nncf/torch/quantization/layers.py
def from_state(cls, state: Dict[str, Any]) -> 'PTQuantizationPoint':
        """
        Creates the object from its state.

        :param state: Output of `get_state()` method.
        """
        kwargs = {
            cls._state_names.TARGET_POINT: PTTargetPoint.from_state(state[cls._state_names.TARGET_POINT]),
            cls._state_names.QSPEC: PTQuantizerSpec.from_state(state[cls._state_names.QSPEC]),
            cls._state_names.NAMES_OF_QUANTIZED_OPS: state[cls._state_names.NAMES_OF_QUANTIZED_OPS]
        }
        return cls(**kwargs)

        Creates the object from its state.

        :param state: Output of `get_state()` method.
        
Creates the object from its state.
Creates the object from its state .
def from_state(cls, state: Dict[str, Any]) -> 'PTQuantizationPoint':
        kwargs = {
            cls._state_names.TARGET_POINT: PTTargetPoint.from_state(state[cls._state_names.TARGET_POINT]),
            cls._state_names.QSPEC: PTQuantizerSpec.from_state(state[cls._state_names.QSPEC]),
            cls._state_names.NAMES_OF_QUANTIZED_OPS: state[cls._state_names.NAMES_OF_QUANTIZED_OPS]
        }
        return cls(**kwargs)
def from_state ( cls , state : Dict [ str , Any ] ) -> 'PTQuantizationPoint' : kwargs = { cls . _state_names . TARGET_POINT : PTTargetPoint . from_state ( state [ cls . _state_names . TARGET_POINT ] ) , cls . _state_names . QSPEC : PTQuantizerSpec . from_state ( state [ cls . _state_names . QSPEC ] ) , cls . _state_names . NAMES_OF_QUANTIZED_OPS : state [ cls . _state_names . NAMES_OF_QUANTIZED_OPS ] } return cls ( ** kwargs )
Creates the object from its state.
Creates the object from its state .
"""
        Creates the object from its state.

        :param state: Output of `get_state()` method.
        """
cls
state
Dict[str, Any]
Output of `get_state()` method.
Output of ` get_state () ` method .