code: def calc_cbow_sentence(sentence, model):
      """
      calc cbow embedding of an input sentence
  
      Args:
          sentence(bytearray): vector of words
          model(gensim.models.Word2Vec): w2v model
  
      Returns:
          numpy.array: cbow_sentence, cbow embedding of the input sentence
  
      """
      cbow_sentence = numpy.zeros(300)
      i = 0
      for word in sentence:
          try:
              wv = model[word]
              cbow_sentence = cbow_sentence + wv
              i += 1
          except KeyError:  # if word is not in embedding dictionary
              pass  # no-operation
          except IndexError:  # if word is not in embedding dictionary
              pass  # no-operation
  
      if i > 0:
          cbow_sentence = cbow_sentence / i
  
      return cbow_sentence
  
file_path: nlp-architect/examples/most_common_word_sense/feature_extraction.py
imports: import re
  import nltk
  import numpy
  from numpy import dot
  from numpy.linalg import norm
  from nlp_architect.utils.generic import license_prompt
label_desc: Use image node_id 6 for calling a global function with example usage: calc_cbow_sentence(sentence, model) and returns: cbow_sentence