-jinSSKJr SSKJr SSKJr \(aSSKJr SSKJ r SSKJ r "SS5r g ) ) annotations) TYPE_CHECKING) TrialState) Container)Study) FrozenTrialcR\rSrSrSr\R 44SSjjrSSjrSr g) MaxTrialsCallbackaSet a maximum number of trials before ending the study. While the ``n_trials`` argument of :meth:`optuna.study.Study.optimize` sets the number of trials that will be run, you may want to continue running until you have a certain number of successfully completed trials or stop the study when you have a certain number of trials that fail. This ``MaxTrialsCallback`` class allows you to set a maximum number of trials for a particular :class:`~optuna.trial.TrialState` before stopping the study. Example: .. testcode:: import optuna from optuna.study import MaxTrialsCallback from optuna.trial import TrialState def objective(trial): x = trial.suggest_float("x", -1, 1) return x**2 study = optuna.create_study() study.optimize( objective, callbacks=[MaxTrialsCallback(10, states=(TrialState.COMPLETE,))], ) Args: n_trials: The max number of trials. Must be set to an integer. states: Tuple of the :class:`~optuna.trial.TrialState` to be counted towards the max trials limit. Default value is ``(TrialState.COMPLETE,)``. If :obj:`None`, count all states. cXlX lg)N _n_trials_states)selfn_trialsstatess K/home/james-whalen/.local/lib/python3.13/site-packages/optuna/_callbacks.py__init__MaxTrialsCallback.__init__5s" cURSURS9n[U5nX@R:aUR 5 gg)NF)deepcopyr) get_trialsrlenrstop)rstudytrialtrials n_completes r__call__MaxTrialsCallback.__call__;s<!!5!F[  ' JJL (rr N)rintrzContainer[TrialState] | NonereturnNone)rrrrr#r$) __name__ __module__ __qualname____firstlineno____doc__rCOMPLETErr __static_attributes__rrr r s:#LFPEXEXDZ%A  rr N) __future__rtypingr optuna.trialrcollections.abcr optuna.studyrrr r,rrr2s&" #)"(00r