code: def test_mu_law_correctness(quantization_channels):
      # test reconstruction
      mu_src = np.arange(0, quantization_channels).astype(np.int)
      src = KPB.mu_law_decoding(
          mu_src, quantization_channels=quantization_channels
      )
      mu_src_recon = KPB.mu_law_encoding(
          src, quantization_channels=quantization_channels
      )
  
      np.testing.assert_equal(mu_src, mu_src_recon)
  
      # test against librosa
      resol = 1 / (2**16)
      src = np.arange(-1.0, 1.0, resol).astype(np.float32)
      mu = quantization_channels - 1
      mu_src_ref = librosa.mu_compress(
          src, mu=quantization_channels - 1, quantize=False
      )
      mu_src_ref = (mu_src_ref + 1.0) / 2.0 * mu + 0.5
      mu_src_ref = mu_src_ref.astype(np.int)
  
      mu_src_kapre = KPB.mu_law_encoding(
          tf.convert_to_tensor(src),
          quantization_channels=quantization_channels,
      )
      np.testing.assert_equal(mu_src_ref, mu_src_kapre.numpy())
  
file_path: kapre/tests/test_backend.py
imports: import pytest
  import numpy
  import librosa
  import tensorflow
  from tensorflow.keras import backend
  from kapre import backend
  from kapre.backend import magnitude_to_decibel, validate_data_format_str
  from utils import SRC
label_desc: Use image node_id 4 for calling a global function with example usage: test_mu_law_correctness(quantization_channels) without return types