code: def test_cqt_white_noise(y_white, sr_white, fmin, n_bins, scale):
      C = np.abs(
          librosa.cqt(
              y=y_white,
              sr=sr_white,
              fmin=fmin,
              n_bins=n_bins,
              scale=scale,
          )
      )
  
      if not scale:
          freqs = librosa.cqt_frequencies(fmin=fmin, n_bins=n_bins)
          lengths, _ = librosa.filters.wavelet_lengths(
              sr=sr_white, freqs=freqs
          )
          C /= np.sqrt(lengths[:, np.newaxis])
  
      # Only compare statistics across the time dimension
      # we want ~ constant mean and variance across frequencies
      assert np.allclose(np.mean(C, axis=1), 1.0, atol=2.5e-1), np.mean(
          C, axis=1
      )
      assert np.allclose(np.std(C, axis=1), 0.5, atol=5e-1), np.std(
          C, axis=1
      )
  
imports: from __future__ import division
  import warnings
  import os
  from typing import Optional
  import librosa
  import numpy
  import scipy.stats
  import pytest
  from test_core import srand
label_desc: Use image node_id 25 for calling a global function with example usage: test_cqt_white_noise(y_white, sr_white, fmin, n_bins, scale) without return types