docstr: Test getting a fixed number of channels without randomization.
  The first few channels will always be selected.
docstr_tok: ["Test","getting","a","fixed","number","of","channels","without","randomization",".","The","first","few","channels","will","always","be","selected","."]
code: def test_channel_selection(self, num_channels):
      """Test getting a fixed number of channels without randomization.
      The first few channels will always be selected.
      """
      num_examples = 100
      batch_size = 4
      num_samples = 100
  
      uut = ChannelAugment(
          permute_channels=False,
          num_channels_min=1,
          num_channels_max=num_channels,
      )
  
      for n in range(num_examples):
          input = torch.rand(batch_size, num_channels, num_samples)
          output = uut(input=input)
  
          num_channels_out = output.size(-2)
  
          assert torch.allclose(
              output, input[:, :num_channels_out, :]
          ), f"Failed for num_channels_out {num_channels_out}, example {n}"
  
file_path: NeMo/tests/collections/asr/test_asr_part_submodules_multichannel.py
imports: import pytest
  import torch
  from nemo.collections.asr.parts.submodules.multichannel_modules import ChannelAttentionPool, ChannelAugment, ChannelAveragePool, TransformAttendConcatenate, TransformAverageConcatenate
label_desc: Use image node_id 1 for calling the TestChannelAugment obj's underlying member method code with example usage: obj.test_channel_selection(num_channels) without return types