def set_device(self, device, memory_backend_params=None, mode='numpy') -> None:
    "\n        An optional function that allows the filter to get the device if it is required to use tensorflow ops\n        :param device: the device to use\n        :memory_backend_params: if not None, holds params for a memory backend for sharing data (e.g. Redis)\n        :param mode: the arithmetic module to use {'tf' | 'numpy'}\n        :return: None\n        "
    if (mode == 'tf'):
        from rl_coach.architectures.tensorflow_components.shared_variables import TFSharedRunningStats
        self.running_observation_stats = TFSharedRunningStats(device, name=self.name, create_ops=False, pubsub_params=memory_backend_params)
    elif (mode == 'numpy'):
        self.running_observation_stats = NumpySharedRunningStats(name=self.name, pubsub_params=memory_backend_params)
An optional function that allows the filter to get the device if it is required to use tensorflow ops
:param device: the device to use
:memory_backend_params: if not None, holds params for a memory backend for sharing data (e.g. Redis)
:param mode: the arithmetic module to use {'tf' | 'numpy'}
:return: None
def set_device(self, device, memory_backend_params=None, mode='numpy') -> None:
    "\n        An optional function that allows the filter to get the device if it is required to use tensorflow ops\n        :param device: the device to use\n        :memory_backend_params: if not None, holds params for a memory backend for sharing data (e.g. Redis)\n        :param mode: the arithmetic module to use {'tf' | 'numpy'}\n        :return: None\n        "
    if (mode == 'tf'):
        from rl_coach.architectures.tensorflow_components.shared_variables import TFSharedRunningStats
        self.running_observation_stats = TFSharedRunningStats(device, name=self.name, create_ops=False, pubsub_params=memory_backend_params)
    elif (mode == 'numpy'):
        self.running_observation_stats = NumpySharedRunningStats(name=self.name, pubsub_params=memory_backend_params)