docstr: Test if the loss is compatible with all the integer types
  for the target segmentation.
docstr_tok: ["Test","if","the","loss","is","compatible","with","all","the","integer","types","for","the","target","segmentation","."]
code: def test_different_target_data_type(self):
      """
      Test if the loss is compatible with all the integer types
      for the target segmentation.
      """
      # define 2d examples
      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)  # shape (1, H, W)
      pred_very_good = (
          1000
          * F.one_hot(target, num_classes=2).permute(0, 3, 1, 2).float()
      )
  
      target_uint8 = target.to(torch.uint8)
      target_int8 = target.to(torch.int8)
      target_short = target.short()
      target_int = target.int()
      target_long = target.long()
      target_list = [
          target_uint8,
          target_int8,
          target_short,
          target_int,
          target_long,
      ]
  
      for w_mode in ["default", "GDL"]:
          # initialize the loss
          loss = GeneralizedWassersteinDiceLoss(
              dist_matrix=np.array([[0.0, 1.0], [1.0, 0.0]]),
              weighting_mode=w_mode,
          )
  
          # The test should pass irrespectively of the integer type used
          for t in target_list:
              # the loss for pred_very_good should be close to 0
              loss_good = float(loss.forward(pred_very_good, t))
              self.assertAlmostEqual(loss_good, 0.0, places=3)
  
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 2 for calling the TestGeneralizedWassersteinDiceLoss obj's underlying member method code with example usage: obj.test_different_target_data_type() without return types