def METHOD_NAME(self):
    GlobalHydra.instance().clear()
    sg_recipes_dir = pkg_resources.resource_filename("super_gradients.recipes", "")
    init_trainer()
    with initialize_config_dir(config_dir=normalize_path(sg_recipes_dir), version_base="1.2"):
        cfg = compose(config_name="cifar10_resnet")
        cfg.experiment_name = "batch_size_selection_test_no_max"
        cfg.training_hyperparams.max_epochs = 1
        OmegaConf.set_struct(cfg, True)
        with open_dict(cfg):
            cfg.pre_launch_callbacks_list = [
                OmegaConf.create(
                    {"AutoTrainBatchSizeSelectionCallback": {"min_batch_size": 64, "size_step": 10000, "num_forward_passes": 3, "scale_lr": False}}
                ),
                OmegaConf.create({"PreLaunchTrainBatchSizeVerificationCallback": {"batch_size": 64}}),
                OmegaConf.create({"PreLaunchLRVerificationCallback": {"lr": cfg.training_hyperparams.initial_lr}}),
            ]
    Trainer.train_from_config(cfg)