code: def test_helper(self, input_data, _unused):
      vals = {
          "y_pred": dict(input_data).pop("y_pred"),
          "y": dict(input_data).pop("y"),
      }
      result = DiceHelper(sigmoid=True)(**vals)
      np.testing.assert_allclose(
          result[0].cpu().numpy(), [0.0, 0.0, 0.0], atol=1e-4
      )
      np.testing.assert_allclose(
          sorted(result[1].cpu().numpy()), [0.0, 1.0, 2.0], atol=1e-4
      )
      result = DiceHelper(softmax=True, get_not_nans=False)(**vals)
      np.testing.assert_allclose(
          result[0].cpu().numpy(), [0.0, 0.0], atol=1e-4
      )
  
      num_classes = vals["y_pred"].shape[1]
      vals["y_pred"] = torch.argmax(vals["y_pred"], dim=1, keepdim=True)
      result = DiceHelper(sigmoid=True, num_classes=num_classes)(**vals)
      np.testing.assert_allclose(
          result[0].cpu().numpy(), [0.0, 0.0, 0.0], atol=1e-4
      )
  
file_path: MONAI/tests/test_compute_meandice.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.metrics import DiceHelper, DiceMetric, compute_dice
label_desc: Use image node_id 3 for calling the TestComputeMeanDice obj's underlying member method code with example usage: obj.test_helper(input_data, _unused) without return types