class_docstr: Estimator class for Jax models.
docstr: Estimator class for Jax models.
  
  :param channels_first: Set channels first or last.
  :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
         maximum values allowed for features. If floats are provided, these will be used as the range of all
         features. If arrays are provided, each value will be considered the bound for a feature, thus
         the shape of clip values needs to match the total number of features.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
         used for data preprocessing. The first value will be subtracted from the input. The input will then
         be divided by the second one.
  :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
code: def __init__(self, **kwargs) -> None:
      """
      Estimator class for Jax models.
  
      :param channels_first: Set channels first or last.
      :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
             maximum values allowed for features. If floats are provided, these will be used as the range of all
             features. If arrays are provided, each value will be considered the bound for a feature, thus
             the shape of clip values needs to match the total number of features.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
             used for data preprocessing. The first value will be subtracted from the input. The input will then
             be divided by the second one.
      :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
      """
      super().__init__(**kwargs)
  
      JaxEstimator._check_params(self)
  
file_path: adversarial-robustness-toolbox/art/experimental/estimators/jax.py
imports: import logging
  import numpy
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
label_desc: Use image node_id 1 to create a new JaxEstimator object from inherited base classes: NeuralNetworkMixin, LossGradientsMixin, BaseEstimator with example: obj = JaxEstimator()