docstr: Test transform-average-concatenate.
          
docstr_tok: ["Test","transform-average-concatenate","."]
code: def test_average(self, num_channels):
      """Test transform-average-concatenate."""
      num_examples = 10
      batch_size = 4
      in_features = 128
      out_features = 96
      num_frames = 20
  
      uut = TransformAverageConcatenate(
          in_features=in_features, out_features=out_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,
              num_channels,
              out_features,
              num_frames,
          ), f"Example {n}: output shape {output.shape} not matching the expected ({batch_size}, {num_channels}, {out_features}, {num_frames})"
  
          # Second half of features must be the same for all channels (concatenated average)
          if num_channels > 1:
              # reference
              avg_ref = output[:, 0, out_features // 2 :, :]
              for m in range(1, num_channels):
                  assert torch.allclose(
                      output[:, m, out_features // 2 :, :], avg_ref
                  ), f"Example {n}: average not matching"
  
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 TestTAC obj's underlying member method code with example usage: obj.test_average(num_channels) without return types