033 Lesson NLP Appliations ( Sentiment Analysis)/Transformers (Hugging Face).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hugging Face\n",
    "\n",
    "Hugging Face is an open-source provider of natural language processing (NLP) technologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='images/2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLP is a field of linguistics and machine learning focused on understanding everything related to human language. The aim of NLP tasks is not only to understand single words individually, but to be able to understand the context of those words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is a list of common NLP tasks, with some examples of each:\n",
    "\n",
    "* Classifying whole sentences: Getting the sentiment of a review, detecting if an email is spam, determining if a sentence is grammatically correct or whether two sentences are logically related or not\n",
    "* Classifying each word in a sentence: Identifying the grammatical components of a sentence (noun, verb, adjective), or the named entities (person, location, organization)\n",
    "* Generating text content: Completing a prompt with auto-generated text, filling in the blanks in a text with masked words\n",
    "* Extracting an answer from a text: Given a question and a context, extracting the answer to the question based on the information provided in the context\n",
    "* Generating a new sentence from an input text: Translating a text into another language, summarizing a text\n",
    "\n",
    "NLP isn’t limited to written text though. It also tackles complex challenges in speech recognition and computer vision, such as generating a transcript of an audio sample or a description of an image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.educative.io/blog/top-uses-gpt-3-deep-learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformers\n",
    "\n",
    "Transformer models are used to solve all kinds of NLP tasks,\n",
    "\n",
    "A transformer is a deep learning model that adopts the mechanism of attention, differentially weighing the significance of each part of the input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Transformers, you can leverage popular NLP models, such as BERT, GPT, XLNet, T5 or DistilBERT and use those models to manipulate text in one way or another. For instance, you can classify text, extract information, automatically answer questions, summarize text, generate text, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "READ MORE : https://jalammar.github.io/illustrated-transformer/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformer history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACLkAAAIUCAIAAABzEftXAAAACXBIWXMAABuvAAAbrwFeGpEcAAAgAElEQVR4nOy9e5wdRZk+/ta5zT0ht0lIgpC4JkqC3AmwCSvxt8CquOBdXEFBwVXBXdHvijfA73pBQXcX0UWEVVgBcVdUdL/AroAEBREEloQ1gyRcMpFMkplkJpnLudXvj5k+Xd39dld11VunC7fPx0995OTtt57nfZ/3rTrTXeewp1f+HQAAA+DRkQNjgTFsFH6FzBEH0ldoEl2blxASOR40Oxo2anikr5hrOWNMHJPUosEoNR6DXMeNMg/h2YEzYP4YW03p8UgJ2VJd8C0FRgRIkmKS0J3a1aM0uiWulqBL9bzb79u6kZEiabP+3elRaZAkxcSBNVoXibWKpoqtueqkSBTxWIitplqoGGVR0ZqxtZUjMkbhl4qUFXqdBUb/e/WfOkfOMzLPEWVk0MUs1FvsryOUjMx6FJVaAMKfYmbGUNtOiyezilZCq2DjmFoI9G+OhHRF08gRGhkn1EJQ0eqsE23I1mhjJGr29vQvxWPSt2OQzOAhR6LevYNAcP8qs6e0cUb/ZIyU7BVjkqyWdjJyo6LbjdYdRVlHq71GFzzPHBmjXkM2UYApHSBueJApxMRTZiMl5A4SeWhDs6BzqdhgeNInGTjn0TEktaiNKSOMCxWjgH8UG8NsIh4458D8kQGbGWHm/wds0MigeFIhwWKiExnuOWLCGILiu451RNRaSEpaBwkWGWNKrbeZP/phhmA4BZvWtUaULBDSRGJR/671KAMkngtkzGaNJpILWUWbxxYbjVUnRaKaaxuxVVULFaPgmElFo5HR71HEsdVnlDq40V5nRS1RRqgN6sgQiXP6d2JLR8rIPEd02+40GymyyNhhBD5Ckx5FoBYOABw4ABNGCP7BEbeRd5hsKhrNSAitis20exfUQql/c91SrGgaOYrF445aCCpaibXchmKNpkGSkf6TPkgbIAFvlkitiUiYJSS4h/i+jXbvOD9/NPonrWjzHEnUEtO9rTByrKI1GWmjzVxR7UELAPprtHeviDFkjJZ0yCbySusAccO8kQv/P72NlJA7SOShDc2CzqVig+FJn2RgjEXHqOBCNqaMMC5UjAL+UWwcs4l4YIwB98fps0SBc0WiDRoZFE8qJFhMdCLDPEdcGENQfNexjohaC0lJ6yDBImNMqfU290c/zBAMp2DTutaIkgVCmkgs6t+1HmWAxHOBjNms0URyIato89hio7HqpEhUc20jtqpqoWIUHDOpaDQy+j2KOLb6jFIHN9rrrKglygi1QR0ZInFO/05s6UgZmeeIbtudZiNFFhk7jMBHaNKjCNTCAIABA+DCCAyYwAS3kXeYbCoazUgIrYrNtHsX1EKpf3PdUqxoGjmKxeOOWggqWom13IZijaZBkpH+kz5IGyABb5ZIrYlIuCUkuIf4vo127zg/fzT6J61o8xxJ1BLTva0wcqyiNRlpo81cUe1BCwD6a3R+rih7JPLQhmZB51KxwfCkTzIQPD2kwQjjQsUo4B/FxjCbiAfOOTB/zM8V0SBRdITayNxo9ShjSq23mT/6YYZgOAWb1rVGlCwQ0kRiUf+u9SgDJJ4LZMxmjSaSC1lFm8cWG41VJ0WimmsbsVVVCxWj4JhJRaOR0e9RxLHVZ5Q6uNFeZ0UtUUaoDerIEIlz+ndiS0fKyDxHdNvuNBspssjYYQQ+QpMeRaAWDgD5uaK4TLmhFkr9m+uWYkXTyFEsHnfUQlDRSqzlNhRrNA2SjPSf9EHaAAl4s0RqTUTCLCHBPeTnitKjVWZkniOJWmK6txVGjlW0JiNttJkrqj1oAUB/jc7PFWWPRB7a0CzoXCo2GJ70SQaCp4c0GGFcqBgF/KPYOGYT8cAYA+6P+bkiGiSKjlAbmRutHmVMqfU290c/zBAMp2DTutaIkgVCmkgs6t+1HmWAxHOBjNms0URyIato89hio7HqpEhUc20jtqpqoWIUHDOpaDQy+j2KOLb6jFIHN9rrrKglygi1QR0ZInFO/05s6UgZmeeIbtudZiNFFhk7jMBHaNKjCNTCACA/VxSXKTfUQql/c91SrGgaOYrF445aCCpaibXchmKNpkGSkf6TPkgbIAFvlkitiUi4JSS4h/xcUXq0yozMcyRRS0z3tsLIsYrWZKSNNnNFtQctAOiv0fm5ouyRyEMbmgWdS8UGw5M+yUDw9JAGI4wLFaOAfxQbw2wiHjjnwPwxP1dEg0TREWojc6PVo4wptd5m/uiHGYLhFGxa1xpRskBIE4lF/bvWowyQeC6QMZs1mkguZBVtHltsNFadFIlqrm3EVlUtVIyCYyYVjUZGv0cRx1afUergRnudFbVEGaE2qCNDJM7p34ktHSkj8xzRbbvTbKTIImOHEfgITXoUgVo4AOTniuIy5YZaKPVvrluKFU0jR7F43FELQUUrsZbbUKzRNEgy0n/SB2kDJODNEqk1EQmzhAT3kJ8rSo9WmZF5jiRqieneVhg5VtGajLTRZq6o9qAFAP01Oj9XlD0SeWhDs6BzqdhgeNInGQieHtJghHGhYhTwj2LjmE3EA2MMuD/m54pokCg6Qm1kbrR6lDGl1tvcH/0wQzCcgk3rWiNKFghpIrGof9d6lAESzwUyZrNGE8mFrKLNY4uNxqqTIlHNtY3YqqqFilFwzKSi0cjo9yji2OozSh3caK+zopYoI9QGdWSIxDn9O7GlI2VkniO6bXeajRRZZOwwAh+hSY8iUAsDgPxcUVym3FALpf7NdUuxomnkKBaPO2ohqGgl1nIbijWaBklG+k/6IG2ABLxZIrUmIuGWkOAe8nNF6dEqMzLPkUQtMd3bCiPHKlqTkTbazBXVHrQAoL9G5+eKskciD21oFnQuFRsMT/okA8HTQxqMMC5UjAL+UWwMs4l44JwD88f8XBENEkVHqI3MjVaPMqbUepv5ox9mCIZTsGlda0TJAiFNJBb171qPMkDiuUDGbNZoIrmQVbR5bLHRWHVSJKq5thFbVbVQMQqOmVQ0Ghn9HkUcW31GqYMb7XVW1BJlhNqgjgyROKd/J7Z0pIzMc0S37U6zkSKLjB1G4CM06VEEauEAkJ8risuUG2qh1L+5bilWNI0cxeJxRy0EFa3EWm5DsUbTIMlI/0kfpA2QgDdLpNZEJMwSEtxDfq4oPVplRuY5kqglpntbYeRYRWsy0kabuaLagxYA9Nfo/FxR9kjkoQ3Ngs6lYoPhSZ9kIHh6SIMRxoWKUcA/io1jNhEPjDHg/pifK6JBougItZG50epRxpRab3N/9MMMwXAKNq1rjShZIKSJxKL+XetRBkg8F8iYzRpNJBeyijaPLTYaq06KRDXXNmKrqhYqRsExk4pGI6Pfo4hjq88odXCjvc6KWqKMUBvUkSES5/TvxJaOlJF5jui23Wk2UmSRscMIfIQmPYpALQwA8nNFcZlyQy2U+jfXLcWKppGjWDzuqIWgopVYy20o1mgaJBnpP+mDtAES8GaJ1JqIhFtCgnvIzxWlR6vMyDxHErXEdG8rjByraE1G2mgzV1R70AKA/hqdnyvKHok8tKFZ0LlUbDA86ZMMBE8PaTDCuFAxCvhHsTHMJuKBcw7MH/NzRTRIFB2hNjI3Wj3KmFLrbeaPfpghGE7BpnWtESULhDSRWNS/az3KAInnAhmzWaOJ5EJW0eaxxUZj1UmRqObaRmxV1ULFKDhmUtFoZPR7FHFs9RmlDm6011lRS5QRaoM6MkTinP6d2NKRMjLPEd22O81GiiwydhiBj9CkRxGohQNAfq4oLlNuqIVS/+a6pVjRNHIUi8cdtRBUtBJruQ3FGk2DJCP9J32QNkAC3iyRWhORMEtIcA/5uaL0aJUZmedIopaY7m2FkWMVrclIG23mimoPWgDQX6Pzc0XZI5GHNjQLOpeKDYYnfZKB4OkhDUYYFypGAf8oNo7ZRDwwxoD7Y36uiAaJoiPURuZGq0cZU2q9zf3RDzMEwynYtK41omSBkCYSi/p3rUcZIPFcIGM2azSRXMgq2jy22GisOikS1VzbiK2qWqgYBcdMKhqNjH6PIo6tPqPUwY32OitqiTJCbVBHhkic078TWzpSRuY5ott2p9lIkUXGDiPwEZr0KAK1MADIzxXFZcoNtVDq31y3FCuaRo5i8bijFoKKVmItt6FYo2mQZKT/pA/SBkjAmyVSayISbgkJ7iE/V5QerTIj8xxJ1BLTva0wcqyiNRlpo81cUe1BCwD6a3R+rih7JPLQhmZB51KxwfCkTzIQPD2kwQjjQsUo4B/FxjCbiAfOOTB/zM8V0SBRdITayNxo9ShjSq23mT/6YYZgOAWb1rVGlCwQ0kRiUf+u9SgDJJ4LZMxmjSaSC1lFm8cWG41VJ0WimmsbsVVVCxWj4JhJRaOR0e9RxLHVZ5Q6uNFeZ0UtUUaoDerIEIlz+ndiS0fKyDxHdNvuNBspssjYYQQ+QpMeRaAWDgD5uaK4TLmhFkr9m+uWYkXTyFEsHnfUQlDRSqzlNhRrNA2SjPSf9EHaAAl4s0RqTUTCLCHBPeTnitKjVWZkniOJWmK6txVGjlW0JiNttJkrqj1oAUB/jc7PFWWPRB7a0CzoXCo2GJ70SQaCp4c0GGFcqBgF/KPYOGYT8cAYA+6P+bkiGiSKjlAbmRutHmVMqfU290c/zBAMp2DTutaIkgVCmkgs6t+1HmWAxHOBjNms0URyIato89hio7HqpEhUc20jtqpqoWIUHDOpaDQy+j2KOLb6jFIHN9rrrKglygi1QR0ZInFO/05s6UgZmeeIbtudZiNFFhk7jMBHaNKjCNTCACA/VxSXKTfUQql/c91SrGgaOYrF445aCCpaibXchmKNpkGSkf6TPkgbIAFvlkitiUi4JSS4h/xcUXq0yozMcyRRS0z3tsLIsYrWZKSNNnNFtQctAOiv0fm5ouyRyEMbmgWdS8UGw5M+yUDw9JAGI4wLFaOAfxQbw2wiHjjnwPwxP1dEg0TREWojc6PVo4wptd5m/uiHGYLhFGxa1xpRskBIE4lF/bvWowyQeC6QMZs1mkguZBVtHltsNFadFIlqrm3EVlUtVIyCYyYVjUZGv0cRx1afUergRnudFbVEGaE2qCNDJM7p34ktHSkj8xzRbbvTbKTIImOHEfgITXoUgVo4AOTniuIy5YZaKPVvrluKFU0jR7F43FELQUUrsZbbUKzRNEgy0n/SB2kDJODNEqk1EQmzhAT3kJ8rSo9WmZF5jiRqieneVhg5VtGajLTRZq6o9qAFAP01Oj9XlD0SeWhDs6BzqdhgeNInGQieHtJghHGhYhTwj2LjmE3EA2MMuD/m54pokCg6Qm1kbrR6lDGl1tvcH/0wQzCcgk3rWiNKFghpIrGof9d6lAESzwUyZrNGE8mFrKLNY4uNxqqTIlHNtY3YqqqFilFwzKSi0cjo9yji2OozSh3caK+zopYoI9QGdWSIxDn9O7GlI2VkniO6bXeajRRZZOwwAh+hSY8iUAsDgPxcUVym3FALpf7NdUuxomnkKBaPO2ohqGgl1nIbijWaBklG+k/6IG2ABLxZIrUmIuGWkOAe8nNF6dEqMzLPkUQtMd3bCiPHKlqTkTbazBXVHrQAoL9G5+eKskciD21oFnQuFRsMT/okA8HTQxqMMC5UjAL+UWwMs4l44JwD88f8XBENEkVHqI3MjVaPMqbUepv5ox9mCIZTsGlda0TJAiFNJBb171qPMkDiuUDGbNZoIrmQVbR5bLHRWHVSJKq5thFbVbVQMQqOmVQ0Ghn9HkUcW31GqYMb7XVW1BJlhNqgjgyROKd/J7Z0pIzMc0S37U6zkSKLjB1G4CM06VEEauEAkJ8risuUG2qh1L+5bilWNI0cxeJxRy0EFa3EWm5DsUbTIMlI/0kfpA2QgDdLpNZEJMwSEtxDfq4oPVplRuY5kqglpntbYeRYRWsy0kabuaLagxYA9Nfo/FxR9kjkoQ3Ngs6lYoPhSZ9kIHh6SIMRxoWKUcA/io1jNhEPjDHg/pifK6JBougItZG50epRxpRab3N/9MMMwXAKNq1rjShZIKSJxKL+XetRBkg8F8iYzRpNJBeyijaPLTYaq06KRDXXNmKrqhYqRsExk4pGI6Pfo4hjq88odXCjvc6KWqKMUBvUkSES5/TvxJaOlJF5jui23Wk2UmSRscMIfIQmPYpALQwA8nNFcZlyQy2U+jfXLcWKppGjWDzuqIWgopVYy20o1mgaJBnpP+mDtAES8GaJ1JqIhFtCgnvIzxWlR6vMyDxHErXEdG8rjByraE1G2mgzV1R70AKA/hqdnyvKHok8tKFZ0LlUbDA86ZMMBE8PaTDCuFAxCvhHsTHMJuKBcw7MH/NzRTRIFB2hNjI3Wj3KmFLrbeaPfpghGE7BpnWtESULhDSRWNS/az3KAInnAhmzWaOJ5EJW0eaxxUZj1UmRqObaRmxV1ULFKDhmUtFoZPR7FHFs9RmlDm6011lRS5QRaoM6MkTinP6d2NKRMjLPEd22O81GiiwydhiBj9CkRxGohQNAfq4oLlNuqIVS/+a6pVjRNHIUi8cdtRBUtBJruQ3FGk2DJCP9J32QNkAC3iyRWhORMEtIcA/5uaL0aJUZmedIopaY7m2FkWMVrclIG23mimoPWgDQX6Pzc0XZI5GHNjQLOpeKDYYnfZKB4OkhDUYYFypGAf8oNo7ZRDwwxoD7Y36uiAaJoiPURuZGq0cZU2q9zf3RDzMEwynYtK41omSBkCYSi/p3rUcZIPFcIGM2azSRXMgq2jy22GisOikS1VzbiK2qWqgYBcdMKhqNjH6PIo6tPqPUwY32OitqiTJCbVBHhkic078TWzpSRuY5ott2p9lIkUXGDiPwEZr0KAK1MADIzxXFZcoNtVDq31y3FCuaRo5i8bijFoKKVmItt6FYo2mQZKT/pA/SBkjAmyVSayISbgkJ7iE/V5QerTIj8xxJ1BLTva0wcqyiNRlpo81cUe1BCwD6a3R+rih7JPLQhmZB51KxwfCkTzIQPD2kwQjjQsUo4B/FxjCbiAfOOTB/zM8V0SBRdITayNxo9ShjSq23mT/6YYZgOAWb1rVGlCwQ0kRiUf+u9SgDJJ4LZMxmjSaSC1lFm8cWG41VJ0WimmsbsVVVCxWj4JhJRaOR0e9RxLHVZ5Q6uNFeZ0UtUUaoDerIEIlz+ndiS0fKyDxHdNvuNBspssjYYQQ+QpMeRaAWDgD5uaK4TLmhFkr9m+uWYkXTyFEsHnfUQlDRSqzlNhRrNA2SjPSf9EHaAAl4s0RqTUTCLCHBPeTnitKjVWZkniOJWmK6txVGjlW0JiNttJkrqj1oAUB/jS5eNH8tAOD3daMlHbKJvNI6QNwwb+TC/09vIyXkDhJ5aEOzoHOp2GB40icZCJ4e0mCEcaFiFPCPYuOYTcQDYwy4P+bnimiQKDpCbWRutHqUMaXW29wf/TBDMJyCTetaI0oWCGkisah/13qUARLPBTJms0YTyYWsos1ji43GqpMiUc21jdiqqoWKUXDMpKLRyOj3KOLY6jNKHdxor7Oiligj1AZ1ZIjEOf07saUjZWSeI7ptd5qNFFlk7DACH6FJj0qrltn9bMGSwrw+aFZhqtbCpnauqJctXFLonwvFKkzUcV4okr4FrH9pYW4f8DpMVVNWdBnmHVhYeCDrbPJ9E2qqQzMSQqtiM+3eBbVQ6t9ctxQrmkaOYvGY9ShKtRBUtBJruQ3FGk2DJCP9J32QNkAC3iyRWhORcEtIcA/5uaL0aJUZmedIopaY7m2FkWMVrclIG23mimoPWgDQX6PZ0yv/DgCwdGL3fpOzEwWO9QS0S2AXK8xFjeclhiSuRqQ2pkgQtWCCs8NI9tKMrSI2CZLAZH40WreLWnuGVHh0kGBu9HIdNgpNjyG0hETSo3Qio9Wj6Lol6iDkHQ1/Ah7jvq0YGeyVEokiHr1KdKtHmSNxZ42mQkJV0RRZRkJjqjopErI60omtjh8DRsSxJasj/R5lJUdknSG6Rie9LFZ0rn+Xc+Rcj0oXGTQmoTdiUBlHxg4jrdgijNTUsujI8mlvKK1aXZxd8a8cH2pu2lD7+Q/rW8dR5N5/l2HFqZU3nFZcvrRQbl27rfn4nbWf3tnYXYvFs/DI0l+8sXRoaNIdzU0b6vf8e33Lfkl2Fh5ffsMbS6sOK3R7/1IbaQw8VL/rlvrASOqYJEc+KV8uqMWi/ql6VMrIaORIGU+6HmVRLRQVrTCLkk00R2n3/5RI7Ovfi7YOHukLs3cGScSL8J03wHEgf1T6t1nR9GqJ6d5WGLld0Q4xekmjjcWjskZ794rwnEZXDAmUlH9ywl5JzSKFzUsIiRzPdHedP6c0r7fQUW8OjtSGJyVIwng6S4fMKfZBc9dI48XJpllkYq6N3pkssnmzivN6CzBZ3z7U3KeOVjW2iXhI/3Qlw4CvgKHFOLaa0uOREiJSXWTexCUfY0SAJCkmCd2pXT1Ko1viagm6VM+7/b6tGxkpkjbrP9qjJDmy1qPSIEmKiQNrtC4SaxVNFVtz1UmRKOKxEFtNtVAxyqKiNWNrK0dkjMIvFSkr9DoLjP736j91jpxnZJ4jysigi1mot9hfRygZmfUouVpmF0+7qOP0I1u3eSKvkcbPr5667bFm+E+K02N/4axLOv9sGd65x7fWvvOF6hNDETwHFE75SMfpRyVO+o9TP/gtlh4AXmYnfKTzrJNiLh9p3PWVqR89yeU5UsmCxMYxtRDo3xwJ6YqmkSM0MuY9ikAtBBWtzjrRhmyNNkaiZm9P/1I8Jn07BskMHnIkMfUIEL0hFACC+1eZPaWNM/onY6RkrxiTZLW0k5EbFd1utO4oyjpa7TU6P1fkOhJ2yIq+c9f2nri8Y76/QW6ODVd/+cTY9+7btzH5plGpdNSaAz5wfNdh/aWO1rVDU/c+tPfaX0/8oa6ApFQ6es0B7zmi3AcAAGPPjFx852QVUYsotcLKI2afs6b76JeVfcy1xrPPT9zxwN5bNtWr8XFDo9eeLCOOZF0OeQUmy88VESOR9CidyGj1KLpuiToIeUfDn4DHuG8rRgZ7pUSiiEevEhN7VH6uaAZ4lvqnqmiKLCOhMVWdc8/sG/vJzxXZzlF+rshebDPVv6M5cq5HpYsMGpPQGzGojCNjh5FWbBFGiWopHlw6/5KOI/plrbra/MUXx29+PIScQU/hrCs6/2xp0uW1bbVv/p/qpv0+ntLy0vsvqRyxUD7p/V+YuPm30ZiwYz7e+b6TCsglrde+xl2XTd6+OUVMIrMo58sFtVjUP1WPys8V6SCR41GYRckmmqO0+39KJPb170VbB4/0hdk7gyTiJT9XRMeIXi0x3dsKI7cr2iFGL2m0sXjyc0UvDSTxeHo73/u2+R9YWe5A/xUAxia/d9vQlQONKBIOwOZ0f/Ls+W9ZXMQv3T566Y3DvxiJQVIqrnhZ59Grek4/vGtln49ubNPOU24cr6EspkXW03Xhu+a/5+X4pAB8+8aRT942tmlStjCoxX/akDzXcaPMQ3j2/FwRCZKkmCR0p3b1KI1uiasl6FI97/b7tm5kpEjarP/8XJEb+lf0o1nR+bkie2rJzxWR5YiMUfilImWFXmeB0f9e/afOkfOMzHNEGRl0MQv1FvvrCCUjsx6VoJbiweWPfLZj5Rw18EO1f/rb6qbxwGeZwy/s/uD6xHs2AACwd8PkpVc2JgAAoLysdOGlypPuqP3T31Sf2h94b976jk/9Tak75orWq7atdvVHqk9XE3OkkgWJjWNqIdC/ORLSFU0jR2hkzHsUgVoIKlqddaIN2RptjETN3p7+pXhM+nYMkhk85Ehi6hEgP1dkhVHfYtY3H8pVGB7kY2Px9qiHPuhfwroAxgb58JiCWjw8fUtZ3zwoVWFkmzcpHSOy2Ert29j/22fjWvxjbbTX6PxckatIKgf2XXH23JPnynbkteoPbvzDFwaaYSSdXZd8qP+t/UmXTw2NfvSa4YcmQ7NX3nJu/4XLy33YOf+xTTtPuXF/7Lmijs6LP7DwrMWSAIxt3v2e6/c9J6JNjF57sow4knU55BWYLD9XRIxE0qN0IqPVo+i6Jeog5B0NfwIe476tGBnslRKJIh69SsR7VH6uKAg8S/1TVTQW2znzCgf0FSr15o7dzb0TKft/V2HJvEIv8OHdjV0T2qpL98x+99xCfzfbP9rcM8prbYitjp/8XJG+n7YxQkbpy2JFO3RmJUv9O5qj/FyRdmTsMNKKLcIoVi3stV/qfvtKwbza3PxA7Rf3NLYN8XJ/cdWp5dPWFYVbMnzL9eNX3MF95Csql3+5skg0uLN6+x31F6uFVW+uvO008drmzz8+cdsAALD1X+4KT7qhtuGe5vM7eGVh4dBTy6edFJz0uokv3yFEqlw85xudJyz039j7ZPUH360/PQRL1pTPfH/5oErg2q/8hCvGJDnySflyQS0W9U/Vo/JzRTpI5HgUZlGyieYo7f6fEol9/XvR1sEjfWH2ziCJeMnPFaW06V9bXP/uwiuPK8zyn9/nE4P8d//RuP/bzedGEz1X4OXvKJ369sLBy1nr5/0mtvAnb63f9X0+MjXzVlS9C9cVTz678Mo1wUm38d/9R+P+a5vP71NjNI+dcFHxiFfNTP3ctbUf/xdpbBOiZ1zRBDZUjF7SaGPx5OeKXhpIEDyVA2d947x5x/QpoASA4bEP/eOuBwMndQp/9tbFXzsm9kuhW69dj+940y0T+wNIKh++ePF7+3H75HNFJ75tydXHlAIX1Brbx6Cvrxi888Qf/eHgBQ81kqOnEv9pQ/Jcx40yD+HZ83NFJEiSYpLQndrVozS6Ja6WoEv1vNvv27qRkSJps/7zc0Vu6F/Rj0ZFL31V55tO7jxyRWmOv+Lw/bvrv3108qd3TQ1MJiIpFQ5d1/2OdZUViwqV1rUv1n+9YfzWDbVddXlgWIkduq77TccUuwEYwP6B8St+XKsmPrPZvaRy5qmdxx1aXtr6U+iERDAAACAASURBVNl4Y+Cp6r33T973dLNqLbaaasnPFWn7scYo/FLZyij0OguM2hZb5/SfOkfOMzLPEWVk0MUs1FvII2OVkVmPSlLL/NL7/77j2H4GALWh+g+/OnnP5oCHZed2XXx60V8/N0998hO13d6nmMMv7vrgOv8ZxBfvnPjSPzcnvGlW/HXXxaf5/7p3w+Qnr2zUAVh/8by/7zh2IQOA2o76D78yde/TAUbLzuv86BsDk37q4/Xd3n/NWt9xmXCoqLalesXHa4PeB9G+tR2f+j+l2a1rt9W+fFF1az0+RypZkNg4phYC/ZsjIV3RNHKERsa8RxGohaCi1Vkn2pCt0cZI1Ozt6V+Kx6RvxyCZwUOOJKYeAfJzRUSM5rP1ny+fuo7F/lF1Z/MXn6r9eENMdg4svOXrpRNfxdBLJ/6nceuH608ORtQyn732i+VTEie9/5LajzfEMir1sYPXFVb/eWH1/1eYK3w/1e8/M/XN7+t0VFWbBPs29v/22VDUSFvQaq/RxYvmr42lkdB8g50m7iX9s1TSH6dU5qLG4wiSyjvP6X+r+F3Stfojj4384x0j3/iv0ds3Tf6hVDp0Uckv/K7yQWPjt7/Q9JEcdMBXz+gRjvs3/vuh3Z++dffV948/Uyofs9S/tntRR8fm0V+NirOX1pzYd2QPjru6c/ymJ6oNRC2Mze399Jm9/jfe1ep3/XjHB28cueGBvd+5f/9ApXLiwa152eJ58KtfTQ7FdA+bsY3NMuJI1uWQV2Cy/FwRMRJJj9KJjFaPouuWqIOQdzT8CXiM+7ZiZLBXSiSKePQqEelR+bmiCPAs9U9V0dP/0Fd+0/tnX/z6zj/pL3QFvgOVVbqLB7+848+PL/dsrz62MyYScysXXDTr/BPK/b2sKF7bW1y2qnP9arbtqdr2iRgkJXbIyytr13df8K7eNx1VPnBOcf6c4rw5xVn7qz9+pMnjs3zoGbM+996uoxYXZ4mfBsqFeYvLx5zQefzC5u+ebOxp2Imtjp/8XJG+n7YxQkbpy2JFO3RmJUv9O5qj/FyRdmTsMNKKLcIoQS0TzUcf4ovWFPv317/z6akHng972LMZDj6ttKh1UqcLNv+kvoMDcIBy8fS/rixp/dO++r99ubZ1wv9z5O6nYdmfl/o9g8558NwdjR1NgH38tw83F60p9u9vfOdTU796Icxoz2YemLQTBn7S2OF9qD3u7MrR/vdVNB/+h6kNgz6j6gvNxpGlVfM9g1ms8Xj9ySGlmChlUJ6j/FxRnIf8XJEOEjkehVmUbKI5Srv/p0RiX/9etHXwSF+YvTNIIl7yc0VqNqVXFs65vrxuNYv7aQ0AgB52yCmFvk3Np56LeO5jb7mxfOKKmAgDlBcUVq9jL/ykuWvKn7b4ysI5/1JeK5v04FMLfU82/+eFCKO+wjvuLJ/18dLxpxUOXsG6gs/wD9/beGRjPGv0lV1FE9hQMXpJo43Fo7JGew8ATf9XaERXDHGMvNI6QNwwb+TC/09vIyXkDpIontr3bt515/DMNnlqeP9Xrh183w9G795S3TpSG9iy/4Zb/vChB6am/CkKrz6ia7GPhJ28tneZgGDrQ0Mfvn3fozsbu0cm77h96OKHasI/lk9Z29MRxLN7pL592PvfWFRxjLHoyHuWdR8m/MHr2QeGLn+oOjz9r436L+4Yuuzxhv/P/Z2vORBZGMToqcQfi3B62Qb9h+ZFEcYogTEG3B+nzxIFzhWJNl705HhSIcFiohMZ5jniwhhd8kUbzBFRayEpaR0kWGSMKbXe5v7ohxmC4RRsWtcaUbJASBOJRf1jPQp5eiK+EtHZQzOm6FEGSDwXyJjNGk0kF7KKZlBe0nXJx2effWixgppMv2aXT79g9gWHIl5YV/mCD/eddlDsV7b2HNR18Yd7juiKZrl4yofm3HTVvK/+7axz13csmx10npRldsQ7D/jMqeX4X3BgS4/t/dyFncvKVmKrqpZ0urW5ohlXNBoZ/R5FHFt9RqmDG+11VtQSZYTaoI4MkTinfye2dKSMzHNEt+1Os5Eii4wdRuAjNOlRErXsql/36Yl/+r9Tj+zEPIw3tgwJ/9nDZvcCcAYMSitLy4TnCMc31R/fw6b/3DAz7q0/uFH4zNhbOGKl53+ocf2nJ//p8qlHd2KM9je3ipP2slmticqFVeL31+1oPLgxxIg/ek9T+FhbWHZ0AckRmhEIZkHFZnpyF9RCqX9z3VKsaBo5isVj1qMo1UJQ0Uqs5TYUazQNkoz0n/RB2gAJeLNEak1Ewi0hwT0InRkYsIiXsE2Mnz8a/WtVdOmVxfOvLR+2JCbl4qujcOKlpZWzwp5XX1I+cbnk8vLy4jsvK3R5aim+snj+dcqTXl5a2Rth1MH6l8YfSIJE1pA+tiGbqH2aitZUnTba9ipKbmMJLQDor9He30Y4R8ZoSYdsIq+0DhA33BuZ8P/T20gJuYMEwzOy/xPf2nXncHNqeN9nvzX0vRea4iwM+H//595fTQiTLOhcUfKQlLpOWSncRJ4Y/85/Tu7z/7vxyH/uEa+dv7L7hJKIpHbLDS+84Yptp1/xwhuu2Pbm2/bvijLi0ZEt6ReOOkH9kU21atDmwUcmBFelQ+aGe0goeirxxyKcXrZB/6F5UYQxSuCcA/NHBmxmhJn/H7DxIiPHkwoJFhOdyHDPERPGEBTfdawjotZCUtI6SLDIGFNqvc380Q8zBMMp2LSuNaJkgZAmEov6x3pUZFEM2eAYaHqUARLPBTJms0YTyYWqoktLui79cO+aeQob63LptHf2HNkVepcd+5be0xZJLq8s6rrwnZXpS0Ug/fOKPXFb8vgszz6q98MnJd7ZAgCAnhU9f/PWUo+F2KqqJZ1uba5oxhWNRka/RxHHVp9R6uBGe50VtUQZoTaoI0MkzunfiS0dKSPzHNFtu9NspMgiY4cR+AhNepRcLbuam7fFeqhVg+6qAIwDh9krCvOFSV54tDkBHPjMv06PA78N3LZZtIL5s+xoDgzGMqrWAnOC95+lZcWDev23x7c2tlTDjPb+rvGicOmiZYXOGLWk29LFbuzcUAul/s11S7GiaeQoFo9Zj6JUC0FFK7GW21Cs0TRIMtJ/0gdpAyTgzRKpNREJs4QE9yB0Zoh+AR2P2MT4+aPRv05FsxMvLf7JAsFgiv/+9vqN51S/9NrqlefUfvofTfEPsbCkcOoZLOD58OLrzxQ/WvJnb61d87qpS19bveXWwLWzXl885dXTl7ITL8MmfXf1ivXVq95d+9nPgpMuLZz6JiaJTPSVwBr10MaK1lSdNtq2KkrBxhJaANBfo/NzRdkjiQntyP5PfOvFD92w666R8CwcgE1WnxwRJukqzO+csSm/rOvVwt/Cxrbsu1e4UwTA2L7xn2xpCtd2nvwySJFkiLkzGTjkyKthS6iONHYLTiqlcA8JRU8l/hBGqyXboH8k2hEMMUpgjAH3x/xcEQ0SRUeojcyNVo8yptR6m/ujH2YIhlOwaV1rRMkCIU0kFvWP9qj8XJEb+lf0I4cCAFA67Z09q2cJb9SaGx/ad+XXRj78meG//droTY/U94vm8zrefnzwWP8hXe8+QXynuXnD2Gcu3/3eT++9ekNNvHbOMd3vOAQgNsuRV1yWS+W3n1kRTxSNDExc/eWRd184/IHPj/7rIw1x0qUn9b7tEPrYqqolnW5trmjGFY1GRr9HEcdWn1Hq4EZ7nRW1RBmhNqgjQyTO6d+JLR0pI/Mc0W2702ykyCJjhxH4CE16lJFayoWDxB+s3c/31mD62fOly0Q7/uJ2DhA8V8RgfGtzr2C0aHmhpIKkXHiZOOk+PurdryovZeLyvntbsxH1M9h8UficW15aODD6IAaaEQhmQcVmenIX1EKpf3PdUqxoGjmKxWPWoyjVQlDRSqzlNhRrNA2SjPSf9EHaAAl4s0RqTUTCLSHBPeTnitKjDdvw+z9Wf2xw5u/ltcHGj86pfuNTjcd/w4f+wLf/pnnPx2rfulF8NoId8rrCHMHz6rOLCwX3O26tfevy5jNbYWw7f+Ty2g23in/LLxx5TqEIwIDff3Fw0rOr3/xE44nf8KFBvv03zXs+WvvWdwOTHvz6wpwwIz68zf/f6GhMrKhiG7JBIpmiojVVp422rYpSsLGEFgD01+j8XFH2SOJDOzL1yE4enYUBcOBToZ/ars/YzH9ZZbH/bnPz5snArSLgHJqPbha/wq508MvKKZIM+J3J4Z0N0efRq8qVgA1U5hTn+QaNP4yFe0goeirx9xhB6tiGXGBjtMtBPBLuRYb5Y36uiAaJoiPURuZGq0cZU2q9zfzRDzMEwynYtK41omSBkCYSi/rHelR+rsgR/Sv6kUMBAKjfcf3Yht0z/1bdPXX9Pwx/+sbJX/6+vm1389nfT/37DXs+d09deE6arTymLD6zddz6zqXCf27bMPa5W6Y27uB7h2v33DL6pQ3CV6dCae36SjmY5T27G0Ot/+0NQozJcnlV53HCWlh9YeLvrx6/b2tzvM53bqvdfv3eK34uAi6efGqlizq2qmpJp1ubK5pxRaOR0e9RxLHVZ5Q6uNFeZ0UtUUaoDerIEIlz+ndiS0fKyDxHdNvuNBspssjYYQQ+QpMeZaKW0urSCuFBhtrW+taZc0Vs9hzxa1r5izsAIHyuqD7UFP8w1X0A61ZAUjqs+IrApI2t3h/D5gW+S4ePbuOInyof3iPM2sPmxqgl3ZYudmPnhloo9W+uW4oVTSNHsXjMehSlWggqWom13IZijaZBkpH+kz5IGyABb5ZIrYlImCUkuIf8XFF6tFGbweZN59QfG+S1wcYt59Tv/++w5XPXNH4nLnjL2JIO71/LhSPXCn+tH23ecw2fFGLyzDV18dpZawuvqgAHYIPNm86uPzbIa9sat55dv//xsFqevzo46XK2pBxktKv5r+urn19f/fzJ1c+vr/7LD4XjASJ+lbhp2ETt01S0puq00bZZUVIbS2gBQH+Nzs8VZY9EHtrQLByAlcorxceSJxq769M2bMWB4gGfxrM7Qz2CMYCx7XXhiA8sO7BUVk8y4Hcmdz0zMeDf6WYr1y64ZFVJtDnhmC7/6wvGpn71fJCRwBRlDVj8W4yMZBv0j0Q7giEmR4wx4P6YnyuiQaLoCLWRudHqUcaUWm9zf/TDDMFwCjata40oWSCkicSi/rEelZ8rckT/in7kUKbfHpm66h/GNuzm1d1TV//D6B1bIRTbgZ+NPzYuXLGwvKy1SJYqaw8V/v41Xr39Z7VxP6d8Y/DaOYd2HFkSs9z46TUjH/jM9P/2XHTTlHjcN+5c0YpDS8Lq3Xz4ZxPPBp784E/9aP/dwlfq9KzqWDObOLaqakmnW5srmnFFo5HR71HEsdVnlDq40V5nRS1RRqgN6sgQiXP6d2JLR8rIPEd02+40GymyyNhhBD5Ckx5loBZ24ptK4hfNbfl1c5TB9LPn84QfK4Iqn9gDAOFzRbDfPxIEANDDuuRI2AlvDky69aFG609h3T0iPT66B/XD94oPRfaybuEr0T1m3qi+pYvd2LmhFkr9m+uWYkXTyFEsHrMeRakWgopWYi23oVijaZBkpP+kD9IGSMCbJVJrIhJuCQnuIT9XlB4tarO9edN7at86v/74dsxyjD83KLw/i/X1zfxr6YjCwcKR2ImHG08Kv8PBGMCu5m8eFv6cP6uw6ghv2sHmTWfXrnt//fFBTC1j/PnQpLMTGUVfCazRq9pY0Zqq00bbfkUl21hCCwD6a3R+rih7JPLQhmZhAOXlPcf0+QZT2yf/e+ZcUWF+X+Be0daRkD/OAaojVfFXiPr6SrPUkwwxdyZ3jX1nk/AIdrl8+jmLv/fWvqPnAOfQu3zOhUf43/Pz7OOjD9bDPSQUPZX4txgZyTboPxrtKIaYHHHOgfljfq6IBomiI9RG5karRxlTar3N/NEPMwTDKdi0rjWiZIGQJhKL+kd7VH6uyA39K/qRQ5l+mwMMT131tT2fu2Z0w24sthP1zeLDEd2FOd0z/7e8rLyy2/+X/U9PPjQKIOZ9tHrP08LzFt2l45YlZ1l44VlmSxYJd6dq9ccGIk2zVr/7QeE7BsrlY1/BaGOrqpZ0urW5ohlXNBoZ/R5FHFt9RqmDG+11VtQSZYTaoI4MkTinfye2dKSMzHNEt+1Os5Eii4wdRuAjNOlR2mqZtbbyhtXCHxhGGvff0wQO059iyuIv89WgxgEgfK4IQj93VJGfK+pbW3nDYaFJfUol8QYVQK2K+uH14M8ddXVD+IVmBIJZULGZntwFtVDq31y3FCuaRo5i8Zj1KEq1EFS0Emu5DcUaTYMkI/0nfZA2QALeLJFaE5EwS0hwD/m5ovRo42wG+TNbY+YCXp8K/lN15l/7Xs3mChcMPtCcFKymOf5+Axe/TW7h4cI9vUH++y2eZVgtvBaadDKRUfSVwBq9qo0VrZkjbbSZKMpq/PX7f9wanZ8ryh6JPLShWTiU/vI13eIXzT25aWJ4xqZwoPjD3bXmvjEMz2Rzt7ix7ir2qicZ4u5M8l/cvvPm7eIxpsLKY+Ze+7Gl3zlnwdXv6juk9fbwvq//Z7Ua6SEQjJ5K/H1GJrIN+o9EG8EQkyPGGHB/zM8V0SBRdITayNxo9ShjSq23uT/6YYZgOAWb1rVGlCwQ0kRiUf94j0rxhCM6e2jGFD3KAInnAhmzWaOJ5EJW0dNxG6lv3BEXW6iFvrLVW/4OWFYSfiWBb91Um/7VUCHvfNOmhviNcEuWFROzLLzwLLOekmA33twT+k1vAMZg8Km68NwYW7aiWCKNrapa0unW5opmXNFoZPR7FHFs9RmlDm6011lRS5QRaoM6MkTinP6d2NKRMjLPEd22O81GiiwydhiBj9CkR2mq5YDS284tz/bd8Bd+Wn1kv/fseQVKwR8BqjEAiJwrAn95BQAoQ7mciGR28W3vKwUm/Untkf2igyC9Ku6nVg3+XST/vaJ0+jfXLcWKppGjWDxmPYpSLQQVrcRabkOxRtMgyUj/SR+kDZCAN0uk1kQk3BIS3EN+rig9WmVG/vsVtniJ8P4oH5uc+dclrwpcNrQl6IABAExu5uLfaPtfycoQr97WvBW2JDRpNZFR9JXAOpmv/YrWzJE22swV1R60AKC/RufnirJHIg9taJb5h8+54OXCb3GPTfzbo3XPptAhHiuq89C9Z89/syr+7axU6FNPMsTfmZyY+uq1L176SDVwf6pcPGxV92GtU1C16s23Dd83GWCERk8l/gIjA9kG/YfmRRHG5IhzDswf83NFNEgUHaE2MjdaPcqYUutt5o9+mCEYTsGmda0RJQuENJFY1H9cj8rPFaVzYEX/in7kUKbflsa2VFgmPN8F480R749Zy5YKKyk0t+3wq6yV933b6uJPISxdWhTX2AgS4YVnmYtPXUOZRf+oxTnAi/Vtwl/cDlhU7CWNrapa0unW5opmXNFoZPR7FHFs9RmlDm6011lRS5QRaoM6MkTinP6d2NKRMjLPEd22O81GiiwydhiBj9CkR2mphZ10Ycex4nebb6vf9pOmf2aoCvXA0gVlDgDIuSIIHT+qJSBhJ/1NeNIf/CT0xelBehWcUfjvIkGovpu4XKjbTE/ugloo9W+uW4oVTSNHsXjMehSlWggqWom13IZijaZBkpH+kz5IGyABb5ZIrYlImCUkuIf8XFF6tMqMWu+Xjiv+ifD7t7XfNZ+rzfxr33xxSeJD4rfGeRxr27j4w0NdC6AT4tXrzVtaU3y5OOn/NJ+bSmQUfSWwTuTbhorWzJE22swV1R60AKC/RufnirJHIg9tYJbeno+d3it8s3Nz8wN77pr0bEpQCfwdi0c2zDOzQOBeEVRKykmGhDuT7Kg1B7z38EoffiEA8M2P7Pnu8zwUNzR6KvEXGenLNugfxcYxm4gHxhhwf8zPFdEgUXSE2sjcaPUoY0qtt7k/+mGGYDgFm9a1RpQsENJEYlH/sT1KHEM2OAaaHmWAxHOBjNms0URyIatoWWzLKzpXC88tV7fVBmaWPzZnlvhj3c3B3Uje68MN8VeIemYVeuMIhRDiWeZ7RoUdXHdxyTwsMt2MCfeKKrOKs0ljq6qWdLq1uaIZVzQaGf0eRRxbfUapgxvtdVbUEmWE2qCODJE4p38ntnSkjMxzRLftTrORIouMHUbgIzTpURpqWXFu59uOEh01f/HP1YE6iGeGasEDQ8AAADlXVBYff6jyWjySle/rCE16/zerA8GDthP7A/9ZrqCMWFfwq+omIqd1dbZ0sRs7N9RCqX9z3VKsaBo5isVj1qMo1UJQ0Uqs5TYUazQNkoz0n/RB2gAJeLNEak1Ewi0hwT3k54rSo1Vm1Hr/2PcVxC+ae+7nzbGZq9hc8S+hUzCxK+hgmukYHxOf6O9j3RCvXm/eY98fnPS/mmPJjKKvBNaJfNtQ0Zo50kabuaLagxYA9Nfo/FxR9kjkoRVmKb/5bfNOExvQ0L6vPFD1beoQODAE0WeWZ2aBwPEjqNaVkwxxdybZ8WccePXrug8p45dN41l5Qv/3zpu1qrPFKDZ6KvEXGenLNug/NC+KMCZHnHNg/pifK6JBougItZG50epRxpRabzN/9MMMwXAKNq1rjShZIKSJxKL+8R6V4glHdPbQjCl6lAESzwUyZrNGE8mFrKIlsS2sP6UiftHcwBNV75xQYUHgK1v5/lEs76GviesqRH8KQUAivGKyPLhD+GE/KL1mXSm0ZvLO0pkXdB8nTtPF5pQpY6uqlnS6tbmiGVc0Ghn9HkUcW31GqYMb7XVW1BJlhNqgjgyROKd/J7Z0pIzMc0S37U6zkSKLjB1G4CM06VFp1bLwNZ3vO70oLEP8hX+fvG1T6MwQ3yvetqmw7m4AiJwr6mFd4sfOGh+NQbLotR3nvTE46b9Nff/JMJfxwL0i1tWDMmKzgzeoxvdFw+qN6lu62I2dG2qh1L+5bilWNI0cxeIx61GUaiGoaCXWchuKNZoGSUb6T/ogbYAEvFkitSYiYZaQ4B7yc0Xp0Sozmn6n77TSqccJf4/f2fzV7f5VpQ7BfoqHnl5oMQ383FGH/FxR3+tKp6wJTPrg7TJG0VcC63i+7alozRxpo81cUe1BCwD6a3R+rih7JPLQep4LR5++4OMrxe/Mqf7g9pFH66INnwoeGIo6YwAAhcDxo3pzSj3JgN+ZXHTUgi+eUG71xqmhfZ/8ygvvuXnkrufCP/w2/+Vzrn5X78GRHgLB6KnEX2BkINug/9C8KMKYHDHGgPtjfq6IBomiI9RG5karRxlTar3N/dEPMwTDKdi0rjWiZIGQJhKL+sd6VH6uyBH9K/qRQ5l+OzG2c47pefsK4fDQ3updD7W+4oZVAt+Ww2sxea8GnqdmZueKYPCJ2jbBaulJvX+7vnTAzIrMDjm6+9OfnvVXywsQckYaW1W1pNOtzRXNuKLRyOj3KOLY6jNKHdxor7Oiligj1AZ1ZIjEOf07saUjZWSeI7ptd5qNFFlk7DACH6FJj0qlllnHdXzwA+IvBsH4xqnr/rVZh9CZIb57RPzbA1u0ECBsA9BfCLga4uMYktnHV/76r4OTPln99o3NRoTL7qHAr3/POoAhjCpslvhFdiN8NEYt6bZ0sRs7N9RCqX9z3VKsaBo5isVj1qMo1UJQ0Uqs5TYUazQNkoz0n/RB2gAJeLNEak1Ewi0hwT3k54rSo1VmBAxgfuGMS4qz/Lf44I2Nx/d5V1Wg3BG4IvSbuT7TwL0iVq4kdW9YUPjLTwYn/W7j8VEZo+grgXUcX3WbqH2aitbMkTbazBXVHrQAoL9GezcMwve6sWJu3RVvjZGX9OZxXJcQXHhjghIUbNAb1S9tJAcfveBLazuE5tPcfO+uK7c0g0gawxPCleVCXydA4AErzgGgk/WJfyOrN4djEx59cc5h5pRMa+zovvB1Xf55p7HxL1y/++4RzneObnx879yX9V1w+gFvPti/y9W3cs7Fx09c9FAjrpOoxRaNcLrYhuZKmFdBCZxzmL4rGyo/aJ0xAs45Y4FRjicVEiwmOpHhgF8mTuy7ju0MBEh8R8k9SoIExaPVo4y7JUfc+JdC8FI0/Al4jPs2WY6kSGJbTHDU0X+0RyFPTyRVYvLsyTYyPOmQoLHNco2mQUJX0QmxnVU5980dwh+R+NZ7xx9oLZElKAdP9FTjchr87p1SKbLxbyERXzFZrm2dvHtL57nLPTbl4pq3zl5zRnNolJdnFefEHMwljq2iWtLp1uaKZlzRaGT0exRxbPUZyQMaTUAiF4uMFNXSht7SXv2nzpHzjMxzpNQIkiODxqQ1b2sjhaEyjowdRlqxjTJSV0vnEZWLLi4vEg7l1LbVvn1VfQeHwGkhxoHDi0NNgNbnODZvAcCA/zDc9Ni1lIlfqrp7qFmPZKr7qMqFH4tM+pX6DoxLbVtzFIotn/OWF4q8UQ9xWVBYKCzztaHmi+jvFSXkQt1mOsAuqIVS/+a6pVjRNHIUi8esR1GqhaCilVin1q3WGk2DJCP9ezY6eBKQzAQ3iKdtSHAP0nNFgb6tlKOQzUtL/6QVDZyd8PnSkcKPBsGW5o9u4v5VVagFH5MPPbrvxyR0/Kia0L3ZCV8IT/rj73A5o+grbUdtY0Vr5sgQrYYNlaLagxYA9Nfo/FxR9kjkoWUA8w6d/7Uzu4WfKYKxZ4Y/cedUVbABAAbNwTHx+22Kh4gPW7X8z6mIu/mx4dqoepIBuTM5/4i+1/h3ipr3/mT3z/b4/zr8wr4vXrP94gfFzlk4cW3fyqC2IRg9lfj7jExkG/QfmhdFGKMExhhwf8zPFdEgUXSE2sjcaPUoY0qtt7k/+mGGYDgFm9a1RpQsENJEYlH/kR6VnyvC3GSjf0U/cijTb8fGtnDKu3vXiY8uvzh5/T3C+lgP/gADQCVOdcEf664jN4paGIRXbJabd986PjAeNC4X+ufF3iiCpIgGxQAAIABJREFUOlRJY6uqlnS6tbmiGVc0Ghn9HkUcW31GqYMb7XVW1BJlhNqgjgyROKd/J7Z0pIzMc0S37U6zkSKLjB1G4CM06VGKaulaVfnYxZWDxG9vG6nf9qWpTXumsYXPDO3e2NwrTLL0sEIpYrN8dUFYxJovbuYhJN2HlT/6sXJ40s9Xn9qLc6lvDdz4mb2sMD/CqPuVhYOES3ZvaY7FqCXdli52Y+eGWij1b65bihVNI0exeMx6FKVaCCpaibXchmKNpkGSkf6TPkgbIAFvlkitiUi4JSS4h/xcUXq0yoxefknpjHXiu81fXV5/ZipwVejL5cJgPabBr6qDOsSq9+WfLP3lScFJL60/U1NgFH0lsEavamNFa+ZIG23mimoPWgDQX6Pz3yvKHok8tL0r5n7jrL5lwgZ8amj0EzePbUWQ8OdGAl9Ct3hu6KtsOAfo7S8tFt7aPlKvqScZOOehEVYvFw48TUz89KlGxKb5izt23jIkuOnvPH5BoIIgGD2V+LcYGck26D80L4owRgmcc2D+mP9eEQ0SRUeojcyNVo8yptR6m/mjH2YIhlOwaV1rRMkCIU0kFvUf7j/o0xMhGxwDTY8yQOK5QMZs1mgiuZBVNB5bWPXmWeetEpe/+p23jG8K3Obh1dCPdcfkPfRVdaFvVQ0iEV7xWa6+MHnFP49v3ou5AQCA6ovVjbuF/x7nexqUsVVVSzrd2lzRjCsajYx+jyKOrT6j1MGN9joraokyQm1QR4ZInNO/E1s6UkbmOaLbdqfZSJFFxg4j8BGa9CgVtXQeWrnokspBPcJlI43vf27y/sEWtvC5ovpA4wXhts38I0sry0GbUvGYo4Q1t9rctDGAoWt1+cJLKgeJ3+I60vj+ZVMbBuOzs7+xaavw7vLS8YeEGLHD14m/e9Tc+tsm4gfNCASzoGIzPbkLaqHUv7luKVY0jRzF4jHrUZRqIahoJdZyG4o1mgZJRvpP+iBtgAS8WSK1JiJhlpDgHvLfK0qPVo1R/xnld58tPhLBB6+r/+jh0FV8bEy4poN1zQo4mWHaB13Be0Xi98mJilpwRvmvzglOem39xw+rMYq+ElijV7WxojVzpI02c0W1By0A6K/R+bmi7JFIQtu3fM63z5q9Uvxb1dj4lTcO/2pfYK7W7INbqrv8twsrl1fE57cAGAN2mHhrBxrPPl9LkWSI3nUszOsTPh6MNXbVsDuT9dqDz4h/qCsu7gtUEADOKCH+HiPQi210rui8KMIYJTDGgPtjfq6IBomiI9RG5karRxlTar3N/dEPMwTDKdi0rjWiZIGQJhKL+g/3n9RPOKKzh2ZM0aMMkHgukDGbNZpILmQVjcV28ZpZF68vCWsf33rXvht+H9o6NfcEv7K1pxvLe3ehJ/iVrTG3eCK5S8zynqcnPvnFvdfeX922N4Bq/wvVu2/ee9GXJ3cK31lQHW2MkMZWVS3pdGtzRTOuaDQy+j2KOLb6jFIHN9rrrKglygi1QR0ZInFO/05s6UgZmeeIbtudZiNFFhk7jMBHaNKjpGrpfEX5I5dUlgduFNW//7mJe54VuUR+i2h/45GNwmq1sPSG0wqizcK/KB8hfJXF+MbG4/t9DN0ryxddUlkeuFFU//5lk/c+m8QFgD9xf1N4qKNwwlmleQKj4orS+sOEy0YajzyJ+UEzAsEsqNhMw3RBLZT6N9ctxYqmkaNYPGY9ilItBBWtxFpuQ7FG0yDJSP9JH6QNkIA3S6TWRCTcEhLcQ36uKD1aBZu+9aX3XloQ7/tMPFy/6au8HrlqeFfgk13/EiwmS1mf8ObEIJ8ERL1960vvvTw46a/r//pVXldkFH0lsEavamNFa+ZIG23WimoTWgDQX6Pz3ytyG0nf8jnXnH3Ayi7B39jEV64f+sFOHpqrhaT2/OTmWt98729bi1f0Hl2afNC/R8N5qevUlf5PB0Ft6ldbEhIefXHOIfSNh1N1AUtfaUkX3zjBGPDgNx5Cb1dEufHRU4k/FuF0WQ7NlTCvghI45zB9VzZUfvnvFRHkSNqjJEhQPFo9yrhbcsSNfykEL0XDn4DHuG+T5UiKJLbFBEcd/Ud6FPb0RFIlJs+ebCPDkw4JGtss12gaJHQVHYnt7MP6LnlnRfz61f0D+6/6ca0a5sKHRpv+IzPAlswFPgoQyunc4gGiq93NwC8AhpCIL0mWOd9bv/uWsbtvge6FxWWzCmVo7tzRGBwF4ADLu18hfHvent3BX2gwj62iWtLp1uaKZlzRaGT0exRxbPUZyQMaTUAiF4uMFNXSht7SXv2nzpHzjMxzpNQIkiODxqQ1b2sjhaEyjowdRlqxjTJKVkvxFeUPfrYjcKNof/M39zRgdfnUo3AuLz5ce2IbAPDf3FE/86iytzSx5e/uPGd48nu/bNY5m3VU+f1vL3b7TpuP/6Q+4eEprSx/4NLgjaJ9zd/8vMEOK59yFNr/+YsP1Z8YBADYvaE+8M7iKu/a2cdXPvA+/s/faQzXobi4+K6/CXyj3Yv31wdq2DqSnAt1m2lCLqiFUv/muqVY0TRyFIvHrEdRqoVi16HCOrVutdZoGiQZ6d+z0cGTgGQmuEE8bUOCe8h/ryg9WplN59ri+VcVFwrP2te2NG68uDmEXTU0yIVViM1dDPBEOCYdywpzhfmHB3lNdMOBMehYW3z/18KT3vTR5pA6o+grbUdtY0Vr5sgQrYYNhaLahxYA9Ndo714RY8CjY/QvRsEx8lL5k1N0DLoI8gVM5wo2LyEkKB4AgL6DZl9z9gGvDtwoGv/K9UPf+wOPzuUjmRy/e0vjT1t3g+b2XLBm74O/bD2VxZatmX2ycCN7bMv4fZMSJGFGjPPAyJ8bafhi6up655rKfb+oVafvT86MAJ2dr3mZeI+q/uxQUvRU4q8WYSkjpf4mYohRQjgy0XNFXBj9yKTHk4BELSbyyMzMFewJIShx/YEWie8osUcBNqaPTAgJFhnjbokFz3cGfrDDI5px+31bN0cyJBb1H+5RMU9PxFdi8ryoTQo8aZDgsc1wjaZBQlfRwdh2v6r30vM6lwongaovTlx1w+QgILEd3C3eKyr2z2PsWR7Ke8+iYr9wydDuBv5zRRB5BlOSZR/P+I7Gph0NwREcfHh5qeDo2YFGlTS2qmpJp1ubK5pxReOR0e5R5LHVZRQOn8pWJtTrLKklzEhZLfZ7S5v1nzpHzjMyzxFS9mkj4y1R+GLW2khh2ynjyNhhpBXbKJ4EtRRfUf7IZztW9gSn7Ckc++aOYzFOAADAN+6pPzHIgbP649U7N5bevtrr05XCiR/vPuLs5ihj8/pZ4Ltan6zd+djM/y+vLF94aWVlb9Brb+HYtyRNumlk5l4R7Kn/7M7Sire0vmiOHfTGzsvWN3fvgVn9he7gTx/99N+bdcCynJwRdRsAV9RCqX9z3VKsaCY5QgAZ9ChKtVDsOlRYy20o1mgaJFnrX4rHpG/HIOGWkOAegreCWOR20YwNeKdCY/z80ejfuKI7jy1+6MrSEvEr43Y2f3RhffNu3H74N3z0/eAdBmJLjmOl/8dbHx6no3HIceKiyYeeCCaJQedxxQ9+NTzpjz9Y37wrDaPoS4W1dvyl0U47i1W02Smq7Yy01+j8XJGrSCoHzf7aeXMDN4omqnc+MgHLZ527Ep2Lb900et9OYNC884HxC1f2zZ95v/Dqv+i/bN/QF56oVYHNWzHnC3/eJdwqatz3wL4x4XPE6kM7F5d9JB0HBn53rdLXccrhUG1NP1n71ebqPsY3PzKx/fg+7zeQ2GGv678Sdl1+3+Su1p3Jzso73zX/dOEG+tT2iemv0YuLnkr8sQiny3JoroR5FZTAOYf8XBF5JfqOknuUBAmKR6tHGXdLjrjxL4XgpWj4E/AY922yHEmRxOCh0D+H/FyRs/pX9aNY0UJsu/+k5+/P7RJ/2w/2Vm+4dt9vR/HYDj1dHzm15J1AYstWlEqP1kK/FbriFeJ32TUHtzaSsiy+9J/ZL/7pocI30NXqG7dy4tgqqiWdbm2uaMYVjUZGv0cRx1afkdKHsegYz8UiI1X92+8t7dV/6hw5z8g8R0qNIDkyaExa87Y2Uhgq48jYYaQV2yijBLW8bH05fKNI6eU/n37PN6tHfDlwt6l7YaE7ZL6v8cNv1nd4/7V0fSl8oyjla8vN1Z8f1Xnacp9oubewKOyz+fh11UdGYrKcnAt1GwBX1EKpf3PdUqxoGjmKxWPWoyjVQrHrUGGdWrdaazQNkoz079no4ElAMhPcIJ62IcE95OeK0qONt+k8tnj+1aUl4tfA7Wz+6Pzag1tjr6o/3tw+VZzl/Ql17rriyyv1zd4P/nEO0MGOWCsQneK/ezjgpuPY4vlfj0z6vtqDW1Myir7SdtQ2VrRmjgzRatiYKardaAFAf43OzxVljwTBUzlo9jfOm3tMV9BTV+W0k+edFsHgvZq/HBv7xU7OAWoDe//lmZ6Pv9x7XLpceeNZS08+rboLiovnFgO/o/bMnn8ZELp3qfM9Zy04uYy5BwCAjpfNuvws4b+H9p41UN0MrPbCnms391y+svWAdunE1y36ybrqxudr2yegr6+0YnnH4oDbxn33jT2fGD2V+E+/nTLXmAu1LgcSJYTv1ubnikiQ+I4SexRgY/rIhJBgkTHulljwfGfgBzs8ohm337d1cyRDYlH/xk84Js+L2qTAk/4pVHPVtdxkrn9FP6oV7UW1+xU9nz2/e5n4V6u9teuvGb1rR2xsa1trW2udc7y1qf/QjtWl2mN1IbOl8tpDhV/jq9Ufezohy8E3JFlmPd0wPo446jmm6+SD/P+sbp36zTBxbFXVQnUKwYGKxiOj3aPIY6vLKBw+la1MqNdZUkuYkbJa7PeWNus/dY6cZ2SeI6Ts00bGW6Lwxay1kcK2U8aRscNIK7ZRPMlq0XoJf1gcrH/zSnbxxyoHxd1z2t/4+ZVT9wwG8Bi+WL35oy9Mzb6s44Slcb6aA9+dun4Dj81yckbUbaYJuaAWSv2b65ZiRTPJEQLIoEdRqoVi16HCWm5DsUbTIMla/1I8Jn07Bgm3hAT3kJ8rSo82xqbz8OL5V5cOCd2zuaB2/+ZEn/uajz3MX7mOzVyypHDqO9jmG3krJgveUTpsge9y4uHGk2O+g47DixdcUzo4NOn7axt+l55R9KUSGfXYhmyk2NLOYhVtForKiJH2Gp2fK3IRSWHlMX3hG0VqLw9D7ZbbR07+0DzRSd/cSl/IemLi6tvHngv/wTrljByAcc6bP7tt1zEfWnD6XJ99R1/l6FWVo7GLnn1w1xc38bju0eICsvi3IGhnOTRXwrwKSuCcQ36uiLwSfUfJPUqCBMWj1aOMuyVH3Ig1GLgUDX8CHuO+TZYjKZIYPBT6n+lLoP2EY/LsyTYyPOmQoLHNco2mQUJX0RyA8e6Duz57fvfKwI2i6vXX7L1jW2JsJ6oPDDSPWuXdDZrX8fZ1E4/d22hlc+m67jXC7wbtH5j69URilsVXYpaXruu7/FS47Z/H7t4WvGpu5fwzO4QfW2o+fn91iDy2impJp1ubK5pxRaOR0e9RxLHVZ6T0YSw6xnOxyEhRLW3oLe3Vf+ocOc/IPEdKjSA5MmhMWvO2NlIYKuPI2GGkFdsoo2S1aL0Cz6dPPFb70v9pvum8yklHFYLPFvJdv63f/u3qI4PBd2OYppieAww1vvuxyYFzKm94bXFeJfCv49vqG75dveNR3khQXXIu1G2m4bigFkr9m+uWYkXTyFEsHrMeRakWil2HCuvUutVao2mQZKR/z0YHTwKSmeAG8bQNCe4hP1eUHi1mU3p18dxrgzeKRpuP/agJxxbX/ymeo6H/amzcCsDhsRubr19XbH0N3SEfLb1jZ/3f/h+vA/T+afHdHyoIf6PlT363OeW5KR5ePPe64I2i0eZjtzfh2OL6tRHiAAAw9F+NjVsAAGAeW30cE76YAuYuCYSz74jCEWP+f9a3NTc+4VBFa+bREK2GjQnaBBtLaAFAf41mT6/8O4C4nEb/YpScHq0/L4ZdBCfRtXkJIYniYYedueSm4+NP9+Cv5i9ve/7CR3kLT9+KudedNXtl3D2nianv3Tz01YF6AEmp96rLks4VhV9De8+6amQzeJKa2/WJs+e/ZXEh8Zrmkxt2feqOie2y6KnEf9qQPNdxo8xDePbQYhxbTenxSAmlV50ShtBbCowIkCTFJKE7tatHaXRLXC1Bl+p5t9+3dSMjRdJm/SNPTyTnyFqPSoMkKSYOrNG6SCgrunRI96Uf6lkt3igar294YHJgL6vgyxnf9sTkwzsAAMqH9l774U7/3kytfs+NY9c+2qgCzDm05zPndgkHlZr3fH3460+1/rOw4tWlBb5/Vlnaee6p5dbD1tUtE9feO/27fZxzxiYbj21qjDMADgcc0/vFszv6ywDjtbt+MH7bI/U9dQBghxzd9Vdndh45z89D9YWJS64Yf65BHFtNtUh163BF40ioehRVJaZnFH6pbGUUep0FRm2LrXP6T50j5xmZ54gyMuhiFuot5JGxysisR1GpBSDyJ8XA8+lQ6i+sWl1YtIR1A+zd1ty6qbF1hwIew4ruYa9YXTxoKevrgYkh/sLvGk8/y2vqqlPJgsTGMbUQ6N8cCemKppEjNDLmPYpALQQVrc460YZsjTZGomZvT/9SPCZ9OwbJDB5yJOrdOwgE968ye0obZ/SfmlHp8OL515b+ZFbEIOnFf/fJ6rdun/bATvpu+YzjmPjPE4PNUWBzlwR/3u/h+lfPaezgwACKhxfPvy79pH9Xve5HABxKJ5U+9e2i+tWj/179/Cd4HVyp6NirNJC4p6hMGWmv0fm5IieRIKFVe4kYxgaGz7lm6sLT57xlZbkjYNXcvnnsn+4YuXsnj0on9Ywc/DuTwxNf+sfBu//0gAuO7zm6P3rHqPns5v0337vnh1uaUbTR6KnEvwVBO8uhuRLmVVAC5xzyc0Xkleg7Su5REiQoHq0eZdwtOeJGrMHApWj4E/AY922yHEmRxDU7Av3P9CXQfsIxefZkGxme/FyRmh+1ii4d3BW+UQQA3aV1p/Suw+advuq3ozP3impPTfxwoOO8FZ7vcmn9eXPWnFEfgUL/vIL4+HJ1YPyHTwG0slwqnXnerDXxj1ZUlndduFx4WOPFiYs37d/KWc8ruj89faMIALrLp54z+9SzmiOjUJlV6Al5qzXu/kH4RhFNbBXVkk63Nlc044pGI6Pfo4hjq89I6cNYdIznYpGRolra0Fvaq//UOXKekXmOlBpBcmTQmLTmbW2kMFTGkbHDSCu2UUYEauEQeDIdfT6ds/pQ44mfN5+QPZ9OXNH7+MCv6wMPaTCKz4W6zTQcF9RCqX9z3VKsaBo5isVjrn8qtRBUtBLr1LrVWqNpkGSkf89GB08CkpngBvG0DQnuIT9XlB5txGbJGcWU92wEnxwA+P2XNVbfGrjx07WkEH6Gf7T5s0tnbhRxgCVnak0q64dyzG5UtGYeDdFq2JigTbCxhBYA9Nfo/PeKskcSxcOfvH3bEbfHzsIVkEyP1Z37r7ph/9VzOk5c3nHIgmIfNHfvrD+5ZfzJEcCTXB//2KefjfhOZMQAQBRZ87e/3H3+A8N9cytHL6ssnlWY1wljY80/DFWf3FL9Q13CSOSlEn+1CIuxjXGh1uUgHslMWTAujvnvFZEg8R0l9ijAxvSRCSHBImPcLbHg+c7AD3Z4RDNuv2/r5kiGxKL+g5XIAVsO/RpEKpG4RxkgwWNrK9fQBv0r+lGs6OUndIVvFKV7NX52y/41H+8VnfTMK4V/i2G89q+3TG6fnlJ/rukVE2o1APG2ULkwZ17UuPnYD0a/97SV2KqqJZ1uba5oxhWNR0a7R5HHVpdROHwqW5lQr7OkljAjZbXY7y1t1n/qHDnPyDxHSNmnjYy3ROGLWWsjhW2njCNjh5FWbKN4CNTCAAA7USQ+nz5jA94vXsT+7oUTFZ2cEXWbaTguqIVS/+a6pVjRTHKEAHJELQQVrcRabkOxRtMgyVr/UjwmfTsGCbeEBPeQ/15RerRRG72X6OfZxg0Xw4euKi2Ju/0z2rz/4tr9W321aL5ERhovZyqaoP+3x8YEbTaMtNfo/FzRSxmJOFeCTXVk6r5HpwDxmRrJDCMO0juTY8PVXwxP2WCE4DGIbWguKbZEJXDOIT9XRK5/31Fyj5IgQfFo9SjjbskRN/6lELwUDX8CHuO+TZYjKZIYPBT6V+hRyZVI3KMMkKCxzXKNpkFCW9FGL75j8os3FP8+8HVzwdd4/Y4bRn+6w3Q3Pt3/x5/e/7mvNy8+r1v8