code: def test_decoder_padding_shape(
      self, input_param, input_shape, expected_shapes
  ):
      if input_param["mode"] == HoVerNet.Mode.FAST:
          input_param["decoder_padding"] = True
          net = HoVerNet(**input_param).to(device)
          with eval_mode(net):
              result = net.forward(torch.randn(input_shape).to(device))
              for item in result:
                  expected_shape = expected_shapes[item]
                  padding_expected_shape = list(expected_shape)
                  padding_expected_shape[2:] = input_shape[2:]
                  self.assertEqual(
                      result[item].shape, tuple(padding_expected_shape)
                  )
      else:
          pass
  
file_path: MONAI/tests/test_hovernet.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode, train_mode
  from monai.networks.nets import HoVerNet
  from monai.networks.nets.hovernet import _DenseLayerDecoder
  from tests.utils import test_script_save
label_desc: Use image node_id 2 for calling the TestHoverNet obj's underlying member method code with example usage: obj.test_decoder_padding_shape(input_param, input_shape, expected_shapes) without return types