code: def get_word_embeddings(batch, idx="word_embeddings"):
      embeddings_dim = len(batch[0][idx][0])
      max_words = max([len(sentence[idx]) for sentence in batch])
      return [
          NerModel.fill(
              [word_embedding for word_embedding in sentence[idx]],
              max_words,
              [0] * embeddings_dim,
          )
          for sentence in batch
      ]
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 18 for calling the NerModel obj's underlying member method code with example usage: obj.get_word_embeddings(batch, idx) and returns: unknown