code: def jaccard(vec1, vec2):
      """Calculate Jaccard distance between two vectors.
  
      Parameters
      ----------
      vec1 : {scipy.sparse, numpy.ndarray, list of (int, float)}
          Distribution vector.
      vec2 : {scipy.sparse, numpy.ndarray, list of (int, float)}
          Distribution vector.
  
      Returns
      -------
      float
          Jaccard distance between `vec1` and `vec2`.
          Value in range `[0, 1]`, where 0 is min distance (max similarity) and 1 is max distance (min similarity).
  
      """
  
      # converting from sparse for easier manipulation
      if scipy.sparse.issparse(vec1):
          vec1 = vec1.toarray()
      if scipy.sparse.issparse(vec2):
          vec2 = vec2.toarray()
      if isbow(vec1) and isbow(vec2):
          # if it's in bow format, we use the following definitions:
          # union = sum of the 'weights' of both the bags
          # intersection = lowest weight for a particular id; basically the number of common words or items
          union = sum(weight for id_, weight in vec1) + sum(
              weight for id_, weight in vec2
          )
          vec1, vec2 = dict(vec1), dict(vec2)
          intersection = 0.0
          for feature_id, feature_weight in vec1.items():
              intersection += min(
                  feature_weight, vec2.get(feature_id, 0.0)
              )
          return 1 - float(intersection) / float(union)
      else:
          # if it isn't in bag of words format, we can use sets to calculate intersection and union
          if isinstance(vec1, np.ndarray):
              vec1 = vec1.tolist()
          if isinstance(vec2, np.ndarray):
              vec2 = vec2.tolist()
          vec1 = set(vec1)
          vec2 = set(vec2)
          intersection = vec1 & vec2
          union = vec1 | vec2
          return 1 - float(len(intersection)) / float(len(union))
  
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 24 for calling a global function with example usage: jaccard(vec1, vec2) and returns: unknown, unknown