class_docstr: This class implements a classifier with the Jax framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","Jax","framework","."]
docstr: Fit the classifier using the generator that yields batches as specified.
  
  :param generator: Batch generator providing `(x, y)` for each epoch.
  :param nb_epochs: Number of epochs to use for training.
  :param kwargs: Dictionary of framework-specific arguments.
docstr_tok: ["Fit","the","classifier","using","the","generator","that","yields","batches","as","specified",".",":","param","generator",":","Batch","generator","providing","`","(","x",",","y",")","`","for","each","epoch",".",":","param","nb_epochs",":","Number","of","epochs","to","use","for","training",".",":","param","kwargs",":","Dictionary","of","framework-specific","arguments","."]
code: def fit_generator(
      self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs
  ) -> None:
      """
      Fit the classifier using the generator that yields batches as specified.
  
      :param generator: Batch generator providing `(x, y)` for each epoch.
      :param nb_epochs: Number of epochs to use for training.
      :param kwargs: Dictionary of framework-specific arguments.
      """
      raise NotImplementedError
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.experimental.estimators.jax import JaxEstimator
label_desc: Use image node_id 10 for calling the JaxClassifier obj's underlying member method code with example usage: obj.fit_generator(generator, nb_epochs) without return types