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

[mdhaber]

Yes, but I don't recall putting [()] on the left of an equals sign. Typically, when code in scipy.stats mutates a 0-d array, it's a special case of mutating a multidimensional array. And usually, that's done with a logical mask generated by some sort of comparison operator, so the 0-d case ends up indexing with a 0-d boolean array, not an empty tuple.

Almost every scipy.stats function uses [()] to the right of an equals sign / return statement to follow the precedent set by NumPy - return scalars instead of 0-d arrays at almost every opportunity. Now that we have to consider array-api-strict, empty tuple __getitem__ is only ever used on 0-d arrays; e.g. like x[() if x.ndim == 0 else x. Fine with me to get rid of all these uses when NumPy makes it less difficult to follow the standard (mdhaber/numpy#2)! But hopefully getting with [()] is not problematic in the meantime? Having to special case that for 0-d arrays is already verbose enough!