code: def genetic_binary_classification_exp_object(request):
      backend = request.param
      dataset = helpers.load_custom_testing_dataset_binary()
      d = dice_ml.Data(
          dataframe=dataset,
          continuous_features=["Numerical"],
          outcome_name="Outcome",
      )
      if backend == "PYT":
          net = FFNetwork(4)
          m = dice_ml.Model(
              model=net, backend=backend, func="ohe-min-max"
          )
      else:
          model = _load_custom_testing_binary_model()
          m = dice_ml.Model(model=model, backend=backend)
      exp = dice_ml.Dice(d, m, method="genetic")
      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 6 for calling a global function with example usage: genetic_binary_classification_exp_object(request) and returns: exp