class_docstr: Wrapper class for importing Keras models.
docstr: Use to ensure `KerasClassifier` can be pickled.
  
  :return: State dictionary with instance parameters.
docstr_tok: ["Use","to","ensure","`","KerasClassifier","`","can","be","pickled",".",":","return",":","State","dictionary","with","instance","parameters","."]
code: def __getstate__(self) -> Dict[str, Any]:
      """
      Use to ensure `KerasClassifier` can be pickled.
  
      :return: State dictionary with instance parameters.
      """
      state = self.__dict__.copy()
  
      # Remove the unpicklable entries
      del state["_model"]
      del state["_input"]
      del state["_output"]
      del state["_predictions_op"]
      del state["_loss"]
      del state["_loss_gradients"]
      del state["_layer_names"]
      del state["_losses"]
      del state["_loss_function"]
  
      if "_orig_loss" in state:
          del state["_orig_loss"]
  
      if "_class_gradients" in state:
          del state["_class_gradients"]
  
      if "_class_gradients_idx" in state:
          del state["_class_gradients_idx"]
  
      if "_activations_func" in state:
          del state["_activations_func"]
  
      if "_custom_loss_func" in state:
          del state["_custom_loss_func"]
  
      model_name = str(time.time()) + ".h5"
      state["model_name"] = model_name
      self.save(model_name)
      return state
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import time
  from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  import six
  from art import config
  from art.estimators.keras import KerasEstimator
  from art.estimators.classification.classifier import ClassifierMixin, ClassGradientsMixin
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 19 for calling the KerasClassifier obj's underlying member method code with example usage: obj.__getstate__() and returns: state