code: def test_sliding_window_with_padding(
      self,
      key,
      image_shape,
      roi_shape,
      sw_batch_size,
      overlap,
      mode,
      device,
      extra_input_padding,
  ):
      n_total = np.prod(image_shape)
      if mode == "constant":
          inputs = torch.arange(n_total, dtype=torch.float).reshape(
              *image_shape
          )
      else:
          inputs = torch.ones(*image_shape, dtype=torch.float)
      if device.type == "cuda" and not torch.cuda.is_available():
          device = torch.device("cpu:0")
  
      def compute(data):
          if key:
              return {key: torch.clone(data[..., 1:-1, 1:-1]) + 1}
          else:
              return torch.clone(data[..., 1:-1, 1:-1]) + 1
  
      if mode == "constant":
          expected_val = (
              np.arange(n_total, dtype=np.float32).reshape(*image_shape)
              + 1.0
          )
      else:
          expected_val = np.ones(image_shape, dtype=np.float32) + 1.0
  
      if extra_input_padding is None:
          expected_val[..., 0, :] = expected_val[..., -1, :] = None
          expected_val[..., 0] = expected_val[..., -1] = None
  
      sliding_inference = SlidingWindowHoVerNetInferer(
          roi_shape,
          sw_batch_size,
          overlap,
          mode,
          extra_input_padding=extra_input_padding,
      )
      result = sliding_inference(inputs.to(device), compute)
      result = result[key] if key else result
      np.testing.assert_string_equal(device.type, result.device.type)
      np.testing.assert_allclose(result.cpu().numpy(), expected_val)
  
file_path: MONAI/tests/test_sliding_window_hovernet_inference.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.apps.pathology.inferers import SlidingWindowHoVerNetInferer
  from monai.data import MetaTensor
  from monai.inferers import sliding_window_inference
  from monai.utils import optional_import
  from tests.test_sliding_window_inference import TEST_CASES
label_desc: Use image node_id 1 for calling the TestSlidingWindowHoVerNetInference obj's underlying member method code with example usage: obj.test_sliding_window_with_padding(key, image_shape, roi_shape, sw_batch_size, overlap, mode, device, extra_input_padding) and returns: dict, unknown