MLX: Subtensor and IncSubtensor fail on a symbolic integer index under mx.compile

x[i] and set_subtensor(x[i], y) with a symbolic integer i fail to compile on the MLX backend. mlx_funcify_Subtensor and mlx_funcify_IncSubtensor call int() on every integer index input, and under mx.compile the index is a traced mx.array, so int() raises. mlx accepts an integer mx.array as an index for both reads and writes, and the same graph compiles when the index is a one-element vector, because AdvancedSubtensor passes the array through.

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

x = pt.matrix("x")
i = pt.iscalar("i")
fn = pytensor.function([x, i], x[i], mode="MLX")
print(fn(np.eye(3, dtype="float32"), 1))  # ValueError: [eval] Attempting to eval an array during function transformations
# workaround: index with a one-element vector, x[i[None]][0], which lowers to AdvancedSubtensor

The int() coercion came in with #2240 for slice bounds. Restricting it to slice bounds and passing an integer mx.array index straight to x[index] would cover both ops.

Start with mlx_funcify_Subtensor and mlx_funcify_IncSubtensor, then run the provided symbolic-index reproducer under the MLX mode. Check both reads and writes with a traced scalar integer index; done means mx.compile accepts those operations without int() coercion while slice bounds retain their required handling.