code: def random_multi_classification_exp_object(request):
      backend, dinterface = request.param
      if dinterface == "public":
          dataset = helpers.load_custom_testing_dataset_multiclass()
          d = dice_ml.Data(
              dataframe=dataset,
              continuous_features=["Numerical"],
              outcome_name="Outcome",
          )
      else:
          d = dice_ml.Data(
              features={
                  "Numerical": [7, 23],
                  "Categorical": ["a", "b", "c"],
              },
              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_multiclass_model()
          m = dice_ml.Model(model=model, backend=backend)
      exp = dice_ml.Dice(d, m, method="random")
      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 3 for calling a global function with example usage: random_multi_classification_exp_object(request) and returns: exp