rrnwq9Z44ObRrz/UrFmKraJa0unW5opmXNFoZPR7FHFs9RkpfRiLjvFcLDJSVEsbekt79Z86R84zMs+RUiNIjgwak9a8rY0Uhso4MnYYacU2yohALRxUzhVhf46Ud5hsKjo5F+o203BcUAul/s11S7GiaeQoFo87aiGoaCXWqXWrtUbTIMlI/56NDp4EJDPBDeJpGxLcQ36uKD3aqI3eK5idyQca//gO/vpLSieuY6Gf9xve0PjpFxuPb5mJEsNoqb5k/VCO2Y2K1syjIVoNGxO0CTaW0AKA/hqd/15R9kjkeNDsaNio4ZG+Yq5F7kzGqkWDUWo8BrmOG2UewrPnv1dEgiQpJgndqV09SqNb4moJulTPu/2+rRsZKZI269+dHpUGSVJMHFijdZGQVfTKd8y5Yl1RAbj44r+9cff/fSiApLKw8ldv6Tl1VTH4s9l8aNPkTf+2/5c7Ij5Klb+7Kuk76MKvFycuvnz82RaXzuJrzux5+/Hl/pCH8cbAU5O3/3TyNzssxlZTLVLdOlzROBKqHkVViekZhV8qWxmFXmeBUdti65z+U+fIeUbmOaKMDLqYhXoLeWSsMjLrUVRqAZD8XlESRxNG0piYqE4lCxIbx9RCoH9zJKQrmkaO0Mg4oRaCilZnnWhDtkYbI1Gzt6d/KR6Tvh2DZAYPORL17h0EgvtXmT2ljTP6J2OkZB8ZS4vZK48r9C+HLoCxLfy5h5vPb5eppZ2M3KjodqN1R1HW0Wqv0d69IpRGQvMNdpq4l/TPUkl/nFKZixrPSwwJOpeKjSkSRC0xdyYtMJK9NGOriE2CJDBZ+FxRa6mm+eNsipjMuNHLtcr2uh05kvYoncho9Si6bok6CHlHw5+Ax7hvK0YGe6VEoohHrxLd6lHmSNxZo6mQUFU0RZZbr/Lc0pErSksXFnqAj+xoPD1Q3TxsCQnnwFip8IpV5WUL2YJu2LOb79ld3zTQ2FOzH1sdP2qMZBGGbCuaqkdZ0T9ZZ4iu0UkvixWtpZY29JY269/RHJExospRusigMQm9EYPKODJ2GGnFFmFkrpZY5MJ/x50rMmakGFsiRiltEMuM1GJR/1Q9KmVkNHKkjMcZtVBUtMIsSjbRHKXd/1Misa9/L9o6eKQvzN4ZJBEv6fu2Ek4FP9no32ZF06slpntbYeR2RTvE6CWNNhaPyhqdnyvKHokcj7yNqtmo4ZG+Yq5155l9C3+6kmHAV8D8XBEJkqSYJHSndvUojW6JqyXoUj3v9vu2bmSkSNqsf3d6VH6uyF5FU8XWXHXZPbNvSy35uSKyHJExCr9UpKzQ6yww+t+r/9Q5cp6ReY4oI4MuZqHeYn8doWRk1qOo1AKQnytCbRxTC4H+zZGQrmgaOUIj44RaCCpanXWiDdkabYxEzd6e/qV4TPp2DJIZPORI1Lt3EAjuX2X2lDbO6J+MkZK9YkyS1dJORm5UdLvRuqMo62i11+iC55kjY9RryCYKMKUDxA0PMoWYeMpspITcQSIPbWgWdC4VGwxP+iQD5zw6Ru9MhmxMGWFcqBgF/KPYGGYT8cA5B+aP3m8U+b9XFLBBI4PiSYUEi4lOZLjniAljCIrvOtYRUWshKWkdJFhkjCm13mb+6IcZguEUbFrXGlGyQEgTiUX9u9ajDJB4LpAxmzWaSC5kFW0eW2w0Vp0UiWqubcRWVS1UjIJjJhWNRka/RxHHVp9R6uBGe50VtUQZoTaoI0MkzunfiS0dKSPzHNFtu9NspMgiY4cR+AhNehSBWjgASH+vCLWRd5hsKhrNSAitis20exfUQql/c91SrGgaOYrF445aCCpaibXchmKNpkGSkf6TPkgbIAFvlkitiUiYJSS4B+nvFYVsYvz80eiftKLNcyRRS0z3tsLIsYrWZKSNNnNFtQctAOiv0d69IsaQMVrSIZvIK60DxA3zRi78//Q2UkLuIJGHNjQLOpeKDYYnfZKBMRYdo4IL2ZgywrhQMQr4R7FxzCbigTEG3B+nzxIFzhWJNmhkUDypkGAx0YkM8xxxYQxB8V3HOiJqLSQlrYMEi4wxpdbb3B/9MEMwnIJN61ojShYIaSKxqH/XepQBEs8FMmazRhPJhayizWOLjcaqkyJRzbWN2KqqhYpRcMykotHI6Pco4tjqM0od3Givs6KWKCPUBnVkiMQ5/TuxpSNlZJ4jum13mo0UWWTsMAIfoUmPIlALAwAGDIALIzBgAhPcRt5hsqloNCMhtCo20+5dUAul/s11S7GiaeQoFo87aiGoaCXWchuKNZoGSUb6T/ogbYAEvFkitSYi4ZaQ4B7i+zbaveP8/NHon7SizXMkUUtM97bCyLGK1mSkjTZzRbUHLQDor9H5uaLskchDG5oFnUvFBsOTPslA8PSQBiOMCxWjgH8UG8NsIh4458D8MT9XRINE0RFqI3Oj1aOMKbXeZv7ohxmC4RRsWtcaUbJASBOJRf271qMMkHgukDGbNZpILmQVbR5bbDRWnRSJaq5txFZVLVSMgmMmFY1GRr9HEcdWn1Hq4EZ7nRW1RBmhNqgjQyTO6d+JLR0pI/Mc0W2702ykyCJjhxH4CE16FIFaOADk54riMuWGWij1b65bihVNI0exeNxRC0FFK7GW21Cs0TR03CG3AAAgAElEQVRIMtJ/0gdpAyTgzRKpNREJs4QE95CfK0qPVpmReY4kaonp3lYYOVbRmoy00WauqPagBQD9NTo/V5Q9EnloQ7Ogc6nYYHjSJxkInh7SYIRxoWIU8I9i45hNxANjDLg/5ueKaJAoOkJtZG60epQxpdbb3B/9MEMwnIJN61ojShYIaSKxqH/XepQBEs8FMmazRhPJhayizWOLjcaqkyJRzbWN2KqqhYpRcMykotHI6Pco4tjqM0od3Givs6KWKCPUBnVkiMQ5/TuxpSNlZJ4jum13mo0UWWTsMAIfoUmPIlALA4D8XFFcptxQC6X+zXVLsaJp5CgWjztqIahoJdZyG4o1mgZJRvpP+iBtgAS8WSK1JiLhlpDgHvJzRenRKjMyz5FELTHd2wojxypak5E22swV1R60AKC/RufnirJHIg9taBZ0LhUbDE/6JAPB00MajDAuVIwC/lFsDLOJeOCcA/PH/FwRDRJFR6iNzI1WjzKm1Hqb+aMfZgiGU7BpXWtEyQIhTSQW9e9ajzJA4rlAxmzWaCK5kFW0eWyx0Vh1UiSqubYRW1W1UDEKjplUNBoZ/R5FHFt9RqmDG+11VtQSZYTaoI4MkTinfye2dKSMzHNEt+1Os5Eii4wdRuAjNOlRBGrhAJCfK4rLlBtqodS/uW4pVjSNHMXicUctBBWtxFpuQ7FG0yDJSP9JH6QNkIA3S6TWRCTMEhLcQ36uKD1aZUbmOZKoJaZ7W2HkWEVrMtJGm7mi2oMWAPTX6PxcUfZI5KENzYLOpWKD4UmfZCB4ekiDEcaFilHAP4qNYzYRD4wx4P6YnyuiQaLoCLWRudHqUcaUWm9zf/TDDMFwCjata40oWSCkicSi/l3rUQZIPBfImM0aTSQXsoo2jy02GqtOikQ11zZiq6oWKkbBMZOKRiOj36OIY6vPKHVwo73OilqijFAb1JEhEuf078SWjpSReY7ott1pNlJkkbHDCHyEJj2KQC0MAPJzRXGZckMtlPo31y3FiqaRo1g87qiFoKKVWMttKNZoGiQZ6T/pg7QBEvBmidSaiIRbQoJ7yM8VpUerzMg8RxK1xHRvK4wcq2hNRtpoM1dUe9ACgP4anZ8ryh6JPLShWdC5VGwwPOmTDARPD2kwwrhQMQr4R7ExzCbigXMOzB/zc0U0SBQdoTYyN1o9yphS623mj36YIRhOwaZ1rRElC4Q0kVjUv2s9ygCJ5wIZs1mjieRCVtHmscVGY9VJkajm2kZsVdVCxSg4ZlLRaGT0exRxbPUZpQ5utNdZUUuUEWqDOjJE4pz+ndjSkTIyzxHdtjvNRoosMnYYgY/QpEcRqIUDQH6uKC5TbqiFUv/muqVY0TRyFIvHHbUQVLQSa7kNxRpNgyQj/Sd9kDZAAt4skVoTkTBLSHAP+bmi9GiVGZnnSKKWmO5thZFjFa3JSBtt5opqD1oA0F+j83NF2SORhzY0CzqXig2GJ32SgeDpIQ1GGBcqRgH/KDaO2UQ8MMaA+2N+rogGiaIj1EbmRqtHGVNqvc390Q8zBMMp2LSuNaJkgZAmEov6d61HGSDxXCBjNms0kVzIKto8tthorDopEtVc24itqlqoGAXHTCoajYx+jyKOrT6j1MGN9joraokyQm1QR4ZInNO/E1s6UkbmOaLbdqfZSJFFxg4j8BGa9CgCtTAAyM8VxWXKDbVQ6t9ctxQrmkaOYvG4oxaCilZiLbehWKNpkGSk/6QP0gZIwJslUmsiEm4JCe4hP1eUHq0yI/McSdQS072tMHKsojUZaaPNXFHtQQsA+mt0fq4oeyTy0IZmQedSscHwpE8yEDw9pMEI40LFKOAfxcYwm4gHzjkwf8zPFdEgUXSE2sjcaPUoY0qtt5k/+mGGYDgFm9a1RpQsENJEYlH/rvUoAySeC2TMZo0mkgtZRZvHFhuNVSdFopprG7FVVQsVo+CYSUWjkdHvUcSx1WeUOrjRXmdFLVFGqA3qyBCJc/p3YktHysg8R3Tb7jQbKbLI2GEEPkKTHkWgFg4A+bmiuEy5oRZK/ZvrlmJF08hRLB531EJQ0Uqs5TYUazQNkoz0n/RB2gAJeLNEak1EwiwhwT3k54rSo1VmZJ4jiVpiurcVRo5VtCYjbbSZK6o9aAFAf43OzxVlj0Qe2tAs6FwqNhie9EkGgqeHNBhhXKgYBfyj2DhmE/HAGAPuj/m5Ihokio5QG5kbrR5lTKn1NvdHP8wQDKdg07rWiJIFQppILOrftR5lgMRzgYzZrNFEciGraPPYYqOx6qRIVHNtI7aqaqFiFBwzqWg0Mvo9iji2+oxSBzfa66yoJcoItUEdGSJxTv9ObOlIGZnniG7bnWYjRRYZO4zAR2jSowjUwgAgP1cUlyk31EKpf3PdUqxoGjmKxeOOWggqWom13IZijaZBkpH+kz5IGyABb5ZIrYlIuCUkuIf8XFF6tMqMzHMkUUtM97bCyLGK1mSkjTZzRbUHLQDor9H5uaLskchDG5oFnUvFBsOTPslA8PSQBiOMCxWjgH8UG8NsIh4458D8MT9XRINE0RFqI3Oj1aOMKbXeZv7ohxmC4RRsWtcaUbJASBOJRf271qMMkHgukDGbNZpILmQVbR5bbDRWnRSJaq5txFZVLVSMgmMmFY1GRr9HEcdWn1Hq4EZ7nRW1RBmhNqgjQyTO6d+JLR0pI/Mc0W2702ykyCJjhxH4CE16FIFaOADk54riMuWGWij1b65bihVNI0exeNxRC0FFK7GW21Cs0TRIMtJ/0gdpAyTgzRKpNREJs4QE95CfK0qPVpmReY4kaonp3lYYOVbRmoy00WauqPagBQD9NTo/V5Q9EnloQ7Ogc6nYYHjSJxkInh7SYIRxoWIU8I9i45hNxANjDLg/5ueKaJAoOkJtZG60epQxpdbb3B/9MEMwnIJN61ojShYIaSKxqH/XepQBEs8FMmazRhPJhayizWOLjcaqkyJRzbWN2KqqhYpRcMykotHI6Pco4tjqM0od3Givs6KWKCPUBnVkiMQ5/TuxpSNlZJ4jum13mo0UWWTsMAIfoUmPIlALA4D8XFFcptxQC6X+zXVLsaJp5CgWjztqIahoJdZyG4o1mgZJRvpP+iBtgAS8WSK1JiLhlpDgHvJzRenRKjMyz5FELTHd2wojxypak5E22swV1R60AKC/RufnirJHIg9taBZ0LhUbDE/6JAPB00MajDAuVIwC/lFsDLOJeOCcA/PH/FwRDRJFR6iNzI1WjzKm1Hqb+aMfZgiGU7BpXWtEyQIhTSQW9e9ajzJA4rlAxmzWaCK5kFW0eWyx0Vh1UiSqubYRW1W1UDEKjplUNBoZ/R5FHFt9RqmDG+11VtQSZYTaoI4MkTinfye2dKSMzHNEt+1Os5Eii4wdRuAjNOlRBGrhAJCfK4rLlBtqodS/uW4pVjSNHMXicUctBBWtxFpuQ7FG0yDJSP9JH6QNkIA3S6TWRCTMEhLcQ36uKD1aZUbmOZKoJaZ7W2HkWEVrMtJGm7mi2oMWAPTX6PxcUfZI5KENzYLOpWKD4UmfZCB4ekiDEcaFilHAP4qNYzYRD4wx4P6YnyuiQaLoCLWRudHqUcaUWm9zf/TDDMFwCjata40oWSCkicSi/l3rUQZIPBfImM0aTSQXsoo2jy02GqtOikQ11zZiq6oWKkbBMZOKRiOj36OIY6vPKHVwo73OilqijFAb1JEhEuf078SWjpSReY7ott1pNlJkkbHDCHyEJj2KQC0MAPJzRXGZckMtlPo31y3FiqaRo1g87qiFoKKVWMttKNZoGiQZ6T/pg7QBEvBmidSaiIRbQoJ7yM8VpUerzMg8RxK1xHRvK4wcq2hNRtpoM1dUe9ACgP4anZ8ryh6JPLShWdC5VGwwPOmTDARPD2kwwrhQMQr4R7ExzCbigXMOzB/zc0U0SBQdoTYyN1o9yphS623mj36YIRhOwaZ1rRElC4Q0kVjUv2s9ygCJ5wIZs1mjieRCVtHmscVGY9VJkajm2kZsVdVCxSg4ZlLRaGT0exRxbPUZpQ5utNdZUUuUEWqDOjJE4pz+ndjSkTIyzxHdtjvNRoosMnYYgY/QpEcRqIUDQH6uKC5TbqiFUv/muqVY0TRyFIvHHbUQVLQSa7kNxRpNgyQj/Sd9kDZAAt4skVoTkTBLSHAP+bmi9GiVGZnnSKKWmO5thZFjFa3JSBtt5opqD1oA0F+j83NF2SORhzY0CzqXig2GJ32SgeDpIQ1GGBcqRgH/KDaO2UQ8MMaA///svX2QntdVJ3iurbXakIw6IUE9YRI3H4PbQKwGY9wOMGlqyEaprRoLqGBR+8doa6mx2JBxp6AmSlW2EEWmorCwUab46GzthzJTQySS3bRnlpn2kGJkPlvEmFYW4zaQuBUnpBWT0ErspBsS3/3jfZ/Pe+5z7z3n3OdeSff+cUt6dN5zf79zfufc5+n3uepmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4gLXSCwAACAASURBVJvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi50jutjvkRkosMnEYQYOQ06ME1KIBoJwrsmUqD7VI6p+vW4kdjZAjK5581CJQ0V6s3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DmSu+0OuZESi0wcRtAg5PQoAbUoACjnimyZykMtkvrn61ZiRyPkyIonH7UIVLQXa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2hdkYHrTWoJq5nCuSQeLpCLVxuSH1KDal+rJq5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36O5G67Q26kxCIThxE0CDk9SkAtGgDKuSJbpvJQi6T++bqV2NEIObLiyUctAhXtxdptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWM2hgelFOhmLueKZJB4OkJtXG5IPYpNqb6sm7kJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifI7nb7pAbKbHIxGEEDUJOjxJQiwKAcq7Ilqk81CKpf75uJXY0Qo6sePJRi0BFe7F220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+j1V/e+XYAwNKJffc7nB0TONYT0C6BfdhjLWk81xkSW404bbhIELVggovDyDWIsfXE5kDSWayJRv11UX3PEISHggRzQ8t136i3PIYwEhJHj6JEhtSj5Lol6qDnHQ3/AB523/aMDDYCkXjioVViXj2KjySfPVoKiVRFS2QZCQ1XdU4kYnVEiS3FD4ORcGzF6ojeo6LkSKwzmHv00IhY0UX/Oecohx710U/v/N5nP/fE33zhU8+/8Nkv7+197Wse0SiDO2ZuveVVX3/7q19y+z3f+LIfnHvFD7/mcPffrSpt/b31vya0nnGwkUNF+9Sdf22O0LednQGNSe+CJT4UPM7B39EIOfLGk41aSHcd4at42RD26IhI4uu/ijYFj3Ng9tkgMbyE920vnB5+0ug/ZkXLq8XSvaMwyruiM2J0XaO14vHZo6vvivCcmjuGAwrpx4s9FwPNIsDmOkLixuNuo342fnicw/JZ85tJu1oIjILxiP7oyoUB3wF7m7G1msLxOAnFUl33kgcjASRDMRnoTmP1KEK3xNXSdemf9/h9mxoZJ5KR9Z9PjwpBMhSTDPZoKpJoFS0VW77qnEg88USILVEtUoxSVDQxtrFyJMaoP3yk7NHrIjC6efUfnKPsGfFzBPDJLz7/0O9euvS5v/GIWBlxx+vmvuFXX//d33boJe2LCsD2hVC3bYd2mGQV7YXWwyZC3x5Y1xkZ9Gaul6ToPUp0RyPkCI1MFmoRu+vwYT1oI7ZHs5H42cfTvxMPZ4+2IJniEUdiqUeke3eB4P59Vg+0yUb/Yoy87D1jMqyWMRnlUdFjo81HUdHRkvfocq7oekaCruVjw0WCqKWcK5pGprVYOVckjMTRoyiRIfUouW6JOuh5R8M/gIfdtz0jg41AJJ54aJWYV4/iI8lnj5ZCIlXREllGQsNVXQ7v7Mv6KeeKYueonCuKF9uk+s80Rwl71G9+YvsnH7s0+fOb3/zme+6555577rn33nsPHTrkEY0yuOPatWsf+9jHHn/88SeeeOJDH/oQANx+4Nb/bfl7jn3LNwHAgEpbfy/niqL2bWdnQGPSu2CJDwWPc/B3NEKOvPFkoxbSXUf4Kl42hD06IpL4+q+iTcHjHJh9NkgML+VckRwjebVYuncURnlXdEaMrmu0VjzlXNH1gcSNx91G/Wz88DiH5bP5vLMf4UdXLgz4DljOFYkgGYrJQHcaq0cRuiWulq5L/7zH79vUyDiRjKz/fHpUOVcUr6LLuaJ4ainnisRyJMaoP3yk7NHrIjC6efUfnKPsGfGQvGfzyX/9xJ8BwGtf+9oLFy7cddddHkErI9b4+Mc//uCDD25tbQHA6Xu/42e++9sn1xVAOVeE2kTo2wPrOiOD3sz1khS9R4nuaIQcoZHJQi1idx0+rAdtxPZoNhI/+3j6d+Lh7NEWJFM84kgs9Yh07y4Q3L/P6oE22ehfjJGXvWdMhtUyJqM8KnpstPkoKjpa8h59S+VZI7PptWdjAgx0gLjRXaZgiafLxkkoHyTu0PZWQdfyscHwhCcZtNbmbH4z2bPhMsK4SDHq+EexKczG8KC1BtXMCtR0humfOzZoZFA8QUiwmFAioytHqjX3oDSurY6EWotISVOQYJFhU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTJiIPm/tj7xr5/4M6XUww8//Pjjj5cvipKPu++++/LlyysrK0qp0x/783/39JWJDjVoANWaofsDR9zG3WHSVDRafT20PjYT9+J921zX97Ez5EHCHmF2j5LY0Qg5suLJRy3BSNx4UNZuG4k9WgZJIv0PPUgzkEC1ilFrna8EIiHBPdj7Ntq9bX5uGP2LVjQ/Rw61WLp3FEaZVTSRERltckWNgxYA6Hv0rf/yFT8AAKAUMpsl3bMxRqgDxI2qZt36c7iNk1A+SNyh7a2CruVjg+EJTzIopczZFFzPhssI4yLFqOMfxaYxG8ODUgp0M0/OEnXOFbVt0MigeIKQYDGhREZVjnRr7kFpXFsdCbUWkZKmIMEiw6ZUX9bN3IQZuuFs2dSfZVGKQIiIJKL+c+tRDCSVC2ROs0cLyUWsovmxxWa26pxIfHMdI7a+apFi1J2TVDQaGXqPEo4tnVFwcM1eF0UtJiPUBnXERJKd/rO4pRNlREVy+fN/++Mf/T0AOHXq1C/90i/deuutloiVMeq49dZbjx49+sILL/zhH/7hb13Z+Wfzr/rGr5tRoAB0awYFqqXa6ZWejbvDpKlotPp6aH1sJu7F+7a5ru9jZ8iDhD3C7B4lsaMRcmTFk49agpG48aCs3TYSe7QMkkT6H3qQZiCBahWj1tpIdCQkuAd730a7t83PDaN/0Yrm58ihFkv3jsIos4omMiKjTa6ocdACAH2PLr+v6HpGYqsRpw0XCaIWTHBxGLkGMbae2BxIOos10Si/r0gEiaNHUSJD6lFy3RJ10POOhn8AD7tve0YGG4FIPPHQKjGvHsVHks8eLYVEqqIlsoyEhqs6JxKxOqLEluKHwUg4tmJ1RO9RUXIk1hnMPXpoRKzoov+cczR+j/rRRx/76Gd2FhYWLl++fNttt3kQL2O88Xd/93eLi4tPPfXUm15z+DeP3o+qtPX38vuKovZtZ2dAY9K7YIkPBY9z8Hc0Qo688WSjFtJdR/gqXjaEPToikvj6r6JNweMcmH02SAwv5fcVyTGSV4ule0dhlHdFZ8ToukZrxeOzR1f/B93kb70Z3THaszFCHSBuVDXr1p/DbZyE8kHiDm1vFXQtHxsMT3iSQeDtIQIjjIsUo45/FJvGbAwPSinQzVzOFckg8XSE2rjckHoUm1J9WTdzE2bohrNlU3+WRSkCISKSiPrPrUcxkFQukDnNHi0kF7GK5scWm9mqK+eKWo66c5KKRiND71HCsaUzCg6u2euiqMVkhNqgjphIstN/Frd0ooxISJ59/oWPfmbnwIEDFy5cKF8UZThuu+22D37wgwcOHPjPn7p65UtfLueKbFUZoW+b6/o+doY8SNgjzO5REjsaIUdWPPmoJRiJGw/K2m0jsUfLIEmk/6EHaQYSqFYxaq2NREdCgnso54rC0Xoz4ufIoRZL947CKLOKJjIio02uqHHQAgB9jy6/ryg9Endoe6uga/nYYHjCkwxaa3M2Bdez4TLCuEgx6vhHsSnMxvCgtQbVzOX3Fckg8XSE2rjckHoUm1J9WTVzE2bohrNlU3+WRSkCISKSiPrPrUcxkFQukDnNHi0kF7GK5scWm9mqcyLxzXWM2PqqRYpRd05S0Whk6D1KOLZ0RsHBNXtdFLWYjFAb1BETSXb6z+KWTpQRCcmHP/kpAHjDG95w9913WwJVRuJx5MiR5eVlAPgPz/x1+X1FtqqM0LfNdX0fO0MeJOwRZvcoiR2NkCMrnnzUEozEjQdl7baR2KNlkCTS/9CDNAMJVKsYtdZGoiIhwT2U31cUjtabET9HDrVYuncURplVNJERGW1yRY2DFgDoe3Q5V5QeiTu0vVXQtXxsMDzhSQaBt4cIjDAuUow6/lFsGrMxPCilQDdzOVckg8TTEWrjckPqUWxK9WXdzE2YoRvOlk39WRalCISISCLqP7cexUBSuUDmNHu0kFzEKpofW2xmq86JxDfXMWLrqxYpRt05SUWjkaH3KOHY0hkFB9fsdVHUYjJCbVBHTCTZ6T+LWzpRRiQkj2x/GgBe97rXWaJURhZjaWkJAB555q/LuSJbVUbo2+a6vo+dIQ8S9gize5TEjkbIkRVPPmoJRuLGg7J220js0TJIEul/6EGagQSqVYxaayPRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYlOYjeFBaw2qmcu5Ihkkno5QG5cbUo9iU6ovq2ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTIiIXnyb3cB4P7777dEqYwsxiRBT37hi+Vcka0qI/Rtc13fx86QBwl7hNk9SmJHI+TIiicftQQjceNBWbttJPZoGSSJ9D/0IM1AAtUqRq21kahISHAP5VxROFpvRvwcOdRi6d5RGGVW0URGZLTJFTUOWgCg79HlXFF6JO7Q9lZB1/KxwfCEJxkE3h4iMMK4SDHq+EexaczG8KCUAt3M5VyRDBJPR6iNyw2pR7Ep1Zd1Mzdhhm44Wzb1Z1mUIhAiIomo/9x6FANJ5QKZ0+zRQnIRq2h+bLGZrTonEt9cx4itr1qkGHXnJBWNRobeo4RjS2cUHFyz10VRi8kItUEdMZFkp/8sbulEGZGQ7H/tRSjnirIfk3NFz//9V8u5IltVRujb5rq+j50hDxL2CLN7lMSORsiRFU8+aglG4saDsnbbSOzRMkgS6X/oQZqBBKpVjFprI9GRkOAeyrmicLTejPg5cqjF0r2jMMqsoomMyGiTK2octABA36PLuaL0SNyh7a2CruVjg+EJTzIIvD1EYIRxkWLU8Y9iU5iN4UFrDaqZy7kiGSSejlAblxtSj2JTqi+rZm7CDN1wtmzqz7IoRSBERBJR/7n1KAaSygUyp9mjheQiVtH82GIzW3VOJL65jhFbX7VIMerOSSoajQy9RwnHls4oOLhmr4uiFpMRaoM6YiLJTv9Z3NKJMmIguf322y1RKiOL8bKXvWzyh3KuyFaVEfq2ua7vY2fIg4Q9wuweJbGjEXJkxZOPWoKRuPGgrN02Enu0DJJE+h96kGYggWoVo9baSFQkJLiHcq4oHK03I36OHGqxdO8ojDKraCIjMtrkihoHLQDQ9+hyrig9Endoe6uga/nYYHjCkwwCbw8RGGFcpBh1/KPYNGZjeFBKgW7mcq5IBomnI9TG5YbUo9iU6su6mZswQzecLZv6syxKEQgRkUTUf249ioGkcoHMafZoIbmIVTQ/ttjMVp0TiW+uY8TWVy1SjLpzkopGI0PvUcKxpTMKDq7Z66KoxWSE2qCOmEiy038Wt3SijPg5KiP7Uc4V2aoyQt821/V97Ax5kBioR26PktjRCDmy4slHLcFI3HhQ1m4biT1aBkki/Q89SDOQQLWKUWttJDoSEtxDOVcUjtabET9HDrVYuncURplVNJERGW1yRY2DFgDoe3Q5V5QeiTu0vVXQtXxsMDzhSQaBt4cIjDAuUow6/lFsCrMxPGitQTVzOVckg8TTEWrjckPqUWxK9WXVzE2YoRvOlk39WRalCISISCLqP7cexUBSuUDmNHu0kFzEKpofW2xmq86JxDfXMWLrqxYpRt05SUWjkaH3KOHY0hkFB9fsdVHUYjJCbVBHTCTZ6T+LWzpRRvwclZH9KOeKbFUZoW+b6/o+doY8SAzUI7dHSexohBxZ8eSjlmAkbjwoa7eNxB4tgySR/ocepBlIoFrFqLU2EhUJCe6hnCsKR+vNiJ8jh1os3TsKo8wqmsiIjDa5osZBCwD0PbqcK0qPxB3a3iroWj42GJ7wJIPA20MERhgXKUYd/yg2jdkYHpRSoJu5nCuSQeLpCLVxuSH1KDal+rJu5ibM0A1ny6b+LItSBEJEJBH1n1uPYiCpXCBzmj1aSC5iFc2PLTazVedE4pvrGLH1VYsUo+6cpKLRyNB7lHBs6YyCg2v2uihqMRmhNqgjJpLs9J/FLZ0oI36Oysh+lHNFtqqM0LfNdX0fO0MeJAbqkdujJHY0Qo6sePJRSzASNx6UtdtGYo+WQZJI/0MP0gwkUK1i1FobiY6EBPdQzhWFo/VmxM+RQy2W7h2FUWYVTWRERptcUeOgBQD6Hl3OFaVH4g5tbxV0LR8bDE94kkHg7SECI4yLFKOOfxSbwmwMD1prUM1czhXJIPF0hNq43JB6FJtSfVk1cxNm6IazZVN/lkUpAiEikoj6z61HMZBULpA5zR4tJBexiubHFpvZqnMi8c11jNj6qkWKUXdOUtFoZOg9Sji2dEbBwTV7XRS1mIxQG9QRE0l2+s/ilk6UET9HZWQ/yrkiW1VG6Nvmur6PnSEPEgP1yO1REjsaIUdWPPmoJRiJGw/K2m0jsUfLIEmk/6EHaQYSqFYxaq2NREVCgnso54rC0Xoz4ufIoRZL947CKLOKJjIio02uqHHQAgB9jy7nitIjcYe2twq6lo8Nhic8ySDw9hCBEcZFilHHP4pNYzaGB6UU6GYu54pkkHg6Qm1cbkg9ik2pvqybuQkzdMPZsqk/y6IUgRARSUT959ajGEgqF8icZo8WkotYRfNji81s1TmR+OY6Rmx91SLFqDsnqWg0MvQeJRxbOqPg4Jq9LopaTEaoDeqIiSQ7/WdxSyfKiJ+jMrIf5VyRrSoj9G1zXd/HzpAHiYF65PYoiR2NkCMrnnzUEozEjQdl7baR2KNlkCTS/9CDNAMJVKsYtdZGoiMhwT2Uc0XhaL0Z8XPkUIule0dhlFlFExmR0SZX1DhoAYC+R5dzRemRuEPbWwVdy8cGwxOeZBB4e4jACOMixajjH8WmMBvDg9YaVDOXc0UySDwdoTYuN6QexaZUX1bN3IQZuuFs2dSfZVGKQIiIJKL+c+tRDCSVC2ROs0cLyUWsovmxxWa26pxIfHMdI7a+apFi1J2TVDQaGXqPEo4tnVFwcM1eF0UtJiPUBnXERJKd/rO4pRNlxM9RGdmPcq7IVpUR+ra5ru9jZ8iDxEA9cnuUxI5GyJEVTz5qCUbixoOydttI7NEySBLpf+hBmoEEqlWMWmsjUZGQ4B7KuaJwtN6M+DlyqMXSvaMwyqyiiYzIaJMrahy0AEDfo8u5ovRI3KHtrYKu5WOD4QlPMgi8PURghHGRYtTxj2LTmI3hQSkFupnLuSIZJJ6OUBuXG1KPYlOqL+tmbsIM3XC2bOrPsihFIEREElH/ufUoBpLKBTKn2aOF5CJW0fzYYjNbdU4kvrmOEVtftUgx6s5JKhqNDL1HCceWzig4uGavi6IWkxFqgzpiIslO/1nc0oky4ueojOxHOVdkq8oIfdtc1/exM+RBYqAeuT1KYkcj5MiKJx+1BCNx40FZu20k9mgZJIn0P/QgzUAC1SpGrbWR6EhIcA/lXFE4Wm9G/Bw51GLp3lEYZVbRREZktMkVNQ5aAKDv0eVcUXok7tD2VkHX8rHB8IQnGQTeHiIwwrhIMer4R7EpzMbwoLUG1czlXJEMEk9HqI3LDalHsSnVl1UzN2GGbjhbNvVnWZQiECIiiaj/3HoUA0nlApnT7NFCchGraH5ssZmtOicS31zHiK2vWqQYdeckFY1Ght6jhGNLZxQcXLPXRVGLyQi1QR0xkWSn/yxu6UQZ8XNURvajnCuyVWWEvm2u6/vYGfIgMVCP3B4lsaMRcmTFk49agpG48aCs3TYSe7QMkkT6H3qQZiCBahWj1tpIVCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9g0ZmN4UEqBbuZyrkgGiacj1MblhtSj2JTqy7qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+DkqI/tRzhXZqjJC3zbX9X3sDHmQGKhHbo+S2NEIObLiyUctwUjceFDWbhuJPVoGSSL9Dz1IM5BAtYpRa20kOhIS3EM5VxSO1psRP0cOtVi6dxRGmVU0kREZbXJFjYMWAOh79IGpg/533Vgx19+K17MxnF8e27pEy0U1DyjBwwb9ovpGQ9Jey8cGwxOGZOpBw/SUTDNjgoP2LMYIwcNl1KzlxDaoBK01TL6V7UUD6jNG4IgMiicICRYTSmQ04B9rL9y4tnYGASSNo+Ee5UCC4iH1KHa31Iib5qPQ/Sga/gE87L4tliMnEgseCf3n1qMYSNDYptyjZZDIVTQ/ttjMVp0TiW+uo8TWUy1SjIRjK1ZH9B4lHFs6I3dAzQQMconI6GbVf3COsmfEz1FuY/vc8eOr24QPLp5aXz02W/91b3d3d29v+CMzM7OzszOEtcYdznNFzf+a0Mzg7jBpKnq47vxtJnBG6NvOzoDGpI5A/SCBxYeCZwAJEhNSZAg5suLJRy0Sdx0+rIN1S9qjZZAk0n9lQ8EzgGQa3C6e0ZDgHpznitx9+4bSv2hF83PkUIule0dhlFlFExkx0RJsMo8/YkPeo6vvipQCbc7mT4y6szF8fuRkzl0XXb5QzYE21xESFM/QKuhaPjakyJhDKaW1OZvfTHZmLiOMixQjr/7WxmDJUT8m5rki3ZrRyHjiGUDiFxNv1XV7Qg+KrT/IImkcDfYowObwyPSQYJFhd0sseI0zaILdn9GMx+/b1By5kETUf249ioEEj22sXMMI+vf041vR/Niy+z8JiXeuI8TWVy1SjDKoaFpso+VIjFE/fD5S7vW6SGrpM7p59R+co+wZ8XOU29jb2dq8dHk/+HMHZ3fbf905d2z+px5zuDl45N2bm6cWgtcaeWBfBanWjx0VTOf2uSKfDpOkooerz99mAke8bw+sa+sMqooMejMH0yhNbNz1yO1REjsaJ0cIoEzUInHX4cPabSOxR8sgSa1/Jx7OHm1BoiMhwT3Y+zbevS1+bhj9i1Y0P0cOtVjUG4VRZhU9NtrkihqPEXmPLr+vKD0Sd2h7q6Br+dhgeMKTDFprczYF17PhMsK4SDHq+EexKczG8KC1BtXM5fcVySDxdITauNyQehSbUn1ZNXMTZuiGs2VTf5ZFKQIhIpKI+s+tRzGQVC6QOc0eLSQXsYrmxxab2apzIvHNdYzY+qpFilF3TlLRaGToPUo4tnRGwcE1e10UtZiMUBvUERNJyXo0KQAAIABJREFUdvrP4pZOlBE/R9mNmdmZg9ioDdB/PTjTPR+0u7sb/n1TpqP8viJbVUbo2+a6vo+dIQ8SA/XI7VESOxohR1Y8+aglGIkbD8rabSOxR8sgSaT/oQdpBhKoVjFqrY1ERUKCeyi/rygcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0SFM/QKuhaPjakyJhD4O0hAiOMixQjHM8AQkuO+jEp54pEkDSOBnsUYHN4ZHpIsMiwuyUWvMYZNMHuz2jG4/dtao5cSCLqP7ceVc4VhfvxrehyrihWjuQYZVDRtNhGy5EYo374fKTc63WR1NJndPPqPzhH2TPi5yi3sbBycXfFvLx3/tjsTzyyDwCHT6zvrC673OzuTI8ZHT7yxqV52/8zN78wa/mXnEY5V2Srygh9e2BdW2dQVWTQmzmYRmli465Hbo+S2NE4OUIAZaIWibsOH9ZuG4k9WgZJav078XD2aAsSHQkJ7qGcKwpH682InyOHWizqjcIos4oeG21yRY3HiLxHl99XdD0jaa/lY4PhCUMy9aAhr98FwmXUrOXENqgErTVMvpXtRaP8viKBHDl7lAMJiofUo9jdUiNumo9C96No+AfwsPu2WI6cSCx4JPSfW49iIEFjm3KPlkEiV9H82GIzW3VOJL65jhJbT7VIMRKOrVgd0XuUcGzpjNwBNRMwyCUio5tV/8E5yp4RP0c36NjdnXxXdHDp1Nra8fx/J9HQKL+vyFaVo/RtZ2dAY1JHoH6QwOJDwTOABIkJKTKEHFnx5KMWibsOH9bBuiXt0TJIEum/sqHgGUAyDW4Xz2hIcA/l9xWFo/VmxM+RQy2W7h2FUWYVTWTEREuwyTz+iA15j67+D7rJ33qzWdI9G2OEOkDcqGrWrT+H2zgJ5YPEHdreKuhaPjYYnvAkg1LKnE3B9Wy4jDAuUow6/lFsGrMxPCilQDczcq6obYNGBsUThASLCSUyqnKkW3MPSuPa6kiotYiUNAUJFhk2pfqybuYmzNANZ8um/iyLUgRCRCQR9Z9bj2IgqVwgc5o9WkguYhXNjy02s1XnROKb6xix9VWLFKPunKSi0cjQe5RwbOmMgoNr9rooajEZoTaoIyaS7PSfxS2dKCN+jm7Msbu7uwcAALNzc9f3F0UAYJwZAgWqpVqF2rg7TJqKRquvh9bHZuJevG+b6/o+doY8SAzUI7dHSexohBxZ8eSjlmAkbjwoa7eNxB4tgySR/ocepBlIoFrFqLU2Eh0JCe7B3rfR7m3zc8PoX7Si+TlyqMXSvaMwyqyiiYzIaJMrahy0AEDfo8vvK0qPxB3a3iroWj42GJ7wJIPW2pxNwfVsuIwwLlKMOv5RbAqzMTxorUE1c/l9RTJIPB2hNi43pB7FplRfVs3chBm64WzZ1J9lUYpAiIgkov5z61EMJJULZE6zRwvJRayi+bHFZrbqnEh8cx0jtr5qkWLUnZNUNBoZeo8Sji2dUXBwzV4XRS0mI9QGdcREkp3+s7ilE2XEz9GNOapjRTA3ez38L3PDo/y+IltVRujb5rq+j50hDxID9cjtURI7GiFHVjz5qCUYiRsPytptI7FHyyBJpP+hB2kGEqhWMWqtjURFQoJ7KL+vKBytNyN+jhxqsXTvKIwyq2giIzLa5IoaBy0A0Pfocq4oPRJ3aHuroGv52GB4wpMMAm8PERhhXKQYdfyj2DRmY3hQSoFu5nKuSAaJpyPUxuWG1KPYlOrLupmbMEM3nC2b+rMsShEIEZFE1H9uPYqBpHKBzGn2aCG5iFU0P7bYzFadE4lvrmPE1lctUoy6c5KKRiND71HCsaUzCg6u2euiqMVkhNqgjphIstN/Frd0ooz4Oboxx+7u7j4AwMHZ+bnUWNijnCuyVWWEvm2u6/vYGfIgMVCP3B4lsaMRcmTFk49agpG48aCs3TYSe7QMkkT6H3qQZiCBahWj1tpIdCQkuIdyrigcrTcjfo4carF07yiMMqtoIiMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTDAJvDxEYYVykGHX8o9gUZmN40FqDauZyrkgGiacj1MblhtSj2JTqy6qZmzBDN5wtm/qzLEoRCBGRRNR/bj2KgaRygcxp9mghuYhVND+22MxWnROJb65jxNZXLVKMunOSikYjQ+9RwrGlMwoOrtnroqjFZITaoI6YSLLTfxa3dKKM+Dm6McfuTnWuaK6cK2oc5VDRaPX10PrYTNyL921zXd/HzpAHiYF65PYoiR2NkCMrnnzUEozEjQdl7baR2KNlkCTS/9CDNAMJVKsYtdZGoiIhwT2Uc0XhaL0Z8XPkUIule0dhlFlFExmR0SZX1DhoAYC+R5dzRemRuEPbWwVdy8cGwxOeZBB4e4jACOMixajjH8WmMRvDg1IKdDOXc0UySDwdoTYuN6QexaZUX9bN3IQZuuFs2dSfZVGKQIiIJKL+c+tRDCSVC2ROs0cLyUWsovmxxWa26pxIfHMdI7a+apFi1J2TVDQaGXqPEo4tnVFwcM1eF0UtJiPUBnXERJKd/rO4pRNlxM/RjTn2pt8Vzc3OXve/rgjKuSJbVUbo2+a6vo+dIQ8SA/XI7VESOxohR1Y8+aglGIkbD8rabSOxR8sgSaT/oQdpBhKoVjFqrY1ER0KCeyjnisLRejPi58ihFkv3jsIos4omMiKjTa6ocdACAH2PLueK0iNxh7a3CrqWjw2GJzzJIPD2EIERxkWKUcc/ik1hNoYHrTWoZi7nimSQeDpCbVxuSD2KTam+rJq5CTN0w9myqT/LohSBEBFJRP3n1qMYSCoXyJxmjxaSi1hF82OLzWzVOZH45jpGbH3VIsWoOyepaDQy9B4lHFs6o+Dgmr0uilpMRqgN6oiJJDv9Z3FLJ8qIn6Mbcuzu7O4BABycnZ/ZWl9dOX50aXFhfmFhcWn52MlTq2ubu6kRhoxyrshWlRH6trmu72NnyIPEQD1ye5TEjkbIkRVPPmoJRuLGg7J220js0TJIEul/6EGagQSqVYxaayNRkZDgHsq5onC03oz4OXKoxdK9ozDKrKKJjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8yCLw9RGCEcZFi1PGPYtOYjeFBKQW6mcu5Ihkkno5QG5cbUo9iU6ov62ZuwgzdcLZs6s+yKEUgREQSUf+59SgGksoFMqfZo4XkIlbR/NhiM1t1TiS+uY4RW1+1SDHqzkkqGo0MvUcJx5bOKDi4Zq+LohaTEWqDOmIiyU7/WdzSiTLi5+iGHLuTr4pgf+v08l1v+qn3XXj00uWnrzz99OVLjz3y/vf81I989/zC8bMb18sXRuVcka0qI/Rtc13fx86QB4mBeuT2KIkdjZAjK5581BKMxI0HZe22kdijZZAk0v/QgzQDCVSrGLXWRqIjIcE9lHNF4Wi9GfFz5FCLpXtHYZRZRRMZkdEmV9Q4aAGAvkeXc0XpkbhD21sFXcvHBsMTnmQQeHuIwAjjIsWo4x/FpjAbw4PWGlQzl3NFMkg8HaE2LjekHsWmVF9WzdyEGbrhbNnUn2VRikCIiCSi/nPrUQwklQtkTrNHC8lFrKL5scVmtuqcSHxzHSO2vmqRYtSdk1Q0Ghl6jxKOLZ1RcHDNXhdFLSYj1AZ1xESSnf6zuKUTZcTP0Q05dnd29wEAYH9//9Cdr3/gnz/08MMPPfTgG++749DE4NrTF962vLxy8br4uqicK7JVZYS+ba7r+9gZ8iAxUI/cHiWxoxFyZMWTj1qCkbjxoKzdNhJ7tAySRPofepBmIIFqFaPW2khUJCS4h3KuKBytNyN+jhxqsXTvKIwyq2giIzLa5IoaBy0A0Pfocq4oPRJ3aHuroGv52GB4wpMMAm8PERhhXKQYdfyj2DRmY3hQSoFu5nKuSAaJpyPUxuWG1KPYlOrLupmbMEM3nC2b+rMsShEIEZFE1H9uPYqBpHKBzGn2aCG5iFU0P7bYzFadE4lvrmPE1lctUoy6c5KKRiND71HCsaUzCg6u2euiqMVkhNqgjphIstN/Frd0ooz4Obohx+LK6q8/eOTwnQ+8+yNP7WxdXDu3evbs6ur59Y3t7ac++NB9ky+M9i+/78TK+nXwbVE5V2Srygh921zX97Ez5EFioB65PUpiRyPkyIonH7UEI3HjQVm7bST2aBkkifQ/9CDNQALVKkattZHoSEhwD+VcUThab0b8HDnUYuneURhlVtFERmS0yRU1DloAoO/R5VxReiTu0PZWQdfyscHwhCcZBN4eIjDCuEgx6vhHsSnMxvCgtQbVzOVckQwST0eojcsNqUexKdWXVTM3YYZuOFs29WdZlCIQIiKJqP/cehQDSeUCmdPs0UJyEatofmyxma06JxLfXMeIra9apBh15yQVjUaG3qOEY0tnFBxcs9dFUYvJCLVBHTGRZKf/LG7pRBnxc3Rjjrmlk+c3d7bWTh1bmOn8w+zC8dX1tYfvPAgAAFfOnz63nQBe2CjnimxVGaFvm+v6PnaGPEgM1CO3R0nsaIQcWfHko5ZgJG48KGu3jcQeLYMkkf6HHqQZSKBaxai1NhIVCQnuoZwrCkfrzYifI4daLN07CqPMKprIiIw2uaLGQQsA9D26nCtKj8Qd2t4q6Fo+Nhie8CSDwNtDBEYYFylGHf8oNo3ZGB6UUqCbuZwrkkHi6Qi1cbkh9Sg2pfqybuYmzNANZ8um/iyLUgRCRCQR9Z9bj2IgqVwgc5o9WkguYhXNjy02s1XnROKb6xix9VWLFKPunKSi0cjQe5RwbOmMgoNr9rooajEZoTaoIyaS7PSfxS2dKCN+jm7CMbt8+syxwwAAsL+5tradFo17lHNFtqqM0LfNdX0fO0MeJAbqkdujJHY0Qo6sePJRSzASNx6UtdtGYo+WQZJI/0MP0gwkUK1i1FobiY6EBPdQzhWFo/VmxM+RQy2W7h2FUWYVTWRERptcUeOgBQD6Hl3OFaVH4g5tbxV0LR8bDE94kkHg7SECI4yLFKOOfxSbwmwMD1prUM1czhXJIPF0hNq43JB6FJtSfVk1cxNm6IazZVN/lkUpAiEikoj6z61HMZBULpA5zR4tJBexiubHFpvZqnMi8c11jNj6qkWKUXdOUtFoZOg9Sji2dEbBwTV7XRS1mIxQG9QRE0l2+s/ilk6UET9HN+WYXT62NDlZtL+5ubWXGI1rlHNFtqqM0LfNdX0fO0MeJAbqkdujJHY0Qo6sePJRSzASNx6UtdtGYo+WQZJI/0MP0gwkUK1i1FobiYqEBPdQzhWFo/VmxM+RQy2W7h2FUWYVTWRERptcUeOgBQD6Hl3OFaVH4g5tbxV0LR8bDE94kkHg7SECI4yLFKOOfxSbxmwMD0op0M1czhXJIPF0hNq43JB6FJtSfVk3cxNm6IazZVN/lkUpAiEikoj6z61HMZBULpA5zR4tJBexiubHFpvZqnMi8c11jNj6qkWKUXdOUtFoZOg9Sji2dEbBwTV7XRS1mIxQG9QRE0l2+s/ilk6UET9HN+eYnZ+fn/xpb2cn9++KyrkiW1VG6Nvmur6PnSEPEgP1yO1REjsaIUdWPPmoJRiJGw/K2m0jsUfLIEmk/6EHaQYSqFYxaq2NREdCgnso54rC0Xoz4ufIoRZL947CKLOKJjIio02uqHHQAgB9jy7nitIjcYe2twq6lo8Nhic8ySDw9hCBEcZFilHHP4pNYTaGB601qGYu54pkkHg6Qm1cbkg9ik2pvqyauQkzdMPZsqk/y6IUgRARSUT959ajGEgqF8icZo8WkotYRfNji81s1TmR+OY6Rmx91SLFqDsnqWg0MvQeJRxbOqPg4Jq9LopaTEaoDeqIiSQ7/WdxSyfKiJ+jMrIf5VyRrSoj9G1zXd/HzpAHiYF65PYoiR2NkCMrnnzUEozEjQdl7baR2KNlkCTS/9CDNAMJVKsYtdZGoiIhwT2Uc0XhaL0Z8XPkUIule0dhlFlFExmR0SZX1DhoAYC+R5dzRemRuEPbWwVdy8cGwxOeZBB4e4jACOMixajjH8WmMRvDg1IKdDOXc0UySDwdoTYuN6QexaZUX9bN3IQZuuFs2dSfZVGKQIiIJKL+c+tRDCSVC2ROs0cLyUWsovmxxWa26pxIfHMdI7a+apFi1J2TVDQaGXqPEo4tnVFwcM1eF0UtJiPUBnXERJKd/rO4pRNlxM/RzTn2trd3Jn+anZubTYvFOcq5IltVRujb5rq+j50hDxID9cjtURI7GiFHVjz5qCUYiRsPytptI7FHyyBJpP+hB2kGEqhWMWqtjURHQoJ7KOeKwtF6M+LnyKEWS/eOwiiziiYyIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMkg8PYQgRHGRYpRxz+KTWE2hgetNahmLueKZJB4OkJtXG5IPYpNqb6smrkJM3TD2bKpP8uiFIEQEUlE/efWoxhIKhfInGaPFpKLWEXzY4vNbNU5kfjmOkZsfdUixag7J6loNDL0HiUcWzqj4OCavS6KWkxGqA3qiIkkO/1ncUsnyoifo5ty7K6tXbwGAAAHF5cXE4NxjnKuyFaVEfq2ua7vY2fIg8RAPXJ7lMSORsiRFU8+aglG4saDsnbbSOzRMkgS6X/oQZqBBKpVjFprI1GRkOAeyrmicLTejPg5cqjF0r2jMMqsoomMyGiTK2octABA36PLuaL0SNyh7a2CruVjg+EJTzIIvD1EYIRxkWLU8Y9i05iN4UEpBbqZy7kiGSSejlAblxtSj2JTqi/rZm7CDN1wtmzqz7IoRSBERBJR/7n1KAaSygUyp9mjheQiVtH82GIzW3VOJL65jhFbX7VIMerOSSoajQy9RwnHls4oOLhmr4uiFpMRaoM6YiLJTv9Z3NKJMuLn6MYbW2ePLa+s79gN9jZOn1mbfFV0aPn40bmRcJFHOVdkq8oIfdtc1/exM+RBYqAeuT1KYkcj5MiKJx+1BCNx40FZu20k9mgZJIn0P/QgzUAC1SpGrbWR6EhIcA/lXFE4Wm9G/Bw51GLp3lEYZVbRREZktMkVNQ5aAKDv0eVcUXok7tD2VkHX8rHB8IQnGQTeHiIwwrhIMer4R7EpzMbwoLUG1czlXJEMEk9HqI3LDalHsSnVl1UzN2GGbjhbNvVnWZQiECIiiaj/3HoUA0nlApnT7NFCchGraH5ssZmtOicS31zHiK2vWqQYdeckFY1Ght6jhGNLZxQcXLPXRVGLyQi1QR0xkWSn/yxu6UQZ8XN0g429jVPHTz3y2PvetLh8am17zzTY3Th77Nj7Lu8DABy888Tp49l/VQTlXJGtKiP0bXNd38fOkAeJgXrk9iiJHY2QIyuefNQSjMSNB2XttpHYo2WQJNL/0IM0AwlUqxi11kaiIiHBPZRzReFovRnxc+RQi6V7R2GUWUUTGZHRJlfUOGgBgL5Hl3NF6ZG4Q9tbBV3LxwbDE55kEHh7iMAI4yLFqOMfxaYxG8ODUgp0M5dzRTJIPB2hNi43pB7FplRf1s3chBm64WzZ1J9lUYpAiIgkov5z61EMJJULZE6zRwvJRayi+bHFZrbqnEh8cx0jtr5qkWLUnZNUNBoZeo8Sji2dUXBwzV4XRS0mI9QGdcREkp3+s7ilE2XEz9GNNmamv3/o6mPv+ZGF+aXjK2fPrV3c2NjcuLi+tnrq+NLC/W979CoAABy67/S5M0szSdF6jXKuyFaVEfq2ua7vY2fIg8RAPXJ7lMSORsiRFU8+aglG4saDsnbbSOzRMkgS6X/oQZqBBKpVjFprI9GRkOAeyrmicLTejPg5cqjF0r2jMMqsoomMyGiTK2octABA36MPTB30v+vGirn+VryejeH88tjWJVouqnlACR426BfVNxqS9lo+NhieMCRTDxqmp2SaGRMctGcxRggeLqNmLSe2QSVorWHyrWwvGqAVqIt/+8k/+uKzl5/f+fT+tat/98K+/ioGpQzhMXPLgbnbXvJNMy9dfOnc/bOvXn75PHB7FLtbasRN81HofrSHBNGecN/2jIzFUQgSCx5WJU6B5NajGEjQ2Kbco2WQePjxUotEbLE5RHW/c/VTf/DcX//p7uee/fKXdr7ywt6LX8OWKeM6GDO33voPb/+6V3/9S77n5a/8/m/8h/907h95qZdSieweVXlyiRWw2c4lIiOg9LoovYXCyJkduRxlz4ifoxtszCyurG/Mr5w4ufrY1f39q5cuvO/Shfchdodf/+7za6euh2+KAJznipr/NaGZwd1h0lT0cN3520zgjNC3nZ0BjUkdgfpBAosPBc8AEiQmpMgQcmTFA1r/9qe+8Luf+ds/+dyXrnzxK5994e/2vvYiblnG9TxmblWvesltr3npwe89/PX/5NX/4A2vmbXrv6oCWzVJ7tHDlSiIBPfgPFfk7ttC3TK4b7u7dyS03ozk77p7arF07yiM+LH1jInfjkZkxERLsMk8/ogNeY9Wf3nn2wFsObX9nNKWHtKPF/suuotQba4jJG48aHYINn54nMPyWfObSbtaCIyC8TBybZtdHvqrV9vwp/aurfzVb/3J83/tAaWMuOO+Q9/0S9/+337L179MWi0h3RJXS9elvwLj921qZJxIcDysShxilE+PCkEyFJMM9mgqErcfP7VEi61Xfp95/tpP/8nv/PEXBn5JRRnX8bj/lXP/5vt+4Ftfeqj/D7EqMVy9/eEjZY9eF4ERtf9H6C1ERs7sSOUoe0YkJP/g/7wAMHlevW7G3vljsz/xyD4AHH7ov+6sLjvtt9ZWz5xdXXvs6Wu9fzl05xuPnzp9+sRS7v/5nNb6lltuAYDn/8Ux2xdC3S0xtMMkq2gvtB42Efr2wLrOyNh+/NpOUvQeJbqjEXLUGZ+89uWf/OhTGzv9Iizjhh/f/6qXrv7wt3zby2YG9e+sAs4ebanEKR5xJJZ6BLD17cHu7bN6oE1w3/bvDAQbCUZe9p4xGVbLmIyk4h8UGf9PRUKbj6KioyXv0dV3RSiNgZuPbqexDdaPIH3WksZznSFB1/Kx4SJB1GL5ZjICI9cQ+GHoADYHks5ikzg88vmn/uVf/dbkn9/85jffc88999xzz7333nvokPHjpzIijGvXrn3sYx97/PHHn3jiiQ996EMAcPutB87eefS/e+W3A7FHyXVL1EHPO3pPNYCH3ben0PidwYnEEw+tEvPqUXwk+ezRUkicfii5JmYZCY1bdf/3s3958vGPTsxLb78xBrJfHDjwa/f9kwde/c1dwyj6F+sM7scJl4cojOLsI2MxcmbnusuRGCM/JNfjd0XUsbu9ubm5tb2zuwswOzs/v7i0vDCbGpTfqL8r+tK/eMBUaevvtnNFiMsMKtqn7vxrc4S+7ewMaEx6FyzxoeBxDv6ORsgRMi78xc7/8Nt/PvlzuTG74Qd2z3bL//6Gb/nRb/8GyyMN6kZc/wAwUIljIzG8hPdtL5wefig/GQhfhYKWykheLZbuHYURP7aeMSHtaBkxuq7RWvH47NHlXFF6JG487jbqZ+OHxzksn83nnX12rm2zy0N/ddD/5tN/9Muf/gMAeO1rX3vhwoW77rrLA00ZscbHP/7xBx98cGtrCwBOffMPvPWO75NTS0i3xNXSdemvwPh9mxoZJxLLlsapxCFG+fSocq7Iw4+fWqLF1pHfX956/MxTH4PS22/c0d4v/ue7v/dt33Gk+bdYlVjOFY3SW4iMyrkiVo5upu+KruNRzhU5bSL07YF1nZEZ/PEr7lkeieiORsjRdLz7Y8/8wh8/A+XG7KYc7Xu2d73u1T9z76uqfwndqTl79OAXIfJILPUIUM4VxWLkZe8Zk2G1jMlIKv5BkfH/VCS0+SgqOlryHn1L5Vkjs+m1Z2MCDHSAuNFdpmCJp8vGSSgfJO7Q9lZB1/KxwfCEJ3n6pNebzW8mezZcRhgXKUYd/yg2hdkYHrTWoJr5N65+/Jc//QdKqYcffvjxxx8v96zJx91333358uWVlRWl1Jlnfv/8Z/+M1KPYJV1fVs08vWeo5YbZ1J8dwsPu21Q3IhtAiyOtEqdAcutRDCSVC2ROs0cLycXpxw1lcpkfW2weVN2/febPzzz1sdLbb+zR3i9+4eOP//tP/kXzbwKVyO5Rlafu7GyXZq+T6i0ORqgN6oiJJBIjZ3bkcpQ9I36Oysh+OH9fEWrj7jBpKhqtvh5aH5uJe/G+ba7r+9gZ8iAxUI/cHiWxoxFy1PnX/+PJz/zCHz9Tbsxu2tG+Z3vnHz679pefBzBqBMBaTTJ7dKvWOl8JREKCe3D+vqKejcVPih87ujuDlTUPrTcjybtuVC2W7h2FET+2aEw8IzOwShAjMtrkihoHLQDQ9+jquyKlkNks6Z6NMUIdIG5UNevWn8NtnITyQeIObW8VdC0fGwxPeJJBKWXO2DeTHRsuI4yLFKOOfxSbxmwMD0op0NP5yS9/7h3P/BcAePvb33727NnbbrvNsnYZo47bbrvtve9978/+7M8CwM88/V/+/EvPhXcGdknXl3Uza6hmmP7ZtKk/O4SH3bepbkQ2gBZHWiVOgeTWoxhIKhfInGaPFpKL048byuQyP7bYbFfd/3ftuZ/ZfAxKb78JRnu/eOsf/96Tu1+Y/oNAJbJ7VOWpOzvbpdnrpHqLgxFqgzpiIonEyJkduRxlz4ifozKyHwoUgG7NoEC1VKtQG3eHSVPRaPX10PrYTNyL921zXd/HzpAHiYF65PYoiR2NkKPmXzef+9JbLz4N5cbs5h7te7b/8bc/+ZWvvtivEQBrNcns0a1aa1eiNqpVBgnuwd630e5t85Pix47uzmBlzUPrzUjyrhtVi6V7R2HEjy0aE8/IDKwSxIiMNrmixkELAPQ9upwrSo/EHdreKuhaPjYYnvAk07+ZZDHCuEgx6vhHsSnMxvCgtQY1nd/zqd8FgIWFhZ//+Z+3rFpGsvGud71r8sbZe7b/ILxkuK99AAAgAElEQVQzsEu6vuz/OuDQS0jCfZvqRmQDaHGkVeIUSG49ioGkcoHMafZoIbk4/bihTC7zY4vNdtW968lLUHr7zTTq/eJdH398ekmgEtk9qvLUnZ3t0ux1Ur3FwQi1QR0xkURi5MyOXI6yZ8TPURnZj3KuyFaVEfq2ua7vY2fIg8RAPXJ7lMSORshR868/t/EJKDdmZQBAdc/25b9/8Rc2Pt2vEQBrNcns0a1aa1eiMqpVBgnuoZwrCkfrzUjyrhtVi6V7R2HEjy0aE8/IDKwSxIiMNrmixkELAPQ9upwrSo/EHdreKuhaPjYYnvAk07+ZZDHCuEgx6vhHsWnMxvCglAINSqnP7H3xsWvbBw4cuHDhQnm5KcNx2223ffCDHzxw4MBHP//JT+9fC+wM7JKuL/u/Djj0EpJw36a6EdkAWhxplTgFkluPYiCpXCBzmj1aSC4Cm/TkMj+22GxR3ae//KXf+dyzpbffVKPeLx7962c/9cKXAEQqkd2jKk/d2dkuzV4n1VscjFAb1BETSSRG9FM4EltaXoz4OSoj+1HOFdmqMkLfNtf1fewMeZAYqEduj5LY0Qg5ml559kt7v/2pL5QbszImo75ne++ffPbJz3/F50G6O3P26FattStRG9UqgwT3UM4VhaP1ZiR5142qxdK9ozDixxaNiWdkBlYJYkRGm1xR46AFAPoeXc4VpUfiDm1vFXQtHxsMT3iS6d9MshhhXKQYdfyj2BRmY3jQWoMCrfV/+PxTAPCGN7zh7rvvtixZRuJx5MiR5eVlAPhPn/urwM7ALun6sv/rgEMvIQn3baobkQ2gxZFWiVMgufUoBpLKBTKn2aOF5CKwSU8u82OLzRbV/T+f/isovf3mG/V+8f8+uw0gUonsHlV56s7Odmn2Oqne4mCE2qCOmEgiMaKfwpHY0vJixM9RGdmPcq7IVpUR+ra5ru9jZ8iDxEA9cnuUxI5GyNH0ym/+5VUoN2ZltEZ9z/YbW3/j8yDdnTl7dKvW2pWojGqVQYJ7KOeKwtF6M5K860bVYuneURjxY4vGxDMyA6sEMSKjTa6ocdACAH2PLueK0iNxh7a3CrqWjw2GJzzJ9G8mWYwwLlKMOv5RbBqzMTwopUCDUuo/feEvAOB1r3udZb0yshhLS0sA8Ft/8xeBnYFd0vVl/9cBh15CEu7bVDciG0CLI60Sp0By61EMJJULZE6zRwvJRWCTnlzmxxabLar7j5/5BJTeflOOyX7xyOS7IoFKZPeoylN3drZLs9dJ9RYHI9QGdcREEokR/RSOxJaWFyMGkhdeeMESpTKyGM8//zwAzNx6SzlXZKvKCH3bXNf3sTPkQcIeYXaPktjRCDmaXvnIJz4H5casjO6Y3LN9cnfP50G6O3P26FattStRG9UqgwT3UM4VhaP1ZiR5142qxdK9ozDixxaNiWdkBlYJYkRGm1xR46AFAPoeXc4VpUfiDm1vFXQtHxsMT3iS6d9MshhhXKQYdfyj2BRmY3jQWoMCrfXTX34OAO6//37LemVkMSYJ2nrhbwI7A7uk68v+rwMOvYQk3LepbkQ2gBZHWiVOgeTWoxhIKhfInGaPFpKLwCY9ucyPLTZbVPfUF78ApbfflGOS9Keu/S2ASCWye1TlqTs726XZ66R6i4MRaoM6YiKJxIh+CkdiS8uLEQnJq77udgD40z/9U0uUyshiPPHEEwDwytsPlnNFtqqM0LfNdX0fO0MeJOwRZvcoiR2NkKPplSc//wKUG7MyumOih09c2/N5kO7OnD26VWvtSlRGtcogwT2Uc0XhaL0ZSd51o2qxdO8ojPixRWPiGZmBVYIYkdEmV9Q4aAGAvkeXc0XpkbhD21sFXcvHBsMTnmT6N5MsRhgXKUYd/yg2jdkYHpRSoEEpta+/BuUVp+zH5J2jF77294GdgV3S9WX/1wGHXkIS7ttUNyIbQIsjrRKnQHLrUQwklQtkTrNHC8lFYJOeXObHFpstqtt/sfT2m3RM9ovnv/r3ACKVyO5Rlafu7GyXZq+T6i0ORqgN6oiJJBIj+ikciS0tL0YkJK+8/SAAbG5uWqJURhZj8l3R4dtnyrkiW1VG6Nvmur6PnSEPEvYIs3uUxI5GyNH0yv7XXoRyY1ZGd0zu2T6xu+/zIN2dOXt0q9balaiNapVBgnso54rC0XozkrzrRtVi6d5RGPFji8bEMzIDqwQxIqNNrqhx0AIAfY8u54rSI3GHtrcKupaPDYYnPMn0byZZjDAuUow6/lFsCrMxPGitQTV8b7/9dst6ZWQxXv7yl0/+ENgZ2CVdX/Z/HXDoJSThvk11I7IBtDjSKnEKJLcexUBSuUDmNHu0kFwENunJZX5ssXlAdaW335Sj3i8ARCqR3aMqT93Z2S7NXifVWxyMUBvUERNJJEb0UzgSW1pejEhI7n3lK6CcK8p+TL4r+r7DLy/nimxVGaFvm+v6PnaGPEjYI8zuURI7GiFHnX8tN2ZltMfknu3LX33R50G6O3P26FattStRGdUqgwT3UM4VhaP1ZiR5142qxdK9ozDixxaNiWdkBlYJYkRGm1xR46AFAPoeXc4VpUfiDm1vFXQtHxsMT3iS6d9MshhhXKQYdfyj2DRmY3hQSoHG+JaR9wjsDOySri/7vw449BKScN+muhHZAFocaZU4BZJbj2IgqVwgc5o9WkguApv05DI/ttg8oLoyyhCoRHaPqjx1Z2e7NHudVG9xMEJtUEdMJJEY0U/hSGxpeTEiIXnzt74GAD784Q8/++yzlkCVkXg888wzH/nIRwDgzd/2j8q5IltVRujb5rq+j50hDxL2CLN7lMSORsiRFU8ZZbSG+0G6O3P26FattStRG9UqgwT3UM4VhaP1ZiR5142qxdK9ozDixxaNiWdkBlYJYkRGm1xR46AFAPoeXc4VpUfiDm1vFXQtHxsMT3iS6d9MshhhXKQYdfyj2BRmY3jQWoPC+JaR9wjsDOySri/7vw449BKScN+muhHZAFocaZU4BZJbj2IgqVwgc5o9WkguApv05DI/ttg8oLoyyhCoRHaPqjx1Z2e7NHudVG9xMEJtUEdMJJEY0U/hSGxpeTEiIbn/8CsXv+FlX/ziF3/8x3/8xRdftMSqjGTjq1/96o/92I+98MIL3/eNL/veb3xZOVdkq8oIfdtc1/exM+RBwh5hdo+S2NEIObLiKaOM1nA/SHdnzh7dqrV2JSqjWmWQ4B7KuaJwtN6MJO+6UbVYuncURvzYojHxjMzAKkGMyGiTK2octABA36PLuaL0SNyh7a2CruVjg+EJTzL9m0kWI4yLFKOOfxSbxmwMD0op0BjfMvIegZ2BXdL1Zf/XAYdeQhLu21Q3IhtAiyOtEqdAcutRDCSVC2ROs0cLyUVgk55c5scWmwdUV0YZApXI7lGVp+7sbJdmr5PqLQ5GqA3qiIkkEiP6KRyJLS0vRlQk/8vS9wDAxsbG6dOnLbEqI9l45zvfOfkfAv/XHzgCAOVcka0qI/Rtc13fx86QBwl7hNk9SmJHI+TIiqeMMlrD/SDdnTl7dKvW2pWojWqVQYJ7KOeKwtF6M5K860bVYuneURjxY4vGxDMyA6sEMSKjTa6ocdACAH2PLueK0iNxh7a3CrqWjw2GJzzJ9G8mWYwwLlKMOv5RbAqzMTxorUFhfMvIewR2BnZJ15f9XwcceglJuG9T3YhsAC2OtEqcAsmtRzGQVC6QOc0eLSQXgU16cpkfW2weUF0ZZQhUIrtHVZ66s7Ndmr1Oqrc4GKE2qCMmkkiM6KdwJLa0vBhRkdx3+BU/d89rAeBd73rXW9/61q985SuWiJUx6vjKV77ylre85Rd/8RcB4D33v/bIKw4BQDlXZKvKCH3bXNf3sTPkQcIeYXaPktjRCDmy4imjjNZwP0h3Z84e3aq1diUqo1plkOAeyrmicLTejCTvulG1WLp3FEb82KIx8YzMwCpBjMhokytqHLQAQN+jy7mi9Ejcoe2tgq7lY4PhCU8y/ZtJFiOMixSjjn8Um8ZsDA9KKdAY3zLyHoGdgV3S9WX/1wGHXkIS7ttUNyIbQIsjrRKnQHLrUQwklQtkTrNHC8lFYJOeXObHFpsHVFdGGQKVyO5Rlafu7GyXZq+T6i0ORqgN6oiJJBIj+ikciS0tL0YMJD9z5Dv+1ZHv0Fr/yq/8yl133fVHf/RHlqCVMdL4/d///bvuuuvXfu3XtNbv/N6F/+m13zrRYTlXZKvKCH3bXNf3sTPkQcIeYXaPktjRCDmy4imjjNZwP0h3Z84e3aq1diVqo1plkOAeyrmicLTejPg5cqjF0r2jMOLHFo2JZ2QGVgliREabXFHjoAUA+h5dzhWlR+IObW8VdC0fGwxPeJLp30yyGGFcpBh1/KPYFGZjeNBag8L4lpH3COwM7JKuL/u/Djj0EpJw36a6EdkAWhxplTgFkluPYiCpXCBzmj1aSC4Cm/TkMj+22DygujLKEKhEdo+qPHVnZ7s0e51Ub3EwQm1QR0wkkRjRT+FIbGl5MeIheec9r/3AD91/2623XLly5fu///u/67u+66d/+qc//OEPP/fcc/hyZUiP55577kMf+tBb3vKW7/zO7/zBH/zBK1euzBy49d/+8L1v/54FAJjosJwrslVlhL5truv72BnyIGGPMLtHSexohBxZ8ZRRRmu4H6S7M2ePbtVauxKVUa0ySHAP5VxROFpvRvwcOdRi6d5RGPFji8bEMzIDqwQxIqNNrqhx0AIAfY8+MHXQeRulns3vfruzMdAPdRyAx5fH7ahCNQfaXEdIUDxDq6Br+diQImMOpZTW5mx+M9mZuYwwLlKMcDwDCC05qqNhWaaMTEdgZ2B3y+py50O1M5j+GUw3gGovet/2rqNAJBY8rEqcAsmtRzGQ4LFNuEfLIHH78VOLRGwD+38ZZQhUIrtHVZ66s4+Ue71Oqrc4GPW4ePe6CL2FyMiRHbkcZc+IjeRHvvk13/2Kl//S5T//jb+68uSTTz755JO/+qu/ioWujOjjv7nllv/+21/9r77nzle/5OuqawoAFCgNGpsnSp3aQOdckU+HSVLRw9XnbzOBI963B9a1dYbqFg2/masfJMArwuweJbGjcXJURhkDo6f/Ae0Be4+2VCL6YC+ABPdg79t497b4EeiWwX07oDP420j2f4EcOdRi6d5RGPFji8bEMzI+q0RFm1xR4zEi79HVd0VaA6j+jIu4NRtD685svYcxfjjVclHNPb6BNj0knniuMyTttXxsMDxhSKYeNIDqzZjgoD2LMULwcBk1azmxDSpBaw3Vt7JlXEcjsB7Z3VIjbpqPQvejxj2V4U24bwO9jgKRWPCwKnEKJLcexUCCxjblHi2DxMOPl1okYovNw3d3ZdzkQ6AS2T2q8uRqkYDNdi4RGQGl10XpLRRGzuzI5Sh7RvwcgZ5/6Ut+5Qfu/bl77n5k+9n/eOUz2196/rNf3tv72tfwCJQhOmZuveVVX3/7HS/9uh/9lm/6Z/Ovevntt3X/XUPntBB+rgj9caS7w6Sp6OG687eZwBmhbzs7AxqTOgL1gwQWHwqeASRITEiRIeTIiqeMMlqjWwW2apLco4crURAJ7sF5rsjdt4W6ZXDfdnfvSGi9GcnfdffUYuneURjxY+sZE78djciIiZZgk3n8ERvyHl3OFaVHguIZWgVdy8eGFBlz5PfOPpeRV39rY7DkqI6GZZkyMh2BnYHdLavLnQ/VzmD6ZzDdAKq96H3bu44CkVjwsCpxCiS3HlXOFYX78VOLRGwD+38ZZQhUYjlX5NkZIvQWIqNyrkguR1U0Xnn7zE/e9Y9/8q5/DACkyIBlM2v1bcQzRqjjAImMAwk+aIzaMxZbR0WjJk4//eyUc0W2+ETo2wPrDuXIfjMH0yhNbLzrkYQEjQkhMpwclVHGwOjpf0B7wN6jLZWo0UrkI/Hv3u3P9Lu3xY9Atwzu2wGdwd9Gsv8L5MihFkv3jsKIH1s0Jp6R8VklKtrkihqPEXmPLueKrmck7bV8bDA8YUimHjTk9c4+l1GzlhPboBK01pDvuaK9ne2tnZ29PYCZ2fmFhbmZ1IDyGYH1yO6WGnHTfBS6H0Wf7gfwsPs20OsoEIkFD6sSp0By61EMJGhsU+7RMkg8/HipRSK22Dx8d5fLKF090RCoRHaPqjy5WiRgs51LREZA6XVReguFkTM7cjnKnhE/R15b+3Bk0JjU69Y3UhgqdmTiMCLF1mQkoBYN5VwRbjOBk4NaJPXP163EjkbIkRVPRmNvd3t7e2d3D2Bmdm5+fn623KeNPrpVYKsmyT16uBIFkeAeyrmicLTejPg5cqjF0r2jMOLH1jMmfjsakRETLcEm8/gjNuQ9upwrSo8ExTO0CrqWjw0pMubI7519LiOv/tbGYMlRHQ3LMknGzsb5c6vnzq9fvHx1v3X54OEjy8dOrKycPLpguW3dOHN0ZW0X+YeZmZmZ2bn5+cXFpaXlo8sLs4bF9rnjx1e3CWAXT62vHjP9xR6BnYHdLavLnQ/VzmD6ZzDdAKq96H3bu44CkVjwsCpxCiS3HsU/+5LPHi2DxO3HTy0SsQ3s/+nHzdLVd7c3Ll7c2Nra2dmDufn5+YWl5eXFLL4QE6hE/lvYU0/d2UfKvV4n1VscjHpcIp5ZicWIegonOEfZM+LnCNnIQyNTbVHWHz+1t7H4+4gAI1JsTTwCalEAUM4V2TKVh1ok9c/XrcSOxslRfmN3c21ym3bpyrXW5YOH71w+euLEysnji5bboq3VYyfO7WD/MjMzOzs3N7+4uLS4jN4L7a6dPHpmkwB24eT5cyfmCR9s36YBzM3Nzy8sLi0tzY//ID84evof0B6w+7alEjVaiXwk/t27/Zl+97b4EeiWAjuaV9UP20j2f4EcOdRi6d5RGPFji8bEMzI+q0RFm1xR4zEi79HlXNH1jKS9lo8NhicMydSDhrze2ecyatZyYhtUgtYacjpXtL1+5uTJ049e2Uf+bf/q5Uff/7ZHz515/clz584enTctdrc2L126OuD+ArwP4ODh+46dOnN2ZXmu9S97O1ubly5jyw6Pg7PYTzHjj8B6ZHdLjbhpPgrdjxr3VIY34b4N9DoKRGLBw6rEKZDcehQDCRrblHu0DBIPP15qkYgtNg/f3aUbN0lX39s8t7Jy+txjBs9DRx44dWb11NE59GOjDYFKZPeoypOrRQI227lEZASUXhelt1AYObMjl6PsGfFz5LW1D0cGjUm9bn0jhaFiRyYOI1JsTUYCatFQzhXhNhM4OahFUv983UrsaIQcWfEkHTsbZ1dOnr5w+Rryb/tXn370A+949ANnzj60eu7sceS9nr3trUuXnh5wf+ED74PpvdDZlaPzLQ97u9ubly4RbtPg2F7wh2B3Y/XUqTPIbdrBO15//NSZsyeXsvnGqFsFtmqS3KOHK1EQCe6hnCsKR+vNiJ8jh1os3TsKI35sPWPit6MRGTHREmwyjz9iQ96jy7mi9EhQPEOroGv52JAiYw6Bt4cIjDAuUoy8+lsbgyVHdTQsy4w5di+eOXb8HY81PxU8ePjI0tLC/OwswO7u9tbGxuWr+wD7Vx9735uWtt67vrayaH1b+9AdR5p3hPb2dvd2dq5cm94b7l+9dOFtP3RxvethZnbm4EHE0/7+9GMH0X+eSfTCeGBnYHfL6nLnQ7UzmP4ZTDeAai963/auo0AkFjysSpwCya1H8c++5LNHyyBx+/FTi0RsA/t/snGTdPXdjbMnTpx65Gn0Bx7XLj/yjjdtXPz1i2snbYenxhgClch/C3vqqTv7SLnX66R6i4NRj0vEMyuxGDmyI5ej7Bnxc4Rs5KGRqbYo64+f2ttY/H1EgBEptsjdC18tCgDKuSJbpvJQi6T++bqV2NE4Ocpm7G2uHj+28kjz/cnBw3cuLi4uzM3OwO7O9tbmxuUr+wBw7dL7f2Jpc3Nt/cyy9RuVQ3fc2fyPdXt7e3u721fqs+TXLj/yjjddXHt728MMzOC3aY77tJnQ+7S97bWV4yfefwn7Ngxg/8pjH/ip5Yvrq+vnT6S8T2tGT/8D2gN237ZUokYrkY/Ev3u3P9Pv3hY/At1SYEfzqvphG8n+L5Ajh1os3TsKI35s0Zh4RsZnlahokytqPEbkPbqcK7qekbTX8rHB8IQhmXrQkNc7+1xGzVpObINK0FpDFueK9jbOHD32juom7vB9D50+c+rE8nznjm13a+3MysrZR6/sA8zMDv3/yYeOrW6eO9pbYXdrc3199eyZ85eu7gNcffRtx1cWN1anN6wLKxd3VxBY54/N/sQj+wBw+MT6zuoyg6HsCKxHdrfUiJvmo9D9qHFPZXgT7ttAr6NAJBY8rEqcAsmtRzGQoLFNuUfLIPHw46UWidhi8/DdXYJxk3T13YsrR4+9b0Lz0J0PnDh18vjSwvws7G5vbZw/e2b10af3Aa4+unLs1MLm2eVkP4UQqER2j6o8uVokYLOdS0RGQOl1UXoLhZEzO3I5yp4RP0deW/twZNCY1OvWN1IYKnZk4jAixdZkJKAWDeVcEW4zgZODWiT1z9etxI5GyJEVT6KxtXrs6E89On2f59CRB0+fOX3iaPf/9N3bXj+7snL6kaedt2kHl85srB/vfZO0u71xcf3c6plzj17ZB7h26T3HTy5unj8+OWk9e2Jt9wTi6eLJuR96/1UAOPjAud2149wbp521E0s/cmHC8tCRB06cPHF8aXF+bmZvd3tzffXMmQ9cugqwf+WRk8dPL26csb+yNN7oVoGtmiT36OFKFESCeyjnisLRejPi58ihFkv3jsKIH1vPmPjtaERGTLQEm8zjj9iQ9+hbpg4mf+vNZkn3bIwR6gBxo6pZt/4cbuMklA8Sd2h7q6Br+dhgeMKTDEopc8a+mezYcBlhXKQYdfyj2DRmY3hQSoHG+I47dtdPHq9+pHjovof/8+bG6snejxQBYHbh2Jn1zYvvfuCNb18L/2+KZ2YXlo6vnNvYXHvoyOQdpafPnTq3xcaeZgR2BnZJ15d1M2uoZpj+2bSpPzuEh923qW5ENoAWR1olToHk1qMYSCoXyJxmjxaSi8AmPbnMjy02D6guxbhZuvrO+urapWsAcOi+hz+yubV29sTRpYX5ubn5haWjJ86ub6w/PIG2//Tq6dXtUaF1hkAlsntU5ak7O9ul2eukeouDEWqDOmIiicTImR25HGXPiJ8judvukBupmPuIACNoEHI2aQG1KAAwzgyBAtVigtu4O0yaikYz0kPrYzNxn4NaJPXP163EjkbIkRVPirG3cer4yvSLooN3/vMPbm6cXzlq/PLHmfmjp9Y2N379wdc/dP78qeAvUmbnl46dXF3fvPju+w4BAMDVC6fPbPDBh4y5o6dW7jsEB+9447v/69bm2tmTx5YW5+fm5uYXlo6tnNu4+OtvPAwAAPuXV0+jvyBz9OF+kO7OnL7dqrV2JWqjWmWQOLt3r2+j3dvmJ8WPHd2dwcqah9abET9HDrVYuncURvzYojHxjMzAKkGMyGiTK2octABA36Or74rqbzXbs1nSPRtjhDpA3NRfhqnWn8NtnITyQeIObW8VdC0fGwxPeJIn30z2Z1NwPRsuI4yLFKOOfxSbwmwMD1prUBjfMcfexumVD1wBAICDdz50fv3s0C92mF06tbZ+hvFfCs8dPXvm+B0AALC/eX59m+wo6QjsDOySri+3X7WBaobpn02b+rNDeNh9m+pGZANocaRV4hRIbj2KgaRygcxp9mghuTj9uKFMLvNji80Dqht/3Dxdfe74ufVff+DIG9978eLZY/PGP88unzl78s4JtI3za6NC6wyBSmT3qMpTd3a2S7PXSfUWByPUBnXERBKJkTM7cjnKnhE/R3K33SE3UjH3EQFG0CDkbNICatEA4DxXhNq4O0yaikYz0kPrYzNxn4NaJPXP163EjkbIkRVPgrF1duXs9Fc6Hn7g3Pq548bbPM2YWTx5/uIq4/cuzi6dWp3eC8HT6+dH/rJoZvHU2toH1zfWTy0jFBZOnl2Zvm907eL6BuE3IYkP94N0d+b07VattStRGdUqg8TZvQE9V9SzsfhJ8WNHd2ewsuah9WbEz5FDLZbuHYURP7ZoTDwjM7BKECMy2uSKGgctAND36HKuKD0Sd2h7q6Br+dhgeMKTTP9mksUI4yLFqOMfxaYxG8ODUgo0xnfEsXP+9Lnp78S888Tq2aPRf7PkzPKxpcnLTftbG1s53BOGj8DOwC7p+rL/64BDLyEJ922qG5ENoMWRVolTILn1KAaSygUyp9mjheQisElPLvNji80Dqht93FRdfWbh5NrmuvU3Lc0sHz96BwAA7G9tbCbbcAQqkd2jKk/d2dkuzV4n1VscjFAb1BETSSRGzuzI5Sh7Rvwcyd12h9xIxdxHBBhBg5CzSQuoRQFAOVdky1QeapHUP1+3EjsaIUdWPKOP3bXTZy9Nvyl6cHX1+Hz0FRePTe+FYHtra/TTO3PLx7HviSZjYWlp+m97Ozs5HCxyP0h3Z07fbtVauxK1Ua0ySJzdG8q5Itn+L5Ajh1os3TsKI35s0Zh4RmZglSBGZLTJFTUOWgCg79HlXFF6JO7Q9lZB1/KxwfCEJ5n+zSSLEcZFilHHP4pNYTaGB601KIzveGPr/OrFyf9TdPCNp06P8tscZubmqnvC3d3r87uiwM7ALun6sv/rgEMvIQn3baobkQ2gxZFWiVMgufUoBpLKBTKn2aOF5CKwSU8u82OLzQOqG3uUrt4Z84sLkzdWU/4QQqAS2T2q8tSdne3S7HVSvcXBCLVBHTGRRGLkzI5cjrJnxM+R3G13yI1UzH1EgBE0CDmbtIBaNACUc0W2TOWhFkn983UrsaMRcmTFM/bYWVtdn/7vc0dWTh+jHxgKGHPzc5N7IdjdzeELmdaYmWn+lMGvK/J4kO7OnL7dqrV2JSqjWmWQOLs3lMhLbIsAACAASURBVHNFsv1fIEcOtVi6dxRG/NiiMfGMzMAqQYzIaJMrahy0AEDfo8u5ovRI3KHtrYKu5WOD4QlPMv2bSRYjjIsUo45/FJvGbAwPSinQGN/Rxvb62ubkvaZDR0+Mc7MKAFD9LHEGcrgnDB+BnYFd0vVl/9cBh15CEu7bVDciG0CLI60Sp0By61EMJJULZE6zRwvJRWCTnlzmxxabB1Q38ihd3TYS/hBCoBLZPary1J2d7dLsdVK9xcEItUEdMZFEYuTMjlyOsmfEz5HcbXfIjVTMfUSAETQIOZu0gFoUAJRzRbZM5aEWSf3zdSuxoxFyZMUz8ti9uLYxfaNn6eTxhXEW3durb9Myu0vb3tzcmfxpfsH4hU0phvtBujtz+nar1tqVqI1qlUHi7N5QzhXJ9n+BHDnUYuneURjxY4vGxDMyA6sEMSKjTa6ocdACAH2PLueK0iNxh7a3CrqWjw2GJzzJ9G8mWYwwLlKMOv5RbAqzMTxorUFhfMcaexsXpz9UPLh0bHmk+7Odra3pPeHc3Fxm96ueI7AzsEu6vuz/OuDQS0jCfZvqRmQDaHGkVeIUSG49ioGkcoHMafZoIbkIbNKTy/zYYvOA6sYdpav3xvbm1iQec/PzyX4IIVCJ7B5VeerOznZp9jqp3uJghNqgjphIIjFyZkcuR9kz4udI7rY75EYq5j4iwAgahJxNWkAtGgDKuSJbpvJQi6T++bqV2NEIObLiGXlsXqy+Klo8ujw/zpp7W5vbk3uh5iB4FmP34pmzj+0DABw8cuLEYmo4AD4P0t2Z07dbtdauRGVUqwwSZ/eGcq5Itv8L5MihFkv3jsKIH1s0Jp6RGVgliBEZbXJFjYMWAOh7dDlXlB6JO7S9VdC1fGwwPOFJpn8zyWKEcZFi1PGPYtOYjeFBKQUa4zvW2Nrcntytwvzi4kh3jttr5y5OX3pfOprFPWH4COwM7JKuL/u/Djj0EpJw36a6EdkAWhxplTgFkluPYiCpXCBzmj1aSC4Cm/TkMj+22DygunFH6erdsXF+/QoAABxaOrqUDIVAJbJ7VOWpOzvbpdnrpHqLgxFqgzpiIonEyJkduRxlz4ifI7nb7pAbqZj7iAAjaBByNmkBtSgAKOeKbJnKQy2S+ufrVmJHI+TIimfc0fzCoNmFxZFOFe2unVuvvp9aXsrllZ7drfMnjx57/9MAAIfuO31uZaRoOIb7Qbo7c/p2q9balaiNapVB4uzeUM4VyfZ/gRw51GLp3lEY8WOLxsQzMgOrBDEio02uqHHQAgB9jz4wddD/rhsr5s4bKxrBCe4vj21douWimgeU4GGDflF9oyFpr+Vjg+EJQzL1oAFUb8YEB+1ZjBGCh8uoWcuJbVAJWmuovpVNMnZ2tid/ODi/MMoPFXc3zpw4NXl9CO44diL+71yPMwLrkd0tNeKm+Sh0P9pDgmhPuG8DvY4CkVjwsCpxCiS3HsVAgsY25R4tg8TDj5daJGKLzTa9jT5KV2+P3fNnzz8NAAB3HD95NN2PRwQqkd2jKk+uFgnYbOcSkRFQel2U3kJh5MyOXI6yZ8TPkdfWPhwZNCb1uvWNFIaKHZk4jEixNRkJqEVD57QQfq5IgTJmjFcWFT2cC3+bCZwc1CKpf75uJXa0/5+9t4+O7KruRPdtt1vlLyTb2C3z1cULpGUI7gJMWviRcbEwr8VjgsuwjMUaJhGTmYWStR4WPLJaXusxI1hkLJK8WP0eQ+T13sTql2RZzRBcnfAhj01Q4xBXg7ElmJhSAFuyMZZsYpds2q4C2/f9UXXrfu1zz7ln73PvVffZf5ylrt53n99vn31+5946dao0xkiIJ1vbbG72Tn8Pj5Sz6LDdnJ+YOt7dKhocm8zuy4nj1lpZbqy3Wq31lUajsbR0YmMbAGBg36HZxcWpSkH2sMKzQDSbONfo5JnIiASPID1XJNdtJrVkWNGUZj0drTIj+hhJqkWg3kYY0XOrmBO1FU2TERGthk/B84/4aK/R3l5R7x3KSBt/xyjcxkzlLad4Gw4R5gtem9JnByFB8ST1gval4qOVmbg5juO68Ta+MxlqqYwwLlyMlPQtiEEwRv1sCLoxbe12y//yYvQNvs2V+nIT+6Xy0shYraL+nmB7s7nSWFqYm1s4sdF7T/GG2dkc37mjWUplIKul93Loon4w6P0N8TCA1p5x3VaeRymRCPCQZmIPSNE0ioAEz22OazQPEnkctWrhyG1K/c/UrKoHrL08M3NsCwBg4ODUVDVHaAwzkaxRXqRwq1LKEa3j0hYJowgXZa0zoC2ajCSjwzdGhWdEHyNkIU+bGW+JEr79FFzGzK8jDIy0covcvdCrxQEAbCvICbzt2POB0LkiFYXJZUYnj4i6TxdOEaqFs/7pdcuxolHGKF9rt3vHimBoCL0vaTWXllZa2P+Uq+Oj6js97db6SmNpcW5u/s613tnvQ7Oz43l+A93y7Nh1xzqBFwb2XzszOztVGynQzWOk/hNqD8i6LZiJLjoT6UjU1Tt4TVS9BXEY1JJhRVOa9ck+nPrPMEaSahGotxFG9NyiOVHMjEovRtHmXlHZMdJeo+25op2MJNiXig+GJx2SXgQXivWZfSojvy8ptsRKcF0Xcj1XhL1fGLLmwtSHjmwg/7H/8L21iuALfbYXx4eXAvd87dbWdvC+EAb2HZqrL+R6s0qzlPORrJYuEsa/FMKXxu6pYtGYdRv051FKJAI8pJnYA1I0jSIgQXOb5xrNg0QhjlK1cOQWa5Pv7jI0q+qetRvTU/PdQ0X7J2cnc/1qE4aZSNYoL5JMIgFrxVwMMgIdrTOiLTqMpKPDN0aFZ0QfI6WlPTkzaE76/fZvpDBU5MyYYaSV2zgjhmpxwZ4rwn26cIpQLZz1T69bjhVNY4yEePIzdIdksz49cdNqJ/4fg7/79fHRMTxSZ3lyZHjK/3e71druhGLsvfrw4mK+90Jx66wtL8zNlkozk2PlomwXhWeBaDZxrtHJM5ERCR7BnitKj1aZEX2MJNUiUG8jjOi5VcyJ2oqmyYiIVsOn4PlHfLTXaHuuKH8kKJ6kXtC+VHy0MhM3hk8PaTDCuHAxUtK3IAbBGPWzIejGtJWGSiWADgBAu90W3LFqWGd7a2sb/6+BfVdPTM/OTo4W6nuK0lpKZSCrpfdy6KJ+MOj9DfEwgNaecd1WnkcpkQjwkGZiD0jRNIp+9qU4azQPEnkctWrhyG1K/c/UTiNVb7darTZ6AKo0JPgwbsBaS1MT8933WvZ9dG4mz0NFwDIT6Z/C7kUKtyqlHNE6Lm2RMIpwMXhmxRQjyejwjVHhGdHHCFnI02bGW6KEbz8FlzHz6wgDI63cIncv9GpxAMCeKxKNVDGqhbP+6XXLsaJRxihfK5W8G5J2qwXA9RmbpNu0vQdr07NzU1Xuz/Okvk+rzjbunWq1W631ZrPRWFpaOrGxvXbi6MdPLM7fMF9fmCjE8aJI/SfUHpB1WzATXXQm0pGoq3fwmqh6C+IwqCXDiqY065N9OPWfYYwk1SJQbyOM6LlFc6KYGZVejKLNvaKyY6S9RttzRTsZSbAvFR8MTzokvQguFOsz+1RGfl9SbImV4Lou5HquaGh4GGAbADqbm+sA8d8kr0zM31H1bwLbm/WZ3z+6Jgu7d//Bsv+uYalUKg0ND5cro6Oj1bHqyI7eJepayvlIVksXCeNfCuFLY/dUsWjMug368yglEgEe0kzsASmaRhGQoLnNc43mQaIQR6laOHKLtcl3dxnaaaPqrcXx4Y/ciXywFmDghjvai7Wki9cXJiZuXesAwMD+Gxdmc/8RJYaZSNYoL5JMIgFrxVwMMgIdrTOiLTqMpKPDN0aFZ0QfI6WlPTkzaE76/fZvpDBU5MyYYaSV2zgjhmpxwZ4rwn26cIpQLZz1T69bjhVNY4yEeLK14fLwAGx0ADrr65sA8YM+5drc4kjgO+hay7OTR06it0MBG9x/MHgzVioNDQ0PD5cr1Wq1OlYxcup7abL8nqPo/tTAodvWlybinQ6VK6NlAAAYgwmYhnazPjM5+bkTW521Yx+ptqFRnyibQJrKwrNANJs41+jkmciIBI9gzxWlR6vMiD5GkmoRqLcRRvTcKuZEbUXTZEREq+FT8PwjPtprtD1XlD8SFE9SL2hfKj5amYkbw6eHNBhhXLgYKelbEINgjPrZEHRj3MqV8gCsdQCg2Wi2IP5TFUOVsVrwvcb1zfmBo2uS29XBsbnGguCI/OlhKZWBrJbey6GL+sGg9zfEwwBae8Z1W3kepUQiwEOaiT0gRdMo+tmX4qzRPEjkcdSqhSO3KfU/W7OqDq3lqdrk8S0AgMGrZxZnq3nvFLHMRPqnsHuRwq1KKUe0jktbJIwiXAyeWTHFSDI6fGNUeEb0MUIW8rSZ8ZYo4dtPwWXM/DrCwEgrt8jdC71aHACw54pEI1WMauGsf3rdcqxolDHK14bL5RKc7ABAq9lch2o56lAaqdaCO0jt9sLkEVnUgdGZpaXx/G930llppDa7VB4aq950Yhu2jk9P12uLtbxJROo/ofaArNuCmeiiM5GORF29g9dE1VsQh0EtGVY0pVmf7MOp/wxjJKkWgXobYUTPLZoTxcyo9GIUbe4VlR0j7TXanivayUiCfan4YHjSIelFcKFYn9mnMvL7kmJLrATXdSHXc0XDlUoZ7lwDgE5jeaU9nvPX9OwYSzkfyWrpImH8SyF8aeyeKhaNWbdBfx6lRCLAQ5qJPSBF0ygCEjS3ea7RPEgU4ihVC0dusTb57i5DO21UvVSdnr9tHP+vcvy4lGftlbna+JHel8/dML84XSlCAhhmIlmjvEgyiQSsFXMxyAh0tM6Itugwko4O3xgVnhF9jJSW9uTMoDnp99u/kcJQkTNjhpFWbuOMGKrFBXuuCPfpwilCtXDWP71uOVY0jTES4snWSpVqZeDYiQ5AZ2Wp0Zos5703om2VifnbqvivZA6rfy1xqTI1Mz7/zls3ALbq8/VWbSLnhIRngWg2ca7RyTOREQkewZ4rSo9WmRF9jCTVIlBvI4zouVXMidqKpsmIiFbDp+D5R3y012h7rih/JCiepF7QvlR8tDITN4ZPD2kwwrhwMVLStyAGwRj1syHoxrxVamP7Pre2AQBb9YXluepYEd5VK76lVAayWnovhy7qB4Pe3xAPA2jtGddt5XmUEokAD2km9oAUTaPoZ1+Ks0bzIJHHUasWjtym1P+M7XRR9VK5OjGR9qL1hfHax0/0jhTdXF8YN/K1K+mNYSbSP4XdixRuVUo5onVc2iJhFOFi8MyKKUaS0eEbo8Izoo8RspCnzYy3RAnffgouY+bXEQZGWrlF7l7o1eIAgD1XJBqpYlQLZ/3T65ZjRaOMUc5Wro6NDJxY7QBsLy/WN8eR72rbGTZcHZ/giFMarVYGbt3oAHSaK02AUY6g+hap/4TaA7JuC2aii85EOhJ19Q5eE1VvQRwGtWRY0ZRmfbIPp/4zjJGkWgTqbYQRPbdoThQzo9KLUbS5V1R2jLTX6F29AK6LtPEijvjELG0AJIzrtU7g7/Q+UkLFQSJPbaQXtC8VHwxP+kEG13XjbbzgIj5URhgXLkah+Cg2B/OJRXBdFxyMb3Y2OjG+v/vX1uLswnp+QHaUpVQG8pTuv+z4rQPh+wTMp39tEh6ybuuGYVkAAhz1ZmIPSNE0ioDEC4G0+azRTOXCsEh3X6bnFmsTqi5rO2NVfX1xvDp5fAMAYODAjfV6MY4UAQDLTCRrlBcp3ErlMq51XNoiYYT6oIGISAwxko4O3xgVnhF9jPhuu9PcSJlcRxgYgY+QskgzVIsLANJzRaiPXGHymdHoiETQqvh0wxehWjjrn163HCuaxhgJ8WRtI+MTlQEAANhemptbyRlNEaxU6t2vtVv4OaUsTf4gHW4puh2Ya8GZ6MRmKw8SqXoDeq4o4iOIk8fbjnJlELKmoVVmRB8jSbUI1NsII3pu0ZwoZiahl1SMtNHmXlHZoAUA/TXa2yvydy8DbXxKR3xiljYAEsbxWjfwd3ofKaHiIJGnNtIL2peKD4Yn/SCD4zjxFtuZDPlQGWFcuBiF4qPYXMwnFsFxHHAxvhlaZXLq0CAAAHROzEyeSe8rEiylMpCndP/lwMdoXPBa6P0d9+lfm4SHrNu6YVgWgABHvZnYA1I0jSIg8UIgbT5rNFO5MCzS3ZfpucXahKrL3M5IVd+sT4xNHNvoAMDAvhsWl+YK8CtFvjHMRLJGeZHCrVQu41rHpS0SRqgPGoiIxBAj6ejwjVHhGdHHiO+2O82NlMl1hIER+AgpizRDtTgAEDszBA44ASa4j1xh8pnR6IhE0Kr4dMMXoVo4659etxwrmsYYCfFkbuWJ6dpeAADorM5Pzq7kvz+Sr62vr/dSMDSc/72b/EE63FJ0OzDXgjPRjc1WHiRS9Y7oNqreojh5vO0oVwYhaxpaZUb0MZJUi0C9jTCi5xbNiWJmEnpJxUgbbe4VlQ1aANBfo+25ovyRyFMb6QXtS8UHw5N+kPV3JkmMMC5cjELxUWwO5hOL4LouOBjfLK08MTt9oPvppq07J2uT9c080ewMS6kM5Cndf1n944BJH0Ji1m3dMCwLQICj3kzsASmaRhGQeCGQNp81mqlcGBbp7sv03GJtQtVlb2ecqm/WJ6vjR9e6P1J07fzSYq1g3+jCMBPJGuVFCrdSuYxrHZe2SBihPmggIhJDjKSjwzdGhWdEHyO+2+40N1Im1xEGRuAjpCzSDNXiAoA9VyQaqWJUC2f90+uWY0XTGCMhnuxtqDY7c3X3Qz3bJ2dqEwvN03a7qDlXHanONlpij3Zjfv5kBwAA9o5WRzLCJTb5g3S4peh2YK4FZ6ITm608SKTqDfZcEa/+M4yRpFoE6m2EET23aE4UM5PQSypG2mhzr6hs0AKA/hptzxXlj0Se2kgvaF8qPhie9IOsvzNJYoRx4WIUio9iczGfWATHccDF+GZqpcrUwmzvhrWzeut1lerU4gp2S7fZmJ+Zb3SyRVdIS6kM5Cndf1n944BJH0Ji1m3dMCwLQICj3kzsASmaRhGQeCGQNp81mqlcGBbp7sv03GJtQtXlYGeUqm8uTY6N39rdKNp76Jb64kT+7zREjWEmkjXKixRupXIZ1zoubZEwQn3QQEQkhhhJR4dvjArPiD5GfLfdaW6kTK4jDIzAR0hZpBmqxQEAe65INFLFqBbO+qfXLceKpjFGQjx5WHlyYe7a3tmijWMfGa1OLDSwz/W0Vhan5pZ36m1auzE1Pn1i7cRN1Uptdhnj116ZHx8/sgYAAAP7J6YK8Bub8gfpcEvR7cBcC85ENzZbeZBI1RvsuSJe/WcYI0m1CNTbCCN6btGcKGYmoZdUjLTR5l5R2aAFAP01encvQHSvG5vMoU+suAhOkG8ei1QiEMJrEypBwQfdqD7dkAT7UvHB8KRD0ovgAjiRFis4CLZsjBA8VEZ+X1JsiZXgui54u7J5WqkyVa+vj9WOnNwGgK0TRz705vnpg2PV0dHKyFAJ2u319cbK8vLy6pZ3rzpYHinYx7YztZTzkayWLhLGvxTCl0aQILXHrNugP49SIhHgIc3EHpCiaRQBCZrbPNdoHiQKcZSqhSO3WCuqt7zsTFH1zaXJsdqtqz0S+0aGVmbHa61Wq91uox/SHZ1ZnsvhnQiGmUjWKC+STCIBa8VcDDICHa0zoi06jKSjwzdGhWdEHyOlpT05M2hO+v32b6QwVOTMmGGklds4I4ZqcSF0Wgg/V4T+fLpcYfKZ0cljoe7ThVOEauGsf3rdcqxoGmMkxJOPlScWl1pjYx8/sQUA2yePfuTti9MHqtVqZXSkXAJot5orjcby0smN/m3ayI67TSsNj46U51fXOp2N4ze9c2n+6vHx8bHqSHloCGBzc2W5vri4eKJHcODA5PxMJWfAAGHFAOFs4lyjk2ciIxI8gvRckVy3mdSSYUVTmvV0tMqM6GMkqRaBehthRM+tYk7UVjRNRkS0Gj4Fzz/io71Ge3tFvXcoI238HaNwGzOVt5zibThEmC94bUqfHYQExZPUC9qXio9WZuLmOI7rxtv4zmSopTLCuHAxUtK3IAbBGPWzIegmQxuqzi03KpPjU0dXtwEAOhsnjx89efwo4jp48KPzC3Pj5WwBFspSKgNZLb2XQxf1g0Hvb4iHAbT2jOu28jxKiUSAhzQTe0CKplEEJHhuc1yjeZDI46hVC0duU+p/XnYmqHpzYbq/UQQAGyeOYfR8GxhO+BoUg8YwE8ka5UUKtyqlHNE6Lm2RMIpwUdY6A9qiyUgyOnxjVHhG9DFCFvK0mfGWKOHbT8FlzPw6wsBIK7fI3Qu9WhwAwLaCnMDbjj0fCJ0rUlGYXGZ08oio+3ThFKFaOOufXrccKxpljIpipcrUUqM8PTE5f2KrAwCdrdU7j63eeQxxHdx/w+zi/GQRtlJSWXl8sTFcmZiYOb7Rgc7GiaOfO3H0c4jf4MEbF+tz1fwPFUGs/hNqD8i6LZiJLjoT6UjU1Tt4TVS9BXEY1JJhRVOa9ck+nPrPMEaSahGotxFG9NyiOVHMjEovRtHmXlHZMdJeo+3vFeWPRJ7aSC9oXyo+GJ70gwyu68bbeMFFfKiMMC5cjELxUWwO5hOL4LouOBjfXKw0MrGwsv7A7YdvOLhvAHMY3Hf1DTff/sB6Y358pBA3c7lZSmUgT+n+y47fOhC+T8B8+tcm4SHrtm4YlgUgwFFvJvaAFE2jCEi8EEibzxrNVC4Mi3T3ZXpusTah6nI0q+oFMYaZSNYoL1K4lcplXOu4tEXCCPVBAxGRGGIkHR2+MSo8I/oY8d12p7mRMrmOMDACHyFlkWaoFhcA7O8ViUaqGNXCWf/0uuVY0TTGSIgnRyuVa3PLzZU7bv7dq/cPYg4Dew/ecPi2e5vNxcnKUNboWGyoOl1vNr/55zceOrAXuRMd2Hf1797yzWZjbqwoh6bkD9LhlqLbgbkWnIlObLbyIJGqN6DniiI+gjh5vO0oVwYhaxpaZUb0MZJUi0C9jTCi5xbNiWJmEnpJxUgbbe4VlQ1aANBfo50f7T8MgI2mg01pyfCIP+DSDyC1SCe6PjsIiRwPOjoaPmp4pCa4FtmZFFaLBqPUeAhjLWplEfo9vqbxJxCYZvlbu9VcaTSb65utdhtKQ8PD5XKlMlremTepnOY4DgD89OpPkKsljVqGq6XXRkKqV6B53dbNjBQJjoc0E5MYFUej0iBJykkB1mhdJPI4atViLLf4+F5yx59DQbTdqnrm1l0vnhr/PWMzMX31Rk2llBW0zgAjXf03oC2ajKSjwzVGhWdEHyPOzKCLWWRdNr+OcDJKlVtjMxog8VxRMkcKI2lOKFWnMgoSn4JVC0P905Gwrmipx+jc//L3AMW4MYtYa31lZaW5vrnZakOpNDRcHhmpVEaGT6NP8rTXVxorzfXNVqsNQ0NDwyOV7hcjF8K692zP3/ibKVdqim4LZmJvPqa9Z5AiUVfvMBA8vkrvKX3oK5oq2qwYKfkr5iS5WrJkxJX/VJlRv8oQ2uJUlHG02mu0t1eE0ki4+QgrjchIb0Gq9MWNZ4chQftS8aEiQaoFO8hmhpHMGN4MTcAmQdLrZt/JPwUo5G2rtbB17yMfq34iTc3wqSUaIBIdvadKwEPWbS8zZGWQIlHEozcTi6VRdCTFWaO5kEjj6Iy15igjqcEBXlIvzF6RtczN3ysyU/9syiB/nJBFMMLIzDqSFSPp6Oy4MWJjxDVG6TKD5iTyggAVOTNmGGnlVnpPqDNGQuSBf4t+r4jMSDG3TIxS+iCeOVWLwfrn0qiUmdEYo54Vd6/IWq7m7RUdDM0CxNjrv9u9aCZmjSQWJb1uK+FUiMOwohlCq8uIv1oE6m2EET23ijnRWtEKxGhHoxXiUVmjve+g6/4r0qJPDME2ZmkDIGEcr3UDf6f3kRIqDhJ5aiO9oH2p+GB40g9yb+mNtNjOZMiHygjjwsUoFB/F5mI+sQiO44CL8bVWbEupDOQp3X/Z9VsXvBZ6f8d9+tcm4SHrtm4YlgUgwFFvJvaAFE2jCEi8EEibzxrNVC4Mi3T3ZXpusTah6qxZY5iJZI3yIoVbqVzGtY5LWySMUB80EBGJIUbS0eEbo8Izoo8R3213mhspk+sIAyPwEVIWaYZqcQAg9ltE4IATYIL7yBUmnxmNjkgErYpPN3wRqoWz/ul1y7GiaYyREI81awGTP0iHW4puB+ZacCa6sdnKg0Sq3hHdRtVbFCePtx3lyiBkTUOrzIg+RpJqEai3EUb03KI5UcxMQi+pGGmjzb2iskELAPprtP29ovyRyFMb6QXtS8UHw5N+kEH7Gw9JjDAuXIxC8VFsDuYTi+C6LjgYX2vFtpTKQJ7S/Zcdv3XAa6H3d9ynf20SHrJu64ZhWQACHPVmYg9I0TSKgMQLgbT5rNFM5cKwSHdfpucWaxOqzpo1hplI1igvUriVymVc67i0RcII9UEDEZEYYiQdHb4xKjwj+hjx3XanuZEyuY4wMAIfIWWRZqgWFwDs7xWJRqoY1cJZ//S65VjRNMZIiMeatYDJH6TDLUW3A3MtOBOd2GzlQSJVb7C/V8Sr/wxjJKkWgXobYUTPLZoTxcwk9JKKkTba3CsqG7QAoL9G23NF+SORpzbSC9qXig+GJ/0g6+9MkhhhXLgYheKj2FzMJxbBcRxwMb7Wim0plYE8pfsvq38cMOlDSMy6rRuGZQEIcNSbiT0gRdMoAhIvBNLms0YzlQvDIt19mZ5brE2oOmvWGGYiWaO8SOFWKpdxrePSFgkj1AcNRERiiJF0dPjGqPCM6GPEd9ud5kbK5DrCwAh8hJRFmqFaHACw54pEI1WMauGsf3rdcqxoGmMkxGPNWsDkD9LhlqLbgbkWnIlubLbyIJGqN9hzRbz6zzBGkmoRqLcRRvTcojlRzExCL6kYhPRGHgAAIABJREFUaaPNvaKyQQsA+mv07l6A6F43NplDn1hxEZwg3zwWqUQghNcmVIKCD7pRfbohCfal4oPhSYekF8EFcCItVnAQbNkYIXiojPy+pNgSK8F1XfB2Za3tIEs5H8lq6SJh/EshfGkECVJ7zLoN+vMoJRIBHtJM7AEpmkYRkKC5zXON5kGiEEepWjhyi7WierNmDVhUl6xRXiSZRALWirkYZAQ6WmdEW3QYSUdHcYy+8djj39rceuDn/7Lxi1Obzz/ffvFF9Bpr1oJWOmvXK84759Xnn/vWSy78rctefs2r94rvMOn170LotBB+rkjwe0UyhclnRidro7pPF04Guo2qdyxCNCf9DPQfJLD86OBJQILkRCszGmMkxGPNWsDCs0A0mwj1H9Oo5JnIiASPID1XJNdtJrVMrdty9TaEVpkR/113pFoE6m2EET23ijlRW9E0GRHRavgUPP+Ij/Ya7e0V9d6hjLTY+5TBNmYqbznF23CIMF/w2pQ+OwgJiiepF7QvFR+tzMTNcRzXjbfxnclQS2WEceFipKRvQQyCMepno/vaqVOnzjvvPEGX1vK3Z599FgBKu3anVAayWnovhy7qB4Pe3xAPA2jtGddt5XmUEokAD2km9oAUTaMISPDc5rhG8yCRx1GrFo7cptR/q+1npPXWi7POAmCZiWSN8iKFW5VSjmgdl7ZIGEW4KGudAW3RZCQZHekYPfzssx+95x9PPvlzzMeatSRrv/jSQ8+ceuiZUyd+9uSfrf7zVcMX/5d/9dbXDZ6H3WHS698BAGwryAm87djzgdC5IhWFyWVGJyukuk8XDrtuJ/QryCp4t2jCt1+Dt3GyDJOQoDnRyAxljADsjZm1sHXv2S7Yc1as/hNqD/TqX6xL3kxEH+wZkKird/CaqHoL4jCoZWrdTqEM6j6c+s8wRpJqEai3EUb03KI5UcyMSi9G0eZeUdkx0l6j7e8V5Y9EntpIL2hfKj4YnvSDDN1zM5E2XnARHyojjAsXo1B8FJuD+cQiuK4LDriuO7znfAB44IEHBP1ZK4R1B+jis89JqQzkKd1/2fFbB8L3CZhP/9okPGTd1g3DsgAEOOrNxB6QomkUAYkXAmnzWaOZyoVhke6+TM8t1gqq7rLSeWC1/Yy07qC/fKAEwDITyRrlRQq3UrmMax2XtkgYoT5oICISQ4yko5M8Rv/toYcrX/7b7kbR9ddfPzs7e9ddd7VaLdeaNQVrtVp33XXXzTfffP311wPAP27+y1Vf/kb9oceQ6mWofxcA7O8ViTTT5dfteL+qj51pHiTEGSbfdsdZp8+Mxhj1XnnFeQNgb8ysha1bD2+46ByVB+lwq/PY6bWBuRaciU5stvIgkao3oOeKIj6COHm87ShXBiFrGlplRpx33Wi1CNTbCCN6btGcKGYmoZdUjLTR5l5R2aAFAP012v5eUf5I5KmN9IL2peKD4Uk/yNA9NxNp4wUX8aEywrhwMQrFR7G5mE8sguM44ILjOBfvPhcAVlZWBP1ZK4Tdf//9AHDpnvNSKgN5SvdfDnyMxgWvhd7fcZ/+tUl4yLqtG4ZlAQhw1JuJPSBF0ygCEi8E0uazRjOVC8Mi3X2ZnlusFVTdywfOAavtZ6R114u9pXMBWGYiWaO8SOFWKpdxrePSFgkj1AcNRERiiJF0dBLG6I9Xf/Dv7/lHAHjTm9704IMPfvGLXzx8+PA111wzODgouMSatZANDg5ec80109PTX/ziF1dXV0dGRp5/4cV/+43v/J+r/xx1Zah/BwDs7xWJNNOAbsf7VX3sTPMgIc4w+babY0XTGKPeK5eeczbYGzNrYeves73pknNVHqTDrc5jp9cG5lpwJrqx2cqDRKreEd1G1VsUJ4+3HeXKIGRNQ6vMiPOuG60WgXobYUTPLZoTxcwk9JKKkTba3CsqG7QAoL9G23NF+SORpzbSC9qXig+GJ/0g6+9MkhhhXLgYheKj2BzMJxbBdV1wwHXdt1zwCrAfcSq8de8j3/Kyy1IqA3lK919W/zhg0oeQmHVbNwzLAhDgqDcTe0CKplEEJF4IpM1njWYqF4ZFuvsyPbdYK6i6Ky/aC1bbz0jrrhdve/mlACwzkaxRXqRwK5XLuNZxaYuEEeqDBiIiMcRI+1zRbf/8oz9a+b7jODfeeON99913+eWX47CtWVOzK664YnV1dWpqynGcme/+01+ubYT+m6H+XQCw54pEmmnPFSHXM6xoGmPUe+Vtw4Ngb8ysha17z/YbF9tzRfZcEaf+M4yRpFoE6m2EET23aE4UM5PQSypG2mhzr6hs0AKA/hptzxXlj0Se2kgvaF8qPhie9IOsvzNJYoRx4WIUio9iczGfWIT+uaJrL74cAL70pS89+uijgi6t5WwPP/zwl7/8ZQC4bu9ISmUgT+n+y+ofB0z6EBKzbuuGYVkAAhz1ZmIPSNE0ioDEC4G0+azRTOXCsEh3X6bnFmsFVff+V78erLafedZfLz6w79cAWGYiWaO8SOFWKpdxrePSFgkj1AcNRERiiJHeuaLVf3lq6t7vAMDhw4fn5ub27NmDY7ZmLY3t2bPnlltu+eQnPwkAf/Ct+//Hv2z7/8dQ/w4A2HNFIs2054qQ6xlWNI0x6r1yw+v3gr0xsxaw/j3bFZecp/IgHW51Hju9NjDXgjPRjc1WHiRS9QZ7rohX/xnGSFItAvU2woieWzQniplJ6CUVI220uVdUNmgBQH+NtueK8kciT22kF7QvFR8MT/pB1t+ZJDHCuHAxCsVHsTmYTyxC/1zR2172qt84d+8zzzzzwQ9+8KWXXhL0ai03e+GFFz7wgQ+cOnXqLS+7rHKBPVfEEoZlAQhw1JuJPSBF0ygCEi8E0uazRjOVC8Mi3X2ZnlusFVTd6MWXXTH4cqvtZ5T114srL770rRdfAsAyE8ka5UUKt1K5jGsdl7ZIGKE+aCAiEkOM9M4Vffr+FQAYGRn59Kc/jaO1Zk3XPvvZz3aPqX3mvgf9Vxnq3wUAe65IpJn2XBFyPcOKpjFGvVeuesXQmy+5wN6YWeta/57tN4fPPzh8nsqDdLjVeez02sBcC85EJzZbeZBI1RvsuSJe/WcYI0m1CNTbCCN6btGcKGYmoZdUjLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHWX9nksQI48LFKBQfxeZiPrEI/XNF4MJnXvsuAGg0GjMzM4JereVmn/rUp7pfVvCfX/eu9MpAntL9l9U/Dpj0ISRm3dYNw7IABDjqzcQekKJpFAGJFwJp81mjmcpFGkcOpfsyPbdYK666mw/8FlhtP5Osv1786ZVX9V5imIlkjfIihVupXMa1jktbJIxQHzQQEYkhRhrnih79xalv/Ozx3bt3Hzt2zJ4ossZue/bsuf3223fv3v31RzY3nj3Ve5Wh/h0AsOeKRJppzxUh1zOsaBpj5P/v3NW/DgCNRsPuylv7j//xPz7wwAPnnr3rr//X1+3atUvlQTrc6jx2em1grgVnohubrTxIpOoN9lwRr/4zjJGkWgTqbYQRPbdoThQzk9BLKkbaaHOvqGzQAoD+Gn3Wx17+DgDAhhPb+00enThwTBNQlcAuVuiLG88OQyKaI1IfKhKkWrCCM8NIZpq5VcQmQeJ384o9FwzsOuvbzzxyzz33/PznP3/nO9959tlnK/dtzZQ9//zzU1NTc3NzAPDpX6u+59LXpdcoPrVEA0SiR5Ag0Zh1uweNrgxSJIp49GZisTSKjqQ4azQXEmkcnbHWHGUkNcJ6e+U55+/ZddY9Tz5mtf20t+B68Z/fPPreV+3z/sdI/bMpQ6iVmsEZncCFbR3JipF0dOKM/mLtR8uPbx46dOgTn/iEwkBYs5bahoeH77nnnoceeuhV559zcO/FAMBR/0IlCfzbdcCJtagVYUaraKO6fmag21L1RnMSeUGQHx08UqOvaBpj5Nsrzy/tOctZ/unT3/rWt+yN2RlrwXu2v3rP69522QXILECMvf4BIGEmZo0kFiW9bivhVIij885A+l500Ooy4q8WgXobYUTPrWJOtFa0AjHa0WiFeFTWaHuuKH8k8tRGekH7UvHB8KQfZCjeZ/apjELxUWwu5hOLEDxX5DjOH7zy4Mde+XbXdT//+c9ffvnl9957r6B7axnZP/zDP1x++eVf+MIXXNf9ZPmqf//qt2hpFHlK9192/dYFr4Xe33Gf/rVJeMi6rRuGZQEIcNSbiT0gRdMoAhIvBNLms0YzlYs0jhxK92V6brE2seqm9r/lE/vfarX99LbgenHTm94yuf+N/v8xzESyRnmRwq1ULuNax6UtEkaoDxqIiMQQI41zRcc3HgGAq666CqxZM2ajo6MAcPyhn/X+zVD/DgDYc0UizbTnipDrGVY0jTEK/e8fvrU8fWXZ3pidsRa8Z/v021/1vtddBBCbIwDC2UR77Az3FZuJriEkUvUGe66IV/8ZxkhSLQL1NsKInls0J4qZSeglFSNttLlXVDZoAUB/jXZ+tP8wAOB7X8iUThydOHBME9Q+VyirBHWfNHh2GBLRHJH6UJFo7kzyMJKZZm4VsUmQhDrr5uGrT/3z1E+++suXXnQc5w1veEO1Wq1Wq1dfffUll1yiDMWavj355JPLnj344IMAUNq1e25k7F9f8uugqVF8aokGiESPIEGiMet2DxpdGaRIFPHozcRiaRQdSXHWaC4k0jg6Y53BuaI+kuOP/eQP7vuG1fbTyZD14qyzvnDw6tprXht2NFL/9lyRudzS42icK7r0L2/vvPTS3Xff/a53vUthIKxZ07Gvfe1r733ve8/bfdbmR94HABz1L1SSwL/tuSKjui1VbzQnkRcE+dHBIzX6iqYxRoh9+cdP/Lu7H/zliy/ZG7MzwZB7tt27/t93/08f+PWLBY80aBj2+geAhJmYNZJYFHuuiI8Rf7UI1NsII3puFXOitaIViNGORivEo7JGe3tF+JjGn4EkUBjepk8SixQ+OwiJHI9cRtV81PBITXBtfGdSXC0ajFLjIYy1qJVFiPbuLcOPtrf/78caf/Pzf3rBtT+5maedveusD+59w8dec/BV57yMu1rSqCVeLeGQ6hVoXrd1MyNFIljSKDMxiVFxNCoNkqScFGCN1kUij6NWLcZyqzS+G6eeuWXt/mOPrr1gf075tLOzd+360Gtf/8k3VF513vnR/zM1E9NXb9RUSllB6www0tV/A9qiyUg6OtE4g0f/GgCee+65c845R2HsrFnTsaeeeuriiy8GgGf/w3UAfPUPINoQCi+JaRUmtxmthFbBx4BuJ/QrzUzi2694ZH4krCuaxhiFbP2Z5//4e+t/1dx84SX15Fvb8Xb2Wc6/vfyS6be94tUvG0isf+ks0JuJ6HyMPEewI1FXb/SBhqSESj6pdVtdGTR8OBgp+SvmJLlasmTElf9UmVG/yhDa4lSUcbTaa7Q9V7STkaB9qfhQkSDVYs8V9TIT6MzPBrgOOE/+6tTSU//89ad+9Ei7tfXLUx33BWUo1vSttGv38J7zX1162W9fuv89L3/dhWeH3rLR0ig+tUQDRKKj91QJeMi6rZgZzFIiUcSjNxOLpVF0JMVZo7mQSOPojHWW54r69mTnua889tBXf/bwxnPPbD5/qv3Si8rdWCuWlc4667Jzzt133gW1V7/2t1/92gv3DChVL1P923NF5nJLj6Nxrqi7V+S6KqNgzZq+db8tpLdXxFD/QiUJ/NueKzKq21L1RnMSeUGQHx08UqOvaBpjlITnied+dfyhJ47/5MmHn3n+8VO/bL9oP9BzGlrpLOcV5+8pv6z0gddfVHv9RRcN7BbXvzcLEGOvfwBImIlZI4lFseeK+BjxV4tAvY0woudWMSdaK1qBGO1otEI89lzRzkAixyOXUTUfNTxSE1xbnM/sk8da1MoiRHuPLMbC2ZQej5SQqaoLv6TAiAFJUk4S1CkrjdJQS7xawiHVx928butmRook4/ovjkbZc0XmZnSm54poSBTxGMitZrVwMcpjRmvm1tQYsTGKmkopK2idAUZnYP3bvSJr2Vhor4ir/gHsuSLUx4BuJ/QrzQx6MxcZJPZ1JAaEc0XTGCM0M4WoFra7DhXWiT5sazQZiZq/ufqX4tFDguIJX8qPRF29w0Dw+Cq9p/QpTP2zMVLyV8xJcrVkyagYMzprtMWpKONotdfoXV5kF2njUSM+cYApAyBh3DBTEORT5iMlVBwk8tRGekH7UvHB8KQf5N5zb6SN70xGfKiMMC5cjELxUWwO5hOL4LouOH7rgNNrofd3yAfNDIonFRIsJzqZcb1ATqCNQPFDCwMxSQvLlNZBgmWGTKn/suO3fpohnM6AT/9aEiUDhDSRGKz/omkUAYkXAmnzWaOZyoVtRtNzi7XkqpMiUR1rE7lVrRYuRuE2lxmNZkZfo5hzq88odXLjWmekWuKMUB80EBFJ4erfmrUsjaH+XQBwwQVwAi2E33DEfeQKk8+MRhUyglbFpxueXbfj/ao+dqZ5kBBnmLxIc6xoGmMkxFOcauG461BhLffhWKN5kORU/0kP0gQk4PUSm2uhLQFDSPAIYt1G1VsU57Spf9YZTR8jSbUI1NsIo4LNaE1G2mhzr6hs0AKA/hrt7RU5DtLGp3TEJ2ZpAyBhHK91A3+n95ESKg4SeWojvaB9qfhgeNIPMnQ/vxZp4wUX8aEywrhwMQrFR7G5mE8sguM44Ppt9yxR6FxR0AfNDIonFRIsJzqZcbxAbqCNQPFDCwMxSQvLlNZBgmWGTKn/suu3fpohnM6AT/9aEiUDhDSRGKz/omkUAYkXAmnzWaOZyoVtRtNzi7XkqpMiUR1rE7lVrRYuRuE2lxmNZkZfo5hzq88odXLjWmekWuKMUB80EBFJ4erfmrUsjaH+HQBwwAFwAy044ASUBfeRK0w+MxpVyAhaFZ9ueHbdjver+tiZ5kFCnGHyIs2xommMkRBPcaqF465DhbXch2ON5kGSU/0nPUgTkIDXS2yuBZG4hpDgEcS6jaq3KM5pU/+sM5o+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYD6xCK7rguO39lwRDxLFQKiPLIyWRpEp9V92/NZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8LAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQxhASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYj6xCI7jgOu39lwRDxLFQKiPLIyWRpEp9V92/dZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8DAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQ1hASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYD6xCK7rguO39lwRDxLFQKiPLIyWRpEp9V92/NZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8LAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQxhASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYj6xCI7jgOu39lwRDxLFQKiPLIyWRpEp9V92/dZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8DAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQ1hASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYD6xCK7rguO39lwRDxLFQKiPLIyWRpEp9V92/NZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8LAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQxhASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012p4ryh+JPLWRXtC+VHwwPOkHGRg+PaTBCOPCxSgUH8XmYj6xCI7jgOu39lwRDxLFQKiPLIyWRpEp9V92/dZPM4TTGfDpX0uiZICQJhKD9V80jSIg8UIgbT5rNFO5sM1oem6xllx1UiSqY20it6rVwsUo3OYyo9HM6GsUc271GaVOblzrjFRLnBHqgwYiIilc/VuzlqUx1L8DAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQ1hASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP01encvQHSvG5vM/V3xfhsz6eaxSCUCIbw2oRIUfNCN6tMNSbAvFR8MTzokvQgu9E7J+C1WcBBs2RgheKiM/L6k2BIrwXVd6O7KRrIB/TNGIMkMiicVEiwnOplxAb8s2LEfWqgMDEj8QMkaJUGC4tHSKLJaukgY/1IIX4qmPwEPWbfZxkiKRICHo/6LplEEJGhu81yjeZDwzWh6brGWXHVSJKpjbSS3itXCxYg5t2zzSF+jmHOrz0ie0PgAJHIxyOhMrf8C2ebiRG2u2f27PLm4OFFO9m8vTVVnGgAAwxML9ckRvZ4iVioNDQ2Xy5VKdXSsWi0PaQWJW2V6ab4WDOaDxyCUhobK5UpldLQ6Vh2JY1ierk4vtxW7Dtjw+Hx9qpL+OkZjqH8XQqeF8HNF/rcm+C3IFSafGZ2sjeo+XTgZ6Daq3rEI0Zz0M9B/kMDyo4MnAQmSE63MaIyREE9xqoXjrkOFdeq61VqjeZDkVP+ejw6eBCS95IbxZIYEjyA9VyTX7dOq/llnNH2MJNUiUG8jjAo2ozUZEdFq+BQ8/4iP9hrt7RX13qGMtPF3jMJtzFTecoq34RBhvuC1KX12EBIUT1IvaF8qPlqZiZvjOK4bb+M7k6GWygjjwsVISd+CGARjFM1J/FyRG2jRzCjiSUCilhPlqgtrQgSKSB94kfiBEjUKsDZ9ZiJIsMyQ1RJLnh8M/GRHW3TEzeu27hjJkBis/6JpFAEJnltTYw0Z1L9iHNUZTc8tWf+1kCiPtYHcqlYLF6MCzGi93BobIzZG0fSplHJE6wxVS5TRmVv/xbHN+vziyZOd7j9WYLE5MZ28/dNura90L9g/2krVVXuzudLvCrVjcARgYN/VkzNzMxMVdMdIHsS3gaFWZGvHBy/FMDEzNxvG0FpvqPYcsn2jGhtMvMZQ/w4AYFtBTuBtx54PhM4VqShMLjM6WSHVfbpw2HU7oV9BVsG7RcNv5qCXpa6PNMMkJGhONDJDGSMEUEGqheOuQ4W13IdjjeZBknf9S/Gkrn/x+HpIXENI8Ahi3cbVWxDntKl/1hlNHyNJtQiq1wijgs3orNHmXlHZMdJeo+25op2MJNiXig+GJx2SXgQXivWZfSojvy8ptsRKcF0X7Lki9vr3AyVrlAQJikdLo8hq6SJh/EshfCma/gQ8ZN1mGyMpEgEejvovmkYRkKC5zXON5kHCN6PpucVactVJkaiOtZHcKlYLFyPm3LLNI32NYs6tPiN5QuMDkMjFIKMztf4LY+v1xUYHAAYGBjqdTmdlsd6clmwWMdjAvgOV4ZL3r3a71d7cXN/a7u7DdDZOHPnI6PJKfXluLOGAEcDAwEByN6USlET/N7B3/4gPAdrtdru1vrHV2wrqbJy49SOjy8uLywu1YT/c0OBAdPcJAKDT6SQgKpWEIDIzhvp3wZ4rwn26cDLQbVS9YxGiOelnoP8ggeVHB08CEiQnWpnRGCMhnuJUC8ddhwrr1HWrtUbzIMmp/j0fHTwJSHrJDePJDAkewZ4rSo9WmRF9jCTVIlBvI4wKNqM1GRHRavgUPP+Ij/YabX+vKH8k8tRGekH7UvHB8KQfZHAcJ97GCy7iQ2WEceFiFIqPYnMxn1gEx3HA9Vv7e0U8SBQDoT6yMFoaRabUf9n1Wz/NEE5nwKd/LYmSAUKaSAzWf9E0ioDEC4G0+azRTOXCNqPpucVactVJkaiOtYncqlYLF6Nwm8uMRjOjr1HMudVnlDq5ca0zUi1xRqgPGoiIpHD1XxRbX+puFe2fnBnfCwCd5mJd9Qve9G2gMlVv+Lay0mxuttpPP3zv7Tdfu38QAAA6q0cmJutJ55YGDs4228nWqk8IN5vK4wsrAWs2m+ub7eef/uE3bz98w4EehrWjExML6/1LxhaLrJDTAAAgAElEQVQ2W0gvjan9PURXz2GIWs3ZUXLKiMZQ/w4A2N8rEmmmAd2O96v62JnmQUKcYfIizbGiaYyREE9xqoXjrkOFtdyHY43mQZJT/Sc9SBOQgNdLbK4FkbiGkOAR7O8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP012tsr6u9qBtv4lI74xCxtACSM67VO4O/0PlJCxUEiT22kF7QvFR8MT/pBBtd142284CI+VEYYFy5GofgoNgfziUVwXRccv/XOEvnnikI+aGZQPKmQYDnRyYzrBXICbQSKH1oYiElaWKa0DhIsM2RK/Zcdv/XTDOF0Bnz615IoGSCkicRg/RdNowhIvBBIm88azVQubDOanlusJVedFInqWJvIrWq1cDEKt7nMaDQz+hrFnFt9RqmTG9c6I9USZ4T6oIGISApX/wUxb6toX7U2WasOAkBnNYvNItSGyqPj0/VG/cYD3cM5W/W5xfVsIZSGRqrjs4uNxi2HuttF23fOzuE/b7SzjKH+XQCQnitCfeQKk8+MRhUyglbFpxueXbfj/ao+dqZ5kBBnmLxIc6xoGmMkxFOcauG461BhLffhWKN5kORU/0kP0gQk4PUSm2tBJI4hJHgE6bmiiI8gzmlT/6wzmj5GkmoRqLcRRgWb0ZqMtNHmXlHZoAUA/TXanivKH4k8tZFe0L5UfDA86QcZGD49pMEI48LFKBQfxeZiPrEIjuOA67f2XBEPEsVAqI8sjJZGkSn1X3b91k8zhNMZ8OlfS6JkgJAmEoP1XzSNIiDxQiBtPms0U7mwzWh6brGWXHVSJKpjbSK3qtXCxSjc5jKj0czoaxRzbvUZpU5uXOuMVEucEeqDBiIiKVz9F8O8L6AbHK2NDlXHRgcAAPLbLAIAgKHqzFS1u1nUWVleyeWXfkojU7OTB7p/ry3XV/LAwGsM9e8AgD1XJNJMA7od71f1sTPNg4Q4w+RFmmNF0xgjIZ7iVAvHXYcKa7kPxxrNgySn+k96kCYgAa+X2FwLInENIcEj2HNF6dEqM6KPkaRaBOpthFHBZrQmI220uVdUNmgBQH+NtueK8kciT22kF7QvFR8MT/pBBoZPD2kwwrhwMQrFR7E5mE8sguu64PitPVfEg0QxEOojC6OlUWRK/Zcdv/XTDOF0Bnz615IoGSCkicRg/RdNowhIvBBIm88azVQubDOanlusJVedFInqWJvIrWq1cDEKt7nMaDQz+hrFnFt9RqmTG9c6I9USZ4T6oIGISApX/4Ww9Xq9u1VUrY2WYGis1t8synV3ZKhSKXf/arfauewVAUBlbHRv96/m+npeIPiMof5dALDnikSaaUC34/2qPnameZAQZ5i8SHOsaBpjJMRTnGrhuOtQYS334VijeZDkVP9JD9IEJOD1EptrQSSOISR4BHuuKD1aZUb0MZJUi0C9jTAq2IzWZKSNNveKygYtAOiv0fZcUf5I5KmN9IL2peKD4Uk/yMDw6SENRhgXLkah+Cg2F/OJRXAcB1y/teeKeJAoBkJ9ZGG0NIpMqf+y67d+miGczoBP/1oSJQOENJEYrP+iaRQBiRcCafNZo5nKhW1G03OLteSqkyJRHWsTuVWtFi5G4TaXGY1mRl+jmHOrzyh1cuNaZ6Ra4oxQHzQQEUnh6r8I5p0qGhitVYcAYLhaq/Q2ixZz3SzqbxCVhoZKeYEYGvJ+6ii/DSs+Y6h/BwDsuSKRZhrQ7Xi/qo+daR4kxBkmL9IcK5rGGAnxFKdaOO46VFjLfTjWaB4kOdV/0oM0AQl4vcTmWhCJawgJHsGeK0qPVpkRfYwk1SJQbyOMCjajNRlpo829orJBCwD6a7Q9V5Q/EnlqI72gfan4YHjSDzIwfHpIgxHGhYtRKD6KzcF8YhFc1wXHb+25Ih4kioFQH1kYLY0iU+q/7Pitn2YIpzPg07+WRMkAIU0kBuu/aBpFQOKFQNp81mimcmGb0fTcYi256qRIVMfaRG5Vq4WLUbjNZUajmdHXKObc6jNKndy41hmpljgj1AcNRERSuPovgK3XF1e8raJhAAAYqdZGAABgdSnPzaJmo7EOAAADlWolt72izc1W94/SUCk3EGzGUP8uANhzRSLNNKDb8X5VHzvTPEiIM0xepDlWNI0xEuIpTrVw3HWosJb7cKzRPEhyqv+kB2kCEvB6ic21IBLHEBI8gj1XlB6tMiP6GEmqRaDeRhgVbEZrMtJGm3tFZYMWAPTXaHuuKH8k8tRGekH7UvHB8KQfZGD49JAGI4wLF6NQfBSbi/nEIjiOA67f2nNFPEgUA6E+sjBaGkWm1H/Z9Vs/zRBOZ8Cnfy2JkgFCmkgM1n/RNIqAxAuBtPms0Uzlwjaj6bnFWnLVSZGojrWJ3KpWCxejcJvLjEYzo69RzLnVZ5Q6uXGtM1ItcUaoDxqIiKRw9Z+/9beKKrWxcu+1ytjYfgAAWK3ntVnUbs5PzZ7sAADsrU2OD+eDAtqNpcZW989yZWTn7xUx1L8DAPZckUgzDeh2vF/Vx840DxLiDJMXaY4VTWOMhHiKUy0cdx0qrOU+HGs0D5Kc6j/pQZqABLxeYnMtiMQ1hASPYM8VpUerzIg+RpJqEai3EUYFm9GajLTR5l5R2aAFAP01encvQHSvG5vM/V3xfhsz6eaxSCUCIbw2oRIUfNCN6tMNSbAvFR8MTzokvQgu9E7J+C1WcBBs2RgheKiM/L6k2BIrwXVd6O7KRrIB/TNGIMkMiicVEiwnOplxAb8s2LEfWqgMDEj8QMkaJUGC4tHSKLJaukgY/1IIX4qmPwEPWbfZxkiKRICHo/6LplEEJGhu81yjeZDwzWh6brGWXHVSJKpjbSS3itXCxYg5t2zzSF+jmHOrz0ie0PgAJHIxyOhMrf/cDdsqAqjUxvZ/bm0NYK2+uDJbqWSJqL3eWKrPz8wcXd0GANh3w9zs+JDsIkNQmvPTC2vdv/ePjY3kg4LTGOrfhdBpIfxckf+tCX4LcoXJZ0Yna6O6TxdOBrqNqncsQjQn/Qz0HySw/OjgSUCC5EQrMxpjJMRTnGrhuOtQYZ26brXWaB4kOdW/55MGz/EfP/3tx569/4lTjzz7y0ef/WXsQmvWcNv3srNfc8HuN+8t/darzv3tX7sg9H/RFS1StwL1Tq0/XIqa4YzWZEREq+FT8PwjPtprtLdX1HuHMtLG3zEKtzFTecsp3oZDhPmC16b02UFIUDxJvaB9qfhoZSZujuO4bryN70yGWiojjAsXIyV9C2IQjFE0J/FzRW6gRTOjiCcBiVpOlKsurAkRKCJ94EXiB0rUKMDa9JmJIMEyQ1ZLLHl+MPCTHW3RETev27pjJENisP6LplEEJHhuTY01ZFD/inFUZzQ9t2T910KiPNYGcqtaLVyMCjCj9XJrbIzYGEXTp1LKEa0zVC1RRmdu/edtzd5WEYzUasGtkNHxsf1HjG8WdVZmxyoL/dM67Xartbm5td3p/Xtg36GpufnZWjk5yMnpytCM6H+HJ5aac6Ma2ForizNTU0dObAMAwN5rZ6Yy3TEzZAz17wAAthXkBLaLej4QOlekojC5zOhkhVT36cJh1+2EfgVZBe8WDb+Zg16Wuj7SDJOQoDnRyAxljBBABakWjrsOFdZyH441mgdJ3vUvxeMCOKtPnvroXQ+vPvkcWLOW3jae+dXGM7+657Hn/6/7nz5wycB/HbvsN17u3QcJ1SBY8rHqTa0/XIqa4YzOGq22T2Hzr6//ojXanivayUiCfan4YHjSIelFcKFYn9mnMvL7kmJLrATXdcGeK2Kvfz9QskZJkKB4tDSKrJYuEsa/FMKXoulPwEPWbbYxkiIR4OGo/6JpFAEJmts812geJHwzmp5brCVXnRSJ6lgbya1itXAxYs4t2zzS1yjm3Oozkic0PgCJXAwyOlPrP2frbxXFTs2M1sb2HVnbAFirLzZmKzq7LQrW2Vpb3RL838C+0bFqZQjaAJLvfuts97eXYlZqt5Mv3mzMTU0FvuKu3W611psrjZU1f8/qwI0LC7l9DR6rMdS/C/ZcEe7ThZOBbqPqHYsQzUk/A/0HCSw/OngSkCA50cqMxhgJ8RSnWjjuOlRYp65brTWaB0lO9e/5KOD5f76/9bFvbgDA0NDQe9/73iuvvPLKK698y1vecu6558autWYtas8999z999//3e9+97777vvKV76y+uQzV/7l+vy7hyd+YwggvqJF6lag3qn1h0tRM5zRmoyIaDV8Cp5/xEd7jbbnivJHguJJ6gXtS8VHKzNxY/j0kAYjjAsXIyV9C2IQjFE0J/ZcEQsSP1CiRgHWps9MBAmWGbJaYsnzg4Gf7GiLjrh53dYdIxkSg/VfNI0ifwqVXnX9MLnXv2Ic1RlNzy1Z/7WQKI+1gdyqVgsXowLMaL3cGhsjNkbR9KmUckTrDFVLlNGZW//5WrNe97aKatFTM6O1sX1Hbt3obRaNmtksGjxwqFoO7gS1263W+npzfWO709k4cetNJ26d2Xf11Pzi7Jh4q2Zg7/6y8EvqhsuSjabtk8eOnExCeMPs/PzkaE7fgsdtDPXvAIA9VyTSTAO6ndCvIKvg3aLhN3PQy1LXR5phEhI0JxqZoYwRAqgg1cJx16HCWu7DsUbzIMm7/iV4pu955Mj9mwBw/fXXf/7zn7/00kvBmrU0du65577jHe94xzveAQCPP/745OTk3/7t307etfnjp3/52d+6VKwGwZKPVW9q/eFS1AxndNZotX0Km399/Ret0fZc0U5GEuxLxQfDkw5JL4ILxfrMPpWR35cUW2IluK4L9lwRe/37gZI1SoIExaOlUWS1dJEw/qUQvhRNfwIesm6zjZEUiQAPR/0XTaMISNDc5rlG8yDhm9H03GItueqkSFTH2khuFauFixFzbtnmkb5GMedWn5E8ofEBSORikNGZWv+5Wv9U0b6x8dheUGm0Vt1769EtgLWlxQYY2SwaGJmYr0+V4//R3lxZWpybnT16cquzceJztepmvbEwhu/XDFSmlxpYECK2fYcmpyYnJmqV02SbCABY6t8Fe64I9+nCyUC3UfWORYjmpJ+B/oMElh8dPAlIkJxoZUZjjIR4ilMtHHcdKqxT163WGs2DJKf693wS8Xz+gc0j929eeOGFCwsL73vf+2Ke1qyls8suu+z48eN33HHH7/3e7/3pfU+94vzdf/DmC8PVG6lbgXqn1h8uRc1wRmsyIqLV8Cl4/hEf7TXanivKHwmKJ6kXtC8VH63MxI3h00MajDAuXIyU9C2IQTBG0ZzYc0UsSPxAiRoFWJs+MxEkWGbIaoklzw8GfrKjLTri5nVbd4xkSAzWf9E0ivwpVHrV9cPkXv+KcVRnND23ZP3XQqI81gZyq1otXIwKMKP1cmtsjNgYRdOnUsoRrTNULVFGZ27952nN+mKzAwAwMLRZn51djv7/ZmsIYAtMbhaJrDRcqU0t1Gpj49UPHduAztrRyemJ5nxVckZIx/Z99OvLMxUvcLs5OzZ2ZK0D0GkPjY6fXhtFwFL/DgDYc0UizTSg2wn9CrIK3i0afjMHvSx1faQZJiFBc6KRGcoYIYAKUi0cdx0qrOU+HGs0D5K861+I57ubv/jDbz0CAHajyBqvXXfddQDw/ve//xPLT7z9lee++dKSuG4F1Ztaf7gUNcMZnTVabZ/C5l9f/0VrtD1XtJORBPtS8cHwpEPSi+BCsT6zT2Xk9yXFllgJruuCPVfEXv9+oGSNkiBB8WhpFFktXSSMfymEL0XTn4CHrNtsYyRFIsDDUf9F0ygCEjS3ea7RPEj4ZjQ9t1hLrjopEtWxNpJbxWrhYsScW7Z5pK9RzLnVZyRPaHwAErkYZHSm1n+O1t8qgs7qsc+tHktwzX6zqGvl8fnZxaUPHd8G2KgvLM9Vx/g3i0qlofKw/wV3wzNz4/X3HN0A2Do2PTNZM7I/lZ8x1L8Lxs4V3f3TJ7/12M+/92TrkWefe/y5dvvFl7LIibUzyUpn7XrF+QOvuaB05aUX/NYrL3z3ay7SqH/xSqru032Bfteh0