write a python code for these requirements,

Objective:

Implement a function to find and return the optimal $\text{'}$K$\text{'}$ for a KNN model, evaluated on given training and testing$/$validation data.

$2\mathrm{.}1\mathrm{.}3\mathrm{.}1$ Requirements:

The function should be named find$\_$best$\_$k $.$

Parameters:

x$\_$train : A $2$D array of the training features.

y$\_$train : A $1$D array of the training labels.

x$\_$test : A $2$D array of the testing$/$validation features.

y$\_$test : A $1$D array of the testing$/$validation labels.

k$\_$max : An integer representing the maximum value of $\text{'}K\text{'}$ to be considered in the search for the optimal $\text{'}K\text{'}.$

Return:

The function should return two values:

best$\_$k:An integer representing the optimal number of neighbors based on the evaluation.

best$\_$error$\_$rate : A float representing the lowest error rate achieved with the optimal $\text{'}$K$\text{'}.$