How to use the datasets.dataset_factory.get_dataset function in datasets

if not FLAGS.dataset_dir:
        raise ValueError('You must supply the dataset directory with --dataset_dir')
    tf.logging.set_verbosity(tf.logging.DEBUG)
    
    config.init_config(image_shape, 
                       batch_size = 1, 
                       seg_conf_threshold = FLAGS.seg_conf_threshold,
                       link_conf_threshold = FLAGS.link_conf_threshold, 
                       train_with_ignored = FLAGS.train_with_ignored,
                       seg_loc_loss_weight = FLAGS.seg_loc_loss_weight, 
                       link_cls_loss_weight = FLAGS.link_cls_loss_weight, 
                       )
        
    
    util.proc.set_proc_name('eval_' + FLAGS.model_name + '_' + FLAGS.dataset_name )
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
    config.print_config(FLAGS, dataset, print_to_file = False)
    
    return dataset

#######################
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=FLAGS.num_clones,
        clone_on_cpu=FLAGS.clone_on_cpu,
        replica_id=FLAGS.task,
        num_replicas=FLAGS.worker_replicas,
        num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        weight_decay=FLAGS.weight_decay,
        is_training=True)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(

#######################
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=FLAGS.num_clones,
        clone_on_cpu=FLAGS.clone_on_cpu,
        replica_id=FLAGS.task,
        num_replicas=FLAGS.worker_replicas,
        num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        weight_decay=FLAGS.weight_decay,
        is_training=True)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(

def imagenet_input(is_training):
  """Data reader for imagenet.

  Reads in imagenet data and performs pre-processing on the images.

  Args:
     is_training: bool specifying if train or validation dataset is needed.
  Returns:
     A batch of images and labels.
  """
  if is_training:
    dataset = dataset_factory.get_dataset('imagenet', 'train',
                                          FLAGS.dataset_dir)
  else:
    dataset = dataset_factory.get_dataset('imagenet', 'validation',
                                          FLAGS.dataset_dir)

  provider = slim.dataset_data_provider.DatasetDataProvider(
      dataset,
      shuffle=is_training,
      common_queue_capacity=2 * FLAGS.batch_size,
      common_queue_min=FLAGS.batch_size)
  [image, label] = provider.get(['image', 'label'])

  image_preprocessing_fn = preprocessing_factory.get_preprocessing(
      'mobilenet_v1', is_training=is_training)

  image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)

def main(_):
  if not FLAGS.dataset_dir:
    raise ValueError('You must supply the dataset directory with --dataset_dir')

  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default():
    tf_global_step = slim.get_or_create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ####################
    # Select the model #
    ####################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=False)

    ##############################################################
    # Create a dataset provider that loads data from the dataset #
    ##############################################################
    provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        shuffle=False,

def imagenet_input(is_training):
  """Data reader for imagenet.

  Reads in imagenet data and performs pre-processing on the images.

  Args:
     is_training: bool specifying if train or validation dataset is needed.
  Returns:
     A batch of images and labels.
  """
  if is_training:
    dataset = dataset_factory.get_dataset('imagenet', 'train',
                                          FLAGS.dataset_dir)
  else:
    dataset = dataset_factory.get_dataset('imagenet', 'validation',
                                          FLAGS.dataset_dir)

  provider = slim.dataset_data_provider.DatasetDataProvider(
      dataset,
      shuffle=is_training,
      common_queue_capacity=2 * FLAGS.batch_size,
      common_queue_min=FLAGS.batch_size)
  [image, label] = provider.get(['image', 'label'])

  image_preprocessing_fn = preprocessing_factory.get_preprocessing(
      'mobilenet_v1', is_training=is_training)

  image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)

#######################
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=FLAGS.num_clones,
        clone_on_cpu=FLAGS.clone_on_cpu,
        replica_id=FLAGS.task,
        num_replicas=FLAGS.worker_replicas,
        num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        weight_decay=FLAGS.weight_decay,
        is_training=True)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(

#######################
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=FLAGS.num_clones,
        clone_on_cpu=FLAGS.clone_on_cpu,
        replica_id=FLAGS.task,
        num_replicas=FLAGS.worker_replicas,
        num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        weight_decay=FLAGS.weight_decay,
        is_training=True)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(

#######################
    deploy_config = model_deploy.DeploymentConfig(
        num_clones=FLAGS.num_clones,
        clone_on_cpu=FLAGS.clone_on_cpu,
        replica_id=FLAGS.task,
        num_replicas=FLAGS.worker_replicas,
        num_ps_tasks=FLAGS.num_ps_tasks)

    # Create global_step
    with tf.device(deploy_config.variables_device()):
      global_step = slim.create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ######################
    # Select the network #
    ######################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        weight_decay=FLAGS.weight_decay,
        is_training=True)

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(

def input_fn(num_gpus, dataset_split="train"):
    batch_size = FLAGS.batch_size * num_gpus
    # Create a dataset provider that loads data from the dataset #
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, dataset_split, FLAGS.dataset_dir)
    provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        num_readers=FLAGS.num_readers,
        common_queue_capacity=20 * batch_size,
        common_queue_min=10 * batch_size)
    [image, label] = provider.get(['image', 'label'])

    image_preprocessing_fn = preprocessing_factory.get_preprocessing(
        FLAGS.model_name,
        is_training=True)
    image = image_preprocessing_fn(
        image, FLAGS.train_image_size, FLAGS.train_image_size)

    images, labels = tf.train.batch(
        [image, label],