code: def __init__(self, config):
      super().__init__()
      self.config = config
      self.output_attentions = self.config.output_attentions
      self.output_hidden_states = self.config.output_hidden_states
  
      # If bert_model_name is not specified, you will need to specify
      # all of the required parameters for BERTConfig and a pretrained
      # model won't be loaded
      self.bert_model_name = self.config.get("bert_model_name", None)
      self.bert_config = BertConfig.from_dict(
          OmegaConf.to_container(self.config, resolve=True)
      )
      if self.bert_model_name is None:
          self.bert = VisualBERTBase(
              self.bert_config,
              visual_embedding_dim=self.config.visual_embedding_dim,
              embedding_strategy=self.config.embedding_strategy,
              bypass_transformer=self.config.bypass_transformer,
              output_attentions=self.config.output_attentions,
              output_hidden_states=self.config.output_hidden_states,
          )
      else:
          self.bert = VisualBERTBase.from_pretrained(
              self.config.bert_model_name,
              config=self.bert_config,
              cache_dir=os.path.join(
                  get_mmf_cache_dir(), "distributed_{}".format(-1)
              ),
              visual_embedding_dim=self.config.visual_embedding_dim,
              embedding_strategy=self.config.embedding_strategy,
              bypass_transformer=self.config.bypass_transformer,
              output_attentions=self.config.output_attentions,
              output_hidden_states=self.config.output_hidden_states,
          )
  
      self.vocab_size = self.bert.config.vocab_size
  
      # TODO: Once omegaconf fixes int keys issue, bring this back
      # See https://github.com/omry/omegaconf/issues/149
      # with omegaconf.open_dict(self.config):
      #     # Add bert config such as hidden_state to our main config
      #     self.config.update(self.bert.config.to_dict())
      if self.bert_model_name is None:
          bert_masked_lm = BertForPreTraining(self.bert.config)
      else:
          bert_masked_lm = BertForPreTraining.from_pretrained(
              self.config.bert_model_name,
              config=self.bert.config,
              cache_dir=os.path.join(
                  get_mmf_cache_dir(), "distributed_{}".format(-1)
              ),
          )
      self.cls = deepcopy(bert_masked_lm.cls)
      self.loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
      self.init_weights()
  
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 1 to create a new VisualBERTForPretraining object from inherited base classes: nn with example: obj = VisualBERTForPretraining(config)