code: def test_shape(self, input_param, input_shape, expected_shape):
      net = DynUNet(**input_param).to(device)
      with torch.no_grad():
          results = net(torch.randn(input_shape).to(device))
          self.assertEqual(results.shape, expected_shape)
  
file_path: MONAI/tests/test_dynunet.py
imports: from __future__ import annotations
  import unittest
  from typing import Any, Sequence
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode
  from monai.networks.nets import DynUNet
  from monai.utils import optional_import
  from tests.utils import assert_allclose, skip_if_no_cuda, skip_if_windows, test_script_save
label_desc: Use image node_id 1 for calling the TestDynUNetDeepSupervision obj's underlying member method code with example usage: obj.test_shape(input_param, input_shape, expected_shape) without return types