class_docstr: This class allows you to specify skipping a `Seq2SeqEncoder` just
  by changing a configuration file. This is useful for ablations and
  measuring the impact of different elements of your model.
  
  Registered as a `Seq2SeqEncoder` with name "pass_through".
docstr: # 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.
code: 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)
  
file_path: allennlp/allennlp/modules/seq2seq_encoders/pass_through_encoder.py
imports: import torch
  from allennlp.modules.seq2seq_encoders.seq2seq_encoder import Seq2SeqEncoder
label_desc: Use image node_id 5 for calling the PassThroughEncoder obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: inputs, unknown