code: def private_data_object():
      """
      Returns a private data object containing meta information about the adult income dataset.
  
      Providing an OrderedDict to make it work for Python<=3.6
      """
      features_dict = OrderedDict(
          [
              ("age", [17, 90]),
              (
                  "workclass",
                  [
                      "Government",
                      "Other/Unknown",
                      "Private",
                      "Self-Employed",
                  ],
              ),
              (
                  "education",
                  [
                      "Assoc",
                      "Bachelors",
                      "Doctorate",
                      "HS-grad",
                      "Masters",
                      "Prof-school",
                      "School",
                      "Some-college",
                  ],
              ),
              (
                  "marital_status",
                  [
                      "Divorced",
                      "Married",
                      "Separated",
                      "Single",
                      "Widowed",
                  ],
              ),
              (
                  "occupation",
                  [
                      "Blue-Collar",
                      "Other/Unknown",
                      "Professional",
                      "Sales",
                      "Service",
                      "White-Collar",
                  ],
              ),
              ("race", ["Other", "White"]),
              ("gender", ["Female", "Male"]),
              ("hours_per_week", [1, 99]),
          ]
      )
      return dice_ml.Data(features=features_dict, outcome_name="income")
  
imports: from collections import OrderedDict
  from itertools import product
  import pandas
  import pytest
  import torch
  from rai_test_utils.models.sklearn import create_complex_classification_pipeline, create_complex_regression_pipeline
  import dice_ml
  from dice_ml.utils import helpers
  from dice_ml.utils.neuralnetworks import FFNetwork
label_desc: Use image node_id 23 for calling a global function with example usage: private_data_object() and returns: dice_ml