code: def calc_word_to_sentence_dist_cbow(target_word, sentence, model):
      """
      calculate cosine similaity between word emb. and sentence cbow emb.
  
      Args:
          target_word(str): input word
          sentence(list(str)): input sentence
          model(gensim.models.Word2Vec): w2v model
  
      Returns:
          numpy.array: cbow_sentence_emb, sentence cbow embedding
          float: cosine_sim, cosine similarity between target word embedding and cbow sentence
           embedding
  
      """
      cbow_sentence_emb = calc_cbow_sentence(sentence, model)
  
      try:
          wv_target_word = model[target_word]
          cosine_sim = cosine_similarity(
              wv_target_word, cbow_sentence_emb
          )
      # if target word is not in embedding dictionary
      except KeyError:
          cosine_sim = 0
  
      return cbow_sentence_emb, cosine_sim
  
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 4 for calling a global function with example usage: calc_word_to_sentence_dist_cbow(target_word, sentence, model) and returns: cbow_sentence_emb,  cosine_sim