code: def test_classification_losses(criterion):
      # Ideal case
      y_pred = torch.tensor(
          [
              [+9, -9, -9, -9],
              [-9, +9, -9, -9],
              [-9, -9, +9, -9],
              [-9, -9, -9, +9],
          ]
      ).float()
      y_true = torch.tensor([0, 1, 2, 3]).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 12 for calling a global function with example usage: test_classification_losses(criterion) without return types