code: def forward(
      self,
      input_ids: 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,
  ) -> Tuple[Tensor, Tensor, List[Tensor]]:
      if attention_mask is None:
          attention_mask = torch.ones_like(input_ids)
      if token_type_ids is None:
          token_type_ids = torch.zeros_like(input_ids)
  
      # We create a 3D attention mask from a 2D tensor mask.
      # Sizes are [batch_size, 1, 1, to_seq_length]
      # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
      # this attention mask is more simple than the triangular masking of
      # causal attention used in OpenAI GPT, we just need to prepare the
      # broadcast dimension here.
      extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
  
      # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
      # masked positions, this operation will create a tensor which is 0.0 for
      # positions we want to attend and -10000.0 for masked positions.
      # Since we are adding it to the raw scores before the softmax, this is
      # effectively the same as removing these entirely.
      # Python builtin next is currently not supported in Torchscript
      if not torch.jit.is_scripting():
          extended_attention_mask = extended_attention_mask.to(
              dtype=next(self.parameters()).dtype
          )  # fp16 compatibility
      extended_attention_mask = (
          1.0 - extended_attention_mask
      ) * -10000.0
  
      embedding_output = self.embeddings(
          input_ids,
          token_type_ids,
          visual_embeddings=visual_embeddings,
          visual_embeddings_type=visual_embeddings_type,
          image_text_alignment=image_text_alignment,
      )
  
      if (
          self.bypass_transformer
          and visual_embeddings is not None
          and hasattr(self, "additional_layer")
      ):
          assert (
              not self.output_hidden_states
          )  # Don't support this for the bypass model
          text_length = input_ids.size(1)
          text_embedding_output = embedding_output[:, :text_length, :]
          visual_part = embedding_output[:, text_length:, :]
  
          text_extended_attention_mask = extended_attention_mask[
              :, :, :text_length, :text_length
          ]
  
          encoded_layers = self.encoder(
              text_embedding_output, text_extended_attention_mask
          )
          sequence_output = encoded_layers[0]
          new_input = torch.cat((sequence_output, visual_part), dim=1)
          final_sequence_output = self.additional_layer(
              new_input, extended_attention_mask
          )
          pooled_output = self.pooler(final_sequence_output[0])
          return final_sequence_output[0], pooled_output, []
  
      else:
          encoded_layers = self.encoder(
              embedding_output, extended_attention_mask
          )
          sequence_output = encoded_layers[0]
          pooled_output = self.pooler(sequence_output)
          attn_data_list: List[Tensor] = []
  
          if not torch.jit.is_scripting():
              if self.output_attentions:
                  attn_data_list = encoded_layers[1:]
          else:
              assert (
                  not self.output_attentions
              ), "output_attentions not supported in script mode"
  
          return sequence_output, pooled_output, attn_data_list
  
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 2 for calling the VisualBERTBase obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids, visual_embeddings, visual_embeddings_type, image_text_alignment) and returns: final_sequence_output,  pooled_output,  list, sequence_output,  pooled_output,  attn_data_list