class_docstr: BERT model ("Bidirectional Encoder Representations from Transformers").
class_docstr_tok: ["BERT","model","(","``","Bidirectional","Encoder","Representations","from","Transformers","''",")","."]
docstr: Get output of the word(piece) embedding lookup.
  
  This is BEFORE positional embeddings and token type embeddings have been
  added.
  
  Returns:
    float Tensor of shape [batch_dim, seq_dim, model_dim] corresponding
    to the output of the word(piece) embedding layer.
code: def get_word_embedding_output(self):
      """Get output of the word(piece) embedding lookup.
  
      This is BEFORE positional embeddings and token type embeddings have been
      added.
  
      Returns:
        float Tensor of shape [batch_dim, seq_dim, model_dim] corresponding
        to the output of the word(piece) embedding layer.
      """
      return self.word_embedding_output
  
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 10 for calling the BertModel obj's underlying member method code with example usage: obj.get_word_embedding_output() and returns: self