code: def _build_word_embedding(self):
      self.bert_config = BertConfig.from_pretrained(
          self.config.bert_model_name
      )
      if self.config.pretrained_bert:
          bert_model = BertForPreTraining.from_pretrained(
              self.config.bert_model_name
          )
          self.word_embedding = bert_model.bert.embeddings
          self.pooler = bert_model.bert.pooler
          self.pooler.apply(self.init_weights)
  
      else:
          self.pooler = BertPooler(self.bert_config)
          self.word_embedding = BertEmbeddings(self.bert_config)
  
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 4 for calling the MMFBert obj's underlying member method code with example usage: obj._build_word_embedding() without return types