3ZqsZERG1OCZo1KGpk7O1a9dK982aNQuxsbEmdYuqe6X+xIkTmDFjRoP71V1INyAgwOhjErUFqamp2LNnj9akIKZ2bayoqMDu3buRkpJiMG7EiBGYMmWK0RdW1GbPns0ZGomIqNVggkYdlqWSMwDo2bMn+vbtKy1qnZiYaNR+mnF+fn7o0aOHScclas30LURtSnKWlZWFuLg45OXl6Y2xt7dHWFiYWS3QCoXC6IXliYiImgP7clCHpCs5Cw8PNys5U4uIiJBuJyQkICMjw2B8RkaGVoKmuT9RW6dSqbRmQzWVEAJHjx7F119/bTA58/b2xpNPPml29+CQkBB2ayQiolaFf5WowxFC4PHHH9dKziIiIrB582azkzMAWLBggTTbo0qlwsqVKw3Gv/HGG9IYHGtrayxYsMDsYxO1NlevXjVrhkagtktjXFwcfvzxR4PjzUaPHo0FCxbA3d3d5GMoFApER0dz3CcREbU67OJIHYoQAk8++STWrFkj3RcVFYWNGzeaPG6lLn9/f8TExEhlf/311xg5ciQee+yxerGrV6/GN998I23Pnz+/3kQjRG1VamoqduzYYda+mZmZ2LJlC/Lz8/XG2NvbIzw8HAMGDDC5fAcHB0RFRXFCECIiarVkQgjR0pUgai6xsbF44IEHpG2ZTIZJkyaZlJw9//zzmDx5ss7HcnJyMGrUKGksGgDMnDkTc+bMQffu3ZGZmYmNGzdi9+7d0uN+fn5ISkoya4r9oqIiuLq6orCwEAqFwuT9iSxN37izhgghcOTIEezbt8/g7I49evRAVFQU3NzczKofW82IiKi5mfp7jS1o1KHUXRhaCIEDBw6YVMacOXP0Pubp6Yk9e/Zg6tSpSEtLAwDs2rULu3bt0hnv6+uLPXv2cP0zatPU0+gXFRXp/awbUl5ejp07d+LPP/80GDd27FhMmjSpwYXjdVEoFAgJCWFyRkRErR4TNCIL69evH86cOYN//OMfWLduHYqKiurFuLq6IiYmBm+99RacnZ1boJZElpGamoq9e/fq/Jwb48aNG9iyZQsKCgr0xjg4OCA8PBz9+/c3uXx7e3tpGn12aSQioraAXRyJmlBFRQUSExORnp6O3NxcdOrUCb1798aECRNgZ2fX6PLZxZFakrndGYHa1uukpCT8/PPPBrs09urVC5GRkXB1dTXrOOzSSERELY1dHIlaEXt7e0ydOrWlq0FkcdXV1VpjKU1RVlaGHTt24OLFiwbjxo0bh4kTJ7JLIxERdShM0IiIyCSpqanYvXt3vTGdxsjIyMCWLVsMdol0dHREREQE/Pz8TCp76tSpcHJygouLC3r16sUujURE1CYxQSMiIqOZ261RpVLh8OHDOHDgAAz1rPfx8UFkZKTJXXYVCgVGjBjBpIyIiNo8JmhERNQglUqF9PR0xMfHm7xvaWkptm/fjsuXLxuMGz9+PIKDg83q0hgSEsLkjIiI2gUmaEREZFBjZmq8du0atmzZguLiYr0xTk5OiIiIQN++fU0un2PNiIiovWGCRkREejWmS+Nvv/2GgwcPGuzS6Ovri4iICLi4uJhUvpeXF6ZMmcKxZkRE1O4wQSMi6sDUi0wXFxfXm1xDpVKZ1aWxpKQE27dvx5UrV/TGyGQyBAcHY/z48SYnWKNHj8aUKVNMrhcREVFbwASNiKiD0tV10cXFBffccw88PDxw5coVlJeXm1RmWloatm7dipKSEr0xzs7OiIyMhK+vr0ll29nZ4e9//ztsbW1N2o+IiKgtYYJGRNQB6eu6WFxcjISEBJPLU6lUOHToEBITEw12aezTpw8iIiLg7Oxs8jHCwsKYnBERUbvHBI2IqINRqVTYu3evxcorLi7Gtm3bkJaWpjdGJpNh4sSJGDdunMldGh0dHTFjxgxOBEJERB0CEzQiog4mIyPDrBkZdbl69Sq2bt2K0tJSvTEuLi6IioqCj4+PyeU7OjpiyZIlsLHhnysiIuoY+BePiKidqzsRiCWSM5VKhcTERCQmJhqM8/PzQ3h4OJycnMw6zowZM5icERFRh8K/ekRE7ZiuiUDkcnmjyiwqKsK2bduQnp6uN0Ymk+G+++7DmDFjzJoG38XFBdOmTWO3RiIi6nCYoBERtVP6JgKpqqoyu8zLly9j27ZtKCsr0xujUCgQFRWFXr16mXWMCRMmICgoiOubERFRh8QEjYioHbL0RCA1NTVISEjAr7/+ajCuf//+mDVrFhwdHU0+hkwmQ2RkJAIDA82tJhERUZvHBI2IqB2y5EQghYWF2Lp1KzIyMvTGWFlZ4f7778fo0aMhk8nMOg6TMyIiIiZoRETtUmpqqkXKuXTpErZt22ZwwWpXV1fMnj0bPXr0MOsYbDkjIiL6P0zQiIjamXPnzuHo0aONKqOmpga//PILfv/9d4NxAwcORFhYGBwcHMw+FpMzIiKi/8MEjYioHUlNTcWWLVsaVUZBQQG2bt2K69ev642xsrLClClTMHLkSLO7NCoUCoSEhHCmRiIiIg1M0IiI2glLTAxy4cIFbN++HRUVFXpj3NzcMHv2bHh7e5tcvqOjIwYPHowBAwagV69enKmRiIioDiZoRERtnEqlQnp6Ok6ePGn2xCDV1dU4cOAAkpKSDMb5+/tj5syZRndpHDx4MIYOHSotks2kjIiIyDAmaEREbVhqairi4+MNTuLRkPz8fGzZsgWZmZl6Y6ytrTF16lQMHz7cpC6NoaGhsLHhnxoiIiJj8a8mEVEbpFKp8OuvvyIhIaFR5aSmpmLnzp0GuzS6u7tj9uzZ6N69u0llBwYGMjkjIiIyEf9yEhG1MampqdizZw+Ki4vNLqO6uhr79+/HkSNHDMYFBgYiNDQU9vb2JpVva2uLiIgIs+tHRETUUTFBIyJqQ1JTUxEbG9uoMvLy8hAXF4esrCy9MdbW1pg2bRruueces2ZpnDVrFseaERERmYEJGhFRG1FdXY3du3c3qoxz585h165dqKys1BvTqVMnzJ49G926dTO5fAcHB4SGhnLqfCIiIjMxQSMiagNSU1Oxe/dulJWVmbV/VVUV9u3bh2PHjhmMGzRoEGbMmAE7OzuTyndwcMDIkSMRFBTEljMiIqJGYIJGRNTKNbZbY25uLuLi4nDr1i29MTY2NvjLX/6CoUOHGt2l0dHREYMGDcLAgQM5fT4REZGFMEEjImrFlEoltm3bZvb+Z8+eRXx8PJRKpd4YT09PzJ49G127djW63MmTJ2PUqFFMyoiIiCyMCRoRUSu1f/9+HD582Kx9q6qqsHfvXiQnJxuMGzJkCKZPnw5bW1ujy7axsWFyRkRE1ESYoBERtUKNSc5ycnIQFxeH27dv642Ry+WYPn067r77bpPLHzNmDJMzIiKiJsIEjYiolamurjY7OTtz5gzi4+NRVVWlN6Zz586YPXs2unTpYnL5tra2CA4ONqtuRERE1DAmaERErczq1atN3kepVGLPnj04efKkwbihQ4di2rRpJnVp1MT1zYiIiJoWEzQiolZk48aNyMnJMWmf7OxsxMXF4c6dO3pj5HI5QkNDMXjwYLPqxfXNiIiImgcTNCKiVuLMmTO4ePGiSfucOnUKP/zwg8EujV27dsXs2bPh6elpcp0GDx6MIUOGoHfv3mw5IyIiagZM0IiIWgFTJwVRKpX44YcfcPr0aYNx99xzD0JCQiCXy02uk0KhQFhYGBMzIiKiZsQEjYiohZ07d86k5Oz27duIi4sz2BXS1tYWoaGhGDRokNn1CgkJYXJGRETUzJigERG1EJVKhfT0dOzYscOoeCEETp48iR9//BHV1dV647p164bZs2ejU6dOZtVLoVAgJCSE482IiIhaABM0IqIWkJqair1796KoqMio+MrKSuzevRtnz541GDd8+HBMmTLFrC6NDg4OiIqK4ngzIiKiFsQEjYiomaWmpiI2Ntbo+Fu3biEuLg65ubl6Y+zs7DBz5kwEBgaaXa/Q0FD06dPH7P2JiIio8ZigERE1I5VKhb179xoVK4RAcnIy9uzZg5qaGr1xXl5emD17Njw8PMyqE7s0EhERtR5M0IiImlFGRoZR3RorKioQHx+Pc+fOGYwbOXIkJk+eDBsb07/O5XI55syZwy6NRERErQgTNCKiZpSamtpgTFZWFuLi4pCXl6c3xt7eHmFhYY1q9QoPD2eXRiIiolaGCRoRUTNJTU3F0aNH9T4uhMCxY8fw008/GezS6O3tjaioKLi7u5tVD3ZpJCIiar2YoBERNQOVSoVdu3bpfbyiogK7du3C+fPnDZYzevRo3HfffWZ1afTz88PYsWPRq1cvdmkkIiJqpZigERE1g/T0dFRUVOh8LDMzE3FxcSgoKNC7v729PcLDwzFgwACzjj9mzBhMnjzZrH2JiIio+TBBIyJqBunp6fXuE0LgyJEj2LdvH1Qqld59e/bsicjISLi5uZl83N69e2Pu3LlmtbgRERFR8+NfbCKiJlRdXY3jx4/j8uXLWveXl5dj586d+PPPPw3uP3bsWEyaNAnW1tYmHzs4OBgTJkwweT8iIiJqOUzQiIiayP79+3H48OF699+4cQNxcXEoLCzUu6+DgwPCw8PRv39/s47t4uKC8ePHm7UvERERtRwmaERETUBXciaEQFJSEn7++WeDXRp79eqFyMhIuLq6mn38adOmcSIQIiKiNogJGhGRhVVXV9dLzsrKyrBjxw5cvHjR4L5BQUGYMGGCWV0aAU6hT0RE1NYxQSMisrBjx45pbWdkZGDLli0oKirSu4+joyMiIiLg5+dn1jG9vb1x//33cwp9IiKiNo4JGhGRhV27dg1A7dpnhw8fxoEDByCE0Bvfu3dvREREQKFQmHU8BwcHPProo0zMiIiI2gEmaEREFmZnZ4fS0lJs37693uyNdQUHByM4OLhRydX06dOZnBEREbUTTNCIiCyssrISq1atQnFxsd4YJycnREZGok+fPo061pgxYxAYGNioMoiIiKj1YIJGRGQhKpUK//rXv/D6668bnKXR19cXERERcHFxMftYtra2mDlzJpMzIiKidoYJGhGRBdy5cwcPP/ww9u/frzdGJpNhwoQJCAoKMrtLooODA0aOHNmoMoiIiKj1YoJGRNRICQkJeOihh5CVlaU3xtnZGZGRkfD19TXrGEFBQejTpw9naSQiImrnmKAREZmppqYGb731FlasWGGwS2Pfvn0RHh4OZ2dns46jUCgwYcIEJmZEREQdABM0IiIz3Lp1C3PnzsUvv/yiN8bKygpvvPEGrK2tUVlZafaxQkJCmJwRERF1EEzQiIhMdODAAcydOxe3b9/WG+Pt7Y2NGzciKCgIqampiI2NNfk4CoUCISEh8Pf3b0x1iYiIqA1hgkZEZKSamhqsWLECb775psGFp6dNm4Zvv/0Wnp6eAAB/f39ER0djx44dUCqVDR6H482IiIg6LiZoRERGuHnzJh566CEkJibqjbG2tsbbb7+NF154oV5i5e/vj379+uE///kPysrK9JbB8WZEREQdG38BEBE14KeffsLdd99tMDnr2bMnDh06hJdeeklvcmVjY4MZM2YYPBbHmxEREXVs/BVARKRHdXU1li5dipCQEGRnZ+uNCw0NxalTpzBmzJgGy1R3d1QoFFr3KxQKREdHc7wZERFRB8cujkREOty4cQMPPvggfvvtN70xNjY2eO+997B48WLIZDKjy/b398eAAQOQkZGB4uJiuLi4cLwZERERAWCCRkRUz48//ohHHnkEubm5emN8fHywefNmjBw50qxjWFlZoXfv3mbWkIiIiNorXq4lIvr/qqqq8NJLL2H69OkGk7NZs2bh5MmTZidnRERERPqwBY2ICEBGRgbmzJmDpKQkvTFyuRwffPABnn32WZO6NBIREREZiwkaEXV48fHxiImJQX5+vt6YPn36YPPmzbj33nubsWZERETU0bCLIxF1WEqlEs8//zxmzpxpMDmLiorCiRMnmJwRERFRk2MLGhF1SGlpaZgzZw6OHj2qN8bW1hb/+c9/8PTTT7NLIxERETULJmhE1OFs374dCxYsQGFhod4YPz8/xMbGYujQoc1YMyIiIuro2MWRiDqMyspKLFq0CBEREQaTszlz5iA5OZnJGRERETU7tqARUYdw5coVPPDAA0hOTtYbY2dnh08++QSPP/44uzQSERFRi2CCRkTtXlxcHB577DEUFRXpjenfvz/i4uIwePDgZqwZERERkTZ2cSSidquiogLPPPMMoqOjDSZnDz/8MJKTk5mcERERUYtjCxoRtUuXLl1CdHQ0Tp06pTfGwcEBn332GRYsWMAujURERNQqMEEjonZn06ZNePzxx1FSUqI3xt/fH7GxsbjrrruasWZEREREhrGLIxG1G+Xl5XjyySfx4IMPGkzOYmJicOzYMSZnRERE1OqwBY2I2oULFy4gOjoaZ86c0Rvj6OiIzz//HDExMc1YMyIiIiLjMUEjojZvw4YNeOqpp1BaWqo35q677kJsbCz8/f2bsWZEREREpmEXRyJqs8rKyrBw4ULMmzfPYHL22GOP4ciRI0zOiIiIqNVjCxoRtUnnz59HdHQ0zp07pzfG2dkZq1evxkMPPdSMNSMiIiIyH1vQiKjNWbduHYYPH24wORsyZAiSk5OZnBEREVGbIhNCCF0PFBQU4MKFC8jJyUFlZSVqamqau25E1ICqqirs3r0bM2bMgFwub+nqNLnq6mqcPn0aGRkZBuN8fX0xaNAgWFtbN1PNqL2Sy+Wwt7dHjx494OfnB3t7+5auEhERtTFFRUVwdXVFYWEhFApFg/H1ErSUlBScPHkSd+7cgVwuR9euXWFvbw8bG/aGJGqNysrK4Ojo2NLVaHIqlQqVlZXQc01JYmdnx8SMLKaqqgolJSW4c+cOrKys0KtXL4wZMwbdunVr6aoREVEb0agELTk5GQkJCejbty8CAgLg6+vbIa7KE7VVKpUKt27dQrdu3WBl1T57LAshUFZWhqKiIoPJmVwuh7u7Oy8mUZMoKSnBpUuXcObMGRQXFyMyMhJeXl4tXS0iImoDzE7QTpw4gYMHD2LEiBEYN24cZDJZk1eWiBqnvSdoKpUKhYWFKC8vNxjn5OQEhULB7y1qckqlElu3bkVubi6ioqLYkkZERA0yNUGzAmq7cPz2228YMmQIkzMiahWqqqqQk5NjMDmTyWRwd3eHq6srv7eoWdja2iIyMhIuLi5ISkpq6eoQEVE7ZAUAaWlpqKqqwr333ssfOUTUooQQKC0tRXZ2Nqqrq/XGyeVydO7cGQ4ODs1YO6LaJG3QoEG4du0aKisrW7o6RETUzlgBwMWLF9GlSxe4ubm1cHWIqCNTqVTIz89HYWGhwTgnJyd4enpyvBm1mP79+0OlUuHy5cstXRUiImpnrADg+vXr6NOnT0vXhYg6MKVSiezsbFRUVOiNYZdGai2cnZ3RpUsXXL9+vaWrQkRE7YyNEAIVFRVwcnJq6boQUQeknqWxoVYzztJIrY2Tk5PBCwpERETmsFGpVFCpVLC1tW3puhBRB6NSqVBQUNDgj1zO0kitkVwub3CGUSIiIlPxUjQRtQilUon8/HzU1NTojbGysoKbmxvs7e2bsWZExuEFAyIiagpM0IioWalnaSwqKjIYZ2trCzc3N3ZpJCIiog6l/a1s20yWL18OmUzGK6gWtm7dOul1TU9Pb+nqkAUolUr069cPMpkMsbGxyM/PbzA5c3Z2RqdOnRpMzjrS5+WZZ56BTCZDTExMS1eFiIiImpBZCVpCQoL0o0jzn42NDTw8PODr64vx48djyZIl2Lp1K5RKpaXrraWmpkYanzJs2DCDsUIIdOrUSarzmjVrDMavX79eiv3iiy8sWW2iDuHjjz/G5cuXERgYiPHjxxscb2ZlZQUPDw+ON9Ph5Zdfhq2tLb777jskJye3dHUs4vjx43jjjTcwZcoU9OjRA3Z2dnB2dkb//v2xYMEC/PbbbxY93rVr1/D8889j4MCBcHJygoeHB4YPH473338fZWVlFj0WERGRuSzaglZTU4P8/Hykp6fj119/xUcffYSoqCj06NEDb775psFFZxvD2toaY8aMAQCcPn3a4NX5c+fOIS8vT9r+9ddfDZat+fj48eMbWdOOqSO1cmjqqM9bU3FxMd59910AtS1AKpVKb6ytrS06d+7M8WZ69OrVCzExMRBC4J///GdLV6fRxo8fj+HDh2PZsmXYv38/MjMzoVQqUVpaikuXLmHdunUICgpCTEyMRS7yxcfHY/Dgwfjwww9x4cIFlJWVIT8/H8ePH8dLL72EoUOHck0zIiJqFRqdoD399NM4e/as9C8pKQk//vgj3nnnHUyePBkymQzZ2dn45z//ibFjxyI7O9sS9a5HnTypVCocPnxYb5w64bK2ttbabije09MTAQEBlqgqGTB//nwIISCEQO/evVu6OtRIn3/+OXJzc+Ht7Y3Q0FC9ceoujerzknR7/vnnAQB79uxp861oN2/eBAB0794dzz33HLZs2YKjR48iKSkJH374Iby9vQEA3377LebPn9+oY508eRIPPPAAioqK4OzsjLfeeguHDx/GgQMH8PjjjwMALl68iOnTp6O4uLhRxyIiImqsRidoXbp0wV133SX9GzVqFKZNm4aXX34Z+/btQ0pKCoYOHQoAOHr0KMLDw5uky6Nm69ahQ4f0xqkfmz17NgDgypUr0g+Fuu7cuYOLFy8CAMaNG8cuV0QmKC8vx6effgoAmDVrFqys6n/dWFlZoVOnTuzSaKQBAwZI3bjVr21bNXDgQGzevBkZGRn46KOPEBkZieHDh2PUqFFYsmQJTp06hf79+wMANm7caPB7vSHPPfccysvLYWNjg3379mHp0qUYPXo0Jk2ahC+//BLvvfcegNok7d///rdFnh8REZG5mnySkICAAPz+++9Skvb777/jv//9r8WPM3z4cKlrlKFWMfVjUVFR6Nu3r8F4dm8kMp0QAsXFxdi2bRsyMzMBAOHh4fXi1F0a7ezsmruKbdrcuXMBAHFxcW26tWf37t2Ijo7W22rq6emplSxt2bLFrOMcPXpU+i5fuHAhRo8eXS/m+eefh7+/P4DaMZNVVVVmHYuIiMgSmmUWRwcHB3z33XfSFfIPPvhA7x9ApVKJzz//HBMnTkTnzp1ha2uLbt264S9/+Qs2bNigdwyLnZ0dRowYAQA4duwYKisr68WkpaVJPxjHjRuHcePGAbBMglZRUYH3338fw4YNg4uLC1xcXDBixAh89tlnOsfeVVVVoVu3bpDJZAgJCTFYNgCkpKRI45nUV3vNcfDgQcTExKBPnz5wdHSEQqHAoEGD8OKLL+ptSVS7efMmXnnlFQwbNgyurq6Qy+Xo2rUrBg0ahAcffBDr1q3TGv+nnkxmwYIF0n2+vr71JpdJSEiQHjc0bqvuzJlFRUVYvnw5Bg0aBGdnZ3Tp0gV/+ctf6nVxvXPnDl577TUEBgbCyckJnTp1QlhYGE6ePGnw+aakpODNN9/E1KlTtSYw6NevH2JiYvDHH3/o3M+c562pMe+ROaqrq/Hpp59ixIgRcHd3h42NDdzc3BAcHIwdO3aYVFZNTQ3y8vJQXFyM+Ph4ALXPXf3jV83FxUWrS6Opny1TmPudAtT/zBUUFGDZsmUIDAyEs7MzPDw8MHHiRGzcuNHo+jT2/Y2MjAQAlJWVYefOnUYfty2aOHGidPvKlStmlaH5GdY8JzVZWVnhkUceAVD7Hh88eNCsYxEREVlEdXW1+OCDD8S5c+eEsQ4ePCgACABi2bJlRu83ZcoUab/ff/+93uNpaWli4MCBUoyuf+PGjRO5ubk6y3/ttdekuMTExHqPr1u3TgAQ/fr1E0II8dVXXwkAYtCgQTrLGzZsmAAgFAqFqK6u1nps2bJl0rFu3bol7r77br11Dg0NFTU1NfXKf/HFFwUAYWVlJW7cuGHwtVuyZIkAIGxsbERWVpbBWF3Ky8vFnDlzDL62Tk5OYteuXTr3P3TokFAoFAb3ByDi4+OlfTQ/J4b+HTx4UNpn7dq10v1paWladdB8zTMyMkT//v11lmdtbS1iY2OFEEKcPn1aeHt764yzs7MTv/zyi87na2zdX3nlFbP31Xze5r5HNTU1IjMzU+fnyxjnz58XQ4YMMXjM999/36iyKioqRFZWlsjMzBSZmZmiZ8+eAoCIjIyU7svKyhIVFRVa+5nz2VIz9HkRovHfKZqfuatXr4q+ffvqLSc6OlpUVVXpfX0aew5q6tatmwAgHnrooQZj27Lc3Fyt71FzBAUFSa+toffn8OHD0rFef/11o8revXu39F1DRESkT2FhoQAgCgsLjYpv1nXQ7r//ful23VarkpIS3Hffffjzzz8B1I5Z2bVrF44fP464uDgEBwcDAH777TeEhoaipqamXvmarVy6WsXU96lbztT/p6SkID8/Xyu2uLgYp0+fBgCMGTPG4OQFEREROH/+PBYtWoT9+/cjOTkZ33//vdRqEB8fj6+++qrefo899hiA2olNvv32W73lV1VVYcOGDQCAadOmoVu3bnpjdRFCICoqCps2bQIAhIaG4rvvvsPvv/+OpKQkfPzxx+jVqxdKS0sRFRWF48ePa+1fWVmJOXPmoKioCC4uLnjppZekSQqSkpLw/fff429/+5s0qF9t+PDhOHv2LN58803pvp9++klrUpmzZ89i+PDhJj0foHYM4Y0bN/Dqq68iMTERx44dw3/+8x8oFArU1NRg4cKFSEtLw4wZM1BeXo633noLv/32G44cOYIVK1bA1tYWlZWVmD9/vs4xkdXV1XByckJ0dDRWrVqFhIQEnDhxAnv37sW///1v+Pj4AADeeecdrF27ttHPu7HvkTmOHDmC0aNH4/Tp0+jTpw8+/fRT/PHHHzh06BBefPFF6TP/yiuvSGMxdRH/v0tjbm6u1Bp18+ZNXL9+HQBw9913A9DdpdHcz5YxLPGdoumBBx5AWloannrqKfz88884duwYvvnmG2mcVGxsLF588UW9r5El3191b4HExETjX5A2SPP51W2FNVZqaioAwM/Pz+C6egMHDqy3DxERUYtozha0n3/+Wdrv0Ucf1XrshRdekB577bXX6u2rUqnE3LlzpZjPP/+8XkxxcbGwsbERAMTUqVPrPa5ucVmzZo10n6enp86r83v37pWO9fbbb9crS/PKulwur9caIkTt1d+uXbsKAGLw4ME6XxP11d3+/fvrfFwIIbZt2yYda/v27Xrj9Pnyyy+leu7Zs0dnTF5enggMDBQAxNixY7UeO3DggMFWDLWqqiqdVwYaauUwNlbzNbezsxN//PFHvf13794txXTu3Fl4enqKy5cv14v773//K8Vt27at3uPZ2dkiPz9fbz0rKyvF5MmTBQDh4+NTr4XV1Odt7ntkbgva7du3pc9mSEiIKC0trRfz8ccfS/V/8cUXdZZTXV0tcnJypBYy9b8vvvhC6zNbVFQkVCpVvf2b8rNlie8Uzc8cAPH999/XiykqKpJaIa2srMTZs2frxTT2HKxrxYoVUp1u3bplMNYQzedm7r+1a9eafXxDampqxIgRI6TjHD9+3OQyysvLpf2nT5/eYLyTk5MAIEaNGmVU+WxBIyIiY7TqFrROnTpJtzVbrCorK/H1118DAAIDA7F8+fJ6+8pkMnz++edSGZ999lm9GGdnZ2kyksOHD2tdEa87I6Pa2LFjAdRvcTNl/Nmzzz6LCRMm1Lvfw8NDGvNw9uxZFBYW1otRt6JdvHgRv//+u87y1S00Xbp0wYwZMwzWpS4hhLQO1aJFi/SOd3N3d8f7778PoHYil0uXLkmP3bp1S7pt6LWwsbGBQqEwqX7mWrx4MUaOHFnv/unTp0utW9nZ2Vi5cqU0GYymBQsWGJxUxtPTE25ubnqPb2trK71e165dw6lTp8x4FrUs8R6Z6u9//ztu374NX19fxMbGwtHRsV7ME088AScnJwDQOd6usrIS2dnZOsd7ZmVlSbf9/Pzg4uKic5bGpvpsWeo7RdOMGTPw4IMP1rvfxcUFX375JYDa1vBVq1ZpPd4U72+XLl2k21evXjVY77bqP//5D44ePQqgtpfCPffcY3IZmpOoODs7Nxiv/ryXlJSYfCwiIiJLadYETfMPpOYfzuTkZBQUFACoXQdLX3dChUKB6OhoAMD58+e1fgSqqX/kFRcXa/1oVk/R3LVrV/Tr10+6X52s1Z3CWf2j3d7evsEueOpZ1XRR/6gQQiAtLa3e47Nnz4arqysA1OsqBwC3b9/Gnj17AADz5s0z2EVHl/Pnz0uD66OiogzGav5ATkpKkm57eXlJt3XVsSXMmTNH72ODBw8GUPsD/IEHHtAZ4+DgIH0OjPmBW1lZiYyMDJw/fx4pKSlISUmBEEJ6XN0d1hyWeI9MceXKFWlSi5UrV8LFxUVnnL29vdR9T3Nxd6GjS2NdmvFdu3bVW5em+mxZ8jtFTd8EE0Btl8PAwEAAwM8//6z1WFO8vx4eHtJtzSTXVHW73Zrzb9asWWYfX5/ExES88sorAGqT0S+++MKscioqKqTbtra2Dcaru9+Wl5ebdTwiIiJLMO3XfiNpJmWaV8NTUlKk27paRTSNHDlS+mOdkpKi9QMPAIKCgqSpmX/99VcpQao7/kwzHqj9QVdeXg4HBwcolUrpyu3IkSMb/MOuOXahLs0fUrqmxHZwcMBDDz2EL774ArGxsfjkk0+0WjO+++47aRbIRx991GA9dNEcy6Jreml9NH/0jRs3Dn369MHVq1exePFi/O9//0N4eDjGjx+P4cOHG/XDx9LUiYMu6pYvT09PuLu7Nxinb6ry0tJSfPLJJ9i0aRPOnTtncIxSTk5Ow5XWwxLvkSnUMxe6ubk1mDCoExu5XA6gdpbG/Pz8BtcyLC0tlW4beg+a6rNlye8UtYYu1IwYMQLnzp3DxYsXoVQqpbo3xfur+Zpqvtamuuuuu8zet6mcO3cO4eHhqK6uhr29PeLi4rRaDE2hbiUHYNT6m+rWYAcHB7OOR0REZAnN2oKm+SNWM3HRvNre0B9izQkyNPdTCwoKkrpSaXZd05egDRs2DI6OjqiqqpK6cR07dky68mrM+me6uoepaS7Oq+8HvrqbY3Fxcb21ftStCiNHjkRAQECDdanrzp07Ju8D1E7hrSaXyxEfHy8N0j927BiWLl2KcePGwc3NDSEhIfj+++8bnGTBkox5zQ3FaMbpqnd6ejoGDRqEpUuX4syZMw0+t8ZccbfEe2SKvXv3AgAmTJjQ4Bpk6mUpfHx8pC6Nhn7oqhee1myVM/TaNNVny5LfKcaWo24pFEJodeFuivdX8zVVJ8/tQVpaGqZMmYL8/HxYW1tj06ZNjVqDUvNzaEy3RXWya0x3SCIioqbSrC1omutODRgwQGeMrnEqpvDw8EBgYCBSUlKkpKyoqEjqglY3QZPL5RgxYgQSEhJw6NAhTJw4sdkXqB42bBiGDh2KkydPYu3atdJ6PEeOHMH58+cBmNd6BmgnH/Hx8ejdu7dR+9X9MRoQEICzZ88iPj4e8fHxOHToEC5fvozy8nL89NNP+Omnn/Dhhx/ixx9/NPtqd2syb948pKWlSeuZzZkzB/7+/tI6WjKZDCqVSmph0uzuaCpLvUfGqKysRHJyMoDaz50ht27dkrr8BQYGIjc312C8nZ0d3NzcYG1tjc6dO0v35+Xl6e1GCTT9Z6ux3ymNLacp3l/NRNLQWMmGaLY0mqtHjx6NqoPazZs3cf/99+PmzZuQyWRYs2YNwsLCGlWmvb09OnXqhNzcXNy4ccNgbH5+vpSg9ezZs1HHJSIiaoxmTdD2798v3dZMlDRb027fvm2w+5pmtx/N/TSNHz8eKSkpyM7Oxp9//om0tDSoVCqtSUQ0jRs3DgkJCVJiph6PJpfLTeqS1BiPPfYYnnnmGSQmJiItLQ2+vr5S65mjo6PBMVeGaE7M4ubm1qguTdbW1pg1a5Y05iQrKwt79+7Ff//7XyQnJyM5ORlPPvkktm/fbvYxWoM///wTv/32GwBg6dKlWtPlazLU2mKKxrxHhhZZ1iUlJUVaJL6hH6HqsY/A/03rro9CoYCTk5OUxGgmaPn5+dLELfpY+rNl6e8UdTmGXrPbt28DqE3kNLsgWvIcVNNsoevVq5fZ5QwaNKjRdVm7di3mz5/fqDJycnIwefJkaTzop59+Kl2oaqyAgAD8+uuvuHz5Mqqrq/WO41UvxwCYP6U/ERGRJTRbF8eUlBQcOHAAQO0Pw3vvvVd6TPMHy5EjRwyWox4bVnc/TepxZUBt10Z14jVq1CidkwWok8U//vgDlZWVOHz4MIDaFgb1rF5Nbe7cuXBwcIAQAuvWrUN5ebm0ZlJkZKTZsyNqJqT6Zok0l5eXFxYsWICkpCSpNWb37t31urRZqgWjuZw7d066rW+SEQANrlVl7PNuyveoLs2JcxpK7tSzEfr6+mqdr5qsra3h6ekJZ2dnreer+cPf0Bpq+hj72dLH0t8pQG33S0PUj/fr109r7FxTvL/q19TOzg5+fn4WKbOlFBYWYurUqVJvgXfeeQfPPPOMxcpXf7+XlpZKrce6aK65pp7dl4iIqCU0S4JWXl6ORx55ROoG9sILL2hdxbznnnukLjLr16/X+8OxuLgYsbGxAGqviuobzK/ZLfHQoUNSi1jd7o1qo0ePhrW1NUpLS7Fu3TppOvzm6N6o5urqKk3YsH79emzZskWqh7ndG4HaJLNHjx4AgC+//FJrVjNLkcvl0qK/1dXV0ux5apoD9XVNyd7aqCdlAQxPwFB3OvW6jH3ezfEeqWkmaGfOnNEZI4TAN998IyUuTz31lM5k087OTuryWde9994rPf+GEhtDGvps6WPp7xR1OfocO3ZM6i54//33az3WFO+v+jUdOnRoo8agCSEa/a8xrWdlZWWYPn06Tpw4AQD4xz/+gZdfftns8nTRnGVS30yhKpUK3377LYDaVs6JEydatA5ERESmaPIE7fz58xg3bpw0/iw4OBhPP/20VoydnZ00UUZKSgpWrlxZrxwhBP72t79JE4387W9/03vM7t27S2tfHTx4UGrp0GxZ06RQKKQr/u+99550f3MmaMD/TRZy7do1vPTSSwCAvn37Sj9QzWFlZYWlS5cCqJ1O/pFHHjGYLBQVFdVbD0rdPUgfpVIpXX12dnbW6t4GaE+lrp5uvDXTXIZh3bp1OmO++OIL7Ny502A5xj5vS7xHxtJM0DZs2FBvAouamhrEx8fjueeeA1CbADz00EP1ylEoFPDw8NCaBEeTra2tNHuiZgtVXY39bOlj6e8UANi1a5eUzGkqKSnBk08+CaD2vVTfVrP0+1tZWSkl11OmTDFY59ZMqVQiPDxcalV87rnn9HYnbohMJoNMJtM5vm/EiBHSd/8333yjc/mCf//730hNTZXq0Z4mXiEioran0WPQ7ty5ozXQvLS0FPn5+Thz5gwOHDiA/fv3Sy1no0aNwpYtW3T+8Xv99dexbds2XL16FcuXL8fZs2exYMECeHl5IS0tDZ999hkSEhIA1LZ4PfHEEwbrFRQUhCtXrkiz0NnY2GDUqFF648eNG4dTp05JYyCsrKz0trg1lfHjx6N///64ePGiNC5m/vz5je4i+NRTT2H//v3Yvn074uLicOLECTz55JMYMWIEXF1dUVRUhD///BMJCQnYtWsX7O3ttX6sHjhwACtXrkRQUBCmT5+OwYMHo3PnzigvL8fFixexatUq6Qr4woUL643xGDp0KOzt7VFRUYF//vOfkMvl8PHxkX7ce3t7t6pprYcOHYq77roLKSkpWL16NfLz8zFv3jx4eXnhxo0b2LBhA7Zs2YKxY8ca7LJmyvNu7HtkDCGE9MP+nnvuQXJyMsaNG4fXX38dAQEBuHnzJmJjY7Fx40ZUV1ejW7du+PLLL7WSMGtra7i7uxs1/X1YWBgSExNx9OhRFBcX65wopLGfLUMs/Z1y77334qGHHkJiYiKioqKgUChw5swZvPvuu7hw4QIA4JlnnpHW4dNkyff30KFD0jjC8PBwo1+P1ubBBx/Evn37AACTJk3CwoULDU5aYmtra3AsoSEff/wxxo4di/LyckyZMgVLly7FxIkTpa7k6oXG+/fvj+eff96sYxAREVlMdXW1+OCDD8S5c+eEsQ4ePCgAGP2vc+fO4q233hJVVVUGy01LSxMDBw40WNbYsWNFbm5ug3Vcs2aN1n7Dhw83GL9p0yat+CFDhhiMX7ZsmRRriOZrdfDgwQbr/e6770rxVlZW4vr16w3uYwylUimefvppIZPJGny/fH19tfbVfK6G/oWFhYmysjKdx3/ppZf07qf5uqxdu1a6Py0tTW89DImJiREAhI+Pj8G44OBgAUAEBwfXe+zkyZPC3d1db50HDRokbt68KW0vW7asUc9bCPPeo5qaGpGZmSlqamoMPlchhLh8+bK0/44dO8S0adP0lh8QECCOHj0qMjMzpX+5ublGHUctJydH2NnZCQBi/fr1OmMa+9ky9HkRovHfKZr1u3r1qvD19dVbTmRkpMHvuMacg5rmz58vAIjAwEC9MW2BKX9DGjqfjYnZtWuXUCgUesvv37+/uHTpkknPYffu3SI2NtakfYiIqOMpLCwUAERhYaFR8Rbt4mhlZQVXV1f06tULQUFBWLx4MbZu3YobN25g6dKlDV797t27N06fPo3PPvsMwcHB6NSpE+RyObp27YqQkBB89913OHTokMGZ1tTqdk9sqDWsbvfH5u7eqDZv3jzp9uTJk6WxK40ll8vx+eef4/Tp03j22WcxaNAguLq6wtraGq6urrj77ruxcOFCbNmyRerqo/bCCy9g69atePrppzFq1Cj06tUL9vb2sLe3R+/evREdHY3du3djx44delvC3nnnHXz11VcICgqCh4eHzslaWpO7774bp06dwlNPPQUfHx/I5XJ4eHhgxIgR+OCDD3D06FGD45XUTHnejXmPjKHZvXHIkCHYsWMH3nzzTQwcOBAODg5wdXXFyJEj8e6772LPnj3w9vaW4hUKBdzd3fV2adSlU6dOiIiIAAB8//33OmMs8dkyxJLfKb6+vkhOTsbSpUvh7+8PR0dHuLq6Yvz48VKrqqHvOEu8vxUVFdi2bRsA4K9//avJr0dHFhoaijNnzmDJkiXo378/HB0d4ebmhnvvvRfvvvsuTp482eYnXCEiovZBVl1dLT766CNMmzbNrIWQybL2798vjSvZvHkzoqOjW7hG1JqpVCrcunUL3bp1azB5eu211/DWW2/B1dUVBQUFqKioQH5+vsE13Ezp0qjLkSNHpNlTr1y50uB0+63N8uXLsWLFCgBo1Fp3lrJhwwbMmzcPnTp1Qnp6OhdUbmE//PADysrKMHv27JauChERtWJFRUVwdXVFYWGhUTOzN9s0+2ScNWvWAKhtfWjsIq1EmtQtaIMGDUJhYSHy8vIMJh329vZ6Z2k01siRIxEREYGamhr861//Mrscqk3G3377bQDAiy++yOSMiIionWKC1opcuXIFW7ZsAQAsWLAAdnZ2LVwjak/UCVq/fv0MLh8A1C77YGqXRn3efvtt2NjYYO3atbhx40ajy+uo4uLikJqail69emHRokUtXR0iIiJqIo2exZEaJzMzE2VlZbh69SpefvllVFdXw97eHkuWLGnpqpEF1NTUoKysDJWVlaiuroZKpYKVlRVsbGxgZ2cHR0fHZhmPl5OTI81oOmDAAL1x1tbW8PDwsOg04wMGDMCaNWtw5coVZGRkWGxcZUdTU1ODZcuWYdKkSa1q1lMiIiKyLCZoLWzu3LnSOk9qK1euRPfu3VuoRmQppaWlKCoqqteNUKVSQalUQqlUoqSkBAqFAk5OTk1WDyGE1nIA/v7+OuPUE4VYotWsLs3Jb8g8utajIyIiovaHCVor4ejoiP79+2Px4sWIiYlp6epQIxUXF6O4uFjrPmtra1hbW6OmpgY1NTUAapOnwsJCqFQqneuENVZ1dTXy8/ORnJwMoHZB37oJmkwmg0KhgKOjY6PX3CMiIiKixuEsjkQWVlFRgby8PGnbxsYGbm5uWpNtKJVKFBQUoLq6WrrPw8MD9vb2Jh3L0CyO5eXlKCgoMDgRiI2NDdzd3S3apZGoo+AsjkREZAyTZ3FUXzFXqVRNXTeidk/dIqZmbW0NT0/PejMh2trawtPTUyupKiwstMhU7kIIFBQUNDiFvoODAzw9PZmcEZlJpVKx1ZmIiCzOysrKCnK5HJWVlS1dF6I2r7y8XOq+CNQu8KxvTJd6YXe1mpoalJeXN+r41dXVyMnJQVlZmd4YmUwGNzc3uLm5Ncl4M6KOoqKiwuRWbyIiooZYAbU/IrOzs1u6LkRtnmaCZWVl1eCPN3t7e60kqaKiwuxjl5WVITs7G1VVVXpjbGxs4OnpyfFmRI0khEBeXl6TjB0lIqKOzQoA/Pz8cOXKFa0r/0RkGiGEVku0vb19g0mQTCbTSuIqKyvN6uZYWFjY4HgzdmkkspzMzEyUlJSgX79+LV0VIiJqZ6yA2nWKKioqkJGR0dL1IWqz6rZc1R13po9mwiSE0Jo4xFiGukaquzRaauFpIgIuXLgAFxcXeHl5tXRViIionbECAE9PT3h6euKXX35BSUlJS9eJqE2qm1gZuwC1jY32aheGuiiaSrNLIxFZRlpaGs6ePYuBAweyqzAREVmcFVB7hT0sLAwqlQqbN29GQUFBC1eLqO2p20XY2AStbpyluho7OjqySyORBQkhcOXKFezcuRO9e/fG2LFjW7pKRETUDsmExqCVgoICxMXFobi4GN27d0f//v3Ro0cP2Nvbw8bGhlcKiQwoKipCaWmptN21a1ejuhSqVCrcvn1b2nZ2dtY78YAQQmucmRACd+7c0YqRyWRwcXGBg4ODqU+BiDQIIaBUKlFWVoarV6/i0qVLKCgoQM+ePTF58mSjL8IQEVHHVlRUhJ49e6KgoEBrBm99tBI0oHaSgsuXL+PixYu4du0aJw4hMpJSqdTqnujk5GT0vpqJnVwu1zt+raqqCkql0vxKEpFJhBBQqVQoKSlBSkoKLly4gOvXr3PtUCIiMtn169fRo0ePBuPqJWiaKisrkZeXh4qKCiZqRA346KOP8NNPP0nbP/zwg9EtaNOnT5e2p06disWLF+uMraqq0koCS0pKEBMTg2+//dakhJCoIygrK8O8efPw3XffmT0OUy6Xw87ODu7u7mwxo3ZDfTX/+vXrUCgULV0dolajqc4NIYTUQ9GY34Y2hh60s7PjDFVERrK2tsbly5el7R49ehj1o7C0tFRrv+nTp8PPz8+oYxYVFeHy5csIDAzkH1miOtTnR0BAAM8PIh0UCgXPDSIdmuLcMKZroxrn3CayEGdnZ63tsrIyo/arG8eFb4mIiIg6LiZoRBbSuXNnre2srCyj9qsb5+npabE6EREREVHbwgSNyEIGDhyotX3t2jWj9qsb5+/vb/Qx7ezssGzZMtjZ2Rm9D1FHwfODSDeeG0S6tZZzw+AkIURkvOvXr6NXr17S9ooVK/D66683uN+KFSuwfPlyrXKMmeGHiIiIiNoftqARWUjPnj3Rt29faTsxMdGo/TTj/Pz8mJwRERERdWBM0IgsKCIiQrqdkJCAjIwMg/EZGRlaCZrm/kRERETU8TBBI7KgBQsWSGslqVQqrFy50mD8G2+8IS14a21tjQULFjR5HYmIiIio9WKCRmRB/v7+iImJkba//vprfP311zpjV69ejW+++Ubanj9/fr2JRoiIiIioY+EkIUQWlpOTg1GjRuHKlSvSfTNnzsScOXPQvXt3ZGZmYuPGjdi9e7f0uJ+fH5KSkoyaYj8zMxMbNmzArl27kJ6ejpycHHh6eqJ3796YOXMmHn74YXh7ezfJcyNqLsXFxTh58iROnDiBEydOIDk5GRcuXEBNTQ0AwMfHB+np6Y06Bs8laosqKyvx+++/IyEhASdOnMD58+eRnZ2NyspKuLq6okePHhg5ciQiIiIwefJkyGQyk4/Bc4PaGiEEzp07h6SkJJw+fRqpqam4du0a7ty5g7KyMjg4OMDd3R0BAQEYN24cHn74YfTu3dvk4zTbuSGIyOIuXrwofH19BYAG//n6+opLly4ZVe4XX3whnJycDJbn7OwsVq1a1cTPkKjp9O/fX8hkMoOfcx8fn0Ydg+cStTW3bt0Sc+bMES4uLkb9bQEgAgMDxR9//GHScXhuUFv09ddfG31eABBWVlbiqaeeEoWFhUYfoznPDSZoRE2kuLhYLFq0SCgUCp0nsaurq1i0aJEoLi42qrwVK1bUK6Nfv34iODhY9O3bt95jK1eubOJnSNQ0jPnj2pgEjecStUXHjh3TeS54eXmJ4cOHi0mTJomAgABhZWWl9biNjY3YunWrUcfguUFt1VdffVXvc+/n5yfGjh0r7r//fjFy5Ejh7u5e7zM8bNgwkZeX12D5zX1uMEEjamLl5eVi7969YtWqVeKtt94Sq1atEnv37hUVFRVGl7Fjxw6tEz8gIEAkJydrxRw7dkz4+/trxe3cudPST4eoyak/v05OTmLMmDHi2WefFWvXrhUhISGNTtB4LlFbpZmgjRo1SqxatUqkpaXVi8vKyhJ/+9vftFqhbW1txZ9//mmwfJ4b1JatXbtWBAUFiffee0/88ccfQqlU1otRqVQiISFBjBw5UuszPHfuXINlt8S5wQSNqJVTKpXCz89POuF79Oih92pPbm6u8Pb21rq6U1VV1cw1JmqcDRs2iPPnz4uamhqt+2NiYhqVoPFcorYsOTlZhIWF1fthqM8nn3yi9WMxMjJSbyzPDepIKioqxLhx46TPsEwmE9euXdMZ21LnBmdxJGrlNm3ahMuXL0vbH374Idzd3XXGenh44MMPP5S2L126hE2bNjV5HYksae7cufD394eVlWX/RPFcorZs2LBh2LFjB4YNG2ZU/LPPPosRI0ZI2z/88APKysp0xvLcoI7Ezs4Ob731lrQthMAvv/yiM7alzg0maEStXGxsrHS7e/fuCA8PNxgfEREBLy8vaTsuLq7J6kbUlvBcoo4mLCxMul1RUaF35lOeG9TR3HvvvVrbWVlZOuNa6txggkbUipWXl2P//v3SdkhICGxsbAzuY2Njg5CQEGl73759qKioaLI6ErUFPJeoI/Lw8NDaLioqqhfDc4M6oqqqKq1thUJRL6Ylzw0maEStWGpqKiorK6XtsWPHGrWfZlxFRQVSU1MtXjeitoTnEnVEdVvMunTpUi+G5wZ1RAcPHtTa1vW5b8lzgwkaUSt27tw5re1+/foZtV/duPPnz1usTkRtEc8l6miEENiyZYu07eXlBV9f33pxPDeoo7l9+zZefPFFafv+++/H3XffXS+uJc8Nw+10RNSi6l797NWrl1H7+fj4aG2npaVZqkpEbRLPJepovv/+e1y5ckXanjt3LmQyWb04nhvU3gkhUFpaiitXrmDPnj348MMPkZ2dDQDo378/1q9fr3O/ljw3mKARtWJ1xwu4ubkZtZ+rq6vWdnFxsaWqRNQm8VyijuTGjRt47rnnpG03Nze8+uqrOmN5blB7NH/+fL2JFwA4OzvjiSeewPLly+Hi4qIzpiXPDXZxJGrFSkpKtLYdHByM2q9uHP9wUkfHc4k6irKyMkRERCA3N1e6b/Xq1fUmDFHjuUEdjZ2dHR599FE8/vjjepMzoGXPDSZoRK1Y3VmGGpo9SF9c3XKIOhqeS9QRVFdXY86cOTh27Jh03zPPPIPo6Gi9+/DcoPZo0KBBmDp1KqZOnYrJkydjxIgRUgtYZWUlPvnkEwQEBOCZZ56BUqnUWUZLnhtM0IhaMScnJ61tY6dqrRtXtxyijobnErV3KpUK8+bNQ3x8vHRfdHQ0Pv74Y4P78dyg9uj555/H3r17sXfvXuzbtw9HjhxBXl4efv/9d0ydOhVA7di0zz//HHPmzNFZRkueG0zQiFoxZ2dnre2ysjKj9qsbZ6gJn6gj4LlE7ZlKpcL8+fOxadMm6b7IyEj873//g7W1tcF9eW5QRyGTyTBmzBjs3bsXS5Yske7fvn27zvFqLXluMEEjasU6d+6sta1vpfu66sZ5enparE5EbRHPJWqvVCoVFi5ciO+++066Lzw8HJs2bTKqSxbPDeqI3nvvPQwYMEDa/vTTT+vFtOS5wQSNqBUbOHCg1va1a9eM2q9unL+/v8XqRNQW8Vyi9kilUuGxxx7DunXrpPtmzZqFzZs3Gz1ehucGdUQ2NjaIioqStk+ePIny8nKtmJY8N5igEbVigYGBWtsnTpwwar+6cQEBARarE1FbxHOJ2ht1crZ27VrpvlmzZiE2NhZyudzocnhuUEelua6ZSqVCfn6+1uMteW4wQSNqxXr27Im+fftK24mJiUbtpxnn5+eHHj16WLxuRG0JzyVqTyyVnAE8N6jjKigo0Np2d3fX2m7Jc4MJGlErFxERId1OSEhARkaGwfiMjAytLwfN/Yk6Mp5L1B7oSs7Cw8PNSs7UeG5QR6T5Gfby8tK5zllLnRtM0IhauQULFkizcKlUKqxcudJg/BtvvAGVSgUAsLa2xoIFC5q8jkRtAc8lauuEEHj88ce1krOIiAhs3rzZ7OQM4LlBHU9iYiL27NkjbYeFhemMa7FzQxBRq/foo48KANK/r776SmfcqlWrtOIWLlzYzDUlajoxMTHSZ9vHx8esMnguUVulUqnE448/rvW5jIqKElVVVRYpn+cGtVUpKSliwYIF4ty5c0bFb926VSgUCukzbG9vLy5fvqw3viXODZkQQpiX2hFRc8nJycGoUaNw5coV6b6ZM2dizpw56N69OzIzM7Fx40bs3r1betzPzw9JSUmc+pjanDfffBNvvvlmvfurqqqkK5MAYGdnVy9m3rx5+Oqrr/SWzXOJ2qrY2Fg88MAD0rZMJsOkSZOMnq0RqF28d/LkyTof47lBbdWpU6cwdOhQALUzJt53330YPHgwvL29oVAoUFVVhezsbJw5cwY7d+5ESkqKtK9MJsNXX32FhQsX6i2/Rc4Ns1M7ImpWFy9eFL6+vlpXZ/T98/X1FZcuXWrpKhOZZdmyZUZ9znX9i4mJabB8nkvUFq1du9bs80L9b+3atQaPwXOD2qKTJ0+adT54eHiI77//3qhjNPe5wTFoRG1Ev379cObMGSxatAgKhUJnjKurKxYtWoQzZ87Az8+vmWtI1DbwXCLSjecGtUW9e/fGP//5T4wYMcKosZjq+AsXLuDBBx806hjNfW6wiyNRG1RRUYHExESkp6cjNzcXnTp1Qu/evTFhwgSd3b6ISDeeS0S68dygtqiiogIpKSm4cuUKsrKyUFJSArlcDoVCAW9vb9x9991a65+Ze4ymPjeYoBEREREREbUS7OJIRERERETUSjBBIyIiIiIiaiWYoBEREREREbUSTNCIiIiIiIhaCSZoRERERERErQQTNCIiIiIiolaCCRoREREREVErwQSNiIiIiIiolWCCRkRERERE1EowQSMiIiIiImolmKARERERERG1EkzQiIiIiIiIWgkmaEREREZIT0+HTCZrkn/p6ekt/fSIiKiVYIJGRERETWb+/PlSIjp//vyWrk6HkZCQoHURgIjaDpuWrgAREVFb4ODggKlTpzYYd/ToUeTn5wMA7O3tERwcbFTZREREABM0IiIio3Tt2hV79+5tMG7ChAlITEw0aR8iIiI1dnEkIiIiIiJqJZigERERERERtRJM0IiIiFqA5gQOCQkJAIDy8nJ8++23CA0NRd++feHk5ASZTIbFixfrLefQoUNYvHgxhg4diq5du8LW1hZdunTB8OHD8corr+DPP/80uk5KpRL79u3Dq6++ismTJ8PHxwdOTk6wtbVF165dMXz4cCxevBjHjh0z+vmtX79eum/9+vV6Z7Jct26d1v7Lly+XHpswYYJ0f0pKChYvXoy77roL7u7usLe3x8CBA7FkyRJkZmbqrMuPP/6I6Oho9OrVC7a2tnB3d8eoUaPw73//G5WVlUa/Pmr5+fn47LPPMGPGDPTp0wfOzs5wcnKCr68voqKi8O2336K6urrBcvRN5FFQUIBPP/0U48aNg5eXF+zs7ODl5YVp06Zh7dq1qKmp0VumelKWiRMnat2v73XXfG2JqJUQREREZDHBwcECgAAgfHx89MapYwCIgwcPilOnTgl/f3+t+9X/nnvuuXr7X7p0Sdx333064zX/WVtbi0WLFomqqiqD9Y6Pjxfu7u4Nlqf+Fx4eLgoKCox6fsb8W7t2rdb+y5Ytkx4LDg4WKpVKrFy5UlhbW+stw9XVVRw9elQqo6CgQPzlL38xeNzAwECRlZVl8LXR9OGHHwo3N7cGn0+/fv3EH3/8YbCsgwcPau2jvs/b29tg2SNGjBDZ2dk6y4yJiTHpdQ8ODjb6uRNR8+AkIURERC0sLS0NkZGRyMvLAwD06NEDvr6+UCqVuHz5cr34pKQkhIaGIjc3V7rP3t4eAQEBcHNzQ15eHlJSUlBdXY2amhp88sknuHTpEnbt2gUbG91/+tPT06XZJwFAoVDAz88Prq6uqKmpQVZWFi5fvgwhBABg+/btuHr1KpKSknTOQqme8fLs2bO4efMmAKB79+4YNGiQzuN7e3sbfI2WL1+ON954Q6pbQEAA7OzskJqaijt37gAACgsLMWXKFJw9exadOnXC5MmTpdY+Ly8v+Pn5oaamBqdPn0ZpaSkA4Ny5cwgLC0NSUhKsrPR3LKqursbChQvx7bffat3v4+ODXr16AQAuXbqEW7duSbcnTpyIXbt24f777zf43NR+/fVXTJ06FUqlEjKZDP7+/ujatSsKCgpw5swZqeXs6NGjmDVrFg4dOlSvzoMGDcLUqVORl5en1dKpbwbSwYMHG1U3ImpGLZ0hEhERtSfmtKApFAoBQIwZM0arBUgIIaqqqkRaWpq0ff36deHp6Snt26NHD7FhwwZRWVmptV9eXp546aWXhEwmk2L/8Y9/6K3Pp59+KoYOHSo++ugjcenSJZ0xWVlZ4tVXXxU2NjZSmUuWLDH4emi26MTExBiM1aTZgubu7i5kMplwc3MTa9euFUqlUopTqVRi9erVWi1rTz/9tHjqqacEABEQECB++eUXrbJLSkrEE088ofUefPvttwbr8/LLL2vFx8TEiIsXL9aL++WXX7RaQjt37ixu3ryps8y6LWjq9/XJJ5+st09WVpaYPn26VvyGDRv01ldX6xwRtQ08Y4mIiCzInAQNgJg0aZKoqKhosPyQkBCt7nn6urqprV69WoqXy+Xixo0bOuOKi4sbPLbaxo0bpTKdnJxEfn6+3lhLJGgAhIODgzhx4oTe+Ndee03recpkMuHv76+3biqVSowZM0baZ+LEiXrLTkpK0kp0P//8c4N1Lygo0ErS/vrXv+qMq5tEARDvvvuu3nIrKyu1yp00aZLeWCZoRG0XJwkhIiJqYXK5HGvXroWdnZ3BuFOnTknrqtnY2GDz5s3w9PQ0uM8TTzyBSZMmAQCqqqqwevVqnXHOzs5G13fOnDkYM2YMAKC0tBQ//fST0fua65VXXsHQoUP1Pv7UU09Jt6uqqiCEwOrVq+Hm5qYzXiaT4a9//au0/ccff+idfOOdd96RunY++OCDePrppw3W1dXVVet1XrduHYqLiw3uAwBjx47FSy+9pPdxW1tbrQljkpKSDE4YQkRtExM0IiKiFjZt2jRpHJMhmjMdTps2DYGBgUaVHxMTI93++eefTa6fLqNHj5ZuHz161CJlGvLEE08YfNzb2xs9e/aUtgcOHIigoCCD+4waNUq6XV5ejrS0tHoxeXl5iI+Pl7ZfeOEFo+obFBQEX19fAEBZWRmSkpIa3EczYdQnODhYul1eXo6rV68aVR8iajs4SQgREVELGz9+vFFxiYmJ0u3JkycbXf6QIUOk28nJyRBCaE3rXld2djb279+P06dP4+bNmygqKqo3Hb3m5CU3btwwui7m8PX1Rbdu3RqM69atG65fvw5AO4HUx8vLS2tbc5IUtV9//RUqlQoA4OHhgWHDhhlTZQC1r7s66Tt+/DimTJliMH7s2LENltmjRw+t7YKCAqPrQ0RtAxM0IiKiFta3b98GY4QQSElJkbbXrFmDH374wajyy8vLpdtKpRJFRUVwdXWtF3ft2jW8+OKL2L59u1HreKk1dZJgTHIGAI6OjibtoxkP1LZ01XXmzBnptlKpREhIiFF1AWpnsFTLzs5uMN6YOjs5OWlt66ozEbVtTNCIiIhamEKhaDCmsLBQK2k6deqU2ccrLCysl6AdO3YMU6ZMMSvZMmexZ1PY2to2yz7qcWaaNJcyKCkpMXu8XWFhYYMxDY1B1EVXnYmobeMYNCIiohZmaP0tNfW6XZag7rKnWXZERISUnMnlcjz88MPYtGkTzp49i7y8PFRUVEDUzv4MIQSWLVtmsfq0ZpZ63eu+5kRE+rAFjYiIqA2oOxthbGwsZs+ebZGy165dK40jk8vl2L9/v9ZkFLoYMythe6D5ugcEBODcuXMtVxki6hDYgkZERNQGODk5aU2Ff/v2bYuVrZ66H6idRr6h5AyANBlHe6c5LuzOnTstWBMi6iiYoBEREbUR6rXHABg1bbuxrl27Jt0eMWJEg/FCCBw+fNiosjW7b7bF8VKar3lOTg4uXbrUgrUxXt1us23xtSfqqJigERERtRHTpk2Tbu/cuVNrAovGqKqqMil+7969yMzMNCpWs9VPczbJtmL48OHo1KmTtP3NN9+0YG2MV3fh8bb42hN1VEzQiIiI2oiFCxfCw8MDQO3kFc8884xFyu3evbt0+9ChQwZjy8rKsGTJEqPL1lxr7OLFi6ZXroXZ2NhoPd9PPvkEJ06caMEaGafuGm9t8bUn6qiYoBEREbURLi4uePvtt6XtzZs3Y86cOToXWK7r+PHjeOSRR/D999/Xe2zSpEnS7S1btmD37t06y8jNzcWMGTNw4cIFo+t8zz33SLfPnDlj9jT1LWnRokXo168fgNqWqClTpuh9jTQVFhZi1apVDS5Q3RS8vLy0krQPP/zQpLXtiKjlcBZHIiKiNuTJJ5/EyZMnsXr1agC1SdoPP/yABx54AEFBQfD29oadnR0KCwuRkZGBU6dOYf/+/UhPTwegnYypPfHEE3j33XdRUlIClUqFsLAwzJs3D6GhoejatSvy8/Px66+/Ys2aNcjNzYVCocD06dOxcePGBus7adIkeHt7IzMzE0IIhISEwN/fH71799Zaq2zRokU669YauLi4YOfOnRg7dizy8/ORm5uL0NBQDB8+HGFhYRg8eDDc3d1RUVGB3NxcnDt3DkeOHEFCQgKUSiV8fHxapN6PPPII3n33XQDAd999hz179mDw4MFwcXGRYu666y68+eabLVI/ItKNCRoREVEb88UXX6Bnz554/fXXoVKpUFJSgm+++cbs8VFdunTB+vXr8cADD6C6uhoqlQrr16/H+vXr68U6OTlh06ZNOHLkiFFl29jYYP369QgLC5PWFEtNTUVqaqpW3KxZs8yqe3Px9/fH0aNHMWvWLGmq/WPHjuHYsWMtXDP9XnvtNRw4cADHjx8HUDvJyS+//KIVY87C5ETUtNjFkYiIqI2RyWT4xz/+gbNnz2Lu3LlwdHQ0GO/u7o6oqChs3boVDz30kM6YiIgI/Pzzz7jrrrt0Pm5tbY0pU6bgxIkTWpOVGOO+++5DSkoKXn31VYwePRqenp6Qy+UmldEa+Pn54cSJE1i1ahUGDhxoMFYmk+Huu+/G66+/jp9//rmZaqjN2dkZv//+O7755hvMmDEDPj4+cHR0hEwma5H6EJFxZILzrhIREbVpSqUSR44cweXLl5GTk4Oqqio4OzvD29sbAwcOhL+/f71p1/URQuDEiRM4fvw4cnNz4eLiAi8vL4wbN05rTTCqXQvujz/+wJ07d1BQUAA7Ozu4u7vDz88PgwYNkiZ0ISIyBRM0IiIiIiKiVoJdHImIiIiIiFoJJmhEREREREStBBM0IiIiIiKiVoIJGhERERERUSvBBI2IiIiIiKiVYIJGRERERETUSjBBIyIiIiIiaiWYoBEREREREbUSTNCIiIiIiIhaCSZoRERERERErQQTNCIiIiIiolaCCRoREREREVErwQSNiIiIiIiolWCCRkRERERE1Er8Pwi49hbbo1K5AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -755,10 +755,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:58.014049Z",
-     "iopub.status.busy": "2024-10-25T15:23:58.013514Z",
-     "iopub.status.idle": "2024-10-25T15:23:59.848366Z",
-     "shell.execute_reply": "2024-10-25T15:23:59.847444Z"
+     "iopub.execute_input": "2024-10-25T15:26:11.873554Z",
+     "iopub.status.busy": "2024-10-25T15:26:11.873357Z",
+     "iopub.status.idle": "2024-10-25T15:26:13.727890Z",
+     "shell.execute_reply": "2024-10-25T15:26:13.726973Z"
     }
    },
    "outputs": [
@@ -775,17 +775,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w1,w0])\n",
+      "────────────(E[Outcome|w0,w1])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w1+w0 | \n",
+      "b: Outcome~Treatment+w0+w1 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.144759400960652\n",
-      "Effect estimates: [[1.1447594]]\n",
+      "Mean value: 1.0590662846792145\n",
+      "Effect estimates: [[1.05906628]]\n",
       "\n"
      ]
     }
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:23:59.851369Z",
-     "iopub.status.busy": "2024-10-25T15:23:59.850934Z",
-     "iopub.status.idle": "2024-10-25T15:28:21.399206Z",
-     "shell.execute_reply": "2024-10-25T15:28:21.398608Z"
+     "iopub.execute_input": "2024-10-25T15:26:13.731947Z",
+     "iopub.status.busy": "2024-10-25T15:26:13.731446Z",
+     "iopub.status.idle": "2024-10-25T15:30:38.881137Z",
+     "shell.execute_reply": "2024-10-25T15:30:38.880352Z"
     }
    },
    "outputs": [
@@ -837,9 +837,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:1.144759400960652\n",
-      "New effect:1.1582026466388928\n",
-      "p value:0.6599999999999999\n",
+      "Estimated effect:1.0590662846792145\n",
