Avoid copying uninitialized float64 buffers to the device in tests

[antonwolfy]

Four tests built an output array with numpy.empty in float64 and copied it to the device with dpnp.array:

• TestDot::test_out_scalar in test_product.py (numpy.result_type(int, float32_array) is float64)
• TestMatmul::test_syrk_out_order in test_product.py (generate_random_numpy_array defaults to float64)
• TestVecdot::test_out_0D in test_product.py
• TestNanProdSum::test_out in test_nanfunctions.py

On a device without fp64 support the copy casts the buffer to float32. If the uninitialized memory happens to hold a value beyond the float32 range, NumPy raises RuntimeWarning: overflow encountered in cast, so the warning appears at random in test runs on such devices (it was seen for TestDot::test_out_scalar[float32] on a Windows GPU).

The buffers are now created with numpy.zeros. In TestNanProdSum::test_out, where only a wrongly shaped out array is needed, it is allocated on the device with dpnp.empty instead. Only test files are changed.

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[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/3110/index.html

[github-actions]

Array API standard conformance tests for dpnp=0.21.0dev14=np2py314h8d9cdd5_6 ran successfully.
Passed: 1376
Failed: 0
Skipped: 6

[coveralls]

coverage: 82.61%. remained the same — fix-uninit-out-arrays-in-tests into master