text: # Copyright (c) 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 torch
  
  from monai.engines import PrepareBatchDefault, SupervisedEvaluator
  from tests.utils import assert_allclose
  
  
  class TestNet(torch.nn.Module):
      def forward(self, x: torch.Tensor):
          return x
  
  
  class TestPrepareBatchDefault(unittest.TestCase):
      def test_content(self):
          device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
          dataloader = [
              {
                  "image": torch.tensor([1, 2]),
                  "label": torch.tensor([3, 4]),
                  "extra1": torch.tensor([5, 6]),
                  "extra2": 16,
                  "extra3": "test",
              }
          ]
          # set up engine
          evaluator = SupervisedEvaluator(
              device=device,
              val_data_loader=dataloader,
              epoch_length=1,
              network=TestNet(),
              non_blocking=False,
              prepare_batch=PrepareBatchDefault(),
              decollate=False,
              mode="eval",
          )
          evaluator.run()
          output = evaluator.state.output
          assert_allclose(output["image"], torch.tensor([1, 2], device=device))
          assert_allclose(output["label"], torch.tensor([3, 4], device=device))
  
      def test_empty_data(self):
          dataloader = []
          evaluator = SupervisedEvaluator(
              val_data_loader=dataloader,
              device=torch.device("cpu"),
              epoch_length=0,
              network=TestNet(),
              non_blocking=False,
              prepare_batch=PrepareBatchDefault(),
              decollate=False,
          )
          evaluator.run()
  
  
  if __name__ == "__main__":
      unittest.main()
  