class_docstr: This class implements a classifier with the Jax framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","Jax","framework","."]
docstr: Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
  `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
  calling `layer_names`.
  
  :param x: Input for computing the activations.
  :param layer: Layer for computing the activations
  :param batch_size: Size of batches.
  :param framework: If true, return the intermediate tensor representation of the activation.
  :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
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code: def get_activations(
      self,
      x: np.ndarray,
      layer: Optional[Union[int, str]] = None,
      batch_size: int = 128,
      framework: bool = False,
  ) -> np.ndarray:
      """
      Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
      `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
      calling `layer_names`.
  
      :param x: Input for computing the activations.
      :param layer: Layer for computing the activations
      :param batch_size: Size of batches.
      :param framework: If true, return the intermediate tensor representation of the activation.
      :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
      """
      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 12 for calling the JaxClassifier obj's underlying member method code with example usage: obj.get_activations(x, layer, batch_size, framework) without return types