You are tasked with creating and training a K$-$Nearest Neighbors $($KNN$)$ model. Your goal is to understand how to implement a KNN classifier and train your model on a given dataset.

Objective:

Create and train a KNN model using the input training set.

Return the trained model.

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

The function should be named create$\_$and$\_$train$\_$knn$\_$model.

Parameters:

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

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

k: An integer representing the number of neighbors to consider. The default value should be $1.$

Return:

The function should return the trained KNN model, which is trained onn the input training data and with the input value of k$.$ def create$\_$and$\_$train$\_$knn$\_$model$($X$\_$train, y$\_$train, k$=1)$:

$"""$

Creates and trains a K$-$Nearest Neighbors $($KNN$)$ model.

Parameters:

$-$ X$\_$train: $2$D array of training features.

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

$-$ k: Integer, number of neighbors $($default: $1).$

Returns:

$-$ Trained KNN model.

$"""$

# Initialize the KNN model with k neighbors

# Train the model with X$\_$train and y$\_$train

# Return the trained model

return model

# Example usage:

# model $=$ create$\_$and$\_$train$\_$knn$\_$model$($X$\_$train, y$\_$train, k$=5)$