def __init__(self, t_prof, eval_env_bldr, chief_handle, evaluator_name, log_conf_interval=False):
    '\n        Args:\n            t_prof (TrainingProfile)\n            chief_handle (class instance or ray ActorHandle)\n            evaluator_name (str):                Name of the evaluator\n        '
    super().__init__(t_prof=t_prof)
    self._eval_env_bldr = eval_env_bldr
    self._chief_handle = chief_handle
    self._is_multi_stack = (len(self._t_prof.eval_stack_sizes) > 1)
    self._log_conf_interval = log_conf_interval
    self._evaluator_name = evaluator_name
    (self._exp_name_total, self._exp_names_conf) = self._create_experiments(self_name=evaluator_name)
    if self._is_multi_stack:
        self._exp_name_multi_stack = {eval_mode: self._ray.get(self._ray.remote(self._chief_handle.create_experiment, ((((((self._t_prof.name + ' ') + eval_mode) + 'Multi_Stack') + ': ') + evaluator_name) + ' Averaged Total'))) for eval_mode in self._t_prof.eval_modes_of_algo}
        if self._log_conf_interval:
            self._exp_names_multi_stack_conf = {eval_mode: self._ray.get([self._ray.remote(self._chief_handle.create_experiment, ((((((self._t_prof.name + ' ') + eval_mode) + ': ') + evaluator_name) + ' Conf_') + bound_end)) for bound_end in ['lower95', 'upper95']]) for eval_mode in self._t_prof.eval_modes_of_algo}
Args:
    t_prof (TrainingProfile)
    chief_handle (class instance or ray ActorHandle)
    evaluator_name (str):                Name of the evaluator
def __init__(self, t_prof, eval_env_bldr, chief_handle, evaluator_name, log_conf_interval=False):
    '\n        Args:\n            t_prof (TrainingProfile)\n            chief_handle (class instance or ray ActorHandle)\n            evaluator_name (str):                Name of the evaluator\n        '
    super().__init__(t_prof=t_prof)
    self._eval_env_bldr = eval_env_bldr
    self._chief_handle = chief_handle
    self._is_multi_stack = (len(self._t_prof.eval_stack_sizes) > 1)
    self._log_conf_interval = log_conf_interval
    self._evaluator_name = evaluator_name
    (self._exp_name_total, self._exp_names_conf) = self._create_experiments(self_name=evaluator_name)
    if self._is_multi_stack:
        self._exp_name_multi_stack = {eval_mode: self._ray.get(self._ray.remote(self._chief_handle.create_experiment, ((((((self._t_prof.name + ' ') + eval_mode) + 'Multi_Stack') + ': ') + evaluator_name) + ' Averaged Total'))) for eval_mode in self._t_prof.eval_modes_of_algo}
        if self._log_conf_interval:
            self._exp_names_multi_stack_conf = {eval_mode: self._ray.get([self._ray.remote(self._chief_handle.create_experiment, ((((((self._t_prof.name + ' ') + eval_mode) + ': ') + evaluator_name) + ' Conf_') + bound_end)) for bound_end in ['lower95', 'upper95']]) for eval_mode in self._t_prof.eval_modes_of_algo}