(self,
                 lbr_bet_set=bet_sets.OFF_TREE_11,
                 n_lbr_hands_per_seat=30000,
                 lbr_check_to_round=None,  # recommended to set to Poker.TURN for 4-round games.
                 n_parallel_lbr_workers=10,
                 use_gpu_for_batch_eval=True,
                 DISTRIBUTED=False,
                 )
Args:
            lbr_bet_set (list):             List of floats; fractions of pot that LBR can consider to bet. (Note:
                                            only relevant in discretized and no-limit games. Limit games always allow 3
                                            actions)
            n_lbr_hands_per_seat (int):              Number of LBR hands to compute.

            lbr_check_to_round (game round from Poker):
                                            In the original paper, LBR has been shown to perform better, when it only
                                            check/calls until the Turn in No-Limit Texas Hold'em. It is also faster if
                                            one does that. We recommend setting this to Poker.TURN in games with 4
                                            rounds and to None in Leduc-like games.
            n_parallel_lbr_workers (int):   Number of workers. Only relevant if running distributed
            use_gpu_for_batch_eval (bool):  Whether to use the GPU for batched strategy queries. Recommended for
                                            big neural networks and games with many different hands (like Hold'em), if
                                            available.
            DISTRIBUTED (bool):             Whether to use ray and run distributed or to run locally.
def __init__(self,
                 lbr_bet_set=bet_sets.OFF_TREE_11,
                 n_lbr_hands_per_seat=30000,
                 lbr_check_to_round=None,  # recommended to set to Poker.TURN for 4-round games.
                 n_parallel_lbr_workers=10,
                 use_gpu_for_batch_eval=True,
                 DISTRIBUTED=False,
                 ):
        """

        Args:
            lbr_bet_set (list):             List of floats; fractions of pot that LBR can consider to bet. (Note:
                                            only relevant in discretized and no-limit games. Limit games always allow 3
                                            actions)
            n_lbr_hands_per_seat (int):              Number of LBR hands to compute.

            lbr_check_to_round (game round from Poker):
                                            In the original paper, LBR has been shown to perform better, when it only
                                            check/calls until the Turn in No-Limit Texas Hold'em. It is also faster if
                                            one does that. We recommend setting this to Poker.TURN in games with 4
                                            rounds and to None in Leduc-like games.
            n_parallel_lbr_workers (int):   Number of workers. Only relevant if running distributed
            use_gpu_for_batch_eval (bool):  Whether to use the GPU for batched strategy queries. Recommended for
                                            big neural networks and games with many different hands (like Hold'em), if
                                            available.
            DISTRIBUTED (bool):             Whether to use ray and run distributed or to run locally.
        """
        self.lbr_bet_set = lbr_bet_set
        self.n_lbr_hands = n_lbr_hands_per_seat
        self.lbr_check_to_round = lbr_check_to_round
        self.n_workers = n_parallel_lbr_workers if DISTRIBUTED else 1
        self.use_gpu_for_batch_eval = use_gpu_for_batch_eval
        self.DISTRIBUTED = DISTRIBUTED
def __init__ ( self , lbr_bet_set = bet_sets . OFF_TREE_11 , n_lbr_hands_per_seat = 30000 , lbr_check_to_round = None , # recommended to set to Poker.TURN for 4-round games. n_parallel_lbr_workers = 10 , use_gpu_for_batch_eval = True , DISTRIBUTED = False , ) : self . lbr_bet_set = lbr_bet_set self . n_lbr_hands = n_lbr_hands_per_seat self . lbr_check_to_round = lbr_check_to_round self . n_workers = n_parallel_lbr_workers if DISTRIBUTED else 1 self . use_gpu_for_batch_eval = use_gpu_for_batch_eval self . DISTRIBUTED = DISTRIBUTED