code: def keyed_vectors():
      model = KeyedVectors(5)
      words = ["cat", "dog", "foo", "bar", "one", "two"]
      vectors = np.array(
          [
              [0, 0, 0, 0, 1],
              [0, 0, 0, 0.28, 0.96],
              [1, 0, 0, 0, 0],
              [0, 0, 1, 0, 0],
              [0.28, 0.96, 0, 0, 0],
              [0.6, 0.8, 0, 0, 0],
          ]
      )
      model.add(words, vectors)
      return model
  
imports: import pytest
  import numpy
  from gensim.models import KeyedVectors
  from sklearn.feature_extraction.text import CountVectorizer
label_desc: Use image node_id 1 for calling a global function with example usage: keyed_vectors() and returns: model