(self, trigger, factor=1)
Backpropagates synthetic gradient from `trigger`.

        Computes synthetic gradient based on `trigger`, scales it by `factor`
        and backpropagates it from `trigger`.

        Works only in `training` mode, otherwise is a no-op.

        Args:
            trigger: `trigger` to compute synthetic gradient based on and to
                backpropagate it from.
            factor (optional): Factor by which to scale the synthetic gradient.
                Defaults to 1.
Backpropagates synthetic gradient from `trigger`.
Backpropagates synthetic gradient from trigger .
def backward(self, trigger, factor=1):
        """Backpropagates synthetic gradient from `trigger`.

        Computes synthetic gradient based on `trigger`, scales it by `factor`
        and backpropagates it from `trigger`.

        Works only in `training` mode, otherwise is a no-op.

        Args:
            trigger: `trigger` to compute synthetic gradient based on and to
                backpropagate it from.
            factor (optional): Factor by which to scale the synthetic gradient.
                Defaults to 1.
        """
        if self.training:
            synthetic_gradient = self.receive(trigger)
            _Manager.backward(trigger, synthetic_gradient.data * factor)
def backward ( self , trigger , factor = 1 ) : if self . training : synthetic_gradient = self . receive ( trigger ) _Manager . backward ( trigger , synthetic_gradient . data * factor )