'Loads data for a specified dataset and split.'

def get_inputs(self, dataset_dir, dataset_name, split_name, batch_size, image_size, vox_size, is_training=True):

    del image_size, vox_size
    with tf.variable_scope(('data_loading_%s/%s' % (dataset_name, split_name))):
        common_queue_min = 64
        common_queue_capacity = 256
        num_readers = 4
        inputs = input_generator.get(dataset_dir, dataset_name, split_name, shuffle=is_training, num_readers=num_readers, common_queue_min=common_queue_min, common_queue_capacity=common_queue_capacity)
        (images, voxels) = tf.train.batch([inputs['image'], inputs['voxel']], batch_size=batch_size, num_threads=8, capacity=(8 * batch_size), name=('batching_queues/%s/%s' % (dataset_name, split_name)))
        outputs = dict()
        outputs['images'] = images
        outputs['voxels'] = voxels
        outputs['num_samples'] = inputs['num_samples']
    return outputs