IuCD8cazYNE7UYw8b4FwlmFSCb7D9JYtsV4pu95BAA+/OEP240ia+x23XXXfehDH7r99tv/j3ue+OoHXhOo3kjdotUL6fVHwwcSZkcWM1qTERGthk/B84/4aD917uoF6P4r0sZFNuITs7QBkDCO17qBv9P7SAkVB4k8tZFe0L5UfDA86QcZHMeJt9jOZMiHygjjwsUoFB/F5mI+sQiO44Drt8i5oqAPmhkUTyokWE50MuN4gdxAG4HihxYGYpIWlimtgwTLDJlS/2XXb/00QzidAZ/+tSRKBghpIjFY/0XTKAISLwTS5rNGM5UL24ym5xZryVUnRaI61iZyq1otXIzCbS4zGs2MvkYx51afUerkxrXOSLXEGaE+aCAiksLVf37WrC+udlSd1+qLDZNghDZUrY0OAABAq7mynkmPYzMzhwYBAGBjYWpuJYs+szOG+ncAIHZmCBxwAsqC+yQozEPPnLrm+D/Uvtb4s9Ufn/jZzx9+9jm7UWTNhLVffOmh7eeXf/r0n97/yLV/t/ruO+7/8dOn0ta/eCVV94FoL5p3HZG+sF4UfDjWaB4kgjsTnbdduB6kAeC+rVP/+LNfvPKVr/z85z8fc7BmjcG+8IUvDA8Pf+OR51afaIdqMlS3aPVCev3R8AHhXMtmRmsy0kar7VPY/Ovrv+ip09sr6u9qBtv4khbxiVnaAEiY/maYk7h5JvOREioOEnlqI72gfan4YHjSD3J3ZzLaxgsu4kNlhHHhYhSKj2JzMJ9YBNd1wfFb7yyRf64o5INmBsWTCgmWE53MuF4gJ9BGoPihhYGYpIVlSusgwTJDptR/2fFbP80QTmfAp38tiZIBQppIDNZ/0TSKgMQLgbT5rNFM5cI2o+m5xVpy1UmRqI61idyqVgsXo3Cby4xGM6OvUcy51WeUOrlxrTNSLXFGqA8aiIikcPWfmzXri6sAADB47W0/fFhkD9xyqLtTs5HbZlGpd66n02q1s+myPDE7fXAAAKCzOjs1v55Nr9kYQ/27ACA9V4T6iBTmiz969IrFbzS2ngaA66+/fnZ29q677mq1Wq41a9zWarXuuuuum2+++frrrweAb/9s++Cx++740dbOeNtFjgfrRcHHZVijeZAI7kx03nbhepAGgK/85GkA+PCHPzw4OBhzsGaNwYaGhj70oQ8BwN/95BehmgzVLVq9kF5/NHxAONeymdGajLTRavsUNv/6+i966rS/V5Q/EhRPUi9oXyo+WpmJm0P/VmINRhgXLkY4ngSEgjGK5sT+XhELEj9QokYB1qbPTAQJlhmyWmLJ84OBn+xoi464ed3WHSMZEoP1XzSNIiDBc2tqrCGD+leMozqjNXPb+mXn7372k+88tfmjZ59uPvv0qRd+hQ+etcJb6ayzLjvnvFefe/5bL77kqksue9dlr1KeR9oaRZ+JZI3yIoVblVuZiNZxaYuEUYQL2xjJI3AxkoyOeIxyspX+VtHYRG2kLPxVnvHx6vSdd3YANuqLy3Oj1azw9W1zc7P7x8DQcGbfBleZmp2Yf+etGwDbJ2amF2uL48Pyi3aEMdS/AwCMv1c0+721z963BgBvetObjh07dvnll2eVC2tnog0ODl5zzTXXXHMNAHz/+9+/4YYbms3mv7nznz69/fwfXllWrH/Z6qni03Wh33WI+krnw7FG8yDBHuRS3reA94iCP8z0H6Sxx2lBtPu2TgHAG97wBrBmzZi98Y1vBID7tp4X162gelPrD8UnaBnO6KzRavsUNv/6+i966rTnivJHIk9tpBe0LxUfDE/6QQbtnUkSI4wLF6NQfBSbg/nEIriuC47f2nNFPEgUA6E+sjBaGkWm1H/Z8Vs/zRBOZ8Cnfy2JkgFCmkgM1n/RNIqAxAuBtPms0UzlwjajU+e2+cxT4/d+9fVf+4tPrJxYfGTte08/YTeKdrS1X3zx4V88860nfnbLD1ev/9bS+7751R8/08Jd2TSKPhPJGuVFCrdSuYxrHZe2SBihPmggIhJDjHbcuaKVRW+rqFqrCjeKAGB4zPsOuI364nJGB3sCtl6vr3S/KW9oZKScWbel6szstXsBoPuzRTkQN2QM9e8CANe5or94cP2z9605jnPjjTfed999dqPIWpZ2xRVXrK6uTk1NOY7znxoP/X8P/qzob7vI8WC9KPjYc0VebvFoG890wHsr35o1Q9YtsI3tX4VqMlS3aPVCev3R8AHhXMtmRmsy0kar7VPY/Ovrv+ip0/5eUf5I5KlFNwsjfan4YHjSDzI4jhNvsZ3JkA+VEcaFi1EoPorNxXxiERzHAddv7e8V8SBRDIT6yMJoaRSZUv/lwEed/DRDOJ0Bn/61JEoGCGkiMVj/RdMoAhIvBNLms0YzlQvbjE6X2y//9Ee/9ffHvrH1CAD8zu/8zm233dZoNJ555hnX2o61/hfOvP/97weAf3ji8av/+x31Rx8yMwG04sjqVrF60UjhViqXca3j0hYJI9QHDUREYoiRdHTEY5SLrSwueVtF42NJW0WhzaKlxUa2eybt5tzk7InuVtG+sfFqZueKAGB4fHb66h7x0+hnixjq3wEAlt8rWv359sfu+T4AHD58eG5ubs+ePaZYW7MmsD179txyyy2f/OQnAWDy75s/ePJZlfpP+QDA97aLHA/Wi4IPxxrNg0RwZ6LztgvXgzQA/Lhl94qsGbfuwbVHn30hVJOhukWrF9Lrj4YPCOdaNjNak5E2Wm2fwuZfX/9FT532XFH+SOSpjfSC9qXig+FJP8j6O5MkRhgXLkah+Cg2B/OJRXBdFxy/teeKeJAoBkJ9ZGG0NIpMqf8yz8ehqMk1NUY6yQ1wJNV/0TSKgMQLgbT5rNFM5cI2o1Pk9s/WvvfR++4GgOuuu+4HP/jB0aNHJyYmDh48eMEFF+Agre0E637hzPT09N/8zd98//vff+Mb3/j8iy/8u3/8+7kfrkZd2TSKPhPJGuVFCrdSuYxrHZe2SBihPmggIhJDjHbYuaKVxXq3/AdGk08VAQCUa7VK9ptFrebS3Pjo6Mfv3AIAgL2HZmbGstwqAoCRydnJ/afbzxYx1L8LACzniv7Tdx4EgJGRkU9/+tOm+FqzpmCf/exnu2faZk4+pFL/KR8A+N52kePBelHw4VijeZAI7kx03nbhepDu/1UqZbwIWTuzrPu8+YtfvRSqyVDdotUL6fVHwwfEsyOTGa3JSButtk9h86+v/6KnTvt7RfkjQfEk9YL2peKjlZm4OYX7LRAqIxxPAkLBGEVzYn+viAWJHyhRowBr02cmggTLDFktseT5wcBPdrRFR9y8buuOkQyJwfovmkaRf5WEXnX9MLnXv2Ic1Rmtmtu/3Hjw5h9+x3GcP/qjP7rpppvwobK2w+1Nb3rT/ffff/jw4SNHjnzm+9+9pHTOv3ntr/v/zaZR9JlI1igvUrhVuZWJaB2XttB/C4Ent8YY7aTfK1pZrK8BAMBAdXxM/kM85bHayPTJ1U5vs6gaP96zvjgx2kjccypPLi5OlGMvd9YXpyebwSvb7dbmerO5srqx3X9t8OCN6NVekJW52uii5D28kan6QtofHSqNzsyN199zdOM0+tkihvp3AID+e0WP/uK5u3/65O7du48dO2ZPFFnL1/bs2XP77bdfeeWVX1//l41n2/teVkquf9nqqeLTdaHfdYj6SufDsUbzIMEe5FLet4D3iII/zPQfpLHHaSEea9YyNGHdCqo3tf5QfEJA0/hI+03bi1G02j6Fzb++/oueOr29ItcFcKItXsSBNmauG2qlb0v135wKhPDaCN+UPhEkinh2GJJgXyo+GJ50SHoRXOidkvFbrOAg2LIxQvBQGfl9SbElVoLrutDdlY1kA/pnjECSGRRPKiRYTnQy4wJ+WbBjP7RQGRiQ+IGSNUqCBMWjpVFktXSRMP6lEL4UTX8CHrJus42RFIkAD0f9F02jCEjQ3Oa5RvMg4ZvRSrn9H9tP/u8rJwDg8OHDdqPo9LbuF86cffbZf/Inf/K/fedblQtf/sahi3r/x6ZR9JlI1igvkkwiAWvFXAwyAh2tM6ItOoykoyMeo+zN3yoarSlsFQGMjI2N3LS6CsLNos7W2smtxBDtGv4rYVsnj916MuG6gX2HJufmZmsjSVtBnY3VkxuJ3cMAbOqciBoam5k5VP/Indvdny2arM1n+j14Boyh/l0InRbCzxWh20hBhflvP34MAN797ndfccUV2dG3Zk1gBw4cqFard9999/GfPPmxN786uf7FK6m6T/cF+l2HQi8KPhxrNA8StUe7xPsWcDGV6+ew/yCNZVuIx5q1DM2v3kjdotUL6fVHwwfEsyOTGa3JiIhWw6fg+Ud8tJ867e8V5Y9EntpIL2hfKj4YnvSDDI7jxFtsZzLkQ2WEceFiFIqPYnMxn1gEx3HA9Vv7e0U8SBQDoT6yMFoaRabUf9n1Wz/NEE5nwKd/LYmSAUKaSAzWf9E0ioDEC4G0+azRTOXCNqOVcvvZB0+C/SacM8n6Xzjz2R/c57/KplH0mUjWKC9SuJXKZVzruLRFwgj1QQMRkRhiJB0d8RhlboGtIpVTRQAAlfGx/d2/NurGv4ZuYHDvvgMHD93w0Ztv+/oPN9eX5pI3igxbeWJ2+sBp9LNFDPXvAAD994rqDz8OAFdddZVBstaspbHR0VEAuOMnTwBI6j/lAwDf2y5yPFgvCj4cazQPEsGdic7bLlwP0rFXrFkzbMGaDNUtWr2QXn80fEA8OzKZ0ZqMtNFq+xQ2//r6L3rqdH60/zAA4HtfyJKWODpx4KKPUMRWSexihb648ewwJKI5IvWhIkGqRbAzaYCRzDRzq4hNgiTUWfRcUf8TeWnx6CDBwuiNddQpvnedzRhJNUonM1oaxaeWaIBIdDT9CXjIuq2YGcxSIlHEo7KA0jgAACAASURBVDcTi6VRdCTFWaO5kHDNaHluHz317Fvu+qvdu3d/73vfsx9wPnNsdXX1yiuvfOGFF1b+9Q2vOa/7k1RcGmWk/tmUIb5GJ5nBGZ3AhW0dyYqRdHTijAaP/jUAuK7KKFizpm/dp/pn/8N1AMBR/0IlCfxbdK7It4v/61c6L7509913v+td7zKeAmvWFOxrX/vae9/73vPPPuuJj/4rjfpP6YN56tx1pO9FyUdjjTaIJOXdC6pykRcE2RZEO+fId8Cu19YMm+u6u3btAoD2x/eH6lxSvdEwTBpV7BldIEY7Gq0Qj8pTpz1XlD8SeWojvaB9qfhgeNIPMhTvM/tURqH4KDYX84lFsOeK4oGYpIVlSusgwTJDptR/2fVbP80QTmfAp38tiZIBQppIDNZ/0TSKgMQLgbT5rNFM5cI2o+W5veOxH4P9Jpwzz7pfOAMAX/npeu8lNo2iz0SyRnmRwq1ULuNax6UtEkaoDxqIiMQQo510rsjamWwM9e8AAP1cUefFl8CeK7JWJOueK/rFr14EkNR/ygcAvrdd5HiwXhR87LkiL7ciPNasZWjBmgzVLVq9kF5/NHxAPDsymdGajLTRavsUNv/6+i966vT2irq76JE2vqRFfGKWNgASxvVaJ/B3eh8poeIgkac20gval4oPhif9IPc+cxFp4wUX8aEywrhwMQrFR7E5mE8sguu64Pit9xtF/u8VhXzQzKB4UiHBcqKTGdcL5ATaCBQ/tDAQk7SwTGkdJFhmyJT6Lzt+66cZwukM+PSvJVEyQEgTicH6L5pGEZB4IZA2nzWaqVzYZrQ8t3/3s5+AfcfqjLTuG0PHH3249282jaLPRLJGeZHCrVQu41rHpS0SRqgPGoiIxBAj6ejYzyNbK4Qx1L8LANLfK0J94gpzzjnnmOFpzVpqu/DCC/1/JNZ/ygcAvrdd5HiwXhR8ONZoHiSCOxOdt124HqRjr1izZtiCNRmqW7R6Ib3+aPiAeHZkMqM1GWmj1fYpbP719V/01GnPFeWPRJ7aSC9oXyo+GJ70g6y/M0lihHHhYhSKj2JzMZ9YBHuuKB6ISVpYprQOEiwzZEr9l3k+DkVNrqkx0klugCOp/oumUQQkXgikzWeNZioXthktz+0Pn3kKAN7+9rfjSKydvtYd9B9uP937N5tG0WciWaO8SOFWKpdxrePSFgkj1AcNRERiiJE9V2RtZxhD/TsAQD9XZM1aoS2x/lM+APC97SLHg/Wi4GPPFXm5FeGxZi1DC9ZkqG7R6oX0+qPhA+LZkcmM1mSkjVbbp7D519d/e66ouEjkqY30gval4oPhST/I+juTJEYYFy5GofgoNgfziUWw54rigZikhWVK6yDBMkOm1H+Z5+NQ1OSaGiOd5AY4kuq/aBpFQOKFQNp81mimcmGb0fLcdl56Eey5ojPSel8488Kvev9m0yj6TCRrlBcp3ErlMq51XNoiYYT6oIGISAwxsueKrO0MY6h/FwC4zhVZs1ZQS6z/lA8AfG+7yPFgvSj42HNFXm5FeKxZy9CCNRmqW7R6Ib3+aPiAeHZkMqM1GWmj1fYpbP719d+eKyouEnlqI72gfan4YHjSD7L+ziSJEcaFi1EoPorNxXxiEey5onggJmlhmdI6SLDMkCn1X+b5OBQ1uabGSCe5AY6k+i+aRhGQeCGQNp81mqlc2Ga0PLdds9+EcwbaRRddFPo3m0bRZyJZo7xI4VYql3Gt49IWCSPUBw1ERGKIkT1XZG1nGEP9OwBgzxVZO80tsf5TPgDwve0ix4P1ouBjzxV5uRXhsWYtQwvWZKhu0eqF9Pqj4QPi2ZHJjNZkpI1W26ew+dfXf9FT5+5egOiuO7aYhXbsXVRXpR+eEK2SgRBem1AJCj7oBzVONyTBvlR8MDzpkPQiuNA7JeO3WMFBsGVjhOChMgptwSZjS6wE13WhuysbyQb0zxiBJDMonlRIsJzoZMYF/LJgx35ooTIwIPEDJWuUBAmKR0ujyGrpImH8SyF8KZr+BDxk3WYbIykSAR6O+i+aRhGQoLnNc43mQcI3o+W5tWatZ2waRZ+JZI3yIskkErBWzMUgI9DROiPaosNIOjpWbawVwhjq34XQaSH8XJH/rQl+C4jCWLNWTEusf/FKqu7j9UK961DoRcGHY43mQaL2aJd43wIupnL9HPYfpLFsC/HkYu2lqepMo/t3aXRmaW6sJL9meXpsernd/cfIVH1hfFjg2VpvLC83ms3NzTYMl8vlkdFqtTKc1INZPKGrWq1WG0pDQ0PyHnq2ubK03FhZWd+EoXK5XKmOVUeG1K5srzeWlhvN5mYLhoZHKtUxSRoyML96I3WLVi+k1x8NHxDPjkxmtCYjIloNn4LnH/HRfur09op671BGWux9ymAbM5W3nOJtOESYL2DqreCzg5CgeJJ6QftS8dHKTNwcx3HdeBvfmQy1VEYYFy5GSvoWxCAYo2hO4ueK3ECLZkYRTwIStZwoV11YEyJQRPrAi8QPlKhRgLXpMxNBgmWGrJZY8vxg4Cc72qIjbl63dcdIhsRg/RdNowhI8NyaGmvIoP4V46jOaHlu8VGxdgYam0bRZyJZo7xI4VblViaidVzaImEU4aKsdQa0RZORZHREjE6dOnXeeedhY2fNGoM9++yzoX8z1L8DANhWkBPYLur5QOhcEaYw1qwV0xLrX7Z6qvh4vVDvOkR9pfPhWKN5kGAPcinvW8B7RMEfZvoP0tjjtBBPLtZura+cPNnp/qM5X58ZG5dtf7SXFxZOnNzq/mMANtuo08rC1NTMwomNTuQ/Bg9cOz07Pz0m2M4xhSdgrebS4tzszMKJrQ7Avhu/uT5XlVwB0G7Wpyen5iN0BvdfOz0/P11N3JpqNeYmJ2eOrW4HXxzYe3BidmFuYiS/DSNh3QqqN7X+UHxCQNP4SPtN24tRtNo+hc2/vv6Lnjrt7xXlj0Se2kgvaF8qPhie9IMMruvG23jBRXyojDAuXIxC8VFsDuYTi+C6Ljh+a3+viAeJYiDURxZGS6PIlPovO37rpxnC6Qz49K8lUTJASBOJwfovmkYRkHghkDafNZqpXNhmtDy31qz1jE2j6DORrFFepHArlcu41nFpi4QR6oMGIiIxxEg6OnFGrzj3HAB44IEHcJzWrHFYt8BedZ73JasM9e8CgP29ImunuSXWf8oHAL63XeR4sF4UfDjWaB4kgjsTnbdduB6kY6/kY9vLi0stmVN7aWFpK9Gj1ZirVUY/cmt8owgAtleP3/Seyth8U7alw4enC6q5tDA3PTFWGR6+/D2/f+uJLQSbqIeVubHqdUfidLbXjt80Njq+uC68dLM+MVr9eGSjCAA6Wydv/chodbohpWfMgjUZqlu0eiG9/mj4gHh2ZDKjNRlpo9X2KWz+9fVf9NRpf68ofyTy1KKbhZG+VHwwPOkHGRzHibfYzmTIh8oI48LFKBQfxeZiPrEIjuOA67f294p4kCgGQn1kYbQ0ikyp/3Lgo05+miGczoBP/1oSJQOENJEYrP+iaRQBiRcCafNZo5nKhW1Gy3NrzVrP2DSKPhPJGuVFCrdSuYxrHZe2SBihPmggIhJDjKSjE2d0SakEACsrKzhOa9Y47P777weAved6n49mqH8HAOzvFVk7zS2x/lM+APC97SLHg/Wi4MOxRvMgEdyZ6LztwvUgHXslJ9teWqhvJru0lhYTt2Zay1NjYx8/vtYBgMH9195429fv/eHDjz/+8A/v/fptNx7aPwAAAFt3TtX63xpnFo9nzfmpj3z8c0fvXE2xSdTrYLL28RNbAACDB2645esPPP70008/fO8dNx/aNwAAnY1jE+OzKyiX5tz4xNG1DgDAvkM33n7vw48//fTjP/zmn//uwUEAgO2TnxuflPEzZsGaDNUtWr2QXn80fEA8OzKZ0ZqMtNFq+xQ2//r6L3rqtOeK8kciT22kF7QvFR8MT/pB1t+ZJDHCuHAxCsVHsTmYTyyCPVcUD8QkLSxTWgcJlhkypf7LPB+HoibX1BjpJDfAkVT/RdMoAhIvBNLms0YzlQvbjJbn1pq1nrFpFH0mkjXKixRupXIZ1zoubZEwQn3QQEQkhhhpnCu68pKXgz1XZM2wdfeKfvPSi3r/Zqh/FwDsuSJrp7kl1n/KBwC+t13keLBeFHzsuSIvtyI8udq+gwf3AUBneaG+nuTXWlpY2vb9Y7a5NF8/uQ0AgwdvvGOlWZ+bGBsdKQ8Pl0dGxybmlhpLNx4YAADorM3PzCf0xIWnb6Vy+cD+vu0bTHT2rd2YmTq6AQAwcODG+vLi1FhleGhoqDxam15qLNywDwCgc3JmaiGOcnNhaubENgDA3mtvW16aGx8tDw8NDY9UJxeWlw4fHAAA2Dg2NaOwZ2bCgjUZqlu0eiG9/mj4gHh2ZDKjNRlpo9X2KWz+9fXfnisqLhJ5aiO9oH2p+GB40g+y/s4kiRHGhYtRKD6KzcV8YhHsuaJ4ICZpYZnSOkiwzJAp9V/m+TgUNbmmxkgnuQGOpPovmkYRkHghkDafNZqpXNhmtDy31qz1jE2j6DORrFFepHArlcu41nFpi4QR6oMGIiIxxEjjXNH1ry0DwJe+9KVHH30Uh2rNGs0efvjhL3/5ywBw/ete1XuJof4dALDnigpi7dZ6c6XRaDQaK03pL4FYS2GJ9Z/yAYDvbRc5HqwXBR97rsjLrQhPrtYeqdX2A0Cnkbg506ovLm8DwP5abQR1GB5fWPrzaw8cumV5ea5Wjv33UHV2bnI/AAB0GosJPXHh6Vtlamml2be5sYFk93782YU1AADYPzk/Ww3/dNLw+NzstYMAAJ0Tc3ONiDquzM3duQ0AMHhodn6iHPq/0ujM/FR302xjcXYxl6NFwZoM1S1avZBefzR8QDw7MpnRmoy00Wr7FDb/+voveurc3QsQ3XXHFrPQjr2L6qr0wxOiVTIQwmsTKkHBB/2gxumGJNiXig+GJx2SXgQXeqdk/BYrOAi2bIwQPFRGoS3YZGyJleC6LnR3ZSPZgP4ZI5BkBsWTCgmWE53MuIBfFuzYDy1UBgYkfqBkjZIgQfFoaRRZLV0kjH8phC9F05+Ah6zbbGMkRSLAw1H/RdMoAhI0t3mu0TxI+Ga0PLfWrPWMTaPoM5GsUV4kmUQC1oq5GGQEOlpnRFt0GElHJ87o7XsvrVx00cpTT33wgx/89re/vWvXLqQba9Z07YUXXvjABz5w6tSp37z0oreFzhUR69+F0Gkh/FyR/60JfguIwuRgjdmxqbri71AMj8/XpyqBFzYXJ2pzTQCAkan6wnjir6aHrTlfm1hQfNexVJ1dnq0mOLTXlxfm5hbqyyc3gj+1Mbjv6rHJqZnJ2kjwXdNWfXJsVue7LkcmFxcmyt6/xHkrlUqloeHhcqVSqVTHqpVhxV+ED6akMl2fr6VIp3FLrH/xSqru4/VCvetQ6EXBh2ON5kGi9miXeN8CLqZy/Rz2H6SxbAvx5Gqtdnm8duDI51Y7K4uLzalpfOdls7641NuaKa/P45FKI5P1lUlhR6Xq+Ni+I2sbAJ1mY6UNZXwu8+GhWGupuxUFAwcnJ0fjSIdrU+P7jt+6AbBWX2jMjlZ9l8ZCfRUAAPbWphAZL1UmJ0fnfv9EB7aXF+qbE5OZS5NfvZG6RasX0uuPhg+IZ0cmM1qTERGthk/B84/4aD91entFvXcoIy32PmWwjZnKW07xNhwizBcw9Vbw2UFIUDxJvaB9qfhoZSZujuO4bryN70yGWiojjAsXIyV9C2IQjFE0J/FzRW6gRTOjiCcBiVpOlKsurAkRKCJ94EXiB0rUKMDa9JmJIMEyQ1ZLLHl+MPCTHW3RETev27pjJENisP6LplEEJHhuTY01ZFD/inFUZ7Q8t/ioWDsDjU2j6DORrFFepHCrcisT0ToubZEwinBR1joD2qLJSDI6KKM/Pnjl//L1/95oNGZmZj7zmc9gI2jNmqZ96lOf6n7D4Z/9zwf8Vxnq3wEAbCvICWwX9XwgdK4IU5gcrNVcOXlS5Sc0AGDfaOTT6O3N5srJkx2AAUh5jKe93jx5ck3Nd6As3stqr9enJ6bm8R+m3zhx7KYT9bmrJ+cXZmve27zt1noXc0obgFqQojxvx44CAOw9cMP03NxUVfru6kp9/vjJVe8f80sztYkCbRYl1r9s9VTx8Xqh3nWI+krnw7FG8yDBHuRS3reA94iCP8z0H6Sxx2khnnyt1S6Pjx/43Opq5+RifWV6uoL4bNYXljsAcKA2PtKe1u2pXBkZgI0OQHtzswUgmJLZ4RHb/8/e20c5dlT3ovvYxpLxR7cBuxuI6eaGZHoAe2RsPOIrIwcnFpcEi+SRkXN5z2LF69J5JnEnl/uQ1wWukpBYw817br+Q0L7vspCT9WLNhcSaXJJo+FijSSDWxI6tNjjWAMHdgGP1NTZqYxspDzjvD+no6Jyz61Sdql06pye1/6g1o95n12/v2vtXJZVKNWg1RltFsFLIo7tV6Wwht3DX3TsA261mF3ITlN1WewsAAOZyhRy6HbacL2RSJ08NYdhutvqrxXlMSaMw85aRvZH5R0XHAzSKDrffqL1oRSutk9j4y/M/612nua8ofiT80Pp6QfsS0cHwRB9ksG072AYTzqej6hHmC5VHHvsoNgvTCViwbRsstzX3FdEgETSE6vDMSHGUskuTly23dcMM3nBO6UyeVXJJg0OSSDTmf9I4SgGJYwJp45mjidKFrKL5sTViZCxkHKVeicoc5Vjytly6DHIdFbdwPEJ1UEOKSDR5xB0d1KODl17yn193AAA+8pGP/Nqv/dr3v/99HLMRI1Hk+9///i233HLkyBEAOPKGyw+8ZOpjLoL8twHgDLmvKMWROcEDMtTdzqcZHfdb5VzmnXdONopSc/sOXn/4ppvee9NNh68/OL7lY7hz8s53ZgvrzoXuaUin8Y7CEaVZKOaWDkzJvn37lhbmXFs7m0d/I58tIDeEeKVTr2+O+waAYavejOsaeVRC8z/iGwC6j134eLBeBHQo5mgaJIyViczHLlRvpAOvzF76/UGmUDyQAoDNWh09JbjVqLeHAHCgWMr0+4InJ8OESQAx4fFLt90ZbRUtZLOMn7hLZ3OZETVtdTouv/Q77c4QACCVySHnkQAAYDmbHe2TDTvtLhVkcZnOSU/eotkL0flHQgfY1TGTipb0SBqttE5i4y/P/6x3nea+oviR8EOLbhb6+hLRwfBEH2SwLCvYYjuTHh1VjzBfqDzy2Eex2ZhOwIJlWWC7rbmviAaJoCFUh2dGiqOUXZq8PPVVJzfM4A3nlM7kWSWXNDgkiURj/ieNoxSQOCaQNp45mihdyCqaH1sjRsZCxlHqlajMUY4lb8ulyyDXUXELxyNUBzWkiESTR9zRYXn0m5e/9j9e8Vrbtj/2sY/t37//vvvuw2EbMSImX/ziF/fv3/9Hf/RHtm3/p6v2/++vfZXnzwT5bwHAGXFf0dxNjUG49LvrWfJuU9fXepxue7U88uCgU80XjpwafTyaWrr+A/c81Ot32816rbZRq9Wb7a3eY399+w37xts2k09650uNPtZNs7QwRnRDDVPot9bQT2DnChudKel2u1u9/uC7j9137x03HRyZHG4fWy1WQz9h7dSbmwCQOrhWyc1B8jaLQvM/4hsAuo9d+HiwXgR0KOZoGiSMlYnMxy5Ub6QDr8xchoNBH1YKpUwKRj+oFlTZatTaQ4DUgWJhBQa9fvSDhGM7ne7o0cXlZeZhmhniYcqg290CAIDUSoZ5G9L8ysp4x6fb2XKxdbaG3j8jspJZGVFpr9vVsNPFkemc9OQtmr0QnX8kdIBdHTOpaEmPpNFK6yQ2/vL8z3rXac4VxY+EH1pfL2hfIjoYnuiDLL8zqeQR5guVRx77KDYL0wlYMOeKgoaIqIWkpGWQYJFRdmnyMs3XoVSDq2uMZII75aNS/ieNoxSQOCaQNp45mihdyCqaH9tkS79VzmVH4n5LGJfuRsHR3Ojif1hrRvvRnHY1n3WkJHi/a2e9MHnGByTpQsZR6pWozFGOJW/Lpcsg11FxC8cjVAc1pIhEk0dy54pGlj545YHaoTefe/bZ29vbb3rTm1772te+733v+/SnP/3kk08yHjFixCNPPvnkpz71qVtuueU1r3nNW97ylu3t7fQ5Z//xW68pXxn4CI0g/20AOEPOFe0lGbTWipXxRtHCodtbnWa1mPF9lptezpcbnfYdNxy6tV5f5dwmTyzzy9nCWq3dvufwEgAADE+tV0NWHJ366L6QlUKxWMiMThbVGgnaLArN/4hvAOg+duHjwXoR0DHnipzYsvDEK4P+AGClUMyON2da/tLq1uudIUAqUyquAAwG0Rb7rrTrzW0AAJjL5kM2ymeGhy293pgu0ouL7F+IW1xeHu/4bLm/GtrrbQEAQGpxmf2zl/PLY7PDXndLEWt0mc5JT96i2QvR+UdCB9jVMZOKlvRIGq20TmLjL8//rHed5r6i+JGgeMJ6QfsS0ZGKTFCsxN0FouoRjicEIWOM/DEx9xWRIHENhXIUYG30yPiQYJFRZksseK4xcIPtb9ER18/bsmPEQ6Ix/5PGUcq3kqhn3cRM7PkvaEe0ovmxxUclKTKfK+YH67dtDgGgW64Xm6zf8+/XK5Vjp3YAYO76TxY8nxFNrklIzfcHABF+VGf6foLORqNSXF3mPdKubRyb3MmwLzv7L+MpCBlHqVeiMkc5lrytyFLGx3VU3KJ+FwJNbLV5JHNf0WR03rm8dOWLX/x/fvkrf/pPjz3yyCOPPPLIH/7hH2LKRoxw5AVnnfXvfvIV/8eVK5ddcJ6eirYAYC/fV7Q3pVNZq50e/WzSvlvrjXKW+eloOrPWaM0Ml1+Wi9XyRuNXTw4BdpqNNuRzqFqn3jgNALCUy2WWF0c3gwzb9UZvdfa3yOMSmv+82VNEx+lFddXB6iuaDsUcTYMEeyMXcd0CzlsU/M3M5I00YLFl4YlXRnsty8Vitnzy5HC7UWut5/JTq/luo35qCJDKlgrLE/Xo0q+v10cL+KXiaj7k3cKM8IRJzzmrND8/z0Y6v+j8rT+5fqnf74/xzM+H3EOUnp8H2BnpUwCOJMy8ZWRvZP5R0fEAjaLD7TdqL1rRSuskNv7y/M9612nOFcWPhB9aXy9oXyI6GJ7ogwzJ+86+qkce+yg2C9MJWDDnioKGiKiFpKRlkGCRUXZp8rLltm6YwRvOKZ3Js0ouaXBIEonG/E8aRykgcUwgbTxzNFG6kFU0P7ZJl8xqtbQEAAC7xyuVwHf3xtKpVhs7AACpg+Wqlvuhh+1aY4ur1R5/ALQnhYyj1CtRmaMcS96WS5dBrqPiFo5HqA5qSBGJJo9UzhWN2uULL/iDNx7s/i/v/L+yr8+9dHH5ggvSZ5/NeMSIEY+kzz7r31x0/rUvu+QP3nLl13/5bX/wlisvO/88ACx7CfLfBgBzrmi20m9UNzZHH4zuK21Uc7O+aD2KLE8uD+l3u4yDQs5W0UIunwFYzuVWAACG7XpyThaF5n/ENwB0H7vw8WC9COiYc0VObFl4EiGLhVJuDgB2mnXPu4FOrbYJAKncaGsGAGQ2ZwatSuXo+J3E2lpO4ItlevGEy8DZ8Am7VwnSaYcs3bNN7r/CH3W2mfo6tro4Mp2TnrxFsxei84+EDrCrYyYVLemRNFppncTGX57/zbmi5CJB8YT1gvYloiMVmaAk7zv7qh7heEIQMsbInCsKMgMBEtdQKEcB1kaPjA8JFhlltsSC5xoDN9j+Fh1x/bwtO0Y8JBrzP2kcZc4VRbcjWtF7/VwRAMznK5Xr6+85vguwXStvrLWDVwj0auXx50hLpepaRg+OYadW766Vw37WZtCqNbb19D4LIeMo9Uo054p0xVabR0rniibtJeelfmXfT/7Kvp8QGp2JAUQR8yjUTiAMKFtK4ImMhCFR8tZFgk5mU/MyYlk9Mlo9EostK3sJ8t8CAHOuaKbSa2w0xtcUHVori3yGG6fML6ZTAENg//KUs1U0lyvk0gCQKeT2Hdk8DcN2vbG1yj+/PAsJzX9ezYroOL2orjpYfUXTMeeKvHNTEE8yZLFQyq0dP7a706g1+/nCeB+kXWucBoC5fKkg/1WxQbu8tjH6ste+1argT1hqxMMXkR2cabKc6E/tFQk+OnMJWbeg2RuZf1R0PECj6HD7jdqLVrTSOomNvzz/m3NFyUXCD62vF7QvER0MT/RBhuR9Z1/VI499FJuF6QQsmHNFQUNE1EJS0jJIsMgouzR52XJbN8zgDeeUzuRZJZc0OCSJRGP+J42jFJA4JpA2njmaKF3IKpof2z0gi6X18sEUAMDwVLXS8P8YwqBdrR7fBQCYu6FS0fAx0tzBgwdSAMPNeqMTpjcY/9D53MGDzu3ae0rIOEq9EpU5yrHkbbl0GeQ6Km7heITqoIYUkWjySP1ckdPGsqQj9Uh9jOiW3VEWUmSR0eMRuAhVOIogW2wAMOeKZin9VqM1OlQ09T395Ep/co8947eh2r6tIoBMIbcEADBs15tbs8DIl9D8Z1euuE6gF8lVh68vrBcBHYo5mgZJTPwf9kY68EqMMp8v5RcAYLdZazpvBtr15nhrJi994rDfXCs53zl777r4OwldePgyORMUumU0mPrzxKfJFlHogaGB408cm0a+lYk//QOrl8j8I6ED7OqYSUVLeiSNVlonsfGX53/WWtfZK3J3L6fa4JTm0wlIVAOIGd/G2OSViDpch5KDhB9aXy9oXyI6GJ7ogwyWZQVbbGfSo6PqEeYLlUce+yg2G9MJWLAsC2y3Rc4VTeugkUHxREKCxUQmMpZjyJ5qfVBc00xDRNRCUtIySLDIKLs0edl2WzfM4A3nlM7kWSWXNDgkiURj/ieNoxSQOCaQNp45mihdyCqaH9s9IStrzg/R7TQqVe+GzVatXDsNoPHn5waLZUvTJgAAIABJREFUhWImBQCbtXrIZpFzqmghX8imhxpw6BYyjlKvRGWOcix5Wy5dBrmOils4HqE6qCFFJJo84o4O3Rgl3iP1MaJbdkdZSJFFRo9H4CJU4SiCbLEAIHBmCCywpjzBdZh+GQmVTqs93irK5HMJuc6HLd1Wa2v0r8Vl9OZ4d6uo6HyUnM4WcgsASdosCs1/duWK6wR6kVx1+PrCehHQoZijaZDExP9hb6QDr8Qpk82ZVn28OTPemlkoyG/NbNVKpbtODwEgte/WWjWKHS14RMTdKwrb8Zn8Ut3UzvX81F5R2KPOZlE67FYjTeJbmfjTP7B6icw/EjrAro6ZVLSkR9JopXUSG395/metdc25oviR8EPr6wXtS0QHwxN9kD37jcn4zr6qRx77KDYL0wlYcM8MWWCbc0VUSAQNoTo8M1IcpezS5GXLbd0wgzecUzqTZ5Vc0uCQJBKN+Z80jlJA4phA2njmaKJ0Iatofmz3hqRzleoNo49SNjfKNfcn/QetavXkLsDo5+fEfjQisvTnc6VsCgBO12ttltKgWWvuAMBCvpQZzP4iWAIh4yj1SlTmKMeSt+XSZZDrqLiF4xGqgxpSRKLJI+7o0I1R4j1SHyO6ZXeUhRRZZPR4BC5CFY4iyBYbAMy5ohnKVrc7nlHnM5nlWKHwZatWrp4a7Wwt5ApZRGP8STKksgX33qV0rph3NosEbkacgYTmP7tyxXUCvUiuOnx9Yb0I6FDM0TRIYuL/sDfSgVdilXSuVFgCd3NmXFBLhZLkzwr0W2uF1WM7AABzhyr1qNehkeMRlPnlxdEPGPT7ffaOT7/vbvg4fqUXx/8c9vshF6Q5j6bmF2e/V+RbmfjTP7B6icw/EjrAro6ZVLSkR9JopXUSG395/metdc25oviR8EPr6wXtS0QHwxN9kD37jcn4zr6qRx77KDYb0wlYcM8M2WCZc0VUSAQNoTo8M1IcpezS5GXbbd0wgzecUzqTZ5Vc0uCQJBKN+Z80jlJA4phA2njmaKJ0Iatofmz3iiwWq+Mfots9Xqk4t8hubVRq2wDafn5uJIP+fL6YTQHAdqPWwt+U9Zv15g4ALBVK2UF/T+4VkXGUeiUqc5Rjydty6TLIdVTcwvEI1UENKSLR5BF3dOjGKPEeqY8R3bI7ykKKLDJ6PAIXoQpHEWSLBQBnxLmi3XphnimLhVrIp4gqMmyVltndZqtd/wO9bm98rGgRPaeTFOl3G+V8bvyhM8wdKpfzyJpkaqsoP+VN4jaLQvOfXbniOoFeJFcdvr6wXgR0KOZoGiQx8X/YG+nAK/FKOlcq7AOA3Wat0YNOY7w1U8zKvAUYdNYLxTvHPz53eKNezkS2QopHXBYXF8cdbG2xuXrLYc/F5eUJnsWV8TZTr8t+sre1NX63g5+P1Cu+lYk//QOrl8j8I6ED7OqYSUVLeiSNVlonsfGX53/WWtecK4ofCT+0vl7QvkR0MDzRB9mz35iM7+yreuSxj2KzMJ2ABffMkAW2OVdEhUTQEKrDMyPFUcouTV623NYNM3jDOaUzeVbJJQ0OSSLRmP9J4ygFJI4JpI1njiZKF7KK5sd278jKanV1HwAAbNfK610AGDQr6yeHoPHn5wAAYNjvD5YLpVzYZlG/WWvuAsBSvpiFsC/5JVjIOEq9EpU5yrHkbbl0GeQ6Km7heITqoIYUkWjyiDs6dGOUeI/Ux4hu2R1lIUUWGT0egYtQhaMIssUGgDPjXNFwuMsUjbNYWLc7g+DtG5OfQoL5GH4LKSiD9saaR1ZLxUJuZXFx/zuPHN8eAgCk9h3eqKEHnd2tomLes2ZJ5wq5OQCAYScRm0Wh+c+uXHGdQC+Sqw5fX1gvAjoUczQNkpj4P+yNdOCVuCU72pwZtuqN5ugXHfcVijLfFtuqFQu/cXJ8pOj2Rq0o9z6CDE8USa9kVkYXuW51uiy23uqOd4NSK1NnMpdXVkbg+t3uFst+t9MdOtrmXFESKlrSI2m00jqJjb88/7PWuueMDXh24ydt8LsP3jYg6EMeAyDw5YnpqAJj84yns4eQoHjCekH7EtGRikxQLMuy7WAb3Jn0tKoeYb5QeYTjCUHIGCN/TILniuypFo2MIJ4QJGIxEc46Lyf4oLD4gRaJayiUowBro0fGhwSLjDJbYsFzjYEbbH+Ljrh+3pYdIx4SjfmfNI5SQILHVtdYwwzyX9COaEXzY4uPSiIlnatUD9ffeXQHhqfWK/VStV+tbwNo/fk5AAAY9PuwmC/mUsePD3eateYgX/C9Cew36q1dGP/qRH99T24V0XGUeiUqc5RjyduKLGV8XEfFLRyPfL4Ic50GbpH0iDM6dGOUeI/UxwiZyKNGxpmi8MlsspDCllPKkdHjkVRsg3gIssUCAPeLbv52hH6sA55zRWy/YpK5pX2LrM8z08vaPh6cW9jH/E2j9PJ8HBepR5Lh6WN3nmb/ObXvhsrGRhm/WMm5qyiVKeSXvX9K5wq5uaPHdkebRWtry8GnZymh+c+rWREdpxfVVQerr2g6FHM0DZK4+Z+FJ1GSKZUOHLltc9iqlDo7AHCgUMJ+7zFcturF3OqxbQCA1IFbGw2JI0WkeKLKSjYzD6d2AHY6rS7kM4jKoNPqjHavM7mMS7rpbC6TOnpyCMNuq91fQ7m+026PT0hmcjrfXzGEmbeM7I3MPyo6HqBRdLj9Ru1FK1ppncTGX57/WWtdc64ofiT80Pp6QfsS0cHwRB9kSN539lU98thHsVmYTsCCe2bIAtucK6JCImgI1eGZkeIoZZcmL1tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUXzY7unZL5QLR9KAQDsHK0Ui9WTQwCYu6Gq7+fnAACgPxgALBZK+TkA2Gk6F9i60mvUm7sAsK9QzE5/C3pvCRlHqVeiMkc5lrwtly6DXEfFLRyPUB3UkCISTR5xR4dujBLvkfoY0S27oyykyCKjxyNwEapwFEG22ABwRpwrmitsdJnSkf2KPVdS2fU2u9vGauBzybR7E3uyf9514eBNt99z31a3wdgoAmjXG9sAAKmVwFYRwHy+mBudE0jCyaLQ/GdXrrhOoBfJVYevL6wXAR2KOZoGSUz8H/ZGOvBK/LJSKB1MAQx3dnYAUgeKJWynJEx6jVK+dHR7CACppcP15nrEW4qo8chINp9bAACAzWazgyn0m6O3JgDLOc+Gz2Iunxn9pnerEXhDAwAA3WZ9EwAA5nIFzT+mh4pvZeJP/8DqJTL/SOgAuzpmUtGSHkmjldZJbPzl+Z+11jX3FcWPhB9aXy9oXyI6GJ7ogwyWZQVbbGfSo6PqEeYLlUce+yg2G9MJWLAsC2y3NfcV0SARNITq8MxIcZSyS5OXbbd1wwzecE7pTJ5VckmDQ5JINOZ/0jhKAYljAmnjmaOJ0oWsovmx3WOyPPkhutMnT23D6OfndH2mNZEBAMB8frRZtNus+d5b9Rq11hAmXyUcAAw1A9IiZBylXonKHOVY8rZcugxyHRW3cDxCdVBDikg0ecQdHboxSrxH6mNEt+yOspAii4wej8BFqMJRBNliAcAZcV/RXpHJHe7DsHs5Zidzh+957AlX7rt9dJMiwCCdLRazIcuR1nirCNLpbq0akI3WYPTwsFNvBK5tmrGE5j+7csV1Ar1Irjp8fWG9COhQzNE0SGLi/7A30oFXEiArhVJ2XHepTKkY7eRLr7GaK959enRJ0Q0bzXpB+U2EEh5JSedXC0sAALC5Xm0Et3y2auuNXQCA1IGSb/NqpTiGu9tY3wjuM/Wb1Y3RVtFCvpSP45c/fSsTf/oHVi+R+UdCB9jVMZOKlvRIGq20TmLjL8//rLWuOVcUPxJ+aH29oH2J6GB4og+yZ78xGd/ZV/XIYx/FZmE6AQvumSELbHOuiAqJoCFUh2dGiqOUXZq8bLmtG2bwhnNKZ/KskksaHJJEojH/k8ZRCkgcE0gbzxxNlC5kFc2P7V6TdLZSvmFu8l/dPz83FmezqLAAzgW2rmw16u0hQOqg81XCRH/1mS1kHKVeicoc5Vjytly6DHIdFbdwPEJ1UEOKSDR5xB0dujFKvEfqY0S37I6ykCKLjB6PwEWowlEE2WIDwBlxrmivyPLkUvaw2zVmKOn55UVXsmvr4y+w7J6slOshu1mtxnirCHZP3X3ktqDcdXz89wRsFoXmP7tyxXUCvUiuOnx9Yb0I6FDM0TRIYuL/sDfSgVeSIM4lopDKlorLER7sNVfzxbtGG0UL19/RqJdI3kNI41GQdG5t7eD4pxbWSrWt6b8NOuulysmRl4VywMflYrm4BAAwPFVZLbc971p6zbW1u7cBAFIHSuVCLJfE+VYm/vQPrF4i84+EDrCrYyYVLemRNFppncTGX57/WWtdc19R/EhQPGG9oH2J6EhFJiiW7C8eKnmE+ULlEY4nBCFjjPwxMfcVkSBxDYVyFGBt9Mj4kGCRUWZLLHiuMXCD7W/REdfP27JjxEOiMf+TxlHKdyGoZ93ETOz5L2hHtKL5scVHJcHSazbau+7/Op0tmNWPaadzpcLS3XdtD1u1Rq+0Ov4i4laj1h4CpDLF2XyVUJuQcZR6JSpzlGPJ24osZXxcR8UtHI+AfxcCTWy1ecQZHboxSrxH6mOETORRI+NMUfhkNllIYcsp5cjo8UgqtkE8BNliAcCZcV/RHpF0JruSOnpqCDDsNNv9VX03KclJOuvcpAg7R8uV1cIG+qO4g8mpIgEZdhqNbrkc45IiNP95NSui4/Siuupg9RVNh2KOpkESN/+z8CROFgvVOhT7MJ+JcCqo11zNF+7aHB/9X1qZ71SLhX6/PxgM0HtGs5XWel7sJ9ik8AAADFrVkudsz2CrPXTgVgrFqUvlMqWNsueYz8rq+lotd2RzCNvH3pPNdSrlYm55vt9tblSrd5/aBQBYuL6K/fjCfL5aPdy88egODE8dyee2yuXVfGYRep16tbJ+fHSr2r7V9cosfkwPEWbeMrI3Mv+o6HiARtHh9hu1F61opXUSG395/metdZ29ItsGsPwtnsRTbUBs29MyOTzw4dSUCaf1+RtRx4dEEM8eQzLdl4gOhicakrEFG8anZNwWSziYbsk8QvCoeuT2xcUWmgm2bcNoV9YXDZicMQJOZFA8kZBgMZGJjA34Y9Mdu6aZzECAxDUUzlEcJCgeKY5SZksbMeM+Ct5H0fCH4FHmbbIx4iJh4KHI/6RxlAISNLZxztE0SOgqmh/bPSaDVqVybAcAIAUwBBieqpbrxYb2X6EbSTpXKizddef2sF1vbK2uLgMAbDXqneFMv0qoS8g4Sr0SlTnKscSjSMBati8aPQIZrtPCLTIecUeHbowS75H6GAlN7eGRQWMy6XeykMJQKUdGj0dSsQ16RJAtNnhOC+HnitBtJKZfRkJlOVdYgVObALDbqjf7xWLCNotgvlCtXN/81eO7ANu1tfXVTjn4AeugXW9uAwDA0k33NisZxufPWxvF/JFTw9HJojg3i0Lzn1254jpOL6qrDoFeBHQo5mgaJDHxv6PDxJM8mc8Uol4L1K2VJxtFALB98ujdofqpxQg/EyCBBwAABlutxtHj6C9XD0+fPHZ6Ck4vt172aqSz1UZtK1c6uj2EnZN3/urJOz1/njtwa71eWkb7XSxuNDpb+SOndmF38+htNx69zfPnpRs2GlW9d8GGiJu9vrxlrF4i84+EDrCrYyYVLemRIloJnYTHH9GRftdp7iuKHwk/tL5e0L5EdDA80QcZLMsKttjOpEdH1SPMFyqPPPZRbDamE7BgWRbYbmvuK6JBImgI1eGZkeIoZZcmL9tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUXzY7u3pLterp0GANh3a/2To5+i2zlWrrTQLwTqkGyxuA8Ahm3n7mlnqyhXUv/B85iFjKPUK1GZoxxL3pZLl0Guo+IWjkeoDmpIEYkmj7ijQzdGifdIfYzolt1RFlJkkdHjEbgIVTiKIFssADD3Fc1UMoXi+E6g3eb6Rtw3+WCyvFotH0gBAAw3q2sbWwGFqa2ifCm/ssySXKkwunR+2I33Z+hC859dueI6gV7kKlrozQZfh2KOpkESE/+HvZEOvGIkSbJcrLebdxw+MOd9ObVw6L2fbLXXc+yt9flstdW+59ZDSynv63P7brj9RLtRWolrpyiwMvGnf2D1Epl/JHSAXR0zqWhJj6TRSuskNv7y/M9a65r7iuJHwg+trxe0LxEdDE/0QR7tTPrbYML5dFQ9wnyh8shjH8VmYToBC7Ztg+W25r4iGiSChlAdnhkpjlJ2afKy5bZumMEbzimdybNKLmlwSBKJxvxPGkcpIHFMIG08czRRupBVND+2e0l6tXL11BAA5q4vlwsl57OZ7draevBuVk2SLZYOgLtZNN4qmsvv/a0iOo5Sr0RljnIseVsuXQa5jopbOB6hOqghRSSaPOKODt0YJd4j9TGiW3ZHWUiRRUaPR+AiVOEogmyxAcDcVzRbWSmt5UefhA5PVVc3tnj6g35/Zl8gGUtmbb20BAD4tUXuVtFCvpgN+wR2JV8YHSYabsa6WRSa/+zKFdcJ9CK56vD1hfUioEMxR9MgiYn/w95IB16ZmcwXGwPbtm17az0b5bncRs+2bdsetNeWp19fKXfsKDJoFD3FSo1nZLTUHAjCaa3ibzQWc2v1Tu+Jh07c+8mP33HHHR//5L0nHu31Whsl1vnFiaRXiuutrd5j9917z8c/fscdH//kPX/90BO9bqOci/ctjW9l4k//wOolMv9I6AC7OmZS0ZIeSaOV1kls/OX5n7XWNeeK4kfCD62vF7QvER0MT/RB9uw3JuM7+6oeeeyj2GxMJ2DBPTNkg2XOFVEhETSE6vDMSHGUskuTl223dcMM3nBO6UyeVXJJg0OSSDTmf9I4SgGJYwJp45mjidKFrKL5sd070m9WKsd2AQD2lSrFRYCV1WpxCQBguFkte2911SjjbziP7p52t4rySftVnOhCxlHqlajMUY4lb8ulyyDXUXELxyNUBzWkiESTR9zRoRujxHukPkZ0y+4oCymyyOjxCFyEKhxFkC0WAJhzRTOWxWK17BwtOrlWWG322Lr9djW/slLY6M52uyidq1RvWACA0bVFntPO01tFhdCtIoBMPr9v9K9YN4tC859dueI6gV4kVx2+vrBeBHQo5mgaJDHxf9gb6cArRpIo6cVMrlBaXVtbWy0VcitR3pPML2cLxdXVtbXVUjGfWYzvONFEfCsTf/oHVi+R+UdCB9jVMZOKlvRIGq20TmLjL8//rLWuOVcUPxJ+aH29oH2J6GB4og+yZ78x0s6kkkeYL1Qeeeyj2CxMJ2DBtm2w3NacK6JBImgI1eGZkeIoZZcmL1tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUXzY7tnpLNRrm0DjA4VjT5smc9XyofmAAB2j1cqzQi/K64iK8VidrxZ1Gw0OkOAhcKZsFVEx1HqlajMUY4lb8ulyyDXUXELxyNUBzWkiESTR9zRoRujxHukPkZ0y+4oCymyyOjxCFyEKhxFkC02AJhzRTOXlXKtemh8tmjzrkK2UG1uBTeDeq31QjZ328mdneNrhbXZ/TotAHg2tLynnQftemMbAAAW8kXuDSCZwtRm0cyOTPslNP/ZlSuuE+hFctXh6wvrRUCHYo6mQRIT/4e9kQ68YsSIZvGtTPzpH1i9ROYfCR1gV8dMKlrSI2m00jqJjb88/7PWuueMDXh24ydt8LsP3jYg6EMeAyDw5YnpqAJj84yns4eQoHjCekH7EtGRikxQLMuy7WAb3Jn0tKoeYb5QeYTjCUHIGCN/TILniuypFo2MIJ4QJGIxEc46Lyf4oLD4gRaJayiUowBro0fGhwSLjDJbYsFzjYEbbH+Ljrh+3pYdIx4SjfmfNI5SQILHVtdYwwzyX9COaEXzY4uPSuJkq1aubg4BIHVgtVqc/DbCcqlaWn/DnacBtutr1dVOlfONXRJZLhaz5ZMnh6fWV7d2hgBLhVJs979SChlHqVeiMkc5lrytyFLGx3VU3MLxyOeLMNdp4BZJjzijQzdGifdIfYyQiTxqZJwpCp/MJgspbDmlHBk9HknFNoiHIFssAHC/6OZvR+jHOuA5V8T2Kx7Zba5ls5xvOaQL660y9mtLw856IVsPnfgy5eZGIWh/2C7nsuucKTNbaa3nfTora41GL184cmoXYLh97La3NdcP5PK5XGZlPg2D/lan3Wo2T+2M74hfyK2uzmI94IW4ur668YY7T4+vLSq2VpcBYNCuNXYAAGAuVxBYLGQL+aU7T28DwGa93ilnMloxMyQ0/3k1K6Lj9KK66mD1FU2HYo6mQRI3/7PwGDEyQ2HmLSN7I/OPio4HaBQdbr9Re9GKVlonsfGX53/WWtfZK7JtAMvf4kk81QbEtj0tk8MDH05NmXBan78RdXxIBPHsMSTTfYnoYHiiIRlbsGF8SsZtsYSD6ZbMIwSPqkduX1xsoZlg2zaMdmV90YDJGSPgRAbFEwkJFhOZyNiAPzbdsWuayQwESFxD4RzFQYLikeIoZba0ETPuo+B9FA1/CB5l3iYbIy4SBh6K/E8aRykgQWMb5xxNg4Suovmx3RPSb1Yqx3cBABYKlfL0D3Cns+XK4fqNR3dgeHq9XFsdfTSjWRYLpVz55PHdne0dOHO2iug4Sr0SlTnKscSjSMBati8aPQIZrtPCLTIecUeHbowS75H6GAlN7eGRQWMy6XeykMJQKUdGj0dSsQ16RJAtNnhOC+HnitBtJKZfMcnO6VM7HJW5FdZ53eH25qntsEdT6R5+rGeX9yRAar4/AAjMq/O5aqu9slpcu3tzFwCGO5vH7948fncQ9cH31urrheXZT8zpbKV6uP7Oozvja4sK9eLioFVrjreKBE4VAYw3i+7aBoDNZr1TjWWzKDT/2ZUrruP0orrqEOhFQIdijqZBEhP/OzpMPEaMzFDc7PXlLWP1Epl/JHSAXR0zqWhJjxTRSugkPP6IjvS7TnNfUfxI+KH19YL2JaKD4Yk+yGBZVrDFdiY9OqoeYb5QeeSxj2KzMZ2ABcuywHZbc18RDRJBQ6gOz4wURym7NHnZdls3zOAN55TO5FkllzQ4JIlEY/4njaMUkDgmkDaeOZooXcgqmh/bPSCDTrVc3wYASB0sV/zfU14sVsY/+zJEbpTWI4uFUm5u/O99xVKkO26TK2QcpV6JyhzlWPK2XLoMch0Vt3A8QnVQQ4pINHnEHR26MUq8R+pjRLfsjrKQIouMHo/ARajCUQTZYgGAua8oLkmvlGqd7n2fvPWGAwup4J/n9l3/3o/f121vxLFRBAAA84Vq5frR8mB0bdGgVW+6p4qEfq02nS3kRjcfwWajHs/P0IXmP7tyxXUCvUiuOnx9Yb0I6FDM0TRIYuL/sDfSgVeMGNEsvpWJP/0Dq5fI/COhA+zqmElFS3okjVZaJ7Hxl+d/1lrX+tq+DwAAvveFTGmhoxMEzvoKRWCWxB4W6Isazx5DwqoRro4qEiRbGDuTGjziiWRsBbFxkHg6c6Ph++mGqHhkkGBm5Mbar+TrHkOoCQmHo2QiI8VRdGyJGvBZR8MfgkeZtwUjg0lEJIJ45CoxWRyljiQ5czQVEqqK5sf2ksbHAcC2bfT5JMjWRi7zqyd3AWDpvSe6G8jXcget1ZVr79oGANj33hMdn0qnvHLlkdMAkLr+nl6zGOFioWZp8W137wDMHb63Xy94/tRvFFfeeXQH4MDtD3XK3m/9dsqZK49sAsC+W+/rrid6I2m03n368M0AQMdRWvKfjBmCc3SYaKzoEF/I5pFZecQdnT03RmQeUY1RtMigMfG9wEClHBk9HknFlrsmlBkjJvKp/7POFblywX/9C0j2zJt8GfQ67XZ3q9frD9Lp+fnllUw2szJ/RpzyjUVs2z7rrLMA4Pn3XSuR/xF1ME2CihboRUhHYo7WiEQ//zvRZlg7786/B8NXRvTL6F3J4Df2efKcu3rxCBVHJbuiE+TRnkbLxCOy1jX3FcWPBMUT1gval4iOVGSCYsn+4qGSR5gvVB4J8ds0BsYY+WNi7isiQeIaCuUowNrokfEhwSKjzJZY8Fxj4Abb36Ijrp+3ZceIh0Rj/ieNoxSQ4LHVNdYwg/wXtCNa0fzY4qOSHOk3ypWTuwAAc9dXKvjvt6Rzlcr19fcc3wU4XSuvr7XLK5ga/5qEdK7arAr9Rsx8vlL7ZL4H6ZV8LHcJ6BAyjlKvRGWOcix5W5GljI/rqLiF45HPF2Gu08Atkh5xRodujBLvkfoYIRN51Mg4UxQ+mU0WUthySjkyejySim0QD0G2WABwRtxXtOclvZjJFc6YCThhEpr/vJoV0XF6UV11sPqKpkMxR9MgiZv/cWsvv+Dcx5/9ly996UtvetObwIgRPXLy5EkAWLroBey8ZWRvZP5R0ZmWGVb0rNFK6yQ2/vL8z1rrmvuK9jKS6b5EdDA80ZCMLdiQrLtAVD1y++JiC80E27bBsiatGw1zXxHBGHE5ioMExSPFUcpsaSNm3EfB+yga/hA8yrxNNkZcJAw8FPmfNI5SQILGNs45mgYJXUXzY5tsGbQr5aM7AACpA6vV0iJLb7FULa+3btscwvBUda1WaqKq3GsSUsusuxkCkl7Jl9Adqb0rZBylXonKHOVY4lEkYC3bF40egQzXaeEWGY+4o0M3Ron3SH2MhKb28MigMZn0O1lIYaiUI6PHI6nYBj0iyBYbzpT7iowYYUpo/rMrV1zH6UV11SHQi4AOxRxNgyQm/nd0GNYufeE5jz/7Lw899JDZKzKiTx566CEAWHjh2VPZ68tbxuolMv9I6ACz1mZT0ZIeKaKV0El4/BEd6Xed5r6i+JHwQ+vrBe1LRAfDE32QwbKsYIvtTHrLsgQVAAAgAElEQVR0VD3CfKHyyGMfxWZjOgELlmWB7bbmviIaJIKGUB2eGSmOUnZp8rLttm6YwRvOKZ3Js0ouaXBIEonG/E8aRykgcUwgbTxzNFG6kFU0P7aJlu5GeeM0AAAsFCrl0O8PZ1arxSUAANg9Xik3hfd8jEyEjKPUK1GZoxxL3pZLl0Guo+IWjkeoDmpIEYkmj7ijQzdGifdIfYzolt1RFlJkkdHjEbgIVTiKIFssADD3FRk5wyU0/9mVK64T6EVy1eHrC+tFQIdijqZBEhP/h72RBoCDixcAQKcTz+VZRv6VyIMPPggA17z0PP/KxJ/+gdVLZP6R0AFmrc2moiU9kkYrrZPY+MvzP2uta+4r2stIWDXC1VFFgmQLY2dSg0c8kYytIDYOEk9nbjTMfUUkSDgcJRMZKY6iY0vUgM86Gv4QPMq8LRgZTCIiEcQjV4nJ4ih1JMmZo6mQUFU0P7bJv6/IiD4ZrXfNfUVs0VjRIb6QzSOz8og7OntujMg8ohqjaJFBY+J7gYFKOTJ6PJKKLXdNKDNGTORT/zf3FRnZe2Kb+4qU5miNSPTzvxNthrX2Pz9z7ae6c3Nzp0+fXlhYCO3diBEZefzxx1dWVp599tm/KS5d89K0J8+5qxePUHFUsis6QR7tabRMPCJrXXOuKH4k/ND6ekH7EtHB8EQfZEjed/ZVPfLYR7HZmE7AgmVZYLutOVdEg0TQEKrDMyPFUcouTV623dYNM3jDOaUzeVbJJQ0OSSLRmP9J4ygFJI4JpI1njiZKF7KK5sfWiJGxkHGUeiUqc5Rjydty6TLIdVTcwvEI1UENKSLR5JE5V8TEE+uyO8pCiiwyejwCF6EKRxFkiwUA5lyRkTNcQvOfXbniOoFeJFcdvr6wXgR0KOZoGiQx8X/YG2kAyL7souxLL9jd3b355psDCkaMEMhNN9307LPPHnxp2pwr4vUSySNptNI6iY2/PP+z1rrOXtHoWz++Njil+XQCEtUAYsZ2Wmvq39F1uA4lBwk/tL5e0L5EdDA80QcZRt8R87XBhPPpqHqE+ULlkcc+is3CdAIWbNsGy22dO4rc+4o8OmhkUDyRkGAxkYmM7RiyplofFNc00xARtZCUtAwSLDLKLk1ettzWDTN4wzmlM3lWySUNDkki0Zj/SeMoBSSOCaSNZ44mSheyiubH1oiRsZBxlHolKnOUY8nbcukyyHVU3MLxCNVBDSki0eQRd3ToxijxHqmPEd2yO8pCiiwyejwCF6EKRxFkiw0A3PuKUB2mX0aMJE1C859dueI6gV4kVx2+vrBeBHQo5mgaJDHxf9gb6VF75KdeAQCf+cxn/uzP/iygY8SIkvzpn/7pF77wBQC449oFgMDKxJ/+gdVLZP6R0IGQ6phFRUt6JI1WWiex8Zfnf9Za15wrih8JP7S+XtC+RHQwPNEH2bPfmIzv7Kt65LGPYrMxnYAF98yQDZY5V0SFRNAQqsMzI8VRyi5NXrbd1g0zeMM5pTN5VsklDQ5JItGY/0njKAUkjgmkjWeOJkoXsormx9aIkbGQcZR6JSpzlGPJ23LpMsh1VNzC8QjVQQ0pItHkEXd06MYo8R6pjxHdsjvKQoosMno8AhehCkcRZIsFAFTnip577jlGxIwYmbU8++yzAJA++ywATv6zK1dcB/y9SK46fH1hvQjoUMzRNEhi4v+wN9Kj9prFCz76U68AgH//7//9X/3VXwXUjBiRlL/8y7983/tuAYD1axdet3geQGBl4k//wOolMv9I6EBIdcyioiU9kkYrrZPY+MvzP2uta84VxY+EH1pfL2hfIjoYnuiD7NlvTMZ39lU98thHsVmYTsCCe2bIAtucK6JCImgI1eGZkeIoZZcmL1tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUXzY2vEyFjIOEq9EpU5yrHkbbl0GeQ6Km7heITqoIYUkWjyiDs6dGOUeI/Ux4hu2R1lIUUWGT0egYtQhaMIssUGAPVzRS87Pw0ADz30ECNiRozMWkYXy19y3gsAOPnPrlxxHfD3Irnq8PWF9SKgQzFH0yCJif/D3khP2l+7cuHW1y0+/fTTb3/722+++ebvfe97AWUjRiLI9773vZtvvvnnfu7nvvvd/vuvftFq5mJ8ZeJP/4BOZP6R0IHQ6tBf0ZIeSaOV1kls/OX5n7XWNeeK4kfCD62vF7QvER0MT/RB9uw3JuM7+6oeeeyj2GxMJ2DBPTNkg2XOFVEhETSE6vDMSHGUskuTl223dcMM3nBO6UyeVXJJg0OSSDTmf9I4SgGJYwJp45mjidKFrKL5sTViZCxkHKVeicoc5Vjytly6DHIdFbdwPEJ1UEOKSDR5xB0dujFKvEfqY0S37I6ykCKLjB6PwEWowlEE2WIBgPq5okvSKQDodDqMiBkxMmsZ7RUtnJ8C4OQ/u3LFdcDfi+Sqw9cX1ouADsUcTYMkJv4PeyM93Vbfctld173ybAs+8YlPLC4u/vRP//SHP/zhz33uc88//3zgQSNGEHn22Wc/+9nPfvjDH7722msXFhY+8YlPnHMW/NefXfzIWy4FYKxM/Okf0InMPxI6EFod+ita0iNptNI6iY2/PP+z1rrnjA34d92xycyzY28jOIH/5QnWLDllwmlDMkFAx4dEEM8eQzLdl4gOhicakrEFG8anZNwWSziYbsk8QvCoeuT2xcUWmgm2bcNoV9YXDZicMQJOZFA8kZBgMZGJjA34Y9Mdu6aZzECAxDUUzlEcJCgeKY5SZksbMeM+Ct5H0fCH4FHmbbIx4iJh4KHI/6RxlAISNLZxztE0SOgqmh/bkTz33HPnn38+BsXIGSuj74Smzz57/H8yjlKvRGWOcizxKBKwlu2LRo9Ahuu0cIuMR9zRoRujxHukPkZCU3t4ZNCYTPqdLKQwVMqR0eORVGyDHhFkiw2e00L4uSL3VxPc1uPXNQsXbz61a84VGUmOjPaKDi5cBMDJf3bliuvAuBfVVYdALwI6FHM0DZKY+N/REcDzv73mJdmXXfCR9uN/9rWnT5w4ceLEicAjRowIyVkWFFcu+tAbXvLj8+eOX/LP0b68ZaxeIvOPhA4IVIeIjmxFS3qkiFZCJ+HxR3Sk33Wac0XxI+GH1tcL2peIDoYn+iBD8r6zr+qRxz6KzcZ0AhYsywLbbc25IhokgoZQHZ4ZKY5Sdmnysu22bpjBG84pncmzSi5pcEgSicb8TxpHKSBxTCBtPHM0UbqQVTQ/ti9Nnw/ml3D+Vcpo0F+SOm/8fzKOUq9EZY5yLHlbLl0GuY6KWzgeoTqoIUUkmjzijg7dGCXeI/Uxolt2R1lIkUVGj0fgIlThKIJssQBA/VzRu171cgD49Kc//a1vfYsRNCNGZiePPfbYvffeCwCH9y0CcPKfXbniOuDvRXLV4esL60VAh2KOpkESE/+HvZEOIvnJi9N//LZXnX7PFb/7psve8vILL7vw3MBTRowwZemiF/zUj73w9rdc8tVfeVXtbS9zN4qAsTLxp39AJzL/SOhAaHXor2hJj6TRSuskNv7y/M9a65pzRXsZyXRfIjoYnmhIxhZsSNZ39lU9cvviYgvNBNu2wZwrIs9/11A4R3GQoHikOEqZLW3EjPsoeB9Fwx+CR5m3ycaIi4SBhyL/k8ZRCkjQ2MY5R9MgoatofmxfkjrvicFznU7nzW9+MwbFyBkr4x+cSTt7RWQcpV6JyhzlWOJRJGAt2xeNHoEM12nhFhmPuKNDN0aJ90h9jISm9vDIoDGZ9DtZSGGolCOjxyOp2AY9IsgWGyjOFb1x8cWZl8x1vrP7S7/0S1/60pfOOussMGIkJvnBD37wi7/4i88999w1ixddfemFAJz8Z1euuA6Me1FddQj0IqBDMUfTIImJ/x2dKHh+7ML0b169+JtXLwYe4XoNAki4dtCYREYSsBLkbTGPODgF7OD5HzAWAQkVWlmPJMbIP0cDFofwvKXiqIRVtKRHimgldBIef0RH+l2nOVcUPxJ+aH29oH2J6GB4og8yJO87+6oeeeyj2GxMJ2DBsiyw3dacK6JBImgI1eGZkeIoZZcmL9tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUXzY3v1ixbAnCv6VymjvaLXv+TS8f/JOEq9EpU5yrHkbbl0GeQ6Km7heITqoIYUkWjyiDs6dGOUeI/Ux4hu2R1lIUUWGT0egYtQhaMIssUCAPVzRQDw+2+8HADa7XalUmHEzYiRWcgHP/jB0SJw/ad+UiT/2ZUrrgP+XiRXHb6+sF4EdCjmaBokMfF/2BtpBSTg9BKYa6aR2JqQcNnbx9soe7PsnDH5T1rR6mPEyRbG6kWLRwmraEmPpNHGnlGzQQsAfP5nrXWtr+37AABgwymw98sTlBNQlsAeFuiLGs8eQ8KqEa6OKhIkWxg7kxo84olkbAWxcZB4OnOjMfXdDTuAUA8SzIzcWPuVfN1jCDUh4XCUTGSkOIqOLVEDPuto+EPwKPO2YGQwiYhEEI9cJSaLo9SRJGeOpkJCVdH82La/88TPf7Fx0UUXfeUrX7nsssv4cTFyRshjjz12+eWXP/fcc5+77h1XvXi0XUTFUWT5/5lvbd/3ZK/z9FPfeu7Zbz//nLZgGDHClFdccP5l578w8+IXvfmll/zby36Mfq7nWmC2IYLOIL4XGKh41sjm6GgeScWWuyaUGSMm8qn/s84V+eX3H/pq5f6uZVm33HLLRz/60fPOOw/TMmJEl3z/+99///vf//GPf9y27f/y5lfdknmFdP5H1ME0CSpaoBchnSDXRV3/UyLRz/9OtGXwcAXTTwySgBVzrojOI/psYaxetHiU7IpOkEd7Gi0Tj8ha19krwsdU7GPiKSH4mD6MLCLo7CEkfDx8GhXTEcPDFcazwZ1JdrZIeBQZj8JYs1qeBX/vvsmYWU3R8XAd0pV13pcEPCJAEhaTEHaaFUdJsCWeLV6T4uOun7dlI8NFMuP8Tw5HRUESFpMEzNGySLRVNB7bt5747w/vfiebzZpfwvlXIj/4wQ+uueaahx566OoXX/rZ694xflUXR8lU4sNPP/W+U3/75e8+rTMMRoxEkytedPFdbzn4mhddrDX/GcJdiKNIQj9+wi0HzKjP0ZQeqXEU1WoBgLUh5F3y8PH8zt93jzz0VQBYWlq655573vCGNwgH0IgRJfniF7/47ne/e3t7GwA+dM0rb7vmlVE4SqRmo9aj7KoD6SuaDved0cyQiOnr438uHhXeZiAZ4yFHIs7eXiC4fZHeI+okJv/JPBLSF4xJeLbM0qNkVPSs0SYno7Sjlf5kzJwr2stI0L5EdFSRINlizhWNIzPVmTlXRIyEw1EykZHiKDq2RA34rKPhD8GjzNuCkcEkIhJBPHKVmCyOUkeSnDmaCglVRQvF9v6ne2//23sB4EMf+tBv//Zv80NjZI/LbbfdVq1WAaD1s4UrLn6J8zIVR6nm/ye/9uh/eOA+AJifn3/7299+9dVXX3311a973ete+MIX6omHESO4PP/88w8++OD999//wAMPfOYzn3nmmWcA4A/e+Pqb9v042VzPtcBsQwSdQXwvMFDxrJlzRSzkU/8XPVc0euLPv/H4zSce+pcf/siyrFe/+tW5XC6Xyx06dOiSSy4RiIkRIxHkySefbDnyj//4jwCQPues//bW/b/wEwuK+R9RB9MkqGiBXoR0glwXdf1PiUQ//zvRlsHDFUw/MUgCVsy5IjqP6LOFsXrR4lGyKzpBHu1ptEw85lzR3kDCx8OnUTEdMTxcYTybnO/sK481q+VZ8PduzhWRIAmLSQg7zYqjJNgSzxavSfFx18/bspHhIplx/ieHo8y5In0VzYzt+lcf/N1/PGVZ1u/93u+Vy2UBV43sSZn+wZnfuzK7+pOvdf+mi6Oi5f+HHvr7P+x+BQDe9a53fexjH7v00kuRLowYmbk88cQTq6urf/EXfwEAv3H5/t+6OqMj/9nCXYijSEI/fsItB8yoz9GUHqlxFNVqAYDqXNHo5a3d5/5L52v/71e//YMf/Ug4gEaMKMkLzj7rf11Z/MBVS5dddF50jhKp2aj1KLvqQPqKpsN9ZzQzJGL6+vifi0eFtxlIxnjIkYiztxcIbl+k94g6icl/Mo+E9AVjEp4ts/QoGRU9a7TJySjtaKU/GTv711/yZqYbIeTrZRqWKH0EKdIXNZ49hgTtS0RHFQmSLeZc0TgyU52Zc0XESDgcJRMZKY6iY0vUgM86Gv4QPMq8LRgZTCIiEcQjV4nJ4ih1JMmZo6mQUFW0aGwPvuilNsDffeefv/CFLzz88MNXXHGF+UbzmSdf/OIX3/rWt37+858HgNtee9Wt+w94/07FUfL5v3H6keqXH7r44ouPHj36wQ9+8Pzzz9cUCiNGosqFF1544403Hjhw4LOf/eyJ7W9dnDr36ktejCnq4X+kDRF0BvG9wEDFs2bOFbGQT/0/2rkiAHs+de7blxZ+5dXLyxe+8F9+aFsAgx/+8Ae2SEyMGIkg6bPPesVF6SsvufA/vG5p46f3/+KrLp1Lv4Ak/yPqYJoEFS3Qi5BOkOuirv8pkejnfyfaMni4guknBknAijlXROcRfbYwVi9aPEp2RSfIoz2NlonHnCvaG0j4ePg0KqYjhocrjGeT85195bFmtTwL/t7NuSISJGExCWGnWXGUBFvi2eI1KT7u+nlbNjJcJDPO/+RwlDlXpK+iObE99vjXb77/c6NnS6XSoUOH9u/f/+pXv/rCCy8UcN5IEuXJJ588ceLEyZMnT5w48eijj45e/H+y1/7i0o/7VXVxlGj+/8NTT/7MZ/8HABw7duwd73gHYtmIkQTIvffe+wu/8AsA8Dfv+NnMi1/k/7M6/+PCXYgHBO19+uMn3HLAjPocTemRGkdRrRYAaM8VaVwtRMg6kVHg6CQsWwjyXx0Jwaqb03u4DhqZRGQL2apDxOtQHe4YzQyJmL6+/OfiUeFtBpIxHnIk4uztBYLbF+k9ok5i8p/MIyF9wZiEZ8ssPUpGRc8abXIySjta6Tna3Fe0l5GgfYnoqCJBssWcKxpHZqozc66IGAmHo2QiI8VRdGyJGvBZR8MfgkeZtwUjg0lEJIJ45CoxWRyljiQ5czQVEqqKjhzbR5956nceOfW5nW1+gIzsQbnpx1d+cyVz2QUXYH+k4ijJ/P+3n//L9pM77373u//kT/5Ei/NGjBDJL//yL99zzz0//bLFxvW5wB/18D/Shgg6g/heYKDiWTPniljIp/4f+VyRztWCvEcRdRDNmLJFY/4T5K1MZCTGSBhPYrKFoqIFehHSCY5R1PU/JRL9+e9EWwYPVzD9xCAJWDHniug8os8WBntr8SjZFZ0gj/Y0WiYec65obyDh4+HTqJiOGB6uMJ5Nznf2lcea1fIs+Hs354pIkITFJISdZsVREmyJZ4vXpPi46+dt2chwkcw4/5PDUeZckb6KFo3tI7tPfexrD3W/9/R3hoPe4DkBz40kVNJnn/3S885fOv/CwmWv/Pkfe+XFqZRw1lFxlFD+P/jUk9d99n+8/OUvf+SRR+bm5nSGxIgRVen3+/v37+/1el98x/VXvPhiz9/U+R8X7kI8IGjv0x8/4ZYDZtTnaEqP1DiKarUAYM4VoToJyxaC/FdHQrDq5vQeroNGJhHZQrbqEPE6VIc7RjNDIqavL/+5eFR4m4FkjIcciTh7e4Hg9kV6j6iTmPwn80hIXzAm4dkyS4+SUdGzRpucjNKOVnqOPsexbANY/hYn36k2CND2tDIfQdqMqEbU8SERxLPHkEz3JaKD4ZH46NC2bQDL1zJ2Jt2WzCMEj6pHbl9cbKGZYNs2WNak9Z8rsm0LgBMZFE8kJFhMZCJjA/7YdMeuaSYzECBxDYVzFAcJikeKo5TZ0kbMuI+C91E0/CF4lHmbbIy4SBh4KPI/aRylgASNbZxzNA0SuoqWjO1r5l788auvw5ABgEzW+ZS439Zh1pGvs5C3wWJIqOwoeEQc23A8AP6xnjJDxVEy+f/Xj38TAN797nebjSIjyZf5+fkbb7zxjjvu+MtvPu7fK1Lnf6GpPbg0D1jwI5n0O1lIYaiCttTnaAKPpGIb9IhgjrYBLNvfgvcDR9a5ooBfs1gtCHnEHgtxnRGcJGQLZf6r5y3BqltmjJh4kpMtFKsOEa8j5y02RhBxjCSRxJT/jo4MnhAk4+B68cwMCW6Bzdsoe4sg8ensrfwnrWj1MeJkC4O9tXiUsIqW9EgRrYROwuOP6EjP0WeNDYz+52uDJe3TCUhUA4gZy2ntqX9H1+E6lBwk/ND6ekH7EtHB8EQfZLAsK9hiO5MeHVWPMF+oPPLYR7HZmE7AgmVZYLvt6CyR51zRtA4aGRRPJCRYTGQiYzmG7KnWB8U1zTRERC0kJS2DBIuMskuTl223dcMM3nBO6UyeVXJJg0OSSDTmf9I4SgGJYwJp45mjidKFrKLVY4u1ylnHRSI61jpiK5otVB5521gqGo2MPEfJxPbBp54EgFe/+tW4TSNGEiavec1rAOAfvvOU/w8UywWslVl2R1lI6ZxH6CYSTyvBUQT8bwGABRaAPdWCBdaUJ7gO4lciZjR0RHxoRXRG5pOQLZT5r563FHO0xBgx8SQnWyhWHSJe83W4Y4Tq6EASU/6HvZFWQAJOL4Fam0Zia0KCW2DzNsreLDtnTP6TVrT6GHGyhcHeWjxKWEVLeiSNNvaMmg1aAJCfo529osmu5nQbLGmfTkCiGkDM2E5rTf07ug7XoeQg4YfW1wval4gOhif6IINt28E2mHA+HVWPMF+oPPLYR7FZmE7Agm3bYLmtc5bIPVfk0UEjg+KJhASLiUxkbMeQNdX6oLimmYaIqIWkpGWQYJFRdmnysuW2bpjBG84pncmzSi5pcEgSicb8TxpHKSBxTCBtPHM0UbqQVbR6bLFWOeu4SETHWkdsRbOFyiNvG0tFo5GR5yiZ2H7zuWfB+fzdiJHkyyhXv/ls4Cc6KZYLWCuz7I6ykNI5j9BNJJ5WgqMI+N8GAO65IlQH8SsRMxo6Ij60Ijoj80nIFsr8V89bijlaYoyYeJKTLRSrDhGv+TrcMUJ1dCCJKf/D3kgrIAGnl0CtTSOxNCHBLXDPFfl0GHbOmPwnrWj1MeJkC4O9tXiUsIqW9EgabewZNRu0ACA/R5tzRfEj4YfW1wval4gOhif6IAPBt4ckPMJ8ofLIYx/FZmM6AQuWZYHttuZcEQ0SQUOoDs+MFEcpuzR52XZbN8zgDeeUzuRZJZc0OCSJRGP+J42jFJA4JpA2njmaKF3IKlo9tlirnHVcJKJjrSO2otlC5ZG3jaWi0cjIc5RMbP/pe8+A2SsysndkdAbu288+7/8DxXIBa2WW3VEWUjrnEbqJxNNKcBQB/1sAYM4VsUYqGdlCmf/qeUsxR0uMERNPcrKFYtUh4jVfhztGqI4OJDHlf9gbaQUk4PQSqLVpJLYmJLgFc64oOlphj9THiJMtDPbW4lHCKlrSI2m0sWfUbNACgPwcbc4VxY+EH1pfL2hfIjoYnuiDDATfHpLwCPOFyiOPfRSbhekELNi2DZbbmnNFNEgEDaE6PDNSHKXs0uRly23dMIM3nFM6k2eVXNLgkCQSjfmfNI5SQOKYQNp45miidCGraPXYYq1y1nGRiI61jtiKZguVR942lopGIyPPUfKxTafTuE0jRhImF110EQA8+4Mf+P9AsVzAWplld5SFlM55hG4i8bQSHEXA/zYAmHNFrJFKRrZQ5r963lLM0RJjxMSTnGyhWHWIeM3X4Y4RqqMDSUz5H/ZGWgEJOL0Eam0aiaUJCW7BnCuKjlbYI/Ux4mQLg721eJSwipb0SBpt7Bk1G7QAID9Hm3NF8SPhh9bXC9qXiA6GJ/ogA8G3hyQ8wnyh8shjH8VmYzoBC5Zlge225lwRDRJBQ6gOz4wURym7NHnZdls3zOAN55TO5FkllzQ4JIlEY/4njaMUkDgmkDaeOZooXcgqWj22WKucdVwkomOtI7ai2ULlkbeNpaLRyMhzlDpdGjGyZ4ViuYC1MsvuKAspnfMI3UTiaSU4ioD/LQAw54pYI5WMbKHMf/W8pZijJcaIiSc52UKx6hDxmq/DHSNURweSmPI/7I20AhJwegnU2jQSWxMS3II5VxQdrbBH6mPEyRYGe2vxKGEVLemRNNrYM2o2aAFAfo4254riR8IPra8XtC8RHQxP9EEGgm8PSXiE+ULlkcc+is3CdAIWbNsGy23NuSIaJIKGUB2eGSmOUnZp8rLltm6YwRvOKZ3Js0ouaXBIEonG/E8aRykgcUwgbTxzNFG6kFW0emyxVjnruEhEx1pHbEWzhcojbxtLRaORkecodbo0YmTPCsVyAWtllt1RFlI65xG6icTTSnAUAf/bAGDOFbFGKhnZQpn/6nlLMUdLjBETT3KyhWLVIeI1X4c7RqiODiQx5X/YG2kFJOD0Eqi1aSSWJiS4BXOuKDpaYY/Ux4iTLQz21uJRwipa0iNptLFn1GzQAoD8HG3OFcWPhB9aXy9oXyI6GJ7ogwwE3x6S8Ajzhcojj30Um43pBCxYlgW225pzRTRIBA2hOjwzUhyl7NLkZdtt3TCDN5xTOpNnlVzS4JAkEo35nzSOUkDimEDaeOZoonQhq2j12GKtctZxkYiOtY7YimYLlUfeNpaKRiMjz1HqdGnEyJ4ViuUC1sosu6MspHTOI3QTiaeV4CgC/rcAwJwrYo1UMrKFMv/V85ZijpYYIyae5GQLxapDxGu+DneMUB0dSGLK/7A30gpIwOklUGvTSGxNSHAL5lxRdLTCHqmPESdbGOytxaOEVbSkR9JoY8+o2aAFAPk52pwrih8JP7S+XtC+RHQwPNEHGQi+PSThEeYLlUce+yg2C9MJWLBtGyy3NeeKaJAIGkJ1eGakOC/FVrkAACAASURBVErZpcnLltu6YQZvOKd0Js8quaTBIUkkGvM/aRylgMQxgbTxzNFE6UJW0eqxxVrlrOMiER1rHbEVzRYqj7xtLBWNRkaeo9Tp0oiRPSsUywWslVl2R1lI6ZxH6CYSTyvBUQT8bwOAOVfEGqlkZAtl/qvnLcUcLTFGTDzJyRaKVYeI13wd7hihOjqQxJT/YW+kFZCA00ug1qaRWJqQ4BbMuaLoaIU9Uh8jTrYw2FuLRwmraEmPpNHGnlGzQQsA8nO0OVcUPxJ+aH29oH2J6GB4og8yEHx7SMIjzBcqjzz2UWw2phOwYFkW2G5rzhXRIBE0hOrwzEhxlLJLk5dtt3XDDN5wTulMnlVySYNDkkg05n/SOEoBiWMCaeOZo4nShayi1WOLtcpZx0UiOtY6YiuaLVQeedtYKhqNjDxHqdOlESN7ViiWC1grs+yOspDSOY/QTSSeVoKjCPjfAgBzrog1UsnIFsr8V89bijlaYoyYeJKTLRSrDhGv+TrcMUJ1dCCJKf/D3kgrIAGnl0CtTSOxNSHBLZhzRdHRCnukPkacbGGwtxaPElbRkh5Jo409o2aDFgDk52hzrih+JPzQ+npB+xLRwfBEH2Qg+PaQhEeYL1Qeeeyj2CxMJ2DBtm2w3NacK6JBImgI1eGZkeIoZZcmL1tu64YZvOGc0pk8q+SSBockkWjM/6RxlAISxwTSxjNHE6ULWUWrxxZrlbOOi0R0rHXEVjRbqDzytrFUNBoZeY5Sp0sjRvasUCwXsFZm2R1lIaVzHqGbSDytBEcR8L8NAOZcEWukkpEtlPmvnrcUc7TEGDHxJCdbKFYdIl7zdbhjhOroQBJT/oe9kVZAAk4vgVqbRmJpQoJbMOeKoqMV9kh9jDjZwmBvLR4lrKIlPZJGG3tGzQYtAMjP0eZcUfxI+KH19YL2JaKD4Yk+yEDw7SEJjzBfqDzy2Eex2ZhOwIJlWWC7rTlXRINE0BCqwzMjxVHKLk1ett3WDTN4wzmlM3lWySUNDkki0Zj/SeMoBSSOCaSNZ44mSheyilaPLdYqZx0XiehY64itaLZQeeRtY6loNDLyHKVOl0aM7FmhWC5grcyyO8pCSuc8QjeReFoJjiLgfwsAzLki1kglI1so8189bynmaIkxYuJJTrZQrDpEvObrcMcI1dGBJKb8D3sjrYAEnF4CtTaNxNaEBLdgzhVFRyvskfoYcbKFwd5aPEpYRUt6JI029oyaDVoAkJ+jzbmi+JHwQ+vrBe1LRAfDE32QgeDbQxIeYb5QeeSxj2KzMJ2ABdu2wXJbc66IBomgIVSHZ0aKo5Rdmrxsua0bZvCGc0pn8qySSxockkSiMf+TxlEKSBwTSBvPHE2ULmQVrR5brFXOOi4S0bHWEVvRbKHyyNvGUtFoZOQ5Sp0ujRjZs0KxXMBamWV3lIWUznmEbiLxtBIcRcD/NgCYc0WskUpGtlDmv3reUszREmPExJOcbKFYdYh4zdfhjhGqowNJTPkf9kZaAQk4vQRqbRqJpQkJbsGcK4qOVtgj9THiZAuDvbV4lLCKlvRIGm3sGTUbtAAgP0ebc0XxI+GH1tcL2peIDoYn+iADwbeHJDzCfKHyyGMfxWZjOgELlmWB7bbmXBENEkFDqA7PjBRHKbs0edl2WzfM4A3nlM7kWSWXNDgkiURj/ieNoxSQOCaQNp45mihdyCpaPbZYq5x1XCSiY60jtqLZQuWRt42lotHIyHOUOl0aMbJnhWK5gLUyy+4oCymd8wjdROJpJTiKgP8tADDnilgjlYxsocx/9bylmKMlxoiJJznZQrHqEPGar8MdI1RHB5KY8j/sjbQCEnB6CdTaNBJbExLcgjlXFB2tsEfqY8TJFgZ7a/EoYRUt6ZE02tgzajZoAUB+jj5nbMC/140V82RXfNIGhLt5zGKJKRNOG5IJAjroRvWZhmS6LxEdDE80JGMLNoxPybgtlnAw3ZJ5hOBR9cjti4stNBNs24bRrqwvGjA5YwScyKB4IiHBYiITGRvwx6Y7dk0zmYEAiWsonKM4SFA8UhylzJY2YsZ9FLyPouEPwaPM22RjxEXCwEOR/0njKAUkaGzjnKNpkNBVtHpssVY567hIRMdaS2wFs4XKI+LYktWRPEcpj1Fc0t0olGo99E/p9Hx6cXF5OZPNZnP57HJaE4RevVRY77IgzC8uL2cyuWw+l1ueD7ES4kjQbK7aqubEMaTn55dXMpksDqJVzpVbA7Gep2WxuNFYy0R/LoFCkP8iRBCk5oAFP5JJv5OFFIYKcUiZWwg8kopt0CMC/rfBc1oIP1fk/mqC22J+JWJGCx8LcZ0RnCRkC2X+q+ctxRyN9Pu5bz71N49/9x/+5zPbzwyeeG44+OGP0N6NGJGW9NnWyy449xUXpa5eOP+nfuyin1m6iIC3IbwSnXpk1XWESsQtcM8V8XmbiC0pVt0CvRCgFfZIfYw42cJgby0eqcdWMCZiM5qkR4poJXQSHn9ER3qOdvaKxp9Q+trgJ0beNiAiHzkFW68Jr7/gtBF19hASFE9YL2hfIjpSkQmKZVm2HWyDO5OeVtUjzBcqj4T4bRoDY4z8MQmeK7KnWjQygnhCkIjFRDjrvJzgg8LiB1okrqFQjgKsjR4ZHxIsMspsiQXPNQZusP0tOuL6eVt2jHhINOZ/0jhKAQkeW11jDTPIf0E7ohWtHltl/pdCIjzWGmIrmi1UHiWgouViq2+M4pLBVvfUqdOhKnffBQBz+24or6+v5TXsGA163c6pU8MQjaNwJ0Bq6dBqZb1SyuA7RgKOTCS13I+M4dhRAIDU0vWr1fVqcWUqDP2tdvijDFnKSmwwJVMI8j9Y9iJEHLDAnMwmCylsOYU4pMwtBB5JxRZZvajzvwUA2FaQNfWx41gHPOeKML8SMaOFj4i4zghOErKFMv/V85Zijvb0/o3d527+/D+2e7sYWiNGyGTwQ/sbu8Nv7A5b33rm9x944k0vu3DjuuVXXXyeLG8zKhF9Y8+q6wiVKM7e08/42Zthh4AtKVbdrL7EdSj5n2CMONnCYG8tHqnHFo2JYGREetGKNvaMmp1H0nO0OVe0l5FM9yWig+GJhmRswYZkfWdf1SO3Ly620EywbRvMuSLy/HcNhXMUBwmKR4qjlNnSRsy4j4L3UTT8IXiUeZtsjLhIGHgo8j9pHKWABI1tnHM0DRK6ilaPLdYqZx0XCdnZF5nYCmYLlUfEsSWrI3mOUh6j+GVuad/yvLsFMhj0+73ezu54E2T39LHb3tZu3tFsrmV0HTCC1NKBzOLE+mDQH/R6Ww6E4fbJO9+TbXUarfV82AEjgFQqFfr39Hya7UJqYd/KoicMg15v2wnDcPv4nTdm251Gs5pzQKTT83OpPrLvMxwOQwClQ0DsNSHIfxEiCFJzwIIfyaTfyUIKQ4U4pMwtBB5JxTboEQH/22DOFeE6IzhJyBbK/FfPW4o52u3x6FefeM/nHhm9+q53veuqq6666qqrXv/618/NzaG9GzEiLbu7u/fff/8DDzzw4IMPfupTn/rSP3/vmj995L/97Ct/4SdeLM/bEF6JTj2y6jpCJeIWzLmi6GiFPVIfI062MNhbi0fqsRWMidiMJumRIloJnYTHH9GRnqPNuaL4kaB4wnpB+xLRkYpMUJL3nX1Vj4T4bRoDY4z8MTHnikiQuIZCOQqwNnpkfEiwyCizJRY81xi4wfa36Ijr523ZMeIh0Zj/SeMoc64ouh3RijbninSNEZ1HCahoudjqG6PYJZWttptF/ybMoNdpNjbWq7WT20OAnZPlYjnbWc/q2eVIZdYa7bVl36v9rXazXq1Uj53eBRhu3llazXXrBfZuUer6Wi/oiDiGcjOIYdDrtJq19erG8dNDgN1TR0pr2U5tDCJf6/VrQUud8sqVR04DQOrQere16jd5RglB/gfLXoSIAxaYk9lkIYUtpxCHknBSRCq2yOpFnf8tADDnilgjlYxsocx/9bylmKPHvd9+/zd+5++/AQCXX3750aNH9+/fjwE2YoRG5ubmrrvuuuuuuw4AHn744cOHD3e73X/3V//0228c/MfXvzw6bzMqEX1jz6rrCJUozt7Tz/jZm2GHgC0pVt2svsR1KPmfYIw42cJgby0eqccWjYlgZER60Yo29oyanUfSc/RZYwO2jbTBJPbpBCSqAcSM7bTW1L+j63AdSg4Sfmh9vaB9iehgeKIPMti2HWyDCefTUfUI84XKI499FJuF6QQs2LYNlts6Z4ncc0UeHTQyKJ5ISLCYyETGdgxZU60PimuaaYiIWkhKWgYJFhlllyYvW27rhhm84ZzSmTyr5JIGhySRaMz/pHGUAhLHBNLGM0cTpQtZRavHFmuVs46LRHSsdcRWNFuoPPK2sVQ0Ghl5jlKny2RKejFTWN1otRvv3ZcCABierlXqgrcCEcn8crZYbrQbtx4YHc7ZaazXt2aKACC9mMmX1pvt1gcOjkBs16sb+PVG/yqFYrmAtTLL7igLKZ3zCN1E4mklOIqA/20A4J4rQnUQvxIxo6Ej4kMrojMyn4Rsocx/9bylmKNtAPjEI9/+nb//hmVZt9566wMPPGA2iozMUq644orNzc21tTXLsj78d4//8SP/Mzpv+z4bgamq9FYrbi1qJXLZG9BzRT4dhp04PnbkszfTazW0wh6pjxEnWxjsrcUj9diiMRGMTEgvkTySRht7Rs0GLQDIz9HOXpG7eznVBkvapxOQqAYQM5bT2lP/jq7DdSg5SPih9fWC9iWig+GJPshgWVawxXYmPTqqHmG+UHnksY9iszGdgAXLssB2W+Rc0bQOGhkUTyQkWExkImM5huyp1gfFNc00REQtJCUtgwSLjLJLk5dtt3XDDN5wTulMnlVySYNDkkg05n/SOEoBiWMCaeOZo4nShayi1WOLtcpZx0UiOtY6YiuaLVQeedtYKhqNjDxHqdNlomUxX63kR7/2s9tutPy3/cxA5nOVtdxon2bYaXXiuelnPluplpbGIJqtrVhAJFEolgtYK7PsjrKQ0jmP0E0knlaCowj43wKAwJkhsMCa8gTXQfxKxIyGjogPrYjOyHwSsoUy/9XzlmKOtjpPPvNrrS4AfOADH1hfXz/33HMZaI0Y0SXnnnvuHXfc8f73vx8A3vv5rS9/57mIvO37bASmqtJbrbi1qJXIZW8fb6PszbITx8eOfPZmeq2GVtgj9THiZAuDvbV4pB5bNCaCkQnpJZJH0mhjz6jZoAUA+Tna3Fe0l5FM9yWig+GJhmRswYZk3QWi6pHbFxdbaCbYtg2jXVlfNMx9RQRjxOUoDhIUjxRHKbOljZhxHwXvo2j4Q/Ao8zbZGHGRMPBQ5H/SOEoBCRrbOOdoGiR0Fa0eW6xVzjouEtGx1hJbwWyh8og4tmR1JM9RymOUdJnP5TOpYyeHALtbWz0A5m+89bfarVa7293qweLy8vJKNpebuoNIBUEmswzHTwPAoD8YAMRy2086m8uk7toeAgy3OlsAyzo66W+12+1Op7PVGwwgvbiSyZAFUZMQ5L8IEQSpOWDBj2TS72QhhaFCHFLmFgKPpGIb9IiA/20w9xXhOiM4ScgWyvxXz1uKOdr+z+1/AoCVlZXf+q3fQnsxYmQ28pGPfOQzn/nMo48+Wvm7x//sHT8BANF4G8Ir0alHVl1HqETcgrmvKDpaYY/Ux4iTLQz21uKRemwFYyI2o0l6pIhWQifh8Ud0pOdoc19R/EhQPGG9oH2J6EhFJiiW+q8SS3iE+ULlkRC/TWNgjJE/Jua+IhIkrqFQjgKsjR4ZHxIsMspsiQXPNQZusP0tOuL6eVt2jHhINOZ/0jhKAQkeW11jDTPIf0E7ohWtHltl/pdCIjzWGmIrmi1UHiWgouViq2+MEi/peWd7aNDDzxX1WuvlcrV+amfofX1u3w2rlWqluKK42TFwDhOl5+dj2zdJz8/PA+wAQH9Afrip166vr6/XGoEQQmrh0OpGbb2wTN0ljRDkf7DsRYg4YIE5mU0WUthyCnFImVsIPJKKLbJ6Ued/CwDMfUWskUpGtlDmv3reqs/R3/re8HPffOqcc845evSoOVFkJF4599xz77nnnquvvvqvHutvP/MvSxedK8zbjEpE39iz6jpCJYqz9/QzfvZm2CFgS4pVN6svcR1K/icYI062MNhbi0fqsUVjIhgZkV60oo09o2bnkfQcbe4rih8JP7S+XtC+RHQwPNEHGWzbDrbBhPPpqHqE+ULlkcc+is3CdAIWbNsGy23NfUU0SAQNoTo8M1IcpezS5GXLbd0wgzecUzqTZ5Vc0uCQJBKN+Z80jlJA4phA2njmaKJ0Iato9dhirXLWcZGIjrWO2IpmC5VH3jaWikYjI89R6nSZdOn3xtcUpdKLwUNF/XY1l7n2N+4O7nIA7J4+duTGTKakeMtQt90eGUhlcpnY9or6/f54p2w+TQhisNUo51eW33DjkaNYCGG4c/LOYq7UmO1VUcJCsVzAWplld5SFlM55hG4i8bQSHEXA/zYAmPuKWCOVjGyhzH/1vFWfo//7154AgJ/5mZ+54oorGCCNGJmdHDhwIJfLAcCxrz8NAMK87ftsBKaq0lutuLWolchlb0DPFfl0GHbi+NiRz95Mr9XQCnukPkacbGGwtxaP1GOLxkQwMiG9RPJIGm3sGTUbtAAgP0eb+4riR8IPra8XtC8RHQxP9EEGy7KCLbYz6dFR9Qjzhcojj30Um43pBCxYlgW225r7imiQCBpCdXhmpDhK2aXJy7bbumEGbzindCbPKrmkwSFJJBrzP2kcpYDEMYG08czRROlCVtHqscVa5azjIhEdax2xFc0WKo+8bSwVjUZGnqPU6TLh0ms1u6M9jPTKyrL3b4POeiF/28kdAACYO3D49k+eeOjRxx577NH77r3jvYeWUgAAw9N3l/KlpuxNR4Puxlr11BAAYKGwWlyUNKMsg3azPQpDajmzQmi41262Tg8BILVw8PCtd9xz4r5HH3viiSceve+v77jp4OimqOH23WuVVjw3NXGEYrmAtTLL7igLKZ3zCN1E4mklOIqA/y0AMPcVsUYqGdlCmf/qeas+R9/7T08CwBvf+EYGQiNGZi3ZbBYA/vzr3wUAYd72fTYCU1XprVbcWtRK5LK3j7dR9mbZieNjRz57M71WQyvskfoYcbKFwd5aPFKPLRoTwciE9BLJI2m0sWfUbNACgPwcbe4r2stIpvsS0cHwREMytmBDsu4CUfXI7YuLLTQTbNuG0a6sLxrmviKCMeJyFAcJikeKo5TZ0kbMuI+C91E0/CF4lHmbbIy4SBh4xPP/xJPbf/fdb3d2d771/Wd2hs8OfvRDzJyRmUr6rLMX0xdc9sILr5y/9E0vefm1l7yCMcpUFa0wE41NYK0M60ZDwsajWtEEHEXsEXFsFcaaiqOUxyjZ0muWq8d3AQBgLlfIeQ7UDDqVUvnkLgBAaulwrVUrLjt/X15eyRZKxXK+cOTULgxP311aLXTrBeZdR5gMttrNxkalcvfmLgDA0uH1ajGSAULpt8vl+mhLLJUt5Al3rNLZcqXYqAxW16uruWU3vouL+ZVsLruYzR3ZHAJs19cb1Vxs/jOFIP9FiCBIzQELfiSTficLKQwV4pAytxB4JBVbZIWpzv82mPuKcJ0RnCRkC2X+q+et+hz9yFPPAsAb3vAG1L4RI7OXUTZ+5TvPA0A03obwSnTqkVXXESoRt2DuK4qOVtgj9THiZAuDvbV4pB5bwZiIzWiSHimildBJePwRHek52txXFD8SFE9YL2hfIjpSkQmKpX5zgIRHmC9UHgnx2zQGxhj5Y2LuKyJB4hoK5SjA2uiR8SHBIqPMlljwXGPgBtvfoiOun7dlx4iHRCH/t77f//Uvf/b+/hMMKEZik8GPfrj1/O7W87t/+51v/99ffzD7opetZ679NxcEPu0kq2iFmWhsQpn/pZCw8ahWNAFHEXtEHlvpsabiKOUxSqwMtpob5bXy0dMAAJA6sFr27vX06uWNzeHob2v1qY0iR+Zz1cZGN3PjsR2AnaPlarlQzeBdDTvVfKY2eX4w6Pd7vZ1d5yfZUkvXr61vVHl39gxbpeX5VcYf0yvlVrsscSBo0GvVymvlu0euwlKxXOLgiCjzhVq3gP8pnS2X8xs3HtsF2G01WoNiIbaf4GMIQf4Hy16EiAMWmEuWyUIKW04hDilzC4FHUrFFVpjq/G8BgLmviDVSycgWyvxXz1v1OXr4wx+BOVdkJEkyOlf03P/3IwAQ5m1GJaJv7Fl1HaESxdl7+hk/ezPsELAlxaqb1Ze4DiX/E4wRJ1sY7K3FI/XYojERjIxIL1rRxp5Rs/NIeo4254r2MpLpvkR0MDzRkIwt2GDOFWEemXNFQWYgQOIaCucoDhIUjxRHKbOljZhxHwXvo2j4Q/Ao8zbZGHGRMPBw8//Pe91bHj4+0n3Xu9511VVXXXXVVa9//evn5uYwc0ZmKru7u/fff/8DDzzw4IMPfupTn2o//c/Xnjz6sSvf+vMve5VHj6yiFWaisQmslWHdaEjMuSLp2CqMNRVHKY9R7DLs1spr7alNiEG/19vqttub7v05C9ev1ytZz0ZFt77RGh04WihUyll8E2OxWFmrHrttEwBO1zZalY0crjfcOb25w8CXWsrmc5l5GABwdkqGw90hcuUPAADsDiD8R9yGW43K2tbUbthg0O9tdTrtze1d56W5Q7fX1/OzPNwzn81nUsdODgEG3e4WAOWv31EIQf6LEEGQmgMW/Egm/U4WUhgqxCFlbiHwSCq2yApTnf9tMOeKcJ0RnCRkC2X+q+ct1Rx93nnnofaNGJm9vOhFL5r6XxTehvBKdOqRVdcRKhG3YM4VRUcr7JH6GHGyhcHeWjxSj61gTMRmNEmPFNFK6CQ8/oiOOVe0h5GgeMJ6QfsS0ZGKTFDMuSJWJphzRUFmIEDiGgrlKMDa6JHxIcEio8yWWPBcY+AG29+iI66ft2XHiIdEKv/v+Mapj369DQCXX3750aNH9+/fz0BjJB6Zm5u77rrrrrvuOgB4+OGHDx8+3O12f+WB4/9p/+6tP3GVq0dW0eZcEdeCNDOYc0Xaxih+2T5+153HmX9NLR1aW69VCr5jQ71WozPal1nIl0K2TzKF4sHK5qkhwE6z0YFcFtWaO3B9ztPBYNDvb211t7Z3h8Ptk3fddvKuytKhtY16Nez33+YW9i2ykKSX5zk7TTsn777zJPvPCwffu15bL67M+GTPfHrs0bC/1UveXhFB/gfLXoSIAxaYS5bJQgpbTiEOJeGkiFRskRWmOv9bAGDOFbFGKhnZQpn/6nlLNkcbMZJQEeRtRiWib+xZdR2hEsXZe/oZP3sz7BCwJcWqm9WXuA4l/xOMESdbGOytxSP12KIxEYyMSC9a0caeUbPzyJwr8uv4kAji2WNIpvsS0cHwREMytmCDOVeEeWTOFQWZgQCJayicozhIUDxSHKXMljZixn0UvI+i4Q/Bo8zbZGPERcLAE5L/f/LtL3/0623Lsn7913/9ox/96Lnnnos9byQpcsUVV2xubn7gAx+48847f/fR9qWpF974Cmdvj6yiFWaisQmslWHdaEjMuSLp2CqMNRVHKY9RcmXuwOHy2mqxmAv8uhwAdNrd0VZRKpPLhO2frGQzi3BqGwB6nU4PstheT2qltNFYWw7+YdDrNOvr1erdp3aG2yePFHK9RrvG2plKZdfbTforfVL7blhbWy2V8jPZJhr0Oq1Wu9Vqdbvdbrd72j3bNQg/FxWPEOS/CBEEqTlgwY9k0u9kIYWhQhxS5hYCj6Rii6ww1fnfBnOuCNcZwUlCtlDmv3reks3RRowkVKLwNoRXolOPrLqOUIm4BXOuKDpaYY/Ux4iTLQz21uKRemwFYyI2o0l6pIhWQifh8Ud0pOfos8YGRv/ztcGS9ukEJKoBxIzltPbUv6Pr/P/svWuUY1eVJrivMSmlDYRwYYegCkJQ0CF3gVMuY1J+0CmqTaeYntUpmoEMelWvDE89EFXV5fAsT6c8a9EtGDNWUt1j5ayiLM8UTXhm1kpFlcsoV1FFZONqIqcorGgYp4KCsqKKxgoosIJHo7CdtjQ87vzQ4772ufc89pVuZJz946xMxb77fPs7++x9ro7OvYEORQdJMLWuXtC+eHQwPOKDDIZheFtsZ9Kho+oR5guVRw77KDYT0/FYMAwDTKtFzhXZdVBmUDxCSDBOZJgxxoZMW+uCYplmGiJKLSRTWgYJxoyyS5OPTau1aAYnnTadybVKLoXgkCQSwfj/6+e/96//5j8BwOnTp6vVqt4o2hdy6NChhx566L777gOAe1r/6et7Pxj9gWxGK1SikQmslcm6YkjYeJSnItGUpvPI2Spzy4knmBn5HKWeLmctsWMPPf2sJZcePjF6hGcfMvlldKMIoNft9ob/SiSTPkd9ACCVTsYAAGDQ7XQFscWTmcLKarN57uQCAMBg+9FiaSOcPZPYkdNP2mh45typheEfBv1kPvyNol57vVosZJKJ1978ng9++Mwja+cvbtk2iqIqFMsFrJVZdosspMKsI3SFxNFK5CiC/G8AgOfMEBhg2DzBdRC/IlHR0BFxoeXRGZqPQrRQxr963JLVaC1aIiqcedv13QjYZqVztuLWRGdiYPZ25W00e7PszOJrx+DszfRaDS23R+pjFBAtjOwdikfq3KKccDLj04uQR9JoZx5R00ELAPI1erxXNNnVtLfeKe3S8YioAcTMZDPMsP1bXCfQoeggCabW1QvaF48Ohkd8kIc7k+7WG3AuHVWPMF+oPHLYR7EZmI7HgmmaYFjt+CyRda7IoYMyg+IRQoJxIsOMOTZk2FoXFMs00xBRaiGZ0jJIMGaUXZp8bPtZiUUzOOm06UyuVXIpBIckkQjG///yt18CgHQ6/dGPfpSBQEtE5YEHHhg+LfDB9uboA9wpmAAAIABJREFUI7IZrVCJRiawVibriiFh41GeikRTms4jZ6vMLSeeYGbkc5R6upy5xJN2yRQr5aMxAIDBVmWl1sGv6U9OucTjAbsolkJf9mhMaqlWGe1g7TRWQ9osctKQWqqUj4+6XF2ptkLpcij9dr2YTd/4nnsfOb+1OwCIzR85fvKeBx8+97knLz3z7LmTsRD7VhWK5QLWyiy7RRZSYdYRukLiaCVyFEH+NwEg8FwRqoP4FYmKho6ICy2PztB8FKKFMv7V45asRmvRElHhzNuu70bANiudsxW3JjoTA7M3oOeKXDoMO7P42jE4ezO9VkPL7ZH6GAVECyN7h+KROrcoJ5zM+PQi5JE02plH1HTQAoB8jdbnimaPJJhaVy9oXzw6GB7xQZbfmVTyCPOFyiOHfRSbiel4LOhzRV5DRKmFZErLIMGYUXZp8rFptRbN4KTTpjO5VsmlEBySRCIS/3//4nMbP9y5+uqr19bW9ImifSeHDh06d+7c1Vdf/R93O9968TkAwhmtUIlGJrBWJuuKIdHniqS55cQTzIx8jlJPl5GTdLFaXAQAgL2L5VIdPQsksAHU499WYksiV8gO90x67VZH1oqQJJerpeBNM2Vp1wq5Dz6yuQsAscXjpz/95DO9bmu9Xi0Vl/LZTCoZhym/IUlIKJYLWCuz7BZZSIVZR+gKiaOVyFEE+d8AAH2uiDVS0YgWyvhXj1t9rkjLlS6cedv13QjYZqVztuLWRGdiYPYGfa6INv8TjFFAtDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI12h9rmj2SIKpdfWC9sWjg+ERH2T5nUkljzBfqDxy2EexGZiOx4I+V+Q1RJRaSKa0DBKMGWWXJh8bVmvRDE46bTqTa5VcCsEhSSQi8d/o/i0AvPvd777pppsY3WuJtBw5ciSXywHAnz37TQDCGa1QiUYmsFYm64oh0eeKpLnlxBPMjHyOUk+X0ZN4tlw5OQ8AALtrpTJ2jieRSo5eDNTrdv2fLNdtd0ZvNkqm/J9W5yeJ8T7ToNeb1ot70iuVwE0zRelUiysXdgEA5o8/3GytV5az+EP/IioUywWslVl2iyykwqwjdIXE0UrkKIL8bwKAPlfEGqloRAtl/KvHrT5XpOVKF8687fpuBGyz0jlbcWuiMzEwe4M+V0Sb/wnGKCBaGNk7FI/UuUU54WTGpxchj6TRzjyipoMWAORrtD5XNHskwdS6ekH74tHB8IgPsvzOpJJHmC9UHjnso9hMTMdjQZ8r8hoiSi0kU1oGCcaMskuTj02rtWgGJ502ncm1Si6F4JAkEpH4/+zu3wHA7bffzuhbyz6QbDYLAJ/97n8BIJzRCpVoZAJrZbKuGBJ9rkiaW048wczI5yj1dBlFSRQq5WO+T2BLZ9OjtxC11pt+mzftZnN3+K9kJiO/VzTZkYolklPbTInnAjfN1KRdX20OAADmjldWi5n9tEs0FIrlAtbKLLtFFlJh1hG6QuJoJXIUQf43AECfK2KNVDSihTL+1eNWnyvScqULZ952fTcCtlnpnK24NdGZGJi9QZ8ros3/BGMUEC2M7B2KR+rcopxwMuPTi5BH0mhnHlHTQQsA8jVanyuaPZJgal29oH3x6GB4xAdZfmdSySPMFyqPHPZRbAam47GgzxV5DRGlFpIpLYMEY0bZpcnHhtVaNIOTTpvO5Foll0JwSBKJSPy3X/ghANx2222MvrXsAxkO39ef+yEA4YxWqEQjE1grk3XFkOhzRdLccuIJZkY+R6mny2hKarlaPOLzBLZkfvxQuN311UaPaae1uro1/NdCvpCRhtNpNFrD00mJdDolbUZYgjfNVKTfbrUHAACxdD4nv482Q6FYLmCtzLJbZCEVZh2hKySOViJHEeR/EwD0uSLWSEUjWijjXz1u9bkiLVe6cOZt13cjYJuVztmKWxOdiYHZG/S5Itr8TzBGAdHCyN6heKTOLcoJJzM+vQh5JI125hE1HbQAIF+j9bmi2SMJptbVC9oXjw6GR3yQ5XcmlTzCfKHyyGEfxWZiOh4L+lyR1xBRaiGZ0jJIMGaUXZp8bFqtRTM46bTpTK5VcikEhySRiMT/4Gc/BX2uaJ/L8FzRiz/9MQDhjFaoRCMTWCuTdcWQ6HNF0txy4glmRj5HqafLiEo8U6osLQAA4wlsqeVifriJsne+zDpy06mVatsAABA7UizmJM/N9NvVYuXicKtoIb8ka0ZOgjbN1GRE26Dfx7fbuhvr7Wk9ck9GKJYLWCuz7BZZSIVZR+gKiaOVyFEE+d8AAH2uiDVS0YgWyvhXj1t9rkjLlS6cedv13QjYZqVztuLWRGdiYPYGfa6INv8TjFFAtDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI12h9rmj2SIKpdfWC9sWjg+ERH2T5nUkljzBfqDxy2EexGZiOx4I+V+Q1RJRaSKa0DBKMGWWXJh8bVmvRDE46bTqTa5VcCsEhSSRC8Q8AAIcPH2b0rWUfyHXXXWf9h2xGK1SikQmslcm6Ykj0uSJpbjnxBDMjn6PU02VkJZGvlE8Mt4OwJ7Allirlo8OjRdtnlwrVlmu3o99pLOdXLuwBAMDCUqUoc6qo116vLmWz9w7f6QPzx8vl/JSf1Ba0aaZiOplKDf/Vrtdb7j2h7ka1kM0/sjWg65BcKJYLWCuz7BZZSIVZR+gKiaOVyFEE+d8EAH2uiDVS0YgWyvhXj1t9rkjLlS6cedv13QjYZqVztuLWRGdiYPYGfa6INv8TjFFAtDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI1+irRwYcv0aZtN69X2frEfQihwHg2Dy2swrjVlBnHyFB8fj1gvbFoyPFjFcMwzBNb+vdmXS0qh5hvlB5hOPxQcgYIzcn3nNFpq1FmeHE44OEjxPuqHPmBBcUVn6gRWIZ8s1RgLXizLiQYMwoZ0uMPMsYWGS7W3TEw8/bsmMUhEQo/rVcYUI2oxUq0ciEcv6XQsLGozqjCXIUsUfk3EqPNVWOUh6jKEtyuVKurd+7ORg+ga3YKjn2e9Ir9dp69u4LuwC7F+7NZhpLxeWlbDoBvU57vV5dPb893CiKHblntZpPsPsZdOqlYtuu0O/3up12u7W1szf5bO7oPfX6coptpVnKZasBO0nZ8kZVdLcpka+UjzfuvrA33DQrFmpUR5uyS/nFs9vbAIOtM/lcr1xazmWS0Ou2m416rba2tQcwPz+/u7tL0x29EMS/d9rzJGKPBeaSZbKQwpZTiEPKuYXAIylukRWmev43AMD6oZu7HaIf6YDjXBHmVyQqmv+I8OsM4UQhWijjXz1uyWr0VKVZya94H6UajyfiiWQqnclms7lcNuVTw9SlXSssr+K/RIjHE4nUCEY+m/KrPrgjuCSXao0VR0n3xxBPJlMpFoheo5ivyDykNV2sr/qU9SgKZ95mzET0xp41rwVmIn/2tl/jzt4MOwTZkmLVzeqLX4cy/xOMUUC0MLJ3KB6pc4tywskMTy+hop15RE3PI+kaPd4rMk0Aw93iQWxrPWKajpa5hvF8BWkzMW5d/grquJBw4tlnSOx98ehgeMSQjCyYMDolY7VYwIG9JfMIwaPqkdVXIDbfSDBNE4a7si42YHLGCAKYQfEIIcE4kWHGBPwye8eWaWZmIEBiGfLPUQFIUDxSOUo5W5qIGetScF6K0u+DRzlvk41RIBIGHjz+tVxhQjajFSrRyATWymRdMSRsPKozmiBHEXtEzK3CWFPlKOUxiraki5Vi7V1nt0dPYFvaKKbsf04tN9b7S4WV8zsDGOxcfPT+i4+6LcwfO12vV3L+37Ltbq49sunz99jC8WK1Wimkffdo9na2Nnd8+4FYotcHEN7pSS5XS7XM/axNM3nJlqun1guPbg8Adjcf+fB7H3GAXTheXl3NNrLvOhvg1syEIP55EoE3NXssuJFM+p0spDBUiEPKuYXAIylukRWmev43wXFaCD9XhH4difgViYrmPxb8OkM4UYgWyvhXj1uyGj1V6bVbm5usLfnzjz4CALH5o4WVcmUl77tXIy/9Tntzc5v554sjGHOLx4uVWrnAQOHriEsWsu6zrAEYAABGIE6UqlUHFf1ep7W5KX4INgaFKD9lFRWRvA3+M3E8H1nzWmAm4hYCzxUF522ibEmx6ubohQAtt0fqYxQQLYzsHYpH6txycsJX0SQ9UkQroRNx/hEd6Rqt31c0eyTB1Lp6Qfvi0cHwiA8yGIbhbbGdSYeOqkeYL1QeOeyj2ExMx2PBMAwwrVa/r4gGCachVCfIjFSOUnZp8rFptRbN4KTTpjO5VsmlEBySRCIU/1quMCGb0QqVaGQCa2WyrhgSNh7lqUg0pek8crbK3HLiCWZGPkepp8toSzxXrpycBwDGE9jimWKj1Tx3z4kjczHXn2ILx049/IXWRiWXlOg4Nje/cOTo8ZMfevDTn3u621mvBmwUhSzplcryIgBQv7YokV/dWH/wxOKc49PY/JETpz/Taq+XcslUKuVmNjpCsVzAWpllt8hCKsw6QldIHK1EjiLI/wYA6PcVsUYqGtFCGf/qcUtWo2ckc/MLY5mftxe1we7m2v3vSWcKNffjVskhLBw5asmRI4sLNiB72xfOvDeTrzQDd1hiATLnV1DnFhaP2GTRgQH2ts/f/55svmp7cGoc4nG8H39A8fgs67qccOZt13cjYJuVztmKWxOdiYHZ25W30ezNsjOLrx2DszfTazW03B6pj1FAtDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI12jj7xZPAwA2nKy9X/boeIGztpA9WQK7mKMvajz7DAlrjgTqqCJBooWxMxmCR0EiyS0ntgAkjs7c54omv+wQxSODBDMjN9ZuJVf3GMKQkATkKBlmpHIUXbZEDbiso/T74FHO25zMYCKIhBOPAWDCa//jWQAwTTNQXUuUZbj4+N4/+226GU1RiRCgUvlfBAnZPAq0QGRHwSNibhXGmipHyXB73bn/AFdgEut1ms1mu9PrQzyRTKWz2Uxy/30LNCvpdZobzVa3B/FEMpXJ5dKhPu5IQoYZ+7m7l5wfc8X/E9959i+f/d5TP/iv33rh8rMv9vs//elMXNASKPGXXfW6aw+//hWHb7nh1e987Wvuev0NWGoGcPyfda7IK1GoaGixkdBBNAkqEbP1EZQT1wcMfmTwBIpqjb7mk0/AtOvj+nLyPY/uAsDcqc/1VvO2v/Q6reZGfbW22tjcHZ2amTt6urEedFZWWFql9M1ntgEgdvxcd33Jbb3X3mjUKuXahZ0hisV7vtCqeh+GynaEAEO/21pv1KqV1YtDELHFezZa1axvmd8oJt/1yC4AxE6c6zWW9u+SYFgBX7rn1iBF/+9GGPkq2I76THQCwbM3t0cBODnsyKy6xXuRQSvrEdEYBWfvUDxS55aTE6mKFiGP9jVaJh6eGq3fVzR7JCgev17Qvnh0pJjxiqH+VGIJjzBfqDziym92DIwxcnOi31dEgsQy5JujAGvFmXEhwZhRzpYYeZYxsMh2t+iIh5+3ZccoCIlQ/Gu5woRsRis/H189/0shYeNRndEEOYrYI3JupceaKkcpj9GVI4lUNp/KzhrFPpVEKlvYh+QFxf83n3v+Q//P5ub3fjBroFq4pP/Tn33zucvffO7yxe/+4H9t/d3tyZ/75LGb3zL3Cv2+ItQjgkqElD2ehYjHAnPJPrmRwG4ngvEIIUE5kWYmGpJIZfLLmfxyqbVaXCqubQ8A9jbPLC1nWo0lmQOz0jjSueVqLp9bzr730R0A2F6tNso5z3ZOqBJPZgrFWqFQKOYKj2wPYLC9Wq6X1penyUMUxC/+2XnJ+V2Keyb6ZCrgm4m4Bf2+InG03B6pj1FAtDCydygeqXOLcsLJDE8voaKdeURNzyPpGj1+Bp1pIq03iF06HhE1gJgxx61h+7e4TqBD0UESTK2rF7QvHh0Mj/ggw/BXP67WG3AuHVWPMF+oPHLYR7EZmI7HgmmaYFjt+B1F1vuKHDooMygeISQYJzLMmGNDhq11QbFMMw0RpRaSKS2DBGNG2aXJx4bVWjSDk06bzuRaJZdCcEgSiVD8a7nChGxGK1SikQmslcm6YkjYeJSnItGUpvPI2Spzy4knmBn5HKWeLrVo2bfiG/9/9I1O5rE/G24Uvf/9769UKp///Od7vZ6pJarS6/U+//nPP/jgg+9///sB4EvdH97+J1/4zDe/A77vK4JR69BBkngkKhpaflxoeXSG5pUrEd1tp8iNhE89Ui3SXq9lmYmWJDLL9Y3GqcXhI9V2z6+sNEJ+FB0myUJ55egQwl6r2Z4+AACAZL5Szg+fmrrXbGzMgIYZS8Aiddy6vhsBcH2LYtfx+2IR+GYibiHwfUUuHYadWXztGJy9mV6roeX2SH2MAqKFkb1D8UidW5QTTmZ8ehHySBrtzCNqOmgBQL5G63NFs0eC4vHrBe2LR0eKGa/oc0WsSNDniryZgQCJZcg3RwHWijPjQoIxo5wtMfIsY2CR7W7REQ8/b8uOURASofjXcoUJ2YzW54rEueXNDPpcUWhjpEXL/hV2/J+59LWPX/oaALztbW9bW1u78cYbZ41VS7DMzc3dddddd911FwB89atfPXnyZLvd/pdPfLl86wv33byozxV57mv0uSLvEF1Z54rskszXVovN3NntAcBuo1xrF0ppP/1ee319o93pdLqQTKXT2Vwum1I9CJTKpBOwuQsAvd7MdmkSuXwmdv7iAGCv0+kChHK6qddubjRb7Xan2+9DPJnOZHP5aDyc1S/+2XnJ+V2Keyb6ZCrgm4m4BX2uSBwtt0fqYxQQLYzsHYpH6tyinHAyw9NLqGhnHlHT80i6Ro/3ikwTwHC3eBDbWo+YpqNlrmE8X0HaTIxbl7+COi4knHj2GRJ7Xzw6GB4xJCMLJoxOyVgtFnBgb8k8QvCoemT1FYjNNxJM04ThrqyLDZicMYIAZlA8QkgwTmSYMQG/zN6xZZqZGQiQWIb8c1QAEhSPVI5SzpYmYsa6FJyXovT74FHO22RjFIiEgQePfy1XmJDNaIVKNDKBtTJZVwwJG4/qjCbIUcQeEXOrMNZUOUp5jLRo2b/CiP9Pt7/x8UtfMwzjd3/3dz/xiU8cOnRo1kC1CMtNN920tbV1+vTps2fPlr/89A3XxP/l4gJ6rojxviLPci0SFQ0tNi60PDpDOMqViKsQepcmHgtuTiYMTG4kMH5k8PggQThRYCaKEs+WyvnVD57fAxhsrdaapSr+4NDuRnVlpdLYGr/iaCSxxeMr1Vo5n5J/bU+/D/3hvxKJ2e2bxCd997vUO1b9zka9Wq3W193sAcQWjpdXV0u5GT/0zi/+PTnKfyaO5yNrXgvMRNxC4Lmi4LxNlC0pVt0cvRCg5fZIfYwCooWRvUPxSJ1bTk74KpqkR4poJXQizj+iI12jx8+gG/7P1aLfDtpbj4gaQMwY49a0/VtcJ9Ch6CAJptbVC9oXjw6GR3yQwTAMb4vtTDp0VD3CfKHyyGEfxWZiOh4LhmGAabXIuSK7DsoMikcICcaJDDPG2JBpa11QLNNMQ0SphWRKyyDBmFF2afKxabUWzeCk06YzuVbJpRAckkQiFP9arjAhm9EKlWhkAmtlsq4YEjYe5alINKXpPHK2ytxy4glmRj5HqadLLVr2rWDxv/WD/3rPl74CAKdPn65Wq3qjaP/KoUOHHnroofvuuw8Afuvipa/9sDf5HbotbY8+Ace5IkCSeCQqGlp+XGh5dIbmlSsR3W2nyI2ETz1SLdJkNTqikiwsj56/BtsbG9hT4HrNSi7zrnvXvFsdMNi+cOY92UKt3Zftvt9ab+4BAMBcNud7qClU6XW7w3/E4knCHat+q76SS6ffdffZ8wh7AIOdC/fn8ysb0vTRSMAiddy6vhsBcH2LYtfx+2IR+GYibsGek515G83eLDuz+NoxOHszvVZDy+2R+hgFRAsje4fikTq3KCeczPj0IuSRNNqZR9R00AKAfI3W54r2MxJ7Xzw6GB4xJCMLJuhzRZhH+lyRNzMQILEM+eeoACQoHqkcpZwtTcSMdSk4L0Xp98GjnLfJxigQCQMPHv9TlWYlz3goeTyeSCSTqVQmk8vlc5mkz88E2Ua8klyqNVYy9k/atcLyahdVjscT8WQylcpks9lcPuv9qWJndWmp1uHr2SGZ0nqtMKXfLJLNaIVKNDKBtTJZVwyJPlckza3CWFPlKHluL1++fO2112JGtWiJljz//PMAcPhlL3P/AYv/j/6/XwWAdDr90Y9+dPpQtZDLAw888NnPfvbpp5/+2Jfbf5Q/CgD6XNEYjnIl4iqE3qWJx4KbkwkDkxsJjB8ZPD5IEE4UmImoxHP5bGztwgAA2hvNXsn5VLR+u1rI339xDwBg/uiHyuViIZdJxqHXaa7XyqXqhZ3B7oWVQinTqmbFDxf1m9WV1W0AAFhYKuZndq6ou7HeHm7lxNPpFJ3dfne9cXFnAACxhWNLS0uFfDadSiag12k1qqXK2vYewGCrtlJeblUygdZCE7/49+Qo/5k4no+seS0wE3EL+lyROFpuj9THKCBaGNk7FI/UueXkhK+iSXqkiFZCJ+L8IzrSNVq/r2j2SFA8fr2gffHoSDHjFUP9qcQSHmG+UHnEld/sGBhj5OZEv6+IBIllyDdHAdaKM+NCgjGjnC0x8ixjYJHtbtERDz9vy45REBKh+J+q9Nqtzc1dP41H4QzA3JGTpWqV9WCEYCOWLGTdP5jrd9qbm9u+Fz36CADMLZ4oVasrjodb9Lvt1uYW8uu8AIklpvgcdLIZrVCJRiaU878UEjYe1RlNkKOIPSLnVnqsqXKUDLevu+ba7754udVq3XHHHbifWrRESS5dugQA1x/2fLnpif9vX37xie90r7766rW1NX2i6MqQQ4cOnTt37u1vf/vnvtX91vMvveGVh/X7isZwlCsRUvZ4FiIeC8wl++RGArudCMYjhATlRJqZqEoinU7BhW0AGHTaHQD7pkWruly6uAcAscWTq+v1pdTkolR2qbKeyyxlP7i2M9iulWorGysp4JZep7ler5Qr57eH1j9Uq+TkH2SnJt31UuXC6HBTrkAJI5GvlI83a6lStbyctd1PJZOpUj2XiWfe8+gOwGBrtbpeXs3Pyn/f+GfnJed3Ke6Z6JOpgG8m4hb0+4rE0XJ7pD5GAdHCyN6heKTOLcoJJzM8vYSKduYRNT2PpGu0Ple0n5HY++LRwfCIIRlZMEGfK8I80ueKvJmBAIllyD9HBSBB8UjlKOVsaSJmrEvBeSlKvw8e5bxNNkaBSBh48PifkcwvHrW9lLbf7/W6nZ3xQxL2ttbuf9fGxsMbjWLa9/YlFov59hL3u3puYTGVsBT6/V6v293dG2PYPn//e5rrD62vr2QmSvFEHO1xMBj4AfKFQS1kM1qhEo1MYK1M1hVDos8VSXOrMNZUOUqG2+tj8e++ePnSpUt6r0jLvpDhXtEN3r0iT/w/9l92AODd7373TTfdNH2cWkKSI0eO5HK5J5544vwz3/lXN71Znysaw1GuRFyF0Ls08VhwczJhYHIjgfEjg8cHCcKJAjORlVQ6GYPtAQB0u44z/916ubI5AIDYkZX6qrVRNJHkUrVcX7/7/N7g4mq9tVJCz8YMmqVspjLJta6VPswtnijVaqVcwKGivXoh0WCt5OO5aquxLPHWn35nvVZaKa0Nf74WO1IsET+AILm83l7G/5TIl0vH6h++OADY3VhvQR5/VdQUxC/+PTnKfyaO5yNrXgvMRNyCPlckjpbbI/UxCogWRvYOxSN1bjk54atokh4popXQiTj/iI4+V7SPkaB4/HpB++LRkWLGK/pcESsS9Lkib2YgQGIZ8s1RgLXizLiQYMwoZ0uMPMsYWGS7W3TEw8/bsmMUhEQo/mcjc/lqczXv+rDfbW00apXyIxd3AWD3wspyNdvE7/6GRk41eh4j3BLLVprrS+77sn63td6oVSurF3cGALsXS0ul7OThFumVjd6K11K/Xkh88PwAAOaX17u1nCwiGiGb0fpckTi3vJlBnyuiHKNbX3PD1o9+2Gq1cCe1aImYPPXUUwBw6/U/5/6DJ/7P7/w9ANx+++3TB6klVMlms0888cT5Z579Vze9WZ8rGsPR54q8Q3SlnyuC+OTXVP1+vw8w/l+nXl0fPnyuUC5l8H2aZGEpt3L+/B60GxudUiaF6eztbG/tMPqOLebyuVQcbN0yZDDYG7AeKtDvBbzxZ9BeLa007Q8p6HW7nXazaXuN0Pzxar0s8SA9eUllsym4uA0A3Xa7C1mJzS4S8Yt/dl5yfpfinok+mQr4ZiJuQZ8rEkfL7ZH6GAVECyN7h+KROrcoJ5zM8PQSKtqZR9T0PNLnitw6LiScePYZEntfPDoYHjEkIwsm6HNFmEf6XJE3MxAgsQz556gAJCgeqRylnC1NxIx1KTgvRen3waOct8nGKBAJAw8e/xGSeDKTL9by+Wwhe/f5XYDBZq22sVKb6pMh4slMoVgrFArFXOGR7QEMtlfL9dK6zC8GZyRkM1qhEo1MYK1M1hVDos8VSXOrMNZUOUqG2/9u4U1/+HdPP/bYYx//+Mfn5+cxu1q0REW+853vPP744wDw/jctuP/mif+v/6gHALfddtv0cWoJVYZj+vX/+hwA6HNFYzjKlYirEHqXJh4Lbk4mDExuJDB+ZPD4IEE4UWBmv0l3vdEaAADM5ZbYrxJKZLKp2PmtwaDdagOkMJX5o8ezjleg9nu9Xrfd3t7dg8H2+bP3nj9bWjxRWV1dyfoc6plbWGS+RzWeCjoNtHPhkbMXmH+NLRxbqa6WC96XpIYrieToRNeg2+0BzOo2xy/+PTnKfyaO5yNrXgvMRNyCPlckjpbbI/UxCogWRvYOxSN1bjk54atokh4popXQiTgAb60VAAAgAElEQVT/iI4+V7SPkaB4/HpB++LRkWLGK/pcESsS9Lkib2YgQGIZ8s1RgLXizLiQYMwoZ0uMPMsYWGS7W3TEw8/bsmMUhEQo/qMnqeXSUuX82W0A6LaaHcilp48hma+U8/UPnt8D2Gs2NnrLngNIkRWyGa3PFYlzy5sZ9LkiyjF6x/Xz73jNDf/5B9/79V//9T/90z/FXdWiJRpy6tSpF1544dbrf+7tHOeKBj/9GehzRVeiZLNZAHjhxz8BAH2uaAxHnyvyDtEVf66o1x+fyonHbQ9sbjXbAwCAWDqT6jkfTmeXfny4OO93uz0AZKEey6zUG9gKvt9pNkbvLBpsn783l+tuNCusgz1zhVpb/jEGTJk7crK0Ulxayk1lm6jXaTY3mhvNZqvVbnfaO9ahpsCTUWGKX/yz85LzuxT3TPTJVMA3E3EL+lyROFpuj9THKCBaGNk7FI/UuUU54WSGp5dQ0c48oqbnkXSNvmpkwDSR1hvELh2PiBpAzJjj1rD9W1wn0KHoIAmm1tUL2hePDoZHfJDBNE1v6w04l46qR5gvVB457KPYDEzHY8E0TTCsdnyWyDpX5NBBmUHxCCHBOJFhxhwbMmytC4plmmmIKLWQTGkZJBgzyi5NPjas1qIZnHTadCbXKrkUgkOSSITiP4qSyaaHr/0Z9Lq9GWFI5PKZIYi9Tod5mxpBIZvRCpVoZAJrZbKuGBI2HuWpSDSl6TxytsrccuIJZkY+R0ly+8DN7wCAz372s3/yJ3+CW9aiJQJSr9f/4i/+AgD+XfYW5M/e+AcAgMOHD08LoJYpyXXXXTf5t+tcEYxax2/YkSQeiYqGlh8XWh6doXnlSkR32ylyI8FmWLlIk9Xo6Eq33R3tWSST1tGWXne0+h9s3n/za9ly870Xh1f3+4LbHfFUdqnUaDUfPj4PADDYqhbLIT3INnbsoaefteTSwyfmhn/oQya/HPpGUa/VqCzn04nkG297z933nnlk7cLmlm2jaNYSsEgdt67vRgBc36LYdfy+WAS+mYhbCDxX5NJh2JnF147B2ZvptRpabo/UxyggWhjZOxSP1LlFOeFkxqcXIY+k0c48oqaDFgDka/R4r8javbS13int0vGIqAHEjGtjbPKJoE6gQ9FBEkytqxe0Lx4dDI/4IINhGN4W25l06Kh6hPlC5ZHDPorNxHQ8FgzDANNqkXNFdh2UGRSPEBKMExlmjLEh09a6oFimmYaIUgvJlJZBgjGj7NLkY9vPaCyawUmn/ac242uVXArBIUkkQvEfRZnc88USgY92CE3iiXHX/ZltWMkI2YxWqEQjE1grk3XFkLDxKE9FoilN55GzVeaWE08wM/I5SpLbt7/mho//8lEA+M3f/M0///M/x41r0TJT+bM/+7Pf+q0PA8C/y95y82uuQzS88a/lAIgBhi1tGzBq7eeKAEnikahoaPlxoeXRGZpXrkR0t50iNxJshpWLNFmNjqz02u3O8F+xtO2FQ+JbP3GpHZd4ulgrHYsBAAy2GqtNGRscvSTtkilWykdHXVZWap1w+gQAgF6rtpxJ3fze+x+9sL03AJhbOHri1D0PffozX3jy6Weefvh4LMS+eSVgkTpuXd+NgG1WGjy39M42cCbiFuw52Zm30ezNsjOLrx2DszfTazW03B6pj1FAtDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI12j9vqL9jMTeF48OhkcMyciCCfp9RZhHpmmCfl8RefxbhvxzVAASFI9UjlLOliZixroUnJei9PvgUc7bZGMUiISBB4//CEq/uTF8TDkkM5kZPIBuKJOHXsTiyX3zADognNEKlWhkAmtlsq4YEjYe1RlNkKOIPSLmVmGsqXKUPLcfXvyl7754+ZPtr/3Tf/pPf+3Xfu2hhx565StfifWhRcu05fnnn7/33ns/9alPAcC9b7vxN9JvwfW88a/lAIh+X9EYjnIl4iqE3qWJx4KbkwkDkxsJjB8ZPD5IEE4UmImo9DfWm8P1fiydzVhrbWvr58iHPlPjeGVoMi27UE/l8+nSxa0BQKfd7oHfW4uoJF2sFmu3nd0G2LtYLtUL9aUwXhbUb5by+TObewDDp92VSsuFjO2tS72QdsYExS/+PTnKfyaO5yNrXgvMRNyCfl+ROFpuj9THKCBaGNk7FI/UueXkhK+iSXqkiFZCJ+L8IzrSNVq/r2j2SFA8fr2gffHoSDHjFUO/r4gxRm5O9PuKSJBYhnxzFGCtODMuJBgzytkSI88yBhbZ7hYd8fDztuwYBSERiv/ISW9jZaW+CwAQO7JUzM0KRndjffiwdIin06lZoZAQshmt/g4b5fwvhYSNR3VGE+QoYo/IuZUea6ocpcTt/3zzO26cS9zzn//qU5/61Llz544ePXrnnXe+853vvOOOO6655hqcAi1awpEXXnjhS1/60he/+MW//Mu/3NzcfOmll642jP/tjnf86lveOJ6iHvHGv5YDIPp9RWM4ypUIKXs8CxGPBeaSfXIjgd1OBOMRQoJyIs1MNKXXWF3fG/4zlc/bfhuWSCYTALsA0Isnhm/3Ck0S1lMEevhLj8glni1XTtbfu7YLsLtWKhcLtRz5g+ha5WJ1uFG0cPLcxurSVN6JJCF+8c/OS87vUtwz0SdTAd9MxC3o9xWJo+X2SH2MAqKFkb1D8UidW5QTTmZ4egkV7cwjanoeSddofa5oPyOx98Wjg+ERQzKyYII+V4R5pM8VeTMDARLLkH+OCkCC4pHKUcrZ0kTMWJeC81KUfh88ynmbbIwCkTDw4PEfHen32s1GrVKuXdgZAMDc0ZVaOTMjLN31UuXC8AZ2Llegv4cLUchmtEIlGpnAWpmsK4ZEnyuS5lZhrKlylCq3/+JNb7n1NfNn/vqpxrc7X/jCF77whS9g3WjRMlW5yjDe/6aF/+nmt77pla8EEJlHWg6A6HNFYzjKlYirEHqXJh4Lbk4mDExuJDB+ZPD4IEE4UWAmitJvViqjraLYkeVlx4I/k03HHt0dAHQ3mm3IhfmIgc7kjUnWs6dDl0ShUj62/uGLewA7qyvVYqtEfL+zsVrfGgAAzJ+q1SK7UQT+8e/JUf4zcTwfWfNaYCbiFvS5InG03B6pj1FAtDCydygeqXPLyQlfRZP0SBGthE7E+Ud09LmifYwExePXC9oXj44UM17R54pYkaDPFXkzAwESy5BvjgKsFWfGhQRjRjlbYuRZxsAi292iIx5+3pYdoyAkQvE/G9lrFDO2p030+71ex3rVamz+6FK5Wi1m/W9u9uqFRIOlEs9VWw2O51Z4pN9Zr5VWSmvbQyhHiqXCfnoEHd2M1ueKxLnlzQz6XFFYY2QYxlteNfepO9/10Rde+JNvffPz3/37nRee//sXL+P+a9ESprzhFdcuvOLa469/3fve+Iafv/bayeQUmEdaDoDoc0VjOPpckXeIrtxzRb2NUrE63M6A+Xyp6NwOSuYLmdjFzQEMtlZrzZVqwP2AgrQaG8P1PqQy0g+yk5DUcrVYy57ZGgxfW7S0UUwRWu+2W8PnaMcyuWk8Vk9e/OKfnZec36W4Z6JPpgK+mYhb0OeKxNFye6Q+RgHRwsjeoXikzi3KCSczPL2EinbmETU9j/S5IreOCwknnn2GxN4Xjw6GRwzJyIIJ+lwR5pE+V+TNDARILEP+OSoACYpHKkcpZ0sTMWNdCs5LUfp98CjnbbIxCkTCwIPH/4xkb2dra4fxt7l0LpdNctwXDgZ7gwHjb/1ewItxB+3V0krT1ku/1+122s3m1mTPCuaPV+vl8O5QQxGyGa1QiUYmsFYm64oh0eeKpLlVGGuqHEXG7c9f+4rfvfFtv3vjTQAsj3yzrtjNGLeFgJtYXjs+vvB6RIRE3U7g6Oy7MaLMUVoOgOhzRWM4ypWIqxB6lyYeC25OwJkHGPzI4PFBgnCiwEzEpLtRWiqcHe0UzR2vVJfc+xmppWK+vHl+D2C7tlJd3ihlwliL95qlYm1r+O/FwlKoz7pzSzxTqizV3/PoTsivLerjd0Od9fGTtmcrfvHvyVH+M3E8H1nzWmAm4hb0uSJxtNweqY9RQLQwsncoHqlzy8kJX0WT9EgRrYROxPlHdPS5on2MBMXj1wvaF4+OFDNe0eeKWJGgzxV5MwMBEsuQb44CrBVnxoUEY0Y5W2LkWcbAItvdoiMeft6WHaMgJELxPxuJLRzNZ+w3Rf1+r9ftdtrbuwPY21o78+G1M6Ujp6qrtWWf28O5hUXmllI8FfRDup0Lj5y94IPw2Ep1tVyI8IMbcCGb0fpckTi3vJlBnysKa4zofoXtoo8nlF31KKRocXt0cONfeIwi75EXiZYDIPpc0RiOPlfkHaIr7VxRv9tsrFYrlbWt0XuKYosfqq8up7yayeVyqbZ+/+YABpv35wvxRn0FOSDTbdY34oUl8Y2kfrfVqJbL1fPbww2ThVMV6sfABUoiXymfaNx9fo/+tUWJVCoBF3cBBs16vVNcSdn/2O+sl5eXz1zcJepMSThrNGMmojf2rHmtsqIbZ2Z9rkgQLbdHlKtuNFoY2TsUj9S5RTnhZIanl1DRzjyipueRdI3W54r2MxJ7Xzw6GB4xJCMLJuhzRZhHkxNFoM8VESKxDPnnqAAkKB6pHKWcLU3EjHUpOC9F6ffBo5y3ycYoEAkDDx7/s5F4rtxYzSN/6LU3xu8s2tt69O5cp9dcX0njN09zhVobNaImc0dOllaKS0u5fbdNBACEM1qhEo1MYK1M1hVDQnb2RYZbzsxA5RExtwpjTZWjiLmV94jrZszbsn0J0aODGv/CYxR5j7xItBwA0eeKxnCmkLcDMwPKyYSByY0Exo8MHh8kCCcKzMxE+huVwtJqHACg3+/3e512q72zZzvMMn/0ntVGNc84TpMprdY2cndf2AXYvXDvben6yeXiUj6TTiXi/V6n3Wo2Vmv1iztw9MFMs8R4o9GgXVspbthX8/1et9vttOzPEID5Yw81aj7Pm95bX8kGPcktXqhulEQPJiWXK+Xa+r2bA+rXFsWzS/n5Rx/dBRhcLOXynfLKUjadhF6n3WzUqqsXtvcgNj8f393do+lPXoRqtP9MHM9H1rxWWdGBCfpckQxabo/oV92uaGFk71A8UueWkxO+iibpkSJaCZ2I84/oSNdofa5o9khQPH69oH3x6Egx4xWCXw9JeIT5QuURV36zY2CMkZsTfa6IBIllyDdHAdaKM+NCgjGjnC0x8ixjYJHtbtERDz9vy45REBKh+I+YJNK55WqusFTK589s7sHexVJxtUD7FO+RxI491KpbT73oNoq5D5/fA4A+ZPLLuRB6nI6QzWj1sybK+V8KyQE6V0HPrfRYU+Uocm5lPXLTxxPKrnoUUrS4PTq48S88RpH3yItEywEQfa5oDIc8b/v0y8oMxpgZdMk+uZHAbieC8QjnKLqKNhMZ7Fw8v4P/KTZ/dKlUqazkfB+7ll5ubMByobi2PQDY3Vw7s7l2xmuqs9HslNIp3MTOxUcfuejTxdziiVK1WsozLh/J7vZm0CGcuXQvQAOVdLFSrL3r7DYQv7YokS9XT6x/8PwuwGDnwtm7L5y1/zW2eLJaryYr6feuzXqziLNGM2YiemPPmtcqK7pxZtbnigTRcntEuepGo4WRvUPxSJ1blBNOZnh6CRXtzCNqeh5J1+irRgaGC31X6w1il45HRA0gZsxxa9j+La4T6FB0kART6+oF7YtHB8MjPshgmqa39QacS0fVI8wXKo8c9lFsBqbjsWCaJhhWOz5LZJ0rcuigzKB4hJBgnMgwY44NGbbWBcUyzTRElFpIprQMEowZZZcmHxtWa9EMTjptOpNrlVwKwSFJJELxH0lJZCu14hEAABg0VxudUDqJJ+2SKVbKR2MAo9u1cLqchpDNaIVKNDKBtTJZVwwJG4/yVCSa0nQeOVtlbjnxBDMjn6OIuZX3SJhcbz0KJVq8HqE6qCFFJJGL/0gs6Ug98iLRcgDEda4IRq3jN+zBGWY2MxqdfS60PDpD8+R529sv722nyI0Em2HlHEVX0WYvsdjc/MLi0WMnTp1++DNPdjrN1YCNIgAAiKeX66325x48eXQh5jW5cOzUQ59pddaXU4JAFo8cO37qnofOfeGZbrsRtFEUrsRz5crJeQAYvbaoS2U4tVRvfuaeYy7eYvNHTz70