def store(
        self,
        parameters: Optional[Iterable[torch.nn.Parameter]] = None
    ) -> None:
        """
        Save the current parameters for restoring later.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                temporarily stored. If `None`, the parameters of with which this
                `ExponentialMovingAverage` was initialized will be used.
        """
        parameters = self._get_parameters(parameters)
        self.collected_params = [
            param.clone()
            for param in parameters
            if param.requires_grad
        ]

        Save the current parameters for restoring later.
        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                temporarily stored. If `None`, the parameters of with which this
                `ExponentialMovingAverage` was initialized will be used.
        
training/src/utils/ema.py