Part $7.$A

Use the cell below to define a function train$\_$logreg$().$ The function should accept four parameters named X$,$ y$,$ alpha, and n$.$ Descriptions of the parameters are as follows:

X is expected to be a $2$D feature array.

y is expected to be a $1$D label array.

alpha is expected to be a learning rate.

steps is the number of iterations of gradient descent to perform during training.

The function should apply gradient descent to determine optimal parameters for a logistic regression model. An outline of the steps that the function should perform is provided below.

The function add$\_$ones$()$ should be used to create an "extended" feature matrix XE$.$

A coeficient array named betas should be created with all values initialized to zero. The following code can be used for this task: betas $=$ np$.$zeros$($XE$.$shape$[1])$