code: # Copyright 2020 MONAI Consortium
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #     http://www.apache.org/licenses/LICENSE-2.0
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import unittest
  import numpy as np
  from parameterized import parameterized
  from monai.transforms import RandSpatialCropSamples
  
  TEST_CASE_1 = [
      {"roi_size": [3, 3, 3], "num_samples": 4, "random_center": True},
      np.random.randint(0, 2, size=[3, 3, 3, 3]),
      (3, 3, 3, 3),
  ]
  
  TEST_CASE_2 = [
      {"roi_size": [3, 3, 3], "num_samples": 8, "random_center": False},
      np.random.randint(0, 2, size=[3, 3, 3, 3]),
      (3, 3, 3, 3),
  ]
  
  
  class TestRandSpatialCropSamples(unittest.TestCase):
      @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
      def test_shape(self, input_param, input_data, expected_shape):
          result = RandSpatialCropSamples(**input_param)(input_data)
          for item in result:
              self.assertTupleEqual(item.shape, expected_shape)
  
  
  if __name__ == "__main__":
      unittest.main()
  
apis: numpy.random.randint