def build_wrapper(img_size: types_of_loco.input_img_size=28, channels: int=3, model_name: str='model1', optimizer: Optimizer=SGD()) -> Union[(ModelBuilder, pytorch_builder.PytorchModelBuilder)]:
    '\n    モデル生成をする関数を返す\n    交差検証をかける際のラッパーとして使う\n    :param img_size:\n    :param channels:\n    :param model_name:\n    :param optimizer:\n    :return:\n    '
    if callable(optimizer):
        return pytorch_builder.PytorchModelBuilder(img_size=img_size, channels=channels, model_name=model_name, opt_builder=optimizer)
    return keras_builder.build_wrapper(img_size, channels, model_name, optimizer)
モデル生成をする関数を返す
交差検証をかける際のラッパーとして使う
:param img_size:
:param channels:
:param model_name:
:param optimizer:
:return:
def build_wrapper(img_size: types_of_loco.input_img_size=28, channels: int=3, model_name: str='model1', optimizer: Optimizer=SGD()) -> Union[(ModelBuilder, pytorch_builder.PytorchModelBuilder)]:
    '\n    モデル生成をする関数を返す\n    交差検証をかける際のラッパーとして使う\n    :param img_size:\n    :param channels:\n    :param model_name:\n    :param optimizer:\n    :return:\n    '
    if callable(optimizer):
        return pytorch_builder.PytorchModelBuilder(img_size=img_size, channels=channels, model_name=model_name, opt_builder=optimizer)
    return keras_builder.build_wrapper(img_size, channels, model_name, optimizer)