How to use the nni.report_intermediate_result function in nni

with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = 200
        for i in range(batch_num):
            batch_size = nni.choice({50: 50, 250: 250, 500: 500}, name=
                'batch_size')
            batch = mnist.train.next_batch(batch_size)
            dropout_rate = nni.choice({1: 1, 5: 5}, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.x: batch[
                0], mnist_network.y: batch[1], mnist_network.keep_prob:
                dropout_rate})
            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(feed_dict={
                    mnist_network.x: mnist.test.images, mnist_network.y:
                    mnist.test.labels, mnist_network.keep_prob: 1.0})
                nni.report_intermediate_result(test_acc)
        test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.x:
            mnist.test.images, mnist_network.y: mnist.test.labels,
            mnist_network.keep_prob: 1.0})
        nni.report_final_result(test_acc)

def on_epoch_end(self, epoch, logs={}):
        '''
        Run on end of each epoch
        '''
        LOG.debug(logs)
        nni.report_intermediate_result(logs["val_acc"])

def on_epoch_end(self, epoch, logs=None):
        """Reports intermediate accuracy to NNI framework"""
        # TensorFlow 2.0 API reference claims the key is `val_acc`, but in fact it's `val_accuracy`
        if 'val_acc' in logs:
            nni.report_intermediate_result(logs['val_acc'])
        else:
            nni.report_intermediate_result(logs['val_accuracy'])

def on_epoch_end(self, epoch, logs=None):
        """
        Run on end of each epoch
        """
        if logs is None:
            logs = dict()
        logger.debug(logs)
        nni.report_intermediate_result(logs["val_acc"])

num_batches = ceil(len(train_set.dataset) / float(bsz))
    total_loss = 0.0
    for epoch in range(3):
        epoch_loss = 0.0
        for data, target in train_set:
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()
            output = model(data)
            loss = F.nll_loss(output, target)
            epoch_loss += loss.item()
            loss.backward()
            average_gradients(model)
            optimizer.step()
        #logger.debug('Rank: ', rank, ', epoch: ', epoch, ': ', epoch_loss / num_batches)
        if rank == 0:
            nni.report_intermediate_result(epoch_loss / num_batches)
        total_loss += (epoch_loss / num_batches)
    total_loss /= 3
    logger.debug('Final loss: {}'.format(total_loss))
    if rank == 0:
        nni.report_final_result(total_loss)

def on_epoch_end(self, epoch, logs={}):
        '''
        Run on end of each epoch
        '''
        LOG.debug(logs)
        nni.report_intermediate_result(logs["val_acc"])

def on_epoch_end(self, epoch, logs=None):
        """
        Run on end of each epoch
        """
        if logs is None:
            logs = dict()
        logger.debug(logs)
        nni.report_intermediate_result(logs["val_acc"])

batch_num = nni.choice(50, 250, 500, name='batch_num')
        for i in range(batch_num):
            batch = mnist.train.next_batch(batch_num)
            dropout_rate = nni.choice(1, 5, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
                                                    mnist_network.labels: batch[1],
                                                    mnist_network.keep_prob: dropout_rate}
                                        )

            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(
                    feed_dict={mnist_network.images: mnist.test.images,
                               mnist_network.labels: mnist.test.labels,
                               mnist_network.keep_prob: 1.0})

                nni.report_intermediate_result(test_acc)
                logger.debug('test accuracy %g', test_acc)
                logger.debug('Pipe send intermediate result done.')

        test_acc = mnist_network.accuracy.eval(
            feed_dict={mnist_network.images: mnist.test.images,
                       mnist_network.labels: mnist.test.labels,
                       mnist_network.keep_prob: 1.0})

        nni.report_final_result(test_acc)
        logger.debug('Final result is %g', test_acc)
        logger.debug('Send final result done.')

answers = generate_predict_json(
                    position1, position2, ids, contexts)
                if save_path is not None:
                    with open(os.path.join(save_path, 'epoch%d.prediction' % epoch), 'w') as file:
                        json.dump(answers, file)
                else:
                    answers = json.dumps(answers)
                    answers = json.loads(answers)
                iter = epoch + 1

                acc = evaluate.evaluate_with_predictions(
                    args.dev_file, answers)

                logger.debug('Send intermediate acc: %s', str(acc))
                nni.report_intermediate_result(acc)

                logger.debug('Send intermediate result done.')

                if acc > bestacc:
                    if acc * improvement_threshold > bestacc:
                        patience = max(patience, iter * patience_increase)
                    bestacc = acc

                    if save_path is not None:
                        saver.save(os.path.join(sess, save_path + 'epoch%d.model' % epoch))
                        with open(os.path.join(save_path, 'epoch%d.score' % epoch), 'wb') as file:
                            pickle.dump(
                                (position1, position2, ids, contexts), file)
                logger.debug('epoch %d acc %g bestacc %g' %
                             (epoch, acc, bestacc))
                if patience <= iter: