import tensorflow as tf

		model_io_fn = model_io.ModelIO(model_io_config)

		tvars = model_io_fn.get_params(model_config.scope, 
										not_storage_params=not_storage_params)
		print(tvars)
		if load_pretrained == "yes":
			model_io_fn.load_pretrained(tvars, 
										init_checkpoint,
										exclude_scope=exclude_scope)

		if mode == tf.estimator.ModeKeys.TRAIN:

			optimizer_fn = optimizer.Optimizer(opt_config)

			model_io_fn.print_params(tvars, string=", trainable params")
			update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
			with tf.control_dependencies(update_ops):
				train_op = optimizer_fn.get_train_op(loss, tvars, 
								opt_config.init_lr, 
								opt_config.num_train_steps,
								**kargs)

				model_io_fn.set_saver()

				if kargs.get("task_index", 1) == 0 and kargs.get("run_config", None):
					training_hooks = []
				elif kargs.get("task_index", 1) == 0:
					model_io_fn.get_hooks(kargs.get("checkpoint_dir", None), 
														kargs.get("num_storage_steps", 1000))

