diff --git a/torch/nn/modules/activation.py b/torch/nn/modules/activation.py
index 0c5258615bfd..5fc6cdf456f7 100644
--- a/torch/nn/modules/activation.py
+++ b/torch/nn/modules/activation.py
@@ -921,9 +921,8 @@ def __setstate__(self, state):
 
         super(MultiheadAttention, self).__setstate__(state)
 
-    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]]
+    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]]:
         r"""
     Args:
         query, key, value: map a query and a set of key-value pairs to an output.
diff --git a/torch/nn/modules/conv.py b/torch/nn/modules/conv.py
index f22c35fa39ff..6a9c4dcd2ef6 100644
--- a/torch/nn/modules/conv.py
+++ b/torch/nn/modules/conv.py
@@ -530,8 +530,9 @@ def __init__(self, in_channels, out_channels, kernel_size, stride,
 
     # dilation being an optional parameter is for backwards
     # compatibility
-    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]
+    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]:
         if output_size is None:
             ret = _single(self.output_padding)  # converting to list if was not already
         else:
diff --git a/torch/nn/modules/utils.py b/torch/nn/modules/utils.py
index 3e0b93c7afc0..97e4195619cb 100644
--- a/torch/nn/modules/utils.py
+++ b/torch/nn/modules/utils.py
@@ -26,8 +26,7 @@ def _reverse_repeat_tuple(t, n):
     return tuple(x for x in reversed(t) for _ in range(n))
 
 
-def _list_with_default(out_size, defaults):
-    # type: (List[int], List[int]) -> List[int]
+def _list_with_default(out_size: List[int], defaults: List[int]) -> List[int]:
     if isinstance(out_size, int):
         return out_size
     if len(defaults) <= len(out_size):