code: def test_softmax_focal_loss_with_logits():
      input_good = torch.tensor(
          [[0, 10, 0], [10, 0, 0], [0, 0, 10]]
      ).float()
      input_bad = torch.tensor(
          [[0, -10, 0], [0, 10, 0], [0, 0, 10]]
      ).float()
      target = torch.tensor([1, 0, 2]).long()
  
      loss_good = F.softmax_focal_loss_with_logits(input_good, target)
      loss_bad = F.softmax_focal_loss_with_logits(input_bad, target)
      assert loss_good < loss_bad
  
      loss_cls = CrossEntropyFocalLoss()
      assert loss_cls(input_good, target) < loss_cls(input_bad, target)
  
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 2 for calling a global function with example usage: test_softmax_focal_loss_with_logits() without return types