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 reorder_decoder_incremental_state(self, incremental_state, inds):
      new_incr_state = []
      for layer_past in incremental_state:
          if torch.is_tensor(layer_past):
              new_incr_state.append(
                  torch.index_select(layer_past, 1, inds)
              )
          else:
              # newer versions of HF split up the intermediate outputs
              assert isinstance(layer_past, tuple)
              layer_past = torch.stack(layer_past, dim=0)
              new_incr_state.append(
                  torch.index_select(layer_past, 1, inds)
              )
  
      return tuple(new_incr_state)
  
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 7 for calling the HFGPT2Model obj's underlying member method code with example usage: obj.reorder_decoder_incremental_state(incremental_state, inds) and returns: tuple