class_docstr: Multimodal Transformers From Pretrained BERT Weights"
code: def forward(
      self,
      input_ids,
      token_type_ids=None,
      vision_feats=None,
      attention_mask=None,
  ):
      language_features = self.embeddings(input_ids, token_type_ids)
      for layer in self.vision_encoder:
          vision_feats = layer(vision_feats, None)[0]
      for layer in self.language_encoder:
          language_features = layer(language_features, attention_mask)[
              0
          ]
      for layer in self.mixed_encoder:
          language_features, vision_feats = layer(
              language_features, vision_feats
          )
      return language_features, vision_feats
  
imports: from __future__ import annotations
  import math
  from collections.abc import Sequence
  import torch
  from torch import nn
  from monai.config.type_definitions import PathLike
  from monai.utils import optional_import
label_desc: Use image node_id 2 for calling the MultiModal obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, vision_feats, attention_mask) and returns: language_features,  vision_feats