diff --git a/captum/_utils/common.py b/captum/_utils/common.py
index f1b5fd9a7e..6336fc4a8a 100644
--- a/captum/_utils/common.py
+++ b/captum/_utils/common.py
@@ -86,6 +86,10 @@ def _is_tuple(inputs: Tuple[Tensor, ...]) -> Literal[True]: ...
 def _is_tuple(inputs: Tensor) -> Literal[False]: ...
 
 
+@typing.overload
+def _is_tuple(inputs: TensorOrTupleOfTensorsGeneric) -> bool: ...
+
+
 def _is_tuple(inputs: Union[Tensor, Tuple[Tensor, ...]]) -> bool:
     return isinstance(inputs, tuple)
 
diff --git a/captum/_utils/typing.py b/captum/_utils/typing.py
index 598c031b2c..5381350033 100644
--- a/captum/_utils/typing.py
+++ b/captum/_utils/typing.py
@@ -2,25 +2,11 @@
 
 # pyre-strict
 
-from typing import (
-    List,
-    Optional,
-    overload,
-    Protocol,
-    Tuple,
-    TYPE_CHECKING,
-    TypeVar,
-    Union,
-)
+from typing import List, Literal, Optional, overload, Protocol, Tuple, TypeVar, Union
 
 from torch import Tensor
 from torch.nn import Module
 
-if TYPE_CHECKING:
-    from typing import Literal
-else:
-    Literal = {True: bool, False: bool, (True, False): bool, "pt": str}
-
 TensorOrTupleOfTensorsGeneric = TypeVar(
     "TensorOrTupleOfTensorsGeneric", Tensor, Tuple[Tensor, ...]
 )
diff --git a/captum/attr/_core/guided_backprop_deconvnet.py b/captum/attr/_core/guided_backprop_deconvnet.py
index 60359bc0dd..5dc444e455 100644
--- a/captum/attr/_core/guided_backprop_deconvnet.py
+++ b/captum/attr/_core/guided_backprop_deconvnet.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 import warnings
-from typing import Any, Callable, List, Tuple, Union
+from typing import Callable, List, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -45,8 +45,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
         Computes attribution by overriding relu gradients. Based on constructor
@@ -58,16 +57,10 @@ def attribute(
 
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
 
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs = _format_tensor_into_tuples(inputs)
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        gradient_mask = apply_gradient_requirements(inputs)
+        inputs_tuple = _format_tensor_into_tuples(inputs)
+        gradient_mask = apply_gradient_requirements(inputs_tuple)
 
         # set hooks for overriding ReLU gradients
         warnings.warn(
@@ -79,14 +72,12 @@ def attribute(
             self.model.apply(self._register_hooks)
 
             gradients = self.gradient_func(
-                self.forward_func, inputs, target, additional_forward_args
+                self.forward_func, inputs_tuple, target, additional_forward_args
             )
         finally:
             self._remove_hooks()
 
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        undo_gradient_requirements(inputs, gradient_mask)
+        undo_gradient_requirements(inputs_tuple, gradient_mask)
         # pyre-fixme[7]: Expected `TensorOrTupleOfTensorsGeneric` but got
         #  `Tuple[Tensor, ...]`.
         return _format_output(is_inputs_tuple, gradients)
@@ -155,8 +146,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
         Args:
@@ -265,8 +255,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
         Args:
diff --git a/captum/attr/_core/guided_grad_cam.py b/captum/attr/_core/guided_grad_cam.py
index bb9beb6a0b..d4d1978496 100644
--- a/captum/attr/_core/guided_grad_cam.py
+++ b/captum/attr/_core/guided_grad_cam.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 import warnings
-from typing import Any, List, Union
+from typing import List, Union
 
 import torch
 from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
@@ -72,8 +72,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         interpolate_mode: str = "nearest",
         attribute_to_layer_input: bool = False,
     ) -> TensorOrTupleOfTensorsGeneric:
@@ -181,15 +180,11 @@ def attribute(
             >>> # attribution size matches input size, Nx3x32x32
             >>> attribution = guided_gc.attribute(input, 3)
         """
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs = _format_tensor_into_tuples(inputs)
+        inputs_tuple = _format_tensor_into_tuples(inputs)
         grad_cam_attr = self.grad_cam.attribute.__wrapped__(
             self.grad_cam,  # self
-            inputs=inputs,
+            inputs=inputs_tuple,
             target=target,
             additional_forward_args=additional_forward_args,
             attribute_to_layer_input=attribute_to_layer_input,
@@ -204,20 +199,18 @@ def attribute(
 
         guided_backprop_attr = self.guided_backprop.attribute.__wrapped__(
             self.guided_backprop,  # self
-            inputs=inputs,
+            inputs=inputs_tuple,
             target=target,
             additional_forward_args=additional_forward_args,
         )
         output_attr: List[Tensor] = []
-        for i in range(len(inputs)):
+        for i in range(len(inputs_tuple)):
             try:
                 output_attr.append(
                     guided_backprop_attr[i]
                     * LayerAttribution.interpolate(
                         grad_cam_attr,
-                        # pyre-fixme[6]: For 2nd argument expected `Union[int,
-                        #  typing.Tuple[int, ...]]` but got `Size`.
-                        inputs[i].shape[2:],
+                        tuple(inputs_tuple[i].shape[2:]),
                         interpolate_mode=interpolate_mode,
                     )
                 )
diff --git a/captum/attr/_core/input_x_gradient.py b/captum/attr/_core/input_x_gradient.py
index 86115bb03b..bfaa75def2 100644
--- a/captum/attr/_core/input_x_gradient.py
+++ b/captum/attr/_core/input_x_gradient.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 # pyre-strict
-from typing import Any, Callable
+from typing import Callable
 
 from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
 from captum._utils.gradient import (
@@ -11,6 +11,7 @@
 from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import GradientAttribution
 from captum.log import log_usage
+from torch import Tensor
 
 
 class InputXGradient(GradientAttribution):
@@ -20,8 +21,7 @@ class InputXGradient(GradientAttribution):
     https://arxiv.org/abs/1605.01713
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Tensor]) -> None:
         r"""
         Args:
 
