def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs):
        """
        Train a model adversarially with FBF protocol using a data generator.
        See class documentation for more information on the exact procedure.

        :param generator: Data generator.
        :param nb_epochs: Number of epochs to use for trainings.
        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
                                  the target classifier.
        """
        logger.info("Performing adversarial training with Fast is better than Free protocol")
        size = generator.size
        batch_size = generator.batch_size
        if size is not None:
            nb_batches = int(np.ceil(size / batch_size))
        else:
            raise ValueError("Size is None.")

        def lr_schedule(step_t):
            return np.interp([step_t], [0, nb_epochs * 2 // 5, nb_epochs], [0, 0.21, 0])[0]

        logger.info("Adversarial Training FBF")

        for i_epoch in trange(nb_epochs, desc="Adversarial Training FBF - Epochs"):
            start_time = time.time()
            train_loss = 0.0
            train_acc = 0.0
            train_n = 0.0

            for batch_id in range(nb_batches):
                l_r = lr_schedule(i_epoch + (batch_id + 1) / nb_batches)

                # Create batch data
                x_batch, y_batch = generator.get_batch()
                x_batch = x_batch.copy()

                _train_loss, _train_acc, _train_n = self._batch_process(x_batch, y_batch, l_r)

                train_loss += _train_loss
                train_acc += _train_acc
                train_n += _train_n

            train_time = time.time()
            logger.info(
                "epoch: %s time(s): %.1f l_r: %.4f loss: %.4f acc: %.4f",
                i_epoch,
                train_time - start_time,
                l_r,
                train_loss / train_n,
                train_acc / train_n,
            )

        Train a model adversarially with FBF protocol using a data generator.
        See class documentation for more information on the exact procedure.

        :param generator: Data generator.
        :param nb_epochs: Number of epochs to use for trainings.
        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
                                  the target classifier.
        
art/defences/trainer/adversarial_trainer_fbf_pytorch.py