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

def test_predict_image(download_release):
    #Load model
    test_model = deepforest.deepforest(weights=get_data("NEON.h5"))    
    assert isinstance(test_model.model,keras.models.Model)
    
    #Predict test image and return boxes
    boxes = test_model.predict_image(image_path=get_data("OSBS_029.tif"), show=False, return_plot = False)
    
    #Returns a 6 column numpy array, xmin, ymin, xmax, ymax, score, label
    assert boxes.shape[1] == 6

def test_random_transform(annotations):
    test_model = deepforest.deepforest()
    test_model.config["random_transform"] = True
    arg_list = utilities.format_args(annotations, test_model.config)
    assert "--random-transform" in arg_list

def test_use_release(download_release):
    test_model = deepforest.deepforest() 
    test_model.use_release()
    
    #Check for release tag
    assert isinstance(test_model.__release_version__, str)
    
    #Assert is model instance    
    assert isinstance(test_model.model,keras.models.Model)
    
    assert test_model.config["weights"] == test_model.weights 
    assert test_model.config["weights"] is not "None"

def test_train(annotations):
    test_model = deepforest.deepforest()
    test_model.config["epochs"] = 1
    test_model.config["save-snapshot"] = False
    test_model.config["steps"] = 1
    test_model.train(annotations=annotations, input_type="fit_generator")
    
    return test_model

def release_model(download_release):
    test_model = deepforest.deepforest() 
    test_model.use_release()
    
    #Check for release tag
    assert isinstance(test_model.__release_version__, str)
    
    #Assert is model instance    
    assert isinstance(test_model.model,keras.models.Model)
    
    return test_model

def test_deepforest():
    model = deepforest.deepforest(weights=None)
    assert model.weights is None