@@ -35,8 +35,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
         Args:
@@ -113,28 +112,20 @@ def attribute(
         """
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
 
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs = _format_tensor_into_tuples(inputs)
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        gradient_mask = apply_gradient_requirements(inputs)
+        inputs_tuple = _format_tensor_into_tuples(inputs)
+        gradient_mask = apply_gradient_requirements(inputs_tuple)
 
         gradients = self.gradient_func(
-            self.forward_func, inputs, target, additional_forward_args
+            self.forward_func, inputs_tuple, target, additional_forward_args
         )
 
         attributions = tuple(
-            input * gradient for input, gradient in zip(inputs, gradients)
+            input * gradient for input, gradient in zip(inputs_tuple, gradients)
         )
 
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        undo_gradient_requirements(inputs, gradient_mask)
+        undo_gradient_requirements(inputs_tuple, gradient_mask)
         # pyre-fixme[7]: Expected `TensorOrTupleOfTensorsGeneric` but got
         #  `Tuple[Tensor, ...]`.
         return _format_output(is_inputs_tuple, attributions)
diff --git a/captum/attr/_core/integrated_gradients.py b/captum/attr/_core/integrated_gradients.py
index e803262937..1abbcc69f7 100644
--- a/captum/attr/_core/integrated_gradients.py
+++ b/captum/attr/_core/integrated_gradients.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 import typing
-from typing import Any, Callable, List, Tuple, Union
+from typing import Any, Callable, List, Literal, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -12,12 +12,7 @@
     _format_output,
     _is_tuple,
 )
-from captum._utils.typing import (
-    BaselineType,
-    Literal,
-    TargetType,
-    TensorOrTupleOfTensorsGeneric,
-)
+from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.approximation_methods import approximation_parameters
 from captum.attr._utils.attribution import GradientAttribution
 from captum.attr._utils.batching import _batch_attribution
@@ -49,8 +44,7 @@ class IntegratedGradients(GradientAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         multiply_by_inputs: bool = True,
     ) -> None:
         r"""
@@ -80,21 +74,16 @@ def __init__(
     # and when return_convergence_delta is True, the return type is
     # a tuple with both attributions and deltas.
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `95`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         n_steps: int = 50,
         method: str = "gausslegendre",
         internal_batch_size: Union[None, int] = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
     ) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]: ...
 
@@ -111,9 +100,6 @@ def attribute(
         n_steps: int = 50,
         method: str = "gausslegendre",
         internal_batch_size: Union[None, int] = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[False] = False,
     ) -> TensorOrTupleOfTensorsGeneric: ...
 
@@ -275,37 +261,35 @@ def attribute(  # type: ignore
         """
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
 
         # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
         #  `Tuple[Tensor, ...]`.
-        inputs, baselines = _format_input_baseline(inputs, baselines)
+        formatted_inputs, formatted_baselines = _format_input_baseline(
+            inputs, baselines
+        )
 
         # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
         #  `TensorOrTupleOfTensorsGeneric`.
-        _validate_input(inputs, baselines, n_steps, method)
+        _validate_input(formatted_inputs, formatted_baselines, n_steps, method)
 
         if internal_batch_size is not None:
-            num_examples = inputs[0].shape[0]
+            num_examples = formatted_inputs[0].shape[0]
             attributions = _batch_attribution(
                 self,
                 num_examples,
                 internal_batch_size,
                 n_steps,
-                inputs=inputs,
-                baselines=baselines,
+                inputs=formatted_inputs,
+                baselines=formatted_baselines,
                 target=target,
                 additional_forward_args=additional_forward_args,
                 method=method,
             )
         else:
             attributions = self._attribute(
-                # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but
-                #  got `TensorOrTupleOfTensorsGeneric`.
-                inputs=inputs,
-                baselines=baselines,
+                inputs=formatted_inputs,
+                baselines=formatted_baselines,
                 target=target,
                 additional_forward_args=additional_forward_args,
                 n_steps=n_steps,
@@ -344,8 +328,7 @@ def _attribute(
         inputs: Tuple[Tensor, ...],
         baselines: Tuple[Union[Tensor, int, float], ...],
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         n_steps: int = 50,
         method: str = "gausslegendre",
         step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
diff --git a/captum/attr/_core/kernel_shap.py b/captum/attr/_core/kernel_shap.py
index 8b6fb44cbf..89d22990d8 100644
--- a/captum/attr/_core/kernel_shap.py
+++ b/captum/attr/_core/kernel_shap.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 
-from typing import Any, Callable, Generator, Tuple, Union
+from typing import Callable, cast, Generator, Tuple, Union
 
 import torch
 from captum._utils.models.linear_model import SkLearnLinearRegression
@@ -27,8 +27,7 @@ class KernelShap(Lime):
     https://arxiv.org/abs/1705.07874
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Tensor]) -> None:
         r"""
         Args:
 
