Enabling passing of primitives/pipelines to hyperparameters

Description

I was trying to construct a primitive for sklearn.model_selection.RandomizedSearchCV when I found that the hyperparameter for estimator is itself can be a primitive.

What I Did

To go around the current implementation, I tried to create a pipeline for the estimator (using LogisticRegression) and passed that as the value for the hyperparameter estimator i input_params as follows:
init_params = {
"sklearn.model_selection.RandomizedSearchCV": {
'estimator': logistic_regression_pipeline,
'scoring': "accuracy",
'n_iter': 5
}
}

In python <3.7 it fails in deepcopy of hyperparameters (fix suggested here: https://stackoverflow.com/questions/6279305/typeerror-cannot-deepcopy-this-pattern-object)

In python 3.7+ it fails where sklearn throws the exception that the estimator object needs to be an object of type sklearn estimator and not MLPipeline

There needs to be a way to pass such primitives as input params

Note It works if I pass the logisticRegression object directly to the init_params as:
'estimator': LogisticRegression(random_state=0)

but looses the capability of:

1. saving the pipeline to disk
2. constructing the complete pipeline using only MLBlocks

Start by reproducing the RandomizedSearchCV case using init_params with a LogisticRegression pipeline as the estimator, checking the deepcopy failure on Python before 3.7 and the sklearn estimator exception on Python 3.7+. Done should allow primitive or pipeline values as hyperparameters while preserving pipeline-to-disk saving and complete construction using only MLBlocks.