+      "New effect:1.0511514897778662\n",
+      "p value:0.72\n",
       "\n"
      ]
     }
@@ -854,10 +854,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-25T15:28:21.401540Z",
-     "iopub.status.busy": "2024-10-25T15:28:21.401308Z",
-     "iopub.status.idle": "2024-10-25T15:28:58.526771Z",
-     "shell.execute_reply": "2024-10-25T15:28:58.525903Z"
+     "iopub.execute_input": "2024-10-25T15:30:38.884047Z",
+     "iopub.status.busy": "2024-10-25T15:30:38.883504Z",
+     "iopub.status.idle": "2024-10-25T15:31:16.368542Z",
+     "shell.execute_reply": "2024-10-25T15:31:16.367590Z"
     }
    },
    "outputs": [
@@ -866,9 +866,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:1.144759400960652\n",
-      "New effect:-0.00012178524613168886\n",
-      "p value:0.44451697404875146\n",
+      "Estimated effect:1.0590662846792145\n",
+      "New effect:0.00011819241215427258\n",
+      "p value:0.38070788967795144\n",
       "\n"
      ]
     }
diff --git a/main/searchindex.js b/main/searchindex.js
index d0c2c2847..3d7bb66e7 100644
--- a/main/searchindex.js
+++ b/main/searchindex.js
@@ -1 +1 @@
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"end": [0, 4, 18, 36, 43, 48, 50, 53, 57, 68, 89, 100], "librari": [0, 1, 2, 4, 6, 18, 22, 24, 31, 35, 38, 39, 42, 47, 49, 53, 55, 56, 65, 71, 79, 92, 100, 102, 108, 109], "causal": [0, 2, 4, 5, 6, 7, 9, 11, 13, 17, 18, 19, 22, 23, 24, 26, 27, 28, 34, 37, 38, 40, 42, 44, 46, 48, 50, 52, 53, 59, 62, 72, 75, 77, 78, 82, 84, 86, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 105, 110, 114, 118, 119, 123, 124], "infer": [0, 1, 4, 5, 6, 7, 13, 17, 18, 19, 22, 26, 27, 28, 34, 39, 44, 47, 55, 57, 59, 60, 63, 65, 66, 75, 79, 85, 87, 101, 102, 109, 113, 121], "2020": [0, 1, 7, 13, 14, 15, 18, 45, 66], "http": [0, 1, 3, 4, 7, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 38, 42, 44, 45, 48, 53, 54, 59, 64, 65, 66, 68, 70, 71], "arxiv": [0, 9, 13, 14, 15, 18, 19, 21, 26, 45, 53, 64, 65], "org": [0, 4, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 44, 45, 53, 54, 64, 65, 66], "ab": [0, 4, 9, 13, 14, 15, 18, 31, 45, 51, 53, 54, 55, 64, 65, 67, 89, 109], "2011": [0, 7, 13, 18, 19, 44], "04216": 0, "patrick": 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74, 75, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 108, 110, 111, 114, 116, 117, 119, 120, 122, 124], "2024": [0, 18, 29, 30, 40, 51, 55, 71, 89, 94], "mloss": 0, "25": [0, 4, 18, 26, 28, 29, 30, 40, 45, 46, 48, 51, 55, 58, 60, 64, 67, 68, 71, 99], "147": 0, "1": [0, 4, 5, 6, 9, 11, 13, 14, 15, 17, 18, 19, 21, 24, 27, 28, 29, 30, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 73, 75, 78, 79, 80, 89, 92, 93, 94, 95, 99, 105, 109, 110, 111, 112, 113, 114, 117, 124], "7": [0, 4, 5, 13, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 92, 93, 99, 110, 114], "jmlr": [0, 34], "paper": [0, 18, 19, 26, 34, 48, 51, 53, 55, 59, 89, 92, 93, 94, 95, 112], "v25": 0, "22": [0, 28, 35, 41, 44, 47, 51, 55, 60, 66, 67], "1258": 0, "html": [0, 1, 4, 9, 13, 18, 21, 25, 42, 48, 57, 64, 66, 68], "bibtex": 0, "articl": [0, 1, 7, 9, 13, 24], "titl": [0, 9, 11, 12, 18, 23, 26, 50, 57], "author": [0, 3, 9, 11, 12], "journal": [0, 7, 9, 19, 34, 44], "preprint": [0, 18], "year": [0, 9, 11, 12, 26, 36, 44, 48, 55], "bl": 0, "o": [0, 4, 5, 14, 15, 17, 27, 29, 58, 60, 61, 66, 67], "baum": 0, "g": [0, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 24, 25, 26, 31, 34, 36, 43, 45, 48, 49, 50, 52, 54, 55, 56, 58, 60, 64, 65, 68, 79, 85, 89, 92, 93, 94, 96, 97, 103, 108, 110, 111, 112, 113, 114], "tz": 0, "machin": [0, 1, 3, 4, 9, 13, 18, 26, 28, 31, 51, 54, 55, 63, 65, 69, 71, 72, 79, 93, 94, 97, 102, 109, 112], "learn": [0, 1, 4, 9, 11, 13, 14, 15, 18, 19, 25, 26, 28, 45, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 71, 72, 79, 89, 93, 94, 97, 101, 102, 103, 109, 110, 112, 113, 114], "research": [0, 13, 18, 26, 27, 34, 51, 55, 66, 68, 69, 75], "volum": [0, 9, 14, 45], "number": [0, 4, 6, 7, 9, 13, 14, 15, 17, 18, 19, 25, 31, 33, 43, 45, 53, 54, 55, 57, 58, 64, 66, 82, 92, 106, 107, 109, 111, 115], "page": [0, 1, 4, 15, 18, 19, 37, 55], "url": [0, 9], "i": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 64, 66, 67, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 118, 121, 122, 123, 124], "host": [1, 56], "github": [1, 2, 3, 4, 25, 42, 47, 57, 63, 64, 67, 70], "can": [1, 2, 3, 4, 5, 6, 7, 11, 13, 17, 18, 19, 21, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 73, 75, 76, 78, 79, 80, 82, 84, 85, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 124], "brows": [1, 71], "code": [1, 2, 4, 7, 9, 13, 14, 15, 24, 25, 35, 39, 45, 51, 55, 59, 64, 68, 69, 70, 71, 82, 89, 94, 102, 109], "friendli": [1, 4], "format": [1, 3, 4, 10, 14, 15, 22, 24, 31, 32, 35, 39, 46, 47, 51, 55, 58, 65, 70, 109], "here": [1, 2, 4, 14, 15, 18, 19, 25, 26, 27, 31, 34, 36, 39, 40, 45, 46, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 64, 68, 69, 70, 79, 80, 89, 92, 93, 94, 95, 96, 97, 99, 103, 109, 111, 112, 113, 114], "decemb": 1, "2022": [1, 7, 9, 12, 13, 18, 34, 64, 93], "see": [1, 2, 3, 6, 7, 9, 13, 14, 15, 18, 19, 21, 24, 25, 26, 27, 29, 31, 32, 33, 35, 41, 42, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 66, 68, 80, 84, 86, 87, 89, 91, 92, 93, 94, 95, 97, 99, 102, 106, 110, 113, 114, 124], "notebook": [1, 2, 4, 31, 34, 37, 42, 43, 45, 46, 49, 51, 53, 54, 55, 59, 64, 65, 67, 68, 70, 72, 73, 75, 79, 84, 87, 101, 102, 103, 106, 107, 117, 122, 123], "The": [1, 3, 4, 5, 6, 7, 9, 11, 12, 13, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29, 31, 33, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 56, 57, 58, 59, 64, 65, 66, 67, 69, 70, 71, 72, 75, 78, 79, 80, 81, 82, 87, 89, 90, 92, 93, 94, 96, 97, 101, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 118, 120, 124], "experiment": [1, 4, 6, 28, 44], "stage": [1, 4, 6, 13, 25, 27, 29, 34, 41, 65, 75, 79, 87, 102], "allow": [1, 3, 4, 5, 6, 13, 17, 18, 19, 28, 48, 49, 51, 60, 67, 68, 71, 79, 80, 92, 94, 97, 112, 113], "modular": [1, 18, 71], "differ": [1, 3, 4, 5, 6, 7, 13, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 28, 31, 34, 36, 37, 39, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 63, 65, 66, 75, 80, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 102, 111, 112, 114, 124], "includ": [1, 3, 4, 13, 14, 15, 18, 24, 26, 36, 45, 48, 51, 55, 56, 60, 65, 66, 67, 68, 70, 71, 81, 87, 89, 93, 95, 109, 114], "separ": [1, 4, 6, 7, 9, 18, 45, 53, 71, 79, 90, 102, 104, 106], "fit": [1, 4, 6, 9, 13, 14, 15, 18, 20, 21, 26, 27, 28, 29, 30, 31, 34, 37, 40, 48, 50, 51, 52, 54, 56, 57, 66, 68, 69, 71, 75, 77, 78, 80, 89, 92, 93, 94, 95, 97, 99, 101, 109, 110, 111, 112], "method": [1, 2, 4, 5, 6, 7, 9, 13, 14, 17, 18, 19, 20, 21, 22, 24, 26, 27, 30, 31, 32, 34, 37, 40, 41, 45, 47, 51, 53, 54, 55, 56, 57, 58, 60, 63, 65, 68, 69, 71, 75, 76, 81, 82, 83, 84, 86, 87, 90, 95, 99, 100, 102, 103, 105, 109, 110, 111, 112, 114, 115, 120, 124], "leav": [1, 13, 51, 54, 60], "feedback": [1, 37], "old": [1, 18, 37, 48, 94, 111], "causalmodel": [1, 4, 7, 23, 25, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 58, 59, 61, 65, 66, 67, 68, 69, 73, 75, 79, 82, 83, 84, 86, 87, 108], "should": [1, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 29, 33, 36, 41, 45, 47, 49, 50, 51, 52, 54, 55, 56, 58, 60, 64, 66, 67, 70, 75, 79, 89, 92, 97, 100, 103, 104, 105, 106, 109, 110, 114, 115, 116, 117, 118, 119, 120, 121, 124], "befor": [1, 2, 13, 14, 15, 18, 23, 25, 27, 36, 39, 47, 48, 49, 50, 53, 54, 55, 56, 57, 60, 65, 75, 87, 94, 105, 109, 113], "andresmor": 1, "m": [1, 3, 4, 13, 14, 15, 18, 24, 26, 29, 34, 45, 51, 53, 54, 64, 89, 97], "analys": [1, 28, 48], "mani": [1, 3, 13, 18, 19, 25, 27, 36, 45, 49, 50, 52, 53, 54, 55, 56, 58, 68, 75, 80, 82, 97, 100, 103, 104, 107, 109, 110, 111, 114], "now": [1, 3, 25, 26, 27, 28, 31, 33, 35, 36, 39, 41, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 68, 70, 80, 89, 93, 95, 99, 106, 109, 110, 111, 113, 114], "joblib": [1, 13, 57], "parallel": [1, 4, 5, 13, 17, 18, 19, 47, 56, 60, 94, 111], "process": [1, 4, 9, 10, 13, 15, 17, 18, 24, 25, 26, 27, 29, 31, 36, 47, 49, 52, 54, 57, 60, 64, 65, 71, 75, 78, 79, 80, 85, 87, 93, 94, 97, 100, 102, 109, 110, 111, 112, 113, 114, 117], "show": [1, 4, 6, 13, 18, 23, 26, 28, 29, 30, 31, 32, 33, 34, 37, 39, 40, 45, 47, 48, 51, 54, 55, 56, 57, 59, 61, 64, 65, 66, 67, 68, 69, 79, 81, 106, 107, 109, 124], "progress": [1, 4, 13, 18, 37, 55, 57], "bar": [1, 4, 18, 24, 26, 27, 30, 39, 50, 51, 55, 56, 57, 60, 75], "astoeffelbau": 1, "yemaedahrav": 1, "non": [1, 4, 7, 13, 14, 15, 18, 19, 25, 26, 27, 34, 37, 41, 44, 45, 48, 49, 50, 52, 54, 55, 56, 57, 63, 64, 67, 68, 69, 71, 75, 79, 89, 93, 95, 97, 102, 104, 109, 110, 113, 114, 118, 121], "linear": [1, 4, 6, 7, 12, 13, 15, 18, 19, 24, 26, 27, 32, 33, 34, 35, 37, 39, 41, 46, 47, 49, 50, 51, 52, 55, 56, 57, 66, 68, 75, 78, 79, 87, 89, 93, 95, 109, 113, 114, 118, 121, 124], "chernozhukov": [1, 13, 51, 65], "cinelli": [1, 13, 66], "newei": [1, 13], "syrgkani": [1, 13], "2021": [1, 4, 7, 9, 11, 18, 34, 55, 56, 57, 94, 95], "exampl": [1, 2, 3, 6, 7, 13, 15, 18, 21, 24, 25, 26, 28, 30, 36, 37, 42, 50, 51, 53, 55, 61, 64, 65, 67, 68, 69, 71, 79, 84, 86, 87, 95, 101, 102, 103, 104, 106, 109, 110, 112, 114, 117, 118], "anusha0409": 1, "e": [1, 3, 4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 79, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 103, 108, 109, 110, 111, 112, 113, 114], "valu": [1, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 75, 77, 78, 81, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 105, 107, 110, 111, 112, 113, 114, 117, 118, 124], "ding": [1, 13, 66], "vanderweel": [1, 13, 66], "2016": 1, "www": [1, 3, 7, 13, 24, 53, 56, 68], "ncbi": [1, 7, 13, 24], "nlm": [1, 7, 13, 24], "nih": [1, 7, 13, 24], "gov": [1, 7, 13, 24], "pmc": [1, 7, 13, 24], "pmc4820664": [1, 13], "jlgleason": 1, "unit": [1, 4, 6, 13, 18, 23, 25, 26, 27, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 55, 57, 65, 66, 67, 68, 92, 94], "chang": [1, 3, 4, 13, 18, 23, 25, 26, 28, 33, 36, 37, 39, 40, 42, 45, 47, 49, 51, 54, 55, 56, 58, 63, 65, 66, 68, 79, 87, 88, 90, 92, 93, 95, 96, 97, 100, 101, 102, 111, 112, 113, 114, 116, 118, 120, 124], "attribut": [1, 4, 5, 7, 9, 11, 17, 21, 24, 26, 31, 34, 51, 53, 54, 60, 63, 67, 88, 89, 92, 95, 96, 101, 102, 113], "kailashbuki": 1, "qualiti": [1, 18, 49, 50, 55, 57, 102, 114], "option": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 19, 24, 25, 26, 46, 47, 48, 49, 50, 51, 54, 55, 57, 60, 64, 70, 95, 109, 114, 124], "best": [1, 4, 13, 18, 19, 24, 49, 51, 55, 57, 64, 68, 69, 102, 107, 109, 114], "auto": [1, 4, 6, 26, 28, 48, 49, 50, 52, 54, 55, 56, 57, 69, 80, 89, 92, 93, 94, 95, 97, 99, 110, 111, 112, 113], "assign": [1, 4, 6, 9, 17, 18, 25, 26, 35, 39, 48, 49, 50, 52, 55, 56, 57, 71, 79, 87, 89, 93, 97, 101, 103, 110, 111, 112, 113], "mechan": [1, 18, 25, 26, 48, 49, 50, 51, 52, 54, 55, 56, 57, 68, 71, 89, 92, 93, 94, 95, 96, 97, 101, 102, 103, 110, 111, 113, 114], "which": [1, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 31, 33, 45, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 61, 64, 65, 66, 67, 68, 69, 75, 80, 82, 88, 89, 91, 93, 94, 95, 97, 102, 104, 106, 109, 111, 112, 114], "ml": [1, 4, 18, 28, 48, 49, 51, 53, 56, 64, 68, 89, 109, 113], "autogluon": [1, 4, 18, 111], "bloebp": 1, "condit": [1, 4, 6, 7, 9, 13, 17, 18, 19, 24, 36, 42, 45, 49, 50, 52, 53, 54, 55, 56, 57, 60, 63, 65, 66, 67, 68, 69, 71, 76, 79, 87, 88, 89, 90, 92, 94, 101, 102, 103, 105, 106, 107, 109, 110, 112, 113, 114, 118], "through": [1, 4, 6, 13, 17, 18, 24, 27, 33, 37, 41, 45, 50, 52, 56, 59, 60, 65, 67, 68, 69, 71, 75, 79, 90, 91, 92, 93, 100, 102, 104, 110], "packag": [1, 25, 28, 35, 36, 39, 47, 48, 52, 57, 58, 60, 64, 67, 68, 71, 73, 95, 96, 101, 104, 109, 110, 111, 113], "algorithm": [1, 4, 7, 8, 13, 18, 26, 31, 34, 37, 49, 50, 51, 52, 54, 55, 56, 57, 63, 67, 68, 69, 71, 72, 85, 87, 94, 102, 104, 109, 110, 111, 112, 114], "comput": [1, 4, 6, 7, 9, 12, 13, 15, 18, 24, 26, 27, 28, 30, 34, 37, 44, 47, 48, 51, 54, 56, 57, 60, 64, 65, 68, 75, 80, 88, 89, 94, 96, 98, 100, 101, 113], "effici": [1, 4, 7, 13, 27, 34, 65, 75], "backdoor": [1, 4, 13, 17, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 83, 85, 86, 87, 88, 101, 102, 124], "set": [1, 4, 5, 7, 10, 13, 14, 15, 17, 18, 19, 21, 24, 26, 27, 28, 32, 33, 36, 37, 41, 44, 45, 46, 47, 48, 49, 51, 53, 54, 55, 57, 60, 63, 65, 66, 67, 68, 70, 71, 75, 76, 80, 82, 85, 87, 89, 93, 94, 95, 96, 97, 102, 104, 106, 111, 112, 113, 114, 124], "esmucl": 1, "control": [1, 4, 5, 6, 7, 18, 19, 25, 27, 35, 39, 45, 47, 48, 49, 60, 75, 78, 101, 113, 115], "direct": [1, 4, 7, 11, 13, 18, 24, 25, 26, 31, 34, 43, 49, 51, 54, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 79, 88, 90, 101, 102, 103, 104, 106, 113, 114], "multi": [1, 4, 9, 26, 42, 94], "egorkraevtransferwis": 1, "pydata": 1, "theme": 1, "homepag": 1, "get": [1, 4, 7, 13, 17, 18, 19, 24, 25, 27, 31, 35, 39, 47, 48, 49, 51, 54, 55, 56, 64, 66, 68, 70, 71, 75, 80, 93, 94, 111, 114], "start": [1, 2, 4, 13, 15, 17, 25, 43, 44, 50, 55, 71, 79, 82, 102, 109], "guid": [1, 2, 4, 33, 49, 69, 71, 112], "revis": 1, "user": [1, 4, 5, 7, 13, 18, 24, 27, 36, 47, 49, 50, 60, 63, 64, 69, 70, 71, 72, 75, 79, 92, 103, 104, 106, 109, 112, 117], "petergtz": 1, "contribut": [1, 18, 48, 51, 54, 55, 57, 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64], "structur": [1, 7, 13, 18, 24, 26, 27, 28, 50, 51, 52, 54, 55, 56, 57, 69, 71, 75, 89, 90, 93, 95, 101, 102, 103, 106, 109, 110, 112, 113, 114], "updat": [1, 2, 3, 4, 7, 18, 37, 55, 56, 67, 70], "check": [1, 2, 3, 4, 7, 13, 18, 21, 24, 25, 27, 29, 36, 37, 41, 46, 47, 49, 50, 52, 54, 55, 56, 58, 60, 65, 69, 71, 72, 75, 79, 83, 84, 85, 86, 87, 100, 102, 103, 106, 107, 110, 114, 117, 120, 124], "out": [1, 2, 4, 9, 12, 13, 19, 25, 26, 27, 37, 45, 47, 51, 54, 55, 56, 64, 69, 75, 79, 84, 85, 87, 88, 94, 101, 102, 103, 107, 109, 112, 117], "elikl": 1, "itsoum": 1, "ryanrussel": 1, "whether": [1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 17, 18, 19, 24, 25, 26, 36, 41, 44, 47, 50, 53, 55, 56, 57, 58, 60, 68, 71, 85, 87, 92, 97, 102, 105, 106, 107, 114, 124], "data": [1, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29, 30, 33, 34, 35, 36, 37, 39, 41, 42, 43, 52, 54, 57, 58, 59, 62, 63, 65, 66, 69, 71, 75, 78, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 112, 114, 115, 117, 118, 124], "conform": [1, 7], "assum": [1, 4, 7, 13, 17, 18, 25, 29, 30, 34, 36, 40, 47, 48, 49, 50, 55, 56, 57, 58, 65, 66, 69, 71, 89, 94, 97, 102, 105, 107, 109, 111, 112, 113, 114, 124], "specif": [1, 4, 5, 6, 7, 9, 13, 17, 18, 19, 24, 26, 27, 28, 31, 47, 50, 55, 60, 64, 66, 71, 75, 82, 90, 91, 92, 93, 95, 97, 103, 104, 108, 111, 112, 124], "paramet": [1, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 33, 37, 41, 45, 47, 49, 51, 53, 55, 57, 58, 60, 64, 65, 67, 68, 71, 75, 79, 89, 92, 97, 109, 111, 114, 117, 121], "directli": [1, 4, 18, 28, 37, 48, 51, 54, 56, 63, 68, 73, 90, 93, 95, 97, 104, 107, 108, 111], "its": [1, 3, 4, 5, 6, 7, 18, 19, 25, 28, 31, 32, 34, 49, 50, 51, 55, 56, 57, 60, 63, 65, 66, 69, 79, 88, 89, 90, 92, 93, 94, 95, 97, 102, 106, 109, 111, 113, 114, 120], "own": [1, 4, 6, 17, 27, 28, 34, 54, 71, 75, 92, 112, 113, 114], "init": [1, 6], "custom": [1, 4, 5, 6, 13, 18, 24, 25, 36, 49, 55, 56, 57, 63, 71, 89, 95, 96, 101, 102, 112, 113, 114, 117], "consist": [1, 4, 13, 18, 21, 26, 31, 45, 48, 49, 50, 54, 55, 56, 57, 64, 69, 93, 104, 105, 106, 113, 114], "init_param": [1, 28, 46, 65, 68, 73, 79], "add": [1, 3, 4, 7, 13, 15, 18, 24, 25, 28, 32, 35, 37, 38, 39, 41, 45, 47, 51, 53, 58, 67, 68, 70, 79, 80, 89, 113, 114, 118, 120, 121], "glm": [1, 6], "hotel": [1, 62, 63, 103], "case": [1, 3, 4, 7, 13, 14, 17, 18, 19, 25, 26, 27, 28, 34, 36, 43, 47, 48, 49, 52, 54, 55, 56, 57, 63, 65, 71, 75, 87, 89, 92, 93, 95, 96, 97, 102, 103, 104, 109, 110, 112, 113, 114, 124], "studi": [1, 13, 14, 15, 26, 44, 45, 48, 56, 57, 65], "ae": 1, "foster": 1, "major": [1, 67, 100], "identif": [1, 4, 5, 6, 7, 13, 17, 25, 27, 31, 35, 39, 41, 59, 60, 68, 69, 71, 75, 81, 82, 83, 85, 86, 87, 102, 103, 108], "minim": [1, 4, 7, 9, 17, 18, 34, 45, 49, 53, 55, 57, 64, 65, 68, 82, 114], "adjust": [1, 4, 7, 13, 18, 19, 26, 38, 39, 45, 48, 49, 50, 55, 56, 63, 65, 66, 67, 68, 82, 91, 114], "maxim": [1, 7, 38, 82], "exhaust": [1, 7, 82], "search": [1, 7, 9, 11, 13, 15, 31, 34, 50, 55, 82], "coverag": 1, "discoveri": [1, 4, 18, 19, 22, 26, 63, 67, 69, 101, 103], "from": [1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 75, 79, 80, 81, 82, 84, 87, 89, 90, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 108, 111, 112, 113, 114, 117, 118], "like": [1, 2, 4, 13, 14, 17, 18, 24, 25, 26, 27, 33, 36, 45, 47, 48, 50, 51, 54, 55, 56, 58, 60, 64, 68, 69, 71, 75, 90, 94, 96, 100, 102, 109, 111, 112], "cdt": [1, 4, 53, 101, 103], "id": [1, 3, 4, 7, 25, 34, 37, 58, 61, 63, 65, 66, 85, 87], "text": [1, 3, 4, 23, 53, 80, 93], "interpret": [1, 2, 13, 18, 33, 41, 42, 47, 49, 50, 54, 55, 56, 63, 65, 66, 89, 91, 92, 93, 95, 114, 124], "": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 73, 75, 79, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 109, 111, 112, 113, 114, 124], "distanc": [1, 6, 9, 13, 18, 19, 76, 87], "match": [1, 4, 6, 13, 14, 33, 36, 45, 58, 76, 79, 87, 104, 117], "reli": [1, 18, 55, 68, 73, 112], "metric": [1, 6, 9, 18, 26, 35, 49, 50, 54, 55, 56, 57, 64, 92, 114], "between": [1, 4, 6, 7, 9, 10, 11, 13, 18, 19, 24, 25, 26, 27, 32, 33, 35, 39, 43, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 71, 75, 79, 80, 90, 92, 93, 94, 102, 103, 109, 110, 111, 112, 114, 118, 124], "input": [1, 4, 13, 18, 19, 20, 21, 24, 26, 31, 35, 39, 45, 50, 53, 56, 57, 63, 64, 67, 68, 70, 88, 92, 95, 96, 101, 102, 104, 109, 111, 112, 114, 124], "heurist": [1, 48, 55], "default": [1, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 24, 26, 27, 31, 37, 47, 51, 53, 54, 56, 58, 60, 64, 65, 66, 71, 75, 82, 89, 92, 94, 95, 111, 114, 124], "strata": [1, 6], "automat": 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69, 70, 71, 73, 75, 80, 89, 96, 97, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 115, 117], "refit": 1, "andrewc19": 1, "ha2trinh": 1, "siddhanthaldar": 1, "vojavocni": 1, "also": [1, 2, 3, 6, 7, 13, 18, 24, 25, 26, 29, 31, 32, 34, 35, 39, 42, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 63, 65, 66, 67, 68, 69, 71, 79, 80, 82, 87, 89, 90, 92, 93, 95, 96, 97, 99, 100, 102, 103, 105, 106, 109, 110, 111, 112, 113, 114, 115, 117, 118, 124], "iv": [1, 4, 6, 25, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 42, 44, 46, 47, 62, 65, 66, 68, 79, 81], "move": [1, 28, 48, 88, 100, 104], "matplotlib": [1, 13, 25, 26, 31, 32, 45, 48, 50, 51, 55, 57, 60, 66, 67, 68], "instal": [1, 3, 18, 24, 26, 51, 63, 64, 67, 68, 71, 104], "pip": [1, 3, 24, 26, 64, 67, 68, 69], "plot": [1, 3, 4, 13, 18, 23, 26, 30, 32, 34, 37, 39, 40, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 65, 67, 68, 109, 124], "align": [1, 56], "all": [1, 2, 3, 4, 6, 7, 9, 10, 12, 13, 15, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 32, 34, 35, 36, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 70, 72, 73, 75, 79, 80, 82, 83, 86, 89, 93, 97, 100, 102, 103, 105, 109, 110, 111, 112, 113, 114, 124], "continu": [1, 4, 5, 13, 14, 15, 17, 18, 19, 21, 23, 37, 44, 45, 49, 50, 54, 55, 56, 57, 58, 60, 67, 92, 97, 106, 109, 112, 114], "log": [1, 13, 18, 24, 25, 28, 29, 30, 32, 33, 35, 37, 39, 40, 41, 44, 46, 56, 66, 68, 93, 109], "configur": [1, 9, 13, 18, 19, 25, 29, 37, 55], "dummyoutcomerefut": [1, 4, 13], "arshiaarya": 1, "n8sty": 1, "moprescu": 1, "conda": [1, 69], "formula": [1, 4, 13, 17, 24, 27, 39, 40, 75], "front": [1, 4, 6, 79, 87], "door": [1, 4, 6, 7, 27, 48, 75, 79, 87], "criterion": [1, 6, 13, 18, 26, 41, 68, 79, 85, 86, 87], "path": [1, 4, 7, 24, 26, 27, 34, 35, 39, 41, 48, 51, 58, 59, 60, 61, 64, 66, 70, 75, 91, 92], "d": [1, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 51, 53, 57, 58, 60, 64, 65, 66, 67, 68, 70, 75, 92, 93, 106], "logist": [1, 4, 6, 18, 24, 25, 51, 66, 78, 87], "erikhambardzumyan": 1, "visual": [1, 4, 23, 31, 45, 51, 55, 56, 68], "how": [1, 4, 13, 17, 18, 19, 23, 25, 26, 31, 33, 34, 36, 39, 40, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 79, 82, 88, 90, 96, 100, 102, 106, 107, 109, 112, 113, 114, 121, 124], "distribut": [1, 3, 4, 9, 12, 13, 14, 17, 18, 23, 24, 26, 27, 39, 40, 48, 49, 50, 51, 52, 54, 55, 56, 57, 60, 68, 70, 75, 76, 80, 85, 88, 89, 90, 92, 93, 95, 96, 97, 101, 102, 103, 104, 109, 110, 111, 112, 113, 114], "friendlier": 1, "error": [1, 4, 6, 7, 13, 18, 19, 24, 27, 30, 34, 37, 40, 49, 50, 51, 53, 54, 55, 56, 57, 60, 64, 66, 68, 70, 75, 94, 103, 111, 114], "messag": [1, 3, 13, 18], "when": [1, 4, 5, 6, 7, 9, 10, 13, 14, 18, 19, 21, 24, 25, 27, 31, 34, 36, 37, 42, 45, 47, 49, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 78, 79, 80, 90, 92, 93, 94, 97, 106, 109, 110, 111, 112, 114, 116, 117, 119, 121], "enough": [1, 18, 25, 36, 65, 66, 68, 114], "bin": [1, 4, 6, 13, 14, 18, 26, 48], "dummi": [1, 13, 33, 79, 118], "outcom": [1, 4, 5, 6, 7, 13, 17, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 58, 59, 60, 61, 63, 65, 66, 67, 68, 69, 75, 76, 78, 79, 87, 88, 90, 95, 97, 100, 101, 102, 108, 112, 118, 121, 124], "compon": [1, 5, 19, 24, 51, 60, 94, 103], "simul": [1, 4, 6, 13, 29, 33, 35, 36, 37, 39, 41, 47, 51, 53, 65, 66, 79, 88, 92, 94, 97, 98, 101, 117, 118, 121], "zero": [1, 4, 13, 14, 18, 25, 29, 30, 33, 36, 41, 44, 47, 51, 53, 54, 55, 65, 66, 79, 89, 107, 118, 119, 124], "too": [1, 4, 13, 18, 25, 27, 47, 53, 57, 72, 73, 75, 79, 80, 95, 114, 121, 124], "readi": [1, 3, 26, 31, 71, 80, 88, 109, 113], "book": [1, 57, 62, 63, 69, 85, 103], "cancel": [1, 51, 62, 63, 103], "membership": [1, 36, 62], "reward": [1, 15, 62, 63, 102], "program": [1, 13, 15, 24, 48, 57, 62, 63, 79, 102], "multipl": [1, 2, 4, 6, 18, 19, 26, 45, 47, 51, 55, 56, 59, 63, 64, 65, 68, 69, 71, 79, 80, 93, 95, 102, 104, 109, 111, 114, 115], "tutori": [1, 3, 18, 59, 63, 69], "organ": [1, 71, 102], "websit": [1, 36, 54, 56], "commun": [1, 2, 51, 71], "creat": [1, 3, 4, 6, 10, 13, 18, 19, 24, 27, 29, 34, 36, 38, 44, 47, 48, 49, 55, 60, 63, 64, 67, 68, 69, 70, 75, 92, 93, 108, 113, 117, 124], "guidelin": 1, "allcontributor": 1, "bot": 1, "so": [1, 2, 3, 4, 5, 12, 14, 18, 25, 26, 27, 29, 31, 37, 45, 47, 49, 50, 55, 58, 60, 65, 68, 75, 79, 97, 103, 109, 124], "just": [1, 4, 12, 24, 27, 30, 34, 37, 45, 47, 49, 50, 51, 53, 54, 55, 56, 60, 65, 68, 79, 107, 114], "after": [1, 4, 12, 13, 18, 23, 24, 34, 36, 39, 47, 49, 50, 51, 52, 54, 55, 56, 57, 63, 65, 66, 79, 87, 89, 93, 94, 97, 102, 110, 114, 118, 120], "pull": [1, 2, 66, 109], "request": [1, 2, 4, 25, 56, 103], "ar": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 35, 36, 39, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 69, 71, 75, 77, 79, 80, 82, 87, 88, 89, 90, 92, 93, 94, 95, 97, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 118, 122, 123, 124], "merg": [1, 3, 18, 45], "tanmai": 1, "kulkarni101": 1, "sid": [1, 25], "darthvad": [1, 25], "increas": [1, 13, 18, 25, 26, 29, 39, 47, 48, 50, 55, 56, 57, 66, 92, 111, 124], "In": [1, 3, 4, 9, 11, 13, 14, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 31, 33, 34, 36, 40, 43, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 64, 65, 67, 68, 69, 70, 71, 73, 75, 78, 79, 80, 87, 89, 90, 92, 93, 95, 96, 97, 102, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114, 117], "addit": [1, 3, 4, 6, 7, 13, 14, 17, 18, 19, 24, 25, 28, 29, 31, 36, 47, 48, 49, 50, 52, 53, 55, 56, 57, 59, 61, 65, 67, 68, 71, 73, 78, 79, 89, 90, 97, 109, 110, 111, 112, 113, 114, 117, 121, 124], "random": [1, 4, 10, 13, 14, 18, 19, 21, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 65, 66, 68, 69, 75, 79, 80, 89, 92, 93, 94, 95, 97, 99, 105, 107, 109, 110, 113, 114, 117, 118, 119], "dummyoutcom": 1, "arbitrari": [1, 4, 13, 18, 21, 89, 93, 95], "alwai": [1, 7, 26, 34, 49, 50, 52, 54, 55, 56, 68, 110, 111, 114], "thi": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 75, 77, 78, 79, 80, 81, 82, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114, 116, 122, 123, 124], "inspir": [1, 57], "idea": [1, 4, 17, 19, 21, 26, 27, 48, 54, 71, 75, 92, 94, 96], "t": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 25, 26, 27, 28, 29, 30, 31, 34, 37, 40, 45, 48, 49, 50, 51, 53, 55, 56, 59, 60, 65, 66, 67, 75, 80, 89, 93, 94, 97, 105, 108, 111, 113], "learner": [1, 28], "we": [1, 2, 3, 4, 5, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 64, 65, 66, 67, 68, 69, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 124], "provid": [1, 3, 4, 5, 7, 13, 14, 18, 19, 20, 24, 25, 26, 29, 31, 34, 35, 37, 40, 42, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 71, 72, 73, 79, 80, 89, 90, 95, 97, 100, 102, 104, 105, 106, 108, 109, 110, 111, 112, 114, 124], "Of": [1, 36], "cours": [1, 13, 36, 45], "specifi": [1, 4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 30, 35, 39, 40, 41, 45, 50, 53, 55, 64, 65, 68, 73, 80, 84, 89, 101, 103, 109, 114], "ani": [1, 2, 3, 4, 6, 7, 13, 14, 15, 18, 19, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 37, 45, 47, 49, 50, 54, 55, 57, 64, 65, 66, 67, 68, 70, 71, 75, 76, 79, 82, 89, 92, 93, 97, 102, 103, 105, 106, 112, 113, 115, 117, 118, 124], "bootstraprefut": [1, 4, 13], "issu": [1, 2, 4, 18, 27, 55, 56, 70, 71, 75], "measur": [1, 4, 13, 14, 18, 19, 31, 34, 37, 44, 51, 53, 54, 55, 56, 65, 66, 89, 90, 93, 94, 97, 105, 107, 114], "rather": [1, 5, 18, 51, 55, 56, 57, 60, 68, 89, 94, 95, 97, 109], "than": [1, 4, 5, 13, 14, 18, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 68, 80, 88, 89, 92, 95, 97, 102, 107, 110, 114, 124], "simpl": [1, 4, 6, 24, 30, 32, 36, 52, 54, 63, 68, 69, 71, 79, 86, 93, 95, 104, 110, 112, 113, 117], "sampl": [1, 4, 5, 6, 10, 13, 14, 15, 17, 18, 19, 24, 25, 26, 27, 28, 33, 35, 37, 39, 46, 47, 48, 49, 50, 51, 53, 56, 57, 58, 60, 63, 64, 68, 69, 75, 80, 93, 94, 97, 99, 101, 102, 111, 112, 113, 114], "nois": [1, 4, 13, 18, 26, 29, 30, 41, 48, 49, 50, 52, 54, 55, 56, 57, 64, 89, 90, 93, 94, 95, 96, 97, 109, 110, 111, 112, 113, 114], "select": [1, 4, 6, 9, 13, 18, 19, 26, 27, 47, 49, 55, 57, 64, 67, 71, 75, 79, 82, 114, 116, 124], "signific": [1, 4, 6, 13, 18, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 65, 66, 67, 79, 107, 110, 114], "causalml": [1, 4, 42, 68, 71], "call": [1, 2, 4, 5, 7, 9, 12, 14, 18, 26, 27, 28, 34, 37, 41, 45, 49, 50, 51, 52, 55, 56, 57, 60, 65, 68, 71, 73, 75, 79, 82, 87, 94, 102, 103, 106, 109, 110, 111, 113], "name": [1, 3, 4, 5, 6, 7, 13, 14, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 48, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 84, 102, 108, 109], "scheme": [1, 21, 35, 39], "To": [1, 3, 4, 5, 9, 13, 18, 21, 24, 25, 26, 27, 34, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 70, 75, 78, 80, 82, 83, 84, 86, 89, 92, 94, 96, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 124], "achiev": [1, 18, 24, 26, 47, 68, 92, 94, 97], "suggest": [1, 4, 18, 21, 26, 57, 64, 65, 68, 97], "prepend": 1, "intern": [1, 9, 11, 12, 13, 14, 15, 18, 21, 27, 35, 45, 51, 64, 75, 89, 93, 94, 95, 109], "string": [1, 4, 5, 6, 13, 14, 15, 18, 19, 21, 23, 24, 28, 31, 34, 46, 47, 48, 49, 55, 57, 58, 60, 61, 66, 108, 109, 112, 114], "For": [1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 34, 35, 36, 37, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 79, 80, 85, 86, 87, 89, 91, 92, 93, 94, 95, 97, 100, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 124], "propensity_score_match": [1, 4, 35, 36, 37, 38, 39, 46, 69, 77, 79], "Not": [1, 6, 13, 25], "break": [1, 3], "keep": [1, 3, 4, 13, 17, 18, 21, 25, 26, 27, 55, 68, 94, 97, 113], "sinc": [1, 4, 12, 13, 18, 25, 26, 27, 29, 30, 31, 34, 39, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 68, 80, 89, 94, 97, 102, 103, 105, 107, 109, 110, 111, 112, 114, 124], "modifi": [1, 4, 6, 17, 18, 21, 24, 26, 28, 31, 42, 47, 53, 54, 73, 93, 95, 104, 105, 118, 124], "subset": [1, 4, 9, 13, 18, 19, 24, 25, 26, 27, 37, 46, 56, 65, 67, 68, 75, 79, 89, 95, 116], "warn": [1, 3, 13, 25, 26, 28, 29, 33, 34, 36, 37, 41, 42, 44, 46, 47, 57, 64, 66, 67, 68], "outsid": [1, 25, 26, 56], "tri": [1, 18, 79, 87, 112], "ci": [1, 4, 18, 25, 48, 53, 66], "standard": [1, 4, 6, 13, 14, 18, 19, 23, 24, 25, 30, 39, 40, 49, 50, 51, 54, 55, 56, 66, 68, 72, 89, 112, 114], "correspond": [1, 4, 6, 7, 10, 13, 14, 18, 24, 28, 34, 46, 48, 49, 51, 52, 53, 54, 55, 56, 66, 68, 89, 93, 94, 109, 110, 113, 114], "parametr": [1, 4, 5, 7, 13, 18, 19, 24, 27, 34, 37, 41, 48, 49, 55, 57, 60, 63, 69, 75, 79, 104, 109, 114, 123], "form": [1, 4, 7, 13, 14, 15, 18, 33, 34, 45, 49, 51, 54, 55, 57, 58, 80, 85, 97, 103, 109, 112, 113, 114], "conveni": [1, 4, 17, 18, 24, 82], "seed": [1, 4, 10, 13, 14, 21, 31, 37, 45, 48, 51, 53, 57, 58, 65, 66, 92], "import": [1, 4, 13, 18, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 69, 70, 73, 79, 80, 82, 84, 87, 89, 91, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 113, 114, 124], "simpler": [1, 28], "multivari": [1, 18, 19], "averag": [1, 13, 18, 21, 24, 25, 26, 31, 36, 38, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 79, 87, 88, 90, 101, 102, 110, 114], "estimate_effect": [1, 2, 4, 6, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 81, 87, 91], "other": [1, 4, 5, 6, 7, 13, 17, 18, 25, 26, 27, 34, 36, 37, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 60, 64, 65, 68, 69, 71, 75, 82, 87, 88, 89, 90, 92, 93, 94, 95, 101, 102, 103, 104, 105, 106, 107, 109, 110, 112, 114], "target": [1, 4, 6, 7, 9, 18, 19, 21, 25, 26, 29, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 46, 47, 50, 51, 54, 55, 57, 58, 61, 65, 66, 67, 69, 71, 80, 85, 89, 90, 92, 93, 94, 95, 96, 108, 112], "estimand": [1, 4, 5, 6, 7, 13, 17, 28, 29, 31, 32, 34, 36, 37, 38, 41, 42, 43, 44, 47, 60, 65, 66, 67, 69, 