code: def test_dict_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)
      )
  
file_path: MONAI/tests/test_prepare_batch_default.py
imports: from __future__ import annotations
  import unittest
  import torch
  from monai.engines import PrepareBatchDefault, SupervisedEvaluator
  from tests.utils import assert_allclose
label_desc: Use image node_id 1 for calling the TestPrepareBatchDefault obj's underlying member method code with example usage: obj.test_dict_content() without return types