code: def _convert_vec(vec1, vec2, num_features=None):
      if scipy.sparse.issparse(vec1):
          vec1 = vec1.toarray()
      if scipy.sparse.issparse(vec2):
          vec2 = (
              vec2.toarray()
          )  # converted both the vectors to dense in case they were in sparse matrix
      if isbow(vec1) and isbow(
          vec2
      ):  # if they are in bag of words format we make it dense
          if (
              num_features is not None
          ):  # if not None, make as large as the documents drawing from
              dense1 = sparse2full(vec1, num_features)
              dense2 = sparse2full(vec2, num_features)
              return dense1, dense2
          else:
              max_len = max(len(vec1), len(vec2))
              dense1 = sparse2full(vec1, max_len)
              dense2 = sparse2full(vec2, max_len)
              return dense1, dense2
      else:
          # this conversion is made because if it is not in bow format, it might be a list within a list after conversion
          # the scipy implementation of Kullback fails in such a case so we pick up only the nested list.
          if len(vec1) == 1:
              vec1 = vec1[0]
          if len(vec2) == 1:
              vec2 = vec2[0]
          return vec1, vec2
  
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 20 for calling a global function with example usage: _convert_vec(vec1, vec2, num_features) and returns: vec1,  vec2, dense1,  dense2, dense1,  dense2