如何用neuraxle实现懒惰数据加载的存储库？

from neuraxle.base import BaseStep, NonFittableMixin
from neuraxle.data_container import DataContainer
from neuraxle.hyperparams.space import HyperparameterSamples
from neuraxle.union import BaseStepCollection


class LazyDataLoader(BaseStep, NonFittableMixin):
    def __init__(self, data_loader_func):
        super().__init__()
        self.data_loader_func = data_loader_func

    def load_data(self):
        return self.data_loader_func()

    def fit_transform(self, data_inputs):
        self._set_hyperparams_space(HyperparameterSamples())
        self._fit_transform_data_container = DataContainer(data_inputs=data_inputs)
        self._fit_transform_data_container = self.data_loader_func()
        self._fit_transform_data_container._set_hyperparams_space(HyperparameterSamples())
        return self

    def transform(self, data_inputs):
        data_container = DataContainer(data_inputs=data_inputs)
        data_container = self.data_loader_func()
        return data_container

from neuraxle.base import Identity
from neuraxle.pipeline import Pipeline

def lazy_data_loader_func():
    # 这里是加载数据的具体实现
    # 可以调用腾讯云的对象存储服务 COS，或者其他存储服务进行数据加载
    # 例如：从腾讯云的COS加载数据：
    # from tencentcloud.cos import CosClient
    # cos_client = CosClient()
    # data = cos_client.get_object(bucket_name, object_key)
    return data

data_loader = LazyDataLoader(lazy_data_loader_func)

# 创建机器学习流程
pipeline = Pipeline([
    data_loader,
    Identity()  # 这里可以添加其他的数据处理步骤
])

# 在训练阶段加载数据
pipeline.fit(X_train, y_train)

# 在预测阶段加载数据
pipeline.predict(X_test)