    def forward(self, inputs: torch.Tensor, mask: torch.BoolTensor = None) -> torch.Tensor:
        """
        # Parameters

        inputs : `torch.Tensor`, required.
            A tensor of shape (batch_size, timesteps, input_dim)
        mask : `torch.BoolTensor`, optional (default = `None`).
            A tensor of shape (batch_size, timesteps).

        # Returns

        A tensor of shape (batch_size, timesteps, output_dim),
        where output_dim = input_dim.
        """
        if mask is None:
            return inputs
        else:
            # We should mask out the output instead of the input.
            # But here, output = input, so we directly mask out the input.
            return inputs * mask.unsqueeze(dim=-1)
# Parameters

inputs : `torch.Tensor`, required.
A tensor of shape (batch_size, timesteps, input_dim)
mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of shape (batch_size, timesteps).

# Returns

A tensor of shape (batch_size, timesteps, output_dim),
where output_dim = input_dim.
allennlp/modules/seq2seq_encoders/pass_through_encoder.py