How to use the optuna.trial.Trial function in optuna

def test_successive_halving_pruner_intermediate_values(direction_value):
    # type: (Tuple[str, float]) -> None

    direction, intermediate_value = direction_value
    pruner = optuna.pruners.SuccessiveHalvingPruner(
        min_resource=1, reduction_factor=2, min_early_stopping_rate=0)
    study = optuna.study.create_study(direction=direction)

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(1, 1)

    # A pruner is not activated at a first trial.
    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    # A pruner is not activated if a trial has no intermediate values.
    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))

    trial.report(intermediate_value, 1)
    # A pruner is activated if a trial has an intermediate value.
    assert pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))

def _create_new_chainermn_trial(study, comm):
    # type: (Study, CommunicatorBase) -> integration.chainermn.ChainerMNTrial

    if comm.rank == 0:
        trial_id = study._storage.create_new_trial(study._study_id)
        trial = Trial(study, trial_id)
        mn_trial = integration.chainermn.ChainerMNTrial(trial, comm)
    else:
        mn_trial = integration.chainermn.ChainerMNTrial(None, comm)

    comm.mpi_comm.barrier()
    return mn_trial

def test_median_pruner_n_warmup_steps():
    # type: () -> None

    pruner = optuna.pruners.MedianPruner(0, 1)
    study = optuna.study.create_study()

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(1, 1)
    trial.report(1, 2)
    study._storage.set_trial_state(trial._trial_id, TrialState.COMPLETE)

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(2, 1)
    # A pruner is not activated during warm-up steps.
    assert not pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))

    trial.report(2, 2)
    # A pruner is activated after warm-up steps.
    assert pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))

def test_successive_halving_pruner_intermediate_values():
    # type: () -> None

    pruner = optuna.pruners.SuccessiveHalvingPruner(
        min_resource=1, reduction_factor=2, min_early_stopping_rate=0)
    study = optuna.study.create_study()

    trial = optuna.trial.Trial(study, study.storage.create_new_trial_id(study.study_id))
    trial.report(1, 1)

    # A pruner is not activated at a first trial.
    assert not pruner.prune(study.storage, study.study_id, trial.trial_id, step=1)

    trial = optuna.trial.Trial(study, study.storage.create_new_trial_id(study.study_id))
    # A pruner is not activated if a trial has no intermediate values.
    assert not pruner.prune(study.storage, study.study_id, trial.trial_id, step=1)

    trial.report(2, 1)
    # A pruner is activated if a trial has an intermediate value.
    assert pruner.prune(study.storage, study.study_id, trial.trial_id, step=1)

def test_percentile_pruner_with_one_trial():
    # type: () -> None

    study = optuna.study.create_study()
    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(1, 1)
    pruner = optuna.pruners.PercentilePruner(25.0, 0, 0)

    # A pruner is not activated at a first trial.
    assert not pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))

def test_nop_pruner():
    # type: () -> None

    study = optuna.study.create_study()
    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(1, 1)
    pruner = optuna.pruners.NopPruner()

    # A NopPruner instance is always deactivated.
    assert not pruner.prune(study=study, trial=study.trials[0])

def test_successive_halving_pruner_min_early_stopping_rate_parameter():
    # type: () -> None

    study = optuna.study.create_study()

    # min_early_stopping_rate=-1: Error (must be `min_early_stopping_rate >= 0`).
    with pytest.raises(ValueError):
        optuna.pruners.SuccessiveHalvingPruner(
            min_resource=1, reduction_factor=2, min_early_stopping_rate=-1)

    # min_early_stopping_rate=0: The rung 0 ends at step 1.
    pruner = optuna.pruners.SuccessiveHalvingPruner(
        min_resource=1, reduction_factor=2, min_early_stopping_rate=0)
    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))

    trial.report(1, step=1)
    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))
    assert 'completed_rung_0' in trial.system_attrs

    # min_early_stopping_rate=1: The rung 0 ends at step 2.
    pruner = optuna.pruners.SuccessiveHalvingPruner(
        min_resource=1, reduction_factor=2, min_early_stopping_rate=1)
    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))

    trial.report(1, step=1)
    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))
    assert 'completed_rung_0' not in trial.system_attrs
    assert 'completed_rung_1' not in trial.system_attrs

    trial.report(1, step=2)

def test_median_pruner_intermediate_values_nan():
    # type: () -> None

    pruner = optuna.pruners.MedianPruner(0, 0)
    study = optuna.study.create_study()

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(float('nan'), 1)
    # A pruner is not activated if the study does not have any previous trials.
    assert not pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))
    study._storage.set_trial_state(trial._trial_id, TrialState.COMPLETE)

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(float('nan'), 1)
    # A pruner is activated if the best intermediate value of this trial is NaN.
    assert pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))
    study._storage.set_trial_state(trial._trial_id, TrialState.COMPLETE)

    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))
    trial.report(1, 1)
    # A pruner is not activated if the median intermediate value is NaN.
    assert not pruner.prune(
        study=study, trial=study._storage.get_trial(trial._trial_id))

def create_trial():
        # type: () -> Trial

        return Trial(study, study._storage.create_new_trial(study._study_id))