code: def init_weights(self):
      if self.config.random_initialize is False:
          if self.bert_model_name is None:
              # No pretrained model, init weights
              self.bert.init_weights()
  
          # Classifier needs to be initialized always as it is task specific
          self.classifier.apply(self.bert._init_weights)
  
      # Set last hidden layer
      if "losses" in self.config and self.config.zerobias:
          for loss in self.config.losses:
              if "bce" in loss["type"]:
                  self.classifier[1].bias.data.fill_(
                      self.config.biasfill
                  )
  
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 VisualBERTForClassification obj's underlying member method code with example usage: obj.init_weights() without return types