def METHOD_NAME(self):
    from neural_compressor.config import AccuracyCriterion, PostTrainingQuantConfig, TuningCriterion
    from neural_compressor.data import DATALOADERS, Datasets
    from neural_compressor.quantization import fit
    # dataset and dataloader
    dataset = Datasets("tensorflow")["dummy"](((100, 3, 3, 1)))
    dataloader = DATALOADERS["tensorflow"](dataset)
    # tuning and accuracy criterion
    accuracy_criterion = AccuracyCriterion(criterion="relative")
    strategy_kwargs = {
        "sigopt_api_token": "sigopt_api_token_test",
        "sigopt_project_id": "sigopt_project_id_test",
        "sigopt_experiment_name": "nc-tune",
    }
    tuning_criterion = TuningCriterion(strategy="sigopt", strategy_kwargs=strategy_kwargs, max_trials=3)
    conf = PostTrainingQuantConfig(
        quant_level=1, approach="static", tuning_criterion=tuning_criterion, accuracy_criterion=accuracy_criterion
    )
    self.assertEqual(conf.tuning_criterion.strategy_kwargs, strategy_kwargs)
    def fake_eval(model):
        return 1
    q_model = fit(model=self.constant_graph, conf=conf, calib_dataloader=dataloader, eval_func=fake_eval)