code: def forward(
      self,
      samples,
      past_key_values=None,
      position_ids=None,
      head_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      use_cache=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      input_embs = self.transformer.wte(samples["input_ids"])
      video_embs = self.video_ff(samples["video_fts"])
      input_embs = torch.cat([video_embs, input_embs], dim=1)
  
      transformer_outputs = self.transformer(
          attention_mask=samples["attn_mask"],
          token_type_ids=samples["token_type_ids"],
          inputs_embeds=input_embs,
          position_ids=position_ids,
          head_mask=head_mask,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
      hidden_states = transformer_outputs[0]
  
      lm_logits = self.lm_head(hidden_states)
  
      loss = None
      if samples["labels"] is not None:
          # Shift so that tokens < n predict n
          shift_logits = lm_logits[..., :-1, :].contiguous()
          shift_labels = samples["labels"][..., 1:].contiguous()
          # Flatten the tokens
          loss_fct = CrossEntropyLoss(ignore_index=-1)
          loss = loss_fct(
              shift_logits.view(-1, shift_logits.size(-1)),
              shift_labels.view(-1),
          )
  
      if samples["video_fts"] is not None:
          len_video_fts = samples["video_fts"].shape[1]
          video_logits = self.video_ff_out(
              hidden_states[:, :len_video_fts, :]
          )
          # Shift so that tokens < n predict n
          shift_logits = video_logits[..., :-1, :].contiguous()
          shift_labels = samples["video_fts"][..., 1:, :].contiguous()
          # Flatten the tokens
          loss_fct = MSELoss(reduction="mean")
          video_loss = loss_fct(shift_logits, shift_labels)
  
          if loss is not None:
              loss = loss + video_loss
          else:
              loss = video_loss
  
      return CausalLMOutputWithCrossAttentions(
          loss=loss,
          logits=lm_logits,
          past_key_values=transformer_outputs.past_key_values,
          hidden_states=transformer_outputs.hidden_states,
          attentions=transformer_outputs.attentions,
          cross_attentions=transformer_outputs.cross_attentions,
      )
  
file_path: lavis/lavis/models/gpt_models/gpt_dialogue.py
imports: import torch
  import torch.nn
  from lavis.common.registry import registry
  from lavis.models.base_model import BaseModel
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers import GPT2LMHeadModel
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
label_desc: Use image node_id 2 for calling the GPTDialogue obj's underlying member method code with example usage: obj.forward(samples, past_key_values, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict) and returns: CausalLMOutputWithCrossAttentions