code: def KD_binary_vars_classification_exp_object():
      backend = "sklearn"
      dataset = load_custom_vars_testing_dataset()
      d = dice_ml.Data(
          dataframe=dataset,
          continuous_features=["Numerical"],
          outcome_name="Outcome",
      )
      model = _load_custom_vars_dataset_model()
      m = dice_ml.Model(model=model, backend=backend)
      exp = dice_ml.Dice(d, m, method="kdtree")
      return exp
  
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 12 for calling a global function with example usage: KD_binary_vars_classification_exp_object() and returns: exp