class_docstr: Filters words with counts below some threshold.
code: def process(self, word_and_count):
      word, count = word_and_count
      if (
          count > self._min_token_frequency
          and len(word) <= self._max_word_length
      ):
          yield word, int(round(count))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import apache_beam
  import tensorflow.compat.v1
  from tensorflow_text.python.ops.bert_tokenizer import BertTokenizer
  from tensorflow_text.python.ops.wordpiece_tokenizer import WordpieceTokenizer
  from tensorflow_text.tools.wordpiece_vocab import wordpiece_tokenizer_learner_lib
label_desc: Use image node_id 2 for calling the FilterByCount obj's underlying member method code with example usage: obj.process(word_and_count) without return types