Feature request: add a take(array, indices) primitive analogous to numpy.take

Many models need to select entries from an array using symbolic indices. Think resource assignment, scheduling, routing—the same pattern.
I emulate those gathers by looping over every possible index and using `where` + `sum` to build a one-hot mask. For an array of length `H`, that costs on the order of `3×H` nodes per lookup.
Having a built-in `take` (scalar and batched) would let us write `value = take(arr, idx)` just like we do in [numpy.take](https://numpy.org/doc/2.2/reference/generated/numpy.take.html). The engine can then translate it into a single node internally.
so, dramatically smaller graphs (fewer nodes, lower state size), faster model build/upload, and much cleaner model code.
Please consider exposing a `gather primitive` similar to `numpy.take` so we don’t have to build the workaround ourselves.

Current workaround
For each lookup I do something like:
```
# array: length H
# choice: symbolic index
```
```
mask = where(range_vec == choice, ones_vec, zeros_vec)
value = (mask * array).sum()
```
If the array has `H` elements, the loop expands to:

H equality checks (`range_vec == choice`)
H `Where` nodes to create the mask
Up to H multiplications for `mask * array`
H-1 additions to accumulate the result.

When this index-selection happens inside a bigger loop—for example, per job, per candidate window, per resource—the node count explodes.