code: def test_hybrid_cqt_white_noise(
      y_white, sr_white, fmin, n_bins, scale
  ):
      C = librosa.hybrid_cqt(
          y=y_white, sr=sr_white, fmin=fmin, n_bins=n_bins, scale=scale
      )
  
      if not scale:
          freqs = fmin * 2.0 ** (np.arange(n_bins) / 12)
          lengths, _ = librosa.filters.wavelet_lengths(
              freqs=freqs, sr=sr_white
          )
          C /= np.sqrt(lengths[:, np.newaxis])
  
      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 26 for calling a global function with example usage: test_hybrid_cqt_white_noise(y_white, sr_white, fmin, n_bins, scale) without return types