uXarXswkU6kwXpAkKJw12vXdCNhmpXO24tZEZyJuIfBckUuHYWcWXzsGZ2+m12pouT2iXHWj0cLI3qF4pM4tygknMz69CHkkjXbmETUdtAAgX6P1uaLZI0Hx+PWC9sWjI8WMVwh+PSThEeYLlUc4Hh+EjDFyc6LPFZEgsQz55ijAWnFmXEgwZpSzJUaeZQwsst0tOuLh523ZMQpCIhT/UZVMPrtwZmsHYNButvowhefBpYvVYu22s9vhvmU2dCGb0fpckTi3vJlBnysKa4zofoXtoo8nlF31KKRocXt0cONfeIwi75EXyWykWcmvNNDfyMfjiUQymUplMrlcPpdhvjDQK936cqHaHv47VazXl1MCF3hRxBOJVDqTyWbzuZz35YQbpVxpA3+hu68kl2qNlWm/swT0uaIxHH2uyDtE+/FcUX61a64S2oun8qV6vtTvtpvNVqfb6/UhkUym0tksMwVlKm2zotyxmiPcGBKFetes82jmal2zxo8gnipUNwrl9sZGs93tQzyRSmey2fQkYdKwpCicNZoxE9Ebe9a8VlnRjTOzPlckiJbbI8pVNxotjOwdikfq3KKccDLD00uoaGceUdPzSLpG6/cV7Wck9r54dDA8YkhGFkzQ7yvCPDJNE4a7si429PuKCMYoMEcFIEHxSOUo5WxpImasS8F5KUq/Dx7lvE02RoFIGHjw+I+sJBIJgB0YPuUcYArvDopny5WT9feu7dK/ZXaKQjajFSrRyATWymRdMSRsPKozmiBHEXtEzK3CWFPlKGJu5T3iuhnztmxfQvTooMa/8BhF3iMvktlIr93a9H/i0qNwZvhuv2q1xHE2AKDbqNU3N0fvB2lBvb3Mer/IWPrddmtyBSrn1wAAYgvHi5VqZcn+XsNep+l/KUMWshIbTMqi31c0hjOFvB2YGVBOJgxMbiQwfmTw+CBBOFFg5gqQeDKdKwSkDS1eSaRzhXRu1iiYIlSj/WfieD6y5rXKig5M0O8rkkHL7RH9qtsVLYzsHYpH6txycsJX0SQ9UkQroRNx/hEd6Ro9fgbd8H+u1julXToeETWAmDHGrWn7t7hOoEPRQRJMrasXtC8eHQyP+CCDYRjeFtuZdOioeoT5QuWRwz6KzcR0PBYMwwDTapFzRXYdlBkUjxASjBMZZoyxIdPWuqBYppmGiFILyZSWQYIxo+zS5GPTai2awUmnTWdyrZJLITgkiUQo/qMq/W539CCGeCIxrS2bRKFSPjYHAMO3zLam1C2pkM1ohUo0MoG1MllXDAkbj/JUJJrSdB45W2VuOfEEMyOfo4i5lfdImFxvPQolWrweoTqoIUUkkYv/SCzpSD3yIpm1zC8etcmRI4sL89YDjfa21u5/VyZfawdvsHQa9eYAAGKxGAAMWvUGfmIIldj84hGbLC4uLMxZKAY7F85+MJsrbdgOQsXjibkYJpZNTObiM/l5iAGGLWoNGLX2c0UQnGFmM6PR2edCy6MzNE+et7398t52itxIsBlWzlF0FU2LlmgKZ412fTcCtlnpnK24NdGZiFuw52Rn3kazN8vOLL52DM7eTK/V0HJ7RLnqRqOFkb1D8UidW5QTTmZ8ehHySBrtzCNqOmgBQL5G6/cVzR5JMLWuXtC+eHQwPOKDPNyZdLfegHPpqHqE+ULlkcM+is3AdDwWTNMEw2r1+4pokHAaQnWCzEjlKGWXJh8bVmvRDE46bTqTa5VcCsEhSSRC8R9R6W80NoY/a46lM+npfX+TWq4Wj+zr1xaRzWiFSjQygbUyWVcMCRuP8lQkmtJ0HjlbZW458QQzI5+jiLmV90iYXG89CiVavB6hOqghRSSRi/9ILOlIPfIimbHM5atNm7Ra7U63/9Kzlz738IeODV+wAbsXVpYDf1fRWR9uFS0Wy0vzADBoC2wWxTKl9ZZN2u1Op9d/6dlLn/v0PccXh9s/e5tnlm2Pzcuvdnt9rzRXFkcmj1XbyN977UpWhB4i0e8rGsMhz9vefnlvO0VuJNgMK+couoqmRUs0hbNGu74bAdusdM5W3JroTMQt6PcViaPl9ohy1Y1GCyN7h+KROrcoJ5zM+PQi5JE02plH1HTQAoB8jdbnimaPJJhaVy9oXzw6GB7xQZbfmVTyCPOFyiOHfRSbiel4LBiGAabV6nNFNEg4DaE6QWakcpSyS5OPTau1aAYnnTadybVKLoXgkCQSofiPpHTXV1ZWdwAAIJZdKqSm2HU8U6osLQAA9VtmpyVkM1qhEo1MYK1M1hVDcoDOVVBzy4knmBn5HEXMrbxHwuR661Eo0eL1CNVBDSkiiVz8R2JJR+qRF0kUJZ7M5Iu1jeanTwy3iwabtZr/y4HGW0ULuUKxkJsDgMGW0MkiBorl6npz4/TR4XbRTr1SUzM5K9HnisZwyPO2t1/e206RGwk2w8o5iq6iadESTeGs0a7vRsA2K52zFbcmOhNxC/pckThabo8oV91otDCydygeqXOLcsLJjE8vQh5Jo515RE0HLQDI12h9rmj2SIKpdfWC9sWjg+ERH2T5nUkljzBfqDxy2EexGZiOx4JpmmBYrT5XRIOE0xCqE2RGKkcpuzT52LBai2Zw0mnTmVyr5FIIDkkiEYr/iEm/01xdyWULj2wPAABii8VKMTVdCIl8pXxi+CC63bVSWeYF2bMUshmtUIlGJrBWJuuKITlA5yqoueXEE8yMfI4i5lbeI2FyvfUolGjxeoTqoIYUkUQu/iOxpCP1yIskwpJaLi2Njul0W82Oj+b4AXRz2UI2kctnYwAAyptFQ0lky5XlBQAAGLTWN/xgRFb0uaIxHPK87e2X97ZT5EaCzbByjqKraFq0RFM4a7TruxGwzUrnbMWtic5E3II+VySOltsjylU3Gi2M7B2KR+rcopxwMuPTi5BH0mhnHlHTQQsA8jX66pEBx69RJq1379fZegS9yGEAODaP7awCY/MsSGcfIUHx+PWC9sWjI8WMVwzDME1v692ZdLSqHmG+UHmE4/FByBgjNyfec0WmrUWZ4cTjg4SPE+6oc+YEFxRWfqBFYhnyzVGAteLMuJBgzChnS4w8yxhYZLtbdMTDz9uyYxSERCj+ZyP9ZrVYbNg/6Pd63U671drenbyQOrZwotaoZJkPoNtbX8lmE/4dxQvVjZLoU2WSy5Vybf3ezcHwtUXFVikjaGGGQjajFSrRyIRy/pdCwsajOqMJchSxR+TcSo81VY4i51bWIzd9PKHsqkchRYvbo4Mb/8JjFHmPvEgiLZlsOnZ2ewAw6HV7bLVOozHcKsoVsnFI5AvZ2IWLA9iqN1olgtoaz+YysUd2BgCDTqsDkFK2OG2ZvAjdBGCcK+LJMDOZ0f6zj19nCIc8b/v0y8oMxpgZdMk+uZHAbieC8QjnKKqKdvny5WuvvRZDqEXLtOX555+3/Y+zRjNmInpjz5rXKiu6cWa2vn1y5G08ezPsEGRLilU3qy9+Hcr8TzBGAdHCyN6heKTOLcoJJzM8vYSKduYRNT2PpGv0eK/INAEMd4sHsa31yPB+YdIy1zCeryBtJsaty19BHRcSTjz7DIm9Lx4dDI8YkpEFE0anZKwWCziwt2QeIXhUPbL6CsTmGwmmacJwV9bFBkzOGEEAMygeISQYJzLMmIBfZu/YMs3MDARILEP+OSoACYpHKkcpZ0sTMWNdCs5LUfp98CjnbbIxCkTCwIPH/2xksH3hkW0/hfkjp0q1yko26ae0u725G9DRXNrnKyy2pIuVYu1dZ7dHry1a2pj24SZ5IZvRCpVoZAJrZbKuGBI2HtUZTZCjiD0i5lZhrKlyFDG38h5x3Yx5W7YvIXp0UONfeIwi75EXSaSl3x+duo0lUuxfbYxPFcWyhVwCAJK5QiZ2cXMAW/V6q5Qh2CxKJBIAuwDQ6++zY8BDcZ0rQr+ODM4ws5nR/vOOX2cIZwp5OzAzoJxMGJjcSGD8yODxQYJwIsHM666Nfffy4NKlS3feeSfahRYtU5ZLly4BwC+88hAACNZo/5k4no+sea2yogMTnGc90XNFwXmbKFtSrLo5eiFAy+0R/arbFS2M7B2KR+rccnLCV9EkPVJEK6ETcf4RHekard9XNHskwdS6ekH74tHB8IgPMhiG4W2xnUmHjqpHmC9UHjnso9hMTMdjwTAMMK1Wv6+IBgmnIVQnyIxUjlJ2afKxabUWzeCk06YzuVbJpRAckkQiFP+RkVhsbmHxyLETp04/dO7JZzut1YCNonAlnitXTg5fxrDPXltENqMVKtHIBNbKZF0xJGw8ylORaErTeeRslbnlxBPMjHyOIuZW3iNhcr31KJRo8XqE6qCGFJFELv4jsaQj9ciLJMrSb260hseAk5lMmqXVadRb462iYUlP5wpD7a31eosAR6/XG/0kJBFnHkOOsuj3FY3hkOdtb7+8t50iNxJshpVzlHpFu+HwIQBotSimmhYtFPLUU08BQPKalwMAd412fTcCtlnpnK24NdGZiFvQ7ysSR8vtEeWqG40WRvYOxSN1blFOOJnx6UXII2m0M4+o6aAFAPkarc8V7Wck9r54dDA8YkhGFkzQ54owj0zTBH2uiDz+LUP+OSoACYpHKkcpZ0sTMWNdCs5LUfp98CjnbbIxCkTCwIPH/1Qlv9o1V2drJFNpmxUexUSh3jXrPJrxpUZ/SRoQtZDNaIVKNDKBtTJZVwzJATpXQc2twlhT5ShibuU9CibUOwC+voTo0UGNf+ExirxHXiTRld7Gykp9FwAgdmSpmGOpTbaKMoV8avRZJiQbPy8AACAASURBVJ9fvH9rG2CrUW9VVE8W9ZvrzeGWVSzF3rKKsuhzRWM4U8jbgZkB5WTCwORGAuNHBo8PEoQTCWZuTb6q9YPnhyc5tGiJggz3io6+dvhQRKEa7T8Tx/ORNa9VVnRggj5XJIOW2yP6VbcrWhjZOxSP1Lnl5ISvokl6pIhWQifi/CM60jVav69o9khQPH69oH3x6Egx4xVD/anEEh5hvlB5xJXf7BgYY+TmRL+viASJZcg3RwHWijPjQoIxo5wtMfIsY2CR7W7REQ8/b8uOURASofjXcoUJ2YxWqEQjE8r5XwoJG4/qjCbIUcQekXMrPdZUOYqcW1mP3PTxhLKrHoUULW6PDm78C49R5D3yIomg9HvtZqNWKdcu7AwAYO7oSq3M3O7BtooAMoX84pntbYBt5c2iXrNUGm5ZQSxbyM/wMLK86PcVjeGQ522fflmZwRgzgy7ZJzcS2O1EMB7hHKVe0U6+5bX/x9e+89hjj5XL5de//vUYSC1apifPPPPM448/DgAfWHwNAHDXaMZMRG/sWfNaZUU3zsyi7yv6fGfv4t+/8JXdF3ee+/+efeHH/Z9GsqwfeIlfbbzuFVe/4ZVX35qM/6PXX/Pu1DVqFY2lYxchncB+RXsJFa20DtWKYnoeSddofa5oPyOx98Wjg+ERQzKyYII+V4R5ZJom6HNF5PFvGfLPUQFIUDxSOUo5W5qIGetScF6K0u+DRzlvk41RIBIGHjz+tVxhQjajFSrRyATWymRdMSQH6FwFNbcKY02Vo4i5lfeI62bM27J9CdGjgxr/wmMUeY+8SGYse41iJmO9iqjf7/U6O7uD0X9j80eXytVqMct88lt7tFUE6ULBfuQnu5RfPKu6WdTvbqyWVkqPbg3xLCyVllNShmYt+lzRGM4U8nZgZkA5mTAwuZHA+JHB44ME4USCmdtfN3fz9a+89P3nPvCBD/zVX/3VVVddhXakRcsU5Cc/+cn73ve+y5cvvyP5inckr/BzRc/0Br924VtPPntZiTItU5H+T8xv9n78zd6PN7790u99+Ud3/vzh2j+54c2vjslWtOB6J6UjW9Eka7QiWgkdqhXFdNACKNRofa5o9khQPH69oH3x6Egx4xWC38NKeIT5QuURV36zY2CMkZsTfa6IBIllyDdHAdaKM+NCgjGjnC0x8ixjYJHtbtERDz9vy45REBKh+NdyhQnZjFY/a6Kc/6WQHKBzFfTcSo81VY4i51bWIzd9PKHsqkchRYvbo4Mb/8JjFHmPvEhmLXs7W1s7jL/NpXO5bNLvDUGTraLFfN75dLhsIb9wdnsHYLtRb1YyWT8Mg06jvNKxdqyg3+91O61Wc2tnb4Ll2IP1aj6BXR590eeKxnDI87ZPv6zMYIyZQZfskxsJ7HYiGI9wjqKoaMa/f+firzz+lWazWS6XP/axj2E4tWiZhnzkIx8ZPg7x7LveIFijGTMRvbFnzWuVFd04M3OeK1pr/2h5fVQ73//+999yyy233HLLrbfeOjc3R0moFiLZ29v78pe//JWvfOWpp5764z/+4y9+56Vb/69vfyp/wz//B6+SqmgsHbsI6QT2K9pLqGildahWFNPzSLpG63NF+xmJvS8eHQyPGJKRBRP0uSLMI9M0QZ8rIo9/y5B/jgpAguKRylHK2dJEzFiXgvNSlH4fPMp5m2yMApEw8ODxr+UKE7IZrVCJRiawVibriiE5QOcqqLlVGGuqHEXMrbxHXDdj3pbtS4geHdT4Fx6jyHvkRTJjiS0czWfsz3Xr93u9brfT3t4dwN7W2pkPr50pHTlVXa0tZ7ybRu1GY7xVVHAfHcoW8gtnH9kZbRZlfTeLdi8+evYi+8/zRz9UXa0upf12rSIt+lzRGM4U8nZgZkA5mTAwuZHA+JHB44ME4USKGTP72sRHs7/4b5v/5YEHHvjRj370iU984vDhw2h3WrSEJC+99NJ999338MMPA8Dv/aM3ZG54hVSN9p+J4/nImtcqKzowgftc0YObux978lkAeNvb3ra2tnbjjTcqMKdlGjI3N3fXXXfdddddAPDVr3715MmT7Xb7X3y2+7E7f/yv33GdeEULrndSOrIVTbJGK6KV0KFaUUwHLYBCjR6f8B3+z9V6U5tLxyOiBhAzxrg1bf8W1wl0KDpIgql19YL2xaOD4REfZDAMw9tiO5MOHVWPMF+oPHLYR7GZmI7HgmEYYFotcq7IroMyg+IRQoJxIsOMMTZk2loXFMs00xBRaiGZ0jJIMGaUXZp8bFqtRTM46bTpTK5VcikEhySRCMW/litMyGa0QiUamcBamawrhoSNR3kqEk1pOo+crTK3nHiCmZHPUcTcynskTK63HoUSLV6PUB3UkCKSyMV/JJZ0pB55kcxY4rlywyHr6xvNVrvb/9HTX/j0PccXYgCwt/Xo3bl8td13Xzw5VbSQX/LsBcWzhdw8AABsr9ebcuhiiydOP/y5pzvN2j7eKAIAAwxb1Bowau3niiA4w8xmRqOzz4WWR2donjxve/vlve0UuZFgM6ycoygqmgEA/+Mtbyy9PWWa5u///u/feOONTz75JAOwFi308sUvfvHGG2/8gz/4A9M0P5J93e/cnBSv0a7vRsA2K52zFbcmOhNxC/ac7MzbVvb+D3/9w489+axhGPfcc89XvvIVvVG07+Smm27a2tpaWVkxDOPffPGH/+fX9sQrmksHfGOSR4fVL19Fk6zR0mildahWFNNBC6BQo/W5ov2MxN4Xjw6GRwzJyILkziSNRwgeVY+svgKx+UaCaZqgzxWRx79lyD9HBSBB8UjlKOVsaSJmrEvBeSlKvw8e5bxNNkaBSBh48PjXcoUJ2YxWqEQjE1grk3XFkBygcxXU3CqMNVWOIuZW3qNgQr0D4OtLiB4d1PgXHqPIe+RFElVJpHPL1VxhqZTPn9ncg72LpeJqYaOYsqm0G/X2AAAglug2KpUNt4luLwGwC6PNIp+TRbEjpzfWVyam+xul3Acf3QGAQT+ZX87v620iAADQ54rGcKaQtwMzA8rJhIHJjQTGjwweHyQIJ1LMjPr6N0ff/Nafe8V//8Tf7Ozs3HHHHf/wH/7DXC6Xy+WOHTt2/fXXo71r0SIt3//+9zfG8jd/8zcAEL/6qj/8J29831t+TqFG+8/E8XxkzWuVFR2YwHGuaOt7L/32X3wbAE6fPv3ggw8q8KdllnLo0KGHHnro5S9/+e/93u/95n/83s3z8bddHxv/UaLqAUdM8ujIVjTJGq2IVkKHakUxHbQACjVav69o9khQPH69oH3x6Egx4xVD/anEEh5hvlB5xJXf7BgYY+TmRL+viASJZcg3RwHWijPjQoIxo5wtMfIsY2CR7W7REQ8/b8uOURASofgHAIDLly9fe+21jO61RF2ef/556z9kM1qhEo1MKOd/KSRsPKozmiBHEXtEzq30WFPlKHJuZT1y08cTyq56FFK0uD06uPEvPEaR98iLJNqSyFZqxfWbz2wBDJqrjU7R2tGxtopgsLV2ZmvNx0zQZlE8mUwmrSfhLVXKq427L+wB7KyuVIutkvv5dvtN9PuKxnDI87ZPv6zMYIyZQZfskxsJ7HYiGI9wjqKoaFbv//zNyV++4VWf+Ern/95+9utf//rXv/71T37ykxhsLVoo5eUvM/7lja8p3fra178qLlujGTMRvbFnzWuVFd04M/u/r+gjf/VdAEin0x/96EfJadQyZXnggQc++9nPPv300//2r374eOF1449Vqp5dhHQC+xXtJVS00jpUK4rpeSRdo8fPoBsu9F2tN+W5dDwiagAxY45bw/ZvcZ1Ah6KDJJhaVy9oXzw6GB7xQQbTNL2tN+BcOqoeYb5QeeSwj2IzMB2PBdM0wbDa8Vki61yRQwdlBsUjhATjRIYZc2zIsLUuKJZppiGi1EIypWWQYMwouzT52LBai2Zw0mnTmVyr5FIIDkkiEYn/18ZeAQDDd41q2acyHL6fP/wKAMIZrVCJRiawVibriiFh41GeikRTms4jZ6vMLSeeYGbkcxQxt/IeCZPrrUehRIvXI1QHNaSIJHLxH4klHalHXiSRl0w+uwAAAIN2s2V7DF27Ud8a8BrZbgg9hi65XC0djQEADLYqK7WOwKWRFNe5Ihi1jt+wB2eY2cxodPa50PLoDM2T521vv7y3nSI3EmyGlXMURUVz9J561TV/8Cs3fuPUnWePLf7KL1z3xlcdjr/sKs8lWrSoSvxlxpvmYr/y+ld98h+nOr9+8yf/cQrbKALuGu36bgRss9I5W3FrojMRt+B/ruhbzw0+v/P81Vdfvba2dujQIXHOtERLDh06dO7cuauvvvrPv3l5Z+/H448lqh74xiSPDqtfvoomWaOl0UrrUK0opoMWQKFG63NFs0eC4vHrBe2LR0eKGa8Q/HpIwiPMFyqPcDw+CBlj5OZEnysiQWIZ8s1RgLXizLiQYMwoZ0uMPMsYWGS7W3TEw8/bsmMUhEQk/l9z6PCzgxdardadd97J6F5L1OWpp54CgBti1wAQzmh9rijQgnRm0OeKwhojMo/c9PGEsqsehRQtbo8ObvwLj1HkPfIiib4kEgmAHQDo9/t9gNED4dqN+hYAAMyd+HSzmmM8Jq7XKGbvvTAA2GnUm1Wfk0VuSa9UiqvvOrsNsHexXKoX6kvJ4IsiK/pc0RiOPlfkHSLKc0WT9oZrDv3GW1//G2/9BU+HgTnTpYJFi78FpBdUM7DCiiLhxyOmwwgWx+hMBwmfPk9lxPi04t/fMi0SVl4az0QTnYk+swD4ZiJuwf9c0R9t9wDg3e9+90033eQ/JFr2ixw5ciSXyz3xxBONb7xwzy2vBgC1qmcXIZ3AfkV7CRWttI4K2tl4JF2j9bmi2SMJptbVC9oXjw6GR3yQgeDXQxIeYb5QeeSwj2IzMB2PBdM0wbBafa6IBgmnIVQnyIxUjlJ2afKxYbUWzeCk06YzuVbJpRAckkQiEv+/nEiCPle0z2W4V/T2VycBCGe0QiUamcBamawrhuQAnaug5pYTTzAz8jmKmFt5j4TJ9dajUKLF6xGqgxpSRBK5+I/Eko7UIy+SyEu/2+0O/xVPJCZbQq3JVlF+uZBOsSSztJQbvgNgp1HfEOk2nitXTs4DAMDuWqm80Q/Qj7Toc0VjOOR529sv722nyI0Em2HlHEVR0STGiIknOtFCserg8TpYh6JG0yCZUfz73UgrIIFxL565ZkdihIQEt+B/rugz3/gRANx+++24L1r2p2SzWQD4zN++MP6AKqNOcUZLZl1ptNI6KmhD5V8+/7Nq9HivaLjQd7XekubS8YioAcSMa2Ns8omgTqBD0UESTK2rF7QvHh0Mj/ggg2EY3hbbmXToqHqE+ULlkcM+is3EdDwWDMMA02qRc0V2HZQZFI8QEowTGWaMsSHT1rqgWKaZhohSC8mUlkGCMaPs0uRj02otmsFJp01ncq2SSyE4JIlEJP7fm1wEgMcee+zb3/42o3stkZZnnnnm8ccfB4B//gv/AIBwRitUopEJrJXJumJI2HiUpyLRlKbzyNkqc8uJJ5gZ+RxFzK28R8LkeutRKNHi9QjVQQ0pIolc/EdiSUfqkRdJ1KW/0djYBQCAWDqTHu8VterjraJcIZfwuTyZL2StzSKhDZ9EoVI+Nje8dnWl2hLDHSkxwLBFrQGj1n6uCIIzzGxmNDr7XGh5dIbmyfO2t1/e206RGwk2w8o5iqKiSYwRE090ooVi1cHjdbAORY2mQTKj+Pe7kVZAAuNePHPNjsQMCQluwZ6TnXkbTICv/aAPALfddhvui5b9KcMB/esfTB6qS5VRpzijJbOuNFppHRW0ofIvn/9ZNVqfK5o9kmBqXb2gffHoYHjEB9m9JzlseXYmlTzCfKHyyGEfxWZgOh4LpmmCYbX6XBENEk5DqE6QGakcpezS5GPDai2awUmnTWdyrZJLITgkiUQk/o9e9/M3vfKG55577gMf+MDPfvYzBgItEZWf/OQn73vf+y5fvvz2V8/f8up5AMIZrVCJRiawVibriiE5QOcqqLnlxBPMjHyOIuZW3iNhcr31KJRo8XqE6qCGFJFELv4jsaQj9ciLJNrSXV9ZWd0BAIBYdqmQGn3cqq+Pt4qW8n5bRY7NovV6U+x0UGq5WjxyJby2SJ8rGsMhz9vefnlvO0VuJNgMK+coioomMUZMPNGJFopVB4/XwToUNZoGyYzi3+9GWgEJjHvxzDU7EiMkJLgF/3NFg5+aoM8VXXEyPFd0+ceTkKDKqFOc0ZJZVxqttI4K2lD5l8//rBqtzxXNHkkwta5e0L54dDA84oPs3pMctjw7k0oeYb5QeeSwj2IzMR2PBcMwwLRafa6IBgmnIVQnyIxUjlJ2afKxabUWzeCk06YzuVbJpRAckkQiGP8P3HgMAJrNZrlcZiDQElH5yEc+Mnx+4Jmbjo0+IpvRCpVoZAJrZbKuGJIDdK6CmltOPMHMyOcoYm7lPRIm11uPQokWr0eoDmpIEUnk4j8SSzpSj7xIoir9TnN1JZctPLI9AACILRYrxdTob616Y7hVFMv6nyoCAEgVChnZzaJ4plRZWgCA0WuLukJXR0f0uaIxHPK87e2X97ZT5EaCzbByjqKoaBJjxMQTnWihWHXweB2sQ1GjaZDMKP79bqQVkMC4F89csyMxQ0KCW/A/VzSUw4cP475o2Z9y3XXXOT+gyqhTnNGSWVcarbSOCtpQ+ZfP/6waffXIgHuvGytmjh17E82WgT+eYFVJm4lx6xMJHDouJJx49hkSe188OhgeMSQjCyaMTslYLRZwYG/JPELwqHpk9RWIzTcSTNOE4a6siw2YnDGCAGZQPEJIME5kmDEBv8zesWWamRkIkFiG/HNUABIUj1SOUs6WJmLGuhScl6L0++BRzttkYxSIhIGHFf+3Jl53/1tuf/DvvvTAAw/86Ec/+sQnPqFXutGXl1566b777nv44YcB4IG33vm2uetHfyCb0QqVaGQCa2WyrhgSNh7VGU2Qo4g9IuZWYaypchQxt/IeBRPqHQBfX0L06KDGv/AYRd4jL5IZS79ZLRYb9g/6vV630261tncnD2SJLZyoNSpZ6wF0je3h57mlfDKwi1S+kC5tbg1Gm0W5XDzwEksS+Ur5eOPuC3vD1xYVCzWhyyMirnNF6OvTgzPMbGa0/7zj1xnCmULeDswMKCcTBiY3Ehg/Mnh8kCCcSDEjMUZMPNGJFopVB4/XwnErVaNpkMwo/sc6Mnh8kIzIdeKZGhLcgv+5Ii0HQ6gy6hRntGTWVUQroUNVI6aDFoAr/+M1erxXNPqG0tVi31PaW4/wfL3obZ0mPPlMSmcfIUHx+PWC9sWjI8WMVwzDME1v692ZdLSqHmG+UHnEld/sGBhj5ObEe67ItLUoM5x4fJDwccIddc6c4ILCyg+0SCxDvjkKsFacGRcSjBnlbImRZxkDi2x3i454+HlbdoyCkIjH/+++8daXfvqT6jf/8+///u//6Z/+6blz5/QDl6MsX/ziF3/1V391Z2cHAP714jt+801HrL+RzWiFSjQyoZz/pZCw8ajOaIIcRewRObfSY02Vo8i5lfXITR9PKLvqUUjR4vbo4Ma/8BhF3iMvkhnLYPvCI9t+CvNHTpVqlZWstSdkbRVlCxxbRQDpfD59/9YWSG0WQXK5Wqpl7t8cDF9bVGyVMiKXR0ImG0ImAONcEU+GmcmM9p99/DpDOOR526dfVmYwxsygS/bJjQR2OxGMRzhHUVQ0lTFCAEUkWihWHTxeB+tQ1GgaJLOO/0A8KjWagcQMCQluAd3IZ6PXckUKVUad4oyeNlppHaoaMT2PpGu0fl/R7JEEU+vqBe2LRwfDIz7IYJqmt/UGnEtH1SPMFyqPHPZRbAam47FgmiYYVqvfV0SDhNMQqhNkRipHKbs0+diwWotmcNJp05lcq+RSCA5JIpGK/9Nvvu1/P/LfHLrqZTs7O3fcccdb3/rW3/md33nssce+//3vM2Bpmap8//vf/+M//uPf/u3f/qVf+qV3vvOdOzs78Zdd/YdvP37f4q0OPbIZrVCJRiawVibriiFh41GeikRTms4jZ6vMLSeeYGbkcxQxt/IeCZPrrUehRIvXI1QHNaSIJHLxH4klHalHXiTRk1hsbmHxyLETp04/dO7JZzutVftGkWOriOdUEQBAZim/OPzXTkP0MXQAkF6pLA+v36+vLdLvKxrDIc/b3n55bztFbiTYDCvnKIqKJjFGTDzRiRaKVQeP18E6FDWaBsmM4t/vRloBCYx78cw1OxIjJCS4BX2uSAtdRp3ijJbMutJopXVU0IbKv3z+Z9Vo4+8WTwOwNr2w3z74DQ97g39iIFB48hmHzj5CEowHHR0JHT48gcK4FtmZZEaLhEfCeBTGmtUGWXD37vpNB3M2ieMJdCisqHN+xOERARI/Tnyy07RylES2xKPFaZJ/3MPP27LMBCKRj/9vvbh39ptf/qNnn/7Jz37GgUzLbOTlV71s6fXpe99yyy9c80r338Ka0SKVyA+JetYNRMKJR3hGh7aio/KIqqKpjzVVjqLKuuIeuYUnlDnWYyF4dHDjX3iMIu+RB8mrPr0GAOxvxrXsYzEMAwCe/80Trt+nj0U0w8xsRnOh5dAJIW/79BvIDLpkdw1S6DmKtKJJjBHKTCSihWzVweO1rw5ZjVZGwqcfXvwH4lGp0QwkIzzkSBjzEcDnXNHh6iUAXa+vQBkW6/7/8BbnxxQZlVcnsN+p5P/p6VDUiKmgla7R+n1F+xmJvS8eHQyPGJKRBRP0+4owj0zThOGurIsN/b4igjEKzFEBSFA8UjlKOVuaiBnrUnBeitLvg0c5b5ONUSASBh6e+H/D4bl//9a77n/z7X/2vW/8+fe+sfPic7uDF/o/+ylmTstUJX7Vy5LxVyxc+8p/9to3/7ev+8VXvzzOGGWqGa1QiUYmsFYm64ohYeNRndEEOYrYI2JuFcaaKkcRcyvvEdfNmLdl+xKiRwc1/oXHKPIeeZFoOQCi31c0hjOFvB2YGVBOJgxMbiQwfmTw+CBBOJFiRmKMmHiiEy0Uqw4er4XjVqpG0yCZUfyPdWTw+CAZkevEMzUkuAV9rkgLXUad4oyWzLqKaCV0qGrEdNACcOV/vEbr9xXNHgmKx68XtC8eHSlmvCL9xEMljzBfqDziym92DIwxcnOi31dEgsQy5JujAGvFmXEhwZhRzpYYeZYxsMh2t+iIh5+3ZccoCIly/L8mfs2pN9x06g038Xkk8gtHVr8+OmxRQuLHCcawrx2eUQ4QIiR0M1qhEvl5JJF1xZCw8ajOaIIcRewRObfSY02Vo8i5lfXITR9PKLtyXUjR4vbo4Ma/8BhF3iMvEi0HQPT7isZwyPO2T7+szGCMmUEXczBiaagTyLByjqKoaCpjhACKSLRQrDp4vA7WoajRNEhmHf+BeFRqNAOJGRIS3IJ+X5EWuow6xRk9bbTSOlQ1YnoeSddo/b6i2SMJptbVC9oXjw6GR3yQwTRNb+sNOJeOqkeYL1QeOeyj2AxMx2PBNE0wrFa/r4gGCachVCfIjFSOUnZp8rFhtRbN4KTTpjO5VsmlEBySRBJi/EctRykgGZtA2tnUaKJwIZvR6txirXLUBSLhHeswuOWNFiqPnO1MZjTKjHyOIuZW3iNhcr25LpRo8XqE6qCGFJFELv4jsaQj9ciLRMsBEP2+ojEc8rzt7Zf3tlPkRoLNsHKOoqhoEmPExBOdaKFYdfB4HaxDUaNpkMwo/v1upBWQwLgXz1yzIzFCQoJb0OeKtNBl1CnOaMmsK41WWkcFbaj8y+d/Vo0e7xVZe++21lvSXDoeETWAmHFtjE0+EdQJdCg6SIKpdfWC9sWjg+ERH2QwDMPbegPOpaPqEeYLlUcO+yg2E9PxWDAMA0yrRc4V2XVQZlA8QkgwTmSYMcaGTFvrgmKZZhoiSi0kU1oGCcaMskuTj02rtWgGJ502ncm1Si6F4JAkkhDjP2o5SgHJ2ATSzqZGE4UL2YxW5xZrlaMuEAnvWIfBLW+0UHnkbGcyo1Fm5HMUMbfyHgmT6811oUSL1yNUBzWkiCRy8R+JJR2pR14kWg6AGGDYotaAUWs/VwTBGWY2MxqdfS60PDpD8+R529sv722nyI0Em2HlHEVR0STGiIknOtFCserg8TpYh6JG0yCZUfz73UgrIIFxL565ZkdihoQEt2DPyc68jXzNrOUKFaqMOsUZLZl1pdFK66igDZV/+fzPqtH6XNHskQRT6+oF7YtHB8MjPshA8OshCY8wX6g8cthHsRmYjseCaZpgWK0+V0SDhNMQqhNkRipHKbs0+diwWotmcNJp05lcq+RSCA5JIgkx/qOWoxSQjE0g7WxqNFG4kM1odW6xVjnqApHwjnUY3PJGC5VHznYmMxplRj5HEXMr75Ewud5cF0q0eD1CdVBDikgiF/+RWNKReuRFouUAiD5XNIZDnre9/fLedorcSLAZVs5RFBVNYoyYeKITLRSrDh6vg3UoajQNkhnFv9+NtAISGPfimWt2JEZISHAL+lyRFrqMOsUZLZl1pdFK66igDZV/+fzPqtH6fUWzR4Li8esF7YtHR4oZrxiGYZre1rsz6WhVPcJ8ofIIx+ODkDFGbk70+4pIkFiGfHMUYK04My4kGDPK2RIjzzIGFtnuFh3x8PO27BgFIQkx/qOWoxSQ4NyGNdYwhfjntMM7o9W5Vc7/Uki4xzoEbnmjhcqjCMxoOW5DGyMyj9z08YSyK9eFFC1ujw5u/AuPUeQ98iIBAIDLly9fe+21GANa9qs8//zzABB/2VUAoN9XNIZDnrd9+mVlBmPMDLqYgxFLQ51AhpVzFEVFUxkjBFBEooVi1cHjdbAORY2mQTLr+A/Eo1KjGUjMkJDgFqL4vqJ2rbC82gUAyJY3qvk494W9RjFfaQEApJZX68W0v3Z/GDIvwgAAIABJREFUfSVXbgIAZEqNWiHJ+mtyebXhY6vfqi4V610AgHhmZbW2lLL+1qzkVxo9PvDJpVpjJcOnSy1UGXWKM3raaKV1qGrE9DySrtHjvSLTBDDcLZ7ybK1HhvcLk5aZwz1fTtlMjFuXv4I6LiScePYZEntfPDoYHjEkIwsmjE7JWC0WcGBvyTxC8Kh6ZPUViM03EkzThOGurIsNmJwxggBmUDxCSDBOZJgxAb/M3rFlmpkZCJBYhvxzVAASFI9UjlLOliZixroUnJei9PvgUc7bZGMUiISBhyL+o5ajFJCg3M6yRtMgoZvR6txirXLUBSLhHetQuOWMFiqPiLklm0fyOYqYW3mPggn1DoCvLyF6dFDjX3iMIu+RB8nrrjn83RdfunTp0p133om7oGV/yqVLlwDg+sMxAHCdK0K/jgzOMLOZ0f7zjl9nCGcKeTswM6CcTBiY3Ehg/Mjg8UGCcCLFjMQYMfFEJ1ooVh08XgvHrVSNpkEyo/gf68jg8UEyIteJZ2pIcAtRPFfU77Q3N7cBIJbo9QH494r6vU5rc3MAAP1cn187Fu96tS1bi1mf3Z5uY7lw7/kdAID5E5+u2zeKAKDXbm1u7vKBX8gGQw5LqDLqFGe0ZNZVRCuhQ1UjpoMWgCv/4zVav69o9kiCqXX1gvbFo4PhER9kMAzD23oDzqWj6hHmC5VHDvsoNhPT8VgwDANMq9XvK6JBwmkI1QkyI5WjlF2afGxarUUzOOm06UyuVXIpBIckkYQY/1HLUQpIxiaQdjY1mihcyGa0OrdYqxx1gUh4xzoMbnmjhcojZzuTGY0yI5+jiLmV90iYXG+uCyVavB6hOqghRSSRi/9ILOlIPfIguf5wHABarRaDAS37VZ566ikAmD8cBwD9vqIxHPK87e2X97ZT5EaCzbByjqKoaBJjxMQTnWihWHXweB2sQ1GjaZDMKP79bqQVkMC4F89csyMxQ0KCW9DvK1KQfqtSWF7bAQCIHbmnvrqcYuvGAmSOf0uMXqgy6hRntGTWlUYrraOCNlT+5fM/q0br9xXNHkkwta5e0L54dDA84oMMpml6W2/AuXRUPcJ8ofLIYR/FZmA6HgumaYJhtfp9RTRIOA2hOkFmpHKUskuTjw2rtWgGJ502ncm1Si6F4JAkkhDjP2o5SgHJ2ATSzqZGE4UL2YxW5xZrlaMuEAnvWIfBLW+0UHnkbGcyo1Fm5HMUMbfyHgmT6811oUSL1yNUBzWkiCRy8R+JJR2pRx4kt17/czA+g6LlSpLhXtE75l8NAPp9RWM45Hnb2y/vbafIjQSbYeUcRVHRJMaIiSc60UKx6uDxOliHokbTIJlR/PvdSCsggXEvnrlmR2KEhAS3EMVzRftEuo3lwv2bewAA88drjWouwdadO9Xo+0uvXc1OC7lHqDLqFGe0ZNaVRiuto4I2VP7l8z+rRutzRbNHEkytqxe0Lx4dDI/4IAPBr4ckPMJ8ofLIYR/FZmI6HguGYYBptfpcEQ0STkOoTpAZqRyl7NLkY9NqLZrBSadNZ3KtkkshOCSJJMT4j1qOUkAyNoG0s6nRROFCNqPVucVa5agLRMI71mFwyxstVB4525nMaJQZ+RxFzK28R8LkenNdKNHi9QjVQQ0pIolc/EdiSUfqkQfJ+39xAQAee+yxb3/72wwStOw/eeaZZx5//HEA+MCbXw8A+lzRGA553vb2y3vbKXIjwWZYOUdRVDSJMWLiiU60UKw6eLwO1qGo0TRIZhT/fjfSCkhg3ItnrtmRmCEhwS3oc0VyYj9StPihen05NWNASkKVUac4oyWzrjRaaR0VtKHyL5//WTVanyuaPZJgal29oH3x6GB4xAcZCH49JOER5guVRw77KDYD0/FYME0TDKvV54pokHAaQnWCzEjlKGWXJh8bVmvRDE46bTqTa5VcCsEhSSQhxn/UcpQCkrEJpJ1NjSYKF7IZ/f+z965BjlzXmeBJkSLQIuUCbUoFypYK8tpTaEtiwaKsBkVrGgxT26BjdxqyRmow1l6CEY4hGJTU6FnNNDrWWkM2J4jWeE10jGSC+2OJ3pgxUUGOhR57raolPUR5ZBNladgoybRQYVoETMtEmRKF4kMN2CJzfwDI58nMm/eBzOq+58eJavTJc7/z3XPPvfm4mezcYpo56zyRkPa1CG5Js4VXRGYdyIhGmaGvUZy5pY/IN7n2WickW+wRoTaoI0Ykocv/UCzpuEZkQ3LLO29I/cT1r7zyyqc+9ak333zTgQcpB0l+9KMffeITn3j99dc//M7rb35nDADkvqI5HO51294u6WmnnxMJZ4aZaxSPGY2ijxzxhCdbeKw6SKL2tuExR/NBElD+u51IMyCBeSu2sWZEoghCgnuQ+4poZNgq6luKaq2625aigyC8KuoCRzRl1aVGS23DglYo//T132mOlvuKgkfiTa2lFbQtEhsMj/9OBg5PD1FEhMXCKyKTfxSbitnYPCiKAqqu5b4iPkgIHaE2Xm6oahRzSNrPqq51msFMp8FGO5YpJAEBUSIRmP9hq1EMSOYuEB3MHM0pXbiNaHZuMc2cdZ5ISPtaBLek2cIrIrMOZESjzNDXKM7c0kfkm1x7rROSLfaIUBvUESOS0OV/KJZ0XCPCkPz79AcBoNPpVCoVBx6kHCT5/Oc/P32p4O/+4k3Y8+mzX0DuK5L7ivjMaBR95IgnPNnCY9VBErW3DY85mg+SgPLf7USaAQnMW7GNNSMSVRAS3IPcV+Rbxt1qvnB+AAAQWb2n0Swmg0bELLwq6gJHNGXVpUZLbcOCVij/9PXfaY6W+4qCR+JNraUVtC0SGwyP/04GDk8PUUSExcIrIpN/FJuC2dg8qKoKiq7lviI+SAgdoTZebqhqFHNI2s+KrnWawUynwUY7likkAQFRIhGY/2GrUQxI5i4QHcwczSlduI1odm4xzZx1nkhI+1oEt6TZwisisw5kRKPM0NcoztzSR+SbXHutE5It9ohQG9QRI5LQ5X8olnRcI8KQHFl+x2/e/AEAuP/++z/zmc9cunTJgQ0pYZdLly7dd999Z8+eBYCzt7x/7YbrsefTZ7+A3Fck9xXxmdEo+sgRT3iyhceqgyRqbxseczQfJAHlv9uJNAMSmLdiG2tGJIogJLgHua/IpwxbxdyZrX0AgKWj1VYte8C3FAEAv4q6wBFNWXWp0VLbsKAVyj99/Xeao+W+ouCReFNraQVti8QGw+O/k4HD00MUEWGx8IrI5B/FpmI2Ng+KooCqa7mviA8SQkeojZcbqhrFHJL2s6prnWYw02mw0Y5lCklAQJRIBOZ/2GoUA5K5C0QHM0dzShduI5qdW0wzZ50nEtK+FsEtabbwisisAxnRKDP0NYozt/QR+SbXXuuEZIs9ItQGdcSIJHT5H4olHdeIHJD8b2vv+7drP6eq6pe+9KXDhw8//fTTDoRICa987WtfO3z48O/93u+pqvobH0re94GfAfz59NkvIPcVyX1FfGY0ij5yxBOebOGx6iCJ2tuGxxzNB0lA+e92Is2ABOat2MaaEYkqCAnuQe4r8iPjbk3bUrRyV6NZSkaDhsRFeFXUBY5oyqpLjZbahgWtUP7p67/THC33FQWPxJtaSytoWyQ2GB7/nQwcnh6iiAiLhVdEJv8oNgWzsXlQVRUUXct9RXyQEDpCbbzcUNUo5pC0nxVd6zSDmU6DjXYsU0gCAqJEIjD/w1ajGJDMXSA6mDmaU7pwG9Hs3GKaOes8kZD2tQhuSbOFV0RmHciIRpmhr1GcuaWPyDe59lonJFvsEaE2qCNGJKHL/1As6bhG5IzkN26+6XzmI9dc9ZbBYHDrrbe+//3v//SnP/3444+/9NJLDuRICV5eeumlxx577L777nvf+9730Y9+dDAYRK++6v+5/UOnP5h0fj599gvIfUVyXxGfGY2ijxzxhCdbeKw6SKL2tuExR/NBElD+u51IMyCBeSu2sWZEoghCgnuQ+4rIZdgq5srzLUWVVj0XDxoRL+FVURc4oimrLjVaahsWtEL5p6//TnP01TMHprvxmrY/+2DWNkEPMjkARJtd2OoZlc0BQoLicWsFbYvEhooZuyiKoqp2bb8zadKsEWGx8IoIx+OC0KGPrJzY9xWpBo0yQ4jHBQkZJ8RZZ64JFihO9YEvEt2Ra40CTPtnxoIEY4a5WmLk6c5AJ9uq0R4XX7dp+8gLicD8D1uNYkCCcyuqr2EB+U/oh3REs3PLXP+pkBD3tQBuSbOFV0QhGNF03ArrI24RWekjSWVLrROULdaIrtz8991HoY/IFcnHf/o9P/+O639n59u//1z/2WefffbZZ7/85S9jtEgJo7z1LW/5X/7Zu//tB1ffc93b9PMXk55mqgIzbdxXRFJhAhnR7qOP3GYKh3vddmnXqTIoc2bQxRzMWJraeDLMXKN4zGgsfYQACkm28Fh1kETtbcNjjuaDJOj898TDMkc7IFEFIcE9ONdtfPxfsTLbUjSZ/isaTyYujy1FAMCvoi5wRC8aLbUNrzlicRFRz9FyX1HwSLyptbSCtkVig+Hx38nA4ekhioiwWHhFZPKPYlMwG5sHVVVB0bXcV8QHCaEj1MbLDVWNYg5J+1nRtU4zmOk02GjHMoUkICBKJALzP2w1igHJ3AWig5mjOaULtxHNzi2mmbPOEwlpX4vgljRbeEVk1oGMaJQZ+hrFmVv6iHyTa691QrLFHhFqgzpiRBK6/A/Fko5rRF5IEm+/7ku3/sLuif/5d2+5+bZ3Lb/37ddGr7rKgRwpwUv0qrf89I9de9tPvuM/fDT13K9m/8M/T737urfZ9wzJfUVzONzrtr1d0tNOPycSzgwz1ygeMxpFHzniCU+28Fh1kETtbcNjjuaDJKD8dzuRZkAC81ZsY82IRBGEBPcg9xURyXCjmJ9tKQIAgL31cqUzJj9+v5mLOUo81xgKwOxDeFXUBY5oyqpLjZbahgWtUP7p67/THC33FQWPBMXj1graFokNFTN24fD0EEVEWCy8IsLxuCB06CMrJ3JfERckuiPXGgWY9s+MBQnGDHO1xMjTnYFOtlWjPS6+btP2kRcSgfkfthol9xX590M6ouW+IlF9xC+iEIxoOm6F9RG3iKz0kaSypdYJyhZrRFdu/vvuo9BHRIbkHYeiv374Z3/98M9gnKBCwQw4TGaG2oJ4xgIyOUCY8UDCMyKjxrj1GNGoiacfOxD7k+lyX9EUDve67dKuUx8pc2Zc8n9u48kwc43iMaOx9BECKCTZwmPVQRK1tw2POZoPkqDz3xMPyxztgEQVhAT3IPcVecu4V82Xzu9OAACWVlZgMNgH2K2XasVOOUnoYzLZn0yc/I983HUSIrwq6gJH9KLRUtvwmiMWFxH1HD2/V6SqAIpV4yXPoG2iqibtWMNtF6cMLubaEq9PGwsSQjwHDImxLRIbDI8/JDMPKsx2yegaSzgwam4RIXhYI9Lb8sTmmgmqqsL0rqyFDdD2GIEHMygeX0gwTmiYUQE/zNiw7tqxMnBAojtyr1EeSFA8VDWKuVqqiBv9UDAfitLvgoe5bnPrI08kDnh45H/YahQDEpTbIOdoPkj4jWh2bjHNnHWeSEj7Wgi3hNnCKyLO3HIbR/Q1ijO39BF5E2rvANdYBEZ0pea/7z4KfUTsfURUCNyZQTnR2tUWUhgqZmbERETFrT0iDtmigmm3EL6vCL0c6V1hghnR7n1BbjOFE4Zs4Zn/7HnLY0aj6CNHPOHJFg4jmihq33lLNUfzQRJQ/s9taPC4IJmRa8azMCS4B7mvyFN2G8Uz0/s8kbWTrXaxk02d2Z7AZLtaauQ3CgkiJ0srq3Gnl9ZFEzFOUGmFV0Vd4IimrLqMaClseM0Ri0ELQFT/8Tla7isKHgmKx60VtC0SGypm7MLh6SGKiLBYeEVEVN+MGBz6yMqJ3FfEBYnuyLVGAab9M2NBgjHDXC0x8nRnoJNt1WiPi6/btH3khURg/oetRjE/hcqedZqbwPOf0A/piGbnlrn+UyEh7msB3JJmC6+IQjCi6bgV1kfcIrLSR5LKllonKFusEV25+e+7j0IfEXsfIcPeLzPzKQqfzLSFFCxoHuEQERW3djwcskUBALmvyKmnwpEtPPOfPW95zGgsfYQACkm2cBjRRFF72/CYo/kgCTr/PfGw1G0HJKogJLgHua/IW2YbgpaP11u1TAzS1WLjtnO7APub5XIr18wR3OlZytV7jaxgnPTCq6IucEQvGi21Da85YnERUc/R8ntFwSPxptbSCtoWiQ2Gx38ng6qqdm1POIsNa0RYLLwiMvlHsSmYjc2Dqqqg6Fp+r4gPEkJHqI2XG6oaxRyS9rOia51mMNNpsNGOZQpJQECUSATmf9hqFAOSuQtEBzNHc0oXbiOanVtMM2edJxLSvhbBLWm28IrIrAMZ0Sgz9DWKM7f0Efkm117rhGSLPSLUBnXEiCR0+R+KJR3XiNj7iN+y289CihszYiICHSFLjeKQLSoAyO8VOfVUOLKFZ/6z5y2PGY2ijxzxhCdbOIxooqi9bXjM0XyQBJT/bifSDEhg3optrBmRKIKQ4B4u031Ffl/q5rTlR5OlI6dbzUICACCaqVRPLAOA/88WhVV4VdQFjmjKqkuNltqGBa1Q/unrv9McPb9XpN97N2j7lGaxsYlfB4gbZa5Vw9/+bTwDCg8Sb2otraBtkdhgePx3MiiKYtf2hLPYsEaExcIrIpN/FJuK2dg8KIoCqq6RfUVGG5QZFI8vJBgnNMwoc0eqQVug6K4dHXEqLVyGNA0SjBnmkLSfVV3rNIOZToONdixTSAICokQiMP/DVqMYkMxdIDqYOZpTunAb0ezcYpo56zyRkPa1CG5Js4VXRGYdyIhGmaGvUZy5pY/IN7n2WickW+wRoTaoI0Ykocv/UCzpuEbE3kf8lt1+FlLcmBETEegIWWoUh2xRAMC2ZwgUUAyR4DbeFSaYEY32iAUtic3UfRiyhWf+s+ctjxmNoo8c8YQnWziMaKKovW14zNF8kASU/24n0gxIYN6KbawZkaiCkOAenOs2cpk57BKNavd8xt53cMaajeE4XFZO1FvVtGYUy1UrR5cAYPrZoh4F1HAJr4q6wBFNWXWp0VLbsKAVyj99/Xeao+X3ig4yEmNbJDYYHn9IZh5UCNe3QFgj0tvyxOaaCaqqwvSurIUN+b0iDn3kWaM8kKB4qGoUc7VUETf6oWA+FKXfBQ9z3ebWR55IHPDwyP+w1SgGJCi3Qc7RfJDwG9Hs3GKaOes8kZD2tRBuCbOFV0ScueU2juhrFDdu/+TF735t78WLL3/vb19/bXjph+M33sDakxJ2iV511Y1vO/Sea6/94A03fDT+zl/6yXcZ/tMz6cGsbRK6Ec1co4gKgTszKCdau9pCCkPFzIyYiKi4tUfEIVtUkN8rwm2mcMKQLTzznz1veczRFH3kiCc82cJj1UESte+8xfrIs/7zQRJQ/s9taPC4IJmRa8azMCS4h8tqX1EsFosCTABgOBqNvXYLjYZD7Tg3y6UjD7Qa+bjpt0SxVqynz+74/WxRSIVXRV3giKasuoxoKWx4zRGLQQtAVP/xOVruKwoeiTe1llbQtkhsMDz+Oxk4PD1EEREWC6+ITP5RbCpmY/OgKAqoupb7ivggIXSE2ni5oapRzCFpP6u61mkGM50GG+1YppAEBESJRGD+h61GMSCZu0B0MHM0p3ThNqLZucU0c9Z5IiHtaxHckmYLr4jMOpARjTJDX6M4cPv8q6/c8cT/+8n2/3fu29/6070X+6+9Km8UHVwZv/HG86++tjXce/Avn/2VJ5+6Y+OJv3nllfl/chgAiA5yRDPXKH7Lbj8LKZHzCL+JxKQpahSHbFEAQO4rcuqpcGQLz/xnz1seczRFHzniCU+28Fh1kETtbePZR6iNCCQB5b/biTQDEpi3YhtrRiSqICS4h8tqXxHE44npH+Neb+hqCTDudfvTDxElEgm3e0XxdCZl//9UpVZYAYDZZ4tG/sGGSHhV1AWOaMqqS42W2oYFrVD+6eu/0xwtv1cUPBJvai2toG2R2GB4/HcyqKpq1/aEs9iwRoTFwisik38Um4LZ2DyoqgqKruX3ivggIXSE2ni5oapRzCFpPyu61mkGM50GG+1YppAEBESJRGD+h61GMSCZu0B0MHM0p3ThNqLZucU0c9Z5IiHtaxHckmYLr4jMOpARjTJDX6NYuX3s+edu/qPHt7/3DwDwyU9+slqtPvHEE6PRSJVyMGU0Gj3xxBMPPPDAJz/5SQD4871/uPUPv9rq/y2vAcAl69Tw1Ch+y24/CymR8wi/icSkKWoUh2xRAUB+r8ipp8KRLTzznz1v0erhkxmKPnLEE55s4bHqIIna28azj1AbEUgCyn+3E2kGJDBvxTbWjEgUQUhwD5fVviJIppPTV8NNuhttj5tF3XZnHwAAlpKpBEVb0Uylevwy+WwRr4q6wBFNWXWp0VLbsKAVyj99/Xeao+fvoDPdjde0/dkHs7YJepDJASDa7MJWz6hsDhASFI9bK2hbJDZUzNhFURRVtWv7nUmTZo0Ii4VXRDgeF4QOfWTlxL6vSDVolBlCPC5IyDghzjpzTbBAcaoPfJHojlxrFGDaPzMWJBgzzNUSI093BjrZVo32uPi6TdtHXkgE5n/YahQDEpxbUX0NC8h/Qj+kI5qdW+b6T4WEuK8FcEuaLbwiCsGIpuNWTB/9+7/sPvCtZwDgAx/4wPr6+uHDh/GwpRwcWVpauv3222+//XYA+OY3v3nixIler3fX1n/7zVfX/vUH3g8AhhT3LDc2Cd2IZq5RyLD3y8x8isInM20hhS2nmJkRExEVt8jqhT1bFADAXjGnGC47zmzAtK8IiysUM5p7j5DbTOGEIVsUAHjyhe9vfff7z/zD/uDVSy++Phm/8SbWlhQpB16iVynvuu6a97w98qHl6/75T/3Yx1ZiANT136U+AHPddkCiCkJCXr1d0IdcoplsOrK+OQGYtOvNfqGUcLIcb9RbAwAAiKRzmRhVa/F8tVzfOLU1mX62qNgpJ6n8BC8ssx6JH9TGs12/rQhFS23Da0WxuIioz6PlvqLgkXhTa2kFbYvEBsPjv5OBw9NDFBFhsfCKyOQfxaZgNjYPqqqComu5r4gPEkJHqI2XG6oaxRyS9rOia51mMNNpsNGOZQpJQECUSATmf9hqFAOSuQtEBzNHc0oXbiOanVtMM2edJxKBz+xzyxZeEZl1ICMaZYa+RtFze/653gPfekZRlJMnT37jG9+QN4ouP7npppt2dnZKpZKiKF94Zuc/PvccADAOAEQHOaKZaxS/ZbefhZTIeYTfRGLSFDWKQ7aoACD3FTn1VBiy5Tv7r//SVzrH/+gbv3vx+fZ3X37+lUvyRpGUy1jGb6jf2Z+0/+6V3/nvf/8vLvRuf/zZ50aXfNZ/txNps2ap25ZrI2BAJQKJZ/WGg7+vCGK5Ym6612eyXS01nLYWjTvlcnMAAABL2UI27mDmKclitbga0Zrr0/oJWihmPXDNSRIbp3bJVnSUczQ1WmobXiuKxaAFsK1bgHj1Ir9XFDwSb2otraBtkdhgePx3MiiKYtfYnUmTDWtEWCy8IjL5R7GpmI3Ng6IooOpafq+IDxJCR6iNlxuqGsUckvazqmudZjDTabDRjmUKSUBAlEgE5n/YahQDkrkLRAczR3NKF24jmp1bTDNnnScS0r4WwS1ptvCKyKwDGdEoM/Q1ipLbb778/VNf/3MAOH36dK1Wu+aaa3D/Ug64XHPNNQ8++ODnPvc5ALjvz7b/8uUfMA4ARAc5oplrFL9lt5+FlMh5hN9EYtIUNYpDtigAIL9X5NRTgWfL+l///Qd+/791hiOQrzCVcmWI5S2vf/b3r37497/1B8+9zOtEmm4kGouCuS0bElUQEs/qDQf/e0UAsVy5tBYBAIC9C8VModGzfklo3N8oZbLndiYAAJHVYjlHfasIIJqu1PIrAACwv1k5sJ8topj1wDUnSWyc2iVb0VHO0dRoqW14rSgWgxbAtm4B4tWL8terpwEA607smR333rEDx9Zy6OoOO5igLd54DhgSpzHiacOKBMkWLOHEROQllNwSYvNAYmpMZ8OyBdgvHhokmBu6vrYaYUuORfSRZ42iYYaqRvGrlqgDi3eUfhc8zHWbkBlMfCIhxEM3EsNVo9iRhGeO5oWE14jm0csINaxZ54mE2zii4ZbGD0NEnLnlNo7oaxQlt/+yvflfX/xuMpnc2dmRN4oue/nHf/zHVCr17W9/O/tTP7n+S0edk95TwjaixdQof8ygnFh+cEDFzIyYiKi49VwT0vSRI3LDv/HX03GIiJBbThH5tEEsF5wt1W8899tffw7kK0ylXKmiveUVAH7rI+/+Nx96F1n9n1cJRHjX7amEBonFS7R2EQBUlcQbP+mWkz9/dhcAYGllLRmPuphGM9WNasZsMe5WM5kz2/uzfy6vHctmMqlkLDoe9bud9sbG9t5k+l+RtdPtTjWNNjBq5uJ3XpgAwOrJp3u1tDOGfiObuntzH6am3ZrB30Yhfsf5PQCA5dUjCY833UVztXbZpR2uMr2+P/7XPwsAVLMeKhQ2nFZ0HGx4RXSg0TriIVm9yO8VBY8ExePWCtoWiQ0VM3ZRaN94yBQRFguviIjqmxGDQx9ZOZHfK+KCRHfkWqMA0/6ZsSDBmGGulhh5ujPQybZqtMfF123aPvJCIjD/w1ajmN9uz551mpvA85/QD+mIZueWuf5TISHuawHckmYLr4hCMKLpuOXaR3/3+mv/9cXvXn311evr6/JG0ZUg11xzzaOPPvqhD31o4+++O3jt9ZXrriMrNzYJ3YhmrlHIsPfLzHyKcroMpxqnMfHzCIeIqLhFVi/s2aIAgPxekVNPBZgt//dfvfDbX39OUZTPfvazX/ziF+U8IuUKlOlbXk+fPn3u3LkNeLXCAAAgAElEQVT/489fWH7bW//Xn3uHn/rvUh+AuW47zESqICTk1dsF/YJlf7CzPXAziCTsO3miqXKrOczlz01vF+3tbJ7f2TxvM1s6crLZcrhR5EsShVq5njqz7frZor3d7T0PP0vJwHYlscx6JH5QG892/bYiFC21Da8VxeIioj6Plt8rCh6JN7WWVtC2SGwwPP47GaZPIli0PeEsNqwRYbHwisjkH8WmYDY2D6qqgqJr+b0iPkgIHaE2Xm6oahRzSNrPiq51msFMp8FGO5YpJAEBUSIRmP9hq1EMSOYuEB3MHM0pXbiNaHZuMc2cdZ5ISPtaBLek2cIrIrMOZESjzNDXKBpu//PgOwDwsY997KabbsLdSrnsZG1tLZPJAMAfDv4WAKgHAKKDHNHMNYrfstvPQkrkPMJvIjFpihrFIVtUAJDfK3LqqaCyZeelVz6z9SzIV5hKueLF+JbXe578zre+90OC+u92Im3WLHXbcm0EDKhEIPGs3nAZfK9oJvFsrdN76sG7jq4sIf8bWTly14Nf7bZr9B8qMkuyVC2sAsCB/WwRxawHrjlJYuPULtmKjnKOpkZLbcNrRbEYtAC2dQsQr17m76DDb3phz+y4dQ/+oI/JgaeQ1DMCmwOExBsP2jsUNmR4PMXhWOTOpGO2UETkGw9DXztpLw/W1i3PdDiOJv94PAMSlXXmnwgi4oDEjROX6rSoGkVRLfFsMbsk73fxdZuWGU8kC87/8NQoP0jcOAnBHE2LRNiI5sUte9Z5IiHEI4BbymzhFVEQI5qSW5599Eub/+Xiy9/77d/+7d/4jd/wjlnK5SKf//zn77///g+/44YnfvkYABCUG5uEfUT7r1G40DGDTmaW2iJ+HuEZEVuN4pUtAK77itxjZInIkxOWrCPpBQ+boLLl+B9948kXvidfYSpFylS0t7z+8ntj//lfJInrv+eoZKnbDjPRDA93JOTVW7c+FMg76LjKqN/tdnv94WgEEIvF44lUOu31NrgrQBRFAf0ddJpQzHqo+J3X/I+jRaNlt+GwolgMWurz6Pk76FQVQLFqfPFt0HaAqknTXIJUHVj1aWNBQojngCExtkVig+GhuHSoqirMdsnoGtvIBkbNLSIED2tEelue2FwzQVVVmN6VtbAB2h4j8GAGxeMLCcYJDTMq4IcZG9ZdO1YGDkh0R+41ygMJioeqRjFXSxVxox8K5kNR+l3wMNdtbn3kicQBD4/8D1uNYkCCchvkHM0HCb8Rzc4tppmzzhMJaV8L4ZYwW3hFxJlbbuOIvkbRcPtXox8AwC233IJ5lHLZyrTHn/3B9EUkaNKDWdskdCOauUYRFQJ3ZlBOtHa1hRSGipkZMRFRcWuPiEO2qGDaLYTvK3L4XpEtrlDMaO59QW4zhRNAtrzw6qUnX/iefIWpFCmaaG95/ePnR4P98cpSxLX+z6uE02zCsW6D+0zEEQnu4fLcV2SQWCKVSaSCRhFyoZj1gCAnSWxoV3SUczQjWgobXiuKxaAFsK1byFcv83fQTf9l0fbSZrGxiV8HiBtlrlXD3/5tPAMKDxJvai2toG2R2GB4/HcyTO9aWzR2Z9JkwxoRFguviEz+UWwqZmPzoCgKqLpGvldktEGZQfH4QoJxQsOMMnekGrQFiu7a0RGn0sJlSNMgwZhhDkn7WdW1TjOY6TTYaMcyhSQgIEokAvM/bDWKAcncBaKDmaM5pQu3Ec3OLaaZs84TCWlfi+CWNFt4RWTWgYxolBn6GkXD7eTNNwDgIx/5CO5TymUq6XQaAF7/0Y8AgHoAIDrIEc1co/gtu/0spETOI/wmEpOmqFEcskUBANu3iEABxRAJboPEFYoZDe0RC1oSm6n7ALLlsedeBPkKUylSzKK95fXC3/yA5UTarFnqtuXaCBhQiUDiWb0tdRu5zCzlMhWKWQ9cc5LExqldshUd5RxNjZbahteKYjFoAWzrFiBevcjvFQWPxJtaSytoWyQ2GB7/nTy9M2nV9oSz2LBGhMXCKyKTfxSbgtnYPKiqCoqu5feK+CAhdITaeLmhqlHMIWk/K7rWaQYznQYb7VimkAQERIlEYP6HrUYxIJm7QHQwczSndOE2otm5xTRz1nkiIe1rEdySZguviMw6kBGNMkNfo+i5PXToEO5TymUqP/7jP274F+UAQHSQI5q5RvFbdvtZSImcR/hNJCZNUaM4ZIsKAPJ7RU49FUi2fOVvhiAfNZAixSbTpzH+4K+/z3IibdYsddtybQQMqEQg8azecFnuK5JCIBSzHrjmJImNU7tkKzrKOZoaLbUNrxXFYtAC2NYtQLx6kfuKgkfiTa2lFbQtEhsMj/9ONt1vDMcz+6wRmfyj2FTMxuZBURRQdS33FfFBQugItfFyQ1WjmEPSflZ1rdMMZjoNNtqxTCEJCIgSicD8D1uNYkAyd4HoYOZoTunCbUSzc4tp5qwL8pl9btnCKyKzDmREo8zQ1yj2cinlyhTKAYDoIEc0e/6zl13NP/lCSuQ8wm8iMWmKGsUhWxQAkPuKnHoqkGx59uXXQL7CVIoUm8ze8vr9H7KcSJs1S922XBsBAyoRSDyrN8h9RVeqUMx64JqTJDZO7ZKt6CjnaGq01Da8VhSLQQtgW7cA8epF7isKHok3tZZW0LZIbDA8/jvZdL8xHM/ss0Zk8o9iUzAbmwdVVUHRtdxXxAcJoSPUxssNVY1iDkn7WdG1TjOY6TTYaMcyhSQgIEokAvM/bDWKAcncBaKDmaM5pQu3Ec3OLaaZsy7IZ/a5ZQuviMw6kBGNMkNfo9jLpZQrUygHAKKDHNHs+c9edjX/5AspkfMIv4nEpClqFIdsUQFA7ity6qlAsmXyxpsg9xVJkWKT6b6i1/7pTZYTabNmqduWayNgQCUCiWf1Brmv6EoVilkPXHOSxMapXbIVHeUcTY2W2obXimIxaAFs6xYgXr3IfUXBI/Gm1tIK2haJDYbHfyeb7jeG45l91ohM/lFsKmZj86AoCqi6lvuK+CAhdITaeLmhqlHMIWk/q7rWaQYznQYb7VimkAQERIlEYP6HrUYxIJm7QHQwczSndOE2otm5xTRz1gX5zD63bOEVkVkHMqJRZuhrFHu5lHJlCuUAQHSQI5o9/9nLruaffCElch7hN5GYNEWN4pAtCgDIfUVOPRVgtshXmEqRYpHrr79+9hfDibRZs9Rty7URIyoRSDyrN8h9RVeqUMx64JqTJDZO7ZKt6CjnaGq01Da8VhSLQQtgW7cA8epF7isKHok3tZZW0LZIbDA8/jvZdL8xHM/ss0Zk8o9iUzAbmwdVVUHRtdxXxAcJoSPUxssNVY1iDkn7WdG1TjOY6TTYaMcyhSQgIEokAvM/bDWKAcncBaKDmaM5pQu3Ec3OLaaZsy7IZ/a5ZQuviMw6kBGNMkNfo9jLpZQrUygHAKKDHNHs+c9edjX/5AspkfMIv4nEpClqFIdsUQFA7ity6qkQZYsUKVI0YTiRNmuWum25NmJEJQKJZ/UGp31Fr7/+Oh6LlIMpr776qvkHilkPXHOSxMapXbIVHeUcTY2W2obXimIxaAFs6xYgXr3IfUXBI/Gm1tIK2haJDYbHfyeb7jeG45l91ohM/lFsKmZj86AoCqi6lvuK+CAhdITaeLmhqlHMIWk/q7rWaQYznQYb7VimkAQERIlEYP6HrUYxIJm7QHQwczSndOE2otm5xTRz1gX5zD63bOEVkVkHMqJRZuhrFHu5lHJlCuUAQHSQI5o9/9nLruaffCElch7hN5GYNEWN4pAtCgDIfUVOPRWibJEiRYomDCfSZs1Sty3XRoyoRCDxrN5g31f0k9e9FQAuXrzowKOUAynTDn3326+e/0Ax64FrTpLYOLVLtqKjnKOp0VLb8FpRLAYtgG3dAsSrF7mvKHgk3tRaWkHbIrHB8PjvZNP9xnA8s88akck/ik3BbGweVFUFRddyXxEfJISOUBsvN1Q1ijkk7WdF1zrNYKbTYKMdyxSSgIAokQjM/7DVKAYkcxeIDmaO5pQu3EY0O7eYZs66IJ/Z55YtvCIy60BGNMoMfY1iL5dSrkyhHACIDnJEs+c/e9nV/JMvpETOI/wmEpOmqFEcskUFALmvyKmnQpQtUqRI0YThRNqsWeq25dqIEZUIJJ7VG+z7it5x6GoA6Ha7DjxKOZDyzDPPAMDytVfNf6CY9cA1J0lsnNolW9FRztHUaKlteK0oFoMWwLZuAeLVi9xXFDwSb2otraBtkdhgePx3sul+Yzie2WeNyOQfxaZiNjYPiqKAqmu5r4gPEkJHqI2XG6oaxRyS9rOqa51mMNNpsNGOZQpJQECUSATmf9hqFAOSuQtEBzNHc0oXbiOanVtMM2ddkM/sc8sWXhGZdSAjGmWGvkaxl0spV6ZQDgBEBzmi2fOfvexq/skXUiLnEX4TiUlT1CgO2aIAgNxX5NRTIcoWKVKkaMJwIm3WLHXbcm3EiEoEEs/qDfZ9RR++8VqQ+4ouO5neKzpyo/ZNO4pZD1xzksTGqV2yFR3lHE2NltqG14piMWgBbOsWIF69yH1FwSPxptbSCtoWiQ2Gx38nm+43huOZfdaITP5RbApmY/Ogqiooupb7ivggIXSE2ni5oapRzCFpPyu61mkGM50GG+1YppAEBESJRGD+h61GMSCZu0B0MHM0p3ThNqLZucU0c9YF+cw+t2zhFZFZBzKiUWboaxR7uZRyZQrlAEB0kCOaPf/Zy67mn3whJXIe4TeRmDRFjeKQLSoAyH1FTj0VomyRIkWKJgwn0mbNUrct10aMqEQg8azeYN9XdGI1BgCPP/74Cy+84ECllAMmzz///B/8wR8AwInk2+e/Ucx64JqTJDZO7ZKt6CjnaGq01Da8VhSLQQtgW7cA8epF7isKHok3tZZW0LZIbDA8/jvZdL8xHM/ss0Zk8o9iUzEbmwdFUUDVtdxXxAcJoSPUxssNVY1iDkn7WdW1TjOY6TTYaMcyhSQgIEokAvM/bDWKAcncBaKDmaM5pQu3Ec3OLaaZsy7IZ/a5ZQuviMw6kBGNMkNfo9jLpZQrUygHAKKDHNHs+c9edjX/5AspkfMIv4nEpClqFIdsUQBA7ity6qkQZYsUKVI0YTiRNmuWum25NmJEJQKJZ/UG+76iW3/y7T//zkOvvPLKpz71qTfffNOJTikHRX70ox994hOfeP3114/cGP3wjdH5zxSzHrjmJImNU7tkKzrKOZoaLbUNrxXFYtAC2NYtQLx6kfuKgkfiTa2lFbQtEhsMj/9ONt1vDMcz+6wRmfyj2BTMxuZBVVVQdC33FfFBQugItfFyQ1WjmEPSflZ0rdMMZjoNNtqxTCEJCIgSicD8D1uNYkAyd4HoYOZoTunCbUSzc4tp5qwL8pl9btnCKyKzDmREo8zQ1yj2cinlyhTKAYDoIEc0e/6zl13NP/lCSuQ8wm8iMWmKGsUhW1QAkPuKnHoqRNkiRYoUTRhOpM2apW5bro0YUYlA4lm9wb6vSAX1dzM/BQCdTqdSqeARSTk48vnPf376RsFzv/QOw88Usx645iSJjVO7ZCs6yjmaGi21Da8VxWLQAtjWLUC8epH7ioJH4k2tpRW0LRIbDI//TjbdbwzHM/usEZn8o9hUzMbmQVEUUHUt9xXxQULoCLXxckNVo5hD0n5Wda3TDGY6DTbasUwhCQiIEonA/A9bjWJAMneB6GDmaE7pwm1Es3OLaeasC/KZfW7Zwisisw5kRKPM0Nco9nIpXsbdWi49lUy5PXIzHTYLM8tstTNG/6PQHPppXD8wXWj0vax79RnSXO1y/9oy5QBAdJAjmj3/2cuu5p98ISVyHuE3kZg0RY3ikC0KAMh9RU49FaJskSJYxsNuu9Wo12q1eqO50em7zqNSAhaGE2mzZqnblmsjRlQikHhWb7DvK1JAueVd1/3WrTcCwP333/+Zz3zm0qVLDpxKCbVcunTpvvvuO3v2LAD8TuaG1DsPGf6TYtYD15wksXFql2xFRzlHU6OltuG1olgMWgDbugWIVy9XzxxY73VjizDtrrimbeL50I/T6s7gYq5dMoHAxv6A0WWIxNgWiQ2Gxx+SmQcVZrtkdI0lHBg1t4gQPKwR6W15YnPNBFVVYXpX1sIGaHuMwIMZFI8vJBgnNMyogB9mbFh37VgZOCDRHbnXKA8kKB6qGsVcLVXEjX4omA9F6XfBw1y3ufWRJxIHPDzyP2w1igEJym2QczQfJPxGNDu3mGbOOk8kpH0thFvCbOEVEWduuY0j+hrF3EcLkGgql41W7t3aB4BuuV7olJO44bhdKZ/fHgBA5MiDjXTU+F/DXnd7ewIQgeEYP9rB5/xAgO1+OZdr5mJu1v3e9vYuAKykfbVCIr1WbaMPAIlsMZeMelkLFzTpwaztB3Eb0X/y3eGfDvcufu/lwWuvD394afzGG4LilCLFl0Svesu7ro2++7q3feidsY++6yduf/c79f/jkP/u447cZgqHeSYimggJKoMUgdJvlQrF+tbexPDbysmn+rVMUIikuIteAbS/sSqBjCbmkWitUQtDgnvw3FekgPJvfmH50j+9+cBf7H3pS1/6wz/8w0cfffSWW27xxbeUYOVrX/var/7qrw4GAwD4/C0//ukPXu9/RvOe76hsKMYRyxzNiJbChteKYjFoAWzrFvLVi9xXFDwSb2otraBtkdhgePx3MoTvmX3WiEz+UWwqZmPzoO8ZUkGR+4p4ISF0hNp4uaGqUcwhaT+rutZpBjOdBhvtWKaQBAREiURg/oetRjEgmbtAdDBzNKd04Tai2bnFNHPWyX1FBkdmHciIRpmhr1Hs5XIRkijUimsRAIDJdq3SdHgkul+vNAYAALBarBYd7iexyN56udLmfg+IUIadevnUqVOnyvVuUBBMQjkAEO0z655/5dX/8Y+f/JUn2rVvfXvrxb3+q6/JG0VSwiPjN978zis/3Pr77/2f3edyf7x97MKfPbf/2uz/OOQ/xZLOcWEn9xVdATLulHP5c+YbRQBL6Uw6IEBSvIXhRNqsWeZoy7URIyoRSHAPnvuKpr//5kdu/E+/nLjmKmUwGNx6663vf//7P/3pTz/++OMvvfSSA8VSApaXXnrpscceu++++973vvd99KMfHQwG0avf8p/+p/j/fssNAOB/RrPYgGtOktg4tUt2IZVyjqZGS23Da0WxGLQAtnULEK9e5L6ig4zE2BaJDYbHH5KZB8o7k3wiQvCwRqS35YnNNRPkviJ7ZeCARHfkXqM8kKB4qGoUc7VUETf6oWA+FKXfBQ9z3ebWR55IHPDwyP+w1SgGJCi3Qc7RfJDwG9Hs3GKaOevkviJHhgMZ0XTcEjJM0UeLkWiqXM037zg/ANhbr1RKuVraurNmtFGpbU0AAJaPV8oZMRtvduvlWtFxX5NQGQ3D9dYgNOnBrO0HsY7ox77T//U/fXr69yc/+cmbb7755ptv/oVf+IWlpSVhkUqR4kP29/e//vWvf+Mb33jmmWcee+yxPxu+fMvjf/p/3Zb6+E+/i8eM5j7uyG0AuKw6iCZCgsogXMYbpUylAwAQLzRaIh4lCKf0G+XazvQ+0cqx09VyPhUb93udYVzQFCmFh+gVQPsbqxLIaGIeidYatTAkuAeSfUVT/Sv/LHbz8qHqX+z9x2//4Nlnn3322We//OUv+2JdSlDy1quUX/u5Hysfuf49P3YN7YzmPd9R2VCMI5Y5mhEthQ2vFcVi0ALY1i3kq5f5vaLZFUqLxq5TGrVNSC452bXZhTlewC4cENgcICQoHrdW0LZIbKiYsYuiKKpq1/Y7kybNGhEWC6+IiOqbEYNDH1k5se8rUg0aZYYQjwsSMk6Is85cEyxQnOoDXyS6I9caBZj2z4wFCcYMc7XEyNOdgU62VaM9Lr5u0/aRFxKB+R+2GsWABOdWVF/DAvKf0A/piGbnlrn+UyEh7msB3JJmC6+IQjCi6bgV10eLkli2UjnWuntzH2C3UWmWNgoJ0//36pXmAAAgcqRczce5t7+0uhrd3d2bbFdLjcJGgX8DXjIajibeVosTknJjP4hpRH9x5y//3cVvAcAHPvCB9fX1w4cPLyxaKVIIZWlp6fbbb7/99tsB4Jvf/OaJEyd6vd6vPfHfKx9+/XMf/FnmGU2bYFxGHIkNAJdVBzLtkUyTi5fxqD97mehqOlx33YXKcKPZmd0puqveqmajAADJlNxTFG7RK4N2Io2dTiPzrK+R6D5HW+oSmKoWfyS4B+1WkEEbj5nZAKgKKImlSP1jK791642t5/Zbz42e3//HF1/7p/EbQS5cpThJ9GrlXdddnfixt/7L1bfnfua6Hz/0FrYZzcnGKL5sPNv124pQtNQ2vFYUi4uI+jxa7is6yEiMbZHYYHj8IZl5UCFcz+yzRqS35YnNNRPkviJ7ZeCARHfkXqM8kKB4qGoUc7VUETf6oWA+FKXfBQ9z3ebWR55IHPDwyP+w1SgGJCi3Qc7RfJDwG9Hs3GKaOevkviJHhgMZ0XTcEjJM0UeLk0ShWq61z+xMYH+zUm3n64YHo0etSm36USFBr58DSJcqifK9m/v7m5XyRq6RdftskQgZjYYLbtFd0KQHs7YfRD+iH9l97t9d/JaiKJ/97Ge/+MUvXnPNNQsIUooUFrnpppt2dnZOnz597ty5yl/0lg9Ffi35Hraq6z7uyG0AuKw6iCZCgsogRYx0O73praLlTF7uJDowolcA7W+sSiCjiXkkWufohSHBPZDvK5r+L4D6zre99V/ddMO/uukGByQkOHFqTDZ43XY/C/DfCg1a2ohIj/JCa3VjzxYStH5nNO/5jsqGYhyxRMSIlsIm5PwjNtTn0fJ7RcEj8abW0graFokNhsd/J4OiKHaN3Zk02bBGhMXCKyKTfxSbitnYPCiKAqqu5feK+CAhdITaeLmhqlHMIWk/q7rWaQYznQYb7VimkAQERIlEYP6HrUYxIJm7QHQwczSndOE2otm5xTRz1nkiIe1rEdySZguviMw6kBGNMkNfo9jL5QIlVawWVgAAYNAo13v6f3SrldYegMjXz41HsVylsAoAMGiWqh32bwaN+u1mvVYtl0qlcq3ebPdcnnYfj/v94azJ8dAko4C+XkQ5ABBNkHU733+59PTXAeD06dO1Wk3eKJJyUOSaa6558MEHP/e5zwHAvVs7f/n9V9iqLsWSznFhx2Em4rXskCJExqPRbFqJx+PyVtGBEYYTabNmmaMt10aMqEQgwT0Qfq9Is3HwE8RlR+/q7Rg1G1riiNj7yCNb0OwVFBE7tygnhMy4tOIrImq0gWfUYtACAP159PxekXYP3KjtQ9piYxO/DhA32s0wxfXmmZeNZ0DhQeJNraUVtC0SGwyP/06e3pm0anvCWWxYI8Ji4RWRyT+KTcFsbB5UVQVF1/O9RPq+IpMNygyKxxcSjBMaZtS5I8WgLVB0146OOJUWLkOaBgnGDHNI2s+KrnWawUynwUY7likkAQFRIhGY/2GrUQxI5i4QHcwczSlduI1odm4xzZx1nkhI+1oEt6TZwisisw5kRKPM0Nco9nK5SIllK5VjSwAAk+1qpTnbaDNsVuo7ExD2+rmpjEaQLleOLwPAZLdmulXl21WnXkjH4++97c57T505e+7cubOn7r3ztsPxRKbU7Jlv/YyauVhUUQ4deu+9m9Pnwydbpw7faJBMpcsQFb1QDgBEE2TdF575JgAkk8kvfOELwiKSIkWU3H///dNXJn7hL3psVZdiSee4sOMwE/FadkgRIuOxNp/E5K2igyMMJ9JmzTJHW66NGFGJQIJ78NxXZLFx8BPEZUfv6u0YNRta4ojY+8gjW9DsFRQRO7coJ4TMuLTiKyJqtIFn1GLQAgD9ebTcVxQ8Em9qLa2gbZHYYHj8dzL9nUmmiLBYeEVk8o9iUzEbmwe5r8juiFNp4TKkaZBgzDCHpP1seMBIpxnMdHo/hMRKrqg+oiHXECNT/oetRjEgmbtAdDBzNKd04Tai2bnFNHPWyX1FBkdmHciIRpmhr1Hs5XKxEi/UykciAAB765Xp7p5urXphH0Dg6+cAACaj0Qji+UppLQIAk61quUn1Trhxt55LZe49v71n+/rQZLB17s50ptoxbjAaj8eh+kyRJpQDANFeWffCa6//yXdfvPrqq9fX1+WOIikHUa655ppHH3306quv/urf7g1euwRAXXUplnSOCzu5r0iKlDAKw4m0WbPM0ZZrI0ZUIpDgHuS+Iv9oiSNi7yOPbEGzV1BE7NyinBAy49KKr4io0QaeUYtBCwD059Hye0UHGYmxLRIbDI8/JDMPKoTrWyCsEelteWJzzQRVVUF+r4h7/uuO3GuUBxIUD1WNYq6WKuJGPxTMh6L0u+Bhrtvc+sgTiQMeHvkfthrFgATlNsg5mg8SfiOanVtMM2edJxLSvhbCLWG28IqIM7fcxhF9jWLuo4VLslQt1G97eACw2yg3Ss1Ypb4DALB8QtTr56YyGo4AIFWs5ut3nB/A3oVypZ2r+2yx38xn772wBwCwtHaiXCkXsql4FMb97kazUq5c2J3sb5/Jl1Ld+feQoqlC5XRqBDBs189vT2+JHT+ZS2rNxhJZUTup3AVNejBr+0E0I/rx5wcA8LGPfeymm25aUHBSpPCWtbW1TCbz5JNP/pfvvPiZm/4H2qrrPu7IbQC4rDqIJkKCynBgZNRrb7S7/X5/CPFEIpHKZDNJ92/XjUfD0RggGovrW3tG/XZ7o9PpDccQTaTS6Uw2nSCYSkb9Trvd6fX6w3E0nkyl0umU7c1yxnbGI31f0XA4HMbsJqzRwWg4HJt9joedVqvd7Q0hkcpk89npbDWjAaLxuOZx2ly314d4IpnKZDP2cEbdjVan2+sNIZ5MpQnw6L43Ntq9WSjJZDqTSSccD0X6aNhttTY6vSHEk+lsPpda6AcK9Qqg/Y1VCWQ0MY9E6xy9MCS4B+MB1uEAACAASURBVIrvFXkhsdiQVkseq26CVjigJY6IvY88sgXNGUERsXNLyInnOGKJiBEthU3I+UdsqM+j5/eKZlcoLRq7TmnUNiG55GTXZhfmeAG7cEBgc4CQoHjcWkHbIrGhYsYuiqKoql3b70yaNGtEWCy8IiKqb0YMDn1k5cS+r0g1aJQZQjwuSMg4Ic46c02wQHGqD3yR6I5caxRg2j8zFiQYM8zVEiNPdwY62VaN9rj4uk3bR15IBOZ/2GoUAxKcW1F9DQvIf0I/pCOanVvm+k+FhLivBXBLmi28IgrBiKbjVlwfLV6imUr1eOvOC3uwv1XJ52Pb+wAQOVquCHv9HABob/OJZSuVY627N/dh0ChVit1qitxFv1EsXph9V+mhdrOo3fGJJlK5ciuTLqazD+9OBufL1VJ26jiaypdTeQDolFrTe0WRRL5SzS/0whUqJOXGfhDNiL7QfwEAPvKRjyw6RClSuEo6nX7yySdb03tFlFVXm2BcRhyJDQCXVQcy7ZFMkwdQxv2NWqlcvbCzb/49sryWK1WrpazDzZ5uJf3zZ3cBVk8+3aulAUadWrFYWTe7iSwfLTWaVef7/uNes1ws17cGXrtMI0cf7LVLiY1CPHfeuHd159xtN56b/T2HwiE6aJdSt53fmzcLo3Y1X6hszmFGOtFMNpkAgH49lzy1NYGlu746amQBhu1qsVi9sGtsL7JyvNZqFlOzpvob5WKptrlrDHlp7a5as15Iut1YG7ZrpVK1tWPZuhtZPVaq1StYJFofnb7Yq6Zg3K0X8qV1reHVUTqX8jHRs4teGbQTaex0GplnfY1E9znaUpc0VIKQ4B7Mt4IU2+2imQ3M9hU5+eFQLXmsup3aIrfhWf859JFHtjhkr5CI2LlFOSFkhqQVoWgDz6jFRUR9Hi2/VxQ8Em9qLa2gbZHYYHj8dzKoqmrX9oSz2LBGhMXCKyKTfxSbgtnYPKiqCoqu5feK+CAhdITaeLmhqlHMIWk/K7rWaQYznQYb7VimkAQERIlEYP6HrUYxIJm7QHQwczSndOE2otm5xTRz1nkiIe1rEdySZguviMw6kBGNMkNfo9jLZQASz1fnL6Lb3toFEPz6ualoX35IFKrl6Yvoduqlep/cwUalsrkPALByT6NRtF/timVqtfwyAMBOs94eW/87ZEI5ABDtlXV/NRoBwC233CIyHClShMs0h599+RUA6qpLsaRzXNhxmIl4LTtCLsONUiZ1xxnbrRQAmOztrJ+5I5Upbbi/lHQ4HI1HnWomecupdZubyd7W2Vyu6vDpuVG7lEnfeW52o2hpeXVtbW1lKeLanJ93l3KIbjIcjmDULmWyZzYN97PiqVTCgms4HI061UzqtjPmG0UAMBlcuDebbw4BYNQuZ9J3nDXfKAKA/Z3zd2eLGyNwkKnrU+vWG0UAMNndPHtHOlfvOU+to+EIxt1qNnPvuqHhpXRG8OLCJgwn0mbNMkdbro0YUYlAgnuQ3yvyj5Y4IvY+8sgWNHsFRcTOLcoJITMurfiKiBpt4Bm1GLQAQH8eLb9XFDwSb2otraBtkdhgePx3MiiKYtfYnUmTDWtEWCy8IjL5R7GpmI3Ng6IooOpafq+IDxJCR6iNlxuqGsUckvaz4TEanWYw02mw0Y5lCklAQJRIBOZ/2GoUA5K5C0QHM0dzShduI5qdW0wzZ50nEtK+FsEtabbwisisAxnRKDP0NYq9XAYiyWJlelcFAACWjlcq6QV+uTtVqhVWAAD2tyrlluNlK7OMWvXWAGC6BSqL7wuKZgq5ZQCAQXujxwWqOKEcAIj2yrrJG2+C3Fck5eBLOp0GgNf+6Q0A6qpLsaRzXNhxmIl4LTvCLKN2KZs7N30DKCwfuevBR5+6+Pzzzz9/8alHH7jryHQe2t8+l8uWOy53+Me9Wj6TObO1B0trJ04/9JWnnn766ae++sjp46tLU4PJdrXSRGaTYbOQn7YeWTn+4FMvDoe9brfbHw2//eg9R6bHRlZPPPjoVBqVbBwAEvny6dOnT588vjrzsnz0ntMzeaCUS/CODkbDbrOYP7czgcjykbtOP/jIo48+8tADlbx1O86k1yxksme29mBp9fjJh77y1NNPP/WVR04fW53d+tq7UK40W6VM9uzWHiyvnTj9yNTkoXuOzmf8QbOM31Ub92q57Jmt6dbdI/c89NWLL15SVVX9wfNPP3r62EoEAPY2SznnQEajXquUr2ztAyytnTj54COPPvroQw9WC4tcXQAAsJxImzXLHG25NmJEJQIJ7kF+r8g/WuKI2PvII1vQ7BUUETu3KCeEzLi04isiarSBZ9Ri0AIA/Xm0/F7RQUZibIvEBsPjD8nMgwrh+hYIa0R6W57YXDNBVVWY3pW1sCG/V8ShjzxrlAcSFA9VjWKuliriRj8UzIei9LvgYa7b3PrIE4kDHh75H7YaxYAE5TbIOZoPEn4jmp1bTDNnnScS0r4Wwi1htvCKiDO33MYRfY1i7qNgZNxptff0f/W6vXF+gddzoplK9UTrzvU92FsvV4rZmvdni8btVnsfACCSKeQc3zQUTaZTkYc3J9Dvdkew2E8k+BQ06cGs7QfRj+hDhw4tICopUsTJ9ddfr/+Dsuq6jztymykE5pmIaCIkqAzhlXG7XDi3MwEAiKzd09qo6y+KSyRSmXwhV8zkH96dwGTnbKGc7TpNBpOdzQsAS0dONpu1bGL+azqTzWUKqTvODwBgf6PRGuUL5rLfqVZmX7g7Wt1olfQdLrFkvr4RG6fuOD+Y7LY70Xozpx+ZzJWrOYBRo1u/sDsBgHi2VC3bt8fwig72Nsqlvb3IyvFaq1F0m7kGmxcGsHz0tPGVe+lMLpvKpe68sAcAg4fv/DgArBx7oNEoZwwmmWQmfWprH2Cy02p2q7a3wnVrhfLWPgBEVk80Npr5hMZUIp2vbmRS+fSd64PJbr1cL7VLCbDLpF0p7u9Nlo890GyWMwFOv3oF0P7GqgQymphHonWOXhgS3IP8XpF/tMQRsfeRR7agOSMoInZuCTnxHEcsETGipbAJOf+IDfV5tNxXFDwSb2otraBtkdhgePx3MoTvmX3WiEz+UWwqZmPzIPcV2R1xKi1chjQNEowZ5pC0n1Vd6zSDmU6DjXYsU0gCAqJEIjD/w1ajGJDMXSA6mDmaU7pwG9Hs3GKaOevkviKDI7MOZESjzNDXKPZyGYR0a6XGAAAgEgEAmOzWyo3+QhHE89XK0SUAgN16uWbYA+R006jX6U6f3E4k4zB0lPHcwWjo/s6fwIVyACD6oGSdFCkchTL/KZZ0jgs7ua/IW3r18nSugaWj1Wbd/kWheK7eqk7nAtitV+p9Z18rxx/qtA03iqYSy5aL01eqwqTbsW6Y6TRbuwAAsJwr29+zGstWSkciALDXqtJMgRyj29vbixyptJquN4oAAGD52IMbG9ZvM8XzlcJ8DxREVk480t7QbxRNJVks52Z7i/qdjnV+HDYr1e0JAETWSs2GfqPI0EKtcnwJACZbjabD2/729/YiqycDvlEEwHIibdYsc7Tl2ogRlQgkuAe5r8g/WuKI2PvII1vQ7BUUETu3KCeEzLi04isiarSBZ9Ri0AIA/Xm0/F5R8Ei8qbW0grZFYoPh8d/J0zuTVm1POIsNa0RYLLwiMvlHsSmYjc2Dqqqg6Fp+r4gPEkJHqI2XG6oaxRyS9rPhsRKdZjDTaX70BMs9VnJF9RENuYYYmfI/bDWKAcncBaKDmaM5pQu3Ec3OLaaZs84TCWlfi+CWNFt4RWTWgYxolBn6GsVeLhcvw0a5ujMBgJW7Go27VgAAJluVcnOxN1cShVpx+tmi7WqpMWs76niFadifmeyeu+NGZ3nv3RcmAACTMYT8g0WUAwDRByPrpEjhKpT5T7Gkc1zYcZiJeC07wiqden17+uWalXzF6Zt4yWIlvwIAAJOtet3hNgREjlWwz9QBQDI9v78y6vfNb6Eb9XrTiSOSTKewYxOpVBwAYNJtu74jDhWO0QHAWqlWQjGaJHKkXEfNkpnU7G18kUylXkjYLaKpTHJ2U23Y65v/r9+sbewDACznKmUHFPFcPrMEANBrtfuoBcBKvlYJ+EYRAMuJtFmzzNGWayNGVCKQ4B7k94r8oyWOiL2PPLIFzV5BEbFzi3JCyIxLK74iokYbeEYtBi0A0J9Hy31FwSPxptbSCtoWiQ2Gx38n09+ZZIoIi4VXRCb/KDYVs7F5kPuK7I44lRYuQ5oGCcYMc0jaz4YHjHSawUyn90NIrOSK6iMacg0xMuV/2GoUA5K5C0QHM0dzShduI5qdW0wzZ53cV2RwZNaBjGiUGfoaxV4uFy2jjXJlcx8AIkdK5Xy+Ujm2BACwt16ptBd6eyWaKlen19D2Nyvl6de2o9Eo/sXx8XjsC1wk6nzbKRxCOQAQfRCyTooUzkKZ/xRLOseFndxX5CXdVnu6qwdWcwXnN41GM4XcyvTP/objp+aijsfHE/HZvDEem+8V6bNG1OFw7Xfrod7CNbrIsVKJ6T2w0XhC20fk4Ccej8/33Vrm0+FGqzsBAFjK5B2+BggAsVQ6EQGASa+LhxFZK5adD1+cMJxImzXLHG25NmJEJQIJ7kHuK/KPljgi9j7yyBY0ewVFxM4tygkhMy6t+IqIGm3gGbUYtABAfx4tv1d0kJEY2yKxwfD4QzLzoEK4vgXCGpHelic210xQVRXk94q457/uyL1GeSBB8VDVKOZqqSJu9EPBfChKvwse5rrNrY88kTjg4ZH/YatRDEhQboOco/kg4Tei2bnFNHPWeSIh7Wsh3BJmC6+IOHPLbRzR1yjmPlqwjLvVcnMAALCcLxeSAJCvFKubZ3cBdhvlWqmDfJJBmMSy1erxjTsv7MGgWamXs+VkNOZwnSkajUYBJgCwcuKRZskbZCyZ4IqVu6BJD2ZtP4hT1ZUi5aALZf67jztymykE5pmIaCIkqAzhlFGvN7unsJTKWD+PY5JUJh05N5gATHqd7hjQ7UPOYrgPZHmkIBaPTyeOSb/XB7BjGPVnO1ajMafJx0n4RheLxx2/w0cm0Zgna9FoFGAf+Y9upzf96FIylXB5f+t49gjGeDgcASB8JRKMQfARvQJof2NVAhlNzCPROkcvDAnuQX6vyD9a4ojY+8gjW9CcERQRO7eEnHiOI5aIGNFS2IScf8SG+jx6fq9odoXSorHrlEZtE5JLTnZtdmGOF7ALBwQ2BwgJisetFbQtEhsqZuyiKIqq2rX9zqRJs0aExcIrIqL6ZsTg0EdWTuz7ilSDRpkhxOOChIwT4qwz1wQLFKf6wBeJ7si1RgGm/TNjQYIxw1wtMfJ0Z6CTbdVoj4uv27R95IVEYP6HrUYxIMG5FdXXsID8J/RDOqLZuWWu/1RIiPtaALek2cIrohCMaDpuxfXRYqXfKNV3JgAQOVIuT7/kHU2XKycad67vTV8GV9goLPBiTzxfLdc2Tm1PJtu1SqvYzDpe7Yon4tOLXCOIpdJMz1+HQ0jKjf0gTlVXipSDLpT5r00wLiOOxGYKgXkmQqY9kmnygIj25lCIJxKuNTsajycAdgEAhsMhQIITgmg6m106v74PsNtqdqsp6y2d0UazvQ8AEEln/c4qwUfnXxxgjobD6aaqyfaZn7/xjLcfn/t8Fy16ZdBOpLHTaWSe9TUS3edoS13SUAlCgnsw3wpSbLeLZjYw21fk5IdDteSx6nZqi9yGZ/3n0Ece2eKQvUIiYucW5YSQGZJWhKINPKMWFxH1ebT8XlHwSLyptbSCtkVig+Hx38mgqqpd2xPOYsMaERYLr4hM/lFsCmZj86CqKii6lt8r4oOE0BFq4+WGqkYxh6T9rOhapxnMdBpstGOZQhIQECUSgfkfthrFgGTuAtHBzNGc0oXbiGbnFtPMWeeJhLSvRXBLmi28IjLrQEY0ygx9jWIvlwuUUatc2doHAFgpGL6uEMtXytNvg+svg1uYJIu14ur04+KVahccNxYl07OPMex32p3FwRMnlAMA0SHPOilSRAhl/lMs6RwXdhxmIl7LjnDKGMbTz/lEPPe86FuD+N6GiOXLs9ltt1YotU2z27jfKJQvzL7SU8z5fUgiBNHxEt+onN7oFxJhOJE2a5Y52nJtxIhKBBLcg/xekX+0xBGx95FHtqDZKygidm5RTgiZcWnFV0TUaAPPqMWgBQD682i5ryh4JCget1bQtkhsqJixS/ie2WeNCMfjgtChj+S+Intl4IBEd+RaowDT/pmxIMGYYa6WGHm6M9DJtmq0x8XXbdo+8kIiMP/DVqPkviL/fkhHtNxXJKqP5L4iYob999HiZNyplNf3AACWjpXLWeOlnmSxWqjd9vAAYNAsV0uZqvcXtrlJNF2p5Vt3nB9MduqVZjoRA9jDzDLZzNL6hX2AQbOxUcn4/SiC4dJWOK7VkZQb+0Gcqq4UKQddKPNfm2BcRhyJzRSC3FfkKtPvz00AJuORR9nVvxbE+zZEqtSobqRPbe1Pds5lU71iuZBNJqLjXqdRq67v7ANAZOVEvZbz/ZmdUETHR3RUa/d8pU6wtTieDMFXiZxFrwzaiTR2Oo3Ms75GovscbalLGipBSHAPcl+Rf7TEEbH3kUe2OGSvkIjYuUU5IWSGpBWhaAPPqMVFJPcVeSCx4wkPEm9qLa2gbZHYYHj8dzKE75l91ohM/lFsCmZj8yD3FdkdcSotXIY0DRKMGeaQtJ8VXes0g5lOg412LFNIAgKiRCIw/8NWoxiQzF0gOpg5mlO6cBvR7Nximjnr5L4igyOzDmREo8zQ1yj2crko6dfL9emHuFeLlULC/J/RTKVybAkAYLJTLzX6C0UWy1anje9fqNS6ThfdYvni7APhe81Spe1391MsHpt+/Xwy7PVpkfIUygGA6DBnHbuMh71ef7F73QBg1O/1hmir7UJcURRFUeKFjUWjkmIQyvynWNI5LuzkviIP0T/BM+z3XW+njPvaC924f/ImmizVZ3uLJoPNc/feecdtt9x2x91npjeKlo+cbLabvjcVQVii4yKxeHx272cUjaUJJBHqW0UsJ9JmzTJHW66NGFGJQIJ7kPuK/KMljoi9jzyyBc1eQRGxc4tyQsiMSyu+IqJGG3hGLQYtANCfR8/vFel3Lw3aPqQtNjbx6wBxY7kxpv3i08YzoPAg8abW0graFokNhsd/J4OiKHaN3Zk02bBGhMXCKyKTfxSbitnYPCiKAqqukX1FRhuUGRSPLyQYJzTMKHNHqkFboOiuHR1xKi1chjQNEowZ5pC0nw2P0eg0g5lOg412LFNIAgKiRCIw/8NWoxiQzF0gOpg5mlO6cBvR7NximjnrPJGQ9rUIbkmzhVdEZh3IiEaZoa9R7OVyMTJslqtbEwCA5RPVMvJhhnihWlybvohuq1JuLfbifLxQm17LG2xtDZyMolntftbuuVyu3EY/wD3qNpvYjaR4cv5B8d7GRpcDZlahHACIDm/WscqwVUzFbzx8+L3xRK7RW1Sr/UY+GX/v4cM3JlKFluNX3qUELJT5T7Gkc1zYcZiJeC07wimxVHr2qtP9Ttu16nba3dn73FLpJOedN8NmPntmexJZPfHgQw/cc/zI2sry8vLK6pFjJ04+9JVv9zq1XILKbzii4yOpdHL6LMWw3VlYpRUnDCfSZs0yR1uujRhRiUCCezDsGQIFFJsXq42DnyAuO3pXb8eo2dASR8TeRx7ZgmavoIjYuUU5IWTGpRVfEVGjDTyjFoMWAOjPo+fvoLPe68YGs3ZXXNM28bx57FQlDC7m2iUTCGzQG9WXGxJjWyQ2GB5/SGYeVJjtktE1lnBg1NwiQvCwRqS35YnNNRNUVYXpXVkLG6DtMQIPZlA8vpBgnNAwowJ+mLFh3bVjZeCARHfkXqM8kKB4qGoUc7VUETf6oWA+FKXfBQ9z3ebWR55IHPDwyP+w1SgGJCi3Qc7RfJDwG9Hs3GKaOes8kZD2tRBuCbOFV0ScueU2juhrFHMfLUTG7Wpl+vq5yJFyxeFlO6lyNd+84/wAYG+9XClla+invifdWi7ddL3clSpv1P2+0CdZqhYbt53bdTVKFBqNjXR+fTCB/a2ztyU3jheKhVw6mYhFx6Nhv9tpNevNzd391ZOJrhV9NJ3NLJ2/sA8w2ankc1AtZZOx8bDX7UYzpVzSoUWBgiY9mLX9IE5VV7T06rlCw3abJRqNRmPxRCKVSqcz2YznW4z6zWpjZx8AYDK4UKl3CrW0ELRm6dSq67sTAID9nWa1Uc6V/WRHp5ot4Xdao9FYLB5PJFKpTCabScVdxtB4o5SpkH6VK1FsNgsJUgzRaCweT6RSqRQKot/I5+t9wpaNQjPo2YQy/93HHbnNFALzTEQ0ERJUhpBKMptdPbOzCwCDVqNdTWfwrB9vNDYGsyNyWb7VeNQsFtcHAKvFRqOUjkKxzM11CKLjJfFsLhXZ2p7AZKdR75Twyf/giF4BtL+xKoGMJuaRaJ2jF4YE9+C5r8jyejoCJBYb0mrJY9VN0AoHtMQRsfeRR7agOSMoInZuCTnxHEcsETGipbAJOf+IDfV5tPxeUfBIUDxuraBtkdhQMWMXhf3LARQRYbHwioiovhkxOPSRlRP5vSIuSHRHrjUKMO2fGQsSjBnmaomRpzsDnWyrRntcfN2m7SMvJALzP2w1igEJzq2ovoYF5D+hH9IRzc4tc/2nQkLc1wK4Jc0WXhGFYETTcSuujxYhPe31cyuFatHxWlUsWykfbd27tQ+wWy/Vi50SZjoZ7GwP3FqLRIcUXwSKZirVE82Pr2PfKtIlnmu2m7F84eHtfYD9nQvn7r1wzm417LR7kE6Zf4zlK+Va+8z2PsBk98KZj184M4N75MFcLpnwj5hRSMqN/SBOVVe0jPu97W3HG3/rcA4gsryWLZSr5bzzLaNoTP+vSCzO407EsNNodkYAsVS+kMFfBhWLz75CYoVAJKNed3vbNYnPw1mApbUT5Vqt7ABhPOp3t7cnRA1GxjnrcPPGsH4eAGB57US5VisZQYyHve72DlnLJhSxhb8mkDL/tQnGZcSR2EwhMM9EyLRHMk0eGEkVS0dr925NAAaNcq3YLiOfwht3a5XmdE5ZOlrM872ZMm632vsAEElm+H+FL/Do+EkiX8xWti9MJ/9aAQ3l4IheGbQTaex0GplnfY1E9znaUpc0VIKQ4B7k94r8oyWOiL2PPLLFIXuFRMTOLcoJITMkrQhFG3hGLS4i6vNo+b2i4JF4U2tpBW2LxAbD47+TQVVVu7YnnMWGNSIsFl4Rmfyj2BTMxuZBVVVQdC2/V8QHCaEj1MbLDVWNYg5J+1nRtU4zmOk02GjHMoUkICBKJALzP2w1igHJ3AWig5mjOaULtxHNzi2mmbPOEwlpX4vgljRbeEVk1oGMaJQZ+hrFXi6Fy7BZrkyvOy8dq1QcHn2eSqJQLawCAMBku1K2bwwRKrFctXJ0ydMskat3ek89dM/RVbttZPnIiQcevdjvlFPIkalyq/XAsZWI5ed+t0dxb4tZKAcAokOZdXOJLK9osrxk4H6yt3Ph7J2pVLaKv0wQAOL5WuOeo6vLyytH7qo10DuXfqXXrJw6derUqWqr72SSLDVqdx1ZWV5ePXpPg+Qz7w6yvHrEIGtrqyvLevj7O+tnbktl656pF/GSaMxlRC+trBlkdXXV1Ad7O+unsumc6fNk0VgUbcYDUHTxF5Yp859iSee4sOMwE/FadoRWEoVqcXX6raDtSq7QsGb8uNfI585MJ6jIWrFaSHCHMAYAmHQ3fH/kzltCEB0viRcq8486bZ/J5modlK1hp9l0/KZgeIThRNqsWeZoy7URIyoRSHAP8ntF/tESR8TeRx7ZgmavoIjYuUU5IWTGpRVfEVGjDTyjFoMWAOjPo+W+ouCRoHjcWkHbIrGhYsYu4XtmnzUiHI8LQoc+kvuK7JWBAxLdkWuNAkz7Z8aCBGOGuVpi5OnOQCfbqtEeF1+3afvIC4nA/A9bjZL7ivz7IR3Rcl+RqD6S+4qIGfbfR+Ilnm+N8oS20XStp9aQ/0iUOuMSTet+DkwU26MiiWE8U6y3i/VRr9Pp9oaj0QiisXgylUqnEq7XrOOZ8ka/2Gu3O73+CGZvBAvoM90k5cZ+EKequzCJZGrdjbyB4PGw22lvNOr15tZgAjAZbJ7JZvqtdj2L3JSJJvP1dr7OEc946H29OJoqNjpEeegmS9lap5G1Nd9tt+rVysNbewCwt1kq1NL4jc2ZrBbbPfo37y3l6l0bBhj1O+1WvVo9v70HMBlcKOar6c7sRXvJUnuEDNdxMxe788IEAJYLG8N6hhYQT6HMf22CcRlxJDZTCFfivqJ+s5DuuNZM02sRo+lqs9LNnNnah8lg/e5Ut1ksFrKpRAxG/W6rUWtsDqb72JaOVhsV7u8+i6Yy6cj61gQGD+fT/Xwhl47HjHc2o9FYNBZPJJNJt7dCOrsPNjqukio36u3M3Zt7AHubp25JNk8UivlsavqO136v22k16s2tARx5INXx9WLOxYteGbQTaex0GplnfY1E9znaUpc0VIKQ4B7kviL/aIkjYu8jj2xxyF4hEbFzi3JCyAxJK0LRBp5Ri4uI+jxafq/oICMxtkVig+Hxh2TmQYVwfQuENSK9LU9srpmgqipM78pa2JDfK+LQR541ygMJioeqRjFXSxVxox8K5kNR+l3wMNdtbn3kicQBD4/8D1uNYkCCchvkHM0HCb8Rzc4tppmzzhMJaV8L4ZYwW3hFxJlbbuOIvkYx95EUeokl09mk7wvpsWQml8wIgONP0KQHs7YfxKnqBifReCqTT2XypXKzlC8+vLMPMNl9OJ9PdtpcNg65y2g4CvSZ+Gg8lS3Ws9l0Ln33hT2AyXa93i7VXTf7cZdYIp0rpXO5bD5z5/oAYLJdq26UGtlQX8e2C2X+u487cpspBOaZiGgiJKgMC5TJygAxjQAAIABJREFU3q77Gw5hnDPdkY2myq0NyOfObO4BTHY3z53atL45NLJyrNpslkS89yxRqNdamXs392B/d/PhM5sP42aRpdVMvlypFtI+Hx0INjq+kiy02lDIFdd3JwB72+tnt9fP2owi/XanXw7gta0+RK8A2t9YlUBGE/NItM7RC0OCe5DfK/KPljgi9j7yyBY0ZwRFxM4tISee44glIka0FDYh5x+xoT6Pnr+Dbvovi0avGBm1Tfw6QNwoc60a/vZv4xlQeJB4U2tpBW2LxAbD47+TQVEUu8buTJpsWCPCYuEVkck/ik3FbGweFEUBVdfIviKjDcoMiscXEowTGmaUuSPVoC1QdNeOjjiVFi5DmgYJxgxzSNrPqq51msFMp8FGO5YpJAEBUSIRmP9hq1EMSOYuEB3MHM0pXbiNaHZuMc2cdZ5ISPtaBLek2cIrIrMOZESjzNDXKPZyKeXKFMoBgOgDmXXRZL7ebp1cm77cbH+rUqz3xbc6Gi38szqYJArl/PRdjzDsdvoBgchXy0en9O9ttDrBgGAQyvynWNI5Luw4zES8lh0hl1i6vNF7+pF7jtnfHLq0euzkI53uRsnvTRpSiSazxeyql9Vkf3fz4bsz6SLFm+qCjI6zRJOFZrf31QdOHLG9rBUgsnL0rge/0u1vFBKLR+ZLGE6kzZpljrZcGzGiEoEE92DYMwQKKDYvVhsHP0FcdvSu3o5Rs6Eljoi9jzyyBc1eQRGxc4tyQsiMSyu+IqJGG3hGLQYtANCfR8t9RQcZibEtEhsMjz8kMw8qhOuZfdaI9LY8sblmgtxXZK8MHJDojtxrlAcSFA9VjWKuliriRj8UzIei9LvgYa7b3PrIE4kDHh75H7YaxYAE5TbIOZoPEn4jmp1bTDNnndxX5MhwICOajltChin6SMoVKmjSg1nbD+JUdcMhsUytWemkzmxPAPa3qtV2wbbDZjQcTjcCRWNxhw/zjPqddrvT6/WH42g8kUikMtlMEr0wOx71+6PpG6HG41F/ONQdmtyP59uPnBtllVQ6GTm3OwGYjAjeiidIEul0ErZ2AGDU6w0hQ/1ppkCEMv/dxx25zRQC80xENBESVAbhEsu3xqSvMnXwkC7UNwr1Ua/d7vaHozFEY4lEKu0wWDVJVXtq1cO125tO+418prg+gJXjj7Tq2Wi/1zfsLRyPx+Nhv9NuNlvbexOAyW6j3Ch1tC2OscLGuCAyOsg0hmqDwH+i1PZ6mWuy3FXLHq3Vh6rnOz2jiWy5mS2Ph71Op9sfjkZjiMXjiWQ6nXJ4Tx9JHy1U9Aqg/Y1VCWQ0MY9E6xy9MCS4B7mvyD9a4ojY+8gjW9CcERQRO7eEnHiOI5aIGNFS2IScf8SG+jxafq8oeCQoHrdW0LZIbKiYsYvC/uUAioiwWHhFRFTfjBgc+sjKifxeERckuiPXGgWY9s+MBQnGDHO1xMjTnYFOtlWjPS6+btP2kRcSgfkfthrFgATnVlRfwwLyn9AP6Yhm55a5/lMhIe5rAdySZguviEIwoum4FddHUq5QISk39oM4Vd3QSLJUydfvOD8AgEGr0a5lzG9Ca5dSt53fA4Dlu746tH95p79RLpZqm7sTy+9La8eLlWoll5w565RTmbM7RqvBw3e81/AyqsjxR0et/Mx63Cokph/nWT19sVd1+ZoQvYzHs+vVkVgwn8sCAIBYPBoBmBjxHByhzH9tgnEZcSQ2UwjMMxEy7ZFMkwdYYslMbnEfu+lV88X1wSSyerLZLKSiAPG4vfF8sVRu5lJ3XtgDmHTb3VHJ6w6Poyw2OqESjR/cWPTKoJ1IY6fTyDzrayS6z9GWuqShEoQE9yC/V+QfLXFE7H3kkS0O2SskInZuUU4ImSFpRSjawDNqcRFRn0fP30Gnqoi2J7HFxiZ+HSBu1LlWDH/7t/EMKDxIvKm1tIK2RWKD4fHfyaCqql3bE85iwxoRFguviEz+UWwKZmPzoKoqKLqe7yXS9xWZbFBmUDy+kGCc0DCjzh0pBm2Bort2dMSptHAZ0jRIMGaYQ9J+VnSt0wxmOg022rFMIQkIiBKJwPwPW41iQDJ3gehg5mhO6cJtRLNzi2nmrPNEQtrXIrglzRZeEZl1ICMaZYa+RrGXSylXplAOAEQf6KyLZgu5lemfe21fb0LrN3KZO87qN4oikcj8rUn7OxfOfjyV0l8nNR5ZbycFK+NOuztFFE+lgrscOxrOeYkJ2j8lUCjzn2JJ57iw4zAT8Vp2SMFkvFGtbU8AIqliKe2a4fFMLjUrHwfwvqkUizCcSJs1yxxtuTZiRCUCCe7Bc1+RxcbBTxCXHb2rt2PUbGiJI2LvI49sQbNXUETs3KKcEDLj0oqviKjRBp5Ri0ELAPTn0XJfUfBIUDxuraBtkdhQMWOX8D2zzxoRjscFoUMfyX1F9srAAYnuyLVGAab9M2NBgjHDXC0x8nRnoJNt1WiPi6/btH3khURg/oetRsl9Rf79kI5oua9IVB/JfUXEDPvg9vXXX7/22mvxOKVcjvLqq68a/kVSbmzCq+qGSdLZzNK58/sAMOx2+5BJEB01bJRKFwYAAJHVE7V6NZ9JxABg1Gu3GrVq/cLuOJnTPlOfyJYeiA5HMO61zl3YBQBYOnJX0fDOtVhqkd+dH7VLpeYeAEBkLV/MLK5hi/Ta7f70r3gicbBeQAfU+a9NMC4jjsRmCkHuKwq1DHu96e3ieNxro9Cw15/dvU0evLEgxSJ6ZdBOpLHTaWSe9TUS3edoS13SUAlCgnuQ+4r8oyWOiL2PPLLFIXuFRMTOLcoJITMkrQhFG3hGLS4i6vNo+b2ig4zE2BaJDYbHH5KZBxXC9S0Q1oj0tjyxuWaCqqowvStrYUN+r4hDH3nWKA8kKB6qGsVcLVXEjX4omA9F6XfBw1y3ufWRJxIHPDzyP2w1igEJym2QczQfJPxGNDu3mGbOOk8kpH0thFvCbOEVEWduuY0j+hpFw+27Dr3t7y/98OLFi7/4i7+IOZVyecrFixcB4KeufRsAOCQ9mLVNeFXdMEk0mUpGzm9PACb9bg8gQXJQv9XY2AcAiByptppFbWtOLJkpVDOFUrvRjRe0nQTxbKmcBYBRo1u/sDsBgHi6WK2meYfiJeNRr9OqVyv1zcEEAJaOlOoVIa+4I5F+o1zdnl4eX87kFs4Fs1Dmv/u4I7eZQmCeiYgmQoLKIAWTWGx6i2jSaW2M8nnn+0W9erm+CwAAq/l8ZhHQpIgUvQJof2NVAhlNzCPROkcvDAnuQX6vyD9a4ojY+8gjW9CcERQRO7eEnHiOI5aIGNFS2IScf8SG+jx6/g666b8sGr1iZNQ28esAcaPMtWr427+NZ0DhQeJNraUVtC0SGwyP/04GRVHsGrszabJhjQiLhVdEJv8oNhWzsXlQFAVUXSP7iow2KDMoHl9IME5omFHmjlSDtkDRXTs64lRauAxpGiQYM8whaT+rutZpBjOdBhvtWKaQBAREiURg/oetRjEgmbtAdDBzNKd04Tai2bnFNHPWeSIh7WsR3JJmC6+IzDqQEY0yQ1+jaLi9IXoIALrdLu5TymUqzzzzDAC889AhAKAeAIimmaTDJPH4/Dn+0Wg0cjXVpN/tTW9zJDMZ5B1u8UwhG/CXNvZbxZRBkslEPHro+sO33X1uczCByPKRux7a2Ki6vxgLYLeejTlKvNCie1vWqNcqZzPFC3sAALB0tFzOHrhX0NHmP8WSznFhx2Em4rXskIJJLJNLLwEA7K0Xs6VWDxsuo26zlMncu7kPAJG1k/XywbttKsUqDCfSZs0yR1uujRhRiUCCezDsGQIFFJsXq42DnyAuO3pXb8eo2dASR8TeRx7ZgmavoIjYuUU5IWTGpRVfEVGjDTyjFoMWAOjPo+W+ooOMxNgWiQ2Gxx+SmQcVwvXMPmtEelue2FwzQe4rslcGDkh0R+41ygMJioeqRjFXSxVxox8K5kNR+l3wMNdtbn3kicQBD4/8D1uNYkCCchvkHM0HCb8Rzc4tppmzTu4rcmQ4kBFNxy0hw2Tcfugn3vHNH3x/ustEypUj03tFH37HDQDgkPRg1jbhVXVDJdFYLAowAQAYjQhvfkSjs5sbw35/BCnaL9GLlP3Bzs7A4f+WkplMOk5yf2ayv+/4qSXP76qMO/VSacP4w2g47Hc7nd097StPqyfqjdJB/II9Zf67jztymykE5pmIaCJEKoN8hSmRJIr1aitz7+Ye7G+f+/jhxsqRTCadTCbiUYDxsNftdjrtncF0MCwdOdls1TJhrCVSiOS1114DgOhViqECaCfSWJVA5lnfI9HmwDJHLwwJ7kHuK/KPljgi9j7yyBY0ZwRFxM4tISee44glIka0FDYh5x+x+f/b+74YO4/rvjOk5V3LVrS2QnGt2uU6siJSUsxtbIvruC7XLw2DIhBt1RQDNBCTIPW6dtpNkVZMIAdbIEAot4FJwFHooojpl5pChIjKE/NQZAU70AooYiqAhRXSgMu4iJdlilKqGS0LWdOHu9/9/sz5vjlzzpk7s9TMw0C6PPfM73fO75yZb++dXfZzdPl7RemRoHiGVkHXotiwIuMOI//LAQxGGBctRqT+1sTQk6NuTMrfK1JBUjsa7FGAzeGR6SDBIiPulljwamdQB7s7oxmP37e5OfIhiaj/3HqUAAke21i5hgnon+iHWtHy2Ir7PwsJOdcRYktVixajDCqaF1vVHP3zuXv/8H+sP/vssysrKx/84AdxqmXcWuPy5ct//Md/DACf+9AcANDajTO0uu6OH/NHFvee+eZVgKvPLB2dnzl/cjG3vzEyte/QkfkmqK2t69c3NzfWX716E157+ZmnvvDMUycPPn763NkTA38raerOfXO9nylN+z5tuvnq82deHYB4/yMrZ8/mFzraYOp/vMEMVBzFZgRBvBMh256nM9zz7qm/vXGz/ApT4ti/dGFtdmVp+fSfXrkJr1156flvvvR812Zq3+HjyyunlndoJZSxPUbfxthz+22NzjB+kMYep5F9NqASfXt0py9VSNAHewUkuIfy94rC0ZIZyXPkUUuPeqMwkscWjQkxMpRVoqJNrqjJMWI/R5d7RTsZSXMtig2GJwzJtgcLeX1nX8qoXsuLbVAJ5V6R2xkUkNSOhnuUBwmKh9WjxN3SIm7qt0L7rWj4B/CI+7ZajrxIevBo6D+3HiVAgsY25R6tg0SvouWxxWax6sq9ot4IJ6loXmyJEabFdmHP3oPvvevl//O/jx079ud//ue7du3CXJdx64w333zz0UcfvXHjxsf3/PjH9twFAD2ih/bsDK2um9XYqm8TzcwQfxfa9JGV049c/IXnrwJcfeE3Pz139vDxpaWlE0dpl3UmMaYXVy6cO4L8w/X11epvFr328jd/aXHj+trF5f09sOdOnF8/rf9LsfYeenx5eenE8YUd/MNxpv6H645uM4Ig3olIG2GrM+x519Tf3rh56dKl8lkRbUzPHT118ejJ9dWLFy+uXlrf2Lh+fWsLYHp6ZmZu//6FhSOLRxb3l9tEt8AYfVY0e/ttjQ4wfpDGugSyzwZUIg6iu0dPDAnuodwrCkdLZiTPkUctqGYiMZLHlhgTbx1JGAnRMmwyjz9iw36OLn+vKD0Sf2g7q6BrUWwwPOFJBmOMO2OfTLZspIwwLlqMWv5RbBazcTwYY8DWc/l7RTpIiI5QG58bVo8SUxq/bOu5DjO0w9mwGb9XRCkCISaSiPrPrUcJkFQukDnNHq0kF7WKlscWm8Wq8yKh5jpGbKlq0WLUnpNUNBoZfo9ixvapjy4AwNra2srKCu65jFtofPnLXx79ysHfO/RxYQEgM2eTzmlsbm5u/9fMzCz5Z7Zzx8+vfuOx++8EAICbV1745m/+wifePzt/dPnsxXXiHz1KM2b2L544ffHS6hOH7gQAeO2Fk0vnNuIsdedj37r8g3q8+LuHpkb/sDW9cHxHf1AEbP0zjnS9BzuFnSj82PHw3jsBoPwK08Axs3/x+PKps+cuXFxdXVtbW1tbXb144dzpU0vHywdFt8oYfVZ06P13SB6k27Nkj+78bAQaqGIgwT2Uv1cUjpbMSJ4jj1pQ9UZiJI8tGhNiZAZWCWLERptcUZNBCwD85+jqs6Lxp5rN2S3pjo0zQh0gbsYfhpnBD898Nl5C+SDxh7azCroWxQbDE57k0SeT3dkVXMdGygjjosWo5R/FZjAbx4O1Fkw9V3eJ6ntFLRs0MiieICRYTDiRsZUj05g7UGrXvY6UWotKSXOQYJERUxq/3PhaSR1maIez/dUTTHvS4MbKESe4DY4i/efWowRIKhfInGaPVpKLWkXLY4vNYtV5kVBzHSO2VLVoMWrPSSoajQy/RzFj+/CevV/+yEcB4Hd+53d+7dd+7Y033sD9l7HDxxtvvPHFL37xqaeeAoBTH//pg3e9T1gAyMzZpDMaW+tr66M/GDI1Nz8X8Mbp/SfOX7r0Z3/w+Z/d/sQI4LWXnz/zhZ87MDd/4uxa1h8YwczCqbNLBwEA4ObauQsbcVaZnpmbrcfC8uml+wEA4LUXVk6e3/S8OfPB1D/jSNd7sFPYicKPHcfuuwcAnn322e9///v+KJVRxttjXL58+bnnngOAYz95l+RBuj1L9ujOz0aggSoGEtyD915Rx6bHT4ofO/q7dy9rGVoyI3mOPGpB1RuJkTy2aEyIkRlYJYgRG21yRU0GLQDwn6PLvaL0SPyh7ayCrkWxwfCEJ5n/yaSIEcZFi1HLP4rNYjaOh3KvyHWk1FpUSpqDBIuMmNL45cYXjOowQzuc/i8hSYMbK0ec4DY4ivSfW48SIKlcIHOaPVpJLmoVLY8tNotVV+4VNRy15yQVjUaG36P4sf31Bw/+xoMHrbVf+9rXDhw48OKLL+JLlLFjx3e+850DBw48/fTT1trfmv/IFx44AADCAkBmziad0Vi7uPba6L9mF4I+KwIAmJ5bXDp7cX3z8ovfeOKxQ/u2r8289vI3v7C4uHQx74+L5o8s7AMAgJvra5e2PMYqY3ph5dRjewEA4OozJ1dWJ7JorMHUP+NI13uwS3Kv6Gfe/975H/+x119//dixY2+99RYhUmWUcYuP8W95fXj2PR8v94oaHsq9onC0ZEbyHHnUgqo3EiN5bNGYECMzsEoQIzba5IqaDFoA4D9Hl3tF6ZH4Q9tZBV2LYoPhCU8y/5NJESOMixajln8Um8FsHA/lXpHrSKm1qJQ0BwkWGTGl8cuNLxjVYYZ2OP1fQpIGN1aOOMFtcBTpP7ceJUBSuUDmNHu0klzUKloeW2wWq67cK2o4as9JKhqNDL9HiWL7Wx/56B9+cvGdu3ZfuXLlk5/85EMPPfSlL33p2WefvXbtGr5cGdmPa9eu/dEf/dEXv/jFBx988FOf+tSVK1emd+8+d/gfP3Hwp1QKAJk5m3Q2Y+viuQtXRv+5d/EI9y/zTM8tnDh1fm1j48VvfP7Q6MOQmy9//cTShaw/LZqZ2f7lV1tbWxP62Gbm6KmVnx3dwrpybvn0pcmsGmUw9c840vUe7JLcKwKA3/vUASi/wrSMMqrx5JNPjn4r45nFD4EVPUi3Z8ke3fnZCDRQxUCCeyj3isLRkhnJc+RRC6reSIzksUVjQozMwCpBjNhokytqMmgBgP8cXe4VpUfiD21nFXQtig2GJzzJ/E8mRYwwLlqMWv5RbBazcTyUe0WuI6XWolLSHCRYZMSUxi83vmBUhxna4WzYjN8rohSBEBNJRP3n1qMESCoXyJxmj1aSi1pFy2OLzWLVlXtFDUftOUlFo5Hh9yhpbD+z7yde+mef/cV7f3K3Md/73vd+//d//3Of+9zdd99tytiZ4+677z527NjTTz/9yiuv3LZr14n7Pvzfj/78Z+b2aRWAiuryGetnT52/MvrPfcdPLE4L3c0unDi7uvrVw6NPQ65eOJvzh0Vb4z/UND0zI2VOHnNLp04enAIAuPnyqeWzG5NaV30w9c840vUe7Ix8J2IdOxZm3/sfDt0H5VeYlvG2H6Pf8vqVr3wFAP7jP9k3f/ftYEQP0u1Zskd3fjYCDVQxkOAeyr2icLRkRvIcedSCqjcSI3ls0ZgQIzOwShAjNtrkipoMWgDgP0eXe0XpkfhD21kFXYtig+EJTzL/k0kRI4yLFqOWfxSbwWwcD+VeketIqbWolDQHCRYZMaXxy40vGNVhhnY4Gzbj94ooRSDERBJR/7n1KAGSygUyp9mjleSiVtHy2GKzWHVeJOVe0UQrGo0Mv0cpxHbfe+448/Anv/fIY//pY59Y3HvP3HvumN69G1+ujOzH9O7dH7rjPYvvnz3ziUN/deyzZ37m0Aff8+7qHxUKAJk5m3QWY+vSqaWVF7b/VtHhk8vij4oAAGB6/9LJo9t3izbWN1r/NF2tkMFvX9tavbB6FQAApvbP75/YZ0UA88unT+wDgB3+Z4uY+mcc6XoPdqnuFQHAb/z0vU989F5bfoVpGW/j0fwtr7+98IEvzc/KH6Tbs2SP7vxsBBqoYiDBPZR7ReFoyYzkOfKoBVVvJEby2KIxIUZmYJUgRmy0yRU1GbQAwH+Ofse2g9an8ePZ/ey3PTsDfVPLARA+PG5GFXo+PPPZ7CAkKJ6hVdC1KDasyLjDGGOtO7ufTLZmKSOMixYjHM8Awp4cdWPi3iuyjRmNDBHPABJaTMiqa/eEDpS+/qCLpHY02KMAm8Mj00GCRUbcLbHg1c6gDnZ3RjMev29zc+RDElH/ufUoARI8trFyDRPQP9EPtaLlsRX3fxYScq4jxJaqFi1GGVQ0L7bRcjRmsWf6Xb/ykwd++b4DxiAHdydY3kGRMqHXoYwGkfR0lQEuRLUEI4nGyJudvhx1JB6SnRqKRmfIYFxfO338+G++MPpTRVOHVk4vzQW9f3Pz+uzszLDN1HT7vs7o/s5NALi+ubkFMMEPaLpj8+Ly8rkrAAAwtXD86Nwk155eXDn1yIVfeP7q6M8WLR09q/Mp3WQHU//D1Ue3GUEQ70TItkftDL/98H0P3XXHr/y3vxz9CtMHHnhgcXFxcXHx8OHDe/bsEce3jDJyHNeuXVutxiuvvAIA0+/Y9V/+6b2Pfvh9WDeA7S4BgHWGgf4A4j26B4mNhAT3UH9NuZ6b79m2ge17RX1+FLqlxqm7by26jWb/V8iRRy096o3CSB5bNCbEyFBWiYo2uaImx4j9HF3uFaVH4g9tZxV0LYoNhic8yZDfd/aljFr+UWwGs3E8lHtFriOl1qJS0hwkWGTElMYvm3quwwztcDZsxu8VUYpAiIkkov5z61ECJJULZE6zRyvJRa2i5bHFZrHqyr2ihqP2nKSi0cjwe5RybPmMgoPr9rooanEZoTaoIyGS7PSfxZEu/di8dPH00sL+xV//0yujF/Y9du78yfkQF9dXl4/MzR85eXEDuSC0eeHsxdGFndmFhbnWP83N758CAIDXVs9fZP56OunVpK2NtXPLiwtHv/7qTQCAqfuXTgV+TCYfs8dPnTw0isSO/bNFTP2j1QftiqPYjNyr92133aHO8Nl7Z797/JOPH/hA+RWmZbxNRuu3vO7e9csP7vnLXzz46Iffp/UgzavEZlNor+UgMZGQ4B7KvaJwtGRGmqduVC2oeiMxkscWjQkxMgOrBDFio02uqMmgBQD+U2e5V5QeCYpnaBV0LYoNKzLuMNl9Z1/KCMczgLAnR92YlHtFKkhqR4M9CrA5PDIdJFhkxN0SC17tDOpgd2c04/H7NjdHPiQR9Z9bjyr3isL9UCu63CuKlSM9RhlUNC+20XKkxqgbPoqUO70uklq6jN6++g/OUSxGkxs3L50+cfzCNADA1tbW1vXNjfVLr169WRvcefDxsxfOHZ8LcXr94vKJMy/fhJef+rn95w+fWF46vriwf252Bq5vrK9dOHVy5fmrAABTh5a6v9Zu/+LifnjpZQC4+szS0dnrJ48vzE1vblxa29y/dGLBc0tpNGbnZgGuAsBrq+fOrc4uzs/Poe/bWju9tHSh+cLW9eubG+uXmvyn9j1y9sKphf5bPRvnTyyseXDNnjh3YWk/BXtj7F86vXT2E2de3f6zRcdXJ/55lXQw9T9cfXSbEYSU94pGY+7H3v304kMrh+57/q83/+Ty/7r8+t//4MbNrR+9JYhsGWXkO6Z3m3ve8865H5t+9L73Hf3wXe+bfgfAqCzQhxnY7hIAWGcY6A8g3qN7kKAP9gpIcA/lXlE4WjIjzVM3qpYe9UZhJI8tGhNiZCirREWbXFGTY8R+6qw+K7IWwHRnXMSN2RnWtma0J6A/nGq4qOYO30CbDhIinh2GpLkWxQbDE4Zk24OF7Vsy9YwJDpqzGiMEj5RRvZYX26ASrLUw+lS2Ew0Y3zECT2RQPEFIsJhwImMBf1tz4dp1b2dQQFI7Gu5RHiQoHlaPEndLi7ip3wrtt6LhH8Aj7ttqOfIi6cGjof/cepQACRrblHu0DhK9ipbHFpvFqvMioeY6SmyJatFipBxbtTri9yjl2PIZ+QPqJmCQS0RGb1f9B+coFqMJjqsvPf/MS/g/3XnwkZOnTi8fmQv9BWgzi8srn19f/vpLr8HNKy98/ddf+LprM7XvsbPnTzqfoMwvrzx2/jPPXAGAqy+c+aUXzmy/vvfx+eMLRyhA5hYW9sHLVwDgyjO/9OkLj3xr88Jx7MOcm6/+6ddfHXK09+DjJ8+eWl6YHTK6efXVl656EN2/wLkhNb2wcuqx85955ur2ny06ev74IJLsBlP/w3VHtxlBmEDf9nYGC2Duftc7f/Whfb/60D/ctu86cOOD77A0JMMDeVdPZPqeIyjr0lG1LXG19J06GEgG8AQi8eMhrEKycXOEFNbEkASqDt0ToR3PnmhTsynZoyeGBPfgvVfU+RiJVmucbqlx6iasooCWzEieI49aerp3FEby2BJjQtrRuIyEaBk2mccfsWE/dVbam4QmAAAgAElEQVS/g270f50Z3TGaszNCHSBuTDXbxn+H23gJ5YPEH9rOKuhaFBsMT3iSwRjjztgnky0bKSOMixajln8Um8VsHA/GGLD1jNwratqgkUHxBCHBYsKJjKkc2cbcgVK77nWk1FpUSpqDBIuMmNL4ZVvPdZihHc6Gzfi9IkoRCDGRRNR/bj1KgKRygcxp9mgluahVtDy22CxWnRcJNdcxYktVixaj9pykotHI8HuUcmz5jIKD6/a6KGpxGaE2qCMhkuz0n8WRLuWYmrpz7/0HDz/y+L/56rf+7PLmpQsnwz8oAgCYnj9xdm3ju9/63ccO7bvTXWXf4c//weql8yfmkLfOHj138RuPH+y+6/r6+gZx7cWVU4/tm9r+v5vra+tk2FNTd+67/+DhRx5/4qvfevEHG5fOeT4oijxmjp5a+dlRIK4+c3JllftL9RINpv7R6oN2xVFsRu7V+7a7LvWxM+RBYqAzSHuUxo7GyFEvHlm31FSLxqmDwtpvo7FH6yBJpP+hB2kBEqhWcWqticRGQoJ7aNwZAgPG8dK16fFzy+hftaLlOfKopad7R2GUWUUzGbHRJlfUZNACAH+PNn91/xMAgKWz77Pf/uy4wPs+Qna6BPZmwlraeHYYkr4a8dpIkSBqwQQXh5FvMGNLxOZB0lqsjkbnCnAoHg4SzA0v112jzvIYwkhIPD2KExlWj9LrlqiDjnc0/AN4xH2bGBlsBCIh4uFVYl49So4knz1aC4lWRWtkGQmNVHVeJGp1xIktx4+AkXJs1eqI36Oi5EitM7h79NCIWNFF/wlzdOe5bwGAtRT/O3BcX19bW9/Y3Ny8DtMzs/vn5xfm/R9AbW2sra6tb2xuwczM3Nz++YX52ZAPrbY2Vi+srm9eh5n9i0eP7Cf98royxMNau2vXLgD44ed/nlvRlLqj1+YE+ra3M6A9ofNCT3w4eLxDvqMxckTGk41aWKeO8FVINow9OiKS+Pqvos3B4x2YfTZIHC/uvSIaIw9Ogp80+o9Z0fpq6eneURjlXdEZMdrRaHvxUPbo6rMiPKfujuGBwvrxYsfFQLMIsNlBSPx4/G2UZkPD4x0973U/mexXC4NRMB7VH135MOA7YGcz7q2mcDxeQrFU136JwEgByVBMBrrTpHoUo1viamm7pOc9ft/mRsaLZML6z6dHhSAZikkGezQXSbSK1oqtXHVeJEQ8EWLLVIsWoxQVzYxtrBypMeoOipQJvS4Co7ev/oNzpMPoFv+sqIy3zWh9ViTpDAC0ivPYROjbA+t6OwN6mOvsrdF7lOqOxsgRGhnZPsJE4t1huacOCutBG7U9WoyEZh9P/148kj26B8k2HnUkPfUIMPj3ivr8U1YPtMlG/2qMSPbEmAyrZZKM8qjoSaPNR1HR0bL36Op30I2eJTqz67Vj4wIMdIC4sW2m0BNPn42XUD5I/KHtrIKuRbHB8IQnefvJszO7n0x2bKSMMC5ajFr+UWwGs3E8WGvB1HP1N4rqv1fUskEjg+IJQoLFhBMZWzkyjbkDpXbd60iptaiUNAcJFhkxpfHLpp7rMEM7nA2b8XtFlCIQYiKJqP/cepQASeUCmdPs0UpyUatoeWyxWaw6LxJqrmPElqoWLUbtOUlFo5Hh9yjl2PIZBQfX7XVR1OIyQm1QR0Ik2ek/iyNdGWXcEoOpf7T6oF1xFJuRe/W+7a5LfewMeZAY6AzSHqWxozFy1ItH1i011aJx6qCw9tto7NE6SBLpf+hBWoAEqlWcWmt9JBAJCe7B+/eKOjY9fm4Z/atWtDxHHrX0dO8ojDKraCYjNtrkipoMWgDg79Hl7xWlR+IPbWcVdC2KDYYnPMng/jZD7Apb10bKCOOixajlH8VmMRvHgzEGbD2Xv1ekg4ToCLXxuWH1KDGl8cu2nuswQzucDZvxe0WUIhBiIomo/9x6lABJ5QKZ0+zRSnJRq2h5bLFZrDovEmquY8SWqhYtRu05SUWjkeH3KOXY8hkFB9ftdVHU4jJCbVBHQiTZ6T+LI10ZZdwSg6l/tPqgXXEUm5F79b7trkt97Ax5kBjoDNIepbGjMXLUi0fWLTXVonHqoLD222js0TpIEul/6EFagASqVZxaayKxkZDgHsrfKwpHS2Ykz5FHLT3dOwqjzCqayYiNNrmiJoMWAPh7dLlXlB6JP7SdVdC1KDYYnvAkg8K3hxiMMC5ajFr+UWwGs3E8WGvB1HO5V6SDhOgItfG5YfUoMaXxy6ae6zBDO5wNm/F7RZQiEGIiiaj/3HqUAEnlApnT7NFKclGraHlssVmsOi8Saq5jxJaqFi1G7TlJRaOR4fco5djyGQUH1+11UdTiMkJtUEdCJNnpP4sjXRll3BKDqX+0+qBdcRSbkXv1vu2uS33sDHmQGOgM0h6lsaMxctSLR9YtNdWiceqgsPbbaOzROkgS6X/oQVqABKpVnFprIjGRkOAeyr2icLRkRvIcedTS072jMMqsopmM2GiTK2oyaAGAv0eXe0XpkfhD21kFXYtig+EJTzIofHuIwQjjosWo5R/FZjEbx4MxBmw9l3tFOkiIjlAbnxtWjxJTGr9s67kOM7TD2bAZv1dEKQIhJpKI+s+tRwmQVC6QOc0erSQXtYqWxxabxarzIqHmOkZsqWrRYtSek1Q0Ghl+j1KOLZ9RcHDdXhdFLS4j1AZ1JESSnf6zONKVUcYtMZj6R6sP2hVHsRm5V+/b7rrUx86QB4mBziDtURo7GiNHvXhk3VJTLRqnDgprv43GHq2DJJH+hx6kBUigWsWptSYSGwkJ7qHcKwpHS2Ykz5FHLT3dOwqjzCqayYiNNrmiJoMWAPh7dLlXlB6JP7SdVdC1KDYYnvAkg8K3hxiMMC5ajFr+UWwGs3E8WGvB1HO5V6SDhOgItfG5YfUoMaXxy6ae6zBDO5wNm/F7RZQiEGIiiaj/3HqUAEnlApnT7NFKclGraHlssVmsOi8Saq5jxJaqFi1G7TlJRaOR4fco5djyGQUH1+11UdTiMkJtUEdCJNnpP4sjXRll3BKDqX+0+qBdcRSbkXv1vu2uS33sDHmQGOgM0h6lsaMxctSLR9YtNdWiceqgsPbbaOzROkgS6X/oQVqABKpVnFprIjGRkOAeyr2icLRkRvIcedTS072jMMqsopmM2GiTK2oyaAGAv0eXe0XpkfhD21kFXYtig+EJTzIofHuIwQjjosWo5R/FZjEbx4MxBmw9l3tFOkiIjlAbnxtWjxJTGr9s67kOM7TD2bAZv1dEKQIhJpKI+s+tRwmQVC6QOc0erSQXtYqWxxabxarzIqHmOkZsqWrRYtSek1Q0Ghl+j1KOLZ9RcHDdXhdFLS4j1AZ1JESSnf6THulu3LjRg6qMMnbG+OEPfwgA07t3AbArGq0+aFccxWbkXr1vu+tSHztDHiT6e6a4R2nsaIwc9eKR9X9NtWicOiis/TYae7QOkkT6H3qQFiCBahWn1ppIbCQkuIdyrygcLZmRPEcetfR07yiMMqtoJiM22uSKmgxaAODv0eVeUXok/tB2VkHXothgeMKTDArfHmIwwrhoMWr5R7EZzMbxYK0FU8/lXpEOEqIj1MbnhtWjxJTGL5t6rsMM7XA2bMbvFVGKQIiJJKL+c+tRAiSVC2ROs0cryUWtouWxxWax6rxIqLmOEVuqWrQYteckFY1Ght+jlGPLZxQcXLfXRVGLywi1QR0JkWSn/0RHuntufxcAfPe73+1BVUYZO2P8xV/8BQDsedcUALui0eqDdsVRbEbu1fu2uy71sTPkQaK/Z4p7lMaOxshRLx5Z/9dUi8apg8Lab6OxR+sgSaT/oQdpARKoVnFqrYnEREKCeyj3isLRkhnJc+RRS0/3jsIos4pmMmKjTa6oyaAFAP4eXe4VpUfiD21nFXQtig2GJzzJoPDtIQYjjIsWo5Z/FJvFbBwPxhiw9VzuFekgITpCbXxuWD1KTGn8sq3nOszQDmfDZvxeEaUIhJhIIuo/tx4lQFK5QOY0e7SSXNQqWh5bbBarzouEmusYsaWqRYtRe05S0Whk+D1KObZ8RsHBdXtdFLW4jFAb1JEQSXb6T3Sk2zM9DQCXLl3qQVVGGTtjjD4r2nv7FAC7otHqg3bFUWxG7tX7trsu9bEz5EGiv2eKe5TGjsbIUS8eWf/XVIvGqYPC2m+jsUfrIEmk/6EHaQESqFZxaq2JxEZCgnso94rC0ZIZyXPkUUtP947CKLOKZjJio02uqMmgBQD+Hl3uFaVH4g9tZxV0LYoNhic8yaDw7SEGI4yLFqOWfxSbwWwcD9ZaMPVc7hXpICE6Qm18blg9Skxp/LKp5zrM0A5nw2b8XhGlCISYSCLqP7ceJUBSuUDmNHu0klzUKloeW2wWq86LhJrrGLGlqkWLUXtOUtFoZPg9Sjm2fEbBwXV7XRS1uIxQG9SREEl2+k90pPvYnrug3CsqY+eP0WdFD+99LwC7otHqg3bFUWxG7tX7trsu9bEz5EGiv2eKe5TGjsbIUS8eWf/XVIvGqYPC2m+jsUfrIEmk/6EHaQESqFZxaq2JxERCgnso94rC0ZIZyXPkUUtP947CKLOKZjJio02uqMmgBQD+Hl3uFaVH4g9tZxV0LYoNhic8yaDw7SEGI4yLFqOWfxSbxWwcD8YYsPVc7hXpICE6Qm18blg9Skxp/LKt5zrM0A5nw2b8XhGlCISYSCLqP7ceJUBSuUDmNHu0klzUKloeW2wWq86LhJrrGLGlqkWLUXtOUtFoZPg9Sjm2fEbBwXV7XRS1uIxQG9SREEl2+k90pPvcT8wBwLPPPvv973+/B1gZZeQ+Ll++/NxzzwHAsfv+AQC7otHqg3bFUWxG7tX7trsu9bEz5EGiv2eKe5TGjsbIUS8eWf/XVIvGqYPC2m+jsUfrIEmk/6EHaQESqFZxaq2JxEZCgnso94rC0ZIZyXPkUUtP947CKLOKZjJio02uqMmgBQD+Hl3uFaVH4g9tZxV0LYoNhic8yaDw7SEGI4yLFqOWfxSbwWwcD9ZaMPVc7hXpICE6Qm18blg9Skxp/LKp5zrM0A5nw2b8XhGlCISYSCLqP7ceJUBSuUDmNHu0klzUKloeW2wWq86LhJrrGLGlqkWLUXtOUtFoZPg9Sjm2fEbBwXV7XRS1uIxQG9SREEl2+k90pPvE3j3zd7339ddfP3bs2FtvvdWDrYwy8h1vvvnmo48+euPGjYfvfu/H9swAsCsarT5oVxzFZuRevW+761IfO0MeJPp7prhHaexojBz14pH1f021aJw6KKz9Nhp7tA6SRPofepAWIIFqFafWmkhMJCS4h3KvKBwtmZE8Rx619HTvKIwyq2gmIzba5IqaDFoA4O/R5V5ReiT+0HZWQdei2GB4wpMMCt8eYjDCuGgxavlHsVnMxvFgjAFbz+VekQ4SoiPUxueG1aPElMYv23quwwztcDZsxu8VUYpAiIkkov5z61ECJJULZE6zRyvJRa2i5bHFZrHqvEiouY4RW6patBi15yQVjUaG36OUY8tnFBxct9dFUYvLCLVBHQmRZKf/dEe6rxz6KACsra2trKz0YCujjHzHk08+Ofolil/91E/JKhqtPmhXHMVm5F69b7vrUh87Qx4k+numuEdp7GiMHPXiyUctGqcOCmu/jcYerYMkkf6HHqQFSKBaxam1JhIbCQnuodwrCkdLZiTPkUctPd07CqPMKprJiI02uaImgxYA+Hv07n/94/8YALB0Yp/9DmfHBY71BLRLYG8mrKWNZ4ch6asRr40UCaIWTHBxGPkGM7ZEbB4krcXqaIw/LhqfGYLwcJBgbni57hp1lscQRkLi6VGcyLB6lF63RB10vKPhH8Aj7tvEyGAjEAkRD68S8+pRciT57NFaSLQqWiPLSGikqvMiUasjTmw5fgSMlGOrVkf8HhUlR2qdwd2jh0bEii76T5ujD7z73VO7dr3wg6vf/va3/+7v/u7Tn/70bbfdRliujDISjzfeeGN5efnMmTMA8JWfefDnP/R+WUVT6o5emxPo297OgPaEzgs98eHg8Q75jsbIERlPNmphnTrCVyHZMPboiEji67+KNgePd2D22SBxvDR+5w1YHMgtpf+YFa2vlp7uHYVR3hWdEaMdjbYXD2WPLveK0iPxh7azCroWxQbDE55kUPj2EIMRxkWLUcs/is1iNo4HYwzYei73inSQEB2hNj43rB4lpjR+2dZzHWZoh7NhM36viFIEQkwkEfWfW48SIKlcIHOaPVpJLmoVLY8tNotVV+4VNRy15yQVjUaG36OUY8tnFBxct9dFUYvLCLVBHQmRZKf/pEe6f/uRB//dRx601n7ta187cODAiy++2AOyjDJyGd/5zncOHDjw9NNPW2uf/Nj9/+ojPwEgrGi0+qBdcRSbkXv1vu2uS33sDHmQ6O+Z4h6lsaMxctSLJx+1aJw6KKz9Nhp7tA6SRPofepAWIIFqFafWmkhsJCS4h3KvKBwtmZE8Rx619HTvKIwyq2gmIzba5IqaDFoA4O/R5q/ufwIAsHQSPvv1DbQn+L6z0GDtXUsbzw5D0lcjXhspEkQtmODiMPINZmyJ2DxIWovV0Sj3ilSQeHoUJzKsHqXXLVEHHe9o+AfwiPs2MTLYCERCxMOrxLx6lBxJPnu0FhKtitbIMhIaqeq8SNTqiBNbjh8BI+XYqtURv0dFyZFaZ3D36KERsaKL/jPJ0XMbf/Mvv/3i//vRW8aYBx54YHFxcXFx8fDhw3v27CGsXkYZ0ce1a9dWq/HKK68AwPQ7dv/nxfnP3nuPRkVT6o5emxPo297OgMak80JPfDh4vEO+ozFyRMaTjVpYp47wVUg2jD06IpL4+q+izcHjHZh9NkgcL+VekR4jfbX0dO8ojPKu6IwY7Wi0vXgoe3T1WRGeU3fH8EBh/Xix42KgWQTY7CAkfjz+NkqzoeHxjp73up9M9quFwSgYj+qPrnwY8B2wsxn3VlM4Hi+hWKprv0RgpIBkKCYD3WlSPYrRLXG1tF3S8x6/b3Mj40UyYf3n06NCkAzFJIM9moskWkVrxVauOi8SIp4IsWWqRYtRiopmxjZWjtQYdQdFyoReF4HR21f/wTmKwmjj//7w9/7ye//1rzfefOstAsgyykg2btu161/c/4F//4/u++AdtwPonToAaBXnsYnQtwfW9XYG9DDX2Vuj9yjVHY2RIzQyWahF7dRBYT1oo7ZHi5HQ7OPp34tHskf3INnGo46kpx4B3A+EWkBw/5TVA22y0b8aI5I9MSbDapkkozwqetJo81FUdLTsPbrcK9rJSNC1KDZSJIhayr2i7cg0Fiv3ipSReHoUJzKsHqXXLVEHHe9o+AfwiPs2MTLYCERCxMOrxLx6lBxJPnu0FhKtitbIMhIaqerKvaJ4sVWrI36PipKjcq8oXmyT6j+jHF174+afXPmbP7nyPzd+eGPz79/Y+tGPCKuXUUb0Mb171z3vnt53x+2fvfeeRz70/vdNv7Pxj3L9U+qOXpsT6NvezoDGpPNCT3w4eLxDvqMxckTGk41aWKeO8FVINow9OiKS+Pqvos3B4x2YfTZIHC/lXpEeI3219HTvKIzyruiMGO1otL14yr2inYHEj8ffRmk2NDze0fPefL6zH+FHVz4M+A5Y7hWpIBmKyUB3mlSPYnRLXC1tl/S8x+/b3Mh4kUxY//n0qHKvKF5Fl3tF8dRS7hWp5UiNUXdQpEzodREYvX31H5yj7BnJc6QZGXQz6/SW+PuIJiNZj9JSC4DzI8XW99MHOUoYeWMiUR0lCx6bzNSioH85EtUdjZEjNDJZqEWhoumsB23U9mgxEpp9PP178Uj6dg+SbTzqSOjduw0E909ZPdAmG/2rMSLZE2MyrJZJMsqjoieNNh9FRUfL3qN3VZ4tMrteOzYuwEAHiBvbZgo98fTZeAnlg8Qf2s4q6FoUGwxPeJLBWuvO7ieTHRspI4yLFqOWfxSbwWwcD9ZaMPVswGzPsP3fLRs0MiieICRYTDiRsZUj05g7UGrXvY6UWotKSXOQYJERUxq/bOq5DjO0w9mwGb9XRCkCISaSiPrPrUcJkFQukDnNHq0kF7WKlscWm8Wq8yKh5jpGbKlq0WLUnpNUNBoZfo9Sji2fUXBw3V4XRS0uI9QGdSREkp3+szjSqTKS50jv2B1ykFKLTBxGUCOU9CgFtVgAsGABTGOG9g8ccRt/h0lT0WhGOmgpNiP3OahFU/9y3WrsaIwc9eLJRy0KFU1i7bfR2KN1kCTS/9CDtAAJVKs4tdZEYiIhwT309220e/f5uWX0r1rR8hx51NLTvaMwyqyimYzYaJMrajJoAYC/R1efFRmDzG5Jd2ycEeoAcWOq2Tb+O9zGSygfJP7QdlZB16LYYHjCkwzGGHd2BdexkTLCuGgxavlHsVnMxvFgjAFbz6O7RK17RU0bNDIoniAkWEw4kTGVI9uYO1Bq172OlFqLSklzkGCREVMav2zruQ4ztMPZsBm/V0QpAiEmkoj6z61HCZBULpA5zR6tJBe1ipbHFpvFqvMioeY6RmypatFi1J6TVDQaGX6PUo4tn1FwcN1eF0UtLiPUBnUkRJKd/rM40qkykudI79gdcpBSi0wcRlAjlPQoBbUYADBgAGxjBgOmwQS38XeYNBWNZqSDlmIzcp+DWjT1L9etxo7GyFEvnnzUolDRJNZ+G409WgdJIv0PPUgLkEC1ilNrTSQ2EhLcQ3/fRrt3n59bRv+qFS3PkUctPd07CqPMKprJiI02uaImgxYA+Ht0uVeUHok/tJ1V0LUoNhie8CSDwreHGIwwLlqMWv5RbAazcTxYa8HUc7lXpIOE6Ai18blh9SgxpfHLpp7rMEM7nA2b8XtFlCIQYiKJqP/cepQASeUCmdPs0UpyUatoeWyxWaw6LxJqrmPElqoWLUbtOUlFo5Hh9yjl2PIZBQfX7XVR1OIyQm1QR0Ik2ek/iyOdKiN5jvSO3SEHKbXIxGEENUJJj1JQiwWAcq+oL1N5qEVT/3LdauxojBz14slHLQoVTWLtt9HYo3WQJNL/0IO0AAlUqzi11kRiIiHBPZR7ReFoyYzkOfKopad7R2GUWUUzGbHRJlfUZNACAH+PLveK0iPxh7azCroWxQbDE55kUPj2EIMRxkWLUcs/is1iNo4HYwzYei73inSQEB2hNj43rB4lpjR+2dZzHWZoh7NhM36viFIEQkwkEfWfW48SIKlcIHOaPVpJLmoVLY8tNotV50VCzXWM2FLVosWoPSepaDQy/B6lHFs+o+Dgur0uilpcRqgN6kiIJDv9Z3GkU2Ukz5HesTvkIKUWmTiMoEYo6VEKajEAUO4V9WUqD7Vo6l+uW40djZGjXjz5qEWhokms/TYae7QOkkT6H3qQFiCBahWn1ppIbCQkuIdyrygcLZmRPEcetfR07yiMMqtoJiM22uSKmgxaAODv0eVeUXok/tB2VkHXothgeMKTDArfHmIwwrhoMWr5R7EZzMbxYK0FU8/lXpEOEqIj1MbnhtWjxJTGL5t6rsMM7XA2bMbvFVGKQIiJJKL+c+tRAiSVC2ROs0cryUWtouWxxWax6rxIqLmOEVuqWrQYteckFY1Ght+jlGPLZxQcXLfXRVGLywi1QR0JkWSn/yyOdKqM5DnSO3aHHKTUIhOHEdQIJT1KQS0WAMq9or5M5aEWTf3LdauxozFy1IsnH7UoVDSJtd9GY4/WQZJI/0MP0gIkUK3i1FoTiYmEBPdQ7hWFoyUzkufIo5ae7h2FUWYVzWTERptcUZNBCwD8PbrcK0qPxB/aziroWhQbDE94kkHh20MMRhgXLUYt/yg2i9k4HowxYOu53CvSQUJ0hNr43LB6lJjS+GVbz3WYoR3Ohs34vSJKEQgxkUTUf249SoCkcoHMafZoJbmoVbQ8ttgsVp0XCTXXMWJLVYsWo/acpKLRyPB7lHJs+YyCg+v2uihqcRmhNqgjIZLs9J/FkU6VkTxHesfukIOUWmTiMIIaoaRHKajFAEC5V9SXqTzUoql/uW41djRGjnrx5KMWhYomsfbbaOzROkgS6X/oQVqABKpVnFprIrGRkOAeyr2icLRkRvIcedTS072jMMqsopmM2GiTK2oyaAGAv0eXe0XpkfhD21kFXYtig+EJTzIofHuIwQjjosWo5R/FZjAbx4O1Fkw9l3tFOkiIjlAbnxtWjxJTGr9s6rkOM7TD2bAZv1dEKQIhJpKI+s+tRwmQVC6QOc0erSQXtYqWxxabxarzIqHmOkZsqWrRYtSek1Q0Ghl+j1KOLZ9RcHDdXhdFLS4j1AZ1JESSnf6zONKpMpLnSO/YHXKQUotMHEZQI5T0KAW1WAAo94r6MpWHWjT1L9etxo7GyFEvnnzUolDRJNZ+G409WgdJIv0PPUgLkEC1ilNrTSQmEhLcQ7lXFI6WzEieI49aerp3FEaZVTSTERttckVNBi0A8Pfocq8oPRJ/aDuroGtRbDA84UkGhW8PMRhhXLQYtfyj2Cxm43gwxoCt53KvSAcJ0RFq43PD6lFiSuOXbT3XYYZ2OBs24/eKKEUgxEQSUf+59SgBksoFMqfZo5XkolbR8this1h1XiTUXMeILVUtWozac5KKRiPD71HKseUzCg6u2+uiqMVlhNqgjoRIstN/Fkc6VUbyHOkdu0MOUmqRicMIaoSSHqWgFgMA5V5RX6byUIum/uW61djRGDnqxZOPWhQqmsTab6OxR+sgSaT/oQdpARKoVnFqrYnERkKCeyj3isLRkhnJc+RRS0/3jsIos4pmMmKjTa6oyaAFAP4eXe4VpUfiD21nFXQtig2GJzzJoPDtIQYjjIsWo5Z/FJvBbBwP1low9VzuFekgITpCbXxuWD1KTGn8sqnnOszQDmfDZvxeEaUIhJhIIuo/tx4lQFK5QOY0e7SSXNQqWh5bbBarzouEmusYsaWqRYtRe05S0Whk+NtbcKoAABBXSURBVD1KObZ8RsHBdXtdFLW4jFAb1JEQSXb6z+JIp8pIniO9Y3fIQUotMnEYQY1Q0qMU1GIBoNwr6stUHmrR1L9ctxo7GiNHvXjyUYtCRZNY+2009mgdJIn0P/QgLUAC1SpOrTWRmEhIcA/lXlE4WjIjeY48aunp3lEYZVbRTEZstMkVNRm0AMDfo8u9ovRI/KHtrIKuRbHB8IQnGRS+PcRghHHRYtTyj2KzmI3jwRgDtp7LvSIdJERHqI3PDatHiSmNX7b1XIcZ2uFs2IzfK6IUgRATSUT959ajBEgqF8icZo9WkotaRctji81i1XmRUHMdI7ZUtWgxas9JKhqNDL9HKceWzyg4uG6vi6IWlxFqgzoSIslO/1kc6VQZyXOkd+wOOUipRSYOI6gRSnqUgloMAJR7RX2ZykMtmvqX61ZjR2PkqBdPPmpRqGgSa7+Nxh6tgySR/ocepAVIoFrFqbUmEhsJCe6h3CsKR0tmJM+RRy093TsKo8wqmsmIjTa5oiaDFgD4e3S5V5QeiT+0nVXQtSg2GJ7wJIPCt4cYjDAuWoxa/lFsBrNxPFhrwdRzuVekg4ToCLXxuWH1KDGl8cumnuswQzucDZvxe0WUIhBiIomo/9x6lABJ5QKZ0+zRSnJRq2h5bLFZrDovEmquY8SWqhYtRu05SUWjkeH3KOXY8hkFB9ftdVHU4jJCbVBHQiTZ6T+LI50qI3mO9I7dIQcptcjEYQQ1QkmPUlCLBYByr6gvU3moRVP/ct1q7GiMHPXiyUctChVNYu230dijdZAk0v/Qg7QACVSrOLXWRGIiIcE9lHtF4WjJjOQ58qilp3tHYZRZRTMZsdEmV9Rk0AIAf48u94rSI/GHtrMKuhbFBsMTnmRQ+PYQgxHGRYtRyz+KzWI2jgdjDNh6LveKdJAQHaE2PjesHiWmNH7Z1nMdZmiHs2Ezfq+IUgRCTCQR9Z9bjxIgqVwgc5o9WkkuahUtjy02i1XnRULNdYzYUtWixag9J6loNDL8HqUcWz6j4OC6vS6KWlxGqA3qSIgkO/1ncaRTZSTPkd6xO+QgpRaZOIygRijpUQpqMQBQ7hX1ZSoPtWjqX65bjR2NkaNePPmoRaGiSaz9Nhp7tA6SRPofepAWIIFqFafWmkhsJCS4h3KvKBwtmZE8Rx619HTvKIwyq2gmIzba5IqaDFoA4O/R5V5ReiT+0HZWQdei2GB4wpMMCt8eYjDCuGgxavlHsRnMxvFgrQVTz+VekQ4SoiPUxueG1aPElMYvm3quwwztcDZsxu8VUYpAiIkkov5z61ECJJULZE6zRyvJRa2i5bHFZrHqvEiouY4RW6patBi15yQVjUaG36OUY8tnFBxct9dFUYvLCLVBHQmRZKf/LI50qozkOdI7doccpNQiE4cR1AglPUpBLRYAyr2ivkzloRZN/ct1q7GjMXLUiycftShUNIm130Zjj9ZBkkj/Qw/SAiRQreLUWhOJiYQE91DuFYWjJTOS58ijlp7uHYVRZhXNZMRGm1xRk0ELAPw9utwrSo/EH9rOKuhaFBsMT3iSQeHbQwxGGBctRi3/KDaL2TgejDFg67ncK9JBQnSE2vjcsHqUmNL4ZVvPdZihHc6Gzfi9IkoRCDGRRNR/bj1KgKRygcxp9mgluahVtDy22CxWnRcJNdcxYktVixaj9pykotHI8HuUcmz5jIKD6/a6KGpxGaE2qCMhkuz0n8WRTpWRPEd6x+6Qg5RaZOIwghqhpEcpqMUAQLlX1JepPNSiqX+5bjV2NEaOevHkoxaFiiax9tto7NE6SBLpf+hBWoAEqlWcWmsisZGQ4B7KvaJwtGRG8hx51NLTvaMwyqyimYzYaJMrajJoAYC/R5d7RemR+EPbWQVdi2KD4QlPMih8e4jBCOOixajlH8VmMBvHg7UWTD2Xe0U6SIiOUBufG1aPElMav2zquQ4ztMPZsBm/V0QpAiEmkoj6z61HCZBULpA5zR6tJBe1ipbHFpvFqvMioeY6RmypatFi1J6TVDQaGX6PUo4tn1FwcN1eF0UtLiPUBnUkRJKd/rM40qkykudI79gdcpBSi0wcRlAjlPQoBbVYACj3ivoylYdaNPUv163GjsbIUS+efNSiUNEk1n4bjT1aB0ki/Q89SAuQQLWKU2tNJCYSEtxDuVcUjpbMSJ4jj1p6uncURplVNJMRG21yRU0GLQDw9+hyryg9En9oO6uga1FsMDzhSQaFbw8xGGFctBi1/KPYLGbjeDDGgK3ncq9IBwnREWrjc8PqUWJK45dtPddhhnY4Gzbj94ooRSDERBJR/7n1KAGSygUyp9mjleSiVtHy2GKzWHVeJNRcx4gtVS1ajNpzkopGI8PvUcqx5TMKDq7b66KoxWWE2qCOhEiy038WRzpVRvIc6R27Qw5SapGJwwhqhJIepaAWAwDlXlFfpvJQi6b+5brV2NEYOerFk49aFCqaxNpvo7FH6yBJpP+hB2kBEqhWcWqticRGQoJ7KPeKwtGSGclz5FFLT/eOwiizimYyYqNNrqjJoAUA/h5d7hWlR+IPbWcVdC2KDYYnPMmg8O0hBiOMixajln8Um8FsHA/WWjD1XO4V6SAhOkJtfG5YPUpMafyyqec6zNAOZ8Nm/F4RpQiEmEgi6j+3HiVAUrlA5jR7tJJc1CpaHltsFqvOi4Sa6xixpapFi1F7TlLRaGT4PUo5tnxGwcF1e10UtbiMUBvUkRBJdvrP4kinykieI71jd8hBSi0ycRhBjVDSoxTUYgGg3Cvqy1QeatHUv1y3GjsaI0e9ePJRi0JFk1j7bTT2aB0kifQ/9CAtQALVKk6tNZGYSEhwD+VeUThaMiN5jjxq6eneURhlVtFMRmy0yRU1GbQAwN+jy72i9Ej8oe2sgq5FscHwhCcZFL49xGCEcdFi1PKPYrOYjePBGAO2nsu9Ih0kREeojc8Nq0eJKY1ftvVchxna4WzYjN8rohSBEBNJRP3n1qMESCoXyJxmj1aSi1pFy2OLzWLVeZFQcx0jtlS1aDFqz0kqGo0Mv0cpx5bPKDi4bq+LohaXEWqDOhIiyU7/WRzpVBnJc6R37A45SKlFJg4jqBFKepSCWgwAlHtFfZnKQy2a+pfrVmNHY+SoF08+alGoaBJrv43GHq2DJJH+hx6kBUigWsWptSYSGwkJ7qHcKwpHS2Ykz5FHLT3dOwqjzCqayYiNNrmiJoMWAPh7dLlXlB6JP7SdVdC1KDYYnvAkg8K3hxiMMC5ajFr+UWwGs3E8WGvB1HO5V6SDhOgItfG5YfUoMaXxy6ae6zBDO5wNm/F7RZQiEGIiiaj/3HqUAEnlApnT7NFKclGraHlssVmsOi8Saq5jxJaqFi1G7TlJRaOR4fco5djyGQUH1+11UdTiMkJtUEdCJNnpP4sjnSojeY70jt0hBym1yMRhBDVCSY9SUIsFgHKvqC9TeahFU/9y3WrsaIwc9eLJRy0KFU1i7bfR2KN1kCTS/9CDtAAJVKs4tdZEYiIhwT2Ue0XhaMmM5DnyqKWne0dhlFlFMxmx0SZX1GTQAgB/jy73itIj8Ye2swq6FsUGwxOeZFD49hCDEcZFi1HLP4rNYjaOB2MM2Hou94p0kBAdoTY+N6weJaY0ftnWcx1maIezYTN+r4hSBEJMJBH1n1uPEiCpXCBzmj1aSS5qFS2PLTaLVedFQs11jNhS1aLFqD0nqWg0MvwepRxbPqPg4Lq9LopaXEaoDepIiCQ7/WdxpFNlJM+R3rE75CClFpk4jKBGKOlRCmoxAFDuFfVlKg+1aOpfrluNHY2Ro148+ahFoaJJrP02Gnu0DpJE+h96kBYggWoVp9aaSGwkJLiHcq8oHC2ZkTxHHrX0dO8ojDKraCYjNtrkipoMWgDg79HlXlF6JP7QdlZB16LYYHjCkwwK3x5iMMK4aDFq+UexGczG8WCtBVPP5V6RDhKiI9TG54bVo8SUxi+beq7DDO1wNmzG7xVRikCIiSSi/nPrUQIklQtkTrNHK8lFraLlscVmseq8SKi5jhFbqlq0GLXnJBWNRobfo5Rjy2cUHFy310VRi8sItUEdCZFkp/8sjnSqjOQ50jt2hxyk1CIThxHUCCU9SkEtFgDKvaK+TOWhFk39y3WrsaMxctSLJx+1KFQ0ibXfRmOP1kGSSP9DD9ICJFCt4tRaE4mJhAT3UO4VhaMlM5LnyKOWnu4dhVFmFc1kxEabXFGTQQsA/D263CtKj8Qf2s4q6FoUGwxPeJJB4dtDDEYYFy1GLf8oNovZOB6MMWDrudwr0kFCdITa+NywepSY0vhlW891mKEdzobN+L0iShEIMZFE1H9uPUqApHKBzGn2aCW5qFW0PLbYLFadFwk11zFiS1WLFqP2nKSi0cjwe5RybPmMgoPr9rooanEZoTaoIyGS7PSfxZFOlZE8R3rH7pCDlFpk4jCCGqGkRymoxQBAuVfUl6k81KKpf7luNXY0Ro568eSjFoWKJrH222js0TpIEul/6EFagASqVZxaayKxkZDgHsq9onC0ZEbyHHnU0tO9ozDKrKKZjNhokytqMmgBgL9Hl3tF6ZH4Q9tZBV2LYoPhCU8yKHx7iMEI46LFqOUfxWYwG8eDtRZMPZd7RTpIiI5QG58bVo8SUxq/bOq5DjO0w9mwGb9XRCkCISaSiPrPrUcJkFQukDnNHq0kF7WKlscWm8Wq8yKh5jpGbKlq0WLUnpNUNBoZfo9Sji2fUXBw3V4XRS0uI9QGdSREkp3+szjSqTKS50jv2B1ykFKLTBxGUCOU9CgFtVgAKPeK+jKVh1o09S/XrcaOxshRL5581KJQ0STWfhuNPVoHSSL9Dz1IC5BAtYpTa00kJhIS3EO5VxSOlsxIniOPWnq6dxRGmVU0kxEbbXJFTQYtAPD36HKvKD0Sf2g7q6BrUWwwPOFJBoVvDzEYYVy0GLX8o9gsZuN4MMaAredyr0gHCdERauNzw+pRYkrjl20912GGdjgbNuP3iihFIMREElH/ufUoAZLKBTKn2aOV5KJW0fLYYrNYdV4k1FzHiC1VLVqM2nOSikYjw+9RyrHlMwoOrtvroqjFZYTaoI6ESLLTfxZHOlVG8hzpHbtDDlJqkYnDCGqEkh6loBYDAOVeUV+m8lCLpv7lutXY0Rg56sWTj1oUKprE2m+jsUfrIEmk/6EHaQESqFZxaq2JxEZCgnso94rC0ZIZyXPkUUtP947CKLOKZjJio02uqMmgBQD+Hl3uFaVH4g9tZxV0LYoNhic8yaDw7SEGI4yLFqOWfxSbwWwcD9ZaMPVc7hXpICE6Qm18blg9Skxp/LKp5zrM0A5nw2b8XhGlCISYSCLqP7ceJUBSuUDmNHu0klzUKloeW2wWq86LhJrrGLGlqkWLUXtOUtFoZPg9Sjm2fEbBwXV7XRS1uIxQG9SREEl2+s/iSKfKSJ4jvWN3yEFKLTJxGEGNUNKjFNRiAaDcK+rLVB5q0dS/XLcaOxojR7148lGLQkWTWPttNPZoHSSJ9D/0IC1AAtUqTq01kZhISHAP5V5ROFoyI3mOPGrp6d5RGGVW0UxGbLTJFTUZtADA36P/Pxso8qRxZ4XJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Image(filename='images/1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Transformer architecture was introduced in June 2017. The focus of the original research was on translation tasks. This was followed by the introduction of several influential models, including:\n",
    "\n",
    "* June 2018: GPT, the first pretrained Transformer model, used for fine-tuning on various NLP tasks and obtained state-of-the-art results\n",
    "\n",
    "* October 2018: BERT, another large pretrained model, this one designed to produce better summaries of sentences (more on this in the next chapter!)\n",
    "\n",
    "* February 2019: GPT-2, an improved (and bigger) version of GPT that was not immediately publicly released due to ethical concerns\n",
    "\n",
    "* October 2019: DistilBERT, a distilled version of BERT that is 60% faster, 40% lighter in memory, and still retains 97% of BERT’s performance\n",
    "\n",
    "* October 2019: BART and T5, two large pretrained models using the same architecture as the original Transformer model (the first to do so)\n",
    "\n",
    "* May 2020, GPT-3, an even bigger version of GPT-2 that is able to perform well on a variety of tasks without the need for fine-tuning (called zero-shot learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why Hugging Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Natural Language Processing is a fast-advancing field. And it is also one of the fields that require a huge amount of computational resources to make important progress\n",
    "*  although breakthroughs are openly announced, and papers are released in free-to-access repositories such as ar \n",
    "χ\n",
    " iv, Open Review, Papers with Code, etc., and despite (sometimes) having the code freely available on GitHub, using those language models is not something widely accessible and easy.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State-of-the-art models\n",
    "* BERT is a state-of-the-art encoder language model. It takes days to train the model from the ground up even when using very powerful GPUs that only a few entities have access to. In 2019, NVIDIA used  \n",
    "1472 NVIDIA V100 GPUs to train BERT from scratch in 53 minutes\n",
    "* One estimate puts the cost of training GPT-3, a  175  billion parameter model, for a single training run at $12 Million USD.\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "watch: Deep Learning State of the Art (2019) - MIT https://www.youtube.com/watch?v=53YvP6gdD7U&t=432s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Such language models are released every now and then. How do you use these powerful language models for your task?\n",
    "\n",
    " Hugging Face comes to the scene. They aim to solve this problem by providing pre-trained models, and simple API so that you can use them, fine-tune them, and use the API in your applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduce the Hugging Face pipeline API to accomplish very interesting tasks by utilizing powerful pre-trained models present in the models hub of Hugging Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will go over, describe, and provide examples for the following tasks using Hugging Face pipeline-\n",
    "\n",
    "* Sentiment Analysis\n",
    "* Zero-Shot Classification\n",
    "* Text Generation\n",
    "* Mask-Filling\n",
    "* Named Entity Recognition\n",
    "* Question Answering\n",
    "* Summarization\n",
    "* Translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation\n",
    "\n",
    "https://huggingface.co/docs/datasets/v0.3.0/installation.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pipeline  It connects a model with its necessary preprocessing and postprocessing steps, allowing us to directly input any text and get an intelligible answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\John\\anaconda3\\lib\\site-packages\\torchaudio\\backend\\utils.py:67: UserWarning: No audio backend is available.\n",
      "  warnings.warn('No audio backend is available.')\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis\n",
    "\n",
    "Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "287630669cdb4d1eaabc531983c4a3ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=267844284.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9995385408401489}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_sentiment = pipeline(\"sentiment-analysis\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.993450939655304}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "classifier_sentiment(\"I like Africa Data School.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* By default, this pipeline selects a particular pretrained model that has been fine-tuned for sentiment analysis in English. The model is downloaded and cached when you create the classifier object.\n",
    "* The model is downloaded and cached when you create the classifier object. If you rerun the command, the cached model will be used instead and there is no need to download the model again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### There are three main steps involved when you pass some text to a pipeline:\n",
    "\n",
    "* 1 The text is preprocessed into a format the model can understand.\n",
    "* 2 The preprocessed inputs are passed to the model.\n",
    "* 3 The predictions of the model are post-processed, so you can make sense of them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zero Shot Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We need to classify texts that haven’t been labelled. \n",
    "* This is a common scenario in real-world projects because annotating text is usually time-consuming and requires domain expertise\n",
    "* For this use case, the zero-shot-classification pipeline is very powerful: it allows you to specify which labels to use for the classification, so you don’t have to rely on the labels of the pretrained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b0d702f15f143fa943271662254b602",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=688.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "833a94dd4c7b47ae9fc6793ad753b3ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1426170180.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFRobertaForSequenceClassification.\n",
      "\n",
      "All the layers of TFRobertaForSequenceClassification were initialized from the model checkpoint at roberta-large-mnli.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaForSequenceClassification for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a1571963db5498fbbddbd99dbcc50ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=898823.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b77caab1d9954842824f6a58279f03f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=456318.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24dec0cd6b1042189895c5f5b80d1182",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1355863.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sequence': 'This is a course about Natural language Processing',\n",
       " 'labels': ['education', 'politics', 'business'],\n",
       " 'scores': [0.9558506011962891, 0.023576518520712852, 0.020572911947965622]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = pipeline(\"zero-shot-classification\")\n",
    "classifier(\n",
    "    \"This is a course about Natural language Processing\",\n",
    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The main idea here is that you provide a prompt and the model will auto-complete it by generating the remaining text.\n",
    "* , this pipeline lets you generate text. It just needs you to supply a prompt, a text that will determine what the generated text will be. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4435502f64674f998ca11b1d883d30cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=665.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99483c1eed844699aa64fece82bf0465",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=497933648.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFGPT2LMHeadModel.\n",
      "\n",
      "All the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at gpt2.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07df900a88a14737be804a0b391a5042",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1042301.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0f171b9df25493bb36039ff9effb55f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=456318.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "394939a3a7a64ad293c527da1035367a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1355256.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Become a Data Scientist. Join the most practical data school in Africa. Learn in-demand skills.\\n\\nPermanent Residence: No permanent residence required for this position.\\n\\nHow to Apply\\n\\nIf you have the qualifications required to'}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = pipeline(\"text-generation\")\n",
    "generator(\"Become a Data Scientist. Join the most practical data school in Africa. Learn in-demand skills.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using any model from the Hub in a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFGPT2LMHeadModel.\n",
      "\n",
      "All the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at distilgpt2.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Intensive training for a career in artificial intelligence and machine learning.'},\n",
       " {'generated_text': 'Intensive training for a career in artificial intelligence and machine learning.'},\n",
       " {'generated_text': 'Intensive training for a career in artificial intelligence and machine learning. What better time?'},\n",
       " {'generated_text': 'Intensive training for a career in artificial intelligence and machine learning for young people,‹ has the potential to be a valuable tool to develop skills for future growth projects,‡ especially if the skills are used in early steps.'},\n",
       " {'generated_text': \"Intensive training for a career in artificial intelligence and machine learning and artificial intelligence are all around. They're exciting as these new discoveries continue to make a mark in the human experience.\\n\\n\\nOne of your favorite examples of the exciting discoveries in AI is the emergence of Google Brain. One of your favorite features in AI is the emergence of Google Brain.\\nGoogle Brain may be a real world AI. One of the things that separates AI from AI is its ability of making decisions. Google Brain is, on the other hand, the most powerful machine learning algorithm on the planet. And Google Brain is a real-time machine learning machine learning algorithm in terms of how many users you need to sign up to participate within a single year. Google has a whole lot going on in the technology world today, but it's not in the algorithms that lead to a real world AI. By showing that Google Brain takes you on a journey beyond the boundaries of the machine learning domain, Google shows it's true to what it does.\\nGoogle just announced it will be launching three big-scale AI training seminars in June this year. These include:\\n- Google Brain Training Center, University of California, Santa Cruz\"},\n",
       " {'generated_text': 'Intensive training for a career in artificial intelligence and machine learning.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTowler: Bored-In:\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAmber Jones, \"A Day and a Day a Dream\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nKendra Sperry, \"A Day and a Dream,\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nK.M. Sperry, \"A Day and a Dream,\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nK.M. Sperry, \"A Day and a Dream,\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBerten, \"A Day and a Dream\",\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nShannon, \"A Day and a Dream\":\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBerten, \"A Day and a Dream,\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTowler: Bored-In:\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n'}]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\n",
    "    \"Intensive training for a career in artificial intelligence and machine learning\",\n",
    "    max_length=300,\n",
    "    num_return_sequences=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Company  : https://www.copy.ai/  \n",
    "* Copy.ai generated monthly recurring revenue (MRR) of $53,600. That figure, up 46% from January, works out to annual\n",
    "* Copy.ai, a startup building AI-powered copywriting tools for business customers, announced a $2.9 million "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mask filling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The idea of this task is to fill in the blanks in a given text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFRobertaForMaskedLM.\n",
      "\n",
      "All the layers of TFRobertaForMaskedLM were initialized from the model checkpoint at distilroberta-base.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaForMaskedLM for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
       "  'score': 0.19619648158550262,\n",
       "  'token': 30412,\n",
       "  'token_str': ' mathematical'},\n",
       " {'sequence': 'This course will teach you all about computational models.',\n",
       "  'score': 0.040526796132326126,\n",
       "  'token': 38163,\n",
       "  'token_str': ' computational'},\n",
       " {'sequence': 'This course will teach you all about predictive models.',\n",
       "  'score': 0.033017534762620926,\n",
       "  'token': 27930,\n",
       "  'token_str': ' predictive'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "unmasker = pipeline(\"fill-mask\")\n",
    "unmasker(\"This course will teach you all about <mask> models.\", top_k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Named entity recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Named entity recognition (NER) is a task where the model has to find which parts of the input text correspond to entities such as persons, locations, or organizations. Let’s look at an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aaa7e380595040dfa381632f5ab68ae1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=998.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e06b1b25ddd84bf28c75c355b4927094",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1334871348.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some layers from the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing TFBertForTokenClassification: ['dropout_147']\n",
      "- This IS expected if you are initializing TFBertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFBertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the layers of TFBertForTokenClassification were initialized from the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForTokenClassification for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c2542157de746cab825939c58608090",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=60.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed43c2dbbf884f38b00e422ff5ccc3e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=213450.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING:tensorflow:From C:\\Users\\John\\anaconda3\\lib\\site-packages\\transformers\\pipelines\\token_classification.py:218: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.identity instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\John\\anaconda3\\lib\\site-packages\\transformers\\pipelines\\token_classification.py:154: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"AggregationStrategy.SIMPLE\"` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'entity_group': 'PER',\n",
       "  'score': 0.99893314,\n",
       "  'word': 'John',\n",
       "  'start': 11,\n",
       "  'end': 15},\n",
       " {'entity_group': 'ORG',\n",
       "  'score': 0.86076367,\n",
       "  'word': 'NLP',\n",
       "  'start': 28,\n",
       "  'end': 31},\n",
       " {'entity_group': 'LOC',\n",
       "  'score': 0.99892247,\n",
       "  'word': 'Nairobi',\n",
       "  'start': 52,\n",
       "  'end': 59}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "ner = pipeline(\"ner\", grouped_entities=True)\n",
    "ner(\"My name is John, and I am a NLP Consultant based in Nairobi.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grouped_entities parameter just ensures that the pipeline can recognize the names that have more than one word, like, you know, Chocolate Factory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question answering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The question-answering pipeline answers questions using information from a given context:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9919862954f04d47891037efd6676447",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=260793700.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "question_answerer = pipeline(\"question-answering\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.9592257142066956, 'start': 0, 'end': 13, 'answer': 'Empress Khema'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_answerer(\n",
    "    question=\"Who became a desciple of the Buddha?\",\n",
    "    context=\"Empress Khema, the wife of Emperor Bimbisara, became a desciple of the Buddha.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.7634803652763367,\n",
       " 'start': 30,\n",
       " 'end': 48,\n",
       " 'answer': 'Africa Data School'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_answerer(\n",
    "    question=\"Where do I work?\",\n",
    "    context=\"My name is John and I work at Africa Data School in Nairobi Kenya\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Company   https://chat.kuki.ai/auth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarization is the task of reducing a text into a shorter text while keeping all (or most) of the important aspects referenced in the text. Here’s an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2529de7c39db495188a1385ae5370d27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1197.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fe82db5f347467b8325a86d4b957e84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=242303832.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n",
      "\n",
      "All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0231592386ab4449b5f3256ae585d841",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=791656.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06976e4111f14d25a96d6da4feace182",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Downloading'), FloatProgress(value=0.0, max=1389353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "summarizer = pipeline(\"summarization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'summary_text': 'in most of the premier American universities engineering curricula now concentrate on and encourage largely the study of engineering science . as a result, there are declining offerings in engineering subjects dealing with infrastructure, the environment, and related issues, and greater concentration on high technology subjects .'}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarizer(\"\"\"\n",
    "     America has changed dramatically during recent years. Not only has the number of \n",
    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
    "    the premier American universities engineering curricula now concentrate on \n",
    "    and encourage largely the study of engineering science. As a result, there \n",
    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
    "    the environment, and related issues, and greater concentration on high \n",
    "    technology subjects, largely supporting increasingly complex scientific \n",
    "    developments. While the latter is important, it should not be at the expense \n",
    "    of more traditional engineering.\n",
    "\n",
    "    Rapidly developing economies such as China and India, as well as other \n",
    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
    "    the teaching of engineering. Both China and India, respectively, graduate \n",
    "    six and eight times as many traditional engineers as does the United States. \n",
    "    Other industrial countries at minimum maintain their output, while America \n",
    "    suffers an increasingly serious decline in the number of engineering graduates \n",
    "    and a lack of well-educated engineers..\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Company : https://www.agolo.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The translation pipeline translates a piece of text from one language to another. It also lets you choose the language model of your choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " let's translate a text from English to German. I will use the t5-small model for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n",
      "\n",
      "All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'translation_text': 'Ich vertraue auf Sie.'}]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translator = pipeline(\"translation\", model=\"t5-small\")\n",
    "translator(\"I LIKE YOU.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'translation_text': 'Amerika hat sich in den letzten Jahren dramatisch verändert.'}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translator(\"America has changed dramatically during recent years.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fine Tunning:\n",
    "    \n",
    "https://towardsdatascience.com/fine-tuning-hugging-face-model-with-custom-dataset-82b8092f5333"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AI Code Generation with GPT Neo (GTP3 Clone )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\John\\anaconda3\\lib\\site-packages\\torchaudio\\backend\\utils.py:67: UserWarning: No audio backend is available.\n",
      "  warnings.warn('No audio backend is available.')\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline # First line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B') # Second line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = generator(prompt, max_length=50, do_sample=True, temperature=0.9) # Fourth line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(res[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build Amazing AI Use Cases using GPT-3?\n",
    "https://towardsdatascience.com/how-to-build-amazing-ai-use-cases-under-10-mins-using-gpt-3-ebc51b2b2b97"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}