How to implement next_batch for mini batch gradient descent in deep learning

Full Code: https://www.kaggle.com/pedrolcn/deep-tensorflow-ccn-cross-validation

class TrainBatcher(object):

    # Class constructor

    def __init__(self, examples, labels):

        self.labels = labels

        self.examples = examples

        self.index_in_epoch = 0

        self.num_examples = examples.shape[0]

    # mini-batching method

    def next_batch(self, batch_size):

        start = self.index_in_epoch

        self.index_in_epoch += batch_size

        # When all the training data is ran, shuffles it

        if self.index_in_epoch > self.num_examples:

            perm = np.arange(self.num_examples)

            np.random.shuffle(perm)

            self.examples = self.examples[perm]

            self.labels = self.labels[perm]

            # Start next epoch

            start = 0

            self.index_in_epoch = batch_size

            assert batch_size <= self.num_examples

        end = self.index_in_epoch

        

        return self.examples[start:end], self.labels[start:end]

mnist = TrainBatcher(train_images, train_labels)

batch_xs, batch_ys = mnist.next_batch(BATCH_SIZE)