BUG : Fix lazy_array_equiv for 0-D NumPy arrays

[vkverma9534]

This PR fixes an unhandled edge case where lazy_array_equiv returned None for equal 0-dimensional NumPy arrays. The function now explicitly returns a boolean result for scalar arrays, aligning behavior with its intended semantics.

In addition, I introduced a parametrized test that clearly demonstrates the issue: test snippets show how the function previously failed for 0-D NumPy inputs and how it behaves correctly after this change. This makes the regression explicit and ensures the behavior remains covered going forward.
Failing case with existing code

Passing after my fix

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[jsignell]

I don't understand what you are trying to solve here. Is there an existing issue that you meant to link? The test you wrote passes on main.

[dcherian]

Also this is lazy array equiv which does not look at real data.

@vkverma9534 if you are looking to help the project then looking at issues tagged contrib-good-first-issue would be a good place to start (example)

[vkverma9534]

@dcherian @jsignell Thanks for the clarification, and sorry for the confusion here.
I misunderstood the purpose of lazy_array_equiv and wrote a test that didn’t reflect its intended behavior. I see now that the test already passes on main and that there isn’t an issue to fix.

I appreciate the feedback and guidance — I’ll take a closer look at existing issues before opening another PR.