Consider adding alternative axis argument to `unpack`

Description

unpack is "awkward" in that it design assumes it should come after pack, and tries to work so that the user can pass the same keep_axes argument:

p, shapes = pt.pack(x, y, z, keep_axes=0)
x, y, z = pt.unpack(p, shapes, keep_axes=0)

When pack is used in isolation it still makes sense for the user to specify keep_axes, as those are the ones that must match among different inputs being packed

But when unpack is used in isolation I think it makes less sense. You end up having to specify everything but the axis you want to operate on:

x = pt.tensor("x", shape=(8, 8, 10, 10))
pt.unpack(p, packed_shapes=[(2,), (2, 3)], keep_axes=[0, 2, 3])

If you never meant to pack these inputs (in which case it's nice that you can use the same keep_axes), it's not very ergonomic. It would be more intuitive to specify axis=1?

We don't even have a good default value if the input is >1D, as keep_axes=None could never produce such input. If you call the second example with the default keep_axes it raises.

But axis=0 if axis is None and keep_axes is None would be a reasonable default, and work with both workflows of unpack. I temporarily considered that default but decided to revert until we discuss having an explicit axis argument.

Start by reviewing the pack and unpack entry points and the documented examples showing keep_axes. Determine the intended behavior for standalone unpack and for the pack/unpack workflow, then confirm that the proposed axis behavior is agreed before defining what a completed change should cover.