In mathematics, a matrix (plural matrices) is a rectangular array of numbers. Matrices have many applications in programming, from performing transformations in 2D space to machine learning. 

One of the most useful operations to perform on matrices is matrix multiplication, which takes a pair of matrices and produces another matrix – known as the dot product. Multiplying matrices is very different to multiplying real numbers, and follows its own set of rules. 

Unlike multiplying real numbers, multiplying matrices is non-commutative: in other words, multiplying matrix ```a``` by matrix ```b``` will not give the same result as multiplying matrix ```b``` by matrix ```a```.

Additionally, not all pairs of matrix can be multiplied. For two matrices to be multipliable, the number of columns in matrix ```a``` must match the number of rows in matrix ```b```.

There are many introductions to matrix multiplication online, including at Khan Academy, and in the classic MIT lecture series by Herbert Gross. 

To complete this kata, write a function that takes two matrices - ```a``` and ```b``` - and returns the dot product of those matrices. If the matrices cannot be multiplied, return ```-1``` for JS/Python, or `null` for Java.

Each matrix will be represented by a two-dimensional list (a list of lists). Each inner list will contain one or more numbers, representing a row in the matrix.

For example, the following matrix:

```|1 2|``````|3 4|```

Would be represented as:

```[[1, 2], [3, 4]]```

It can be assumed that all lists will be valid matrices, composed of lists with equal numbers of elements, and which contain only numbers. The numbers may include integers and/or decimal points.

```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 = 'def getMatrixProduct(a, b):\n    """If a and b can be multiplied, returns the product of a and b as a two-dimensional array. Otherwise returns -1."""\n    # Get dimensions\n    arl, acl = len(a), len(a[0])\n    brl, bcl = len(b), len(b[0])\n    # Validate compatible dimensions\n    if acl != brl:\n        return -1\n    # Handy data extraction lambdas\n    row = lambda ar, n: ar[n]\n    col = lambda ar, n: [r[n] for r in ar]\n    # Sum of the products of parallel arrays\n    prod_sum = lambda ar1, ar2: sum((v1 * v2 for v1, v2 in zip(ar1, ar2)))\n    # Answer!\n    return [[prod_sum(row(a, r), col(b, c)) for c in range(bcl)] for r in range(arl)]\n'
_co = compile(_CODE, USER_FILENAME, "exec")
exec(_co, ns, ns)

_target = None
_fn_name = 'getMatrixProduct'
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: 8672