Suppose you needed to generate a Gaussian $($normal$)$ distribution

$(,|)=\frac{1}{\sqrt[\phantom{2}]{2}}{e}^{-\frac{(x-{)}^2}{2}{}^2}$

of $N_{RV}$ random variates, but the only random number generator you know about

is numpy.random.uniform $(0,1,N).$ However, assume you do know about the central

limit theorem. Create a Python script

gengauss.py which does the following:

Reads $,$ and $N_{RV}$ from the command line. Be sure that your script checks that

it has the proper inputs, and prompts the user if not.

Uses the central limit theorem to generate $N_{RV}$ values of $x$ having the proper

distribution, using only random numbers from numpy.random. uniform $(0,1,N).$

Creates a plot in the file gengauss.png$,$ showing a histogram of $x$ values with

the $(,|)$ superimposed.

Be sure the distribution you generate has mean and width as specified on the command

line.