code: def test_uses_separator_mem_tokens(
      n_mics, num_spk, memory_types, mode, ch_mode
  ):
      model = USESSeparator(
          input_dim=None,
          num_spk=num_spk,
          enc_channels=12,
          bottleneck_size=8,
          num_blocks=3,
          num_spatial_blocks=2,
          ref_channel=0,
          segment_size=10,
          memory_size=2,
          memory_types=memory_types,
          # Transformer-related arguments
          hidden_size=4,
          att_heads=2,
          ch_mode=ch_mode,
          ch_att_dim=8,
      )
      model.train()
  
      real = torch.rand(2, 18, n_mics, 33)
      imag = torch.rand(2, 18, n_mics, 33)
      x = ComplexTensor(real, imag)
      x_lens = torch.tensor([18, 16], dtype=torch.long)
  
      output, flens, others = model(
          x, ilens=x_lens, additional={"mode": mode}
      )
      assert len(output) == num_spk
      sum(output).abs().mean().backward()
  
imports: import pytest
  import torch
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.uses_separator import USESSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_uses_separator_mem_tokens(n_mics, num_spk, memory_types, mode, ch_mode) without return types