diff --git a/ax/models/tests/test_botorch_model.py b/ax/models/tests/test_botorch_model.py
index f5a75551598..9e42efd6d14 100644
--- a/ax/models/tests/test_botorch_model.py
+++ b/ax/models/tests/test_botorch_model.py
@@ -12,7 +12,6 @@
 from unittest import mock
 
 import numpy as np
-
 import torch
 from ax.core.search_space import SearchSpaceDigest
 from ax.exceptions.core import DataRequiredError
@@ -29,6 +28,7 @@
 from ax.models.torch.utils import sample_simplex
 from ax.models.torch_base import TorchOptConfig
 from ax.utils.common.testutils import TestCase
+from ax.utils.common.typeutils import not_none
 from ax.utils.testing.mock import fast_botorch_optimize
 from ax.utils.testing.torch_stubs import get_torch_test_data
 from botorch.acquisition.utils import get_infeasible_cost
@@ -120,7 +120,7 @@ def test_fixed_prior_BotorchModel(
         Xs2, Ys2, Yvars2, _, _, _, _ = get_torch_test_data(
             dtype=dtype, cuda=cuda, constant_noise=True
         )
-        kwargs = {
+        kwargs: Dict[str, Any] = {
             "prior": {
                 "covar_module_prior": {
                     "lengthscale_prior": GammaPrior(6.0, 3.0),
@@ -131,7 +131,7 @@ def test_fixed_prior_BotorchModel(
                 "eta": 0.6,
             }
         }
-        model = BotorchModel(**kwargs)  # pyre-ignore [6]
+        model = BotorchModel(**kwargs)
         datasets = [
             SupervisedDataset(
                 X=Xs1[0],
@@ -329,8 +329,7 @@ def test_BotorchModel(
 
                 if not use_input_warping:
                     expected_train_inputs = expected_train_inputs.unsqueeze(0).expand(
-                        torch.Size([2])
-                        + Xs1[0].shape  # pyre-fixme[58]: Unsupported operand
+                        2, *Xs1[0].shape
                     )
                     expected_train_targets = torch.cat(Ys1 + Ys2, dim=-1).permute(1, 0)
                 else:
@@ -475,10 +474,8 @@ def test_BotorchModel(
                 )
 
             # test get_rounding_func
-            dummy_rounding = get_rounding_func(rounding_func=dummy_func)
+            dummy_rounding = not_none(get_rounding_func(rounding_func=dummy_func))
             X_temp = torch.rand(1, 2, 3, 4)
-            # pyre-fixme[29]: `Optional[typing.Callable[[torch._tensor.Tensor],
-            #  torch._tensor.Tensor]]` is not a function.
             self.assertTrue(torch.equal(X_temp, dummy_rounding(X_temp)))
 
             # Check best point selection
@@ -773,18 +770,18 @@ def test_botorchmodel_raises_when_no_data(self) -> None:
             )
 
     def test_get_feature_importances_from_botorch_model(self) -> None:
-        tkwargs = {"dtype": torch.double}
-        train_X = torch.rand(5, 3, **tkwargs)  # pyre-ignore [6]
+        tkwargs: Dict[str, Any] = {"dtype": torch.double}
+        train_X = torch.rand(5, 3, **tkwargs)
         train_Y = train_X.sum(dim=-1, keepdim=True)
         simple_gp = SingleTaskGP(train_X=train_X, train_Y=train_Y)
         simple_gp.covar_module.base_kernel.lengthscale = torch.tensor(
-            [1, 3, 5], **tkwargs  # pyre-ignore [6]
+            [1, 3, 5], **tkwargs
         )
         importances = get_feature_importances_from_botorch_model(simple_gp)
         self.assertTrue(np.allclose(importances, np.array([15 / 23, 5 / 23, 3 / 23])))
         self.assertEqual(importances.shape, (1, 1, 3))
         # Model with no base kernel
-        simple_gp.covar_module.base_kernel = None  # pyre-ignore [16]
+        simple_gp.covar_module.base_kernel = None
         with self.assertRaisesRegex(
             NotImplementedError,
             "Failed to extract lengthscales from `m.covar_module.base_kernel`",