@@ -50,8 +49,7 @@ def attribute(  # type: ignore
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         n_samples: int = 25,
         perturbations_per_eval: int = 1,
@@ -279,10 +277,7 @@ def attribute(  # type: ignore
         )
         num_features_list = torch.arange(num_interp_features, dtype=torch.float)
         denom = num_features_list * (num_interp_features - num_features_list)
-        # pyre-fixme[58]: `/` is not supported for operand types
-        # `int` and `torch._tensor.Tensor`.
-        probs = (num_interp_features - 1) / denom
-        # pyre-fixme[16]: `float` has no attribute `__setitem__`.
+        probs = torch.tensor((num_interp_features - 1)) / denom
         probs[0] = 0.0
         return self._attribute_kwargs(
             inputs=inputs,
@@ -309,8 +304,7 @@ def kernel_shap_similarity_kernel(
         _,
         __,
         interpretable_sample: Tensor,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Tensor:
         assert (
             "num_interp_features" in kwargs
@@ -332,8 +326,7 @@ def kernel_shap_similarity_kernel(
     def kernel_shap_perturb_generator(
         self,
         original_inp: Union[Tensor, Tuple[Tensor, ...]],
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Generator[Tensor, None, None]:
         r"""
         Perturbations are sampled by the following process:
@@ -361,11 +354,13 @@ def kernel_shap_perturb_generator(
             device = original_inp.device
         else:
             device = original_inp[0].device
-        num_features = kwargs["num_interp_features"]
+        num_features = cast(int, kwargs["num_interp_features"])
         yield torch.ones(1, num_features, device=device, dtype=torch.long)
         yield torch.zeros(1, num_features, device=device, dtype=torch.long)
         while True:
-            num_selected_features = kwargs["num_select_distribution"].sample()
+            num_selected_features = cast(
+                Categorical, kwargs["num_select_distribution"]
+            ).sample()
             rand_vals = torch.randn(1, num_features)
             threshold = torch.kthvalue(
                 rand_vals, num_features - num_selected_features
diff --git a/captum/attr/_core/lime.py b/captum/attr/_core/lime.py
index 21bae8677e..152bae2c3a 100644
--- a/captum/attr/_core/lime.py
+++ b/captum/attr/_core/lime.py
@@ -6,7 +6,7 @@
 import typing
 import warnings
 from collections.abc import Iterator
-from typing import Any, Callable, cast, List, Optional, Tuple, Union
+from typing import Any, Callable, cast, List, Literal, Optional, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -23,12 +23,7 @@
 from captum._utils.models.linear_model import SkLearnLasso
 from captum._utils.models.model import Model
 from captum._utils.progress import progress
-from captum._utils.typing import (
-    BaselineType,
-    Literal,
-    TargetType,
-    TensorOrTupleOfTensorsGeneric,
-)
+from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import PerturbationAttribution
 from captum.attr._utils.batching import _batch_example_iterator
 from captum.attr._utils.common import (
@@ -73,18 +68,18 @@ class LimeBase(PerturbationAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         interpretable_model: Model,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        similarity_func: Callable,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        perturb_func: Callable,
+        similarity_func: Callable[
+            ...,
+            Union[float, Tensor],
+        ],
+        perturb_func: Callable[..., object],
         perturb_interpretable_space: bool,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        from_interp_rep_transform: Optional[Callable],
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        to_interp_rep_transform: Optional[Callable],
+        from_interp_rep_transform: Optional[
+            Callable[..., Union[Tensor, Tuple[Tensor, ...]]]
+        ],
+        to_interp_rep_transform: Optional[Callable[..., Tensor]],
     ) -> None:
         r"""
 
@@ -249,13 +244,11 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         n_samples: int = 50,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Tensor:
         r"""
         This method attributes the output of the model with given target index
@@ -551,7 +544,7 @@ def generate_perturbation() -> (
                     curr_sample, inputs, **kwargs
                 )
 
-            return interpretable_inp, curr_model_input
+            return interpretable_inp, curr_model_input  # type: ignore
 
         return generate_perturbation
 
@@ -568,8 +561,7 @@ def _evaluate_batch(
         self,
         curr_model_inputs: List[TensorOrTupleOfTensorsGeneric],
         expanded_target: TargetType,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        expanded_additional_args: Any,
+        expanded_additional_args: object,
         device: torch.device,
     ) -> Tensor:
         model_out = _run_forward(
@@ -630,8 +622,7 @@ def default_from_interp_rep_transform(curr_sample, original_inputs, **kwargs):
 def get_exp_kernel_similarity_function(
     distance_mode: str = "cosine",
     kernel_width: float = 1.0,
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-) -> Callable:
+) -> Callable[..., float]:
     r"""
     This method constructs an appropriate similarity function to compute
     weights for perturbed sample in LIME. Distance between the original
@@ -680,8 +671,9 @@ def default_exp_kernel(original_inp, perturbed_inp, __, **kwargs):
     return default_exp_kernel
 
 
-# pyre-fixme[2]: Parameter must be annotated.
-def default_perturb_func(original_inp, **kwargs) -> Tensor:
+def default_perturb_func(
+    original_inp: TensorOrTupleOfTensorsGeneric, **kwargs: object
+) -> Tensor:
     assert (
         "num_interp_features" in kwargs
     ), "Must provide num_interp_features to use default interpretable sampling function"
@@ -690,7 +682,7 @@ def default_perturb_func(original_inp, **kwargs) -> Tensor:
     else:
         device = original_inp[0].device
 
-    probs = torch.ones(1, kwargs["num_interp_features"]) * 0.5
+    probs = torch.ones(1, cast(int, kwargs["num_interp_features"])) * 0.5
     return torch.bernoulli(probs).to(device=device).long()
 
 
@@ -698,17 +690,17 @@ def construct_feature_mask(
     feature_mask: Union[None, Tensor, Tuple[Tensor, ...]],
     formatted_inputs: Tuple[Tensor, ...],
 ) -> Tuple[Tuple[Tensor, ...], int]:
+    feature_mask_tuple: Tuple[Tensor, ...]
     if feature_mask is None:
-        feature_mask, num_interp_features = _construct_default_feature_mask(
+        feature_mask_tuple, num_interp_features = _construct_default_feature_mask(
             formatted_inputs
         )
     else:
-        feature_mask = _format_tensor_into_tuples(feature_mask)
+        feature_mask_tuple = _format_tensor_into_tuples(feature_mask)
         min_interp_features = int(
             min(
                 torch.min(single_mask).item()
-                # pyre-fixme[16]: `None` has no attribute `__iter__`.
-                for single_mask in feature_mask
+                for single_mask in feature_mask_tuple
                 if single_mask.numel()
             )
         )
@@ -718,14 +710,12 @@ def construct_feature_mask(
                 " start at 0.",
                 stacklevel=2,
             )
-            feature_mask = tuple(
-                single_mask - min_interp_features for single_mask in feature_mask
+            feature_mask_tuple = tuple(
+                single_mask - min_interp_features for single_mask in feature_mask_tuple
             )
 
