Add ShrunkCovariance Estimator

[icfaust]

The output of EmpiricalCovariance is regularized by a shrinkage value impacted by the overall mean of the data.
The goal would be to implement this estimator with post-processing changes to the fitted empirical covariance.
This is very similar to the OAS project and would combine into a medium project.
When implemented in python re-using our EmpiricalCovariance estimator, this would be an easy project with a
small time commitment. Implementing the super-computing distributed version using python would only work for
distributed-aware frameworks. Extended goals would make this a hard difficulty, medium commitment project. This
would require implementing the regularization in C++ in oneDAL both for CPU and GPU. Then this must be made
available in Scikit-learn-intelex for making a new estimator. This would hopefully follow the design strategy
used for our Ridge Regression estimator.

https://scikit-learn.org/stable/modules/generated/sklearn.covariance.ShrunkCovariance.html