code: def test_svoice_separator_output_train():
      x = torch.rand(2, 800)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      for num_spk in range(1, 3):
          model = SVoiceSeparator(
              input_dim=12,
              enc_dim=8,
              kernel_size=8,
              hidden_size=8,
              num_spk=num_spk,
              num_layers=4,
              segment_size=2,
              bidirectional=False,
              input_normalize=False,
          )
          model.train()
          waveforms, _, _ = model(x, x_lens)
          assert isinstance(waveforms, list)
          assert isinstance(waveforms[0], list)
          assert x[0].shape == waveforms[0][0][0].shape
  
imports: import pytest
  import torch
  from torch import Tensor
  from espnet2.enh.separator.svoice_separator import SVoiceSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_svoice_separator_output_train() without return types