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