class_docstr: Llama Decoder.
  
  This decoder is initialized with the pretrained model from Hugging Face.
code: def forward(
      self,
      input,
      encoder_state,
      incr_state=None,
      sequence_classification=False,
  ):
      attention_mask = None
      position_ids = None
      input_cut_start_token = False
      if sequence_classification:
          assert incr_state is None
      if incr_state is None:
          if sequence_classification:
              # for sequence classification, ignore the input (is None anyway)
              model_input = encoder_state
          elif (
              input.size(1) == 1
              and (input[:, 0] == self.start_idx).all()
          ):
              # first step generating: ignore the start token
              model_input = encoder_state
              input_cut_start_token = True
          else:
              # forced decoding: concatenate the context with the labels
              # cut decoder_start_token:
              if (input[:, 0] == self.start_idx).all():
                  input = input[:, 1:]
                  input_cut_start_token = True
              model_input = torch.cat([encoder_state, input], dim=-1)
  
          attention_mask = model_input != self.padding_idx
          position_ids = (
              attention_mask.cumsum(dim=-1, dtype=torch.int64) - 1
          ).clamp_(min=0)
      else:
          # generating with continuation
          # cut decoder_start_token
          if (input[:, 0] == self.start_idx).all():
              input = input[:, 1:]
              input_cut_start_token = True
          # generation: get the last token input
          model_input = input[:, -1:]
          # get the position ids
          position_ids = (encoder_state != self.padding_idx).sum(
              -1, True, dtype=torch.int64
          ) - 1
          delta = ((input != self.padding_idx)).sum(
              -1, True, dtype=torch.int64
          )
          position_ids += delta
          attention_mask = (
              torch.cat([encoder_state, input], dim=-1)
              != self.padding_idx
          )
  
      model_input = model_input.clamp_(min=0)
      transformer_outputs = self.model(
          input_ids=model_input,
          attention_mask=attention_mask,
          position_ids=position_ids,
          past_key_values=incr_state,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      )
      hidden_states = transformer_outputs[0]
      new_incr_state = transformer_outputs[1]
  
      if sequence_classification is True:
          # return full hidden states for sequence level classification
          output = hidden_states
      elif incr_state is None:
          # pull out only the hidden states for the label tokens
          output = hidden_states[
              :, -input.size(1) - int(input_cut_start_token) :
          ]
      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 torch
  from parlai.agents.hugging_face.dict import LlamaDictionaryAgent, _init_llama_path
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.utils.torch import IdentityLayer, padded_tensor
  from parlai.agents.transformer.transformer import _check_positional_embeddings
  import parlai.utils.logging
label_desc: Use image node_id 2 for calling the ParlaiLlamaDecoder obj's underlying member method code with example usage: obj.forward(input, encoder_state, incr_state, sequence_classification) and returns: output,  new_incr_state