(self, t_prof, mode=None, device=None)
Args:
            t_prof (TrainingProfile):
            mode:                       Any mode your algorithm's eval agent can be evaluated in. Specify modes
                                        as class variables and pass one of them here. Can be changed later by calling
                                        .to_mode(new_mode) on this instance
            device (torch.device):      The device the eval agent shall live and act on.
Args:
            t_prof (TrainingProfile):
            mode:                       Any mode your algorithm's eval agent can be evaluated in. Specify modes
                                        as class variables and pass one of them here. Can be changed later by calling
                                        .to_mode(new_mode) on this instance
            device (torch.device):      The device the eval agent shall live and act on.
Args : t_prof ( TrainingProfile ) : mode : Any mode your algorithm s eval agent can be evaluated in . Specify modes as class variables and pass one of them here . Can be changed later by calling . to_mode ( new_mode ) on this instance device ( torch . device ) : The device the eval agent shall live and act on .
def __init__(self, t_prof, mode=None, device=None):
        """
        Args:
            t_prof (TrainingProfile):
            mode:                       Any mode your algorithm's eval agent can be evaluated in. Specify modes
                                        as class variables and pass one of them here. Can be changed later by calling
                                        .to_mode(new_mode) on this instance
            device (torch.device):      The device the eval agent shall live and act on.
        """
        self.t_prof = t_prof
        self.ray = MaybeRay(runs_distributed=t_prof.DISTRIBUTED, runs_cluster=t_prof.CLUSTER)
        self.env_bldr = rl_util.get_env_builder(t_prof=t_prof)

        self._internal_env_wrapper = self.env_bldr.get_new_wrapper(is_evaluating=True, stack_size=None)
        self._mode = mode

        if device is None:
            self.device = self.t_prof.device_inference
        else:
            self.device = device
def __init__ ( self , t_prof , mode = None , device = None ) : self . t_prof = t_prof self . ray = MaybeRay ( runs_distributed = t_prof . DISTRIBUTED , runs_cluster = t_prof . CLUSTER ) self . env_bldr = rl_util . get_env_builder ( t_prof = t_prof ) self . _internal_env_wrapper = self . env_bldr . get_new_wrapper ( is_evaluating = True , stack_size = None ) self . _mode = mode if device is None : self . device = self . t_prof . device_inference else : self . device = device