code: def test_svoice_separator_forward_backward(
      input_dim,
      enc_dim,
      kernel_size,
      hidden_size,
      num_spk,
      num_layers,
      segment_size,
      bidirectional,
      input_normalize,
  ):
      model = SVoiceSeparator(
          input_dim=input_dim,
          enc_dim=enc_dim,
          kernel_size=kernel_size,
          hidden_size=hidden_size,
          num_spk=num_spk,
          num_layers=num_layers,
          segment_size=segment_size,
          bidirectional=bidirectional,
          input_normalize=input_normalize,
      )
      model.train()
  
      x = torch.rand(2, 800)
      x_lens = torch.tensor([400, 300], dtype=torch.long)
  
      separated, _, _ = model(x, ilens=x_lens)
  
      assert isinstance(separated[0][0], Tensor)
      assert len(separated) == num_layers
  
      separated[0][0].mean().backward()
  
imports: import pytest
  import torch
  from torch import Tensor
  from espnet2.enh.separator.svoice_separator import SVoiceSeparator
label_desc: Use image node_id 1 for calling a global function with example usage: test_svoice_separator_forward_backward(input_dim, enc_dim, kernel_size, hidden_size, num_spk, num_layers, segment_size, bidirectional, input_normalize) without return types