code: def test_pad_shape(self, input_param, input_shape, expected_shape, _):
      for p in TEST_NDARRAYS_ALL:
          if isinstance(p(0), torch.Tensor) and (
              "constant_values" in input_param
              or input_param["mode"] == "reflect"
          ):
              continue
          padcropper = ResizeWithPadOrCrop(**input_param)
          result = padcropper(p(np.zeros(input_shape)))
          np.testing.assert_allclose(result.shape, expected_shape)
          result = padcropper(p(np.zeros(input_shape)), mode="constant")
          np.testing.assert_allclose(result.shape, expected_shape)
          self.assertIsInstance(result, MetaTensor)
          self.assertEqual(len(result.applied_operations), 1)
          inv = padcropper.inverse(result)
          self.assertTupleEqual(inv.shape, input_shape)
          self.assertIsInstance(inv, MetaTensor)
          self.assertEqual(inv.applied_operations, [])
  
file_path: MONAI/tests/test_resize_with_pad_or_crop.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.data.meta_tensor import MetaTensor
  from monai.transforms import ResizeWithPadOrCrop
  from monai.transforms.lazy.functional import apply_pending
  from tests.utils import TEST_NDARRAYS_ALL, assert_allclose, pytorch_after
label_desc: Use image node_id 1 for calling the TestResizeWithPadOrCrop obj's underlying member method code with example usage: obj.test_pad_shape(input_param, input_shape, expected_shape, _) without return types