class_docstr: This class implements a regressor with the PyTorch framework.
class_docstr_tok: ["This","class","implements","a","regressor","with","the","PyTorch","framework","."]
docstr: Return the loss scaling value.
  
  :return: Loss scaling. Possible values for string: a string representing a number, e.g., “1.0”,
           or the string “dynamic”.
docstr_tok: ["Return","the","loss","scaling","value",".",":","return",":","Loss","scaling",".","Possible","values","for","string",":","a","string","representing","a","number",",","e.g.",",","\u201c","1.0","\u201d",",","or","the","string","\u201c","dynamic","\u201d","."]
code: def loss_scale(self) -> Union[float, str]:
      """
      Return the loss scaling value.
  
      :return: Loss scaling. Possible values for string: a string representing a number, e.g., “1.0”,
               or the string “dynamic”.
      """
      return self._loss_scale
  
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 9 for calling the PyTorchRegressor obj's underlying member method code with example usage: obj.loss_scale() and returns: self