class_docstr: Wrap a transformer in a linear layer.
class_docstr_tok: ["Wrap","a","transformer","in","a","linear","layer","."]
docstr: Forward pass.
  
  Apply transformer, then additional linear layer.
docstr_tok: ["Forward","pass",".","Apply","transformer",",","then","additional","linear","layer","."]
code: def forward(self, *args) -> torch.Tensor:
      """
      Forward pass.
  
      Apply transformer, then additional linear layer.
      """
      context_h = self.transformer(*args)
      return self.additional_linear_layer(context_h)
  
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 2 for calling the TransformerLinearWrapper obj's underlying member method code with example usage: obj.forward() and returns: self