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `Optional[typing.Tuple[typing.Any, ...]]`.
-        num_interp_features = _get_max_feature_index(feature_mask) + 1
-    return feature_mask, num_interp_features
+        num_interp_features = _get_max_feature_index(feature_mask_tuple) + 1
+    return feature_mask_tuple, num_interp_features
 
 
 class Lime(LimeBase):
@@ -766,8 +756,7 @@ class Lime(LimeBase):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         interpretable_model: Optional[Model] = None,
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
         similarity_func: Optional[Callable] = None,
@@ -887,8 +876,7 @@ def attribute(  # type: ignore
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         n_samples: int = 25,
         perturbations_per_eval: int = 1,
@@ -1133,18 +1121,14 @@ def _attribute_kwargs(  # type: ignore
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         n_samples: int = 25,
         perturbations_per_eval: int = 1,
         return_input_shape: bool = True,
         show_progress: bool = False,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> TensorOrTupleOfTensorsGeneric:
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
         formatted_inputs, baselines = _format_input_baseline(inputs, baselines)
         bsz = formatted_inputs[0].shape[0]
@@ -1263,33 +1247,35 @@ def _attribute_kwargs(  # type: ignore
             return coefs
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `_convert_output_shape` does not accept
-    #  all possible arguments of overload defined on line `1201`.
     def _convert_output_shape(
         self,
         formatted_inp: Tuple[Tensor, ...],
         feature_mask: Tuple[Tensor, ...],
         coefs: Tensor,
         num_interp_features: int,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         is_inputs_tuple: Literal[True],
     ) -> Tuple[Tensor, ...]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `_convert_output_shape` does not accept
-    #  all possible arguments of overload defined on line `1211`.
     def _convert_output_shape(  # type: ignore
         self,
         formatted_inp: Tuple[Tensor, ...],
         feature_mask: Tuple[Tensor, ...],
         coefs: Tensor,
         num_interp_features: int,
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         is_inputs_tuple: Literal[False],
     ) -> Tensor: ...
 
+    @typing.overload
+    def _convert_output_shape(
+        self,
+        formatted_inp: Tuple[Tensor, ...],
+        feature_mask: Tuple[Tensor, ...],
+        coefs: Tensor,
+        num_interp_features: int,
+        is_inputs_tuple: bool,
+    ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
+
     def _convert_output_shape(
         self,
         formatted_inp: Tuple[Tensor, ...],
diff --git a/captum/attr/_core/lrp.py b/captum/attr/_core/lrp.py
index 06c2fd5ae7..d08b7b4de8 100644
--- a/captum/attr/_core/lrp.py
+++ b/captum/attr/_core/lrp.py
@@ -4,7 +4,7 @@
 
 import typing
 from collections import defaultdict
-from typing import Any, Callable, cast, List, Tuple, Union
+from typing import Any, Callable, cast, Dict, List, Literal, Tuple, Union
 
 import torch.nn as nn
 from captum._utils.common import (
@@ -18,7 +18,7 @@
     apply_gradient_requirements,
     undo_gradient_requirements,
 )
-from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
+from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import GradientAttribution
 from captum.attr._utils.common import _sum_rows
 from captum.attr._utils.custom_modules import Addition_Module
@@ -43,6 +43,12 @@ class LRP(GradientAttribution):
     Ancona et al. [https://openreview.net/forum?id=Sy21R9JAW].
     """
 
+    verbose: bool = False
+    _original_state_dict: Dict[str, Any] = {}
+    layers: List[Module] = []
+    backward_handles: List[RemovableHandle] = []
+    forward_handles: List[RemovableHandle] = []
+
     def __init__(self, model: Module) -> None:
         r"""
         Args:
@@ -62,33 +68,22 @@ def multiplies_by_inputs(self) -> bool:
         return True
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `75`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         *,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         return_convergence_delta: Literal[True],
         verbose: bool = False,
     ) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `65`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
-        # pyre-fixme[9]: return_convergence_delta has type `Literal[]`; used as `bool`.
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
+        additional_forward_args: object = None,
         return_convergence_delta: Literal[False] = False,
         verbose: bool = False,
     ) -> TensorOrTupleOfTensorsGeneric: ...
@@ -100,7 +95,7 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         return_convergence_delta: bool = False,
         verbose: bool = False,
     ) -> Union[
@@ -199,35 +194,22 @@ def attribute(
                 >>> attribution = lrp.attribute(input, target=5)
 
         """
-        # pyre-fixme[16]: `LRP` has no attribute `verbose`.
         self.verbose = verbose
-        # pyre-fixme[16]: `LRP` has no attribute `_original_state_dict`.
         self._original_state_dict = self.model.state_dict()
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
-        self.layers: List[Module] = []
+        self.layers = []
         self._get_layers(self.model)
         self._check_and_attach_rules()
-        # pyre-fixme[16]: `LRP` has no attribute `backward_handles`.
         self.backward_handles: List[RemovableHandle] = []
-        # pyre-fixme[16]: `LRP` has no attribute `forward_handles`.
         self.forward_handles: List[RemovableHandle] = []
 
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs = _format_tensor_into_tuples(inputs)
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        gradient_mask = apply_gradient_requirements(inputs)
+        input_tuple = _format_tensor_into_tuples(inputs)
+        gradient_mask = apply_gradient_requirements(input_tuple)
 
         try:
             # 1. Forward pass: Change weights of layers according to selected rules.
             output = self._compute_output_and_change_weights(
-                # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but
-                #  got `TensorOrTupleOfTensorsGeneric`.
-                inputs,
+                input_tuple,
                 target,
                 additional_forward_args,
             )
@@ -235,7 +217,7 @@ def attribute(
             # propagation and execute back-propagation.
             self._register_forward_hooks()
             normalized_relevances = self.gradient_func(
-                self._forward_fn_wrapper, inputs, target, additional_forward_args
+                self._forward_fn_wrapper, input_tuple, target, additional_forward_args
             )
             relevances = tuple(
                 normalized_relevance
@@ -245,9 +227,7 @@ def attribute(
         finally:
             self._restore_model()
 
