How to use the axelrod.strategies.finite_state_machines.FSMPlayer function in Axelrod

}

    def __init__(self) -> None:
        transitions = (
            (1, C, 2, D),
            (1, D, 1, D),
            (2, C, 2, C),
            (2, D, 3, C),
            (3, C, 3, C),
            (3, D, 3, C),
        )

        super().__init__(transitions=transitions, initial_state=1, initial_action=D)


class SolutionB5(FSMPlayer):
    """

    FSM player described in http://DOI.org/10.1109/TCIAIG.2014.2326012.

    Names

    - Solution B5: [Ashlock2015]_
    """

    name = "SolutionB5"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,

def __init__(self) -> None:
        transitions = (
            (0, C, 0, C),
            (0, D, 2, D),
            (1, C, 3, D),
            (1, D, 0, C),
            (2, C, 2, D),
            (2, D, 1, C),
            (3, C, 3, D),
            (3, D, 1, D),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


class EvolvedFSM16(FSMPlayer):
    """
    A 16 state FSM player trained with an evolutionary algorithm.

    Names:

        - Evolved FSM 16: Original name by Marc Harper

    """

    name = "Evolved FSM 16"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,

) -> None:

        super().__init__()
        self.initial_state = initial_state
        self.initial_action = initial_action
        self.fsm = SimpleFSM(transitions, initial_state)

    def strategy(self, opponent: Player) -> Action:
        if len(self.history) == 0:
            return self.initial_action
        else:
            action = self.fsm.move(opponent.history[-1])
            return action


class Fortress3(FSMPlayer):
    """Finite state machine player specified in http://DOI.org/10.1109/CEC.2006.1688322.

    Note that the description in http://www.graham-kendall.com/papers/lhk2011.pdf
    is not correct.


    Names:

    - Fortress 3: [Ashlock2006b]_
    """

    name = "Fortress3"
    classifier = {
        "memory_depth": 2,
        "stochastic": False,
        "makes_use_of": set(),

(11, C, 6, D),
            (11, D, 5, D),
            (12, C, 6, D),
            (12, D, 9, D),
            (13, C, 9, D),
            (13, D, 8, D),
            (14, C, 8, D),
            (14, D, 13, D),
            (15, C, 4, C),
            (15, D, 5, C),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


class TF2(FSMPlayer):
    """
    A FSM player trained to maximize Moran fixation probabilities.

    Names:

        - TF2: Original name by Marc Harper
    """

    name = "TF2"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,

(11, C, 7, D),
            (11, D, 13, D),
            (12, C, 3, C),
            (12, D, 8, D),
            (13, C, 7, C),
            (13, D, 10, D),
            (14, C, 10, D),
            (14, D, 7, D),
            (15, C, 15, C),
            (15, D, 11, D),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


class TF3(FSMPlayer):
    """
    A FSM player trained to maximize Moran fixation probabilities.

    Names:

        - TF3: Original name by Marc Harper
    """

    name = "TF3"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,

"memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,
        "manipulates_state": False,
    }

    def __init__(self) -> None:
        transitions = ((1, C, 1, C), (1, D, 2, D), (2, C, 1, D), (2, D, 1, D))

        super().__init__(transitions=transitions, initial_state=1, initial_action=C)


class EvolvedFSM4(FSMPlayer):
    """
    A 4 state FSM player trained with an evolutionary algorithm.

    Names:

        - Evolved FSM 4: Original name by Marc Harper
    """

    name = "Evolved FSM 4"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,

"manipulates_source": False,
        "manipulates_state": False,
    }

    def __init__(self) -> None:
        transitions = (
            (1, C, 1, C),
            (1, D, 2, C),
            (2, C, 1, D),
            (2, D, 1, C),
        )

        super().__init__(transitions=transitions, initial_state=1, initial_action=C)


class UsuallyDefects(FSMPlayer):
    """
    This strategy defects except after a D following a C.

    Names:

    - Usually Defects (UD): [Ashlock2009]_
    """

    name = "UsuallyDefects"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,

(11, C, 15, D),
            (11, D, 5, D),
            (12, C, 8, C),
            (12, D, 11, D),
            (13, C, 13, D),
            (13, D, 7, D),
            (14, C, 13, D),
            (14, D, 13, D),
            (15, C, 15, D),
            (15, D, 2, C),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


class EvolvedFSM16Noise05(FSMPlayer):
    """
    A 16 state FSM player trained with an evolutionary algorithm with
    noisy matches (noise=0.05).

    Names:

        - Evolved FSM 16 Noise 05: Original name by Marc Harper
    """

    name = "Evolved FSM 16 Noise 05"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,

(12, D, 2, C),
            (13, C, 13, C),
            (13, D, 6, C),
            (14, C, 3, D),
            (14, D, 13, D),
            (15, C, 5, D),
            (15, D, 11, C),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


# Strategies trained with Moran process objectives


class TF1(FSMPlayer):
    """
    A FSM player trained to maximize Moran fixation probabilities.

    Names:

        - TF1: Original name by Marc Harper
    """

    name = "TF1"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,

(4, C, 2, C),
            (4, D, 6, D),
            (5, C, 7, D),
            (5, D, 3, D),
            (6, C, 7, C),
            (6, D, 7, D),
            (7, C, 8, D),
            (7, D, 7, D),
            (8, C, 8, D),
            (8, D, 6, D),
        )

        super().__init__(transitions=transitions, initial_state=0, initial_action=C)


class Pun1(FSMPlayer):
    """FSM player described in [Ashlock2006]_.

    Names:

    - Pun1: [Ashlock2006]_
    """

    name = "Pun1"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "makes_use_of": set(),
        "long_run_time": False,
        "inspects_source": False,
        "manipulates_source": False,
        "manipulates_state": False,