//  [python]
def attack_stage1(
        self,
        audios,
        trans,
        th_batch,
        psd_max_batch,
        maxlen,
        sample_rate,
        masks,
        masks_freq,
        num_loop,
        data,
        lr_stage2,
    ):
        sess = self.sess
        # initialize and load the pretrained model
        sess.run(tf.compat.v1.global_variables_initializer())
        saver = tf.train.Saver(
            [x for x in tf.compat.v1.global_variables() if x.name.startswith("librispeech")]
        )
        saver.restore(sess, FLAGS.checkpoint)

        # reassign the variables
        sess.run(
            tf.compat.v1.assign(self.rescale, np.ones((self.batch_size, 1), dtype=np.float32))
        )
        sess.run(
            tf.compat.v1.assign(
                self.delta_large,
                np.zeros((self.batch_size, FLAGS.max_length_dataset), dtype=np.float32),
            )
        )

        # noise = np.random.normal(scale=2, size=audios.shape)
        noise = np.zeros(audios.shape)
        feed_dict = {
            self.input_tf: audios,
            self.tgt_tf: trans,
            self.sample_rate_tf: sample_rate,
            self.th: th_batch,
            self.psd_max_ori: psd_max_batch,
            self.mask: masks,
            self.mask_freq: masks_freq,
            self.noise: noise,
            self.maxlen: maxlen,
            self.lr_stage2: lr_stage2,
        }
        losses, predictions = sess.run((self.celoss, self.decoded), feed_dict)

        # show the initial predictions
        for i in range(self.batch_size):
            print(
                "example: {}, loss: {}".format(
                    num_loop * self.batch_size + i, losses[i]
                )
            )
            print("pred:{}".format(predictions["topk_decoded"][i, 0]))
            print("targ:{}".format(trans[i].lower()))
            print("true: {}".format(data[1, i].lower()))

        # We'll make a bunch of iterations of gradient descent here
        now = time.time()
        MAX = self.num_iter_stage1
        loss_th = [np.inf] * self.batch_size
        final_deltas = [None] * self.batch_size
        clock = 0

        for i in range(MAX):
            now = time.time()

            # Actually do the optimization
            sess.run(self.train1, feed_dict)
            if i % 10 == 0:
                d, cl, predictions, new_input = sess.run(
                    (self.delta, self.celoss, self.decoded, self.new_input), feed_dict
                )

            for ii in range(self.batch_size):
                # print out the prediction each 100 iterations
                if i % 1000 == 0:
                    print("pred:{}".format(predictions["topk_decoded"][ii, 0]))
                    # print("rescale: {}".format(sess.run(self.rescale[ii])))
                if i % 10 == 0:
                    if i % 100 == 0:
                        print("example: {}".format(num_loop * self.batch_size + ii))
                        print("iteration: {}. loss {}".format(i, cl[ii]))

                    if predictions["topk_decoded"][ii, 0] == trans[ii].lower():
                        print(
                            "-------------------------------True--------------------------"
                        )

                        # update rescale
                        rescale = sess.run(self.rescale)
                        if rescale[ii] * FLAGS.initial_bound > np.max(np.abs(d[ii])):
                            rescale[ii] = np.max(np.abs(d[ii])) / FLAGS.initial_bound
                        rescale[ii] *= 0.8

                        # save the best adversarial example
                        final_deltas[ii] = new_input[ii]

                        print(
                            "Iteration i=%d, worked ii=%d celoss=%f bound=%f"
                            % (i, ii, cl[ii], FLAGS.initial_bound * rescale[ii])
                        )
                        sess.run(tf.compat.v1.assign(self.rescale, rescale))

                # in case no final_delta return
                if i == MAX - 1 and final_deltas[ii] is None:
                    final_deltas[ii] = new_input[ii]

            if i % 10 == 0:
                print("ten iterations take around {} ".format(clock))
                clock = 0

            clock += time.time() - now

        return final_deltas