code: def forward(
      self,
      input_ids: Tensor,
      input_mask: Tensor,
      attention_mask: Optional[Tensor] = None,
      token_type_ids: Optional[Tensor] = None,
      visual_embeddings: Optional[Tensor] = None,
      visual_embeddings_type: Optional[Tensor] = None,
      image_text_alignment: Optional[Tensor] = None,
      masked_lm_labels: Optional[Tensor] = None,
  ) -> Dict[str, Tensor]:
      sequence_output, pooled_output, attention_weights = self.bert(
          input_ids,
          attention_mask,
          token_type_ids,
          visual_embeddings,
          visual_embeddings_type,
          image_text_alignment,
      )
  
      output_dict: Dict[str, Tensor] = {}
      if not torch.jit.is_scripting():
          if self.output_attentions:
              output_dict["attention_weights"] = attention_weights
  
          if self.output_hidden_states:
              output_dict["sequence_output"] = sequence_output
              output_dict["pooled_output"] = pooled_output
      else:
          assert not (
              self.output_attentions or self.output_hidden_states
          ), "output_attentions or output_hidden_states not supported in script mode"
  
      prediction_scores, seq_relationship_score = self.cls(
          sequence_output, pooled_output
      )
      if masked_lm_labels is not None:
          output_dict["logits"] = prediction_scores
          masked_lm_loss = self.loss_fct(
              prediction_scores.contiguous().view(-1, self.vocab_size),
              masked_lm_labels.contiguous().view(-1),
          )
          output_dict["masked_lm_loss"] = masked_lm_loss
          output_dict["loss"] = masked_lm_loss
  
      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 4 for calling the VisualBERTForPretraining obj's underlying member method code with example usage: obj.forward(input_ids, input_mask, attention_mask, token_type_ids, visual_embeddings, visual_embeddings_type, image_text_alignment, masked_lm_labels) and returns: output_dict