def __init__(self, num_doors: int, nrollouts: int, rollout_len: int, dataset_size: int):
    super(PoisonedDoorsExpertTrajectoryIterator, self).__init__()
    (self.np_seeded_random_gen, _) = typing.cast(Tuple[(np.random.RandomState, Any)], seeding.np_random(0))
    self.ndoors = num_doors
    self.nrollouts = nrollouts
    self.rollout_len = rollout_len
    self.dataset_size = dataset_size
    self.initial_observations = np.zeros((rollout_len, nrollouts, 1), dtype=np.int64)
    self.mask = np.zeros((rollout_len, nrollouts, 1), dtype=np.float32)
    self.expert_actions = np.random.randint(4, (3 + num_doors), size=(self.dataset_size, 1))
    self.current_ind = 0
def __init__(self, num_doors: int, nrollouts: int, rollout_len: int, dataset_size: int):
    super(PoisonedDoorsExpertTrajectoryIterator, self).__init__()
    (self.np_seeded_random_gen, _) = typing.cast(Tuple[(np.random.RandomState, Any)], seeding.np_random(0))
    self.ndoors = num_doors
    self.nrollouts = nrollouts
    self.rollout_len = rollout_len
    self.dataset_size = dataset_size
    self.initial_observations = np.zeros((rollout_len, nrollouts, 1), dtype=np.int64)
    self.mask = np.zeros((rollout_len, nrollouts, 1), dtype=np.float32)
    self.expert_actions = np.random.randint(4, (3 + num_doors), size=(self.dataset_size, 1))
    self.current_ind = 0