class_docstr: GPT2 Decoder.
  
  This decoder is initialized with the pretrained model from Hugging Face.
class_docstr_tok: ["GPT2","Decoder",".","This","decoder","is","initialized","with","the","pretrained","model","from","Hugging","Face","."]
code: def __init__(self, opt, dict):
      super().__init__()
      self.transformer = self._init_from_pretrained(opt)
      # add special tokens
      if opt["add_special_tokens"]:
          size_before = self.transformer.wte.weight.size(0)
          self.transformer.resize_token_embeddings(
              len(dict.hf_tokenizer)
          )
          with torch.no_grad():
              # first reduce the random jitter of the initialization
              self.transformer.wte.weight[size_before:] *= 0.1
              # next center it on the endoftext token
              self.transformer.wte.weight[
                  size_before:
              ] += self.transformer.wte.weight[
                  size_before - 1
              ].unsqueeze(
                  0
              )
  
      self.add_start_token = opt["add_start_token"]
      self.START_IDX = dict.start_idx
      self.NULL_IDX = dict.null_idx
      self.END_IDX = dict.end_idx
      # use cuda
      self.use_cuda = not opt["no_cuda"] and torch.cuda.is_available()
  
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 GPT2Decoder object from inherited base classes: torch.nn with example: obj = GPT2Decoder(opt, dict)