Revert D25718705: Clean up type annotations in caffe2/torch/nn/modules

Test Plan: revert-hammer

Differential Revision:
D25718705 (891759f)

Original commit changeset: 6a9e3e6d17aa

fbshipit-source-id: 1a4ef0bfdec8eb8e7ce149bfbdb34a4ad8d964b6

torch/nn/modules/activation.py

@@ -921,8 +921,9 @@ def __setstate__(self, state):
 
         super(MultiheadAttention, self).__setstate__(state)
 
-    def forward(self, query: Tensor, key: Tensor, value: Tensor, key_padding_mask: Optional[Tensor] = None,
-                need_weights: bool = True, attn_mask: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
+    def forward(self, query, key, value, key_padding_mask=None,
+                need_weights=True, attn_mask=None):
+        # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
         r"""
     Args:
         query, key, value: map a query and a set of key-value pairs to an output.

torch/nn/modules/conv.py

@@ -530,7 +530,8 @@ def __init__(self, in_channels, out_channels, kernel_size, stride,
 
     # dilation being an optional parameter is for backwards
     # compatibility
-    def _output_padding(self, input: Tensor, output_size: Optional[List[int]], stride: List[int], padding: List[int], kernel_size: List[int], dilation: Optional[List[int]] = None) -> List[int]:
+    def _output_padding(self, input, output_size, stride, padding, kernel_size, dilation=None):
+        # type: (Tensor, Optional[List[int]], List[int], List[int], List[int], Optional[List[int]]) -> List[int]
         if output_size is None:
             ret = _single(self.output_padding)  # converting to list if was not already
         else:

torch/nn/modules/utils.py

@@ -26,7 +26,8 @@ def _reverse_repeat_tuple(t, n):
     return tuple(x for x in reversed(t) for _ in range(n))
 
 
-def _list_with_default(out_size: List[int], defaults: List[int]) -> List[int]:
+def _list_with_default(out_size, defaults):
+    # type: (List[int], List[int]) -> List[int]
     if isinstance(out_size, int):
         return out_size
     if len(defaults) <= len(out_size):