Add dpnp.linalg.eigh() function

[antonwolfy]

The PR is about to close #1344, since it covers the last functional gap of pca benchmark.

PR adds a missing dpnp.linalg.eigh() function to calculate the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric input matrix. OneMKL LAPACK library is used for that.
The implementation is written as a pybind11 extension above required LAPACK functions.

This is a base PR which assumes to be extended later with remaining functions from OneMKL LAPACK library, once new functionality required.

Numbers for a laptop with Iris Xe:

import dpnp, numpy

sh = (2024, 2024)
a = numpy.random.uniform(-1, 1, sh) + 1.j * numpy.random.uniform(-1, 1, sh)
a = a.astype(numpy.complex64)
x = numpy.matrix(a)

nx = x.getH()
%timeit _, _ = numpy.linalg.eigh(nx)
Out: 2.21 s ± 199 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

dx = dpnp.array(nx, device='level_zero:gpu')
%timeit _, _ = dpnp.linalg.eigh(dx)
Out: 858 ms ± 25.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

dx = dpnp.array(nx, device='opencl:gpu')
%timeit _, _ = dpnp.linalg.eigh(dx)
Out: 849 ms ± 37.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

dx = dpnp.array(nx, device='opencl:cpu')
%timeit _, _ = dpnp.linalg.eigh(dx)
Out: 841 ms ± 47.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

nx.size, dx.size
Out: (4096576, 4096576)

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[npolina4]

Works in pca workload. Thanks!