code: def forward(
      self, sample_list: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      if torch.jit.is_scripting():
          assert (
              "image_feature_0" in sample_list
          ), "Key 'image_feature_0' is required in TorchScript model"
  
      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)
      sample_list = self.add_post_flatten_params(sample_list)
  
      output_dict = 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["visual_embeddings_type"],
          getattr_torchscriptable(
              sample_list, "image_text_alignment", None
          ),
          getattr_torchscriptable(
              sample_list, "masked_lm_labels", None
          ),
      )
  
      if self.training_head_type == "pretraining":
          if not torch.jit.is_scripting():
              loss_key = "{}/{}".format(
                  sample_list["dataset_name"],
                  sample_list["dataset_type"],
              )
              output_dict["losses"] = {}
              output_dict["losses"][
                  loss_key + "/masked_lm_loss"
              ] = output_dict.pop("masked_lm_loss")
          else:
              raise RuntimeError(
                  "Pretraining head can't be used in script mode."
              )
  
      return output_dict
  
imports: import os
  from copy import deepcopy
  from typing import Dict, List, Optional, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.modules.hf_layers import BertEncoderJit, BertLayerJit
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.torchscript import getattr_torchscriptable
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 11 for calling the VisualBERT obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output_dict