import tensorflow as tf

                        global_step,
                        decay_steps=lr_decay_params["lr_decay_steps"],
                        decay_rate=lr_decay_params["lr_decay_rate"],
                        staircase=lr_decay_params.get("staircase", True),
                    )
                    optimizer_params["learning_rate"] = learning_rate

                var_list = None
                if self.params["warm_start_dir"]:
                    output_vars1 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_dependent")
                    output_vars2 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_independent/Variable_1")
                    var_list = [output_vars1, output_vars2]

                train_op = tf.train.AdamOptimizer(
                    # learning_rate=self.params["learning_rate"]
                    # **self.params.get("optimizer_params", {})
                    # learning_rate=learning_rate
                    **optimizer_params
                ).minimize(loss, global_step=global_step, var_list=var_list)

