This also prevents `SeqOptimizer.apply` from calling `Rewriter.add_requirements`
on each iteration through its list of sub-`Rewriter`s.

A few core `Feature` implementations were using `assert`s and generic
`Exception` instead of `AlreadyThere`, which unnecessarily produces errors
when already-attached `Feature` requirements are added by `Rewriter`s.

These changes allow one to pass an `fgraph` argument to all key functions in the
`Function` compilation pipeline.  The given `FunctionGraph` will be directly
compiled without cloning.  Unlike the previous `FunctionMaker.__init__`, this
one's `fgraph` argument *will* be optimized according to the given `mode` unless
the keyword argument `no_fgraph_prep` is `True`.

- Use newer, NumPy-recommended `numpy.random` functions (e.g. replace `rand`
  `random`, `randn` with `standard_normal`, etc.)
- Seed some unseeded/inconsistently seeded tests
- Minor `pytest` usage updates (e.g. use parameterizations, `pytest.raises`, and
  `pytest.warns`)
- Use `np.array_equal` instead of `np.all(... == ...)`

This removes an inconsistency between Numpy and Aesara broadcasting rules, where a variable dimension with unknown shape was always assumed to be non-broadcastable (i.e., different than 1)