Fix layer conductance pyre fixme issues

Differential Revision: D67705320

Author: vivekmig

captum/attr/_core/layer/layer_conductance.py

@@ -2,7 +2,7 @@
 
 # pyre-strict
 import typing
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Callable, cast, Dict, List, Literal, Optional, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -44,8 +44,7 @@ class LayerConductance(LayerAttribution, GradientAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         layer: Module,
         device_ids: Union[None, List[int]] = None,
     ) -> None:
@@ -73,8 +72,7 @@ def has_convergence_delta(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`.
+    @log_usage()
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -91,8 +89,7 @@ def attribute(
     ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `91`.
+    @log_usage()
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -108,8 +105,6 @@ def attribute(
     ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
 
     @log_usage()
-    # pyre-fixme[43]: This definition does not have the same decorators as the
-    #  preceding overload(s).
     def attribute(
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
@@ -376,7 +371,7 @@ def _attribute(
             layer_evals,
         ) = compute_layer_gradients_and_eval(
             forward_fn=self.forward_func,
-            layer=self.layer,
+            layer=cast(Module, self.layer),
             inputs=scaled_features_tpl,
             additional_forward_args=input_additional_args,
             target_ind=expanded_target,
@@ -389,8 +384,6 @@ def _attribute(
         # This approximates the total input gradient of each step multiplied
         # by the step size.
         grad_diffs = tuple(
-            # pyre-fixme[58]: `-` is not supported for operand types `Tuple[Tensor,
-            #  ...]` and `Tuple[Tensor, ...]`.
             layer_eval[num_examples:] - layer_eval[:-num_examples]
             for layer_eval in layer_evals
         )
@@ -403,8 +396,7 @@ def _attribute(
                 grad_diff * layer_gradient[:-num_examples],
                 n_steps,
                 num_examples,
-                # pyre-fixme[16]: `tuple` has no attribute `shape`.
-                layer_eval.shape[1:],
+                tuple(layer_eval.shape[1:]),
             )
             for layer_gradient, layer_eval, grad_diff in zip(
                 layer_gradients, layer_evals, grad_diffs