code: def test_soft_ce_loss():
      soft_ce_criterion = L.SoftCrossEntropyLoss(smooth_factor=0.0)
      ce_criterion = nn.CrossEntropyLoss()
  
      y_pred = torch.tensor(
          [
              [+1, -1, -1, -1],
              [-1, +1, -1, -1],
              [-1, -1, +1, -1],
              [-1, -1, -1, +1],
          ]
      ).float()
      y_true = torch.tensor([0, 1, 2, 3]).long()
  
      actual = soft_ce_criterion(y_pred, y_true).item()
      expected = ce_criterion(y_pred, y_true).item()
      np.testing.assert_almost_equal(actual, expected)
  
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 10 for calling a global function with example usage: test_soft_ce_loss() without return types