code: def data_object():
      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]),
          ]
      )  # providing an OrderedDict to make it work for Python<3.6
      return dice_ml.Data(
          features=features_dict,
          outcome_name="income",
          type_and_precision={"hours_per_week": ["float", 2]},
          mad={"age": 10},
      )
  
imports: from collections import OrderedDict
  import pytest
  import dice_ml
label_desc: Use image node_id 1 for calling a global function with example usage: data_object() and returns: dice_ml