class_docstr: Wrap transformer response.
  
  Pushes input through transformer and MLP.
class_docstr_tok: ["Wrap","transformer","response",".","Pushes","input","through","transformer","and","MLP","."]
code: def __init__(self, transformer: TransformerEncoder, hdim: int):
      super(TransformerResponseWrapper, self).__init__()
      dim = transformer.out_dim
      self.transformer = transformer
      self.mlp = nn.Sequential(
          nn.Linear(dim, hdim),
          nn.ReLU(),  # TODO: should this also be gelu?
          nn.Linear(hdim, dim),
      )
  
file_path: ParlAI/parlai/agents/transformer/modules/wrappers.py
imports: import torch
  import torch.nn
  from parlai.agents.transformer.modules import TransformerEncoder
label_desc: Use image node_id 1 to create a new TransformerResponseWrapper object from inherited base classes: nn with example: obj = TransformerResponseWrapper(transformer, hdim)