code: def _init_classifier(self, hidden_size):
      if "pretraining" in self.config.training_head_type:
          self.classifier = BertPreTrainingHeads(self.bert_config)
      if "vqa" in self.config.training_head_type:
          self.dropout = nn.Dropout(
              self.bert_config.hidden_dropout_prob
          )
          self.answer_space_size = 3129
          self.classifier = nn.Sequential(
              BertPredictionHeadTransform(self.bert_config),
              nn.Linear(
                  self.bert_config.hidden_size, self.answer_space_size
              ),
          )
          # self.classifier = nn.Linear(self.bert_config.hidden_size,
          # self.answer_space_size)
      elif "vizwiz" in self.config.training_head_type:
          self.dropout = nn.Dropout(
              self.bert_config.hidden_dropout_prob
          )
          self.answer_space_size = 7371
          self.classifier = nn.Sequential(
              BertPredictionHeadTransform(self.bert_config),
              nn.Linear(
                  self.bert_config.hidden_size, self.answer_space_size
              ),
          )
          # self.classifier = nn.Linear(self.bert_config.hidden_size,
          # self.answer_space_size)
      elif self.config.training_head_type == "visual_entailment":
          self.dropout = nn.Dropout(
              self.bert_config.hidden_dropout_prob
          )
          self.classifier = nn.Sequential(
              BertPredictionHeadTransform(self.bert_config),
              nn.Linear(self.bert_config.hidden_size, 3),
          )
  
imports: import torch
  from mmf.common.registry import registry
  from mmf.models.pythia import Pythia
  from mmf.modules.embeddings import ProjectionEmbedding
  from mmf.utils.transform import transform_to_batch_sequence
  from torch import nn
label_desc: Use image node_id 5 for calling the MMFBert obj's underlying member method code with example usage: obj._init_classifier(hidden_size) without return types