code: def _load_custom_vars_dataset_model():
      dataset = load_custom_vars_testing_dataset()
      X_train = dataset[["Categorical", "CategoricalNum", "Numerical"]]
      y_train = dataset["Outcome"].values
      num_feature_names = ["Numerical"]
      cat_feature_names = ["Categorical", "CategoricalNum"]
      model = create_complex_classification_pipeline(
          X_train, y_train, num_feature_names, cat_feature_names
      )
      return model
  
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 31 for calling a global function with example usage: _load_custom_vars_dataset_model() and returns: model