How to use the deepforest.utilities.read_config function in deepforest

def config():
    config  = utilities.read_config(get_data("deepforest_config.yml"))
    return config

def config():
    config = utilities.read_config(get_data("deepforest_config.yml"))
    config["patch_size"] = 200
    config["patch_overlap"] = 0.25
    config["annotations_xml"] = get_data("OSBS_029.xml")
    config["rgb_dir"] = "tests/data"
    config["annotations_file"] = "tests/data/OSBS_029.csv"
    config["path_to_raster"] = get_data("OSBS_029.tif")
    
    #Create a clean config test data
    annotations = utilities.xml_to_annotations(xml_path = config["annotations_xml"])
    annotations.to_csv("tests/data/OSBS_029.csv",index=False)
    
    return config

def __init__(self, weights=None, saved_model=None):        
        self.weights = weights
        self.saved_model = saved_model
        
        #Read config file - if a config file exists in local dir use it, if not use installed.
        if os.path.exists("deepforest_config.yml"):
            config_path = "deepforest_config.yml"
        else:
            try:
                config_path = get_data("deepforest_config.yml")
            except Exception as e:
                raise ValueError("No deepforest_config.yml found either in local directory or in installed package location. {}".format(e))
        
        print("Reading config file: {}".format(config_path))                    
        self.config = utilities.read_config(config_path)
        
        #release version id to flag if release is being used
        self.__release_version__ = None
        
        #Load saved model if needed
        if self.saved_model:
            print("Loading saved model")
            #Capture user warning, not relevant here
            with warnings.catch_warnings():
                warnings.filterwarnings("ignore",category=UserWarning)                    
                self.model = utilities.load_model(saved_model)
            self.prediction_model = convert_model(self.model)
            
        if self.weights is not None:
            print("Creating model from weights")
            backbone = models.backbone(self.config["backbone"])