def METHOD_NAME(self,
                  num_channels: int,
                  channel_display_groups: Any,
                  num_classes: int = 5):
    """Tests learner init, plots, bundle, train and pred."""
    with get_tmp_dir() as tmp_dir:
        class_config = ClassConfig(
            names=[f'class_{i}' for i in range(num_classes)])
        dataset_cfg = DatasetConfig(
            class_config=class_config,
            train_scenes=[
                make_scene(num_channels, num_classes, tmp_dir)
                for _ in range(2)
            ],
            validation_scenes=[
                make_scene(num_channels, num_classes, tmp_dir)
                for _ in range(2)
            ],
            test_scenes=[])
        data_cfg = ObjectDetectionGeoDataConfig(
            scene_dataset=dataset_cfg,
            window_opts=ObjectDetectionGeoDataWindowConfig(
                method=GeoDataWindowMethod.random, size=20, max_windows=8),
            class_names=class_config.names,
            class_colors=class_config.colors,
            plot_options=PlotOptions(
                channel_display_groups=channel_display_groups),
            num_workers=0)
        backend_cfg = PyTorchObjectDetectionConfig(
            data=data_cfg,
            model=ObjectDetectionModelConfig(
                backbone=Backbone.resnet18, pretrained=False),
            solver=SolverConfig(batch_sz=4, num_epochs=1),
            log_tensorboard=False)
        pipeline_cfg = ObjectDetectionConfig(
            root_uri=tmp_dir, dataset=dataset_cfg, backend=backend_cfg)
        pipeline_cfg.update()
        backend = backend_cfg.build(pipeline_cfg, tmp_dir)
        learner = backend.learner_cfg.build(tmp_dir, training=True)
        learner.plot_dataloaders()
        learner.train()
        learner.plot_predictions(split='valid')
        learner.save_model_bundle()
        learner = None
        backend.learner = None
        backend.load_model()
        pred_scene = dataset_cfg.validation_scenes[0].build(
            class_config, tmp_dir)
        _ = backend.predict_scene(pred_scene, chip_sz=100)