Fix pyre errors in Integrated Gradients (pytorch#1398)

Summary:

Initial work on fixing Pyre errors in Integrated Gradients

Reviewed By: csauper

Differential Revision: D64677345

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,