[BUG] Numpy proxy array fails with AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'

[Matt711]

Describe the bug
Apart of #17490. Several of the third-party integration tests fail with AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'. I've checked that ndarray in this case is a proxy array (ie cudf.pandas._wrappers.numpy.ndarray) not a true numpy array (ie. np.ndarray). So this makes me think this is a real numpy proxying bug because proxy arrays get a _fsproxy_wrapped attribute immediately after instance creation in __array_finalize__.

The failing tests occur during the comparison step in the third-party integration tests CI job. I've checked that the "gold" run (ie. no cudf.pandas) and the "cudf" (ie. with cudf.pandas) pass. To illustrate what is happening, we can use one of the failing numpy integration tests.

def test_numpy_fft(sr):
    fft = np.fft.fft(sr)
    return fft

During the "gold" run the result from test_numpy_fft is a real np.ndarray. And during the "cudf" run, the result is a proxy array cudf.pandas._wrappers.numpy.ndarray. The results are stored in a binary file for use during the "compare" run. It's during this that the test fails. The assertion function used to compare the "gold" and "cudf" runs is np.testing.assert_allclose. So effectively,

np.testing.assert_allclose(gold_result, cudf_result)

produces the test failure we see

AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'`

Steps/Code to reproduce bug
I have not come up with a minimal reproducer yet. But we can reproduce the CI failures locally in a conda environment

# env.yaml
channels:
- rapidsai-nightly
- rapidsai
- conda-forge
- nvidia
dependencies:
- cuda-version=12.8
- cudf==25.2.*,>=0.0.0a0
- numpy
- pandas
- pytest
- pytest-xdist
- python=3.12
name: test_numpy_cuda-128_arch-x86_64_py-312

./ci/cudf_pandas_scripts/third-party-integration/test.sh ./python/cudf/cudf_pandas_tests/third_party_integration_tests/dependencies.yaml

[Matt711]

TODO: Try serializing/deserializing the wrapped array and comparing to a real numpy array.