code: def test_content(self, input_args, expected_value):
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      dataloader = [
          {
              "image": torch.tensor([1, 2]),
              "label": torch.tensor([1, 2]),
              "extra_label1": torch.tensor([3, 4]),
              "extra_label2": 16,
          }
      ]
      # set up engine
      evaluator = SupervisedEvaluator(
          device=device,
          val_data_loader=dataloader,
          epoch_length=1,
          network=TestNet(),
          non_blocking=True,
          prepare_batch=PrepareBatchHoVerNet(**input_args),
          decollate=False,
      )
      evaluator.run()
      output = evaluator.state.output
      assert_allclose(
          output["image"], torch.tensor([1, 2], device=device)
      )
      for k, v in output["pred"].items():
          if isinstance(v, torch.Tensor):
              assert_allclose(v, expected_value[k].to(device))
          else:
              self.assertEqual(v, expected_value[k])
  
file_path: MONAI/tests/test_prepare_batch_hovernet.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.apps.pathology.engines import PrepareBatchHoVerNet
  from monai.engines import SupervisedEvaluator
  from monai.utils.enums import HoVerNetBranch
  from tests.utils import assert_allclose
label_desc: Use image node_id 1 for calling the TestPrepareBatchHoVerNet obj's underlying member method code with example usage: obj.test_content(input_args, expected_value) without return types