How to use the mindmeld.components._config.get_classifier_config function in mindmeld

def test_get_classifier_config():
    """Tests that the default config is returned when an app specified config doesn't exist."""
    actual = get_classifier_config("domain", APP_PATH)["param_selection"]

    expected = {
        "type": "k-fold",
        "k": 10,
        "grid": {"fit_intercept": [True, False], "C": [10, 100, 1000, 10000, 100000]},
    }

    assert actual == expected

def test_get_classifier_config_func():
    """Tests that the app config provider is called."""
    actual = get_classifier_config(
        "entity", APP_PATH, domain="domain", intent="intent"
    )["params"]

    expected = {"penalty": "l2", "C": 100}

    assert actual == expected

):
        """Initializes an entity resolver

        Args:
            app_path (str): The application path
            resource_loader (ResourceLoader): An object which can load resources for the resolver
            entity_type: The entity type associated with this entity resolver
            es_host (str): The Elasticsearch host server
        """
        self._app_namespace = get_app_namespace(app_path)
        self._resource_loader = resource_loader
        self._normalizer = resource_loader.query_factory.normalize
        self.type = entity_type
        self._is_system_entity = Entity.is_system_entity(self.type)
        self._exact_match_mapping = None
        self._er_config = get_classifier_config("entity_resolution", app_path=app_path)
        self._es_host = es_host
        self._es_config = {"client": es_client, "pid": os.getpid()}

def _get_model_config(self, **kwargs):  # pylint: disable=arguments-differ
        """Gets a machine learning model configuration

        Returns:
            ModelConfig: The model configuration corresponding to the provided config name
        """
        kwargs["example_type"] = QUERY_EXAMPLE_TYPE
        kwargs["label_type"] = ENTITIES_LABEL_TYPE
        loaded_config = get_classifier_config(
            self.CLF_TYPE,
            self._resource_loader.app_path,
            domain=self.domain,
            intent=self.intent,
        )
        return super()._get_model_config(loaded_config, **kwargs)

def _get_model_config(self, **kwargs):
        """Gets a machine learning model configuration

        Returns:
            ModelConfig: The model configuration corresponding to the provided config name
        """
        kwargs["example_type"] = QUERY_EXAMPLE_TYPE
        kwargs["label_type"] = CLASS_LABEL_TYPE
        loaded_config = get_classifier_config(
            self.CLF_TYPE, self._resource_loader.app_path
        )
        return super()._get_model_config(loaded_config, **kwargs)

def _get_model_config(self, **kwargs):
        """Gets a machine learning model configuration

        Returns:
            ModelConfig: The model configuration corresponding to the provided config name
        """
        kwargs["example_type"] = ENTITY_EXAMPLE_TYPE
        kwargs["label_type"] = CLASS_LABEL_TYPE
        loaded_config = get_classifier_config(
            self.CLF_TYPE,
            self._resource_loader.app_path,
            domain=self.domain,
            intent=self.intent,
            entity=self.entity_type,
        )
        return super()._get_model_config(loaded_config, **kwargs)

def _get_model_config(self, **kwargs):
        """Gets a machine learning model configuration

        Returns:
            ModelConfig: The model configuration corresponding to the provided config name
        """
        kwargs["example_type"] = QUERY_EXAMPLE_TYPE
        kwargs["label_type"] = CLASS_LABEL_TYPE
        loaded_config = get_classifier_config(
            self.CLF_TYPE, self._resource_loader.app_path, domain=self.domain
        )
        return super()._get_model_config(loaded_config, **kwargs)