ENH: use torch.clamp for wrapped_torch.clip

[ev-br]

Closes gh-350

Otherwise, the version which emulates "clip" fails with torch.vmap.

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This patch is less innocuous than it looks, because it changes the promotion rules. Previously, min and max were not upcasting the result:

In [9]: xpn = array_namespace(np.arange(3))

In [10]: xpn.clip(xpn.arange(5, dtype=xpn.int8), 2, xpn.asarray(3.0))
Out[10]: array([2, 2, 2, 3, 3], dtype=int8)

and now they are:

In [11]: xpt = array_namespace(torch.ones(3))

In [12]: xpt.clip(xpt.arange(5, dtype=xpt.int8), 2, xpt.asarray(3.0))
Out[12]: tensor([2., 2., 2., 3., 3.])

OTOH, the wording in the spec, https://data-apis.org/array-api/draft/API_specification/generated/array_api.clip.html is

min ... should have the same data type as x.

and

If either min or max is an array having a different data type than x, behavior is unspecified and thus implementation-dependent.

[ev-br]

cc/fyi @mdhaber : IIRC scipy.stats is a big user of the [()] empty tuple indexing. Apparently, it is problematic for torch.vmap, especially if used on the left of the = assignment. This PR and the issue it closes is triggered by this line:
https://github.com/data-apis/array-api-compat/blob/main/array_api_compat/common/_aliases.py#L424