class_docstr: BERT model ("Bidirectional Encoder Representations from Transformers").
class_docstr_tok: ["BERT","model","(","``","Bidirectional","Encoder","Representations","from","Transformers","''",")","."]
code: def embedding_initializer(self):
      initializer = self.dense_initializer
      if isinstance(initializer, mtf.layers.DenseInitializer):
          # embedding matrix is also used as classifier weight matrix.
          # scale it appropriately.
          return initializer(
              reduced_dims=[self.model_dim], new_dims=[self.vocab_dim]
          )
      else:
          return initializer
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import copy
  import json
  import math
  import re
  import mesh_tensorflow
  import mesh_tensorflow.transformer.moe
  import six
  import tensorflow.compat.v1
label_desc: Use image node_id 25 for calling the BertModel obj's underlying member method code with example usage: obj.embedding_initializer() and returns: initializer, initializer