code: def jensen_shannon(vec1, vec2, num_features=None):
      """Calculate Jensen-Shannon distance between two probability distributions using `scipy.stats.entropy`.
  
      Parameters
      ----------
      vec1 : {scipy.sparse, numpy.ndarray, list of (int, float)}
          Distribution vector.
      vec2 : {scipy.sparse, numpy.ndarray, list of (int, float)}
          Distribution vector.
      num_features : int, optional
          Number of features in the vectors.
  
      Returns
      -------
      float
          Jensen-Shannon distance between `vec1` and `vec2`.
  
      Notes
      -----
      This is a symmetric and finite "version" of :func:`gensim.matutils.kullback_leibler`.
  
      """
      vec1, vec2 = _convert_vec(vec1, vec2, num_features=num_features)
      avg_vec = 0.5 * (vec1 + vec2)
      return 0.5 * (entropy(vec1, avg_vec) + entropy(vec2, avg_vec))
  
imports: from __future__ import with_statement
  import logging
  import math
  from gensim import utils
  import numpy
  import scipy.sparse
  from scipy.stats import entropy
  from scipy.linalg import get_blas_funcs, triu
  from scipy.linalg.lapack import get_lapack_funcs
  from scipy.special import psi
label_desc: Use image node_id 22 for calling a global function with example usage: jensen_shannon(vec1, vec2, num_features) and returns: unknown