sklearn : scaling x (data) and y (target) using both Pipeline and TransformedTargetRegressor

I'd like to use both Pipeline and TransformedTargetRegressor to handle all the scaling (on data and target) : is this possible to mix Pipeline and TransformedTargetRegressor ? How to get results out of TransformedTargetRegressor ?

$ cat test_ttr.py
#!/usr/bin/python
# -*- coding: UTF-8 -*-

from sklearn.datasets import make_regression
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.pipeline import Pipeline
from sklearn.compose import TransformedTargetRegressor

def main():
    x, y = make_regression()

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

    model = linear_model.Ridge(alpha=1)

    pipe = Pipeline([('scale', preprocessing.StandardScaler()), ('model', model)])
    treg = TransformedTargetRegressor(regressor=pipe, transformer=preprocessing.MinMaxScaler())

    treg.fit(x_train, y_train)

    print(pipe.get_params()['model__alpha']) # OK !
    print(treg.get_params()['regressor__model__coef']) # KO ?!

if __name__ == '__main__':
    main()

But can't get results (coefs for instance) out of TransformedTargetRegressor

1
Traceback (most recent call last):
  File ".\test_ttr.py", line 26, in <module>
    main()
  File ".\test_ttr.py", line 23, in main
    print(treg.get_params()['regressor__model__coef']) # KO ?!
TypeError: 'TransformedTargetRegressor' object is not subscriptable

from sklearn : scaling x (data) and y (target) using both Pipeline and TransformedTargetRegressor