code: def return_w2v(word_a, model):
      """
      extract embedding vector of word_a
  
      Args:
           word_a(str):   input word
          model(gensim.models.Word2Vec): w2v model
  
      Returns:
          numpy.array: w2v, embedding vector of word_a
  
      """
      w2v = None
      try:
          w2v = numpy.array
          w2v = model[word_a]
          return True, w2v
      except ValueError:
          return False, w2v
      except IndexError:
          return False, w2v
      except KeyError:
          return False, w2v
  
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 10 for calling a global function with example usage: return_w2v(word_a, model) and returns: True,  w2v, False,  w2v, False,  w2v, False,  w2v