Write a function linear$\_$regression that fits a linear regression model, and takes the following two arguments as input:

X: A numpy array of the shape $($N$,$d$)$ where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer.

Y: A numpy array of the shape $($N$,)$ where N is the number of data points.

lam: The regularization coefficient $$

$,$ which is a scalar positive value. See the objective function below.

and returns the linear regression weight vector