code: def test_script(self):
      target = torch.tensor(
          [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]]
      )
  
      # add another dimension corresponding to the batch (batch size = 1 here)
      target = target.unsqueeze(0)
      pred_very_good = (
          1000
          * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float()
      )
  
      loss = GeneralizedWassersteinDiceLoss(
          dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]),
          weighting_mode="default",
      )
  
      test_script_save(loss, pred_very_good, target)
  
file_path: MONAI/tests/test_generalized_wasserstein_dice_loss.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from monai.losses import GeneralizedWassersteinDiceLoss
  from tests.utils import test_script_save
label_desc: Use image node_id 6 for calling the TestGeneralizedWassersteinDiceLoss obj's underlying member method code with example usage: obj.test_script() without return types