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 on the training set `(x, y)`.
  
  :param x: Training data.
  :param y: Target values.
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
  :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
         and providing it takes no effect.
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code: def fit(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int = 128,
      nb_epochs: int = 10,
      **kwargs,
  ) -> None:
      """
      Fit the classifier on the training set `(x, y)`.
  
      :param x: Training data.
      :param y: Target values.
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
             and providing it takes no effect.
      """
      # Apply preprocessing
      x_preprocessed, y_preprocessed = self._apply_preprocessing(
          x, y, fit=True
      )
  
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
      ind = np.arange(len(x_preprocessed)).tolist()
  
      # Start training
      for _ in range(nb_epochs):
          # Shuffle the examples
          random.shuffle(ind)
  
          # Train for one epoch
          for m in range(num_batch):
              i_batch = x_preprocessed[
                  ind[m * batch_size : (m + 1) * batch_size]
              ]
              o_batch = y_preprocessed[
                  ind[m * batch_size : (m + 1) * batch_size]
              ]
              self._model = self.update_func(
                  self.model, i_batch, o_batch
              )
  
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 8 for calling the JaxClassifier obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs) without return types