Method __pow__

machine_learning/automatic_differentiation.py:110–122

(self, power: int)

        return result

    def __pow__(self, power: int) -> Variable:
        result = Variable(self.value**power)

        with GradientTracker() as tracker:
            # if tracker is enabled, computation graph will be updated
            if tracker.enabled:
                tracker.append(
                    OpType.POWER,
                    params=[self],
                    output=result,
                    other_params={"power": power},
                )
        return result

    def add_param_to(self, param_to: Operation) -> None:
        self.param_to.append(param_to)