code: def get_cat_data(rs):
      X_cont = rs.rand(20, 5)
      X_cat = rs.randint(low=0, high=3, size=(20, 5))
      X = np.concatenate([X_cat, X_cont], axis=1)
      Y = rs.rand(10, 1)
      cat_dims = [0, 1, 2, 3, 4]
      cont_dims = [5, 6, 7, 8, 9]
  
      return X, Y, cat_dims, cont_dims
  
imports: from unittest.mock import patch
  import numpy
  import pytest
  import scipy.optimize
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import CategoricalHyperparameter, ConfigurationSpace, EqualsCondition, UniformFloatHyperparameter
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.utils.configspace import convert_configurations_to_array
label_desc: Use image node_id 3 for calling a global function with example usage: get_cat_data(rs) and returns: X,  Y,  cat_dims,  cont_dims