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        undo_gradient_requirements(inputs, gradient_mask)
+        undo_gradient_requirements(input_tuple, gradient_mask)
 
         if return_convergence_delta:
             # pyre-fixme[7]: Expected `Union[Tuple[Variable[TensorOrTupleOfTensorsGen...
@@ -310,13 +290,11 @@ def compute_convergence_delta(
     def _get_layers(self, model: Module) -> None:
         for layer in model.children():
             if len(list(layer.children())) == 0:
-                # pyre-fixme[16]: `LRP` has no attribute `layers`.
                 self.layers.append(layer)
             else:
                 self._get_layers(layer)
 
     def _check_and_attach_rules(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if hasattr(layer, "rule"):
                 layer.activations = {}  # type: ignore
@@ -355,50 +333,41 @@ def _check_rules(self) -> None:
                     )
 
     def _register_forward_hooks(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if type(layer) in SUPPORTED_NON_LINEAR_LAYERS:
                 backward_handles = _register_backward_hook(
                     layer, PropagationRule.backward_hook_activation, self
                 )
-                # pyre-fixme[16]: `LRP` has no attribute `backward_handles`.
                 self.backward_handles.extend(backward_handles)
             else:
                 forward_handle = layer.register_forward_hook(
                     layer.rule.forward_hook  # type: ignore
                 )
-                # pyre-fixme[16]: `LRP` has no attribute `forward_handles`.
                 self.forward_handles.append(forward_handle)
-                # pyre-fixme[16]: `LRP` has no attribute `verbose`.
                 if self.verbose:
                     print(f"Applied {layer.rule} on layer {layer}")
 
     def _register_weight_hooks(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if layer.rule is not None:
                 forward_handle = layer.register_forward_hook(
                     layer.rule.forward_hook_weights  # type: ignore
                 )
-                # pyre-fixme[16]: `LRP` has no attribute `forward_handles`.
                 self.forward_handles.append(forward_handle)
 
     def _register_pre_hooks(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if layer.rule is not None:
                 forward_handle = layer.register_forward_pre_hook(
                     layer.rule.forward_pre_hook_activations  # type: ignore
                 )
-                # pyre-fixme[16]: `LRP` has no attribute `forward_handles`.
                 self.forward_handles.append(forward_handle)
 
     def _compute_output_and_change_weights(
         self,
         inputs: Tuple[Tensor, ...],
         target: TargetType,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any,
+        additional_forward_args: object,
     ) -> Tensor:
         try:
             self._register_weight_hooks()
@@ -416,15 +385,12 @@ def _compute_output_and_change_weights(
         return cast(Tensor, output)
 
     def _remove_forward_hooks(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `forward_handles`.
         for forward_handle in self.forward_handles:
             forward_handle.remove()
 
     def _remove_backward_hooks(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `backward_handles`.
         for backward_handle in self.backward_handles:
             backward_handle.remove()
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if hasattr(layer.rule, "_handle_input_hooks"):
                 for handle in layer.rule._handle_input_hooks:  # type: ignore
@@ -433,13 +399,11 @@ def _remove_backward_hooks(self) -> None:
                 layer.rule._handle_output_hook.remove()  # type: ignore
 
     def _remove_rules(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if hasattr(layer, "rule"):
                 del layer.rule
 
     def _clear_properties(self) -> None:
-        # pyre-fixme[16]: `LRP` has no attribute `layers`.
         for layer in self.layers:
             if hasattr(layer, "activation"):
                 del layer.activation
diff --git a/captum/attr/_core/noise_tunnel.py b/captum/attr/_core/noise_tunnel.py
index 7247ccc00d..5d9eb19626 100644
--- a/captum/attr/_core/noise_tunnel.py
+++ b/captum/attr/_core/noise_tunnel.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 from enum import Enum
-from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -27,8 +27,7 @@ class NoiseTunnelType(Enum):
     vargrad = 3
 
 
-# pyre-fixme[5]: Global expression must be annotated.
-SUPPORTED_NOISE_TUNNEL_TYPES = list(NoiseTunnelType.__members__.keys())
+SUPPORTED_NOISE_TUNNEL_TYPES: List[str] = list(NoiseTunnelType.__members__.keys())
 
 
 class NoiseTunnel(Attribution):
@@ -58,6 +57,10 @@ class NoiseTunnel(Attribution):
     It is assumed that the batch size is the first dimension of input tensors.
     """
 
+    is_delta_supported: bool
+    _multiply_by_inputs: bool
+    is_gradient_method: bool
+
     def __init__(self, attribution_method: Attribution) -> None:
         r"""
         Args:
@@ -66,19 +69,15 @@ def __init__(self, attribution_method: Attribution) -> None:
                         Conductance or Saliency.
         """
         self.attribution_method = attribution_method
-        # pyre-fixme[4]: Attribute must be annotated.
         self.is_delta_supported = self.attribution_method.has_convergence_delta()
-        # pyre-fixme[4]: Attribute must be annotated.
         self._multiply_by_inputs = self.attribution_method.multiplies_by_inputs
-        # pyre-fixme[4]: Attribute must be annotated.
         self.is_gradient_method = isinstance(
             self.attribution_method, GradientAttribution
         )
         Attribution.__init__(self, self.attribution_method.forward_func)
 
     @property
-    # pyre-fixme[3]: Return type must be annotated.
-    def multiplies_by_inputs(self):
+    def multiplies_by_inputs(self) -> bool:
         return self._multiply_by_inputs
 
