code: def test_decompose_multi():
      srand()
      X = np.random.random_sample(size=(2, 20, 100))
  
      # Fit with multichannel data
      components, activations = librosa.decompose.decompose(
          X, n_components=20, random_state=0
      )
  
      # Reshape the data
      Xflat = np.vstack([X[0], X[1]])
      c_flat, a_flat = librosa.decompose.decompose(
          Xflat, n_components=20, random_state=0
      )
  
      assert np.allclose(c_flat[: X.shape[1]], components[0])
      assert np.allclose(c_flat[X.shape[1] :], components[1])
      assert np.allclose(activations, a_flat)
  
imports: import os
  import numpy
  import scipy.sparse
  import librosa
  import sklearn.decomposition
  import pytest
  from test_core import srand
label_desc: Use image node_id 5 for calling a global function with example usage: test_decompose_multi() without return types