class_docstr: This class implements a classifier with the TensorFlow framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","TensorFlow","framework","."]
docstr: Use to ensure `TensorFlowClassifier` can be pickled.
  
  :return: State dictionary with instance parameters.
docstr_tok: ["Use","to","ensure","`","TensorFlowClassifier","`","can","be","pickled",".",":","return",":","State","dictionary","with","instance","parameters","."]
code: def __getstate__(self) -> Dict[str, Any]:
      """
      Use to ensure `TensorFlowClassifier` can be pickled.
  
      :return: State dictionary with instance parameters.
      """
      state = self.__dict__.copy()
  
      # Remove the unpicklable entries
      del state["_sess"]
      del state["_input_ph"]
      state["_output"] = self.output.name
  
      if self.labels_ph is not None:
          state["_labels_ph"] = self.labels_ph.name
  
      if self._loss is not None:
          state["_loss"] = self._loss.name
  
      if hasattr(self, "_loss_grads"):
          state["_loss_grads"] = self._loss_grads.name
      else:
          state["_loss_grads"] = False
  
      if self.learning is not None:
          state["_learning"] = self.learning.name
  
      if self.train is not None:
          state["_train"] = self.train.name
  
      if hasattr(self, "_class_grads"):
          state["_class_grads"] = [
              ts if ts is None else ts.name for ts in self._class_grads
          ]
      else:
          state["_class_grads"] = False
  
      model_name = str(time.time())
      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 random
  import shutil
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  from tqdm.auto import tqdm
  import numpy
  import six
  from art import config
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 21 for calling the TensorFlowClassifier obj's underlying member method code with example usage: obj.__getstate__() and returns: state