code: def forward(self, sample_list):
      sample_list = self.update_sample_list_based_on_head(sample_list)
      sample_list = self.add_custom_params(sample_list)
      sample_list = self.flatten_for_bert(sample_list)
  
      if self.feed_graph_to_vb:
          if self.graph_feed_mode == "feed_graph_hid_to_vb":
              assert "graph_special_node_out" in sample_list
              graph_input = sample_list["graph_special_node_out"]
          else:
              assert False
      else:
          graph_input = None
  
      output = self.model(
          sample_list.input_ids,
          sample_list.input_mask,
          sample_list.attention_mask,
          sample_list.token_type_ids,
          sample_list.visual_embeddings,
          sample_list.position_embeddings_visual,
          sample_list.visual_embeddings_type,
          sample_list.image_text_alignment,
          sample_list.masked_lm_labels,
          graph_input,
      )
  
      return output
  
imports: import logging
  import math
  import os
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.file_io import PathManager
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 9 for calling the VisualBERTModule obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output