docstr: Test attention for channel pooling.
          
docstr_tok: ["Test","attention","for","channel","pooling","."]
code: def test_attention(self, num_channels):
      """Test attention for channel pooling."""
      num_examples = 10
      batch_size = 4
      in_features = 128
      num_frames = 20
  
      uut = ChannelAttentionPool(in_features=in_features)
  
      for n in range(num_examples):
          input = torch.rand(
              batch_size, num_channels, in_features, num_frames
          )
          output = uut(input=input)
  
          # Dimensions must match
          assert output.shape == (
              batch_size,
              in_features,
              num_frames,
          ), f"Example {n}: output shape {output.shape} not matching the expected ({batch_size}, {in_features}, {num_frames})"
  
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 2 for calling the TestChannelPool obj's underlying member method code with example usage: obj.test_attention(num_channels) without return types