def __setstate__(self, state):
self.__dict__.update(state)<EOL>self.__restoringFromState = True<EOL>self.__logger = initLogger(self)<EOL>if not hasattr(self, "<STR_LIT>"):<EOL><INDENT>self.__restoringFromV1 = True<EOL>self._hasSP = True<EOL>if self.__temporalNetInfo is not None:<EOL><INDENT>self._Model__inferenceType = InferenceType.TemporalNextStep<EOL>self._netInfo = self.__temporalNetInfo<EOL>self._hasTP = True<EOL><DEDENT>else:<EOL><INDENT>raise RuntimeError("<STR_LIT>")<EOL><DEDENT>self._Model__inferenceArgs = {}<EOL>self._Model__learningEnabled = True<EOL>self._Model__inferenceEnabled = True<EOL>self.__dict__.pop("<STR_LIT>", None)<EOL>self.__dict__.pop("<STR_LIT>", None)<EOL>self.__dict__.pop("<STR_LIT>", None)<EOL><DEDENT>if not hasattr(self, "<STR_LIT>"):<EOL><INDENT>self._hasSP = False<EOL>self._hasTP = False<EOL>if self.__encoderNetInfo is not None:<EOL><INDENT>self._netInfo = self.__encoderNetInfo<EOL><DEDENT>elif self.__nonTemporalNetInfo is not None:<EOL><INDENT>self._netInfo = self.__nonTemporalNetInfo<EOL>self._hasSP = True<EOL><DEDENT>else:<EOL><INDENT>self._netInfo = self.__temporalNetInfo<EOL>self._hasSP = True<EOL>self._hasTP = True<EOL><DEDENT>self.__dict__.pop("<STR_LIT>", None)<EOL>self.__dict__.pop("<STR_LIT>", None)<EOL>self.__dict__.pop("<STR_LIT>", None)<EOL><DEDENT>self._classifierInputEncoder = None<EOL>if not hasattr(self, '<STR_LIT>'):<EOL><INDENT>self._minLikelihoodThreshold = DEFAULT_LIKELIHOOD_THRESHOLD<EOL><DEDENT>if not hasattr(self, '<STR_LIT>'):<EOL><INDENT>self._maxPredictionsPerStep = DEFAULT_MAX_PREDICTIONS_PER_STEP<EOL><DEDENT>if not hasattr(self, '<STR_LIT>'):<EOL><INDENT>self._hasCL = (self._getClassifierRegion() is not None)<EOL><DEDENT>self.__logger.debug("<STR_LIT>" % self.__class__.__name__)<EOL>
Set the state of ourself from a serialized state.

See also: _deSerializeExtraData