     @log_usage()
@@ -205,9 +204,10 @@ def attribute(
                 nt_samples_batch_size, kwargs_copy, inputs, draw_baseline_from_distrib
             )
 
-            sum_attributions: List[Union[None, Tensor]] = []
-            sum_attributions_sq: List[Union[None, Tensor]] = []
+            sum_attributions: Sequence[Union[None, Tensor]] = []
+            sum_attributions_sq: Sequence[Union[None, Tensor]] = []
             delta_partial_list: List[Tensor] = []
+            is_attrib_tuple = is_inputs_tuple
 
             for _ in range(nt_samples_partition):
                 inputs_with_noise = self._add_noise_to_inputs(
@@ -225,11 +225,7 @@ def attribute(
                 )
 
                 if len(sum_attributions) == 0:
-                    # pyre-fixme[9]: sum_attributions has type
-                    #  `List[Optional[Tensor]]`; used as `List[None]`.
                     sum_attributions = [None] * len(attributions_partial)
-                    # pyre-fixme[9]: sum_attributions_sq has type
-                    #  `List[Optional[Tensor]]`; used as `List[None]`.
                     sum_attributions_sq = [None] * len(attributions_partial)
 
                 self._update_partial_attribution_and_delta(
@@ -297,7 +293,6 @@ def attribute(
 
         return self._apply_checks_and_return_attributions(
             attributions,
-            # pyre-fixme[61]: `is_attrib_tuple` is undefined, or not always defined.
             is_attrib_tuple,
             return_convergence_delta,
             delta,
@@ -348,9 +343,7 @@ def _add_noise_to_input(
         bsz = input.shape[0]
 
         # expand input size by the number of drawn samples
-        # pyre-fixme[58]: `+` is not supported for operand types `Tuple[int]`
-        #  and `Size`.
-        input_expanded_size = (bsz * nt_samples_partition,) + input.shape[1:]
+        input_expanded_size = (bsz * nt_samples_partition,) + tuple(input.shape[1:])
 
         # expand stdev for the shape of the input and number of drawn samples
         stdev_expanded = torch.tensor(stdev, device=input.device).repeat(
@@ -375,14 +368,13 @@ def _update_sum_attribution_and_sq(
         bsz = attribution.shape[0] // nt_samples_batch_size_inter
         attribution_shape = cast(Tuple[int, ...], (bsz, nt_samples_batch_size_inter))
         if len(attribution.shape) > 1:
-            # pyre-fixme[22]: The cast is redundant.
-            attribution_shape += cast(Tuple[int, ...], tuple(attribution.shape[1:]))
+            attribution_shape += tuple(attribution.shape[1:])
 
         attribution = attribution.view(attribution_shape)
         current_attribution_sum = attribution.sum(dim=1, keepdim=False)
-        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-        #  `int`.
-        current_attribution_sq = torch.sum(attribution**2, dim=1, keepdim=False)
+        current_attribution_sq = torch.sum(
+            torch.pow(attribution, 2), dim=1, keepdim=False
+        )
 
         sum_attribution[i] = (
             current_attribution_sum
@@ -398,8 +390,7 @@ def _update_sum_attribution_and_sq(
     def _compute_partial_attribution(
         self,
         inputs_with_noise_partition: Tuple[Tensor, ...],
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        kwargs_partition: Any,
+        kwargs_partition: object,
         is_inputs_tuple: bool,
         return_convergence_delta: bool,
     ) -> Tuple[Tuple[Tensor, ...], bool, Union[None, Tensor]]:
@@ -505,14 +496,12 @@ def _apply_checks_and_return_attributions(
     ) -> Union[
         TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
     ]:
-        # pyre-fixme[9]: Unable to unpack `Union[Tensor, typing.Tuple[Tensor,
-        #  ...]]`, expected a tuple.
-        attributions = _format_output(is_attrib_tuple, attributions)
+        attributions_tuple = _format_output(is_attrib_tuple, attributions)
 
         ret = (
-            (attributions, cast(Tensor, delta))
+            (attributions_tuple, cast(Tensor, delta))
             if self.is_delta_supported and return_convergence_delta
-            else attributions
+            else attributions_tuple
         )
         ret = cast(
             # pyre-fixme[34]: `Variable[TensorOrTupleOfTensorsGeneric <:
diff --git a/captum/attr/_core/occlusion.py b/captum/attr/_core/occlusion.py
index 33c1531108..62ac38e84d 100644
--- a/captum/attr/_core/occlusion.py
+++ b/captum/attr/_core/occlusion.py
@@ -37,8 +37,7 @@ class Occlusion(FeatureAblation):
     /tensorflow/methods.py#L401
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Tensor]) -> None:
         r"""
         Args:
 
@@ -58,8 +57,7 @@ def attribute(  # type: ignore
         ] = None,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
     ) -> TensorOrTupleOfTensorsGeneric:
@@ -377,9 +375,7 @@ def _occlusion_mask(
         padded_tensor = torch.nn.functional.pad(
             sliding_window_tsr, tuple(pad_values)  # type: ignore
         )
-        # pyre-fixme[58]: `+` is not supported for operand types `Tuple[int]` and
-        #  `Size`.
-        return padded_tensor.reshape((1,) + padded_tensor.shape)
+        return padded_tensor.reshape((1,) + tuple(padded_tensor.shape))
 
     def _get_feature_range_and_mask(
         self, input: Tensor, input_mask: Optional[Tensor], **kwargs: Any
@@ -389,8 +385,7 @@ def _get_feature_range_and_mask(
 
     def _get_feature_counts(
         self,
-        # pyre-fixme[2]: Parameter must be annotated.
-        inputs,
+        inputs: TensorOrTupleOfTensorsGeneric,
         feature_mask: Tuple[Tensor, ...],
         **kwargs: Any,
     ) -> Tuple[int, ...]:
diff --git a/captum/attr/_core/saliency.py b/captum/attr/_core/saliency.py
index 29205725c0..8698099db7 100644
--- a/captum/attr/_core/saliency.py
+++ b/captum/attr/_core/saliency.py
@@ -2,7 +2,7 @@
 
