Implement several subtensor lift rewrites

[ricardoV94]

This allows reducing computations on batch dimensions by lifting simple indexing operations closer to the inputs.

An obvious example is:

import numpy as np
import pytensor
import pytensor.tensor as pt
from pytensor.compile.mode import get_default_mode

mode = get_default_mode()

x = pt.matrix("x", shape=(512, 512))
x_test = np.random.normal(size=x.type.shape)

x_sum = x.sum(axis=1)
out = x_sum[0]

fn_before = pytensor.function([x], out, mode=mode.excluding("local_subtensor_of_reduce"))
fn_before.dprint(print_type=True)
%timeit fn_before(x_test)
# Subtensor{i} [id A] <Scalar(float64, shape=())> 1
#  ├─ Sum{axis=1} [id B] <Vector(float64, shape=(512,))> 0
#  │  └─ x [id C] <Matrix(float64, shape=(512, 512))>
#  └─ 0 [id D] <uint8>
# 762 μs ± 7.55 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

print()
fn_after = pytensor.function([x], out, mode=mode)
fn_after.dprint(print_type=True)
%timeit fn_after(x_test)
# Sum{axes=None} [id A] <Scalar(float64, shape=())> 1
#  └─ Subtensor{i} [id B] <Vector(float64, shape=(512,))> 0
#     ├─ x [id C] <Matrix(float64, shape=(512, 512))>
#     └─ 0 [id D] <uint8>
# 5.26 μs ± 86 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

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📚 Documentation preview 📚: https://pytensor--1158.org.readthedocs.build/en/1158/