71, 78, 87], "att": [1, 4, 25, 28, 35, 36, 39, 47, 74, 77], "atc": [1, 4, 25, 28, 35, 39, 47, 77], "reproduc": [1, 4, 47, 55, 57, 92], "j": [1, 18, 19, 24, 26, 44, 46, 51, 58, 64, 67, 93, 94], "chou": 1, "ktmud": 1, "jrfiedler": 1, "shounak112358": 1, "lnk2past": 1, "pywhi": [2, 70, 71, 104], "welcom": [2, 31, 37], "There": [2, 11, 13, 18, 25, 26, 27, 28, 44, 45, 47, 48, 49, 50, 51, 55, 56, 64, 66, 69, 75, 82, 93, 95, 114, 124], "wai": [2, 3, 4, 9, 18, 27, 29, 33, 35, 47, 49, 51, 52, 54, 55, 56, 57, 63, 68, 70, 71, 75, 79, 82, 89, 96, 97, 102, 104, 110, 114, 124], "some": [2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 25, 27, 28, 32, 34, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 65, 68, 69, 70, 79, 80, 88, 89, 92, 93, 94, 95, 97, 99, 100, 102, 105, 106, 108, 109, 110, 111, 113, 114, 118, 124], "ad": [2, 3, 4, 5, 12, 13, 15, 24, 26, 30, 45, 48, 51, 53, 54, 55, 60, 64, 68, 80, 90], "jupyt": [2, 63, 68], "describ": [2, 4, 7, 13, 15, 18, 34, 39, 41, 50, 52, 54, 55, 57, 60, 64, 65, 66, 68, 69, 88, 100, 102, 110, 112, 113], "solv": [2, 13, 15, 68, 70, 71], "problem": [2, 3, 6, 15, 18, 21, 25, 27, 29, 35, 36, 37, 46, 47, 50, 55, 56, 68, 69, 70, 71, 75, 79, 87, 95, 102, 103, 104, 106, 112, 118], "help": [2, 4, 13, 18, 19, 25, 33, 47, 49, 54, 55, 56, 60, 64, 65, 66, 68, 71, 89, 97, 102, 103, 113, 114], "document": [2, 3, 4, 6, 13, 18, 25, 28, 31, 55, 70, 87], "new": [2, 3, 4, 5, 6, 9, 13, 15, 18, 19, 24, 25, 27, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 52, 54, 57, 60, 64, 67, 68, 69, 70, 71, 75, 80, 85, 88, 92, 94, 102, 110, 111, 114, 118, 124], "four": [2, 4, 28, 29, 69, 71, 79, 82, 87, 93, 94], "step": [2, 3, 4, 6, 7, 13, 17, 18, 26, 27, 28, 29, 46, 47, 50, 54, 57, 59, 64, 67, 69, 70, 75, 79, 80, 87, 88, 94, 103, 114], "analysi": [2, 4, 6, 13, 18, 25, 32, 36, 37, 48, 54, 63, 68, 69, 71, 73, 79, 87, 88, 89, 90, 92, 93, 94, 97, 100, 101, 103, 106, 113, 115], "identifi": [2, 4, 5, 6, 7, 13, 18, 19, 26, 27, 28, 29, 42, 43, 44, 47, 55, 56, 60, 62, 63, 66, 67, 69, 70, 71, 76, 78, 81, 82, 83, 84, 86, 87, 88, 89, 93, 94, 96, 101, 112, 124], "estim": [2, 4, 6, 7, 9, 13, 14, 15, 18, 19, 21, 23, 27, 34, 45, 48, 49, 50, 51, 52, 55, 56, 57, 62, 63, 64, 69, 71, 75, 77, 78, 85, 86, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 108, 110, 112, 113, 114, 116, 117, 119, 120, 121, 124], "refut": [2, 4, 13, 18, 29, 45, 62, 69, 87, 101, 102, 103, 104, 122, 123, 124], "integr": [2, 3, 18, 26, 79, 102, 104], "api": [2, 4, 6, 13, 18, 27, 40, 49, 52, 56, 57, 63, 66, 68, 69, 71, 73, 75, 78, 79, 80, 82, 84, 87, 100, 102, 110, 113], "extern": [2, 26, 57, 79], "seamlessli": 2, "identify_effect": [2, 4, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 59, 65, 66, 67, 68, 69, 79, 82, 83, 84, 86, 87, 91, 124], "refute_estim": [2, 4, 25, 28, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 65, 66, 68, 69, 79, 87, 116, 117, 119, 120, 124], "extend": [2, 26, 48, 55, 109, 114], "support": [2, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 35, 37, 39, 41, 42, 47, 51, 55, 58, 60, 61, 63, 64, 66, 68, 70, 71, 72, 76, 78, 87, 88, 91, 97, 100, 101, 104, 109, 112, 124], "function": [2, 4, 5, 6, 7, 9, 10, 12, 13, 14, 18, 19, 20, 21, 24, 25, 26, 28, 34, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 67, 71, 77, 78, 82, 84, 87, 89, 90, 94, 95, 96, 97, 102, 105, 108, 109, 110, 111, 112, 113, 114, 117], "counterfactu": [2, 6, 18, 36, 63, 68, 69, 71, 88, 96, 98, 100, 101, 102, 112, 113], "predict": [2, 4, 6, 13, 14, 15, 18, 19, 20, 21, 25, 26, 33, 49, 50, 54, 55, 56, 63, 65, 71, 88, 89, 95, 100, 101, 102, 104, 109, 111, 112, 114], "would": [2, 4, 13, 18, 25, 26, 34, 36, 49, 50, 51, 52, 53, 54, 55, 56, 57, 65, 66, 68, 69, 80, 88, 89, 93, 94, 96, 97, 103, 104, 105, 109, 110, 111, 112, 113, 114], "rais": [2, 5, 7, 13, 18, 34, 60], "info": [2, 32], "have": [2, 4, 5, 6, 7, 13, 14, 17, 18, 19, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 79, 82, 88, 89, 93, 94, 96, 97, 102, 103, 104, 106, 107, 109, 111, 112, 113, 114, 124], "question": [2, 18, 24, 36, 49, 50, 54, 57, 68, 69, 80, 88, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 106, 112, 113, 114], "open": [2, 4, 55, 70], "list": [2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 31, 34, 42, 45, 51, 56, 59, 60, 64, 67, 102, 106, 114], "md": [2, 13], "our": [2, 18, 25, 27, 29, 31, 32, 33, 36, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 68, 69, 71, 75, 80, 89, 92, 93, 94, 95, 96, 97, 105, 106, 109, 110, 111, 112, 113, 114], "conduct": [2, 18, 26, 45, 68, 102], "avail": [2, 3, 4, 6, 18, 24, 25, 26, 47, 48, 55, 57, 63, 64, 65, 67, 68, 69, 79, 82, 85, 87, 106, 114], "join": [2, 26, 45, 51, 56, 71, 108], "develop": [2, 3, 34, 63, 70, 79, 102], "channel": 2, "discord": [2, 37, 71], "basic": [3, 13, 33, 37, 63, 69, 80, 82, 93, 99, 113], "knowledg": [3, 25, 31, 47, 48, 49, 53, 55, 56, 57, 68, 69, 72, 79, 87, 97, 101, 103, 104, 109, 113, 114, 124], "git": [3, 70], "requir": [3, 4, 5, 6, 7, 13, 17, 18, 24, 25, 27, 31, 35, 38, 39, 45, 47, 51, 55, 57, 58, 60, 61, 64, 65, 67, 68, 70, 75, 80, 87, 89, 97, 100, 102, 103, 109, 111, 112, 114, 124], "unfamiliar": 3, "workflow": [3, 45, 79, 87], "checkout": [3, 18], "fork": [3, 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119, 120], "strength": [4, 6, 13, 18, 47, 54, 55, 65, 66, 88, 90, 101, 113, 121], "param": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 24, 33, 46, 60, 64], "dictionari": [4, 5, 6, 7, 10, 13, 14, 15, 17, 18, 21, 24, 26, 45, 51, 60, 64, 80], "size": [4, 6, 10, 11, 12, 13, 18, 19, 23, 24, 27, 29, 30, 31, 36, 45, 48, 49, 50, 52, 55, 56, 57, 64, 66, 69, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "boolean": [4, 6, 13, 14, 15, 21, 26, 28, 45], "split": [4, 6, 10, 13, 18, 36, 40, 44, 45, 51], "enabl": [4, 6, 13, 64, 94, 100, 102, 109], "instanc": [4, 6, 7, 9, 11, 12, 13, 15, 18, 19, 21, 23, 26, 31, 34, 45, 47, 49, 55, 56, 57, 60, 66, 67, 69, 71, 80, 82, 83, 84, 86, 89, 92, 93, 100, 105, 108, 109, 111, 112, 113, 114], "bootstrapestim": 4, "tupl": [4, 13, 18, 21, 24, 26, 42, 58, 66], "alia": [4, 6, 13], "field": [4, 5, 13, 60], "default_confidence_level": 4, "default_interpret_method": 4, "default_notimplementederror_msg": 4, "yet": [4, 27], "next": [4, 17, 29, 34, 51, 54, 55, 56, 64, 87, 88, 89, 92, 93, 94, 97, 99, 103, 106, 110, 111], "microsoft": [4, 13, 69], "default_number_of_simulations_ci": 4, "default_number_of_simulations_stat_test": 4, "default_sample_size_fract": 4, "temp_cat_column_prefix": 4, "__categorical__": 4, "x": [4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 26, 28, 30, 31, 33, 34, 36, 38, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 65, 66, 69, 80, 87, 88, 89, 92, 93, 94, 95, 96, 97, 99, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "data_df": [4, 6, 31], "oper": [4, 5, 7, 14, 17, 18, 25, 55, 56, 85, 109], "expect": [4, 9, 13, 17, 18, 19, 21, 33, 36, 39, 46, 47, 49, 50, 53, 54, 55, 56, 64, 65, 76, 80, 87, 89, 92, 94, 95, 97, 104, 105, 109, 112, 114], "interven": [4, 5, 26, 27, 49, 52, 54, 60, 75, 80, 97, 99, 100, 110], "appli": [4, 6, 7, 9, 11, 13, 14, 18, 19, 20, 21, 23, 24, 31, 36, 40, 51, 53, 54, 55, 63, 67, 68, 92, 102, 103, 111], "estimate_confidence_interv": 4, "estimate_valu": 4, "estimate_effect_na": 4, "panda": [4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 63, 66, 67, 68, 69, 75, 80, 89, 92, 93, 94, 95, 97, 99, 104, 105, 108, 109, 110, 111, 113, 114], "estimate_std_error": 4, "get_new_estimator_object": 4, "same": [4, 6, 13, 14, 18, 19, 20, 25, 26, 27, 28, 29, 33, 35, 36, 37, 45, 47, 49, 51, 52, 53, 55, 56, 57, 60, 61, 65, 66, 67, 68, 69, 71, 75, 79, 80, 83, 86, 94, 104, 105, 106, 107, 110, 111, 113, 114], "type": [4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 76, 80, 84, 89, 92, 97, 101, 109, 113, 114], "respect": [4, 5, 7, 9, 10, 13, 18, 34, 36, 49, 50, 52, 54, 55, 56, 57, 58, 60, 66, 67, 71, 79, 80, 92, 94, 95, 96, 97, 110, 112, 114], "pathwai": [4, 66, 91], "emploi": [4, 48, 50, 51, 66, 90, 92, 94, 112], "had": [4, 25, 26, 36, 50, 55, 65, 88, 96, 97, 105, 112], "static": [4, 7, 18, 23, 34], "is_bootstrap_parameter_chang": 4, "bootstrap_estimates_param": 4, "given_param": 4, "fresh": 4, "again": [4, 5, 49, 53, 55, 60, 69, 70, 97, 105, 109, 111, 113, 114], "current": [4, 5, 6, 7, 9, 13, 18, 19, 24, 26, 36, 37, 41, 42, 45, 47, 51, 58, 60, 64, 66, 79, 97, 109], "denot": [4, 6, 13, 18, 24, 59, 65, 66, 68, 92, 112, 114], "reset_encod": 4, "remov": [4, 7, 13, 18, 24, 25, 27, 29, 31, 33, 34, 35, 36, 37, 39, 46, 53, 54, 64, 65, 66, 67, 68, 90, 92, 124], "encod": [4, 12, 13, 19, 21, 47, 48, 49, 51, 52, 68, 79, 87, 102, 103, 110], "re": [4, 13, 14, 18, 25, 27, 40, 44, 51, 55, 56, 60, 65, 66, 69, 75, 94, 111], "It": [4, 5, 6, 7, 13, 18, 19, 25, 26, 27, 32, 37, 43, 47, 50, 53, 54, 57, 60, 64, 65, 66, 68, 69, 71, 75, 78, 79, 82, 83, 86, 90, 97, 100, 106, 107, 109, 114, 116, 117, 120, 121, 124], "produc": [4, 6, 13, 18, 26, 33, 49, 55, 57, 60, 114], "inconsist": [4, 52, 110], "output": [4, 6, 9, 13, 15, 18, 19, 23, 24, 26, 37, 44, 46, 47, 50, 53, 55, 56, 57, 58, 60, 65, 66, 76, 104, 107, 124], "particular": [4, 7, 18, 26, 48, 50, 54, 56, 57, 71, 92, 97], "hot": [4, 21, 48], "map": [4, 5, 14, 15, 18, 19, 21, 24, 26, 45, 51, 54, 93], "must": [4, 5, 7, 13, 18, 23, 24, 26, 27, 45, 56, 59, 60, 66, 68, 109, 111], "ident": [4, 6, 8, 12, 18, 53], "train": [4, 8, 9, 10, 12, 13, 15, 18, 19, 21, 26, 44, 49, 50, 51, 69, 89, 97, 109, 113], "time": [4, 5, 18, 21, 24, 25, 29, 30, 31, 40, 43, 44, 49, 51, 55, 56, 60, 64, 66, 67, 68, 89, 92, 105, 111, 114], "common": [4, 6, 9, 13, 17, 18, 24, 25, 29, 33, 35, 38, 39, 54, 65, 66, 68, 73, 78, 79, 80, 81, 93, 95, 97, 100, 102, 108, 112, 114, 118, 121, 124], "signif_results_tostr": 4, "signif_result": 4, "target_units_tostr": 4, "procedur": [4, 13, 27, 47, 60, 68, 75, 115, 118, 124], "self": [4, 6, 7, 13, 15, 24, 27, 35, 39, 48, 66, 75, 109], "update_input": 4, "target_unit": [4, 6, 25, 28, 35, 36, 37, 39, 40, 45, 47, 73, 74, 77, 78, 79], "realizedestimand": 4, "estimator_nam": [4, 6], "update_assumpt": 4, "estimator_assumpt": 4, "update_estimand_express": 4, "estimand_express": 4, "identifier_nam": [4, 7], "ate": [4, 5, 7, 25, 28, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 60, 65, 66, 68, 77, 78], "fit_estim": [4, 28], "method_param": [4, 6, 28, 33, 35, 38, 39, 40, 41, 42, 45, 46, 65, 68, 73, 74, 77, 79, 81, 91], "dict": [4, 5, 6, 7, 13, 14, 15, 17, 18, 21, 24, 26, 32, 33, 37, 44, 46, 48, 49, 52, 56, 57, 60, 66, 69, 80, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "convent": 4, "doubl": [4, 28, 47, 49, 50, 52, 54, 55, 56, 68, 79, 110, 114], "dml": [4, 13, 28, 37, 46, 65, 68, 79], "locat": [4, 34], "insid": 4, "demo": [4, 53, 63], "group": [4, 9, 13, 26, 36, 39, 47, 48], "variat": [4, 13, 25, 51, 65, 66, 68], "treat": [4, 6, 15, 23, 25, 36, 39, 40, 44, 45, 60, 71, 79, 80, 113], "three": [4, 13, 17, 18, 19, 25, 26, 27, 28, 31, 50, 55, 56, 61, 64, 75, 76, 79, 89, 100, 105, 106, 109, 111, 112], "2": [4, 6, 7, 9, 10, 11, 13, 15, 18, 20, 21, 24, 27, 28, 29, 30, 31, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 74, 79, 80, 89, 92, 93, 94, 95, 97, 98, 99, 105, 109, 110, 111, 112, 113, 114, 117], "lambda": [4, 6, 9, 13, 18, 24, 26, 28, 36, 47, 48, 49, 50, 51, 55, 56, 57, 73, 79, 80, 89, 97, 99, 111], "onli": [4, 5, 6, 7, 9, 13, 14, 15, 18, 19, 21, 24, 25, 26, 28, 29, 34, 36, 37, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 66, 67, 68, 80, 87, 92, 93, 94, 97, 104, 105, 109, 110, 111, 112, 114], "thei": [4, 5, 13, 18, 19, 24, 25, 26, 27, 29, 34, 36, 48, 49, 50, 52, 54, 55, 56, 57, 58, 60, 65, 66, 68, 75, 82, 89, 102, 103, 106, 110, 113, 114, 118], "previous": [4, 18, 21], "causalgraph": [4, 34, 37], "common_cause_nam": 4, "instrument_nam": [4, 7, 32, 33, 35, 39, 68], "mediator_nam": 4, "observed_node_nam": [4, 34], "missing_nodes_as_confound": [4, 41], "accept": [4, 26, 105], "networkx": [4, 18, 24, 26, 31, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 67, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "digraph": [4, 5, 7, 17, 18, 24, 25, 26, 31, 36, 43, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59, 60, 61, 65, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "gml": [4, 39, 47, 53, 58, 108], "dot": [4, 22, 24, 31, 32, 33, 35, 44, 47, 51, 58, 67, 68, 70, 117], "edg": [4, 7, 17, 18, 24, 26, 27, 31, 34, 36, 43, 48, 49, 55, 61, 65, 66, 67, 75, 80, 90, 92, 103, 109, 113, 114], "node": [4, 7, 15, 18, 24, 25, 26, 31, 34, 36, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 65, 66, 67, 69, 71, 80, 89, 90, 92, 93, 94, 95, 96, 97, 103, 106, 109, 110, 111, 112, 113, 114], "style": [4, 15], "dash": [4, 13, 15, 53], "ensur": [4, 13, 15, 18, 21, 48, 49, 55, 57, 67, 68, 70, 96, 97, 100, 114], "drawn": [4, 13, 18, 25, 55, 56, 111], "line": [4, 13, 24, 32, 36, 47, 53, 54, 55, 56, 65, 66, 68, 71, 94], "confound": [4, 6, 9, 13, 18, 23, 25, 27, 29, 36, 37, 40, 41, 47, 50, 54, 56, 59, 63, 64, 66, 68, 79, 87, 108, 118, 121, 124], "instrument": [4, 6, 7, 13, 31, 32, 33, 39, 48, 59, 63, 68, 79, 83, 85, 87, 102, 108], "add_missing_nodes_as_common_caus": 4, "add_node_attribut": 4, "color": [4, 11, 13, 21, 24, 26, 48, 51, 55, 56, 64, 66, 102], "grai": 4, "all_observ": 4, "node_nam": [4, 7, 18, 24, 109], "build_graph": [4, 37], "semant": 4, "consid": [4, 6, 13, 14, 15, 18, 19, 21, 25, 26, 34, 45, 48, 49, 50, 52, 54, 55, 56, 57, 64, 65, 66, 67, 68, 87, 89, 105, 106, 108, 110, 114], "represent": [4, 9, 11, 13, 18, 49, 50, 52, 53, 54, 55, 56, 65, 72, 100, 108, 110, 114], "thu": [4, 13, 18, 25, 26, 28, 31, 34, 36, 52, 53, 55, 65, 68, 70, 73, 79, 89, 95, 102, 104, 110, 114], "unless": [4, 58, 64, 102], "taxonomi": 4, "vanderwheel": [4, 7], "robin": [4, 17], "modif": [4, 18, 45], "acycl": [4, 7, 18, 24, 26, 31, 49, 55, 59, 63, 102, 103, 104, 106, 113], "epidemiologi": 4, "2007": [4, 18, 19], "check_dsepar": 4, "nodes1": [4, 7], "nodes2": [4, 7], "nodes3": 4, "new_graph": 4, "dseparation_algo": [4, 7], "check_valid_backdoor_set": 4, "backdoor_path": [4, 7], "second": [4, 6, 9, 13, 18, 19, 27, 29, 34, 41, 45, 51, 53, 56, 75, 94, 102, 107, 109, 111, 118], "third": [4, 13, 14, 15, 18, 27, 45, 51, 71, 75, 105, 106], "candid": [4, 102, 104, 106], "check_valid_frontdoor_set": 4, "candidate_nod": 4, "frontdoor_path": 4, "frontdoor": [4, 7, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 65, 66, 68, 79, 85, 86, 87, 102], "check_valid_mediation_set": 4, "mediation_path": 4, "mediat": [4, 6, 7, 13, 34, 59, 63, 87, 88, 90, 101, 103, 108], "do_surgeri": 4, "remove_outgoing_edg": 4, "remove_incoming_edg": 4, "target_node_nam": 4, "remove_only_direct_edges_to_target": 4, "concept": [4, 26, 54, 71, 89, 95, 113], "surgeri": 4, "focal": 4, "outgo": 4, "incom": [4, 18, 92], "filter_unobserved_vari": 4, "get_adjacency_matrix": 4, "adjac": [4, 18, 24, 67], "matrix": [4, 13, 14, 18, 19, 21, 24, 30, 40, 56], "get_all_directed_path": 4, "singleton": [4, 15], "get_all_nod": 4, "include_unobserv": [4, 7], "get_ancestor": 4, "get_backdoor_path": 4, "get_caus": 4, "remove_edg": [4, 53], "get_common_caus": 4, "get_descend": 4, "get_effect_modifi": [4, 78], "get_instru": 4, "treatment_nod": 4, "outcome_nod": [4, 7, 17, 27, 34, 37], "get_par": 4, "get_unconfounded_observed_subgraph": 4, "has_directed_path": 4, "action_nod": [4, 7, 17, 27, 34, 37], "least": [4, 7, 13, 15, 18, 28, 29, 30, 34, 40, 45, 47, 48, 66, 79, 112, 115, 124], "And": [4, 31, 36, 56, 106, 111], "conditioned_nod": 4, "criteria": [4, 25, 34, 45, 69], "decid": [4, 13, 18, 25, 27, 31, 34, 50, 68, 75], "block": [4, 24, 27, 34, 41, 48, 75], "view_graph": [4, 34], "layout": [4, 24, 32, 44, 68], "file_nam": 4, "common_caus": [4, 5, 7, 27, 29, 30, 32, 38, 40, 44, 45, 47, 60, 68, 108], "estimand_typ": [4, 5, 6, 7, 17, 34, 37, 41, 60, 91], "nonparametr": [4, 5, 7, 13, 36, 41, 44, 46, 49, 60, 91], "proceed_when_unidentifi": [4, 5, 25, 27, 28, 29, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 60, 65, 66, 68, 91], "identify_var": 4, "store": [4, 6, 7, 13, 18, 32, 51, 55], "state": [4, 5, 12, 17, 24, 26, 34, 48, 60, 64, 68, 71, 79, 89, 100], "At": [4, 13, 18, 47, 49, 51, 55, 58, 65, 68, 89, 113], "later": [4, 5, 25, 34, 36, 45, 48, 51, 57, 60, 70], "dag": [4, 18, 26, 31, 47, 49, 50, 51, 52, 53, 55, 56, 57, 59, 67, 103, 104, 106, 107, 110, 113, 114], "_outcom": 4, "futur": [4, 7, 13, 36, 57, 60, 68, 97, 104], "proce": [4, 5, 56, 60, 70], "potenti": [4, 18, 27, 49, 50, 54, 55, 56, 64, 69, 75, 79, 80, 100, 102, 103, 108, 114], "unobserv": [4, 7, 13, 18, 25, 26, 29, 36, 37, 49, 55, 56, 57, 66, 68, 79, 89, 94, 97, 112, 113, 114, 118, 124], "while": [4, 12, 13, 14, 17, 18, 25, 26, 27, 29, 45, 52, 54, 55, 56, 57, 66, 68, 71, 73, 75, 82, 89, 92, 93, 94, 95, 97, 100, 110, 112, 114], "sens": [4, 26, 34, 51, 53, 54, 55, 89, 111, 114], "intervent": [4, 5, 6, 7, 17, 18, 26, 31, 34, 44, 48, 49, 52, 54, 63, 71, 80, 88, 89, 90, 97, 98, 100, 101, 102, 110, 112], "These": [4, 5, 13, 14, 18, 19, 27, 31, 34, 45, 46, 51, 55, 57, 60, 63, 66, 68, 75, 89, 92, 102, 103, 106, 112, 115], "explicit": [4, 17, 27, 71, 75, 79, 100, 103], "estimation_method_nam": 4, "propensity_score_stratif": [4, 35, 38, 39, 47, 68, 77], "invers": [4, 6, 18, 20, 23, 27, 35, 39, 75, 79, 87], "weight": [4, 5, 6, 9, 10, 13, 15, 17, 18, 21, 23, 27, 30, 31, 40, 51, 54, 55, 60, 64, 67, 75, 79, 87, 94], "propensity_score_weight": [4, 25, 35, 38, 39, 40, 44, 45, 46, 77], "linear_regress": [4, 28, 31, 32, 35, 38, 42, 45, 66, 67, 68, 73, 78], "generalized_linear_model": 4, "instrumental_vari": [4, 7, 29, 33, 35, 81], "discontinu": [4, 6, 79, 87], "regression_discontinu": [4, 35, 81], "two_stage_regress": [4, 41, 91], "addition": [4, 18, 25, 55, 68, 94, 102, 114], "signfic": 4, "rel": [4, 15, 18, 50, 51, 55, 65, 90, 93, 114], "compar": [4, 13, 18, 19, 21, 23, 26, 34, 36, 38, 43, 46, 49, 50, 53, 54, 55, 56, 60, 65, 66, 68, 79, 80, 87, 94, 100, 107, 112, 114], "sequenti": [4, 9, 64, 68, 94], "get_estim": 4, "retriev": 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"dowhy.gcm.ml.autogluon module": [[20, "dowhy-gcm-ml-autogluon-module"]], "dowhy.gcm.ml.classification module": [[20, "module-dowhy.gcm.ml.classification"]], "dowhy.gcm.ml.prediction_model module": [[20, "module-dowhy.gcm.ml.prediction_model"]], "dowhy.gcm.ml.regression module": [[20, "module-dowhy.gcm.ml.regression"]], "dowhy.gcm.util package": [[21, "dowhy-gcm-util-package"]], "dowhy.gcm.util.catboost_encoder module": [[21, "module-dowhy.gcm.util.catboost_encoder"]], "dowhy.gcm.util.general module": [[21, "module-dowhy.gcm.util.general"]], "dowhy.gcm.util.plotting module": [[21, "module-dowhy.gcm.util.plotting"]], "dowhy.graph_learners package": [[22, "dowhy-graph-learners-package"]], "dowhy.graph_learners.cdt module": [[22, "module-dowhy.graph_learners.cdt"]], "dowhy.graph_learners.ges module": [[22, "module-dowhy.graph_learners.ges"]], "dowhy.graph_learners.lingam module": [[22, "module-dowhy.graph_learners.lingam"]], "dowhy.interpreters package": [[23, "dowhy-interpreters-package"]], "dowhy.interpreters.confounder_distribution_interpreter module": [[23, "module-dowhy.interpreters.confounder_distribution_interpreter"]], "dowhy.interpreters.propensity_balance_interpreter module": [[23, "module-dowhy.interpreters.propensity_balance_interpreter"]], "dowhy.interpreters.textual_effect_interpreter module": [[23, "module-dowhy.interpreters.textual_effect_interpreter"]], "dowhy.interpreters.textual_interpreter module": [[23, "module-dowhy.interpreters.textual_interpreter"]], "dowhy.interpreters.visual_interpreter module": [[23, "module-dowhy.interpreters.visual_interpreter"]], "dowhy.utils package": [[24, "dowhy-utils-package"]], "dowhy.utils.api module": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit module": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers module": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp module": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations module": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting module": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting module": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set module": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting module": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score module": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression module": [[24, "module-dowhy.utils.regression"]], "Exploring Causes of Hotel Booking Cancellations": [[25, "Exploring-Causes-of-Hotel-Booking-Cancellations"]], "Data Description": [[25, "Data-Description"]], "Feature Engineering": [[25, "Feature-Engineering"]], "Calculating Expected Counts": [[25, "Calculating-Expected-Counts"]], "Using DoWhy to estimate the causal effect": [[25, "Using-DoWhy-to-estimate-the-causal-effect"]], "Step-1. Create a Causal Graph": [[25, "Step-1.-Create-a-Causal-Graph"]], "Step-2. Identify the Causal Effect": [[25, "Step-2.-Identify-the-Causal-Effect"]], "Step-3. Estimate the identified estimand": [[25, "Step-3.-Estimate-the-identified-estimand"]], "Step-4. Refute results": [[25, "Step-4.-Refute-results"]], "Method-1": [[25, "Method-1"]], "Method-2": [[25, "Method-2"]], "Method-3": [[25, "Method-3"]], "Counterfactual Fairness": [[26, "Counterfactual-Fairness"]], "When to apply counterfactual fairness?": [[26, "When-to-apply-counterfactual-fairness?"]], "What is required to estimate counterfactual fairness?": [[26, "What-is-required-to-estimate-counterfactual-fairness?"]], "Notation": [[26, "Notation"]], "1. Load and Clean the Dataset": [[26, "1.-Load-and-Clean-the-Dataset"]], "2. A Formal Definition of Counterfactual Fairness": [[26, "2.-A-Formal-Definition-of-Counterfactual-Fairness"]], "2.1 Measuring Counterfactual Fairness": [[26, "2.1-Measuring-Counterfactual-Fairness"]], "3. Measuring Counterfactual Fairness using DoWhy": [[26, "3.-Measuring-Counterfactual-Fairness-using-DoWhy"]], "3.1 Univariate Analysis": [[26, "3.1-Univariate-Analysis"]], "3.2 Intersectional Analysis": [[26, "3.2-Intersectional-Analysis"]], "3.3 Some Additional Uses of the Counterfactuals df_obs , df_cf for Fairness": [[26, "3.3-Some-Additional-Uses-of-the-Counterfactuals-df_obs-,-df_cf-for-Fairness"]], "4. Limitation of Counterfactual Fairness": [[26, "4.-Limitation-of-Counterfactual-Fairness"]], "References": [[26, "References"], [44, "References"], [64, "References"]], "Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unfair Estimator": [[26, "Appendix-:-Fairness-Through-Unawareness-(FTU)-creates-A-Counterfactually-Unfair-Estimator"]], "Do-sampler Introduction": [[27, "Do-sampler-Introduction"]], "Pearlian Interventions": [[27, "Pearlian-Interventions"], [75, "pearlian-interventions"]], "Statefulness": [[27, "Statefulness"], [75, "statefulness"]], "Integration": [[27, "Integration"]], "Specifying Interventions": [[27, "Specifying-Interventions"], [60, "Specifying-Interventions"]], "Demo": [[27, "Demo"]], "Conditional Average Treatment Effects (CATE) with DoWhy and EconML": [[28, "Conditional-Average-Treatment-Effects-(CATE)-with-DoWhy-and-EconML"]], "Linear Model": [[28, "Linear-Model"]], "EconML methods": [[28, "EconML-methods"]], "CATE Object and Confidence Intervals": [[28, "CATE-Object-and-Confidence-Intervals"]], "Can provide a new inputs as target units and estimate CATE on them.": [[28, "Can-provide-a-new-inputs-as-target-units-and-estimate-CATE-on-them."]], "Can also retrieve the raw EconML estimator object for any further operations": [[28, "Can-also-retrieve-the-raw-EconML-estimator-object-for-any-further-operations"]], "Works with any EconML method": [[28, "Works-with-any-EconML-method"]], "Binary treatment, Binary outcome": [[28, "Binary-treatment,-Binary-outcome"]], "Using DRLearner estimator": [[28, "Using-DRLearner-estimator"]], "Instrumental Variable Method": [[28, "Instrumental-Variable-Method"]], "Metalearners": [[28, "Metalearners"]], "Avoiding retraining the estimator": [[28, "Avoiding-retraining-the-estimator"]], "Refuting the estimate": [[28, "Refuting-the-estimate"], [47, "Refuting-the-estimate"]], "Adding a random common cause variable": [[28, "Adding-a-random-common-cause-variable"], [32, "Adding-a-random-common-cause-variable"], [47, "Adding-a-random-common-cause-variable"]], "Adding an unobserved common cause variable": [[28, "Adding-an-unobserved-common-cause-variable"], [47, "Adding-an-unobserved-common-cause-variable"]], "Replacing treatment with a random (placebo) variable": [[28, "Replacing-treatment-with-a-random-(placebo)-variable"], [32, "Replacing-treatment-with-a-random-(placebo)-variable"], [47, "Replacing-treatment-with-a-random-(placebo)-variable"]], "Removing a random subset of the data": [[28, "Removing-a-random-subset-of-the-data"], [32, "Removing-a-random-subset-of-the-data"], [47, "Removing-a-random-subset-of-the-data"]], "Simple example on using Instrumental Variables method for estimation": [[29, "Simple-example-on-using-Instrumental-Variables-method-for-estimation"]], "Loading the dataset": [[29, "Loading-the-dataset"]], "Using DoWhy to estimate the causal effect of education on future income": [[29, "Using-DoWhy-to-estimate-the-causal-effect-of-education-on-future-income"]], "Demo for the DoWhy causal API": [[30, "Demo-for-the-DoWhy-causal-API"]], "Comparing the estimate to Linear Regression": [[30, "Comparing-the-estimate-to-Linear-Regression"]], "Causal Discovery example": [[31, "Causal-Discovery-example"]], "Utility function": [[31, "Utility-function"]], "Experiments on the Auto-MPG dataset": [[31, "Experiments-on-the-Auto-MPG-dataset"]], "1. Load the data": [[31, "1.-Load-the-data"], [31, "id1"], [40, "1.-Load-the-data"]], "Causal Discovery with causal-learn": [[31, "Causal-Discovery-with-causal-learn"], [31, "id2"]], "Estimate causal effects using Linear Regression": [[31, "Estimate-causal-effects-using-Linear-Regression"]], "Experiments on the Sachs dataset": [[31, "Experiments-on-the-Sachs-dataset"]], "Estimate effects using Linear Regression": [[31, "Estimate-effects-using-Linear-Regression"]], "Confounding Example: Finding causal effects from observed data": [[32, "Confounding-Example:-Finding-causal-effects-from-observed-data"]], "Let\u2019s create a mystery dataset for which we need to determine whether there is a causal effect.": [[32, "Let's-create-a-mystery-dataset-for-which-we-need-to-determine-whether-there-is-a-causal-effect."]], "Using DoWhy to resolve the mystery: Does Treatment cause Outcome?": [[32, "Using-DoWhy-to-resolve-the-mystery:-Does-Treatment-cause-Outcome?"]], "STEP 1: Model the problem as a causal graph": [[32, "STEP-1:-Model-the-problem-as-a-causal-graph"]], "STEP 2: Identify causal effect using properties of the formal causal graph": [[32, "STEP-2:-Identify-causal-effect-using-properties-of-the-formal-causal-graph"]], "STEP 3: Estimate the causal effect": [[32, "STEP-3:-Estimate-the-causal-effect"]], "Checking if the estimate is correct": [[32, "Checking-if-the-estimate-is-correct"]], "Step 4: Refuting the estimate": [[32, "Step-4:-Refuting-the-estimate"]], "A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions": [[33, "A-Simple-Example-on-Creating-a-Custom-Refutation-Using-User-Defined-Outcome-Functions"]], "Insert Dependencies": [[33, "Insert-Dependencies"]], "Create the Dataset": [[33, "Create-the-Dataset"]], "Creating the Causal Model": [[33, "Creating-the-Causal-Model"]], "Identify the Estimand": [[33, "Identify-the-Estimand"]], "Estimating the Effect": [[33, "Estimating-the-Effect"]], "Refuting the Estimate": [[33, "Refuting-the-Estimate"]], "Using a Randomly Generated Outcome": [[33, "Using-a-Randomly-Generated-Outcome"]], "Using a Function that Generates the Outcome from the Confounders": [[33, "Using-a-Function-that-Generates-the-Outcome-from-the-Confounders"]], "Finding optimal adjustment sets": [[34, "Finding-optimal-adjustment-sets"]], "Preliminaries": [[34, "Preliminaries"]], "The design of an observational study": [[34, "The-design-of-an-observational-study"]], "An example in which sufficient conditions to guarantee the existence of an optimal backdoor set do not hold": [[34, "An-example-in-which-sufficient-conditions-to-guarantee-the-existence-of-an-optimal-backdoor-set-do-not-hold"]], "An example in which there are no observable adjustment sets": [[34, "An-example-in-which-there-are-no-observable-adjustment-sets"]], "An example with costs": [[34, "An-example-with-costs"]], "DoWhy: Different estimation methods for causal inference": [[35, "DoWhy:-Different-estimation-methods-for-causal-inference"]], "Identifying the causal estimand": [[35, "Identifying-the-causal-estimand"], [39, "Identifying-the-causal-estimand"]], "Method 1: Regression": [[35, "Method-1:-Regression"]], "Method 2: Distance Matching": [[35, "Method-2:-Distance-Matching"]], "Method 3: Propensity Score Stratification": [[35, "Method-3:-Propensity-Score-Stratification"]], "Method 4: Propensity Score Matching": [[35, "Method-4:-Propensity-Score-Matching"]], "Method 5: Weighting": [[35, "Method-5:-Weighting"]], "Method 6: Instrumental Variable": [[35, "Method-6:-Instrumental-Variable"]], "Method 7: Regression Discontinuity": [[35, "Method-7:-Regression-Discontinuity"]], "Estimating the Effect of a Member Rewards Program": [[36, "Estimating-the-Effect-of-a-Member-Rewards-Program"]], "I. Formulating the causal model": [[36, "I.-Formulating-the-causal-model"]], "The importance of time": [[36, "The-importance-of-time"]], "II. Identifying the causal effect": [[36, "II.-Identifying-the-causal-effect"]], "III. Estimating the effect": [[36, "III.-Estimating-the-effect"]], "IV. Refuting the estimate": [[36, "IV.