Implement `infer_shape` automatically from `gufunc_signature`

[ricardoV94]

Description

For Ops with a gufunc_signature, we can automate infer_shape implementation:

pytensor/pytensor/tensor/slinalg.py

Lines 28 to 53 in e036caf

	class Cholesky(Op):
	# TODO: LAPACK wrapper with in-place behavior, for solve also
	__props__ = ("lower", "check_finite", "on_error", "overwrite_a")
	gufunc_signature = "(m,m)->(m,m)"
	def __init__(
	self,
	*,
	lower: bool = True,
	check_finite: bool = True,
	on_error: Literal["raise", "nan"] = "raise",
	overwrite_a: bool = False,
	):
	self.lower = lower
	self.check_finite = check_finite
	if on_error not in ("raise", "nan"):
	raise ValueError('on_error must be one of "raise" or ""nan"')
	self.on_error = on_error
	self.overwrite_a = overwrite_a
	if self.overwrite_a:
	self.destroy_map = {0: [0]}
	def infer_shape(self, fgraph, node, shapes):
	return [shapes[0]]

We actually already do it for the Blockwise Wrapper:

pytensor/pytensor/tensor/blockwise.py

Lines 208 to 210 in 3cdcfde

	# The output dim is the same as another input dim
	if dim_name in core_dims:
	core_out_shape.append(core_dims[dim_name])