class_docstr: Assumes that the input is already vectorized in some way,
  and just returns it.
  
  Registered as a `TokenEmbedder` with name "pass_through".
  
  # Parameters
  
  hidden_dim : `int`, required.
class_docstr_tok: ["Assumes","that","the","input","is","already","vectorized","in","some","way",",","and","just","returns","it",".","Registered","as","a","`","TokenEmbedder","`","with","name","``","pass_through","''",".","#","Parameters","hidden_dim",":","`","int","`",",","required","."]
code: def forward(self, tokens: torch.Tensor) -> torch.Tensor:
      return tokens
  
imports: import torch
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
label_desc: Use image node_id 3 for calling the PassThroughTokenEmbedder obj's underlying member method code with example usage: obj.forward(tokens) and returns: tokens