-Refuting-the-estimate"]], "Functional API Preview": [[37, "Functional-API-Preview"]], "Import Dependencies": [[37, "Import-Dependencies"], [46, "Import-Dependencies"]], "Create the Datasets": [[37, "Create-the-Datasets"], [46, "Create-the-Datasets"]], "Identify Effect - Functional API (Preview)": [[37, "Identify-Effect---Functional-API-(Preview)"]], "Estimate Effect - Functional API (Preview)": [[37, "Estimate-Effect---Functional-API-(Preview)"]], "Refute Estimate - Functional API (Preview)": [[37, "Refute-Estimate---Functional-API-(Preview)"]], "Backwards Compatibility": [[37, "Backwards-Compatibility"]], "Identify Effect": [[37, "Identify-Effect"], [46, "Identify-Effect"]], "Estimate Effect": [[37, "Estimate-Effect"], [46, "Estimate-Effect"]], "Refute Estimate": [[37, "Refute-Estimate"], [46, "Refute-Estimate"]], "DoWhy example on ihdp (Infant Health and Development Program) dataset": [[38, "DoWhy-example-on-ihdp-(Infant-Health-and-Development-Program)-dataset"]], "Loading Data": [[38, "Loading-Data"]], "1.Model": [[38, "1.Model"]], "2.Identify": [[38, "2.Identify"]], "3. Estimate (using different methods)": [[38, "3.-Estimate-(using-different-methods)"]], "3.1 Using Linear Regression": [[38, "3.1-Using-Linear-Regression"]], "3.2 Using Propensity Score Matching": [[38, "3.2-Using-Propensity-Score-Matching"]], "3.3 Using Propensity Score Stratification": [[38, "3.3-Using-Propensity-Score-Stratification"]], "3.4 Using Propensity Score Weighting": [[38, "3.4-Using-Propensity-Score-Weighting"]], "4. Refute": [[38, "4.-Refute"]], "4.3 Data Subset Refuter": [[38, "4.3-Data-Subset-Refuter"]], "DoWhy: Interpreters for Causal Estimators": [[39, "DoWhy:-Interpreters-for-Causal-Estimators"]], "Method 1: Propensity Score Stratification": [[39, "Method-1:-Propensity-Score-Stratification"]], "Textual Interpreter": [[39, "Textual-Interpreter"]], "Visual Interpreter": [[39, "Visual-Interpreter"]], "Method 2: Propensity Score Matching": [[39, "Method-2:-Propensity-Score-Matching"]], "Method 3: Weighting": [[39, "Method-3:-Weighting"]], "DoWhy example on the Lalonde dataset": [[40, "DoWhy-example-on-the-Lalonde-dataset"]], "2. Run DoWhy analysis: model, identify, estimate": [[40, "2.-Run-DoWhy-analysis:-model,-identify,-estimate"]], "3. Interpret the estimate": [[40, "3.-Interpret-the-estimate"]], "4. Sanity check: compare to manual IPW estimate": [[40, "4.-Sanity-check:-compare-to-manual-IPW-estimate"]], "Mediation analysis with DoWhy: Direct and Indirect Effects": [[41, "Mediation-analysis-with-DoWhy:-Direct-and-Indirect-Effects"]], "Creating a dataset": [[41, "Creating-a-dataset"]], "Step 1: Modeling the causal mechanism": [[41, "Step-1:-Modeling-the-causal-mechanism"]], "Step 2: Identifying the natural direct and indirect effects": [[41, "Step-2:-Identifying-the-natural-direct-and-indirect-effects"]], "Step 3: Estimation of the effect": [[41, "Step-3:-Estimation-of-the-effect"]], "Natural Indirect Effect": [[41, "Natural-Indirect-Effect"]], "Natural Direct Effect": [[41, "Natural-Direct-Effect"]], "Step 4: Refutations": [[41, "Step-4:-Refutations"]], "Estimating effect of multiple treatments": [[42, "Estimating-effect-of-multiple-treatments"]], "Linear model": [[42, "Linear-model"]], "More methods": [[42, "More-methods"]], "Example to demonstrate optimized backdoor variable search for Causal Identification": [[43, "Example-to-demonstrate-optimized-backdoor-variable-search-for-Causal-Identification"]], "Create Random Graph": [[43, "Create-Random-Graph"]], "Testing optimized backdoor search": [[43, "Testing-optimized-backdoor-search"]], "Applying refutation tests to the Lalonde and IHDP datasets": [[44, "Applying-refutation-tests-to-the-Lalonde-and-IHDP-datasets"]], "Import the Dependencies": [[44, "Import-the-Dependencies"]], "Loading the Dataset": [[44, "Loading-the-Dataset"]], "Infant Health and Development Program Dataset (IHDP)": [[44, "Infant-Health-and-Development-Program-Dataset-(IHDP)"]], "Reference": [[44, "Reference"]], "Lalonde Dataset": [[44, "Lalonde-Dataset"]], "Step 1: Building the model": [[44, "Step-1:-Building-the-model"]], "IHDP": [[44, "IHDP"], [44, "id1"], [44, "id3"], [44, "id5"]], "Lalonde": [[44, "Lalonde"], [44, "id2"], [44, "id4"], [44, "id6"]], "Step 2: Identification": [[44, "Step-2:-Identification"]], "Step 3: Estimation (using propensity score weighting)": [[44, "Step-3:-Estimation-(using-propensity-score-weighting)"]], "Step 4: Refutation": [[44, "Step-4:-Refutation"]], "Add Random Common Cause": [[44, "Add-Random-Common-Cause"], [44, "id7"]], "Replace Treatment with Placebo": [[44, "Replace-Treatment-with-Placebo"], [44, "id8"]], "Remove Random Subset of Data": [[44, "Remove-Random-Subset-of-Data"], [44, "id9"]], "Assessing Support and Overlap with OverRule": [[45, "Assessing-Support-and-Overlap-with-OverRule"]], "Motivation": [[45, "Motivation"]], "Acknowledgements": [[45, "Acknowledgements"]], "Table of contents": [[45, "Table-of-contents"]], "Illustration on a simple 2D example": [[45, "Illustration-on-a-simple-2D-example"]], "Problem Data": [[45, "Problem-Data"]], "Applying OverRule with default arguments": [[45, "Applying-OverRule-with-default-arguments"]], "Example usage using CausalModel": [[45, "Example-usage-using-CausalModel"]], "Example usage using the Functional API": [[45, "Example-usage-using-the-Functional-API"]], "Only fitting support or overlap rules": [[45, "Only-fitting-support-or-overlap-rules"]], "Interpreting the output of OverRule": [[45, "Interpreting-the-output-of-OverRule"]], "Configuring OverRule": [[45, "Configuring-OverRule"]], "Illustration on Lalonde and PSID datasets": [[45, "Illustration-on-Lalonde-and-PSID-datasets"]], "Checking for Support/Overlap in the Experimental Sample": [[45, "Checking-for-Support/Overlap-in-the-Experimental-Sample"]], "Assessing Support/Overlap on the combined sample": [[45, "Assessing-Support/Overlap-on-the-combined-sample"]], "Filtering with OverRule before performing a causal analysis": [[45, "Filtering-with-OverRule-before-performing-a-causal-analysis"]], "Performing causal inference after filtering": [[45, "Performing-causal-inference-after-filtering"]], "Iterating over multiple refutation tests": [[46, "Iterating-over-multiple-refutation-tests"]], "Inspection Parameters": [[46, "Inspection-Parameters"]], "Estimator List": [[46, "Estimator-List"]], "Refuter List": [[46, "Refuter-List"]], "Inspect Data": [[46, "Inspect-Data"]], "Create the CausalModels": [[46, "Create-the-CausalModels"]], "View Models": [[46, "View-Models"]], "Identified Estimands": [[46, "Identified-Estimands"]], "Estimate Values": [[46, "Estimate-Values"]], "Refutation Values": [[46, "Refutation-Values"]], "Basic Example for Calculating the Causal Effect": [[47, "Basic-Example-for-Calculating-the-Causal-Effect"]], "Interface 1 (recommended): Input causal graph": [[47, "Interface-1-(recommended):-Input-causal-graph"]], "DoWhy philosophy: Keep identification and estimation separate": [[47, "DoWhy-philosophy:-Keep-identification-and-estimation-separate"]], "Identification": [[47, "Identification"], [91, "identification"]], "Estimation": [[47, "Estimation"], [91, "estimation"]], "Interface 2: Specify common causes and instruments": [[47, "Interface-2:-Specify-common-causes-and-instruments"]], "Impact of 401(k) eligibility on net financial assets": [[48, "Impact-of-401(k)-eligibility-on-net-financial-assets"]], "Background": [[48, "Background"]], "Data": [[48, "Data"]], "Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income": [[48, "Effect-of-401(k)-Eligibility-on-Net-Financial-Assets,-Conditioned-on-Income"]], "Basic Example for Graphical Causal Models": [[49, "Basic-Example-for-Graphical-Causal-Models"]], "Step 1: Modeling cause-effect relationships as a structural causal model (SCM)": [[49, "Step-1:-Modeling-cause-effect-relationships-as-a-structural-causal-model-(SCM)"]], "Step 2: Fitting the SCM to the data": [[49, "Step-2:-Fitting-the-SCM-to-the-data"]], "Step 3: Answering a causal query based on the SCM": [[49, "Step-3:-Answering-a-causal-query-based-on-the-SCM"]], "Counterfactual Analysis in a Medical Case": [[50, "Counterfactual-Analysis-in-a-Medical-Case"]], "The data": [[50, "The-data"]], "Modeling of the graph": [[50, "Modeling-of-the-graph"]], "Answering Alice\u2019s counterfactual queries": [[50, "Answering-Alice's-counterfactual-queries"]], "Appendix: What the tele-app uses internally. Data generation of the patients\u2019 log": [[50, "Appendix:-What-the-tele-app-uses-internally.-Data-generation-of-the-patients'-log"]], "Decomposing the Gender Wage Gap": [[51, "Decomposing-the-Gender-Wage-Gap"]], "Read and prepare data": [[51, "Read-and-prepare-data"]], "Causal Model": [[51, "Causal-Model"]], "Implementation with dowhy.gcm.distribution_change_robust": [[51, "Implementation-with-dowhy.gcm.distribution_change_robust"]], "Manual Implementation (Advanced)": [[51, "Manual-Implementation-(Advanced)"]], "Interpretation": [[51, "Interpretation"]], "Basic Example for generating samples from a GCM": [[52, "Basic-Example-for-generating-samples-from-a-GCM"]], "Falsification of User-Given Directed Acyclic Graphs": [[53, "Falsification-of-User-Given-Directed-Acyclic-Graphs"]], "Synthetic Data": [[53, "Synthetic-Data"]], "Real Data (Protein Network dataset by Sachs et al., 2005)": [[53, "Real-Data-(Protein-Network-dataset-by-Sachs-et-al.,-2005)"]], "Edge Suggestions": [[53, "Edge-Suggestions"]], "Estimating intrinsic causal influences in real-world examples": [[54, "Estimating-intrinsic-causal-influences-in-real-world-examples"]], "Intrinsic influence on car MPG consumption": [[54, "Intrinsic-influence-on-car-MPG-consumption"]], "Intrinsic influence on river flow": [[54, "Intrinsic-influence-on-river-flow"]], "Causal Attributions and Root-Cause Analysis in an Online Shop": [[55, "Causal-Attributions-and-Root-Cause-Analysis-in-an-Online-Shop"]], "The scenario": [[55, "The-scenario"]], "Step 1: Define causal model": [[55, "Step-1:-Define-causal-model"]], "Step 2: Fit causal models to data": [[55, "Step-2:-Fit-causal-models-to-data"]], "Step 3: Answer causal questions": [[55, "Step-3:-Answer-causal-questions"]], "Generate new samples": [[55, "Generate-new-samples"]], "What are the key factors influencing the variance in profit?": [[55, "What-are-the-key-factors-influencing-the-variance-in-profit?"]], "What are the key factors explaining the Profit drop on a particular day?": [[55, "What-are-the-key-factors-explaining-the-Profit-drop-on-a-particular-day?"]], "What caused the profit drop in Q1 2022?": [[55, "What-caused-the-profit-drop-in-Q1-2022?"]], "Data generation process": [[55, "Data-generation-process"]], "Finding the Root Cause of Elevated Latencies in a Microservice Architecture": [[56, "Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture"]], "Setting up the causal model": [[56, "Setting-up-the-causal-model"]], "Scenario 1: Observing a single outlier": [[56, "Scenario-1:-Observing-a-single-outlier"]], "Attributing an outlier latency at a target service to other services": [[56, "Attributing-an-outlier-latency-at-a-target-service-to-other-services"]], "Scenario 2: Observing permanent degradation of latencies": [[56, "Scenario-2:-Observing-permanent-degradation-of-latencies"]], "Attributing permanent degradation of latencies at a target service to other services": [[56, "Attributing-permanent-degradation-of-latencies-at-a-target-service-to-other-services"]], "Scenario 3: Simulating the intervention of shifting resources": [[56, "Scenario-3:-Simulating-the-intervention-of-shifting-resources"]], "Appendix: Data generation process": [[56, "Appendix:-Data-generation-process"]], "Finding Root Causes of Changes in a Supply Chain": [[57, "Finding-Root-Causes-of-Changes-in-a-Supply-Chain"]], "Week-over-week changes": [[57, "Week-over-week-changes"]], "Why did the average value of received quantity change week-over-week?": [[57, "Why-did-the-average-value-of-received-quantity-change-week-over-week?"]], "Ad-hoc attribution analysis": [[57, "Ad-hoc-attribution-analysis"]], "Causal Attribution Analysis": [[57, "Causal-Attribution-Analysis"]], "Graphical causal model": [[57, "Graphical-causal-model"]], "Attributing change": [[57, "Attributing-change"]], "Appendix: Ground Truth": [[57, "Appendix:-Ground-Truth"]], "Testing Assumptions in model with DoWhy: A simple example": [[58, "Testing-Assumptions-in-model-with-DoWhy:-A-simple-example"]], "Step 1: Load dataset": [[58, "Step-1:-Load-dataset"]], "Step 2: Input causal graph": [[58, "Step-2:-Input-causal-graph"]], "Step 3: Create Causal Model": [[58, "Step-3:-Create-Causal-Model"], [66, "Step-3:-Create-Causal-Model"]], "Step 4: Testing for Conditional Independence": [[58, "Step-4:-Testing-for-Conditional-Independence"]], "Testing for a set of edges": [[58, "Testing-for-a-set-of-edges"]], "Testing with a wrong graph input": [[58, "Testing-with-a-wrong-graph-input"]], "Identifying Effect using ID Algorithm": [[59, "Identifying-Effect-using-ID-Algorithm"]], "Case 1": [[59, "Case-1"]], "Case 2": [[59, "Case-2"]], "Case 3": [[59, "Case-3"]], "Case 4": [[59, "Case-4"]], "Case 5": [[59, "Case-5"]], "Case 6": [[59, "Case-6"]], "Lalonde Pandas API Example": [[60, "Lalonde-Pandas-API-Example"]], "Getting the Data": [[60, "Getting-the-Data"]], "The causal Namespace": [[60, "The-causal-Namespace"]], "The do Operation": [[60, "The-do-Operation"]], "Treatment Effect Estimation": [[60, "Treatment-Effect-Estimation"]], "Different ways to load an input graph": [[61, "Different-ways-to-load-an-input-graph"]], "I. Generating dummy data": [[61, "I.-Generating-dummy-data"]], "II. Loading GML or DOT graphs": [[61, "II.-Loading-GML-or-DOT-graphs"]], "GML format": [[61, "GML-format"]], "DOT format": [[61, "DOT-format"]], "Case Studies": [[62, null]], "Example notebooks": [[63, "example-notebooks"]], "Introductory examples": [[63, "introductory-examples"]], "Real world-inspired examples": [[63, "real-world-inspired-examples"]], "Examples on benchmark datasets": [[63, "examples-on-benchmark-datasets"]], "Modeling and refuting causal assumptions": [[63, "modeling-and-refuting-causal-assumptions"]], "Miscellaneous": [[63, "miscellaneous"]], "Demo for DoWhy Causal Prediction on MNIST": [[64, "Demo-for-DoWhy-Causal-Prediction-on-MNIST"]], "Multi-attribute distribution shift datasets": [[64, "Multi-attribute-distribution-shift-datasets"]], "Multi-attribute MNIST": [[64, "Multi-attribute-MNIST"]], "Domains in multi-attribute MNIST": [[64, "Domains-in-multi-attribute-MNIST"]], "Initialize dataset": [[64, "Initialize-dataset"]], "Initialize data loaders": [[64, "Initialize-data-loaders"]], "Method 1: Provide validation domain explicitly": [[64, "Method-1:-Provide-validation-domain-explicitly"]], "Method 2: Validation set using subset of training data": [[64, "Method-2:-Validation-set-using-subset-of-training-data"]], "Initialize model and algorithm": [[64, "Initialize-model-and-algorithm"]], "Initialize algorithm class: ERM": [[64, "Initialize-algorithm-class:-ERM"]], "Fit predictor and start training": [[64, "Fit-predictor-and-start-training"]], "Evaluate on test domain": [[64, "Evaluate-on-test-domain"]], "Prediction with CACM": [[64, "Prediction-with-CACM"]], "Extending to different datasets and algorithms": [[64, "Extending-to-different-datasets-and-algorithms"]], "MNIST Independent and Causal+Independent datasets": [[64, "MNIST-Independent-and-Causal+Independent-datasets"]], "MNISTIndAttribute: Single-attribute Independent shift": [[64, "MNISTIndAttribute:-Single-attribute-Independent-shift"]], "MNISTCausalIndAttribute: Multi-attribute Causal+Independent shift": [[64, "MNISTCausalIndAttribute:-Multi-attribute-Causal+Independent-shift"]], "Additional datasets and algorithms": [[64, "Additional-datasets-and-algorithms"]], "Sensitivity analysis for non-parametric causal estimators": [[65, "Sensitivity-analysis-for-non-parametric-causal-estimators"]], "I. Sensitivity analysis for partially linear models": [[65, "I.-Sensitivity-analysis-for-partially-linear-models"]], "Creating a dataset with unobserved confounding": [[65, "Creating-a-dataset-with-unobserved-confounding"]], "Obtaining a causal estimate using Model, Identify, Estimate steps": [[65, "Obtaining-a-causal-estimate-using-Model,-Identify,-Estimate-steps"]], "Sensitivity Analysis using the Refute step": [[65, "Sensitivity-Analysis-using-the-Refute-step"]], "II. Sensitivity Analysis for general non-parametric models": [[65, "II.-Sensitivity-Analysis-for-general-non-parametric-models"]], "Sensitivity Analysis for Regression Models": [[66, "Sensitivity-Analysis-for-Regression-Models"]], "Step 1: Load required packages": [[66, "Step-1:-Load-required-packages"]], "Step 2: Load the dataset": [[66, "Step-2:-Load-the-dataset"]], "Step 4: Identification": [[66, "Step-4:-Identification"]], "Step 5: Estimation": [[66, "Step-5:-Estimation"]], "Step 6a: Refutation and Sensitivity Analysis - Method 1": [[66, "Step-6a:-Refutation-and-Sensitivity-Analysis---Method-1"]], "Parameter List for plot function": [[66, "Parameter-List-for-plot-function"]], "Step 6b: Refutation and Sensitivity Analysis - Method 2": [[66, "Step-6b:-Refutation-and-Sensitivity-Analysis---Method-2"]], "Effect inference with timeseries data": [[67, "Effect-inference-with-timeseries-data"]], "Loading timeseries data and causal graph": [[67, "Loading-timeseries-data-and-causal-graph"]], "Dataset Shifting and Filtering": [[67, "Dataset-Shifting-and-Filtering"]], "Causal Effect Estimation": [[67, "Causal-Effect-Estimation"]], "Importing temporal causal graph from Tigramite library": [[67, "Importing-temporal-causal-graph-from-Tigramite-library"]], "Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)": [[68, "Tutorial-on-Causal-Inference-and-its-Connections-to-Machine-Learning-(Using-DoWhy+EconML)"]], "Why causal inference?": [[68, "Why-causal-inference?"]], "Defining a causal effect": [[68, "Defining-a-causal-effect"]], "The difference between prediction and causal inference": [[68, "The-difference-between-prediction-and-causal-inference"]], "Two fundamental challenges for causal inference": [[68, "Two-fundamental-challenges-for-causal-inference"]], "The four steps of causal inference": [[68, "The-four-steps-of-causal-inference"]], "The DoWhy+EconML solution": [[68, "The-DoWhy+EconML-solution"]], "A mystery dataset: Can you find out if if there is a causal effect?": [[68, "A-mystery-dataset:-Can-you-find-out-if-if-there-is-a-causal-effect?"]], "Model assumptions about the data-generating process using a causal graph": [[68, "Model-assumptions-about-the-data-generating-process-using-a-causal-graph"]], "Identify the correct estimand for the target quantity based on the causal model": [[68, "Identify-the-correct-estimand-for-the-target-quantity-based-on-the-causal-model"]], "Estimate the target estimand": [[68, "Estimate-the-target-estimand"]], "Check robustness of the estimate using refutation tests": [[68, "Check-robustness-of-the-estimate-using-refutation-tests"]], "Case-studies using DoWhy+EconML": [[68, "Case-studies-using-DoWhy+EconML"]], "Estimating the impact of a customer loyalty program": [[68, "Estimating-the-impact-of-a-customer-loyalty-program"]], "Recommendation A/B testing at an online company": [[68, "Recommendation-A/B-testing-at-an-online-company"]], "User segmentation for targeting interventions": [[68, "User-segmentation-for-targeting-interventions"]], "Multi-investment attribution at a software company": [[68, "Multi-investment-attribution-at-a-software-company"]], "Connections to fundamental machine learning challenges": [[68, "Connections-to-fundamental-machine-learning-challenges"]], "Further resources": [[68, "Further-resources"], [69, "further-resources"]], "DoWhy+EconML libraries": [[68, "DoWhy+EconML-libraries"]], "Video Lecture on causal inference and its connections to machine learning": [[68, "Video-Lecture-on-causal-inference-and-its-connections-to-machine-learning"]], "Detailed KDD Tutorial on Causal Inference": [[68, "Detailed-KDD-Tutorial-on-Causal-Inference"]], "Book chapters on causality and machine learning": [[68, "Book-chapters-on-causality-and-machine-learning"]], "Causality and Machine Learning group at Microsoft": [[68, "Causality-and-Machine-Learning-group-at-Microsoft"]], "Getting Started": [[69, "getting-started"]], "Installation": [[69, "installation"], [70, "installation"]], "\u201cHello causal inference world\u201d": [[69, "hello-causal-inference-world"]], "Effect inference": [[69, "effect-inference"]], "Graphical causal model-based inference": [[69, "graphical-causal-model-based-inference"]], "Installing with pip": [[70, "installing-with-pip"]], "Installing with Conda": [[70, "installing-with-conda"]], "Installing on Azure Machine Learning": [[70, "installing-on-azure-machine-learning"]], "DoWhy documentation": [[71, "dowhy-documentation"]], "Key differences compared to available causal inference software": [[71, "key-differences-compared-to-available-causal-inference-software"]], "Predicting outcome for out-of-distribution inputs": [[72, "predicting-outcome-for-out-of-distribution-inputs"]], "Estimating conditional average causal effect": [[73, "estimating-conditional-average-causal-effect"]], "Linear regression": [[73, "linear-regression"]], "DML method from EconML": [[73, "dml-method-from-econml"]], "Distance-based matching": [[74, "distance-based-matching"]], "Do-sampler": [[75, "do-sampler"]], "Using do-sampler": [[75, "using-do-sampler"]], "Estimating average causal effect using backdoor": [[76, "estimating-average-causal-effect-using-backdoor"]], "Propensity-based methods": [[77, "propensity-based-methods"]], "Propensity-Based Matching": [[77, "propensity-based-matching"]], "Propensity-based Stratification": [[77, "propensity-based-stratification"]], "Inverse Propensity Weighting": [[77, "inverse-propensity-weighting"]], "Regression-based methods": [[78, "regression-based-methods"]], "Effect Estimation Using specific Effect Estimators (for ACE, mediation effect, \u2026)": [[79, "effect-estimation-using-specific-effect-estimators-for-ace-mediation-effect"]], "Generating sample data and the causal model": [[79, "generating-sample-data-and-the-causal-model"]], "Identify a target estimand under the model": [[79, "identify-a-target-estimand-under-the-model"]], "Supported identification criteria": [[79, "supported-identification-criteria"]], "Estimate causal effect based on the identified estimand": [[79, "estimate-causal-effect-based-on-the-identified-estimand"]], "Supported estimation methods": [[79, "supported-estimation-methods"]], "Using EconML and CausalML estimation methods in DoWhy": [[79, "using-econml-and-causalml-estimation-methods-in-dowhy"]], "Refute the obtained estimate": [[79, "refute-the-obtained-estimate"]], "Supported refutation methods": [[79, "supported-refutation-methods"]], "Comparison to other packages": [[79, "comparison-to-other-packages"]], "Key difference: Causal assumptions as first-class citizens": [[79, "key-difference-causal-assumptions-as-first-class-citizens"]], "Estimating average causal effect using GCM": [[80, "estimating-average-causal-effect-using-gcm"]], "How to use it": [[80, "how-to-use-it"], [89, "how-to-use-it"], [92, "how-to-use-it"], [93, "how-to-use-it"], [94, "how-to-use-it"], [95, "how-to-use-it"], [97, "how-to-use-it"], [99, "how-to-use-it"], [111, "how-to-use-it"]], "Related example notebooks": [[80, "related-example-notebooks"], [89, "related-example-notebooks"], [91, "related-example-notebooks"], [92, "related-example-notebooks"], [93, "related-example-notebooks"], [94, "related-example-notebooks"], [97, "related-example-notebooks"], [99, "related-example-notebooks"], [113, "related-example-notebooks"]], "Understanding the method": [[80, "understanding-the-method"], [89, "understanding-the-method"], [92, "understanding-the-method"], [93, "understanding-the-method"], [94, "understanding-the-method"], [97, "understanding-the-method"]], "Estimating average causal effect with natural experiments": [[81, "estimating-average-causal-effect-with-natural-experiments"]], "Instrumental variable estimator for binary action": [[81, "instrumental-variable-estimator-for-binary-action"]], "Regression discontinuity estimator": [[81, "regression-discontinuity-estimator"]], "Backdoor criterion": [[82, "backdoor-criterion"]], "Frontdoor criterion": [[83, "frontdoor-criterion"]], "ID algorithm for discovering new identification strategies": [[84, "id-algorithm-for-discovering-new-identification-strategies"]], "Identifying causal effect": [[85, "identifying-causal-effect"]], "Natural experiments and instrumental variables": [[86, "natural-experiments-and-instrumental-variables"]], "Estimating Causal Effects": [[87, "estimating-causal-effects"]], "Performing Causal Tasks": [[88, "performing-causal-tasks"]], "Quantifying Intrinsic Causal Influence": [[89, "quantifying-intrinsic-causal-influence"]], "Quantify Causal Influence": [[90, "quantify-causal-influence"]], "Mediation Analysis: Estimating natural direct and indirect effects": [[91, "mediation-analysis-estimating-natural-direct-and-indirect-effects"]], "Direct Effect: Quantifying Arrow Strength": [[92, "direct-effect-quantifying-arrow-strength"]], "Customize the distance measure": [[92, "customize-the-distance-measure"]], "Anomaly Attribution": [[93, "anomaly-attribution"]], "Attributing Distributional Changes": [[94, "attributing-distributional-changes"]], "Feature Relevance": [[95, "feature-relevance"]], "Root-Cause Analysis and Explanation": [[96, "root-cause-analysis-and-explanation"]], "Computing Counterfactuals": [[97, "computing-counterfactuals"]], "Asking and Answering What-If Questions": [[98, "asking-and-answering-what-if-questions"]], "Simulating the Impact of Interventions": [[99, "simulating-the-impact-of-interventions"]], "Foreword": [[100, "foreword"]], "The need for causal inference": [[100, "the-need-for-causal-inference"]], "User Guide": [[101, "user-guide"]], "Introduction to DoWhy": [[102, "introduction-to-dowhy"]], "Supported causal tasks": [[102, "supported-causal-tasks"]], "Testing validity of a causal analysis": [[102, "testing-validity-of-a-causal-analysis"]], "Who this user guide is for": [[102, "who-this-user-guide-is-for"]], "Modeling Causal Relations": [[103, "modeling-causal-relations"]], "Learning causal structure from data": [[104, "learning-causal-structure-from-data"]], "Graph discovery using CDT": [[104, "graph-discovery-using-cdt"]], "Graph discovery using dodiscover": [[104, "graph-discovery-using-dodiscover"]], "Graph discovery using causal-learn": [[104, "graph-discovery-using-causal-learn"]], "Performing independence tests": [[105, "performing-independence-tests"]], "Refuting a Causal Graph": [[106, "refuting-a-causal-graph"]], "Graph refutations": [[107, "graph-refutations"]], "Specifying a causal graph using domain knowledge": [[108, "specifying-a-causal-graph-using-domain-knowledge"]], "Customizing Causal Mechanism Assignment": [[109, "customizing-causal-mechanism-assignment"]], "Using ground truth models": [[109, "using-ground-truth-models"]], "Creating causal model (GCM) from equations": [[109, "creating-causal-model-gcm-from-equations"]], "Generate samples from a GCM": [[110, "generate-samples-from-a-gcm"]], "Estimating Confidence Intervals": [[111, "estimating-confidence-intervals"]], "Computing confidence intervals for causal queries": [[111, "computing-confidence-intervals-for-causal-queries"]], "Conveniently bootstrapping graph training on random subsets of training data": [[111, "conveniently-bootstrapping-graph-training-on-random-subsets-of-training-data"]], "Runtime cost versus confidence": [[111, "runtime-cost-versus-confidence"]], "Understanding the need for confidence intervals": [[111, "understanding-the-need-for-confidence-intervals"]], "Types of graphical causal models": [[112, "types-of-graphical-causal-models"]], "Available Causal Mechanisms": [[112, "available-causal-mechanisms"]], "Modeling Graphical Causal Models (GCMs)": [[113, "modeling-graphical-causal-models-gcms"]], "Fitting an SCM to the data": [[113, "fitting-an-scm-to-the-data"]], "Evaluating a fitted SCM": [[113, "evaluating-a-fitted-scm"]], "Other topics": [[113, "other-topics"]], "Evaluate a GCM": [[114, "evaluate-a-gcm"]], "Summary of auto assignment": [[114, "summary-of-auto-assignment"]], "Evaluating a fitted GCM": [[114, "evaluating-a-fitted-gcm"]], "Refuting causal estimates": [[115, "refuting-causal-estimates"]], "Data Subsample Refuter": [[116, "data-subsample-refuter"]], "Dummy Outcome Refuter": [[117, "dummy-outcome-refuter"]], "Testing for zero causal effect": [[117, "testing-for-zero-causal-effect"]], "Testing for non-zero causal effect": [[117, "testing-for-non-zero-causal-effect"]], "Refuting Effect Estimates": [[118, "refuting-effect-estimates"]], "Refutations based on negative control": [[118, "refutations-based-on-negative-control"]], "Refutations based on sensitivity analysis": [[118, "refutations-based-on-sensitivity-analysis"]], "Placebo Treatment Refuter": [[119, "placebo-treatment-refuter"]], "Random Common Cause Refuter": [[120, "random-common-cause-refuter"]], "Sensitivity Analysis": [[121, "sensitivity-analysis"]], "Partial-R2 based sensitivity analysis for 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"dowhy.causal_refuters.partial_linear_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.partial_linear_sensitivity_analyzer"]], "dowhy.causal_refuters.placebo_treatment_refuter": [[13, "module-dowhy.causal_refuters.placebo_treatment_refuter"]], "dowhy.causal_refuters.random_common_cause": [[13, "module-dowhy.causal_refuters.random_common_cause"]], "dowhy.causal_refuters.refute_estimate": [[13, "module-dowhy.causal_refuters.refute_estimate"]], "dowhy.causal_refuters.reisz": [[13, "module-dowhy.causal_refuters.reisz"]], "filter_dataframe() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.filter_dataframe"]], "fit() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.fit"]], "fit() (dowhy.causal_refuters.reisz.pluginreisz method)": [[13, "dowhy.causal_refuters.reisz.PluginReisz.fit"]], "fit() 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dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.get_generic_regressor"]], "get_phi_lower_upper() (dowhy.causal_refuters.non_parametric_sensitivity_analyzer.nonparametricsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer.get_phi_lower_upper"]], "get_phi_lower_upper() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_phi_lower_upper"]], "get_regression_r2() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_regression_r2"]], "include_simulated_confounder() (dowhy.causal_refuters.