Computes the forward pass for an affine $($fully$-$connected$)$ layer.

The input x has shape $($N$,$ d$\_1,...,$ d$\_$k$)$ and contains a minibatch of N

examples, where each example x$[$i$]$ has shape $($d$\_1,...,$ d$\_$k$).$ We will

reshape each input into a vector of dimension D $=$ d$\_1*...*$ d$\_$k$,$ and

then transform it to an output vector of dimension M$.$

Inputs:

$-$ x: A numpy array containing input data, of shape $($N$,$ d$\_1,...,$ d$\_$k$)$

$-$ w: A numpy array of weights, of shape $($D$,$ M$)$

$-$ b: A numpy array of biases, of shape $($M$,)$

Returns a tuple of:

$-$ out: output, of shape $($N$,$ M$)$

$-$ cache: $($x$,$ w$,$ b$)$