 # pyre-strict
 
-from typing import Any, Callable
+from typing import Callable
 
 import torch
 from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
@@ -13,6 +13,7 @@
 from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import GradientAttribution
 from captum.log import log_usage
+from torch import Tensor
 
 
 class Saliency(GradientAttribution):
@@ -25,8 +26,7 @@ class Saliency(GradientAttribution):
         https://arxiv.org/abs/1312.6034
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Tensor]) -> None:
         r"""
         Args:
 
@@ -41,8 +41,7 @@ def attribute(
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
         abs: bool = True,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
         Args:
@@ -124,29 +123,21 @@ def attribute(
         """
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
 
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs = _format_tensor_into_tuples(inputs)
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        gradient_mask = apply_gradient_requirements(inputs)
+        inputs_tuple = _format_tensor_into_tuples(inputs)
+        gradient_mask = apply_gradient_requirements(inputs_tuple)
 
         # No need to format additional_forward_args here.
         # They are being formated in the `_run_forward` function in `common.py`
         gradients = self.gradient_func(
-            self.forward_func, inputs, target, additional_forward_args
+            self.forward_func, inputs_tuple, target, additional_forward_args
         )
         if abs:
             attributions = tuple(torch.abs(gradient) for gradient in gradients)
         else:
             attributions = gradients
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        undo_gradient_requirements(inputs, gradient_mask)
+        undo_gradient_requirements(inputs_tuple, gradient_mask)
         # pyre-fixme[7]: Expected `TensorOrTupleOfTensorsGeneric` but got
         #  `Tuple[Tensor, ...]`.
         return _format_output(is_inputs_tuple, attributions)
diff --git a/captum/attr/_core/shapley_value.py b/captum/attr/_core/shapley_value.py
index 081bd75067..8f8d791377 100644
--- a/captum/attr/_core/shapley_value.py
+++ b/captum/attr/_core/shapley_value.py
@@ -5,7 +5,7 @@
 import itertools
 import math
 import warnings
-from typing import Any, Callable, cast, Iterable, Sequence, Tuple, Union
+from typing import Callable, cast, Iterable, Sequence, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -56,9 +56,7 @@ def _shape_feature_mask(
             f"input shape: {inp.shape}, feature mask shape {mask.shape}"
         )
         if mask.dim() < inp.dim():
-            # pyre-fixme[58]: `+` is not supported for operand types `Tuple[int,
-            #  ...]` and `Size`.
-            mask = mask.reshape((1,) * (inp.dim() - mask.dim()) + mask.shape)
+            mask = mask.reshape((1,) * (inp.dim() - mask.dim()) + tuple(mask.shape))
 
         mask_list.append(mask)
 
@@ -89,8 +87,7 @@ class ShapleyValueSampling(PerturbationAttribution):
     https://pdfs.semanticscholar.org/7715/bb1070691455d1fcfc6346ff458dbca77b2c.pdf
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Union[int, float, Tensor]]) -> None:
         r"""
         Args:
 
@@ -111,8 +108,7 @@ def attribute(
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
         n_samples: int = 25,
         perturbations_per_eval: int = 1,
@@ -301,45 +297,25 @@ def attribute(
         """
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
-        # pyre-fixme[9]: inputs has type `TensorOrTupleOfTensorsGeneric`; used as
-        #  `Tuple[Tensor, ...]`.
-        inputs, baselines = _format_input_baseline(inputs, baselines)
+        inputs_tuple, baselines = _format_input_baseline(inputs, baselines)
         additional_forward_args = _format_additional_forward_args(
             additional_forward_args
         )
-        # pyre-fixme[9]: feature_mask has type
-        #  `Optional[Variable[TensorOrTupleOfTensorsGeneric <: [Tensor,
-        #  typing.Tuple[Tensor, ...]]]]`; used as `Tuple[Tensor, ...]`.
-        # pyre-fixme[6]: For 2nd argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        feature_mask = _format_feature_mask(feature_mask, inputs)
-        # pyre-fixme[9]: feature_mask has type
-        #  `Optional[Variable[TensorOrTupleOfTensorsGeneric <: [Tensor,
-        #  typing.Tuple[Tensor, ...]]]]`; used as `Tuple[Tensor, ...]`.
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `Optional[Variable[TensorOrTupleOfTensorsGeneric <: [Tensor,
-        #  typing.Tuple[Tensor, ...]]]]`.
-        # pyre-fixme[6]: For 2nd argument expected `Tuple[Tensor, ...]` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
-        feature_mask = _shape_feature_mask(feature_mask, inputs)
+        formatted_feature_mask = _format_feature_mask(feature_mask, inputs_tuple)
+        reshaped_feature_mask = _shape_feature_mask(
+            formatted_feature_mask, inputs_tuple
+        )
 
         assert (
             isinstance(perturbations_per_eval, int) and perturbations_per_eval >= 1
         ), "Ablations per evaluation must be at least 1."
 
         with torch.no_grad():
-            # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-            #  `TensorOrTupleOfTensorsGeneric`.
-            baselines = _tensorize_baseline(inputs, baselines)
-            num_examples = inputs[0].shape[0]
+            baselines = _tensorize_baseline(inputs_tuple, baselines)
+            num_examples = inputs_tuple[0].shape[0]
 
-            # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-            #  `Optional[Variable[TensorOrTupleOfTensorsGeneric <: [Tensor,
-            #  typing.Tuple[Tensor, ...]]]]`.
-            total_features = _get_max_feature_index(feature_mask) + 1
+            total_features = _get_max_feature_index(reshaped_feature_mask) + 1
 
             if show_progress:
                 attr_progress = progress(
@@ -362,7 +338,7 @@ def attribute(
                 initial_eval,
                 num_examples,
                 perturbations_per_eval,
-                feature_mask,
+                reshaped_feature_mask,
                 allow_multi_outputs=True,
             )
 
