'Predict each data slice/task with a series of independent estimators.
The number of tasks in X should match the number of tasks/estimators
given at fit time.
Parameters
X : array, shape (n_samples, nd_features, n_tasks)
The input samples. For each data slice, the corresponding estimator
makes the sample predictions, e.g.:
``[estimators[ii].predict(X[..., ii]) for ii in range(n_estimators)]``.
The feature dimension can be multidimensional e.g.
X.shape = (n_samples, n_features_1, n_features_2, n_tasks)
Returns
y_pred : array, shape (n_samples, n_estimators) | (n_samples, n_tasks, n_targets)
Predicted values for each estimator/data slice.'

def predict(self, X):

    return self._transform(X, 'predict')
