code: def test_soft_bce_loss():
      criterion = L.SoftBCEWithLogitsLoss(
          smooth_factor=0.1, ignore_index=-100
      )
  
      # Ideal case
      y_pred = torch.tensor([-9, 9, 1, 9, -9]).float()
      y_true = torch.tensor([0, 1, -100, 1, 0]).long()
  
      loss = criterion(y_pred, y_true)
      print(loss)
  
imports: import numpy
  import pytest
  import pytorch_toolbelt.losses.functional
  import torch
  from pytorch_toolbelt import losses
  from torch import nn
  from pytorch_toolbelt.losses import BinaryFocalLoss, CrossEntropyFocalLoss
label_desc: Use image node_id 11 for calling a global function with example usage: test_soft_bce_loss() without return types