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (dowhy.causal_refuters.add_unobserved_common_cause.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (in module dowhy.causal_refuters.add_unobserved_common_cause)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.include_simulated_confounder"]], "is_benchmarking_needed() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.is_benchmarking_needed"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.iterMax"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": 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"dowhy.causal_refuters.dummy_outcome_refuter.noise"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.num_thresh"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.num_thresh"]], "other (dowhy.causal_refuters.dummy_outcome_refuter.testfraction attribute)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction.other"]], "partial_correlation() (dowhy.causal_refuters.graph_refuter.graphrefuter method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter.partial_correlation"]], "partial_r2_func() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.partial_r2_func"]], "perform_benchmarking() 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(in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.estimate_distribution_change_scores"]], "estimate_entropy_discrete() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_discrete"]], "estimate_entropy_kmeans() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_kmeans"]], "estimate_entropy_of_probabilities() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_of_probabilities"]], "estimate_entropy_using_discretization() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_using_discretization"]], "estimate_ftest_pvalue() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.estimate_ftest_pvalue"]], "estimate_gaussian_entropy() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_gaussian_entropy"]], "estimate_geometric_median() (in module dowhy.gcm.confidence_intervals)": [[18, "dowhy.gcm.confidence_intervals.estimate_geometric_median"]], "estimate_kl_divergence_categorical() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_categorical"]], "estimate_kl_divergence_continuous_clf() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_clf"]], "estimate_kl_divergence_continuous_knn() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_knn"]], "estimate_kl_divergence_of_probabilities() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_of_probabilities"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.invertiblefunctionalcausalmodel method)": [[18, "dowhy.gcm.causal_mechanisms.InvertibleFunctionalCausalModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.estimate_noise"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.estimate_probabilities"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.probabilityestimatormodel method)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel.estimate_probabilities"]], "estimate_shapley_values() (in module dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.estimate_shapley_values"]], "estimate_variance() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_variance"]], "evaluate() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.evaluate"]], "evaluate() 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"dowhy.gcm.ml package", "dowhy.gcm.util package", "dowhy.graph_learners package", "dowhy.interpreters package", "dowhy.utils package", "Exploring Causes of Hotel Booking Cancellations", "Counterfactual Fairness", "Do-sampler Introduction", "Conditional Average Treatment Effects (CATE) with DoWhy and EconML", "Simple example on using Instrumental Variables method for estimation", "Demo for the DoWhy causal API", "Causal Discovery example", "Confounding Example: Finding causal effects from observed data", "A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions", "Finding optimal adjustment sets", "DoWhy: Different estimation methods for causal inference", "Estimating the Effect of a Member Rewards Program", "Functional API Preview", "DoWhy example on ihdp (Infant Health and Development Program) dataset", "DoWhy: Interpreters for Causal Estimators", "DoWhy example on the Lalonde dataset", "Mediation analysis with DoWhy: Direct and Indirect Effects", "Estimating effect of multiple treatments", "Example to demonstrate optimized backdoor variable search for Causal Identification", "Applying refutation tests to the Lalonde and IHDP datasets", "Assessing Support and Overlap with OverRule", "Iterating over multiple refutation tests", "Basic Example for Calculating the Causal Effect", "Impact of 401(k) eligibility on net financial assets", "Basic Example for Graphical Causal Models", "Counterfactual Analysis in a Medical Case", "Decomposing the Gender Wage Gap", "Basic Example for generating samples from a GCM", "Falsification of User-Given Directed Acyclic Graphs", "Estimating intrinsic causal influences in real-world examples", "Causal Attributions and Root-Cause Analysis in an Online Shop", "Finding the Root Cause of Elevated Latencies in a Microservice Architecture", "Finding Root Causes of Changes in a Supply Chain", "Testing Assumptions in model with DoWhy: A simple example", "Identifying Effect using ID Algorithm", "Lalonde Pandas 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"Identifying causal effect", "Natural experiments and instrumental variables", "Estimating Causal Effects", "Performing Causal Tasks", "Quantifying Intrinsic Causal Influence", "Quantify Causal Influence", "Mediation Analysis: Estimating natural direct and indirect effects", "Direct Effect: Quantifying Arrow Strength", "Anomaly Attribution", "Attributing Distributional Changes", "Feature Relevance", "Root-Cause Analysis and Explanation", "Computing Counterfactuals", "Asking and Answering What-If Questions", "Simulating the Impact of Interventions", "Foreword", "User Guide", "Introduction to DoWhy", "Modeling Causal Relations", "Learning causal structure from data", "Performing independence tests", "Refuting a Causal Graph", "Graph refutations", "Specifying a causal graph using domain knowledge", "Customizing Causal Mechanism Assignment", "Generate samples from a GCM", "Estimating Confidence Intervals", "Types of graphical causal models", "Modeling Graphical Causal Models (GCMs)", 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37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 75, 77, 78, 81, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 105, 107, 110, 111, 112, 113, 114, 117, 118, 124], "ding": [1, 13, 66], "vanderweel": [1, 13, 66], "2016": 1, "www": [1, 3, 7, 13, 24, 53, 56, 68], "ncbi": [1, 7, 13, 24], "nlm": [1, 7, 13, 24], "nih": [1, 7, 13, 24], "gov": [1, 7, 13, 24], "pmc": [1, 7, 13, 24], "pmc4820664": [1, 13], "jlgleason": 1, "unit": [1, 4, 6, 13, 18, 23, 25, 26, 27, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 55, 57, 65, 66, 67, 68, 92, 94], "chang": [1, 3, 4, 13, 18, 23, 25, 26, 28, 33, 36, 37, 39, 40, 42, 45, 47, 49, 51, 54, 55, 56, 58, 63, 65, 66, 68, 79, 87, 88, 90, 92, 93, 95, 96, 97, 100, 101, 102, 111, 112, 113, 114, 116, 118, 120, 124], "attribut": [1, 4, 5, 7, 9, 11, 17, 21, 24, 26, 31, 34, 51, 53, 54, 60, 63, 67, 88, 89, 92, 95, 96, 101, 102, 113], "kailashbuki": 1, "qualiti": [1, 18, 49, 50, 55, 57, 102, 114], "option": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 19, 24, 25, 26, 46, 47, 48, 49, 50, 51, 54, 55, 57, 60, 64, 70, 95, 109, 114, 124], "best": [1, 4, 13, 18, 19, 24, 49, 51, 55, 57, 64, 68, 69, 102, 107, 109, 114], "auto": [1, 4, 6, 26, 28, 48, 49, 50, 52, 54, 55, 56, 57, 69, 80, 89, 92, 93, 94, 95, 97, 99, 110, 111, 112, 113], "assign": [1, 4, 6, 9, 17, 18, 25, 26, 35, 39, 48, 49, 50, 52, 55, 56, 57, 71, 79, 87, 89, 93, 97, 101, 103, 110, 111, 112, 113], "mechan": [1, 18, 25, 26, 48, 49, 50, 51, 52, 54, 55, 56, 57, 68, 71, 89, 92, 93, 94, 95, 96, 97, 101, 102, 103, 110, 111, 113, 114], "which": [1, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 31, 33, 45, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 61, 64, 65, 66, 67, 68, 69, 75, 80, 82, 88, 89, 91, 93, 94, 95, 97, 102, 104, 106, 109, 111, 112, 114], "ml": [1, 4, 18, 28, 48, 49, 51, 53, 56, 64, 68, 89, 109, 113], "autogluon": [1, 4, 18, 111], "bloebp": 1, "condit": [1, 4, 6, 7, 9, 13, 17, 18, 19, 24, 36, 42, 45, 49, 50, 52, 53, 54, 55, 56, 57, 60, 63, 65, 66, 67, 68, 69, 71, 76, 79, 87, 88, 89, 90, 92, 94, 101, 102, 103, 105, 106, 107, 109, 110, 112, 113, 114, 118], "through": [1, 4, 6, 13, 17, 18, 24, 27, 33, 37, 41, 45, 50, 52, 56, 59, 60, 65, 67, 68, 69, 71, 75, 79, 90, 91, 92, 93, 100, 102, 104, 110], "packag": [1, 25, 28, 35, 36, 39, 47, 48, 52, 58, 60, 64, 67, 68, 71, 73, 95, 96, 101, 104, 109, 110, 111, 113], "algorithm": [1, 4, 7, 8, 13, 18, 26, 31, 34, 37, 49, 50, 51, 52, 54, 55, 56, 57, 63, 67, 68, 69, 71, 72, 85, 87, 94, 102, 104, 109, 110, 111, 112, 114], "comput": [1, 4, 6, 7, 9, 12, 13, 15, 18, 24, 26, 27, 28, 30, 34, 37, 44, 47, 48, 51, 54, 56, 57, 60, 64, 65, 68, 75, 80, 88, 89, 94, 96, 98, 100, 101, 113], "effici": [1, 4, 7, 13, 27, 34, 65, 75], "backdoor": [1, 4, 13, 17, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 83, 85, 86, 87, 88, 101, 102, 124], "set": [1, 4, 5, 7, 10, 13, 14, 15, 17, 18, 19, 21, 24, 26, 27, 28, 32, 33, 36, 37, 41, 44, 45, 46, 47, 48, 49, 51, 53, 54, 55, 57, 60, 63, 65, 66, 67, 68, 70, 71, 75, 76, 80, 82, 85, 87, 89, 93, 94, 95, 96, 97, 102, 104, 106, 111, 112, 113, 114, 124], "esmucl": 1, "control": [1, 4, 5, 6, 7, 18, 19, 25, 27, 35, 39, 45, 47, 48, 49, 60, 75, 78, 101, 113, 115], "direct": [1, 4, 7, 11, 13, 18, 24, 25, 26, 31, 34, 43, 49, 51, 54, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 79, 88, 90, 101, 102, 103, 104, 106, 113, 114], "multi": [1, 4, 9, 26, 42, 94], "egorkraevtransferwis": 1, "pydata": 1, "theme": 1, "homepag": 1, "get": [1, 4, 7, 13, 17, 18, 19, 24, 25, 27, 31, 35, 39, 47, 48, 49, 51, 54, 55, 56, 64, 66, 68, 70, 71, 75, 80, 93, 94, 111, 114], "start": [1, 2, 4, 13, 15, 17, 25, 43, 44, 50, 55, 71, 79, 82, 102, 109], "guid": [1, 2, 4, 33, 49, 69, 71, 112], "revis": 1, "user": [1, 4, 5, 7, 13, 18, 24, 27, 36, 47, 49, 50, 60, 63, 64, 69, 70, 71, 72, 75, 79, 92, 103, 104, 106, 109, 112, 117], "petergtz": 1, "contribut": [1, 18, 48, 51, 54, 55, 57, 68, 71, 89, 90, 93, 95, 96, 100, 111], "simplifi": [1, 48, 68], "instruct": [1, 64, 69], "contributor": [1, 2], "michaelmarien": 1, "streamlin": [1, 111], "dev": [1, 3, 70], "environ": [1, 3, 8, 9, 10, 11, 54, 63, 64, 70], "poetri": [1, 3], "manag": [1, 51], "depend": [1, 3, 4, 13, 15, 18, 19, 26, 34, 45, 49, 50, 51, 52, 55, 56, 61, 64, 65, 67, 68, 70, 89, 92, 94, 95, 102, 103, 105, 106, 108, 110, 111, 112, 114], "project": [1, 2, 3, 22], "build": [1, 5, 7, 26, 27, 28, 29, 34, 45, 51, 57, 60, 68, 71, 75, 79, 87], "darthtrevino": 1, "bug": [1, 3, 28, 56], "fix": [1, 3, 4, 15, 17, 18, 28, 49, 53, 56, 97, 99, 115], "juli": [1, 25], "18": [1, 25, 28, 31, 40, 42, 44, 45, 47, 48, 49, 51, 53, 55, 60, 64, 66, 67], "big": [1, 36, 106], "thank": [1, 40, 51], "implement": [1, 2, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 20, 21, 24, 27, 34, 43, 45, 60, 64, 66, 68, 71, 75, 79, 87, 92, 94, 102, 104, 109, 111, 112, 113], "scm": [1, 18, 26, 50, 53, 55, 97, 101, 109, 111, 112], "root": [1, 4, 11, 18, 49, 50, 52, 54, 63, 69, 71, 80, 88, 89, 92, 93, 94, 99, 101, 102, 109, 110, 112, 113, 114], "caus": [1, 3, 4, 6, 13, 18, 23, 24, 26, 27, 29, 33, 35, 36, 38, 39, 50, 51, 63, 64, 65, 66, 68, 69, 70, 71, 75, 79, 80, 88, 89, 92, 93, 94, 97, 99, 101, 102, 103, 108, 111, 112, 113, 114, 118, 121, 124], "what": [1, 6, 18, 24, 25, 27, 33, 36, 49, 52, 53, 54, 64, 68, 69, 71, 75, 79, 80, 88, 92, 94, 97, 99, 100, 101, 102, 103, 105, 110, 112, 117, 119], "more": [1, 2, 3, 4, 9, 13, 14, 15, 17, 18, 19, 21, 25, 26, 27, 28, 31, 34, 36, 37, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 60, 64, 65, 66, 68, 69, 71, 75, 78, 79, 80, 87, 89, 90, 91, 94, 95, 97, 102, 107, 110, 111, 112, 113, 114, 124], "gener": [1, 4, 6, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 24, 26, 27, 28, 29, 31, 32, 34, 36, 37, 43, 45, 47, 49, 51, 53, 54, 57, 58, 60, 63, 64, 66, 69, 71, 75, 78, 80, 87, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 105, 106, 109, 111, 112, 113, 114, 117], "hazlett": [1, 66], "doc": [1, 4, 6, 18, 21, 64], "structur": [1, 7, 13, 18, 24, 26, 27, 28, 50, 51, 52, 54, 55, 56, 57, 69, 71, 75, 89, 90, 93, 95, 101, 102, 103, 106, 109, 110, 112, 113, 114], "updat": [1, 2, 3, 4, 7, 18, 37, 55, 56, 67, 70], "check": [1, 2, 3, 4, 7, 13, 18, 21, 24, 25, 27, 29, 36, 37, 41, 46, 47, 49, 50, 52, 54, 55, 56, 58, 60, 65, 69, 71, 72, 75, 79, 83, 84, 85, 86, 87, 100, 102, 103, 106, 107, 110, 114, 117, 120, 124], "out": [1, 2, 4, 9, 12, 13, 19, 25, 26, 27, 37, 45, 47, 51, 54, 55, 56, 64, 69, 75, 79, 84, 85, 87, 88, 94, 101, 102, 103, 107, 109, 112, 117], "elikl": 1, "itsoum": 1, "ryanrussel": 1, "whether": [1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 17, 18, 19, 24, 25, 26, 36, 41, 44, 47, 50, 53, 55, 56, 57, 58, 60, 68, 71, 85, 87, 92, 97, 102, 105, 106, 107, 114, 124], "data": [1, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29, 30, 33, 34, 35, 36, 37, 39, 41, 42, 43, 52, 54, 57, 58, 59, 62, 63, 65, 66, 69, 71, 75, 78, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 112, 114, 115, 117, 118, 124], "conform": [1, 7], "assum": [1, 4, 7, 13, 17, 18, 25, 29, 30, 34, 36, 40, 47, 48, 49, 50, 55, 56, 57, 58, 65, 66, 69, 71, 89, 94, 97, 102, 105, 107, 109, 111, 112, 113, 114, 124], "specif": [1, 4, 5, 6, 7, 9, 13, 17, 18, 19, 24, 26, 27, 28, 31, 47, 50, 55, 60, 64, 66, 71, 75, 82, 90, 91, 92, 93, 95, 97, 103, 104, 108, 111, 112, 124], "paramet": [1, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 33, 37, 41, 45, 47, 49, 51, 53, 55, 57, 58, 60, 64, 65, 67, 68, 71, 75, 79, 89, 92, 97, 109, 111, 114, 117, 121], "directli": [1, 4, 18, 28, 37, 48, 51, 54, 56, 63, 68, 73, 90, 93, 95, 97, 104, 107, 108, 111], "its": [1, 3, 4, 5, 6, 7, 18, 19, 25, 28, 31, 32, 34, 49, 50, 51, 55, 56, 57, 60, 63, 65, 66, 69, 79, 88, 89, 90, 92, 93, 94, 95, 97, 102, 106, 109, 111, 113, 114, 120], "own": [1, 4, 6, 17, 27, 28, 34, 54, 71, 75, 92, 112, 113, 114], "init": [1, 6], "custom": [1, 4, 5, 6, 13, 18, 24, 25, 36, 49, 55, 56, 57, 63, 71, 89, 95, 96, 101, 102, 112, 113, 114, 117], "consist": [1, 4, 13, 18, 21, 26, 31, 45, 48, 49, 50, 54, 55, 56, 57, 64, 69, 93, 104, 105, 106, 113, 114], "init_param": [1, 28, 46, 65, 68, 73, 79], "add": [1, 3, 4, 7, 13, 15, 18, 24, 25, 28, 32, 35, 37, 38, 39, 41, 45, 47, 51, 53, 58, 67, 68, 70, 79, 80, 89, 113, 114, 118, 120, 121], "glm": [1, 6], "hotel": [1, 62, 63, 103], "case": [1, 3, 4, 7, 13, 14, 17, 18, 19, 25, 26, 27, 28, 34, 36, 43, 47, 48, 49, 52, 54, 55, 56, 57, 63, 65, 71, 75, 87, 89, 92, 93, 95, 96, 97, 102, 103, 104, 109, 110, 112, 113, 114, 124], "studi": [1, 13, 14, 15, 26, 44, 45, 48, 56, 57, 65], "ae": 1, "foster": 1, "major": [1, 67, 100], "identif": [1, 4, 5, 6, 7, 13, 17, 25, 27, 31, 35, 39, 41, 59, 60, 68, 69, 71, 75, 81, 82, 83, 85, 86, 87, 102, 103, 108], "minim": [1, 4, 7, 9, 17, 18, 34, 45, 49, 53, 55, 57, 64, 65, 68, 82, 114], "adjust": [1, 4, 7, 13, 18, 19, 26, 38, 39, 45, 48, 49, 50, 55, 56, 63, 65, 66, 67, 68, 82, 91, 114], "maxim": [1, 7, 38, 82], "exhaust": [1, 7, 82], "search": [1, 7, 9, 11, 13, 15, 31, 34, 50, 55, 82], "coverag": 1, "discoveri": [1, 4, 18, 19, 22, 26, 63, 67, 69, 101, 103], "from": [1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 75, 79, 80, 81, 82, 84, 87, 89, 90, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 108, 111, 112, 113, 114, 117, 118], "like": [1, 2, 4, 13, 14, 17, 18, 24, 25, 26, 27, 33, 36, 45, 47, 48, 50, 51, 54, 55, 56, 58, 60, 64, 68, 69, 71, 75, 90, 94, 96, 100, 102, 109, 111, 112], "cdt": [1, 4, 53, 101, 103], "id": [1, 3, 4, 7, 25, 34, 37, 58, 61, 63, 65, 66, 85, 87], "text": [1, 3, 4, 23, 53, 80, 93], "interpret": [1, 2, 13, 18, 33, 41, 42, 47, 49, 50, 54, 55, 56, 63, 65, 66, 89, 91, 92, 93, 95, 114, 124], "": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 73, 75, 79, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 109, 111, 112, 113, 114, 124], "distanc": [1, 6, 9, 13, 18, 19, 76, 87], "match": [1, 4, 6, 13, 14, 33, 36, 45, 58, 76, 79, 87, 104, 117], "reli": [1, 18, 55, 68, 73, 112], "metric": [1, 6, 9, 18, 26, 35, 49, 50, 54, 55, 56, 57, 64, 92, 114], "between": [1, 4, 6, 7, 9, 10, 11, 13, 18, 19, 24, 25, 26, 27, 32, 33, 35, 39, 43, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 71, 75, 79, 80, 90, 92, 93, 94, 102, 103, 109, 110, 111, 112, 114, 118, 124], "input": [1, 4, 13, 18, 19, 20, 21, 24, 26, 31, 35, 39, 45, 50, 53, 56, 57, 63, 64, 67, 68, 70, 88, 92, 95, 96, 101, 102, 104, 109, 111, 112, 114, 124], "heurist": [1, 48, 55], "default": [1, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 24, 26, 27, 31, 37, 47, 51, 53, 54, 56, 58, 60, 64, 65, 66, 71, 75, 82, 89, 92, 94, 95, 111, 114, 124], "strata": [1, 6], "automat": [1, 3, 4, 5, 6, 13, 15, 18, 19, 27, 45, 47, 48, 49, 52, 55, 57, 60, 65, 71, 75, 79, 89, 93, 106, 108, 110, 113, 114, 121], "propens": [1, 4, 6, 13, 23, 27, 42, 45, 47, 60, 68, 75, 76, 79, 87], "score": [1, 4, 6, 13, 15, 18, 23, 26, 27, 45, 47, 49, 50, 51, 54, 55, 56, 57, 60, 67, 68, 75, 79, 87, 93, 94, 95, 111, 114], "stratif": [1, 4, 23, 47, 68, 79, 87], "confid": [1, 4, 6, 13, 18, 24, 25, 27, 29, 30, 37, 48, 55, 56, 57, 60, 65, 66, 75, 79, 101, 113], "interv": [1, 4, 6, 13, 18, 24, 30, 48, 55, 56, 57, 60, 66, 79, 101, 113], "regress": [1, 4, 6, 13, 18, 25, 26, 28, 29, 32, 33, 40, 41, 48, 49, 51, 55, 57, 63, 65, 68, 76, 79, 87, 94, 112, 114, 122], "version": [1, 3, 4, 13, 18, 21, 26, 31, 32, 33, 37, 44, 46, 54, 55, 66, 69, 70, 71], "bootstrap": [1, 4, 6, 13, 18, 19, 27, 37, 46, 48, 51, 56, 57, 60, 75, 79], "without": [1, 3, 4, 18, 19, 30, 47, 55, 56, 65, 66, 85, 105, 109, 111, 114], "need": [1, 4, 6, 7, 12, 13, 18, 19, 21, 25, 27, 28, 34, 36, 37, 47, 51, 54, 55, 59, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 89, 96, 97, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 115, 117], "refit": 1, "andrewc19": 1, "ha2trinh": 1, "siddhanthaldar": 1, "vojavocni": 1, "also": [1, 2, 3, 6, 7, 13, 18, 24, 25, 26, 29, 31, 32, 34, 35, 39, 42, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 63, 65, 66, 67, 68, 69, 71, 79, 80, 82, 87, 89, 90, 92, 93, 95, 96, 97, 99, 100, 102, 103, 105, 106, 109, 110, 111, 112, 113, 114, 115, 117, 118, 124], "iv": [1, 4, 6, 25, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 42, 44, 46, 47, 62, 65, 66, 68, 79, 81], "move": [1, 28, 48, 88, 100, 104], "matplotlib": [1, 13, 25, 26, 31, 32, 45, 48, 50, 51, 55, 57, 60, 66, 67, 68], "instal": [1, 3, 18, 24, 26, 51, 63, 64, 67, 68, 71, 104], "pip": [1, 3, 24, 26, 64, 67, 68, 69], "plot": [1, 3, 4, 13, 18, 23, 26, 30, 32, 34, 37, 39, 40, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 65, 67, 68, 109, 124], "align": [1, 56], "all": [1, 2, 3, 4, 6, 7, 9, 10, 12, 13, 15, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 32, 34, 35, 36, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 70, 72, 73, 75, 79, 80, 82, 83, 86, 89, 93, 97, 100, 102, 103, 105, 109, 110, 111, 112, 113, 114, 124], "continu": [1, 4, 5, 13, 14, 15, 17, 18, 19, 21, 23, 37, 44, 45, 49, 50, 54, 55, 56, 57, 58, 60, 67, 92, 97, 106, 109, 112, 114], "log": [1, 13, 18, 24, 25, 28, 29, 30, 32, 33, 35, 37, 39, 40, 41, 44, 46, 56, 66, 68, 93, 109], "configur": [1, 9, 13, 18, 19, 25, 29, 37, 55], "dummyoutcomerefut": [1, 4, 13], "arshiaarya": 1, "n8sty": 1, "moprescu": 1, "conda": [1, 69], "formula": [1, 4, 13, 17, 24, 27, 39, 40, 75], "front": [1, 4, 6, 79, 87], "door": [1, 4, 6, 7, 27, 48, 75, 79, 87], "criterion": [1, 6, 13, 18, 26, 41, 68, 79, 85, 86, 87], "path": [1, 4, 7, 24, 26, 27, 34, 35, 39, 41, 48, 51, 58, 59, 60, 61, 64, 66, 70, 75, 91, 92], "d": [1, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 51, 53, 57, 58, 60, 64, 65, 66, 67, 68, 70, 75, 92, 93, 106], "logist": [1, 4, 6, 18, 24, 25, 51, 66, 78, 87], "erikhambardzumyan": 1, "visual": [1, 4, 23, 31, 45, 51, 55, 56, 68], "how": [1, 4, 13, 17, 18, 19, 23, 25, 26, 31, 33, 34, 36, 39, 40, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 79, 82, 88, 90, 96, 100, 102, 106, 107, 109, 112, 113, 114, 121, 124], "distribut": [1, 3, 4, 9, 12, 13, 14, 17, 18, 23, 24, 26, 27, 39, 40, 48, 49, 50, 51, 52, 54, 55, 56, 57, 60, 68, 70, 75, 76, 80, 85, 88, 89, 90, 92, 93, 95, 96, 97, 101, 102, 103, 104, 109, 110, 111, 112, 113, 114], "friendlier": 1, "error": [1, 4, 6, 7, 13, 18, 19, 24, 27, 30, 34, 37, 40, 49, 50, 51, 53, 54, 55, 56, 57, 60, 64, 66, 68, 70, 75, 94, 103, 111, 114], "messag": [1, 3, 13, 18], "when": [1, 4, 5, 6, 7, 9, 10, 13, 14, 18, 19, 21, 24, 25, 27, 31, 34, 36, 37, 42, 45, 47, 49, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 78, 79, 80, 90, 92, 93, 94, 97, 106, 109, 110, 111, 112, 114, 116, 117, 119, 121], "enough": [1, 18, 25, 36, 65, 66, 68, 114], "bin": [1, 4, 6, 13, 14, 18, 26, 48], "dummi": [1, 13, 33, 79, 118], "outcom": [1, 4, 5, 6, 7, 13, 17, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 58, 59, 60, 61, 63, 65, 66, 67, 68, 69, 75, 76, 78, 79, 87, 88, 90, 95, 97, 100, 101, 102, 108, 112, 118, 121, 124], "compon": [1, 5, 19, 24, 51, 60, 94, 103], "simul": [1, 4, 6, 13, 29, 33, 35, 36, 37, 39, 41, 47, 51, 53, 65, 66, 79, 88, 92, 94, 97, 98, 101, 117, 118, 121], "zero": [1, 4, 13, 14, 18, 25, 29, 30, 33, 36, 41, 44, 47, 51, 53, 54, 55, 65, 66, 79, 89, 107, 118, 119, 124], "too": [1, 4, 13, 18, 25, 27, 47, 53, 72, 73, 75, 79, 80, 95, 114, 121, 124], "readi": [1, 3, 26, 31, 71, 80, 88, 109, 113], "book": [1, 57, 62, 63, 69, 85, 103], "cancel": [1, 51, 62, 63, 103], "membership": [1, 36, 62], "reward": [1, 15, 62, 63, 102], "program": [1, 13, 15, 24, 48, 57, 62, 63, 79, 102], "multipl": [1, 2, 4, 6, 18, 19, 26, 45, 47, 51, 55, 56, 59, 63, 64, 65, 68, 69, 71, 79, 80, 93, 95, 102, 104, 109, 111, 114, 115], "tutori": [1, 3, 18, 59, 63, 69], "organ": [1, 71, 102], "websit": [1, 36, 54, 56], "commun": [1, 2, 51, 71], "creat": [1, 3, 4, 6, 10, 13, 18, 19, 24, 27, 29, 34, 36, 38, 44, 47, 48, 49, 55, 60, 63, 64, 67, 68, 69, 70, 75, 92, 93, 108, 113, 117, 124], "guidelin": 1, "allcontributor": 1, "bot": 1, "so": [1, 2, 3, 4, 5, 12, 14, 18, 25, 26, 27, 29, 31, 37, 45, 47, 49, 50, 55, 58, 60, 65, 68, 75, 79, 97, 103, 109, 124], "just": [1, 4, 12, 24, 27, 30, 34, 37, 45, 47, 49, 50, 51, 53, 54, 55, 56, 60, 65, 68, 79, 107, 114], "after": [1, 4, 12, 13, 18, 23, 24, 34, 36, 39, 47, 49, 50, 51, 52, 54, 55, 56, 57, 63, 65, 66, 79, 87, 89, 93, 94, 97, 102, 110, 114, 118, 120], "pull": [1, 2, 66, 109], "request": [1, 2, 4, 25, 56, 103], "ar": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 35, 36, 39, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 69, 71, 75, 77, 79, 80, 82, 87, 88, 89, 90, 92, 93, 94, 95, 97, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 118, 122, 123, 124], "merg": [1, 3, 18, 45], "tanmai": 1, "kulkarni101": 1, "sid": [1, 25], "darthvad": [1, 25], "increas": [1, 13, 18, 25, 26, 29, 39, 47, 48, 50, 55, 56, 57, 66, 92, 111, 124], "In": [1, 3, 4, 9, 11, 13, 14, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 31, 33, 34, 36, 40, 43, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 64, 65, 67, 68, 69, 70, 71, 73, 75, 78, 79, 80, 87, 89, 90, 92, 93, 95, 96, 97, 102, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114, 117], "addit": [1, 3, 4, 6, 7, 13, 14, 17, 18, 19, 24, 25, 28, 29, 31, 36, 47, 48, 49, 50, 52, 53, 55, 56, 57, 59, 61, 65, 67, 68, 71, 73, 78, 79, 89, 90, 97, 109, 110, 111, 112, 113, 114, 117, 121, 124], "random": [1, 4, 10, 13, 14, 18, 19, 21, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 65, 66, 68, 69, 75, 79, 80, 89, 92, 93, 94, 95, 97, 99, 105, 107, 109, 110, 113, 114, 117, 118, 119], "dummyoutcom": 1, "arbitrari": [1, 4, 13, 18, 21, 89, 93, 95], "alwai": [1, 7, 26, 34, 49, 50, 52, 54, 55, 56, 68, 110, 111, 114], "thi": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 75, 77, 78, 79, 80, 81, 82, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114, 116, 122, 123, 124], "inspir": [1, 57], "idea": [1, 4, 17, 19, 21, 26, 27, 48, 54, 71, 75, 92, 94, 96], "t": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 25, 26, 27, 28, 29, 30, 31, 34, 37, 40, 45, 48, 49, 50, 51, 53, 55, 56, 59, 60, 65, 66, 67, 75, 80, 89, 93, 94, 97, 105, 108, 111, 113], "learner": [1, 28], "we": [1, 2, 3, 4, 5, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 64, 65, 66, 67, 68, 69, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 124], "provid": [1, 3, 4, 5, 7, 13, 14, 18, 19, 20, 24, 25, 26, 29, 31, 34, 35, 37, 40, 42, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 71, 72, 73, 79, 80, 89, 90, 95, 97, 100, 102, 104, 105, 106, 108, 109, 110, 111, 112, 114, 124], "Of": [1, 36], "cours": [1, 13, 36, 45], "specifi": [1, 4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 30, 35, 39, 40, 41, 45, 50, 53, 55, 64, 65, 68, 73, 80, 84, 89, 101, 103, 109, 114], "ani": [1, 2, 3, 4, 6, 7, 13, 14, 15, 18, 19, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 37, 45, 47, 49, 50, 54, 55, 57, 64, 65, 66, 67, 68, 70, 71, 75, 76, 79, 82, 89, 92, 93, 97, 102, 103, 105, 106, 112, 113, 115, 117, 118, 124], "bootstraprefut": [1, 4, 13], "issu": [1, 2, 4, 18, 27, 55, 56, 70, 71, 75], "measur": [1, 4, 13, 14, 18, 19, 31, 34, 37, 44, 51, 53, 54, 55, 56, 65, 66, 89, 90, 93, 94, 97, 105, 107, 114], "rather": [1, 5, 18, 51, 55, 56, 57, 60, 68, 89, 94, 95, 97, 109], "than": [1, 4, 5, 13, 14, 18, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 68, 80, 88, 89, 92, 95, 97, 102, 107, 110, 114, 124], "simpl": [1, 4, 6, 24, 30, 32, 36, 52, 54, 63, 68, 69, 71, 79, 86, 93, 95, 104, 110, 112, 113, 117], "sampl": [1, 4, 5, 6, 10, 13, 14, 15, 17, 18, 19, 24, 25, 26, 27, 28, 33, 35, 37, 39, 46, 47, 48, 49, 50, 51, 53, 56, 57, 58, 60, 63, 64, 68, 69, 75, 80, 93, 94, 97, 99, 101, 102, 111, 112, 113, 114], "nois": [1, 4, 13, 18, 26, 29, 30, 41, 48, 49, 50, 52, 54, 55, 56, 57, 64, 89, 90, 93, 94, 95, 96, 97, 109, 110, 111, 112, 113, 114], "select": [1, 4, 6, 9, 13, 18, 19, 26, 27, 47, 49, 55, 57, 64, 67, 71, 75, 79, 82, 114, 116, 124], "signific": [1, 4, 6, 13, 18, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 65, 66, 67, 79, 107, 110, 114], "causalml": [1, 4, 42, 68, 71], "call": [1, 2, 4, 5, 7, 9, 12, 14, 18, 26, 27, 28, 34, 37, 41, 45, 49, 50, 51, 52, 55, 56, 57, 60, 65, 68, 71, 73, 75, 79, 82, 87, 94, 102, 103, 106, 109, 110, 111, 113], "name": [1, 3, 4, 5, 6, 7, 13, 14, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 48, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 84, 102, 108, 109], "scheme": [1, 21, 35, 39], "To": [1, 3, 4, 5, 9, 13, 18, 21, 24, 25, 26, 27, 34, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 70, 75, 78, 80, 82, 83, 84, 86, 89, 92, 94, 96, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 124], "achiev": [1, 18, 24, 26, 47, 68, 92, 94, 97], "suggest": [1, 4, 18, 21, 26, 57, 64, 65, 68, 97], "prepend": 1, "intern": [1, 9, 11, 12, 13, 14, 15, 18, 21, 27, 35, 45, 51, 64, 75, 89, 93, 94, 95, 109], "string": [1, 4, 5, 6, 13, 14, 15, 18, 19, 21, 23, 24, 28, 31, 34, 46, 47, 48, 49, 55, 57, 58, 60, 61, 66, 108, 109, 112, 114], "For": [1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 34, 35, 36, 37, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 79, 80, 85, 86, 87, 89, 91, 92, 93, 94, 95, 97, 100, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 124], "propensity_score_match": [1, 4, 35, 36, 37, 38, 39, 46, 69, 77, 79], "Not": [1, 6, 13, 25], "break": [1, 3], "keep": [1, 3, 4, 13, 17, 18, 21, 25, 26, 27, 55, 68, 94, 97, 113], "sinc": [1, 4, 12, 13, 18, 25, 26, 27, 29, 30, 31, 34, 39, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 68, 80, 89, 94, 97, 102, 103, 105, 107, 109, 110, 111, 112, 114, 124], "modifi": [1, 4, 6, 17, 18, 21, 24, 26, 28, 31, 42, 47, 53, 54, 73, 93, 95, 104, 105, 118, 124], "subset": [1, 4, 9, 13, 18, 19, 24, 25, 26, 27, 37, 46, 56, 65, 67, 68, 75, 79, 89, 95, 116], "warn": [1, 3, 13, 25, 26, 28, 29, 33, 34, 36, 37, 41, 42, 44, 46, 47, 64, 66, 67, 68], "outsid": [1, 25, 26, 56], "tri": [1, 18, 79, 87, 112], "ci": [1, 4, 18, 25, 48, 53, 66], "standard": [1, 4, 6, 13, 14, 18, 19, 23, 24, 25, 30, 39, 40, 49, 50, 51, 54, 55, 56, 66, 68, 72, 89, 112, 114], "correspond": [1, 4, 6, 7, 10, 13, 14, 18, 24, 28, 34, 46, 48, 49, 51, 52, 53, 54, 55, 56, 66, 68, 89, 93, 94, 109, 110, 113, 114], "parametr": [1, 4, 5, 7, 13, 18, 19, 24, 27, 34, 37, 41, 48, 49, 55, 57, 60, 63, 69, 75, 79, 104, 109, 114, 123], "form": [1, 4, 7, 13, 14, 15, 18, 33, 34, 45, 49, 51, 54, 55, 57, 58, 80, 85, 97, 103, 109, 112, 113, 114], "conveni": [1, 4, 17, 18, 24, 82], "seed": [1, 4, 10, 13, 14, 21, 31, 37, 45, 48, 51, 53, 57, 58, 65, 66, 92], "import": [1, 4, 13, 18, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 69, 70, 73, 79, 80, 82, 84, 87, 89, 91, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 113, 114, 124], "simpler": [1, 28], "multivari": [1, 18, 19], "averag": [1, 13, 18, 21, 24, 25, 26, 31, 36, 38, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 79, 87, 88, 90, 101, 102, 110, 114], "estimate_effect": [1, 2, 4, 6, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 81, 87, 91], "other": [1, 4, 5, 6, 7, 13, 17, 18, 25, 26, 27, 34, 36, 37, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 60, 64, 65, 68, 69, 71, 75, 82, 87, 88, 89, 90, 92, 93, 94, 95, 101, 102, 103, 104, 105, 106, 107, 109, 110, 112, 