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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_proba(self, X):
 """Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting and the predicted class probabilities of a an input sample represents the proportion of estimators predicting each class. Parameters X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- p : array of shape = [n_samples, n_classes] The class probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """

        # Check data
        # X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])  # Dont in version 0.15

        if self.n_features_ != X.shape[1]:
            raise ValueError("Number of features of the model must "
                             "match the input. Model n_features is {0} and "
                             "input n_features is {1}."
                             "".format(self.n_features_, X.shape[1]))

        # Parallel loop
        n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators, self.n_jobs)

        all_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
            delayed(_parallel_predict_proba)(
                self.estimators_[starts[i]:starts[i + 1]],
                self.estimators_features_[starts[i]:starts[i + 1]],
                X,
                self.n_classes_,
                self.combination,
                self.estimators_weight_[starts[i]:starts[i + 1]])
            for i in range(n_jobs))

        # Reduce
        if self.combination in ['majority_voting', 'majority_bmr']:
            proba = sum(all_proba) / self.n_estimators
        elif self.combination in ['weighted_voting', 'weighted_bmr']:
            proba = sum(all_proba)
        elif self.combination in ['stacking', 'stacking_proba', 'stacking_bmr', 'stacking_proba_bmr']:
            X_stacking = _create_stacking_set(self.estimators_, self.estimators_features_,
                                              self.estimators_weight_, X, self.combination)
            proba = self.f_staking.predict_proba(X_stacking)

        return proba