code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      images: torch.Tensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = None,
      labels: Optional[torch.Tensor] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[dict, 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
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if input_ids is not None and inputs_embeds is not None:
          raise ValueError(
              "You cannot specify both input_ids and inputs_embeds at the same time"
          )
      elif input_ids is None and inputs_embeds is None:
          raise ValueError(
              "You have to specify either input_is or inputs_embeds"
          )
  
      if inputs_embeds is None and past_key_values is None:
          inputs_embeds = self.get_input_embeddings()(input_ids)
  
          if images is not None:
              image_embeds = self.get_images_embeddings(images)
              inputs_embeds = self.gather_continuous_embeddings(
                  input_ids, inputs_embeds, image_embeds
              )
  
      if position_ids is None:
          seq_length = (
              inputs_embeds.shape[1]
              if inputs_embeds is not None
              else input_ids.shape[1]
          )
          past_key_values_length = 0
  
          if past_key_values is not None:
              past_key_values_length = past_key_values[0][0].shape[2]
  
          device = (
              input_ids.device
              if input_ids is not None
              else inputs_embeds.device
          )
          position_ids = torch.arange(
              past_key_values_length,
              seq_length + past_key_values_length,
              dtype=torch.long,
              device=device,
          )
          position_ids = position_ids.unsqueeze(0)
  
      outputs = self.text_decoder(
          inputs_embeds=inputs_embeds,
          input_ids=input_ids if past_key_values is not None else None,
          attention_mask=attention_mask,
          position_ids=position_ids,
          past_key_values=past_key_values,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          use_cache=use_cache,
          return_dict=return_dict,
      )
  
      return outputs
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.transforms import *
  from transformers import AutoConfig, AutoTokenizer
  from transformers.configuration_utils import PretrainedConfig
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
  from transformers.processing_utils import ProcessorMixin
  from transformers.tokenization_utils_base import BatchEncoding
  from uform.models import VisualEncoder
label_desc: Use image node_id 6 for calling the VLMForCausalLM obj's underlying member method code with example usage: obj.forward(input_ids, images, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, labels, output_attentions, output_hidden_states, return_dict) and returns: outputs