code: def get_input_words(word_counts, reserved_tokens, max_token_length):
      """Filters out words that are longer than max_token_length or are reserved.
  
      Args:
        word_counts: list of (string, int) tuples
        reserved_tokens: list of strings
        max_token_length: int, maximum length of a token
  
      Returns:
        list of (string, int) tuples of filtered wordcounts
      """
  
      all_counts = []
  
      for word, count in word_counts:
          if len(word) > max_token_length or word in reserved_tokens:
              continue
          all_counts.append((word, count))
  
      return all_counts
  
file_path: text/tensorflow_text/tools/wordpiece_vocab/wordpiece_tokenizer_learner_lib.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import List, Optional
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: get_input_words(word_counts, reserved_tokens, max_token_length) and returns: all_counts