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torch/nn/modules/rnn.py

@@ -531,6 +531,8 @@ class LSTM(RNNBase):
     def __init__(self, *args, **kwargs):
         super(LSTM, self).__init__('LSTM', *args, **kwargs)
 
+    # In the future, we should prevent mypy from applying contravariance rules here.
+    # See torch/nn/modules/module.py::_forward_unimplemented
     def check_forward_args(self, input: Tensor, hidden: Tuple[Tensor, Tensor], batch_sizes: Optional[Tensor]):  # type: ignore
         self.check_input(input, batch_sizes)
         expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
@@ -540,17 +542,20 @@ def check_forward_args(self, input: Tensor, hidden: Tuple[Tensor, Tensor], batch
         self.check_hidden_size(hidden[1], expected_hidden_size,
                                'Expected hidden[1] size {}, got {}')
 
+    # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
     def permute_hidden(self, hx: Tuple[Tensor, Tensor], permutation: Optional[Tensor]) -> Tuple[Tensor, Tensor]:  # type: ignore
         if permutation is None:
             return hx
         return apply_permutation(hx[0], permutation), apply_permutation(hx[1], permutation)
 
+    # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
     @overload  # type: ignore
     @torch._jit_internal._overload_method  # noqa: F811
     def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
                 ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:  # noqa: F811
         pass
 
+    # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
     @overload
     @torch._jit_internal._overload_method  # noqa: F811
     def forward(self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None