code: def test_trim_multichannel(y_multi):
      y, sr = y_multi
  
      # Make one channel much quieter than the other
      y = y * np.array([[1e-6, 1e6]]).T
      yt, ival = librosa.effects.trim(y)
  
      yt0, ival0 = librosa.effects.trim(y[0])
      yt1, ival1 = librosa.effects.trim(y[1])
  
      # Trim uses max aggregation across channels by default
      # So the multichannel trimming window will be the
      # intersection of the individual intervals
      assert ival[0] == max(ival0[0], ival1[0])
      assert ival[1] == min(ival0[1], ival1[1])
  
imports: import os
  import librosa
  import glob
  import numpy
  import scipy.io
  import scipy.stats
  import pytest
  import warnings
  from unittest import mock
  from typing import List, Union
  from contextlib import nullcontext
  from test_core import srand
label_desc: Use image node_id 51 for calling a global function with example usage: test_trim_multichannel(y_multi) without return types