content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
  #  or implied. See the License for the specific language governing
  #  permissions and limitations under the License.
  
  from steps.evaluators import evaluator
  from steps.importers import importer
  from steps.normalizers import normalizer
  from steps.trainers import trainer
  
  from zenml import pipeline
  from zenml.config import DockerSettings
  from zenml.integrations.constants import TENSORFLOW
  
  docker_settings = DockerSettings(required_integrations=[TENSORFLOW])
  
  
  @pipeline(enable_cache=True, settings={"docker": docker_settings})
  def mnist_pipeline(epochs: int = 2, lr: float = 0.001):
      X_train, X_test, y_train, y_test = importer()
      X_trained_normed, X_test_normed = normalizer(
          X_train=X_train, X_test=X_test
      )
      model = trainer(
          X_train=X_trained_normed, y_train=y_train, epochs=epochs, lr=lr
      )
      evaluator(X_test=X_test_normed, y_test=y_test, model=model)
  