def _check_reg_targets(y_true, y_pred, multioutput):
    check_consistent_length(y_true, y_pred)
    y_true = check_array(y_true, ensure_2d=False)
    y_pred = check_array(y_pred, ensure_2d=False)

    if y_true.ndim == 1:
        y_true = y_true.reshape((-1, 1))

    if y_pred.ndim == 1:
        y_pred = y_pred.reshape((-1, 1))

    if y_true.shape[1] != y_pred.shape[1]:
        raise ValueError("y_true and y_pred have different number of output "
                         "({0}!={1})".format(y_true.shape[1], y_pred.shape[1]))

    n_outputs = y_true.shape[1]
    multioutput_options = (None, 'raw_values', 'uniform_average',
                           'variance_weighted')
    if multioutput not in multioutput_options:
        multioutput = check_array(multioutput, ensure_2d=False)
        if n_outputs == 1:
            raise ValueError("Custom weights are useful only in "
                             "multi-output cases.")
        elif n_outputs != len(multioutput):
            raise ValueError(("There must be equally many custom weights "
                              "(%d) as outputs (%d).") %
                             (len(multioutput), n_outputs))
    y_type = 'continuous' if n_outputs == 1 else 'continuous-multioutput'

    return y_type, y_true, y_pred, multioutput
Check that y_true and y_pred belong to the same regression task
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/regression.py/_check_reg_targets