@@ -372,11 +348,11 @@ def attribute(
             # attr shape (*output_shape, *input_feature_shape)
             total_attrib = [
                 torch.zeros(
-                    output_shape + input.shape[1:],
+                    tuple(output_shape) + tuple(input.shape[1:]),
                     dtype=torch.float,
-                    device=inputs[0].device,
+                    device=inputs_tuple[0].device,
                 )
-                for input in inputs
+                for input in inputs_tuple
             ]
 
             iter_count = 0
@@ -393,17 +369,11 @@ def attribute(
                     current_target,
                     current_masks,
                 ) in self._perturbation_generator(
-                    # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]`
-                    #  but got `TensorOrTupleOfTensorsGeneric`.
-                    inputs,
+                    inputs_tuple,
                     additional_forward_args,
                     target,
                     baselines,
-                    # pyre-fixme[6]: For 5th argument expected
-                    #  `TensorOrTupleOfTensorsGeneric` but got
-                    #  `Optional[Variable[TensorOrTupleOfTensorsGeneric <: [Tensor,
-                    #  typing.Tuple[Tensor, ...]]]]`.
-                    feature_mask,
+                    reshaped_feature_mask,
                     feature_permutation,
                     perturbations_per_eval,
                 ):
@@ -445,10 +415,8 @@ def attribute(
                         # have the same dim as the mask tensor.
                         formatted_eval_diff = eval_diff.reshape(
                             (-1,)
-                            # pyre-fixme[58]: `+` is not supported for operand types
-                            #  `Tuple[int]` and `Size`.
-                            + output_shape
-                            + (len(inputs[j].shape) - 1) * (1,)
+                            + tuple(output_shape)
+                            + (len(inputs_tuple[j].shape) - 1) * (1,)
                         )
 
                         # mask in shape (n_perturb, *mask_shape_broadcastable_to_input)
@@ -460,11 +428,9 @@ def attribute(
                         # )
                         cur_mask = current_masks[j]
                         cur_mask = cur_mask.reshape(
-                            cur_mask.shape[:2]
+                            tuple(cur_mask.shape[:2])
                             + (len(output_shape) - 1) * (1,)
-                            # pyre-fixme[58]: `+` is not supported for operand types
-                            #  `Tuple[int, ...]` and `Size`.
-                            + cur_mask.shape[2:]
+                            + tuple(cur_mask.shape[2:])
                         )
 
                         # aggregate n_perturb
@@ -495,18 +461,16 @@ def attribute_future(self) -> Callable:
             "attribute_future is not implemented for ShapleyValueSampling"
         )
 
-    # pyre-fixme[3]: Return annotation cannot contain `Any`.
     def _perturbation_generator(
         self,
         inputs: Tuple[Tensor, ...],
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_args: Any,
+        additional_args: object,
         target: TargetType,
         baselines: Tuple[Tensor, ...],
         input_masks: TensorOrTupleOfTensorsGeneric,
         feature_permutation: Sequence[int],
         perturbations_per_eval: int,
-    ) -> Iterable[Tuple[Tuple[Tensor, ...], Any, TargetType, Tuple[Tensor, ...]]]:
+    ) -> Iterable[Tuple[Tuple[Tensor, ...], object, TargetType, Tuple[Tensor, ...]]]:
         """
         This method is a generator which yields each perturbation to be evaluated
         including inputs, additional_forward_args, targets, and mask.
@@ -578,9 +542,9 @@ def _perturbation_generator(
                 combined_masks,
             )
 
-    # pyre-fixme[3]: Return type must be annotated.
-    # pyre-fixme[2]: Parameter must be annotated.
-    def _get_n_evaluations(self, total_features, n_samples, perturbations_per_eval):
+    def _get_n_evaluations(
+        self, total_features: int, n_samples: int, perturbations_per_eval: int
+    ) -> int:
         """return the total number of forward evaluations needed"""
         return math.ceil(total_features / perturbations_per_eval) * n_samples
 
@@ -642,8 +606,7 @@ class ShapleyValues(ShapleyValueSampling):
     evaluations, and we plan to add this approach in the future.
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Union[int, float, Tensor]]) -> None:
         r"""
         Args:
 
@@ -664,8 +627,7 @@ def attribute(
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
diff --git a/captum/attr/_utils/common.py b/captum/attr/_utils/common.py
index 92c1ccafb7..9cb38b10d8 100644
--- a/captum/attr/_utils/common.py
+++ b/captum/attr/_utils/common.py
@@ -82,6 +82,12 @@ def _format_input_baseline(  # type: ignore
 ) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]: ...
 
 
+@typing.overload
+def _format_input_baseline(  # type: ignore
+    inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType
+) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]: ...
+
+
 def _format_input_baseline(
     inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType
 ) -> Tuple[Tuple[Tensor, ...], Tuple[Union[Tensor, int, float], ...]]:
@@ -236,6 +242,21 @@ def _compute_conv_delta_and_format_attrs(
 ) -> Union[Tensor, Tuple[Tensor, Tensor]]: ...
 
 
+@typing.overload
+def _compute_conv_delta_and_format_attrs(
+    attr_algo: "GradientAttribution",
+    return_convergence_delta: bool,
+    attributions: Tuple[Tensor, ...],
+    start_point: Union[int, float, Tensor, Tuple[Union[int, float, Tensor], ...]],
+    end_point: Union[Tensor, Tuple[Tensor, ...]],
+    additional_forward_args: Any,
+    target: TargetType,
+    is_inputs_tuple: bool = False,
+) -> Union[
+    Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]
+]: ...
+
+
 # FIXME: GradientAttribution is provided as a string due to a circular import.
 # This should be fixed when common is refactored into separate files.
 def _compute_conv_delta_and_format_attrs(