docstr: Test initialization of the mask estimator and make sure it can process input tensor.
          
docstr_tok: ["Test","initialization","of","the","mask","estimator","and","make","sure","it","can","process","input","tensor","."]
code: def test_flex_channels(
      self,
      channel_reduction_position: int,
      channel_reduction_type: str,
      channel_block_type: str,
  ):
      """Test initialization of the mask estimator and make sure it can process input tensor."""
      # Model parameters
      num_subbands_tests = [32, 65]
      num_outputs_tests = [1, 2]
      num_blocks_tests = [1, 5]
  
      # Input configuration
      num_channels_tests = [1, 4]
      batch_size = 4
      num_frames = 50
  
      for num_subbands in num_subbands_tests:
          for num_outputs in num_outputs_tests:
              for num_blocks in num_blocks_tests:
                  logging.debug(
                      "Instantiate with num_subbands=%d, num_outputs=%d, num_blocks=%d",
                      num_subbands,
                      num_outputs,
                      num_blocks,
                  )
  
                  # Instantiate
                  uut = MaskEstimatorFlexChannels(
                      num_outputs=num_outputs,
                      num_subbands=num_subbands,
                      num_blocks=num_blocks,
                      channel_reduction_position=channel_reduction_position,
                      channel_reduction_type=channel_reduction_type,
                      channel_block_type=channel_block_type,
                  )
  
                  # Process different channel configurations
                  for num_channels in num_channels_tests:
                      logging.debug(
                          "Process num_channels=%d", num_channels
                      )
                      input_size = (
                          batch_size,
                          num_channels,
                          num_subbands,
                          num_frames,
                      )
  
                      # multi-channel input
                      spec = torch.randn(input_size, dtype=torch.cfloat)
                      spec_length = torch.randint(
                          1, num_frames, (batch_size,)
                      )
  
                      # UUT
                      mask, mask_length = uut(
                          input=spec, input_length=spec_length
                      )
  
                      # Check output dimensions match
                      expected_mask_shape = (
                          batch_size,
                          num_outputs,
                          num_subbands,
                          num_frames,
                      )
                      assert (
                          mask.shape == expected_mask_shape
                      ), f"Output shape mismatch: expected {expected_mask_shape}, got {mask.shape}"
  
                      # Check output lengths match
                      assert torch.all(
                          mask_length == spec_length
                      ), f"Output length mismatch: expected {spec_length}, got {mask_length}"
  
imports: import importlib
  from typing import Optional
  import numpy
  import pytest
  import torch
  from nemo.collections.asr.modules.audio_modules import MaskBasedDereverbWPE, MaskEstimatorFlexChannels, MaskEstimatorGSS, MaskReferenceChannel, SpectrogramToMultichannelFeatures, WPEFilter
  from nemo.collections.asr.modules.audio_preprocessing import AudioToSpectrogram
  from nemo.collections.asr.parts.utils.audio_utils import convmtx_mc_numpy
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the TestMaskEstimator obj's underlying member method code with example usage: obj.test_flex_channels(channel_reduction_position, channel_reduction_type, channel_block_type) without return types