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 forward(self, input, encoder_state, incr_state=None):
      attention_mask = None
      position_ids = None
      if incr_state is None:
          # first step
          if (
              not self.add_start_token
              and input.size(1) == 1
              and int(input[0][0]) == self.START_IDX
          ):
              # generating: ignore the start token
              # without deep copy, the padding_idx (-1) in encoder_state can be reset to 0 with clamp_ inplace operation
              model_input = encoder_state.clone()
          else:
              # forced decoding: concatenate the context
              # with the labels
              model_input = torch.cat([encoder_state, input], dim=-1)
          attention_mask = model_input != self.NULL_IDX
          position_ids = (
              attention_mask.cumsum(dim=-1, dtype=torch.int64) - 1
          ).clamp_(min=0)
      else:
          if not self.add_start_token:
              input = input[:, 1:]
          # generating with continuation
          # get the position ids
          position_ids = (encoder_state != self.NULL_IDX).sum(
              -1, True, dtype=torch.int64
          ) - 1
          delta = ((input != self.NULL_IDX)).sum(
              -1, True, dtype=torch.int64
          )
          position_ids += delta
          # generation: get the last token input
          model_input = input[:, -1:]
          attention_mask = (
              torch.cat([encoder_state, input], dim=-1) != self.NULL_IDX
          )
  
      model_input = model_input.clamp_(min=0)
      transformer_outputs = self.transformer(
          model_input,
          past_key_values=incr_state,
          attention_mask=attention_mask,
          position_ids=position_ids,
      )
      hidden_states = transformer_outputs[0]
      new_incr_state = transformer_outputs[1]
  
      if incr_state is None:
          # pull out only the hidden states for the label tokens
          output = hidden_states[
              :, -input.size(1) - 1 + int(self.add_start_token) :
          ]
          # hack: we need the last state of the encoder-side to be the first
          # element of the decoder-side
          lengths = (input != self.NULL_IDX).sum(dim=-1)
          for i in range(input.size(0)):
              output[i, input.size(1) - lengths[i]] = output[i, 0]
  
      else:
          # generation, we're only doing one token at a time. no need to
          # shove things back in
          output = hidden_states
  
      return output, 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 3 for calling the GPT2Decoder obj's underlying member method code with example usage: obj.forward(input, encoder_state, incr_state) and returns: output,  new_incr_state