class_docstr: Calculates smoothing coefficient for each language.
code: def extract_output(self, accumulator):
      lang_count = accumulator["lang_count"]
      total = accumulator["total_count"]
      probs, exp = {}, {}
      for lang in lang_count:
          probs[lang] = lang_count[lang] / total
          exp[lang] = pow(probs[lang], self._smoothing_exponent)
      total_weight = sum(exp.values())
      for lang in exp:
          exp[lang] = exp[lang] / (total_weight * probs[lang])
      return exp
  
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 5 for calling the CalculateCoefficients obj's underlying member method code with example usage: obj.extract_output(accumulator) and returns: exp