code: def test_rand_grid_patchd(
      self, in_type, input_parameters, image_dict, expected
  ):
      image_key = "image"
      input_dict = {}
      for k, v in image_dict.items():
          input_dict[k] = v
          if k == image_key:
              input_dict[k] = in_type(v)
      splitter = RandGridPatchd(keys=image_key, **input_parameters)
      splitter.set_random_state(1234)
      output = splitter(input_dict)
      self.assertEqual(len(output[image_key]), len(expected))
      for output_patch, expected_patch in zip(
          output[image_key], expected
      ):
          assert_allclose(
              output_patch,
              in_type(expected_patch),
              type_test=False,
              device_test=bool(
                  isinstance(in_type(expected_patch), torch.Tensor)
              ),
          )
  
file_path: MONAI/tests/test_rand_grid_patchd.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.transforms.spatial.dictionary import RandGridPatchd
  from monai.utils import set_determinism
  from tests.utils import TEST_NDARRAYS, SkipIfBeforePyTorchVersion, assert_allclose
label_desc: Use image node_id 3 for calling the TestRandGridPatchd obj's underlying member method code with example usage: obj.test_rand_grid_patchd(in_type, input_parameters, image_dict, expected) without return types