code: def main(args):
      output = Path(args.output)
      output.mkdir(parents=True, exist_ok=True)
  
      run_context = Run.get_context()
      dataset = run_context.input_datasets["train_10_models"]
      df = dataset.to_pandas_dataframe()
  
      # Drop the column "Revenue" from the dataset to avoid information leak as
      # "Quantity" = "Revenue" / "Price". Please modify the logic based on your data.
      drop_column_name = "Revenue"
      if drop_column_name in df.columns:
          df.drop(drop_column_name, axis=1, inplace=True)
  
      # Apply any data pre-processing techniques here
  
      df.to_parquet(
          output / "data_prepared_result.parquet", compression=None
      )
  
imports: from pathlib import Path
  from azureml.core import Run
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types