code: def test_cross_similarity_multi():
      srand()
      X1 = np.random.randn(2, 10, 100)
      X2 = np.random.randn(2, 10, 50)
  
      R = librosa.segment.cross_similarity(X1, X2, mode="affinity")
      # This should give the same output as if we stacked out the leading channel
  
      X1f = np.concatenate([X1[0], X1[1]], axis=0)
      X2f = np.concatenate([X2[0], X2[1]], axis=0)
      Rf = librosa.segment.cross_similarity(X1f, X2f, mode="affinity")
  
      assert np.allclose(R, Rf)
  
imports: from typing import Union
  import warnings
  import os
  import numpy
  import scipy
  from scipy.spatial.distance import cdist, pdist, squareform
  import pytest
  from test_core import srand
  import librosa
label_desc: Use image node_id 9 for calling a global function with example usage: test_cross_similarity_multi() without return types