def METHOD_NAME(self):
    opt = NevergradOptimizer(facade="NGO", n_configurations=10)
    pipeline_elements = [PipelineElement('SVC', hyperparameters={'kernel': ["sigmoid", "rbf"],
                                                                 'C': [0.6], 'coef0': Categorical([0.5])})]
    of = lambda x: x ** 2
    with warnings.catch_warnings(record=True) as w:
        opt.prepare(pipeline_elements=pipeline_elements, maximize_metric=True, objective_function=of)
        assert any("PHOTONAI has detected some" in s for s in [e.message.args[0] for e in w])
    pipeline_elements = [PipelineElement("SVC", hyperparameters={'C': FloatRange(0.1, 0.5,
                                                                                 range_type='geomspace')})]
    opt = NevergradOptimizer(facade="NGO")
    with self.assertRaises(NotImplementedError):
        opt.prepare(pipeline_elements=pipeline_elements, maximize_metric=True, objective_function=of)
    pipeline_elements = [PipelineElement("SVC", hyperparameters={'C': NumberRange(1, 3, range_type='range')})]
    opt = NevergradOptimizer(facade="NGO")
    with self.assertRaises(ValueError):
        opt.prepare(pipeline_elements=pipeline_elements, maximize_metric=True, objective_function=of)