code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      images: Optional[torch.FloatTensor] = None,
      prompts: Optional[List[str]] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, CausalLMOutputWithPast]:
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if not self.training:
          if images[0].device != self.device:
              images[0] = images[0].to(device=self.device)
          if input_ids.device != self.device:
              input_ids = input_ids.to(device=self.device)
  
      (
          input_ids,
          attention_mask,
          past_key_values,
          inputs_embeds,
          labels,
      ) = self.prepare_inputs_labels_for_multimodal(
          input_ids,
          attention_mask,
          past_key_values,
          labels,
          images,
          prompts=prompts,
      )
  
      torch.cuda.empty_cache()
  
      # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
      outputs = self.model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      hidden_states = outputs[0]
      logits = self.lm_head(hidden_states)
  
      loss = None
      if labels is not None:
          # Shift so that tokens < n predict n
          shift_logits = logits[..., :-1, :].contiguous()
          shift_labels = labels[..., 1:].contiguous()
          # Flatten the tokens
          loss_fct = CrossEntropyLoss()
          shift_logits = shift_logits.view(-1, self.config.vocab_size)
          shift_labels = shift_labels.view(-1)
          # Enable model/pipeline parallelism
          shift_labels = shift_labels.to(shift_logits.device)
          loss = loss_fct(shift_logits, shift_labels)
  
      if not return_dict:
          output = (logits,) + outputs[1:]
          return (loss,) + output if loss is not None else output
  
      return CausalLMOutputWithPast(
          loss=loss,
          logits=logits,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
file_path: llama-vid/llamavid/model/language_model/llava_llama_vid.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss
  from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from llamavid.model.llamavid_arch import LLaMAVIDMetaModel, LLaMAVIDMetaForCausalLM
label_desc: Use image node_id 3 for calling the LlavaLlamaAttForCausalLM obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, images, prompts, return_dict) and returns: CausalLMOutputWithPast, unknown