114], "target": [1, 4, 6, 7, 9, 18, 19, 21, 25, 26, 29, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 46, 47, 50, 51, 54, 55, 57, 58, 61, 65, 66, 67, 69, 71, 80, 85, 89, 90, 92, 93, 94, 95, 96, 108, 112], "estimand": [1, 4, 5, 6, 7, 13, 17, 28, 29, 31, 32, 34, 36, 37, 38, 41, 42, 43, 44, 47, 60, 65, 66, 67, 69, 71, 78, 87], "att": [1, 4, 25, 28, 35, 36, 39, 47, 74, 77], "atc": [1, 4, 25, 28, 35, 39, 47, 77], "reproduc": [1, 4, 47, 55, 57, 92], "j": [1, 18, 19, 24, 26, 44, 46, 51, 58, 64, 67, 93, 94], "chou": 1, "ktmud": 1, "jrfiedler": 1, "shounak112358": 1, "lnk2past": 1, "pywhi": [2, 70, 71, 104], "welcom": [2, 31, 37], "There": [2, 11, 13, 18, 25, 26, 27, 28, 44, 45, 47, 48, 49, 50, 51, 55, 56, 64, 66, 69, 75, 82, 93, 95, 114, 124], "wai": [2, 3, 4, 9, 18, 27, 29, 33, 35, 47, 49, 51, 52, 54, 55, 56, 57, 63, 68, 70, 71, 75, 79, 82, 89, 96, 97, 102, 104, 110, 114, 124], "some": [2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 25, 27, 28, 32, 34, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 65, 68, 69, 70, 79, 80, 88, 89, 92, 93, 94, 95, 97, 99, 100, 102, 105, 106, 108, 109, 110, 111, 113, 114, 118, 124], "ad": [2, 3, 4, 5, 12, 13, 15, 24, 26, 30, 45, 48, 51, 53, 54, 55, 60, 64, 68, 80, 90], "jupyt": [2, 63, 68], "describ": [2, 4, 7, 13, 15, 18, 34, 39, 41, 50, 52, 54, 55, 57, 60, 64, 65, 66, 68, 69, 88, 100, 102, 110, 112, 113], "solv": [2, 13, 15, 68, 70, 71], "problem": [2, 3, 6, 15, 18, 21, 25, 27, 29, 35, 36, 37, 46, 47, 50, 55, 56, 68, 69, 70, 71, 75, 79, 87, 95, 102, 103, 104, 106, 112, 118], "help": [2, 4, 13, 18, 19, 25, 33, 47, 49, 54, 55, 56, 60, 64, 65, 66, 68, 71, 89, 97, 102, 103, 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34, 35, 36, 45, 46, 48, 49, 50, 51, 53, 54, 55, 56, 57, 64, 66, 67, 68, 71, 75, 79, 82, 88, 89, 90, 92, 93, 94, 96, 102, 103, 105, 106, 107, 109, 111, 112, 113, 114], "get_confidence_interv": 4, "confidence_level": [4, 6, 18, 57], "done": [4, 6, 10, 13, 18, 24, 35, 39, 47, 49, 52, 67, 71, 102, 110], "overridden": [4, 12, 18], "level": [4, 6, 13, 18, 24, 25, 26, 27, 29, 32, 33, 37, 44, 46, 49, 50, 51, 52, 53, 55, 56, 57, 60, 63, 65, 66, 68, 75, 95, 97, 110, 112, 114], "arg": [4, 6, 7, 10, 13, 16, 17, 18, 21, 22, 23, 26], "get_standard_error": 4, "method_nam": [4, 6, 7, 17, 22, 23, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 59, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 81, 91, 116, 117, 119, 120, 124], "test_stat_signific": 4, "statist": [4, 6, 9, 13, 14, 15, 18, 19, 25, 27, 29, 30, 32, 40, 44, 45, 48, 49, 50, 51, 53, 56, 60, 65, 66, 68, 69, 71, 75, 79, 87, 89, 92, 94, 95, 103, 114], "resampl": [4, 18, 111], "individu": [4, 6, 18, 19, 26, 29, 31, 34, 45, 48, 51, 53, 67, 95], "child": [4, 44, 56, 92, 109], "p": [4, 5, 6, 9, 11, 13, 18, 19, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 53, 55, 56, 57, 59, 60, 64, 65, 66, 67, 68, 74, 76, 77, 92, 93, 94, 105, 110, 112, 114, 118, 124], "test_signific": [4, 6, 29, 31, 35, 37, 38, 41, 65, 66, 67, 78, 91], "bool": [4, 5, 6, 7, 12, 13, 14, 15, 17, 18, 19, 21, 24, 26, 38, 44, 60], "str": [4, 5, 6, 7, 13, 14, 15, 17, 18, 20, 21, 24, 26, 31, 32, 35, 38, 39, 40, 43, 44, 45, 47, 48, 51, 60, 68, 77, 81], "fals": [4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 73, 79, 91, 107, 110, 111, 114], "evaluate_effect_strength": [4, 6, 37], "num_null_simul": [4, 6], "int": [4, 5, 6, 7, 13, 14, 15, 18, 19, 20, 21, 24, 26, 28, 51, 60], "1000": [4, 6, 13, 18, 19, 25, 29, 30, 36, 49, 51, 52, 56, 57, 65, 69, 80, 89, 92, 93, 94, 95, 99, 105, 109, 110, 111, 113, 114], "num_simul": [4, 6, 13, 28, 36, 68], "399": [4, 6, 67], "sample_size_fract": [4, 6], "float": [4, 5, 6, 13, 14, 15, 18, 19, 20, 21, 24, 26, 33, 67, 80], "0": [4, 6, 9, 10, 12, 13, 14, 15, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 64, 65, 66, 67, 68, 69, 73, 78, 79, 80, 81, 89, 92, 93, 94, 95, 97, 99, 105, 107, 109, 110, 111, 112, 113, 114, 116, 117, 118, 124], "95": [4, 6, 13, 15, 18, 24, 25, 28, 30, 45, 51, 55, 56, 57, 60, 66, 97, 111], "need_conditional_estim": [4, 6, 42], "num_quantiles_to_discretize_cont_col": [4, 6], "_": [4, 6, 13, 18, 26, 28, 51, 53, 89, 94, 109], "subclass": [4, 12, 18, 24], "initi": [4, 9, 12, 13, 14, 15, 17, 18, 21, 23, 27, 32, 43, 50, 54, 56, 59, 75, 95], "relev": [4, 13, 17, 18, 19, 26, 36, 45, 47, 49, 51, 54, 55, 65, 88, 96, 101, 112, 124], "constructor": [4, 18], "probabl": [4, 6, 7, 13, 18, 25, 27, 34, 47, 49, 50, 51, 53, 54, 55, 56, 57, 58, 68, 71, 75, 76, 112, 114], "express": [4, 6, 7, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 53, 65, 66, 67, 68, 79, 85, 87, 109, 111], "repres": [4, 6, 7, 18, 19, 20, 24, 25, 26, 27, 34, 39, 49, 50, 52, 54, 55, 56, 57, 60, 65, 66, 67, 68, 75, 90, 92, 96, 97, 108, 109, 110, 111, 112, 113, 114], "binari": [4, 5, 6, 9, 10, 11, 13, 14, 15, 18, 19, 33, 44, 45, 50, 55, 56, 58, 65, 68, 79, 80, 87], "flag": [4, 5, 6, 9, 10, 11, 13, 27, 47, 60], "indic": [4, 6, 9, 10, 11, 13, 14, 18, 19, 24, 25, 29, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 93, 97, 107, 110, 114, 124], "overrid": [4, 6, 9, 12, 13, 14, 15, 17, 27, 64, 66, 75], "true": [4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 18, 19, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 51, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 75, 78, 79, 80, 91, 100, 104, 105, 106, 112, 114, 116, 117, 118, 119, 120], "strength": [4, 6, 13, 18, 47, 54, 55, 65, 66, 88, 90, 101, 113, 121], "param": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 24, 33, 46, 60, 64], "dictionari": [4, 5, 6, 7, 10, 13, 14, 15, 17, 18, 21, 24, 26, 45, 51, 60, 64, 80], "size": [4, 6, 10, 11, 12, 13, 18, 19, 23, 24, 27, 29, 30, 31, 36, 45, 48, 49, 50, 52, 55, 56, 57, 64, 66, 69, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "boolean": [4, 6, 13, 14, 15, 21, 26, 28, 45], "split": [4, 6, 10, 13, 18, 36, 40, 44, 45, 51], "enabl": [4, 6, 13, 64, 94, 100, 102, 109], "instanc": [4, 6, 7, 9, 11, 12, 13, 15, 18, 19, 21, 23, 26, 31, 34, 45, 47, 49, 55, 56, 57, 60, 66, 67, 69, 71, 80, 82, 83, 84, 86, 89, 92, 93, 100, 105, 108, 109, 111, 112, 113, 114], "bootstrapestim": 4, "tupl": [4, 13, 18, 21, 24, 26, 42, 58, 66], "alia": [4, 6, 13], "field": [4, 5, 13, 60], "default_confidence_level": 4, "default_interpret_method": 4, "default_notimplementederror_msg": 4, "yet": [4, 27], "next": [4, 17, 29, 34, 51, 54, 55, 56, 64, 87, 88, 89, 92, 93, 94, 97, 99, 103, 106, 110, 111], "microsoft": [4, 13, 69], "default_number_of_simulations_ci": 4, "default_number_of_simulations_stat_test": 4, "default_sample_size_fract": 4, "temp_cat_column_prefix": 4, "__categorical__": 4, "x": [4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 26, 28, 30, 31, 33, 34, 36, 38, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 65, 66, 69, 80, 87, 88, 89, 92, 93, 94, 95, 96, 97, 99, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "data_df": [4, 6, 31], "oper": [4, 5, 7, 14, 17, 18, 25, 55, 56, 85, 109], "expect": [4, 9, 13, 17, 18, 19, 21, 33, 36, 39, 46, 47, 49, 50, 53, 54, 55, 56, 64, 65, 76, 80, 87, 89, 92, 94, 95, 97, 104, 105, 109, 112, 114], "interven": [4, 5, 26, 27, 49, 52, 54, 60, 75, 80, 97, 99, 100, 110], "appli": [4, 6, 7, 9, 11, 13, 14, 18, 19, 20, 21, 23, 24, 31, 36, 40, 51, 53, 54, 55, 63, 67, 68, 92, 102, 103, 111], "estimate_confidence_interv": 4, "estimate_valu": 4, "estimate_effect_na": 4, "panda": [4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 63, 66, 67, 68, 69, 75, 80, 89, 92, 93, 94, 95, 97, 99, 104, 105, 108, 109, 110, 111, 113, 114], "estimate_std_error": 4, "get_new_estimator_object": 4, "same": [4, 6, 13, 14, 18, 19, 20, 25, 26, 27, 28, 29, 33, 35, 36, 37, 45, 47, 49, 51, 52, 53, 55, 56, 57, 60, 61, 65, 66, 67, 68, 69, 71, 75, 79, 80, 83, 86, 94, 104, 105, 106, 107, 110, 111, 113, 114], "type": [4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 76, 80, 84, 89, 92, 97, 101, 109, 113, 114], "respect": [4, 5, 7, 9, 10, 13, 18, 34, 36, 49, 50, 52, 54, 55, 56, 57, 58, 60, 66, 67, 71, 79, 80, 92, 94, 95, 96, 97, 110, 112, 114], "pathwai": [4, 66, 91], "emploi": [4, 48, 50, 51, 66, 90, 92, 94, 112], "had": [4, 25, 26, 36, 50, 55, 65, 88, 96, 97, 105, 112], "static": [4, 7, 18, 23, 34], "is_bootstrap_parameter_chang": 4, "bootstrap_estimates_param": 4, "given_param": 4, "fresh": 4, "again": [4, 5, 49, 53, 55, 60, 69, 70, 97, 105, 109, 111, 113, 114], "current": [4, 5, 6, 7, 9, 13, 18, 19, 24, 26, 36, 37, 41, 42, 45, 47, 51, 58, 60, 64, 66, 79, 97, 109], "denot": [4, 6, 13, 18, 24, 59, 65, 66, 68, 92, 112, 114], "reset_encod": 4, "remov": [4, 7, 13, 18, 24, 25, 27, 29, 31, 33, 34, 35, 36, 37, 39, 46, 53, 54, 64, 65, 66, 67, 68, 90, 92, 124], "encod": [4, 12, 13, 19, 21, 47, 48, 49, 51, 52, 68, 79, 87, 102, 103, 110], "re": [4, 13, 14, 18, 25, 27, 40, 44, 51, 55, 56, 60, 65, 66, 69, 75, 94, 111], "It": [4, 5, 6, 7, 13, 18, 19, 25, 26, 27, 32, 37, 43, 47, 50, 53, 54, 57, 60, 64, 65, 66, 68, 69, 71, 75, 78, 79, 82, 83, 86, 90, 97, 100, 106, 107, 109, 114, 116, 117, 120, 121, 124], "produc": [4, 6, 13, 18, 26, 33, 49, 55, 57, 60, 114], "inconsist": [4, 52, 110], "output": [4, 6, 9, 13, 15, 18, 19, 23, 24, 26, 37, 44, 46, 47, 50, 53, 55, 56, 57, 58, 60, 65, 66, 76, 104, 107, 124], "particular": [4, 7, 18, 26, 48, 50, 54, 56, 57, 71, 92, 97], "hot": [4, 21, 48], "map": [4, 5, 14, 15, 18, 19, 21, 24, 26, 45, 51, 54, 93], "must": [4, 5, 7, 13, 18, 23, 24, 26, 27, 45, 56, 59, 60, 66, 68, 109, 111], "ident": [4, 6, 8, 12, 18, 53], "train": [4, 8, 9, 10, 12, 13, 15, 18, 19, 21, 26, 44, 49, 50, 51, 69, 89, 97, 109, 113], "time": [4, 5, 18, 21, 24, 25, 29, 30, 31, 40, 43, 44, 49, 51, 55, 56, 60, 64, 66, 67, 68, 89, 92, 105, 111, 114], "common": [4, 6, 9, 13, 17, 18, 24, 25, 29, 33, 35, 38, 39, 54, 65, 66, 68, 73, 78, 79, 80, 81, 93, 95, 97, 100, 102, 108, 112, 114, 118, 121, 124], "signif_results_tostr": 4, "signif_result": 4, "target_units_tostr": 4, "procedur": [4, 13, 27, 47, 60, 68, 75, 115, 118, 124], "self": [4, 6, 7, 13, 15, 24, 27, 35, 39, 48, 66, 75, 109], "update_input": 4, "target_unit": [4, 6, 25, 28, 35, 36, 37, 39, 40, 45, 47, 73, 74, 77, 78, 79], "realizedestimand": 4, "estimator_nam": [4, 6], "update_assumpt": 4, "estimator_assumpt": 4, "update_estimand_express": 4, "estimand_express": 4, "identifier_nam": [4, 7], "ate": [4, 5, 7, 25, 28, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 60, 65, 66, 68, 77, 78], "fit_estim": [4, 28], "method_param": [4, 6, 28, 33, 35, 38, 39, 40, 41, 42, 45, 46, 65, 68, 73, 74, 77, 79, 81, 91], "dict": [4, 5, 6, 7, 13, 14, 15, 17, 18, 21, 24, 26, 32, 33, 37, 44, 46, 48, 49, 52, 56, 57, 60, 66, 69, 80, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "convent": 4, "doubl": [4, 28, 47, 49, 50, 52, 54, 55, 56, 68, 79, 110, 114], "dml": [4, 13, 28, 37, 46, 65, 68, 79], "locat": [4, 34], "insid": 4, "demo": [4, 53, 63], "group": [4, 9, 13, 26, 36, 39, 47, 48], "variat": [4, 13, 25, 51, 65, 66, 68], "treat": [4, 6, 15, 23, 25, 36, 39, 40, 44, 45, 60, 71, 79, 80, 113], "three": [4, 13, 17, 18, 19, 25, 26, 27, 28, 31, 50, 55, 56, 61, 64, 75, 76, 79, 89, 100, 105, 106, 109, 111, 112], "2": [4, 6, 7, 9, 10, 11, 13, 15, 18, 20, 21, 24, 27, 28, 29, 30, 31, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 74, 79, 80, 89, 92, 93, 94, 95, 97, 98, 99, 105, 109, 110, 111, 112, 113, 114, 117], "lambda": [4, 6, 9, 13, 18, 24, 26, 28, 36, 47, 48, 49, 50, 51, 55, 56, 57, 73, 79, 80, 89, 97, 99, 111], "onli": [4, 5, 6, 7, 9, 13, 14, 15, 18, 19, 21, 24, 25, 26, 28, 29, 34, 36, 37, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 66, 67, 68, 80, 87, 92, 93, 94, 97, 104, 105, 109, 110, 111, 112, 114], "thei": [4, 5, 13, 18, 19, 24, 25, 26, 27, 29, 34, 36, 48, 49, 50, 52, 54, 55, 56, 57, 58, 60, 65, 66, 68, 75, 82, 89, 102, 103, 106, 110, 113, 114, 118], "previous": [4, 18, 21], "causalgraph": [4, 34, 37], "common_cause_nam": 4, "instrument_nam": [4, 7, 32, 33, 35, 39, 68], "mediator_nam": 4, "observed_node_nam": [4, 34], "missing_nodes_as_confound": [4, 41], "accept": [4, 26, 105], "networkx": [4, 18, 24, 26, 31, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 67, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "digraph": [4, 5, 7, 17, 18, 24, 25, 26, 31, 36, 43, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59, 60, 61, 65, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "gml": [4, 39, 47, 53, 58, 108], "dot": [4, 22, 24, 31, 32, 33, 35, 44, 47, 51, 58, 67, 68, 70, 117], "edg": [4, 7, 17, 18, 24, 26, 27, 31, 34, 36, 43, 48, 49, 55, 61, 65, 66, 67, 75, 80, 90, 92, 103, 109, 113, 114], "node": [4, 7, 15, 18, 24, 25, 26, 31, 34, 36, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 65, 66, 67, 69, 71, 80, 89, 90, 92, 93, 94, 95, 96, 97, 103, 106, 109, 110, 111, 112, 113, 114], "style": [4, 15], "dash": [4, 13, 15, 53], "ensur": [4, 13, 15, 18, 21, 48, 49, 55, 57, 67, 68, 70, 96, 97, 100, 114], "drawn": [4, 13, 18, 25, 55, 56, 111], "line": [4, 13, 24, 32, 36, 47, 53, 54, 55, 56, 65, 66, 68, 71, 94], "confound": [4, 6, 9, 13, 18, 23, 25, 27, 29, 36, 37, 40, 41, 47, 50, 54, 56, 59, 63, 64, 66, 68, 79, 87, 108, 118, 121, 124], 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"module-dowhy.causal_refuters.overrule.ruleset"]], "dowhy.causal_refuters.overrule.utils module": [[14, "module-dowhy.causal_refuters.overrule.utils"]], "dowhy.causal_refuters.overrule.BCS package": [[15, "dowhy-causal-refuters-overrule-bcs-package"]], "dowhy.causal_refuters.overrule.BCS.beam_search module": [[15, "module-dowhy.causal_refuters.overrule.BCS.beam_search"]], "dowhy.causal_refuters.overrule.BCS.load_process_data_BCS module": [[15, "module-dowhy.causal_refuters.overrule.BCS.load_process_data_BCS"]], "dowhy.causal_refuters.overrule.BCS.overlap_boolean_rule module": [[15, "module-dowhy.causal_refuters.overrule.BCS.overlap_boolean_rule"]], "dowhy.data_transformers package": [[16, "dowhy-data-transformers-package"]], "dowhy.data_transformers.pca_reducer module": [[16, "module-dowhy.data_transformers.pca_reducer"]], "dowhy.do_samplers package": [[17, "dowhy-do-samplers-package"]], "dowhy.do_samplers.kernel_density_sampler module": [[17, "module-dowhy.do_samplers.kernel_density_sampler"]], "dowhy.do_samplers.mcmc_sampler module": [[17, "module-dowhy.do_samplers.mcmc_sampler"]], "dowhy.do_samplers.multivariate_weighting_sampler module": [[17, "module-dowhy.do_samplers.multivariate_weighting_sampler"]], "dowhy.do_samplers.weighting_sampler module": [[17, "module-dowhy.do_samplers.weighting_sampler"]], "dowhy.gcm package": [[18, "dowhy-gcm-package"]], "dowhy.gcm.anomaly module": [[18, "module-dowhy.gcm.anomaly"]], "dowhy.gcm.anomaly_scorer module": [[18, "module-dowhy.gcm.anomaly_scorer"]], "dowhy.gcm.anomaly_scorers module": [[18, "module-dowhy.gcm.anomaly_scorers"]], "dowhy.gcm.auto module": [[18, "module-dowhy.gcm.auto"]], "dowhy.gcm.causal_mechanisms module": [[18, "module-dowhy.gcm.causal_mechanisms"]], "dowhy.gcm.causal_models module": [[18, "module-dowhy.gcm.causal_models"]], "dowhy.gcm.confidence_intervals module": [[18, "module-dowhy.gcm.confidence_intervals"]], "dowhy.gcm.confidence_intervals_cms module": [[18, "module-dowhy.gcm.confidence_intervals_cms"]], "dowhy.gcm.config module": [[18, "module-dowhy.gcm.config"]], "dowhy.gcm.constant module": [[18, "module-dowhy.gcm.constant"]], "dowhy.gcm.density_estimator module": [[18, "module-dowhy.gcm.density_estimator"]], "dowhy.gcm.density_estimators module": [[18, "module-dowhy.gcm.density_estimators"]], "dowhy.gcm.distribution_change module": [[18, "module-dowhy.gcm.distribution_change"]], "dowhy.gcm.divergence module": [[18, "module-dowhy.gcm.divergence"]], "dowhy.gcm.falsify module": [[18, "module-dowhy.gcm.falsify"]], "Attributes": [[18, "attributes"]], "dowhy.gcm.feature_relevance module": [[18, "module-dowhy.gcm.feature_relevance"]], "dowhy.gcm.fitting_sampling module": [[18, "module-dowhy.gcm.fitting_sampling"]], "dowhy.gcm.influence module": [[18, "module-dowhy.gcm.influence"]], "dowhy.gcm.model_evaluation module": [[18, "module-dowhy.gcm.model_evaluation"]], "dowhy.gcm.shapley module": [[18, "module-dowhy.gcm.shapley"]], "dowhy.gcm.stats module": [[18, "module-dowhy.gcm.stats"]], "dowhy.gcm.stochastic_models module": [[18, "module-dowhy.gcm.stochastic_models"]], "dowhy.gcm.uncertainty module": [[18, "module-dowhy.gcm.uncertainty"]], "dowhy.gcm.unit_change module": [[18, "module-dowhy.gcm.unit_change"]], "dowhy.gcm.validation module": [[18, "module-dowhy.gcm.validation"]], "dowhy.gcm.whatif module": [[18, "module-dowhy.gcm.whatif"]], "dowhy.gcm.independence_test package": [[19, "dowhy-gcm-independence-test-package"]], "dowhy.gcm.independence_test.generalised_cov_measure module": [[19, "module-dowhy.gcm.independence_test.generalised_cov_measure"]], "dowhy.gcm.independence_test.kernel module": [[19, "module-dowhy.gcm.independence_test.kernel"]], "dowhy.gcm.independence_test.kernel_operation module": [[19, "module-dowhy.gcm.independence_test.kernel_operation"]], "dowhy.gcm.independence_test.regression module": [[19, "module-dowhy.gcm.independence_test.regression"]], "dowhy.gcm.ml package": [[20, "dowhy-gcm-ml-package"]], "dowhy.gcm.ml.autogluon module": [[20, "dowhy-gcm-ml-autogluon-module"]], "dowhy.gcm.ml.classification module": [[20, "module-dowhy.gcm.ml.classification"]], "dowhy.gcm.ml.prediction_model module": [[20, "module-dowhy.gcm.ml.prediction_model"]], "dowhy.gcm.ml.regression module": [[20, "module-dowhy.gcm.ml.regression"]], "dowhy.gcm.util package": [[21, "dowhy-gcm-util-package"]], "dowhy.gcm.util.catboost_encoder module": [[21, "module-dowhy.gcm.util.catboost_encoder"]], "dowhy.gcm.util.general module": [[21, "module-dowhy.gcm.util.general"]], "dowhy.gcm.util.plotting module": [[21, "module-dowhy.gcm.util.plotting"]], "dowhy.graph_learners package": [[22, "dowhy-graph-learners-package"]], "dowhy.graph_learners.cdt module": [[22, "module-dowhy.graph_learners.cdt"]], "dowhy.graph_learners.ges module": [[22, "module-dowhy.graph_learners.ges"]], "dowhy.graph_learners.lingam module": [[22, "module-dowhy.graph_learners.lingam"]], "dowhy.interpreters package": [[23, "dowhy-interpreters-package"]], "dowhy.interpreters.confounder_distribution_interpreter module": [[23, "module-dowhy.interpreters.confounder_distribution_interpreter"]], "dowhy.interpreters.propensity_balance_interpreter module": [[23, "module-dowhy.interpreters.propensity_balance_interpreter"]], "dowhy.interpreters.textual_effect_interpreter module": [[23, "module-dowhy.interpreters.textual_effect_interpreter"]], "dowhy.interpreters.textual_interpreter module": [[23, "module-dowhy.interpreters.textual_interpreter"]], "dowhy.interpreters.visual_interpreter module": [[23, "module-dowhy.interpreters.visual_interpreter"]], "dowhy.utils package": [[24, "dowhy-utils-package"]], "dowhy.utils.api module": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit module": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers module": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp module": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations module": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting module": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting module": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set module": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting module": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score module": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression module": [[24, "module-dowhy.utils.regression"]], "Exploring Causes of Hotel Booking Cancellations": [[25, "Exploring-Causes-of-Hotel-Booking-Cancellations"]], "Data Description": [[25, "Data-Description"]], "Feature Engineering": [[25, "Feature-Engineering"]], "Calculating Expected Counts": [[25, "Calculating-Expected-Counts"]], "Using DoWhy to estimate the causal effect": [[25, "Using-DoWhy-to-estimate-the-causal-effect"]], "Step-1. Create a Causal Graph": [[25, "Step-1.-Create-a-Causal-Graph"]], "Step-2. Identify the Causal Effect": [[25, "Step-2.-Identify-the-Causal-Effect"]], "Step-3. Estimate the identified estimand": [[25, "Step-3.-Estimate-the-identified-estimand"]], "Step-4. Refute results": [[25, "Step-4.-Refute-results"]], "Method-1": [[25, "Method-1"]], "Method-2": [[25, "Method-2"]], "Method-3": [[25, "Method-3"]], "Counterfactual Fairness": [[26, "Counterfactual-Fairness"]], "When to apply counterfactual fairness?": [[26, "When-to-apply-counterfactual-fairness?"]], "What is required to estimate counterfactual fairness?": [[26, "What-is-required-to-estimate-counterfactual-fairness?"]], "Notation": [[26, "Notation"]], "1. Load and Clean the Dataset": [[26, "1.-Load-and-Clean-the-Dataset"]], "2. A Formal Definition of Counterfactual Fairness": [[26, "2.-A-Formal-Definition-of-Counterfactual-Fairness"]], "2.1 Measuring Counterfactual Fairness": [[26, "2.1-Measuring-Counterfactual-Fairness"]], "3. Measuring Counterfactual Fairness using DoWhy": [[26, "3.-Measuring-Counterfactual-Fairness-using-DoWhy"]], "3.1 Univariate Analysis": [[26, "3.1-Univariate-Analysis"]], "3.2 Intersectional Analysis": [[26, "3.2-Intersectional-Analysis"]], "3.3 Some Additional Uses of the Counterfactuals df_obs , df_cf for Fairness": [[26, "3.3-Some-Additional-Uses-of-the-Counterfactuals-df_obs-,-df_cf-for-Fairness"]], "4. Limitation of Counterfactual Fairness": [[26, "4.-Limitation-of-Counterfactual-Fairness"]], "References": [[26, "References"], [44, "References"], [64, "References"]], "Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unfair Estimator": [[26, "Appendix-:-Fairness-Through-Unawareness-(FTU)-creates-A-Counterfactually-Unfair-Estimator"]], "Do-sampler Introduction": [[27, "Do-sampler-Introduction"]], "Pearlian Interventions": [[27, "Pearlian-Interventions"], [75, "pearlian-interventions"]], "Statefulness": [[27, "Statefulness"], [75, "statefulness"]], "Integration": [[27, "Integration"]], "Specifying Interventions": [[27, "Specifying-Interventions"], [60, "Specifying-Interventions"]], "Demo": [[27, "Demo"]], "Conditional Average Treatment Effects (CATE) with DoWhy and EconML": [[28, "Conditional-Average-Treatment-Effects-(CATE)-with-DoWhy-and-EconML"]], "Linear Model": [[28, "Linear-Model"]], "EconML methods": [[28, "EconML-methods"]], "CATE Object and Confidence Intervals": [[28, "CATE-Object-and-Confidence-Intervals"]], "Can provide a new inputs as target units and estimate CATE on them.": [[28, "Can-provide-a-new-inputs-as-target-units-and-estimate-CATE-on-them."]], "Can also retrieve the raw EconML estimator object for any further operations": [[28, "Can-also-retrieve-the-raw-EconML-estimator-object-for-any-further-operations"]], "Works with any EconML method": [[28, "Works-with-any-EconML-method"]], "Binary treatment, Binary outcome": [[28, "Binary-treatment,-Binary-outcome"]], "Using DRLearner estimator": [[28, "Using-DRLearner-estimator"]], "Instrumental Variable Method": [[28, "Instrumental-Variable-Method"]], "Metalearners": [[28, "Metalearners"]], "Avoiding retraining the estimator": [[28, "Avoiding-retraining-the-estimator"]], "Refuting the estimate": [[28, "Refuting-the-estimate"], [47, "Refuting-the-estimate"]], "Adding a random common cause variable": [[28, "Adding-a-random-common-cause-variable"], [32, "Adding-a-random-common-cause-variable"], [47, "Adding-a-random-common-cause-variable"]], "Adding an unobserved common cause variable": [[28, "Adding-an-unobserved-common-cause-variable"], [47, "Adding-an-unobserved-common-cause-variable"]], "Replacing treatment with a random (placebo) variable": [[28, "Replacing-treatment-with-a-random-(placebo)-variable"], [32, "Replacing-treatment-with-a-random-(placebo)-variable"], [47, "Replacing-treatment-with-a-random-(placebo)-variable"]], "Removing a random subset of the data": [[28, "Removing-a-random-subset-of-the-data"], [32, "Removing-a-random-subset-of-the-data"], [47, "Removing-a-random-subset-of-the-data"]], "Simple example on using Instrumental Variables method for estimation": [[29, "Simple-example-on-using-Instrumental-Variables-method-for-estimation"]], "Loading the dataset": [[29, "Loading-the-dataset"]], "Using DoWhy to estimate the causal effect of education on future income": [[29, "Using-DoWhy-to-estimate-the-causal-effect-of-education-on-future-income"]], "Demo for the DoWhy causal API": [[30, "Demo-for-the-DoWhy-causal-API"]], "Comparing the estimate to Linear Regression": [[30, "Comparing-the-estimate-to-Linear-Regression"]], "Causal Discovery example": [[31, "Causal-Discovery-example"]], "Utility function": [[31, "Utility-function"]], "Experiments on the Auto-MPG dataset": [[31, "Experiments-on-the-Auto-MPG-dataset"]], "1. Load the data": [[31, "1.-Load-the-data"], [31, "id1"], [40, "1.-Load-the-data"]], "Causal Discovery with causal-learn": [[31, "Causal-Discovery-with-causal-learn"], [31, "id2"]], "Estimate causal effects using Linear Regression": [[31, "Estimate-causal-effects-using-Linear-Regression"]], "Experiments on the Sachs dataset": [[31, "Experiments-on-the-Sachs-dataset"]], "Estimate effects using Linear Regression": [[31, "Estimate-effects-using-Linear-Regression"]], "Confounding Example: Finding causal effects from observed data": [[32, "Confounding-Example:-Finding-causal-effects-from-observed-data"]], "Let\u2019s create a mystery dataset for which we need to determine whether there is a causal effect.": [[32, "Let's-create-a-mystery-dataset-for-which-we-need-to-determine-whether-there-is-a-causal-effect."]], "Using DoWhy to resolve the mystery: Does Treatment cause Outcome?": [[32, "Using-DoWhy-to-resolve-the-mystery:-Does-Treatment-cause-Outcome?"]], "STEP 1: Model the problem as a causal graph": [[32, "STEP-1:-Model-the-problem-as-a-causal-graph"]], "STEP 2: Identify causal effect using properties of the formal causal graph": [[32, "STEP-2:-Identify-causal-effect-using-properties-of-the-formal-causal-graph"]], "STEP 3: Estimate the causal effect": [[32, "STEP-3:-Estimate-the-causal-effect"]], "Checking if the estimate is correct": [[32, "Checking-if-the-estimate-is-correct"]], "Step 4: Refuting the estimate": [[32, "Step-4:-Refuting-the-estimate"]], "A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions": [[33, "A-Simple-Example-on-Creating-a-Custom-Refutation-Using-User-Defined-Outcome-Functions"]], "Insert Dependencies": [[33, "Insert-Dependencies"]], "Create the Dataset": [[33, "Create-the-Dataset"]], "Creating the Causal Model": [[33, "Creating-the-Causal-Model"]], "Identify the Estimand": [[33, "Identify-the-Estimand"]], "Estimating the Effect": [[33, "Estimating-the-Effect"]], "Refuting the Estimate": [[33, "Refuting-the-Estimate"]], "Using a Randomly Generated Outcome": [[33, "Using-a-Randomly-Generated-Outcome"]], "Using a Function that Generates the Outcome from the Confounders": [[33, "Using-a-Function-that-Generates-the-Outcome-from-the-Confounders"]], "Finding optimal adjustment sets": [[34, "Finding-optimal-adjustment-sets"]], "Preliminaries": [[34, "Preliminaries"]], "The design of an observational study": [[34, "The-design-of-an-observational-study"]], "An example in which sufficient conditions to guarantee the existence of an optimal backdoor set do not hold": [[34, "An-example-in-which-sufficient-conditions-to-guarantee-the-existence-of-an-optimal-backdoor-set-do-not-hold"]], "An example in which there are no observable adjustment sets": [[34, "An-example-in-which-there-are-no-observable-adjustment-sets"]], "An example with costs": [[34, "An-example-with-costs"]], "DoWhy: Different estimation methods for causal inference": [[35, "DoWhy:-Different-estimation-methods-for-causal-inference"]], "Identifying the causal estimand": [[35, "Identifying-the-causal-estimand"], [39, "Identifying-the-causal-estimand"]], "Method 1: Regression": [[35, "Method-1:-Regression"]], "Method 2: Distance Matching": [[35, "Method-2:-Distance-Matching"]], "Method 3: Propensity Score Stratification": [[35, "Method-3:-Propensity-Score-Stratification"]], "Method 4: Propensity Score Matching": [[35, "Method-4:-Propensity-Score-Matching"]], "Method 5: Weighting": [[35, "Method-5:-Weighting"]], "Method 6: Instrumental Variable": [[35, "Method-6:-Instrumental-Variable"]], "Method 7: Regression Discontinuity": [[35, "Method-7:-Regression-Discontinuity"]], "Estimating the Effect of a Member Rewards Program": [[36, "Estimating-the-Effect-of-a-Member-Rewards-Program"]], "I. Formulating the causal model": [[36, "I.-Formulating-the-causal-model"]], "The importance of time": [[36, "The-importance-of-time"]], "II. Identifying the causal effect": [[36, "II.-Identifying-the-causal-effect"]], "III. Estimating the effect": [[36, "III.-Estimating-the-effect"]], "IV. Refuting the estimate": [[36, "IV.-Refuting-the-estimate"]], "Functional API Preview": [[37, "Functional-API-Preview"]], "Import Dependencies": [[37, "Import-Dependencies"], [46, "Import-Dependencies"]], "Create the Datasets": [[37, "Create-the-Datasets"], [46, "Create-the-Datasets"]], "Identify Effect - Functional API (Preview)": [[37, "Identify-Effect---Functional-API-(Preview)"]], "Estimate Effect - Functional API (Preview)": [[37, "Estimate-Effect---Functional-API-(Preview)"]], "Refute Estimate - Functional API (Preview)": [[37, "Refute-Estimate---Functional-API-(Preview)"]], "Backwards Compatibility": [[37, "Backwards-Compatibility"]], "Identify Effect": [[37, "Identify-Effect"], [46, "Identify-Effect"]], "Estimate Effect": [[37, "Estimate-Effect"], [46, "Estimate-Effect"]], "Refute Estimate": [[37, "Refute-Estimate"], [46, "Refute-Estimate"]], "DoWhy example on ihdp (Infant Health and Development Program) dataset": [[38, "DoWhy-example-on-ihdp-(Infant-Health-and-Development-Program)-dataset"]], "Loading Data": [[38, "Loading-Data"]], "1.Model": [[38, "1.Model"]], "2.Identify": [[38, "2.Identify"]], "3. Estimate (using different methods)": [[38, "3.