class_docstr: Hugging Face GPT2 Model.
  
  GPT2 is a multi-layer decoder-only Transformer. As such, the encoder
  is simply an identity layer. The decoder is initialized with pretrained
  weights from Hugging Face. Read more about this model here
  <https://huggingface.co/transformers/model_doc/gpt2.html>.
class_docstr_tok: ["Hugging","Face","GPT2","Model",".","GPT2","is","a","multi-layer","decoder-only","Transformer",".","As","such",",","the","encoder","is","simply","an","identity","layer",".","The","decoder","is","initialized","with","pretrained","weights","from","Hugging","Face",".","Read","more","about","this","model","here","<","https",":","\/\/huggingface.co\/transformers\/model_doc\/gpt2.html",">","."]
code: def __init__(self, opt, dict):
      self.add_start_token = opt["add_start_token"]
      super().__init__(*self._get_special_tokens(opt, dict))
  
      # init the model
      self.encoder = IdentityLayer()
      self.decoder = self._get_decoder(opt, dict)
      self.config = self.decoder.transformer.config
      self.lm_head = torch.nn.Linear(
          self.config.n_embd, self.config.vocab_size, bias=False
      )
      self._tie_weights(self.lm_head, self.decoder.transformer.wte)
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import os
  import torch
  from parlai.agents.hugging_face.dict import Gpt2DictionaryAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.utils.io import PathManager
  from parlai.utils.misc import warn_once
  from parlai.utils.torch import IdentityLayer, padded_tensor
label_desc: Use image node_id 1 to create a new HFGPT2Model object from inherited base classes: TorchGeneratorModel with example: obj = HFGPT2Model(opt, dict)