code: def forward(
      self,
      src_tokens,
      src_lengths,
      prev_output_tokens,
      features_only: bool = False,
      classification_head_name: Optional[str] = None,
      token_embeddings: Optional[torch.Tensor] = None,
      return_all_hiddens: bool = True,
      alignment_layer: Optional[int] = None,
      alignment_heads: Optional[int] = None,
  ):
      if classification_head_name is not None:
          features_only = True
  
      encoder_out = self.encoder(
          src_tokens,
          src_lengths=src_lengths,
          token_embeddings=token_embeddings,
          return_all_hiddens=return_all_hiddens,
      )
      x, extra = self.decoder(
          prev_output_tokens,
          encoder_out=encoder_out,
          features_only=features_only,
          alignment_layer=alignment_layer,
          alignment_heads=alignment_heads,
          src_lengths=src_lengths,
          return_all_hiddens=return_all_hiddens,
      )
      eos: int = self.eos
      if classification_head_name is not None:
          sentence_representation = x[src_tokens.eq(eos), :].view(
              x.size(0), -1, x.size(-1)
          )[:, -1, :]
          for k, head in self.classification_heads.items():
              # for torch script only supports iteration
              if k == classification_head_name:
                  x = head(sentence_representation)
                  break
      return x, extra
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import TransformerModel
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from .hub_interface import BARTHubInterface
label_desc: Use image node_id 5 for calling the BARTModel obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths, prev_output_tokens, features_only, classification_head_name, token_embeddings, return_all_hiddens, alignment_layer, alignment_heads) and returns: x,  extra