content: # Apache Software License 2.0
  #
  # Copyright (c) ZenML GmbH 2023. 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
  #
  # http://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.
  #
  
  import pandas as pd
  from sklearn.pipeline import Pipeline
  from typing_extensions import Annotated
  
  from zenml import step
  
  
  @step
  def inference_preprocessor(
      dataset_inf: pd.DataFrame,
      preprocess_pipeline: Pipeline,
      target: str,
  ) -> Annotated[pd.DataFrame, "inference_dataset"]:
      """Data preprocessor step.
  
      This is an example of a data processor step that prepares the data so that
      it is suitable for model inference. It takes in a dataset as an input step
      artifact and performs any necessary preprocessing steps based on pretrained
      preprocessing pipeline.
  
      Args:
          dataset_inf: The inference dataset.
          preprocess_pipeline: Pretrained `Pipeline` to process dataset.
          target: Name of target columns in dataset.
  
      Returns:
          The processed dataframe: dataset_inf.
      """
      # artificially adding `target` column to avoid Pipeline issues
      dataset_inf[target] = pd.Series([1] * dataset_inf.shape[0])
      dataset_inf = preprocess_pipeline.transform(dataset_inf)
      dataset_inf.drop(columns=[target], inplace=True)
      return dataset_inf
  