code: def test_correct_results(self, _, spatial_axis):
      for p in TEST_NDARRAYS_ALL:
          init_param = {
              "keys": "img",
              "prob": 1.0,
              "spatial_axis": spatial_axis,
          }
          flip = RandFlipd(**init_param)
          im = p(self.imt[0])
          call_param = {"data": {"img": im}}
          result = flip(**call_param)
  
          # test lazy
          test_resampler_lazy(
              flip, result, init_param, call_param, output_key="img"
          )
          flip.lazy = False
  
          expected = [
              np.flip(channel, spatial_axis) for channel in self.imt[0]
          ]
          expected = np.stack(expected)
          assert_allclose(
              result["img"], p(expected), type_test="tensor"
          )
          test_local_inversion(
              flip, {"img": result["img"]}, {"img": im}, "img"
          )
  
          set_track_meta(False)
          result = flip({"img": im})["img"]
          self.assertNotIsInstance(result, MetaTensor)
          self.assertIsInstance(result, torch.Tensor)
          set_track_meta(True)
  
file_path: MONAI/tests/test_rand_flipd.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.data import MetaTensor, set_track_meta
  from monai.transforms import RandFlipd
  from tests.lazy_transforms_utils import test_resampler_lazy
  from tests.utils import TEST_NDARRAYS_ALL, NumpyImageTestCase2D, assert_allclose, test_local_inversion
label_desc: Use image node_id 1 for calling the TestRandFlipd obj's underlying member method code with example usage: obj.test_correct_results(_, spatial_axis) without return types