How to use the dnn.DatasetMiniBatchIterator function in dnn

"""
        import time, copy
        if x_dev == None or y_dev == None:
            from sklearn.cross_validation import train_test_split
            x_train, x_dev, y_train, y_dev = train_test_split(x_train, y_train,
                    test_size=split_ratio, random_state=42)
        if method == 'sgd':
            train_fn = self.get_SGD_trainer()
        elif method == 'adagrad':
            train_fn = self.get_adagrad_trainer()
        elif method == 'adadelta':
            train_fn = self.get_adadelta_trainer()
        elif method == 'rmsprop':
            train_fn = self.get_rmsprop_trainer(with_step_adapt=True,
                    nesterov=False)
        train_set_iterator = DatasetMiniBatchIterator(x_train, y_train)
        dev_set_iterator = DatasetMiniBatchIterator(x_dev, y_dev)
        train_scoref = self.score_classif(train_set_iterator)
        dev_scoref = self.score_classif(dev_set_iterator)
        best_dev_loss = numpy.inf
        epoch = 0
        # TODO early stopping (not just cross val, also stop training)
        if plot:
            verbose = True
            self._costs = []
            self._train_errors = []
            self._dev_errors = []
            self._updates = []

        init_lr = INIT_LR
        if method == 'rmsprop':
            init_lr = 1.E-6  # TODO REMOVE HACK

import time, copy
        if x_dev == None or y_dev == None:
            from sklearn.cross_validation import train_test_split
            x_train, x_dev, y_train, y_dev = train_test_split(x_train, y_train,
                    test_size=split_ratio, random_state=42)
        if method == 'sgd':
            train_fn = self.get_SGD_trainer()
        elif method == 'adagrad':
            train_fn = self.get_adagrad_trainer()
        elif method == 'adadelta':
            train_fn = self.get_adadelta_trainer()
        elif method == 'rmsprop':
            train_fn = self.get_rmsprop_trainer(with_step_adapt=True,
                    nesterov=False)
        train_set_iterator = DatasetMiniBatchIterator(x_train, y_train)
        dev_set_iterator = DatasetMiniBatchIterator(x_dev, y_dev)
        train_scoref = self.score_classif(train_set_iterator)
        dev_scoref = self.score_classif(dev_set_iterator)
        best_dev_loss = numpy.inf
        epoch = 0
        # TODO early stopping (not just cross val, also stop training)
        if plot:
            verbose = True
            self._costs = []
            self._train_errors = []
            self._dev_errors = []
            self._updates = []

        init_lr = INIT_LR
        if method == 'rmsprop':
            init_lr = 1.E-6  # TODO REMOVE HACK
        n_seen = 0

def score(self, x, y):
        """ error rates """
        iterator = DatasetMiniBatchIterator(x, y)
        scoref = self.score_classif(iterator)
        return numpy.mean(scoref())