class_docstr: A class to transform data based on user-defined function to get predicted outcomes.
  This class calls FunctionTransformer of scikit-learn internally
  (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html).
code: def initialize_transform_func(self):
      if self.func == "ohe-min-max":
          self.data_transformer = FunctionTransformer(
              func=ohe_min_max_transformation,
              inverse_func=inverse_ohe_min_max_transformation,
              check_inverse=False,
              validate=False,
              kw_args=self.kw_args,
              inv_kw_args=self.kw_args,
          )
      elif self.func is None:
          # identity transformation
          # add more ready-to-use transformers (such as label-encoding) in elif loops.
          self.data_transformer = FunctionTransformer(
              func=self.func, kw_args=None, validate=False
          )
      else:
          # add more ready-to-use transformers (such as label-encoding) in elif loops.
          self.data_transformer = FunctionTransformer(
              func=self.func, kw_args=self.kw_args, validate=False
          )
  
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 3 for calling the DataTransfomer obj's underlying member method code with example usage: obj.initialize_transform_func() without return types