code: def test_flip_numpy(self, device, dtype):
      make_arg = partial(make_tensor, dtype=dtype, device=device)
  
      for ndim in [3, 4]:
          shape = self._rand_shape(ndim, 5, 10)
          data = make_arg(shape)
  
          # Axis to sample for given shape.
          for i in range(1, ndim + 1):
              # Check all combinations of `i` axis.
              for flip_dim in combinations(range(ndim), i):
                  torch_fn = partial(torch.flip, dims=flip_dim)
                  np_fn = partial(np.flip, axis=flip_dim)
                  self.compare_with_numpy(torch_fn, np_fn, data)
  
imports: import torch
  import numpy
  from itertools import product, combinations, permutations, chain
  from functools import partial
  import random
  import warnings
  import unittest
  from torch import nan
  from torch.testing import make_tensor
  from torch.testing._internal.common_utils import TestCase, run_tests, skipIfTorchDynamo, torch_to_numpy_dtype_dict, IS_JETSON
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyNativeDeviceTypes, dtypesIfCUDA, largeTensorTest
  from torch.testing._internal.common_dtype import all_types_and_complex_and, all_types, all_types_and
label_desc: Use image node_id 16 for calling the TestShapeOps obj's underlying member method code with example usage: obj.test_flip_numpy(device, dtype) without return types