code: def test_torch(
      self, spatial_axis, img: torch.Tensor, track_meta: bool, device
  ):
      set_track_meta(track_meta)
      img = img.to(device)
      init_param = {"keys": "image", "spatial_axis": spatial_axis}
      xform = Flipd(**init_param)
      call_param = {"data": {"image": img}}
      res = xform(**call_param)  # type: ignore
      self.assertEqual(img.shape, res["image"].shape)
      if track_meta:
          test_resampler_lazy(
              xform, res, init_param, call_param, output_key="image"
          )
          self.assertIsInstance(res["image"], MetaTensor)
      else:
          self.assertNotIsInstance(res["image"], MetaTensor)
          self.assertIsInstance(res["image"], torch.Tensor)
          with self.assertRaisesRegex(ValueError, "MetaTensor"):
              xform.inverse(res)
  
file_path: MONAI/tests/test_flipd.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai import config
  from monai.data.meta_obj import set_track_meta
  from monai.data.meta_tensor import MetaTensor
  from monai.transforms import Flipd
  from tests.lazy_transforms_utils import test_resampler_lazy
  from tests.utils import TEST_DEVICES, TEST_NDARRAYS_ALL, NumpyImageTestCase2D, assert_allclose, test_local_inversion
label_desc: Use image node_id 3 for calling the TestFlipd obj's underlying member method code with example usage: obj.test_torch(spatial_axis, img, track_meta, device) without return types