from sklearn.compose import TransformedTargetRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LinearRegression
import numpy as np

X = np.arange(100).reshape(10, 10)
y = np.arange(60).reshape(10, 3, 2)

def flatten_coords(coords):
    return coords.reshape(coords.shape[0], -1)
def unflatten_coords(coords):
    return coords.reshape(coords.shape[0], -1, 2)
coords_flattener = FunctionTransformer(flatten_coords, unflatten_coords)

model = TransformedTargetRegressor(
    regressor=MultiOutputRegressor(LinearRegression()),
    transformer=coords_flattener
)
model.fit(X, y)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-10-a584e7c9bc04> in <module>
     18     transformer=coords_flattener
     19 )
---> 20 model.fit(X, y)

~/.pyenv/versions/3.7.4/envs/unspun_analysis/lib/python3.7/site-packages/sklearn/compose/_target.py in fit(self, X, y, **fit_params)
    177         """
    178         y = check_array(y, accept_sparse=False, force_all_finite=True,
--> 179                         ensure_2d=False, dtype='numeric')
    180 
    181         # store the number of dimension of the target to predict an array of

~/.pyenv/versions/3.7.4/envs/unspun_analysis/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~/.pyenv/versions/3.7.4/envs/unspun_analysis/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    639         if not allow_nd and array.ndim >= 3:
    640             raise ValueError("Found array with dim %d. %s expected <= 2."
--> 641                              % (array.ndim, estimator_name))
    642 
    643         if force_all_finite:

ValueError: Found array with dim 3. Estimator expected <= 2.