-Estimate-(using-different-methods)"]], "3.1 Using Linear Regression": [[38, "3.1-Using-Linear-Regression"]], "3.2 Using Propensity Score Matching": [[38, "3.2-Using-Propensity-Score-Matching"]], "3.3 Using Propensity Score Stratification": [[38, "3.3-Using-Propensity-Score-Stratification"]], "3.4 Using Propensity Score Weighting": [[38, "3.4-Using-Propensity-Score-Weighting"]], "4. Refute": [[38, "4.-Refute"]], "4.3 Data Subset Refuter": [[38, "4.3-Data-Subset-Refuter"]], "DoWhy: Interpreters for Causal Estimators": [[39, "DoWhy:-Interpreters-for-Causal-Estimators"]], "Method 1: Propensity Score Stratification": [[39, "Method-1:-Propensity-Score-Stratification"]], "Textual Interpreter": [[39, "Textual-Interpreter"]], "Visual Interpreter": [[39, "Visual-Interpreter"]], "Method 2: Propensity Score Matching": [[39, "Method-2:-Propensity-Score-Matching"]], "Method 3: Weighting": [[39, "Method-3:-Weighting"]], "DoWhy example on the Lalonde dataset": [[40, "DoWhy-example-on-the-Lalonde-dataset"]], "2. Run DoWhy analysis: model, identify, estimate": [[40, "2.-Run-DoWhy-analysis:-model,-identify,-estimate"]], "3. Interpret the estimate": [[40, "3.-Interpret-the-estimate"]], "4. Sanity check: compare to manual IPW estimate": [[40, "4.-Sanity-check:-compare-to-manual-IPW-estimate"]], "Mediation analysis with DoWhy: Direct and Indirect Effects": [[41, "Mediation-analysis-with-DoWhy:-Direct-and-Indirect-Effects"]], "Creating a dataset": [[41, "Creating-a-dataset"]], "Step 1: Modeling the causal mechanism": [[41, "Step-1:-Modeling-the-causal-mechanism"]], "Step 2: Identifying the natural direct and indirect effects": [[41, "Step-2:-Identifying-the-natural-direct-and-indirect-effects"]], "Step 3: Estimation of the effect": [[41, "Step-3:-Estimation-of-the-effect"]], "Natural Indirect Effect": [[41, "Natural-Indirect-Effect"]], "Natural Direct Effect": [[41, "Natural-Direct-Effect"]], "Step 4: Refutations": [[41, "Step-4:-Refutations"]], "Estimating effect of multiple treatments": [[42, "Estimating-effect-of-multiple-treatments"]], "Linear model": [[42, "Linear-model"]], "More methods": [[42, "More-methods"]], "Example to demonstrate optimized backdoor variable search for Causal Identification": [[43, "Example-to-demonstrate-optimized-backdoor-variable-search-for-Causal-Identification"]], "Create Random Graph": [[43, "Create-Random-Graph"]], "Testing optimized backdoor search": [[43, "Testing-optimized-backdoor-search"]], "Applying refutation tests to the Lalonde and IHDP datasets": [[44, "Applying-refutation-tests-to-the-Lalonde-and-IHDP-datasets"]], "Import the Dependencies": [[44, "Import-the-Dependencies"]], "Loading the Dataset": [[44, "Loading-the-Dataset"]], "Infant Health and Development Program Dataset (IHDP)": [[44, "Infant-Health-and-Development-Program-Dataset-(IHDP)"]], "Reference": [[44, "Reference"]], "Lalonde Dataset": [[44, "Lalonde-Dataset"]], "Step 1: Building the model": [[44, "Step-1:-Building-the-model"]], "IHDP": [[44, "IHDP"], [44, "id1"], [44, "id3"], [44, "id5"]], "Lalonde": [[44, "Lalonde"], [44, "id2"], [44, "id4"], [44, "id6"]], "Step 2: Identification": [[44, "Step-2:-Identification"]], "Step 3: Estimation (using propensity score weighting)": [[44, "Step-3:-Estimation-(using-propensity-score-weighting)"]], "Step 4: Refutation": [[44, "Step-4:-Refutation"]], "Add Random Common Cause": [[44, "Add-Random-Common-Cause"], [44, "id7"]], "Replace Treatment with Placebo": [[44, "Replace-Treatment-with-Placebo"], [44, "id8"]], "Remove Random Subset of Data": [[44, "Remove-Random-Subset-of-Data"], [44, "id9"]], "Assessing Support and Overlap with OverRule": [[45, "Assessing-Support-and-Overlap-with-OverRule"]], "Motivation": [[45, "Motivation"]], "Acknowledgements": [[45, "Acknowledgements"]], "Table of contents": [[45, "Table-of-contents"]], "Illustration on a simple 2D example": [[45, "Illustration-on-a-simple-2D-example"]], "Problem Data": [[45, "Problem-Data"]], "Applying OverRule with default arguments": [[45, "Applying-OverRule-with-default-arguments"]], "Example usage using CausalModel": [[45, "Example-usage-using-CausalModel"]], "Example usage using the Functional API": [[45, "Example-usage-using-the-Functional-API"]], "Only fitting support or overlap rules": [[45, "Only-fitting-support-or-overlap-rules"]], "Interpreting the output of OverRule": [[45, "Interpreting-the-output-of-OverRule"]], "Configuring OverRule": [[45, "Configuring-OverRule"]], "Illustration on Lalonde and PSID datasets": [[45, "Illustration-on-Lalonde-and-PSID-datasets"]], "Checking for Support/Overlap in the Experimental Sample": [[45, "Checking-for-Support/Overlap-in-the-Experimental-Sample"]], "Assessing Support/Overlap on the combined sample": [[45, "Assessing-Support/Overlap-on-the-combined-sample"]], "Filtering with OverRule before performing a causal analysis": [[45, "Filtering-with-OverRule-before-performing-a-causal-analysis"]], "Performing causal inference after filtering": [[45, "Performing-causal-inference-after-filtering"]], "Iterating over multiple refutation tests": [[46, "Iterating-over-multiple-refutation-tests"]], "Inspection Parameters": [[46, "Inspection-Parameters"]], "Estimator List": [[46, "Estimator-List"]], "Refuter List": [[46, "Refuter-List"]], "Inspect Data": [[46, "Inspect-Data"]], "Create the CausalModels": [[46, "Create-the-CausalModels"]], "View Models": [[46, "View-Models"]], "Identified Estimands": [[46, "Identified-Estimands"]], "Estimate Values": [[46, "Estimate-Values"]], "Refutation Values": [[46, "Refutation-Values"]], "Basic Example for Calculating the Causal Effect": [[47, "Basic-Example-for-Calculating-the-Causal-Effect"]], "Interface 1 (recommended): Input causal graph": [[47, "Interface-1-(recommended):-Input-causal-graph"]], "DoWhy philosophy: Keep identification and estimation separate": [[47, "DoWhy-philosophy:-Keep-identification-and-estimation-separate"]], "Identification": [[47, "Identification"], [91, "identification"]], "Estimation": [[47, "Estimation"], [91, "estimation"]], "Interface 2: Specify common causes and instruments": [[47, "Interface-2:-Specify-common-causes-and-instruments"]], "Impact of 401(k) eligibility on net financial assets": [[48, "Impact-of-401(k)-eligibility-on-net-financial-assets"]], "Background": [[48, "Background"]], "Data": [[48, "Data"]], "Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income": [[48, "Effect-of-401(k)-Eligibility-on-Net-Financial-Assets,-Conditioned-on-Income"]], "Basic Example for Graphical Causal Models": [[49, "Basic-Example-for-Graphical-Causal-Models"]], "Step 1: Modeling cause-effect relationships as a structural causal model (SCM)": [[49, "Step-1:-Modeling-cause-effect-relationships-as-a-structural-causal-model-(SCM)"]], "Step 2: Fitting the SCM to the data": [[49, "Step-2:-Fitting-the-SCM-to-the-data"]], "Step 3: Answering a causal query based on the SCM": [[49, "Step-3:-Answering-a-causal-query-based-on-the-SCM"]], "Counterfactual Analysis in a Medical Case": [[50, "Counterfactual-Analysis-in-a-Medical-Case"]], "The data": [[50, "The-data"]], "Modeling of the graph": [[50, "Modeling-of-the-graph"]], "Answering Alice\u2019s counterfactual queries": [[50, "Answering-Alice's-counterfactual-queries"]], "Appendix: What the tele-app uses internally. Data generation of the patients\u2019 log": [[50, "Appendix:-What-the-tele-app-uses-internally.-Data-generation-of-the-patients'-log"]], "Decomposing the Gender Wage Gap": [[51, "Decomposing-the-Gender-Wage-Gap"]], "Read and prepare data": [[51, "Read-and-prepare-data"]], "Causal Model": [[51, "Causal-Model"]], "Implementation with dowhy.gcm.distribution_change_robust": [[51, "Implementation-with-dowhy.gcm.distribution_change_robust"]], "Manual Implementation (Advanced)": [[51, "Manual-Implementation-(Advanced)"]], "Interpretation": [[51, "Interpretation"]], "Basic Example for generating samples from a GCM": [[52, "Basic-Example-for-generating-samples-from-a-GCM"]], "Falsification of User-Given Directed Acyclic Graphs": [[53, "Falsification-of-User-Given-Directed-Acyclic-Graphs"]], "Synthetic Data": [[53, "Synthetic-Data"]], "Real Data (Protein Network dataset by Sachs et al., 2005)": [[53, "Real-Data-(Protein-Network-dataset-by-Sachs-et-al.,-2005)"]], "Edge Suggestions": [[53, "Edge-Suggestions"]], "Estimating intrinsic causal influences in real-world examples": [[54, "Estimating-intrinsic-causal-influences-in-real-world-examples"]], "Intrinsic influence on car MPG consumption": [[54, "Intrinsic-influence-on-car-MPG-consumption"]], "Intrinsic influence on river flow": [[54, "Intrinsic-influence-on-river-flow"]], "Causal Attributions and Root-Cause Analysis in an Online Shop": [[55, "Causal-Attributions-and-Root-Cause-Analysis-in-an-Online-Shop"]], "The scenario": [[55, "The-scenario"]], "Step 1: Define causal model": [[55, "Step-1:-Define-causal-model"]], "Step 2: Fit causal models to data": [[55, "Step-2:-Fit-causal-models-to-data"]], "Step 3: Answer causal questions": [[55, "Step-3:-Answer-causal-questions"]], "Generate new samples": [[55, "Generate-new-samples"]], "What are the key factors influencing the variance in profit?": [[55, "What-are-the-key-factors-influencing-the-variance-in-profit?"]], "What are the key factors explaining the Profit drop on a particular day?": [[55, "What-are-the-key-factors-explaining-the-Profit-drop-on-a-particular-day?"]], "What caused the profit drop in Q1 2022?": [[55, "What-caused-the-profit-drop-in-Q1-2022?"]], "Data generation process": [[55, "Data-generation-process"]], "Finding the Root Cause of Elevated Latencies in a Microservice Architecture": [[56, "Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture"]], "Setting up the causal model": [[56, "Setting-up-the-causal-model"]], "Scenario 1: Observing a single outlier": [[56, "Scenario-1:-Observing-a-single-outlier"]], "Attributing an outlier latency at a target service to other services": [[56, "Attributing-an-outlier-latency-at-a-target-service-to-other-services"]], "Scenario 2: Observing permanent degradation of latencies": [[56, "Scenario-2:-Observing-permanent-degradation-of-latencies"]], "Attributing permanent degradation of latencies at a target service to other services": [[56, "Attributing-permanent-degradation-of-latencies-at-a-target-service-to-other-services"]], "Scenario 3: Simulating the intervention of shifting resources": [[56, "Scenario-3:-Simulating-the-intervention-of-shifting-resources"]], "Appendix: Data generation process": [[56, "Appendix:-Data-generation-process"]], "Finding Root Causes of Changes in a Supply Chain": [[57, "Finding-Root-Causes-of-Changes-in-a-Supply-Chain"]], "Week-over-week changes": [[57, "Week-over-week-changes"]], "Why did the average value of received quantity change week-over-week?": [[57, "Why-did-the-average-value-of-received-quantity-change-week-over-week?"]], "Ad-hoc attribution analysis": [[57, "Ad-hoc-attribution-analysis"]], "Causal Attribution Analysis": [[57, "Causal-Attribution-Analysis"]], "Graphical causal model": [[57, "Graphical-causal-model"]], "Attributing change": [[57, "Attributing-change"]], "Appendix: Ground Truth": [[57, "Appendix:-Ground-Truth"]], "Testing Assumptions in model with DoWhy: A simple example": [[58, "Testing-Assumptions-in-model-with-DoWhy:-A-simple-example"]], "Step 1: Load dataset": [[58, "Step-1:-Load-dataset"]], "Step 2: Input causal graph": [[58, "Step-2:-Input-causal-graph"]], "Step 3: Create Causal Model": [[58, "Step-3:-Create-Causal-Model"], [66, "Step-3:-Create-Causal-Model"]], "Step 4: Testing for Conditional Independence": [[58, "Step-4:-Testing-for-Conditional-Independence"]], "Testing for a set of edges": [[58, "Testing-for-a-set-of-edges"]], "Testing with a wrong graph input": [[58, "Testing-with-a-wrong-graph-input"]], "Identifying Effect using ID Algorithm": [[59, "Identifying-Effect-using-ID-Algorithm"]], "Case 1": [[59, "Case-1"]], "Case 2": [[59, "Case-2"]], "Case 3": [[59, "Case-3"]], "Case 4": [[59, "Case-4"]], "Case 5": [[59, "Case-5"]], "Case 6": [[59, "Case-6"]], "Lalonde Pandas API Example": [[60, "Lalonde-Pandas-API-Example"]], "Getting the Data": [[60, "Getting-the-Data"]], "The causal Namespace": [[60, "The-causal-Namespace"]], "The do Operation": [[60, "The-do-Operation"]], "Treatment Effect Estimation": [[60, "Treatment-Effect-Estimation"]], "Different ways to load an input graph": [[61, "Different-ways-to-load-an-input-graph"]], "I. Generating dummy data": [[61, "I.-Generating-dummy-data"]], "II. Loading GML or DOT graphs": [[61, "II.-Loading-GML-or-DOT-graphs"]], "GML format": [[61, "GML-format"]], "DOT format": [[61, "DOT-format"]], "Case Studies": [[62, null]], "Example notebooks": [[63, "example-notebooks"]], "Introductory examples": [[63, "introductory-examples"]], "Real world-inspired examples": [[63, "real-world-inspired-examples"]], "Examples on benchmark datasets": [[63, "examples-on-benchmark-datasets"]], "Modeling and refuting causal assumptions": [[63, "modeling-and-refuting-causal-assumptions"]], "Miscellaneous": [[63, "miscellaneous"]], "Demo for DoWhy Causal Prediction on MNIST": [[64, "Demo-for-DoWhy-Causal-Prediction-on-MNIST"]], "Multi-attribute distribution shift datasets": [[64, "Multi-attribute-distribution-shift-datasets"]], "Multi-attribute MNIST": [[64, "Multi-attribute-MNIST"]], "Domains in multi-attribute MNIST": [[64, "Domains-in-multi-attribute-MNIST"]], "Initialize dataset": [[64, "Initialize-dataset"]], "Initialize data loaders": [[64, "Initialize-data-loaders"]], "Method 1: Provide validation domain explicitly": [[64, "Method-1:-Provide-validation-domain-explicitly"]], "Method 2: Validation set using subset of training data": [[64, "Method-2:-Validation-set-using-subset-of-training-data"]], "Initialize model and algorithm": [[64, "Initialize-model-and-algorithm"]], "Initialize algorithm class: ERM": [[64, "Initialize-algorithm-class:-ERM"]], "Fit predictor and start training": [[64, "Fit-predictor-and-start-training"]], "Evaluate on test domain": [[64, "Evaluate-on-test-domain"]], "Prediction with CACM": [[64, "Prediction-with-CACM"]], "Extending to different datasets and algorithms": [[64, "Extending-to-different-datasets-and-algorithms"]], "MNIST Independent and Causal+Independent datasets": [[64, "MNIST-Independent-and-Causal+Independent-datasets"]], "MNISTIndAttribute: Single-attribute Independent shift": [[64, "MNISTIndAttribute:-Single-attribute-Independent-shift"]], "MNISTCausalIndAttribute: Multi-attribute Causal+Independent shift": [[64, "MNISTCausalIndAttribute:-Multi-attribute-Causal+Independent-shift"]], "Additional datasets and algorithms": [[64, "Additional-datasets-and-algorithms"]], "Sensitivity analysis for non-parametric causal estimators": [[65, "Sensitivity-analysis-for-non-parametric-causal-estimators"]], "I. Sensitivity analysis for partially linear models": [[65, "I.-Sensitivity-analysis-for-partially-linear-models"]], "Creating a dataset with unobserved confounding": [[65, "Creating-a-dataset-with-unobserved-confounding"]], "Obtaining a causal estimate using Model, Identify, Estimate steps": [[65, "Obtaining-a-causal-estimate-using-Model,-Identify,-Estimate-steps"]], "Sensitivity Analysis using the Refute step": [[65, "Sensitivity-Analysis-using-the-Refute-step"]], "II. Sensitivity Analysis for general non-parametric models": [[65, "II.-Sensitivity-Analysis-for-general-non-parametric-models"]], "Sensitivity Analysis for Regression Models": [[66, "Sensitivity-Analysis-for-Regression-Models"]], "Step 1: Load required packages": [[66, "Step-1:-Load-required-packages"]], "Step 2: Load the dataset": [[66, "Step-2:-Load-the-dataset"]], "Step 4: Identification": [[66, "Step-4:-Identification"]], "Step 5: Estimation": [[66, "Step-5:-Estimation"]], "Step 6a: Refutation and Sensitivity Analysis - Method 1": [[66, "Step-6a:-Refutation-and-Sensitivity-Analysis---Method-1"]], "Parameter List for plot function": [[66, "Parameter-List-for-plot-function"]], "Step 6b: Refutation and Sensitivity Analysis - Method 2": [[66, "Step-6b:-Refutation-and-Sensitivity-Analysis---Method-2"]], "Effect inference with timeseries data": [[67, "Effect-inference-with-timeseries-data"]], "Loading timeseries data and causal graph": [[67, "Loading-timeseries-data-and-causal-graph"]], "Dataset Shifting and Filtering": [[67, "Dataset-Shifting-and-Filtering"]], "Causal Effect Estimation": [[67, "Causal-Effect-Estimation"]], "Importing temporal causal graph from Tigramite library": [[67, "Importing-temporal-causal-graph-from-Tigramite-library"]], "Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)": [[68, "Tutorial-on-Causal-Inference-and-its-Connections-to-Machine-Learning-(Using-DoWhy+EconML)"]], "Why causal inference?": [[68, "Why-causal-inference?"]], "Defining a causal effect": [[68, "Defining-a-causal-effect"]], "The difference between prediction and causal inference": [[68, "The-difference-between-prediction-and-causal-inference"]], "Two fundamental challenges for causal inference": [[68, "Two-fundamental-challenges-for-causal-inference"]], "The four steps of causal inference": [[68, "The-four-steps-of-causal-inference"]], "The DoWhy+EconML solution": [[68, "The-DoWhy+EconML-solution"]], "A mystery dataset: Can you find out if if there is a causal effect?": [[68, "A-mystery-dataset:-Can-you-find-out-if-if-there-is-a-causal-effect?"]], "Model assumptions about the data-generating process using a causal graph": [[68, "Model-assumptions-about-the-data-generating-process-using-a-causal-graph"]], "Identify the correct estimand for the target quantity based on the causal model": [[68, "Identify-the-correct-estimand-for-the-target-quantity-based-on-the-causal-model"]], "Estimate the target estimand": [[68, "Estimate-the-target-estimand"]], "Check robustness of the estimate using refutation tests": [[68, "Check-robustness-of-the-estimate-using-refutation-tests"]], "Case-studies using DoWhy+EconML": [[68, "Case-studies-using-DoWhy+EconML"]], "Estimating the impact of a customer loyalty program": [[68, "Estimating-the-impact-of-a-customer-loyalty-program"]], "Recommendation A/B testing at an online company": [[68, "Recommendation-A/B-testing-at-an-online-company"]], "User segmentation for targeting interventions": [[68, "User-segmentation-for-targeting-interventions"]], "Multi-investment attribution at a software company": [[68, "Multi-investment-attribution-at-a-software-company"]], "Connections to fundamental machine learning challenges": [[68, "Connections-to-fundamental-machine-learning-challenges"]], "Further resources": [[68, "Further-resources"], [69, "further-resources"]], "DoWhy+EconML libraries": [[68, "DoWhy+EconML-libraries"]], "Video Lecture on causal inference and its connections to machine learning": [[68, "Video-Lecture-on-causal-inference-and-its-connections-to-machine-learning"]], "Detailed KDD Tutorial on Causal Inference": [[68, "Detailed-KDD-Tutorial-on-Causal-Inference"]], "Book chapters on causality and machine learning": [[68, "Book-chapters-on-causality-and-machine-learning"]], "Causality and Machine Learning group at Microsoft": [[68, "Causality-and-Machine-Learning-group-at-Microsoft"]], "Getting Started": [[69, "getting-started"]], "Installation": [[69, "installation"], [70, "installation"]], "\u201cHello causal inference world\u201d": [[69, "hello-causal-inference-world"]], "Effect inference": [[69, "effect-inference"]], "Graphical causal model-based inference": [[69, "graphical-causal-model-based-inference"]], "Installing with pip": [[70, "installing-with-pip"]], "Installing with Conda": [[70, "installing-with-conda"]], "Installing on Azure Machine Learning": [[70, "installing-on-azure-machine-learning"]], "DoWhy documentation": [[71, "dowhy-documentation"]], "Key differences compared to available causal inference software": [[71, "key-differences-compared-to-available-causal-inference-software"]], "Predicting outcome for out-of-distribution inputs": [[72, "predicting-outcome-for-out-of-distribution-inputs"]], "Estimating conditional average causal effect": [[73, "estimating-conditional-average-causal-effect"]], "Linear regression": [[73, "linear-regression"]], "DML method from EconML": [[73, "dml-method-from-econml"]], "Distance-based matching": [[74, "distance-based-matching"]], "Do-sampler": [[75, "do-sampler"]], "Using do-sampler": [[75, "using-do-sampler"]], "Estimating average causal effect using backdoor": [[76, "estimating-average-causal-effect-using-backdoor"]], "Propensity-based methods": [[77, "propensity-based-methods"]], "Propensity-Based Matching": [[77, "propensity-based-matching"]], "Propensity-based Stratification": [[77, "propensity-based-stratification"]], "Inverse Propensity Weighting": [[77, "inverse-propensity-weighting"]], "Regression-based methods": [[78, "regression-based-methods"]], "Effect Estimation Using specific Effect Estimators (for ACE, mediation effect, \u2026)": [[79, "effect-estimation-using-specific-effect-estimators-for-ace-mediation-effect"]], "Generating sample data and the causal model": [[79, "generating-sample-data-and-the-causal-model"]], "Identify a target estimand under the model": [[79, "identify-a-target-estimand-under-the-model"]], "Supported identification criteria": [[79, "supported-identification-criteria"]], "Estimate causal effect based on the identified estimand": [[79, "estimate-causal-effect-based-on-the-identified-estimand"]], "Supported estimation methods": [[79, "supported-estimation-methods"]], "Using EconML and CausalML estimation methods in DoWhy": [[79, "using-econml-and-causalml-estimation-methods-in-dowhy"]], "Refute the obtained estimate": [[79, "refute-the-obtained-estimate"]], "Supported refutation methods": [[79, "supported-refutation-methods"]], "Comparison to other packages": [[79, "comparison-to-other-packages"]], "Key difference: Causal assumptions as first-class citizens": [[79, "key-difference-causal-assumptions-as-first-class-citizens"]], "Estimating average causal effect using GCM": [[80, "estimating-average-causal-effect-using-gcm"]], "How to use it": [[80, "how-to-use-it"], [89, "how-to-use-it"], [92, "how-to-use-it"], [93, "how-to-use-it"], [94, "how-to-use-it"], [95, "how-to-use-it"], [97, "how-to-use-it"], [99, "how-to-use-it"], [111, "how-to-use-it"]], "Related example notebooks": [[80, "related-example-notebooks"], [89, "related-example-notebooks"], [91, "related-example-notebooks"], [92, "related-example-notebooks"], [93, "related-example-notebooks"], [94, "related-example-notebooks"], [97, "related-example-notebooks"], [99, "related-example-notebooks"], [113, "related-example-notebooks"]], "Understanding the method": [[80, "understanding-the-method"], [89, "understanding-the-method"], [92, "understanding-the-method"], [93, "understanding-the-method"], [94, "understanding-the-method"], [97, "understanding-the-method"]], "Estimating average causal effect with natural experiments": [[81, "estimating-average-causal-effect-with-natural-experiments"]], "Instrumental variable estimator for binary action": [[81, "instrumental-variable-estimator-for-binary-action"]], "Regression discontinuity estimator": [[81, "regression-discontinuity-estimator"]], "Backdoor criterion": [[82, "backdoor-criterion"]], "Frontdoor criterion": [[83, "frontdoor-criterion"]], "ID algorithm for discovering new identification strategies": [[84, "id-algorithm-for-discovering-new-identification-strategies"]], "Identifying causal effect": [[85, "identifying-causal-effect"]], "Natural experiments and instrumental variables": [[86, "natural-experiments-and-instrumental-variables"]], "Estimating Causal Effects": [[87, "estimating-causal-effects"]], "Performing Causal Tasks": [[88, "performing-causal-tasks"]], "Quantifying Intrinsic Causal Influence": [[89, "quantifying-intrinsic-causal-influence"]], "Quantify Causal Influence": [[90, "quantify-causal-influence"]], "Mediation Analysis: Estimating natural direct and indirect effects": [[91, "mediation-analysis-estimating-natural-direct-and-indirect-effects"]], "Direct Effect: Quantifying Arrow Strength": [[92, "direct-effect-quantifying-arrow-strength"]], "Customize the distance measure": [[92, "customize-the-distance-measure"]], "Anomaly Attribution": [[93, "anomaly-attribution"]], "Attributing Distributional Changes": [[94, "attributing-distributional-changes"]], "Feature Relevance": [[95, "feature-relevance"]], "Root-Cause Analysis and Explanation": [[96, "root-cause-analysis-and-explanation"]], "Computing Counterfactuals": [[97, "computing-counterfactuals"]], "Asking and Answering What-If Questions": [[98, "asking-and-answering-what-if-questions"]], "Simulating the Impact of Interventions": [[99, "simulating-the-impact-of-interventions"]], "Foreword": [[100, "foreword"]], "The need for causal inference": [[100, "the-need-for-causal-inference"]], "User Guide": [[101, "user-guide"]], "Introduction to DoWhy": [[102, "introduction-to-dowhy"]], "Supported causal tasks": [[102, "supported-causal-tasks"]], "Testing validity of a causal analysis": [[102, "testing-validity-of-a-causal-analysis"]], "Who this user guide is for": [[102, "who-this-user-guide-is-for"]], "Modeling Causal Relations": [[103, "modeling-causal-relations"]], "Learning causal structure from data": [[104, "learning-causal-structure-from-data"]], "Graph discovery using CDT": [[104, "graph-discovery-using-cdt"]], "Graph discovery using dodiscover": [[104, "graph-discovery-using-dodiscover"]], "Graph discovery using causal-learn": [[104, "graph-discovery-using-causal-learn"]], "Performing independence tests": [[105, "performing-independence-tests"]], "Refuting a Causal Graph": [[106, "refuting-a-causal-graph"]], "Graph refutations": [[107, "graph-refutations"]], "Specifying a causal graph using domain knowledge": [[108, "specifying-a-causal-graph-using-domain-knowledge"]], "Customizing Causal Mechanism Assignment": [[109, "customizing-causal-mechanism-assignment"]], "Using ground truth models": [[109, "using-ground-truth-models"]], "Creating causal model (GCM) from equations": [[109, "creating-causal-model-gcm-from-equations"]], "Generate samples from a GCM": [[110, "generate-samples-from-a-gcm"]], "Estimating Confidence Intervals": [[111, "estimating-confidence-intervals"]], "Computing confidence intervals for causal queries": [[111, "computing-confidence-intervals-for-causal-queries"]], "Conveniently bootstrapping graph training on random subsets of training data": [[111, "conveniently-bootstrapping-graph-training-on-random-subsets-of-training-data"]], "Runtime cost versus confidence": [[111, "runtime-cost-versus-confidence"]], "Understanding the need for confidence intervals": [[111, "understanding-the-need-for-confidence-intervals"]], "Types of graphical causal models": [[112, "types-of-graphical-causal-models"]], "Available Causal Mechanisms": [[112, "available-causal-mechanisms"]], "Modeling Graphical Causal Models (GCMs)": [[113, "modeling-graphical-causal-models-gcms"]], "Fitting an SCM to the data": [[113, "fitting-an-scm-to-the-data"]], "Evaluating a fitted SCM": [[113, "evaluating-a-fitted-scm"]], "Other topics": [[113, "other-topics"]], "Evaluate a GCM": [[114, "evaluate-a-gcm"]], "Summary of auto assignment": [[114, "summary-of-auto-assignment"]], "Evaluating a fitted GCM": [[114, "evaluating-a-fitted-gcm"]], "Refuting causal estimates": [[115, "refuting-causal-estimates"]], "Data Subsample Refuter": [[116, "data-subsample-refuter"]], "Dummy Outcome Refuter": [[117, "dummy-outcome-refuter"]], "Testing for zero causal effect": [[117, "testing-for-zero-causal-effect"]], "Testing for non-zero causal effect": [[117, "testing-for-non-zero-causal-effect"]], "Refuting Effect Estimates": [[118, "refuting-effect-estimates"]], "Refutations based on negative control": [[118, "refutations-based-on-negative-control"]], "Refutations based on sensitivity analysis": [[118, "refutations-based-on-sensitivity-analysis"]], "Placebo Treatment Refuter": [[119, "placebo-treatment-refuter"]], "Random Common Cause Refuter": [[120, "random-common-cause-refuter"]], "Sensitivity Analysis": [[121, "sensitivity-analysis"]], "Partial-R2 based sensitivity analysis for linear estimators": [[122, "partial-r2-based-sensitivity-analysis-for-linear-estimators"]], "Reisz estimator-based sensitivity analysis for non-linear estimators": [[123, "reisz-estimator-based-sensitivity-analysis-for-non-linear-estimators"]], "Simulation-based sensitivity analysis": [[124, "simulation-based-sensitivity-analysis"]], "Automatically inferring effect strength parameters": [[124, "automatically-inferring-effect-strength-parameters"]]}, "indexentries": {"auto (dowhy.causal_refuter.significancetesttype attribute)": [[4, "dowhy.causal_refuter.SignificanceTestType.AUTO"]], "bootstrap (dowhy.causal_refuter.significancetesttype attribute)": [[4, "dowhy.causal_refuter.SignificanceTestType.BOOTSTRAP"]], "causalestimate (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.CausalEstimate"]], "causalestimator (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.CausalEstimator"]], "causalestimator.bootstrapestimates (class in dowhy.causal_estimator)": [[4, 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(class in dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.DummyOutcomeRefuter"]], "evaluesensitivityanalyzer (class in dowhy.causal_refuters.evalue_sensitivity_analyzer)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer"]], "graphrefutation (class in dowhy.causal_refuters.graph_refuter)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefutation"]], "graphrefuter (class in dowhy.causal_refuters.graph_refuter)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter"]], "k (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.K"]], "k (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.K"]], "linearsensitivityanalyzer (class in dowhy.causal_refuters.linear_sensitivity_analyzer)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer"]], "nonparametricsensitivityanalyzer (class in