Improve performance of dnp.nan_to_num (#2228)

This PR adds a dedicated kernel for `dnp.nan_to_num` to improve its
performance. This reduces the number of kernel calls to at most one in
all cases.

A kernel for both strided and contiguous inputs have been added, to
avoid additional allocation of device memory for trivial strides when
input is fully C- or F-contiguous.

For example of performance gains, using Max GPU

master:
```python
In [1]: import dpnp as dnp

In [2]: import numpy as np

In [3]: x_np = np.random.randn(10**9)

In [4]: x_np[np.random.choice(x_np.size, 200, replace=False)] = np.nan

In [5]: x = dnp.asarray(x_np)

In [6]: q = x.sycl_queue

In [7]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 394 ms, sys: 43.8 ms, total: 438 ms
Wall time: 304 ms

In [8]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 333 ms, sys: 31.8 ms, total: 364 ms
Wall time: 134 ms
```

on branch:
```python
In [8]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 49.6 ms, sys: 8.1 ms, total: 57.7 ms
Wall time: 60.9 ms

In [9]: %time r = dnp.nan_to_num(x); q.wait()
CPU times: user 22.9 ms, sys: 16 ms, total: 38.9 ms
Wall time: 19.7 ms
``` 77702b3

Author: github-actions[bot]

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