You will be given a certain array of length ```n```, such that ```n > 4```, having positive and negative integers but there will be no zeroes and all the elements will occur once in it.

We may obtain an amount of ```n``` sub-arrays of length ```n - 1```, removing one element at a time (from left to right). 

For each subarray, let's calculate the product and sum of its elements with the corresponding absolute value of the quotient, ```q = SubProduct/SubSum``` (if it is possible, SubSum cannot be 0). 
Then we select the array with the lowest value of ```|q|```(absolute value)

e.g.: we have the array, ```arr = [1, 23, 2, -8, 5]```
```
Sub Arrays            SubSum    SubProduct         |q|
[23, 2, -8, 5]         22         -1840         83.636363
[1, 2, -8, 5]           0           -80          No value
[1, 23, -8, 5]         21          -920         43.809524
[1, 23, 2, 5]          31           230          7.419355  <--- selected array
[1, 23, 2, -8]         18           368         20.444444
```
Let's compare the given array with the selected subarray:
```
[1, 23, 2, -8, 5]
[1, 23, 2,     5]
```
The difference between them is at the index ```3``` for the given array, with element ```-8```, so we put both things for a result ```[3, -8]```.

That means that to obtain the selected subarray we have to take out the value -8 at index 3.
We need a function that receives an array as an argument and outputs the the pair ```[index, arr[index]]``` that generates the subarray with  the lowest value of ```|q|```.

```python 
select_subarray([1, 23, 2, -8, 5]) == [3, -8]
```
Another case:
```python 
select_subarray([1, 3, 23, 4, 2, -8, 5, 18]) == [2, 23]
```
In Javascript the function will be ```selectSubarray()```.

We may have some special arrays that may have more than one solution as the one that follows below.
```python 
select_subarray([10, 20, -30, 100, 200]) == [[3, 100], [4, 200]]
```
If there is more than one result the function should output a 2Darray sorted by the index of the element removed from the array.

Thanks to Unnamed for detecting the special cases when we have multiple solutions.

Features of the random tests:
```
Number of tests = 200
length of the array, l, such that 20 <= l <= 100
```

Enjoy it!!

```python

USER_FILENAME = "<user_code>"
import sys, json, builtins
import math, heapq, bisect, itertools, functools, collections, statistics, fractions, decimal, operator, array, random, re, string, os, pathlib
try:
    import resource as _res
except Exception:
    _res = None
from collections import Counter, defaultdict, deque, OrderedDict
from functools import lru_cache, reduce
from itertools import accumulate, permutations, combinations, product
from math import gcd, ceil, floor, sqrt
from decimal import Decimal
try:
    from typing import *
except Exception:
    pass
try:
    sys.setrecursionlimit(1_000_000)
except Exception:
    pass
try:
    import site as _site
    try:
        _sys_sites = _site.getsitepackages() if hasattr(_site, "getsitepackages") else []
        for _p in _sys_sites:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
    try:
        _usp = _site.getusersitepackages()
        if isinstance(_usp, str):
            _usp = [_usp]
        for _p in _usp:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
except Exception:
    pass

def _disabled_input(*args, **kwargs):
    raise RuntimeError("input() is disabled in callable mode")
builtins.input = _disabled_input

ns = {"__name__": "__judge__"}  # prevent if __name__ == "__main__" blocks
for _k, _v in list(globals().items()):
    if not _k.startswith("_"):
        ns[_k] = _v
_CODE = 'import numpy as np\n\n\ndef calc_one(value, tot_sum, tot_prod):\n    value[value==tot_sum] = np.nan\n    return (tot_prod / value) / (tot_sum - value)\n\n\ndef select_subarray(arr):\n    np_arr = np.array(arr).astype(np.float64)\n    tot_sum = np_arr.sum()\n    tot_prod = np_arr.prod()\n    qs = np.apply_along_axis(\n        calc_one,\n        0,\n        np_arr,\n        tot_sum,\n        tot_prod,\n    )\n    qs = np.abs(qs)\n    min_ = np.nanmin(qs)\n    where_min = np.where(qs == min_)[0]\n    out = [[where, arr[where]] for where in where_min]\n    return out if len(out)>1 else out[0]'
_co = compile(_CODE, USER_FILENAME, "exec")
exec(_co, ns, ns)

_target = None
_fn_name = 'select_subarray'
if _fn_name:
    if "Solution" in ns:
        try:
            _obj = ns["Solution"]()
            if hasattr(_obj, _fn_name):
                _target = getattr(_obj, _fn_name)
        except Exception:
            _target = None
    if _target is None and _fn_name in ns and callable(ns[_fn_name]):
        _target = ns[_fn_name]
# Fallback: if there's exactly one user-defined function, use it
if _target is None:
    _cands = [v for k, v in ns.items() if callable(v) and getattr(v, "__module__", "") in ("__judge__", "__main__")]
    _user_funcs = [c for c in _cands if getattr(c, "__name__", "").startswith("_") is False]
    if len(_user_funcs) == 1:
        _target = _user_funcs[0]
if _target is None:
    raise RuntimeError(f"Could not resolve callable '{_fn_name}'")

_raw = sys.stdin.read().strip()
if _raw:
    _parsed = json.loads(_raw)
    if isinstance(_parsed, dict):
        _kwargs = _parsed.get("kwargs", {})
        _args = _parsed.get("args", [])
    else:
        _args, _kwargs = _parsed, {}
else:
    _args, _kwargs = [], {}
if not isinstance(_args, (list, tuple)):
    _args = [_args]
_res = _target(*_args, **_kwargs)
try:
    sys.stdout.write(repr(_res) + "\n")
except Exception:
    pass

```

Measured memory_bytes: 12631