dowhy.causal_refuters.non_parametric_sensitivity_analyzer)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer"]], "overlapconfig (class in dowhy.causal_refuters.assess_overlap_overrule)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig"]], "overruleanalyzer (class in dowhy.causal_refuters.assess_overlap_overrule)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer"]], "permute (dowhy.causal_refuters.placebo_treatment_refuter.placebotype attribute)": [[13, "dowhy.causal_refuters.placebo_treatment_refuter.PlaceboType.PERMUTE"]], "partiallinearsensitivityanalyzer (class in dowhy.causal_refuters.partial_linear_sensitivity_analyzer)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer"]], "placebotreatmentrefuter (class in dowhy.causal_refuters)": [[13, "dowhy.causal_refuters.PlaceboTreatmentRefuter"]], "placebotreatmentrefuter (class in dowhy.causal_refuters.placebo_treatment_refuter)": [[13, "dowhy.causal_refuters.placebo_treatment_refuter.PlaceboTreatmentRefuter"]], "placebotype (class in dowhy.causal_refuters.placebo_treatment_refuter)": [[13, "dowhy.causal_refuters.placebo_treatment_refuter.PlaceboType"]], "pluginreisz (class in dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.PluginReisz"]], "randomcommoncause (class in dowhy.causal_refuters)": [[13, "dowhy.causal_refuters.RandomCommonCause"]], "randomcommoncause (class in dowhy.causal_refuters.random_common_cause)": [[13, "dowhy.causal_refuters.random_common_cause.RandomCommonCause"]], "reiszrepresenter (class in dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.ReiszRepresenter"]], "supportconfig (class in dowhy.causal_refuters.assess_overlap_overrule)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig"]], "testfraction (class in dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction"]], "add_conditional_independence_test_result() (dowhy.causal_refuters.graph_refuter.graphrefutation method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefutation.add_conditional_independence_test_result"]], "alpha (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.alpha"]], "alpha (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.alpha"]], "assess_support_and_overlap_overrule() (in module dowhy.causal_refuters.assess_overlap)": [[13, "dowhy.causal_refuters.assess_overlap.assess_support_and_overlap_overrule"]], "base (dowhy.causal_refuters.dummy_outcome_refuter.testfraction attribute)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction.base"]], "benchmark() (dowhy.causal_refuters.evalue_sensitivity_analyzer.evaluesensitivityanalyzer method)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer.benchmark"]], "calculate_robustness_value() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.calculate_robustness_value"]], "check_sensitivity() (dowhy.causal_refuters.evalue_sensitivity_analyzer.evaluesensitivityanalyzer method)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer.check_sensitivity"]], "check_sensitivity() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.check_sensitivity"]], "check_sensitivity() (dowhy.causal_refuters.non_parametric_sensitivity_analyzer.nonparametricsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer.check_sensitivity"]], "check_sensitivity() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.check_sensitivity"]], "compute_bias_adjusted() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.compute_bias_adjusted"]], "compute_r2diff_benchmarking_covariates() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.compute_r2diff_benchmarking_covariates"]], "conditional_mutual_information() (dowhy.causal_refuters.graph_refuter.graphrefuter method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter.conditional_mutual_information"]], "describe_all_rules() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.describe_all_rules"]], "describe_overlap_rules() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.describe_overlap_rules"]], "describe_support_rules() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.describe_support_rules"]], "dowhy.causal_refuters": [[13, "module-dowhy.causal_refuters"]], "dowhy.causal_refuters.add_unobserved_common_cause": [[13, "module-dowhy.causal_refuters.add_unobserved_common_cause"]], "dowhy.causal_refuters.assess_overlap": [[13, "module-dowhy.causal_refuters.assess_overlap"]], "dowhy.causal_refuters.assess_overlap_overrule": [[13, "module-dowhy.causal_refuters.assess_overlap_overrule"]], "dowhy.causal_refuters.bootstrap_refuter": [[13, "module-dowhy.causal_refuters.bootstrap_refuter"]], "dowhy.causal_refuters.data_subset_refuter": [[13, "module-dowhy.causal_refuters.data_subset_refuter"]], "dowhy.causal_refuters.dummy_outcome_refuter": [[13, "module-dowhy.causal_refuters.dummy_outcome_refuter"]], "dowhy.causal_refuters.evalue_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.evalue_sensitivity_analyzer"]], "dowhy.causal_refuters.graph_refuter": [[13, "module-dowhy.causal_refuters.graph_refuter"]], "dowhy.causal_refuters.linear_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.linear_sensitivity_analyzer"]], "dowhy.causal_refuters.non_parametric_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.non_parametric_sensitivity_analyzer"]], "dowhy.causal_refuters.partial_linear_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.partial_linear_sensitivity_analyzer"]], "dowhy.causal_refuters.placebo_treatment_refuter": [[13, "module-dowhy.causal_refuters.placebo_treatment_refuter"]], "dowhy.causal_refuters.random_common_cause": [[13, "module-dowhy.causal_refuters.random_common_cause"]], "dowhy.causal_refuters.refute_estimate": [[13, "module-dowhy.causal_refuters.refute_estimate"]], "dowhy.causal_refuters.reisz": [[13, "module-dowhy.causal_refuters.reisz"]], "filter_dataframe() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.filter_dataframe"]], "fit() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.fit"]], "fit() (dowhy.causal_refuters.reisz.pluginreisz method)": [[13, "dowhy.causal_refuters.reisz.PluginReisz.fit"]], "fit() (dowhy.causal_refuters.reisz.reiszrepresenter method)": [[13, "dowhy.causal_refuters.reisz.ReiszRepresenter.fit"]], "get_alpha_estimator() (in module dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.get_alpha_estimator"]], "get_alpharegression_var() (dowhy.causal_refuters.non_parametric_sensitivity_analyzer.nonparametricsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer.get_alpharegression_var"]], "get_confidence_levels() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_confidence_levels"]], "get_evalue() (dowhy.causal_refuters.evalue_sensitivity_analyzer.evaluesensitivityanalyzer method)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer.get_evalue"]], "get_generic_regressor() (in module dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.get_generic_regressor"]], "get_phi_lower_upper() (dowhy.causal_refuters.non_parametric_sensitivity_analyzer.nonparametricsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer.get_phi_lower_upper"]], "get_phi_lower_upper() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_phi_lower_upper"]], "get_regression_r2() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_regression_r2"]], "include_simulated_confounder() (dowhy.causal_refuters.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (dowhy.causal_refuters.add_unobserved_common_cause.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (in module dowhy.causal_refuters.add_unobserved_common_cause)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.include_simulated_confounder"]], "is_benchmarking_needed() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.is_benchmarking_needed"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.iterMax"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": 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"dowhy.causal_refuters.dummy_outcome_refuter.noise"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.num_thresh"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.num_thresh"]], "other (dowhy.causal_refuters.dummy_outcome_refuter.testfraction attribute)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction.other"]], "partial_correlation() (dowhy.causal_refuters.graph_refuter.graphrefuter method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter.partial_correlation"]], "partial_r2_func() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.partial_r2_func"]], "perform_benchmarking() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.perform_benchmarking"]], "permute() (in module dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.permute"]], "plot() (dowhy.causal_refuters.evalue_sensitivity_analyzer.evaluesensitivityanalyzer method)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer.plot"]], "plot() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.plot"]], "plot() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.plot"]], "plot_estimate() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.plot_estimate"]], "plot_t() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.plot_t"]], "predict() (dowhy.causal_refuters.reisz.pluginreisz method)": [[13, "dowhy.causal_refuters.reisz.PluginReisz.predict"]], "predict() (dowhy.causal_refuters.reisz.reiszrepresenter method)": [[13, "dowhy.causal_refuters.reisz.ReiszRepresenter.predict"]], "predict_overlap_support() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.predict_overlap_support"]], "preprocess_data_by_treatment() (in module dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.preprocess_data_by_treatment"]], "preprocess_observed_common_causes() (in module dowhy.causal_refuters.add_unobserved_common_cause)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.preprocess_observed_common_causes"]], "process_data() (in module dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.process_data"]], "propensity() (dowhy.causal_refuters.reisz.pluginreisz method)": [[13, "dowhy.causal_refuters.reisz.PluginReisz.propensity"]], "refute_bootstrap() (in module dowhy.causal_refuters)": [[13, "dowhy.causal_refuters.refute_bootstrap"]], "refute_bootstrap() (in module dowhy.causal_refuters.bootstrap_refuter)": [[13, "dowhy.causal_refuters.bootstrap_refuter.refute_bootstrap"]], "refute_data_subset() (in module dowhy.causal_refuters)": [[13, "dowhy.causal_refuters.refute_data_subset"]], "refute_data_subset() (in module dowhy.causal_refuters.data_subset_refuter)": [[13, "dowhy.causal_refuters.data_subset_refuter.refute_data_subset"]], "refute_dummy_outcome() (in module dowhy.causal_refuters)": [[13, "dowhy.causal_refuters.refute_dummy_outcome"]], "refute_dummy_outcome() (in module dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.refute_dummy_outcome"]], "refute_estimate() (dowhy.causal_refuters.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.AddUnobservedCommonCause.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.bootstraprefuter method)": [[13, "dowhy.causal_refuters.BootstrapRefuter.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.datasubsetrefuter method)": [[13, "dowhy.causal_refuters.DataSubsetRefuter.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.dummyoutcomerefuter method)": [[13, "dowhy.causal_refuters.DummyOutcomeRefuter.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.placebotreatmentrefuter method)": [[13, "dowhy.causal_refuters.PlaceboTreatmentRefuter.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.randomcommoncause method)": [[13, "dowhy.causal_refuters.RandomCommonCause.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.add_unobserved_common_cause.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.AddUnobservedCommonCause.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.assess_overlap.assessoverlap method)": [[13, "dowhy.causal_refuters.assess_overlap.AssessOverlap.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.bootstrap_refuter.bootstraprefuter method)": [[13, "dowhy.causal_refuters.bootstrap_refuter.BootstrapRefuter.refute_estimate"]], "refute_estimate() (dowhy.causal_refuters.data_subset_refuter.datasubsetrefuter method)": [[13, "dowhy.causal_refuters.data_subset_refuter.DataSubsetRefuter.refute_estimate"]], "refute_estimate() 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(class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel"]], "probabilisticcausalmodel (class in dowhy.gcm.causal_models)": [[18, "dowhy.gcm.causal_models.ProbabilisticCausalModel"]], "probabilityestimatormodel (class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel"]], "rejected (dowhy.gcm.validation.rejectionresult attribute)": [[18, "dowhy.gcm.validation.RejectionResult.REJECTED"]], "rankbasedanomalyscorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.RankBasedAnomalyScorer"]], "rejectionresult (class in dowhy.gcm.validation)": [[18, "dowhy.gcm.validation.RejectionResult"]], "rescaledmediancdfquantilescorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.RescaledMedianCDFQuantileScorer"]], "subset_sampling (dowhy.gcm.shapley.shapleyapproximationmethods attribute)": [[18, "dowhy.gcm.shapley.ShapleyApproximationMethods.SUBSET_SAMPLING"]], "scipydistribution (class in dowhy.gcm.stochastic_models)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution"]], "shapleyapproximationmethods (class in dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.ShapleyApproximationMethods"]], "shapleyconfig (class in dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.ShapleyConfig"]], "sklearnlinearregressionmodel (class in dowhy.gcm.unit_change)": [[18, "dowhy.gcm.unit_change.SklearnLinearRegressionModel"]], "stochasticmodel (class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.StochasticModel"]], "structuralcausalmodel (class in dowhy.gcm.causal_models)": [[18, "dowhy.gcm.causal_models.StructuralCausalModel"]], "validate_cm (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_CM"]], "validate_lmc (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_LMC"]], "validate_pd (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_PD"]], "validate_tpa (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_TPA"]], "add_model_performance() (dowhy.gcm.auto.autoassignmentsummary method)": [[18, "dowhy.gcm.auto.AutoAssignmentSummary.add_model_performance"]], "add_node_log_message() (dowhy.gcm.auto.autoassignmentsummary method)": [[18, "dowhy.gcm.auto.AutoAssignmentSummary.add_node_log_message"]], "anomaly_scores() (in module dowhy.gcm.anomaly)": [[18, "dowhy.gcm.anomaly.anomaly_scores"]], "apply_suggestions() (in module dowhy.gcm.falsify)": [[18, "dowhy.gcm.falsify.apply_suggestions"]], "arrow_strength() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.arrow_strength"]], "arrow_strength_of_model() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.arrow_strength_of_model"]], "assign_causal_mechanism_node() (in module dowhy.gcm.auto)": [[18, "dowhy.gcm.auto.assign_causal_mechanism_node"]], "assign_causal_mechanisms() 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"dowhy.gcm.causal_models.StructuralCausalModel.causal_mechanism"]], "classifier_model (dowhy.gcm.causal_mechanisms.classifierfcm property)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.classifier_model"]], "clone() (dowhy.gcm.causal_mechanisms.additivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.AdditiveNoiseModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.clone"]], "clone() (dowhy.gcm.causal_mechanisms.conditionalstochasticmodel method)": [[18, "dowhy.gcm.causal_mechanisms.ConditionalStochasticModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.stochasticmodel method)": [[18, 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(dowhy.gcm.density_estimators.kerneldensityestimator1d method)": [[18, "dowhy.gcm.density_estimators.KernelDensityEstimator1D.density"]], "disable_progress_bars() (in module dowhy.gcm.config)": [[18, "dowhy.gcm.config.disable_progress_bars"]], "distribution_change() (in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.distribution_change"]], "distribution_change_of_graphs() (in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.distribution_change_of_graphs"]], "dowhy.gcm": [[18, "module-dowhy.gcm"]], "dowhy.gcm.anomaly": [[18, "module-dowhy.gcm.anomaly"]], "dowhy.gcm.anomaly_scorer": [[18, "module-dowhy.gcm.anomaly_scorer"]], "dowhy.gcm.anomaly_scorers": [[18, "module-dowhy.gcm.anomaly_scorers"]], "dowhy.gcm.auto": [[18, "module-dowhy.gcm.auto"]], "dowhy.gcm.causal_mechanisms": [[18, "module-dowhy.gcm.causal_mechanisms"]], "dowhy.gcm.causal_models": [[18, "module-dowhy.gcm.causal_models"]], "dowhy.gcm.confidence_intervals": [[18, 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(in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.estimate_distribution_change_scores"]], "estimate_entropy_discrete() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_discrete"]], "estimate_entropy_kmeans() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_kmeans"]], "estimate_entropy_of_probabilities() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_of_probabilities"]], "estimate_entropy_using_discretization() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_using_discretization"]], "estimate_ftest_pvalue() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.estimate_ftest_pvalue"]], "estimate_gaussian_entropy() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_gaussian_entropy"]], "estimate_geometric_median() (in module dowhy.gcm.confidence_intervals)": [[18, 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"estimate_noise() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.estimate_noise"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.estimate_probabilities"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.probabilityestimatormodel method)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel.estimate_probabilities"]], "estimate_shapley_values() (in module dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.estimate_shapley_values"]], "estimate_variance() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_variance"]], "evaluate() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.functionalcausalmodel method)": [[18, "dowhy.gcm.causal_mechanisms.FunctionalCausalModel.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.evaluate"]], "evaluate_causal_model() (in module dowhy.gcm.model_evaluation)": [[18, "dowhy.gcm.model_evaluation.evaluate_causal_model"]], "falsify_graph() (in module dowhy.gcm.falsify)": [[18, "dowhy.gcm.falsify.falsify_graph"]], "feature_relevance_distribution() (in module dowhy.gcm.feature_relevance)": [[18, "dowhy.gcm.feature_relevance.feature_relevance_distribution"]], "feature_relevance_sample() (in module dowhy.gcm.feature_relevance)": [[18, "dowhy.gcm.feature_relevance.feature_relevance_sample"]], "find_best_model() (in module dowhy.gcm.auto)": [[18, "dowhy.gcm.auto.find_best_model"]], "find_suitable_continuous_distribution() (dowhy.gcm.stochastic_models.scipydistribution static method)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution.find_suitable_continuous_distribution"]], "fit() (dowhy.gcm.anomaly_scorer.anomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorer.AnomalyScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.itanomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorers.ITAnomalyScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.inversedensityscorer method)": [[18, "dowhy.gcm.anomaly_scorers.InverseDensityScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.meandeviationscorer method)": [[18, "dowhy.gcm.anomaly_scorers.MeanDeviationScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.mediancdfquantilescorer method)": [[18, "dowhy.gcm.anomaly_scorers.MedianCDFQuantileScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.mediandeviationscorer method)": [[18, "dowhy.gcm.anomaly_scorers.MedianDeviationScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.rankbasedanomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorers.RankBasedAnomalyScorer.fit"]], "fit() 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"get_class_names() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.get_class_names"]], "graph_falsification (dowhy.gcm.model_evaluation.causalmodelevaluationresult attribute)": [[18, "dowhy.gcm.model_evaluation.CausalModelEvaluationResult.graph_falsification"]], "has_linear_relationship() (in module dowhy.gcm.auto)": [[18, "dowhy.gcm.auto.has_linear_relationship"]], "interventional_samples() (in module dowhy.gcm.whatif)": [[18, "dowhy.gcm.whatif.interventional_samples"]], "intrinsic_causal_influence() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.intrinsic_causal_influence"]], "intrinsic_causal_influence_sample() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.intrinsic_causal_influence_sample"]], "invertible_function (dowhy.gcm.causal_mechanisms.postnonlinearmodel property)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.invertible_function"]], "is_probability_matrix() (in module 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dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.geometric_median"]], "has_categorical() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.has_categorical"]], "is_categorical() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.is_categorical"]], "is_discrete() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.is_discrete"]], "means_difference() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.means_difference"]], "plot() (in module dowhy.gcm.util.plotting)": [[21, "dowhy.gcm.util.plotting.plot"]], "plot_adjacency_matrix() (in module dowhy.gcm.util.plotting)": [[21, "dowhy.gcm.util.plotting.plot_adjacency_matrix"]], "set_random_seed() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.set_random_seed"]], "setdiff2d() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.setdiff2d"]], "shape_into_2d() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.shape_into_2d"]], "transform() (dowhy.gcm.util.catboost_encoder.catboostencoder method)": [[21, "dowhy.gcm.util.catboost_encoder.CatBoostEncoder.transform"]], "variance_of_deviations() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.variance_of_deviations"]], "variance_of_matching_values() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.variance_of_matching_values"]], "cdt (class in dowhy.graph_learners.cdt)": [[22, "dowhy.graph_learners.cdt.CDT"]], "ges (class in dowhy.graph_learners.ges)": [[22, "dowhy.graph_learners.ges.GES"]], "lingam (class in dowhy.graph_learners.lingam)": [[22, "dowhy.graph_learners.lingam.LINGAM"]], "dowhy.graph_learners": [[22, "module-dowhy.graph_learners"]], "dowhy.graph_learners.cdt": [[22, "module-dowhy.graph_learners.cdt"]], "dowhy.graph_learners.ges": [[22, "module-dowhy.graph_learners.ges"]], "dowhy.graph_learners.lingam": [[22, "module-dowhy.graph_learners.lingam"]], "get_discovery_class_object() (in module dowhy.graph_learners)": [[22, "dowhy.graph_learners.get_discovery_class_object"]], "get_library_class_object() (in module dowhy.graph_learners)": [[22, "dowhy.graph_learners.get_library_class_object"]], "learn_graph() (dowhy.graph_learners.cdt.cdt method)": [[22, "dowhy.graph_learners.cdt.CDT.learn_graph"]], "learn_graph() (dowhy.graph_learners.ges.ges method)": [[22, "dowhy.graph_learners.ges.GES.learn_graph"]], "learn_graph() (dowhy.graph_learners.lingam.lingam method)": [[22, "dowhy.graph_learners.lingam.LINGAM.learn_graph"]], "confounderdistributioninterpreter (class in dowhy.interpreters.confounder_distribution_interpreter)": [[23, "dowhy.interpreters.confounder_distribution_interpreter.ConfounderDistributionInterpreter"]], "propensitybalanceinterpreter (class in dowhy.interpreters.propensity_balance_interpreter)": [[23, "dowhy.interpreters.propensity_balance_interpreter.PropensityBalanceInterpreter"]], "supported_estimators 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dowhy.interpreters.visual_interpreter)": [[23, "dowhy.interpreters.visual_interpreter.VisualInterpreter"]], "discrete_dist_plot() (dowhy.interpreters.confounder_distribution_interpreter.confounderdistributioninterpreter static method)": [[23, "dowhy.interpreters.confounder_distribution_interpreter.ConfounderDistributionInterpreter.discrete_dist_plot"]], "dowhy.interpreters": [[23, "module-dowhy.interpreters"]], "dowhy.interpreters.confounder_distribution_interpreter": [[23, "module-dowhy.interpreters.confounder_distribution_interpreter"]], "dowhy.interpreters.propensity_balance_interpreter": [[23, "module-dowhy.interpreters.propensity_balance_interpreter"]], "dowhy.interpreters.textual_effect_interpreter": [[23, "module-dowhy.interpreters.textual_effect_interpreter"]], "dowhy.interpreters.textual_interpreter": [[23, "module-dowhy.interpreters.textual_interpreter"]], "dowhy.interpreters.visual_interpreter": [[23, "module-dowhy.interpreters.visual_interpreter"]], "get_class_object() (in module dowhy.interpreters)": [[23, "dowhy.interpreters.get_class_object"]], "interpret() (dowhy.interpreters.confounder_distribution_interpreter.confounderdistributioninterpreter method)": [[23, "dowhy.interpreters.confounder_distribution_interpreter.ConfounderDistributionInterpreter.interpret"]], "interpret() (dowhy.interpreters.propensity_balance_interpreter.propensitybalanceinterpreter method)": [[23, "dowhy.interpreters.propensity_balance_interpreter.PropensityBalanceInterpreter.interpret"]], "interpret() (dowhy.interpreters.textual_effect_interpreter.textualeffectinterpreter method)": [[23, "dowhy.interpreters.textual_effect_interpreter.TextualEffectInterpreter.interpret"]], "show() (dowhy.interpreters.textual_interpreter.textualinterpreter method)": [[23, "dowhy.interpreters.textual_interpreter.TextualInterpreter.show"]], "show() (dowhy.interpreters.visual_interpreter.visualinterpreter method)": [[23, "dowhy.interpreters.visual_interpreter.VisualInterpreter.show"]], 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"dowhy.utils.propensity_score.binarize_discrete"]], "binary_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.binary_treatment_model"]], "categorical_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.categorical_treatment_model"]], "compute_ci() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.compute_ci"]], "conditional_mi() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.conditional_MI"]], "continuous_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.continuous_treatment_model"]], "convert_to_binary() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.convert_to_binary"]], "convert_to_undirected_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.convert_to_undirected_graph"]], "create_polynomial_function() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.create_polynomial_function"]], "daggity_to_dot() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.daggity_to_dot"]], "del_edge() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.del_edge"]], "difference() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.difference"]], "discrete_to_integer() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.discrete_to_integer"]], "dowhy.utils": [[24, "module-dowhy.utils"]], "dowhy.utils.api": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression": [[24, "module-dowhy.utils.regression"]], "entropy() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.entropy"]], "find_ancestor() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_ancestor"]], "find_c_components() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_c_components"]], "find_predecessor() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_predecessor"]], "generate_data() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.generate_data"]], "generate_moment_function() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.generate_moment_function"]], "generation_process() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.generation_process"]], "get_all() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.get_all"]], "get_numeric_features() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.get_numeric_features"]], "get_random_node_pair() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_random_node_pair"]], "get_simple_ordered_tree() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_simple_ordered_tree"]], "get_type_string() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.get_type_string"]], "induced_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.induced_graph"]], "intersection() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.intersection"]], "is_connected() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.is_connected"]], "is_empty() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.is_empty"]], "parse_state() (in module dowhy.utils.api)": [[24, "dowhy.utils.api.parse_state"]], "partial_corr() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.partial_corr"]], "plot() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot"]], "plot_adjacency_matrix() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot_adjacency_matrix"]], "plot_causal_graph_graphviz() (in module dowhy.utils.graphviz_plotting)": [[24, "dowhy.utils.graphviz_plotting.plot_causal_graph_graphviz"]], "plot_causal_graph_networkx() (in module dowhy.utils.networkx_plotting)": [[24, "dowhy.utils.networkx_plotting.plot_causal_graph_networkx"]], "pretty_print_graph() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.pretty_print_graph"]], "propensity_of_treatment_score() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.propensity_of_treatment_score"]], "query_yes_no() (in module dowhy.utils.cli_helpers)": [[24, "dowhy.utils.cli_helpers.query_yes_no"]], "state_propensity_score() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.state_propensity_score"]], "str_to_dot() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.str_to_dot"]], "union() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.union"]]}})
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