code: def ohe_min_max_transformation(data, data_interface):
      """the data is one-hot-encoded and min-max normalized and fed to the ML model"""
      return data_interface.get_ohe_min_max_normalized_data(data)
  
imports: import os
  import pickle
  import shutil
  import zipfile
  from urllib.request import urlretrieve
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import FunctionTransformer, OneHotEncoder
  import dice_ml
label_desc: Use image node_id 19 for calling a global function with example usage: ohe_min_max_transformation(data, data_interface) and returns: data_interface