class_docstr: This is abstract class for different backend-specific implementations of TRADES protocol
  for adversarial training.
  
  | Paper link: https://proceedings.mlr.press/v97/zhang19p.html
docstr: Train a model adversarially 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.
docstr_tok: ["Train","a","model","adversarially","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","."]
code: def fit_generator(
      self, generator: DataGenerator, nb_epochs: int = 20, **kwargs
  ):
      """
      Train a model adversarially 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.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_trades.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from art.defences.trainer.trainer import Trainer
  from art.attacks.attack import EvasionAttack
  from art.data_generators import DataGenerator
label_desc: Use image node_id 3 for calling the AdversarialTrainerTRADES obj's underlying member method code with example usage: obj.fit_generator(generator, nb_epochs) without return types