code: def get_word_lengths(batch, idx="char_ids"):
      max_words = max([len(row[idx]) for row in batch])
      return [
          NerModel.fill(
              [len(chars) for chars in row[idx]], max_words, 0
          )
          for row 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 14 for calling the NerModel obj's underlying member method code with example usage: obj.get_word_lengths(batch, idx) and returns: unknown