code: def test_nn_filter_mean():
      srand()
      X = np.random.randn(10, 100)
  
      # Build a recurrence matrix, just for testing purposes
      rec = librosa.segment.recurrence_matrix(X)
  
      X_filtered = librosa.decompose.nn_filter(X)
  
      # Normalize the recurrence matrix so dotting computes an average
      rec = librosa.util.normalize(rec.astype(float), axis=0, norm=1)
  
      assert np.allclose(X_filtered, X.dot(rec))
  
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 14 for calling a global function with example usage: test_nn_filter_mean() without return types