Add implicit NumPy conversion for dpctl.tensor.usm_ndarray types

[icfaust]

The functionality introduced in #1964 can be better optimized by moving code from _copy_utils to the usm_ndarray itself. This will make seamless integration into other larger codebases like scikit-learn, where use of asarray is common. It attempts to solve #2129

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[coveralls]

coverage: 85.903% (+0.03%) from 85.878%
when pulling 1b8b30b on icfaust:dev/array_fix
into 3381d14 on IntelPython:master.

[icfaust]

@ndgrigorian I am unable to check the jenkins CI checks. Is there any way someone from your team can help me?

[ndgrigorian]

@ndgrigorian I am unable to check the jenkins CI checks. Is there any way someone from your team can help me?

I can help with that, but there are still concerns with this PR that need to be addressed, some of which I mentioned in the issue

If this PR were modified to accept an environment variable to permit implicit conversion, that would be acceptable, but implicit conversion under all circumstances should not be allowed

[sycloid]

Implicit conversion means GPU resident array data is going to be copied to host without explicit user control. This introduces source of hard to pin-point performance bottlenecks.

CuPy does not do it for a good reason. Computation on GPU using data-parallel algorithms is generally quite a lot faster. So CuPy promotes coercion of np.ndarray to cp.ndarray by implementing __array_function__ hook in cp.ndarray, so that cp.sin( inp: np.ndarrray ) actually returns cp.ndarray on default device.

I hope this change does not get merged, it is a regression