Sparse TensorVariables are not using the Sparse Elemwise methods

[ricardoV94]

Description

I think during the merging of SparseVariable and TensorVariables Type classes still in Aesara aesara-devs/aesara#766, there was a mistake where the Structured methods were not borrowed, instead we always convert to dense with a warning

import numpy as np
import pytensor.tensor as pt
import pytensor.sparse as ps


X_sp = ps.csc_from_dense(pt.arange(5))
X_sp.sin()  # Method sin is not implemented for sparse variables. The variable will be converted to dense.

Either way, we have a sparse Sin implemented, so we should use them:

out = ps.sin(X_sp)
out.eval()  # <1x5 sparse matrix of type '<class 'numpy.float64'>'

pytensor/pytensor/sparse/basic.py

Lines 3028 to 3051 in 0b632bd

	def structured_monoid(tensor_op):
	# Generic operation to perform many kinds of monoid element-wise
	# operations on the non-zeros of a sparse matrix.
	# The first parameter must always be a sparse matrix. The other parameters
	# must be scalars which will be passed as argument to the tensor_op.
	def decorator(f):
	def wrapper(*args):
	x = as_sparse_variable(args[0])
	assert x.format in ("csr", "csc")
	xs = [aes.as_scalar(arg) for arg in args[1:]]
	data, ind, ptr, _shape = csm_properties(x)
	data = tensor_op(data, *xs)
	return CSM(x.format)(data, ind, ptr, _shape)
	wrapper.__name__ = str(tensor_op.scalar_op)
	return wrapper
	return decorator