def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
        """
        Copy current averaged parameters into given collection of parameters.

        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                updated with the stored moving averages. If `None`, the parameters with which this
                `ExponentialMovingAverage` was initialized will be used.
        """
        parameters = list(parameters)
        if self.foreach:
            torch._foreach_copy_(
                [param.data for param in parameters],
                [s_param.to(param.device).data for s_param, param in zip(self.shadow_params, parameters)],
            )
        else:
            for s_param, param in zip(self.shadow_params, parameters):
                param.data.copy_(s_param.to(param.device).data)

        Copy current averaged parameters into given collection of parameters.

        Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                updated with the stored moving averages. If `None`, the parameters with which this
                `ExponentialMovingAverage` was initialized will be used.
        
src/diffusers/training_utils.py