code: def test_dc_crn_separator_multich_input(
      num_spk,
      input_channels,
      enc_kernel_size,
      enc_padding,
      enc_last_kernel_size,
      enc_last_stride,
      enc_last_padding,
      skip_last_kernel_size,
      skip_last_stride,
      skip_last_padding,
  ):
      model = DC_CRNSeparator(
          input_dim=33,
          num_spk=num_spk,
          input_channels=input_channels,
          enc_hid_channels=2,
          enc_kernel_size=enc_kernel_size,
          enc_padding=enc_padding,
          enc_last_kernel_size=enc_last_kernel_size,
          enc_last_stride=enc_last_stride,
          enc_last_padding=enc_last_padding,
          enc_layers=3,
          skip_last_kernel_size=skip_last_kernel_size,
          skip_last_stride=skip_last_stride,
          skip_last_padding=skip_last_padding,
      )
      model.train()
  
      real = torch.rand(2, 10, input_channels[0] // 2, 33)
      imag = torch.rand(2, 10, input_channels[0] // 2, 33)
      x = (
          torch.complex(real, imag)
          if is_torch_1_9_plus
          else ComplexTensor(real, imag)
      )
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      assert is_complex(masked[0])
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
file_path: espnet/test/espnet2/enh/separator/test_dc_crn_separator.py
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.layers.complex_utils import is_complex
  from espnet2.enh.separator.dc_crn_separator import DC_CRNSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_dc_crn_separator_multich_input(num_spk, input_channels, enc_kernel_size, enc_padding, enc_last_kernel_size, enc_last_stride, enc_last_padding, skip_last_kernel_size, skip_last_stride, skip_last_padding) without return types