Normally-distributed random numbers

It is common to need a random sample from either a real or complex normal distribution.

The standard library provides static `random(in:)` methods which sample uniformly, in a constrained extension of `BinaryFloatingPoint`. (The constraint is `RawSignificand: FixedWidthInteger`.)

I propose that we add similar functionality for sampling from a normal distribution on `Real` and `Complex` with the corresponding constraints.

Start by reviewing the standard library's static random(in:) methods and the existing Real and Complex types in Swift Numerics. Define the corresponding normal-distribution sampling APIs and constraints for real and complex values, then verify that both kinds of samples behave as specified.

Written by the indexing model from the issue text.