def METHOD_NAME(self) -> dict:
    return dict(
        start=0,
        stop=None,
        target_dir=MISSING,
        cache_dir=None,
        remove_all_cache=False,
        prepare_data=dict(
            dataset_root=MISSING,
        ),
        build_dataset=dict(
            train=dict(
                chunk_secs=4.0,
                step_secs=4.0,
            ),
            valid=dict(
                chunk_secs=4.0,
                step_secs=4.0,
            ),
            test=dict(
                chunk_secs=4.0,
                step_secs=4.0,
            ),
        ),
        build_batch_sampler=dict(
            train=dict(
                batch_size=5,
                shuffle=True,
            ),
        ),
        build_upstream=dict(
            name=MISSING,
        ),
        build_featurizer=dict(
            layer_selections=None,
            normalize=False,
        ),
        build_downstream=dict(
            hidden_layers=2,
        ),
        build_model=dict(
            upstream_trainable=False,
        ),
        build_task=dict(
            prediction_type="multilabel",
            scores=["event_onset_200ms_fms", "segment_1s_er"],
            postprocessing_grid={
                "median_filter_ms": [250],
                "min_duration": [125, 250],
            },
        ),
        build_optimizer=dict(
            name="Adam",
            conf=dict(
                lr=1.0e-3,
            ),
        ),
        build_scheduler=dict(
            name="ExponentialLR",
            gamma=0.9,
        ),
        save_model=dict(),
        save_task=dict(),
        train=dict(
            total_steps=15000,
            log_step=100,
            eval_step=500,
            save_step=500,
            gradient_clipping=1.0,
            gradient_accumulate=1,
            valid_metric="event_onset_200ms_fms",
            valid_higher_better=True,
            auto_resume=True,
            resume_ckpt_dir=None,
        ),
        evaluate=dict(),
    )