Fix layer gradient x activation pyre fixme issues (#1472)

Summary:
Pull Request resolved: #1472

Fixing unresolved pyre fixme issues in corresponding file

Reviewed By: cyrjano

Differential Revision: D67705758

fbshipit-source-id: e05d67d46a59211cd959f8824f6069d990f13720

Author: Vivek Miglani

captum/attr/_core/layer/layer_gradient_x_activation.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 # pyre-strict
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
 
 from captum._utils.common import (
     _format_additional_forward_args,
@@ -24,8 +24,7 @@ class LayerGradientXActivation(LayerAttribution, GradientAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         layer: ModuleOrModuleList,
         device_ids: Union[None, List[int]] = None,
         multiply_by_inputs: bool = True,
@@ -186,11 +185,10 @@ def attribute(
         if isinstance(self.layer, Module):
             return _format_output(
                 len(layer_evals) > 1,
-                # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but
-                #  got `List[typing.Tuple[Tensor, ...]]`.
-                # pyre-fixme[6]: For 2nd argument expected `Tuple[Tensor, ...]` but
-                #  got `List[typing.Tuple[Tensor, ...]]`.
-                self.multiply_gradient_acts(layer_gradients, layer_evals),
+                self.multiply_gradient_acts(
+                    cast(Tuple[Tensor, ...], layer_gradients),
+                    cast(Tuple[Tensor, ...], layer_evals),
+                ),
             )
         else:
             return [