docstr: Cover the case with multi-channel input signal and single-
  or multi-channel output.
docstr_tok: ["Cover","the","case","with","multi-channel","input","signal","and","single-","or","multi-channel","output","."]
code: def test_multi_channel_input(
      self,
      num_channels: int,
      channel_selector: Type[Union[str, int, List[int]]],
  ):
      """Cover the case with multi-channel input signal and single-
      or multi-channel output.
      """
      num_samples = 1000
      signal_in = np.random.rand(self.num_samples, num_channels)
  
      # calculate golden output
      if channel_selector is None:
          golden_out = signal_in
      elif channel_selector == "average":
          golden_out = np.mean(signal_in, axis=1)
      else:
          golden_out = signal_in[:, channel_selector].squeeze()
  
      # UUT
      signal_out = select_channels(signal_in, channel_selector)
  
      # Check difference
      max_diff = np.max(np.abs(signal_out - golden_out))
      assert max_diff < self.max_diff_tol
  
imports: import os
  from collections import namedtuple
  from typing import List, Type, Union
  import librosa
  import matplotlib.pyplot
  import numpy
  import pytest
  import scipy
  import soundfile
  import torch
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.asr.parts.utils.audio_utils import SOUND_VELOCITY
  from nemo.collections.asr.parts.utils.audio_utils import calculate_sdr_numpy, convmtx_mc_numpy, db2mag, estimated_coherence, generate_approximate_noise_field, get_segment_start, mag2db, pow2db, rms, select_channels, theoretical_coherence, toeplitz
label_desc: Use image node_id 2 for calling the TestSelectChannels obj's underlying member method code with example usage: obj.test_multi_channel_input(num_channels, channel_selector) without return types