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,
      )
  
      if self.training_head_type == "nlvr2":
          # 2B * H => B * 2H
          b, h = pooled_output.size()
          pooled_output = torch.cat(
              [pooled_output[: b // 2], pooled_output[b // 2 :]], dim=1
          )
  
      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"
  
      if self.pooler_strategy == "vqa":
          # In VQA2 pooling strategy, we use representation from second last token
          index_to_gather = input_mask.sum(1) - 2
          pooled_output = torch.gather(
              sequence_output,
              1,
              index_to_gather.unsqueeze(-1)
              .unsqueeze(-1)
              .expand(
                  index_to_gather.size(0), 1, sequence_output.size(-1)
              ),
          )
  
      pooled_output = self.dropout(pooled_output)
      logits = self.classifier(pooled_output)
      reshaped_logits = logits.contiguous().view(-1, self.num_labels)
      output_dict["scores"] = reshaped_logits
      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 3 for calling the VisualBERTForClassification 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