Write a python code:

$1.$ Build $5$ different regression models, and measure their accuracy on a training, validation, and test set.

Use the mean squared error to represent the accuracy of $5$ models.

Diabetes dataset will be used.

"from sklearn import datasets, linear$\_$model $"$

"diabetes $=$ datasets.load$\_$diabetes$()"$

$2.$ Use the same training and test split $($with same seed$)$ as used the example above, and use a validation set to do any hyperparameter tuning$/$selection$.$

Can use ElasticNetCV to do hyperparameter tuning.

$3.$ Visualize the accuracy of your $5$ models as a barplot, with different subfigures for the accuracy on your $($a$)$ training, $($b$)$ validation, and $($c$)$ test data.