code: def test_multi_res_l1_spec_loss_forward_backward(
      window_sz,
      time_domain_weight,
      dtype,
      normalize_variance,
      reduction,
  ):
      if dtype == torch.float16 and not is_torch_1_12_1_plus:
          pytest.skip(
              "Skip tests for dtype=torch.float16 due to lack of torch.complex32"
          )
      criterion = MultiResL1SpecLoss(
          window_sz=window_sz,
          time_domain_weight=time_domain_weight,
          normalize_variance=normalize_variance,
          reduction=reduction,
      )
  
      batch = 2
      inf = torch.rand(batch, 2000, dtype=dtype, requires_grad=True)
      ref = torch.rand(batch, 2000, dtype=dtype)
  
      loss = criterion(ref, inf)
      loss.sum().backward()
      assert loss.shape == (batch,), (
          "Invlid loss shape with " + criterion.name
      )
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.enh.loss.criterions.time_domain import CISDRLoss, MultiResL1SpecLoss, SDRLoss, SISNRLoss, SNRLoss, TimeDomainL1, TimeDomainMSE
label_desc: Use image node_id 3 for calling a global function with example usage: test_multi_res_l1_spec_loss_forward_backward(window_sz, time_domain_weight, dtype, normalize_variance, reduction) without return types