code: def test_dtw_multi():
      srand()
      X = np.random.randn(2, 5, 10)
      Y = np.random.randn(2, 5, 20)
  
      D, wp, steps = librosa.sequence.dtw(
          X=X, Y=Y, backtrack=True, return_steps=True
      )
  
      # Should give identical results to calling with concatenated inputs
      Xf = np.concatenate([X[0], X[1]], axis=0)
      Yf = np.concatenate([Y[0], Y[1]], axis=0)
      Df, wpf, stepsf = librosa.sequence.dtw(
          X=Xf, Y=Yf, backtrack=True, return_steps=True
      )
  
      assert np.allclose(D, Df)
      assert np.allclose(wp, wpf)
      assert np.allclose(steps, stepsf)
  
imports: import librosa
  import numpy
  from scipy.spatial.distance import cdist
  import pytest
  from test_core import srand
label_desc: Use image node_id 25 for calling a global function with example usage: test_dtw_multi() without return types