code: def test_result(self, input_param, input_data, expected_val):
      loss = DeepSupervisionLoss(DiceLoss(**input_param))
      result = loss(**input_data)
      np.testing.assert_allclose(
          result.detach().cpu().numpy(),
          expected_val,
          atol=1e-4,
          rtol=1e-4,
      )
  
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.losses import DeepSupervisionLoss, DiceCELoss, DiceFocalLoss, DiceLoss
  from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
label_desc: Use image node_id 1 for calling the TestDSLossDice obj's underlying member method code with example usage: obj.test_result(input_param, input_data, expected_val) without return types