code: def __init__(self, config, extra_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"
      )
  
      # Graph input params
      self.feed_graph_to_vb = extra_config["feed_graph_to_vb"]
      self.graph_node_hid_dim = extra_config["node_hid_dim"]
      self.graph_feed_mode = extra_config["feed_mode"]
      self.graph_topk = extra_config["topk_ans_feed"]
  
      # If doing graph, make a graph embedding layer
      if self.feed_graph_to_vb:
          self.graph_embedding = nn.Sequential(
              nn.Linear(self.graph_node_hid_dim, config.hidden_size),
              nn.LayerNorm(config.hidden_size, eps=1e-12),
              nn.Dropout(
                  config.hidden_dropout_prob
              ),  # hidden_dropout_prb
          )
  
      # 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
          or self.bert_model_name == "nopretrain"
      ):
          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.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)
      )
  
      self.init_weights()
  
imports: import logging
  import math
  import os
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.file_io import PathManager
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 1 to create a new VisualBERTForClassification object from inherited base classes: nn with example: obj = VisualBERTForClassification(config, extra_config)