code: def mle_bigram_model(vocabulary, bigram_training_data):
      model = MLE(2, vocabulary=vocabulary)
      model.fit(bigram_training_data)
      return model
  
imports: import math
  from operator import itemgetter
  import pytest
  from nltk.lm import MLE, AbsoluteDiscountingInterpolated, KneserNeyInterpolated, Laplace, Lidstone, StupidBackoff, Vocabulary, WittenBellInterpolated
  from nltk.lm.preprocessing import padded_everygrams
label_desc: Use image node_id 5 for calling a global function with example usage: mle_bigram_model(vocabulary, bigram_training_data) and returns: model