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_from_pretrained(self, opt):
      # load model
      if opt.get("model_name"):
          fle_key = opt["model_name"]
      else:
          model_sz = opt["gpt2_size"]
          if model_sz == "small":
              model_key = "gpt2"
          elif model_sz == "distilgpt2":
              model_key = "distilgpt2"
          else:
              model_key = f"gpt2-{model_sz}"
  
          # check if datapath has the files that hugging face code looks for
          hf_dir = os.path.join(opt["datapath"], "hf", model_key)
          if all(
              PathManager.exists(os.path.join(hf_dir, file_name))
              for file_name in ["pytorch_model.bin", "config.json"]
          ):
              fle_key = PathManager.get_local_path(
                  hf_dir, recursive=True
              )
          else:
              fle_key = model_key
      return GPT2Model.from_pretrained(fle_key)
  
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 2 for calling the GPT2Decoder obj's underlying member method code with example usage: obj._init_from_pretrained(opt) and returns: GPT2Model