class_docstr: This class implements a regressor with the PyTorch framework.
class_docstr_tok: ["This","class","implements","a","regressor","with","the","PyTorch","framework","."]
docstr: Use to ensure `PyTorchRegressor` can be pickled.
  
  :return: State dictionary with instance parameters.
docstr_tok: ["Use","to","ensure","`","PyTorchRegressor","`","can","be","pickled",".",":","return",":","State","dictionary","with","instance","parameters","."]
code: def __getstate__(self) -> Dict[str, Any]:
      """
      Use to ensure `PyTorchRegressor` can be pickled.
  
      :return: State dictionary with instance parameters.
      """
      # pylint: disable=W0212
      # disable pylint because access to _model required
      state = self.__dict__.copy()
      state["inner_model"] = copy.copy(state["_model"]._model)
  
      # Remove the unpicklable entries
      del state["_model_wrapper"]
      del state["_device"]
      del state["_model"]
  
      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 copy
  import logging
  import os
  import time
  from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  import six
  from art import config
  from art.estimators.regression.regressor import RegressorMixin
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 23 for calling the PyTorchRegressor obj's underlying member method code with example usage: obj.__getstate__() and returns: state