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
      self.pooler_strategy = self.config.get(
          "pooler_strategy", "default"
      )
  
      # 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 = getattr(
          self.config, "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.training_head_type = self.config.training_head_type
      self.num_labels = self.config.num_labels
      self.dropout = nn.Dropout(self.bert.config.hidden_dropout_prob)
      if self.config.training_head_type == "nlvr2":
          self.bert.config.hidden_size *= 2
      self.classifier = nn.Sequential(
          BertPredictionHeadTransform(self.bert.config),
          nn.Linear(
              self.bert.config.hidden_size, self.config.num_labels
          ),
      )
  
      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 VisualBERTForClassification object from inherited base classes: nn with example: obj = VisualBERTForClassification(config)