code: def test_numeric_categories(self, desired_range, method):
      (
          x_train,
          x_test,
          y_train,
          y_test,
          feature_names,
      ) = create_housing_data()
  
      x_train = pd.DataFrame(data=x_train, columns=feature_names)
      x_test = pd.DataFrame(data=x_test, columns=feature_names)
  
      rfc = RandomForestRegressor(
          n_estimators=10, max_depth=4, random_state=777
      )
      model = rfc.fit(x_train, y_train)
  
      dataset_train = x_train.copy()
      dataset_train["Outcome"] = y_train
  
      d = dice_ml.Data(
          dataframe=dataset_train,
          continuous_features=feature_names,
          outcome_name="Outcome",
      )
      m = dice_ml.Model(
          model=model, backend="sklearn", model_type="regressor"
      )
      exp = dice_ml.Dice(d, m, method=method)
  
      cf_explanation = exp.generate_counterfactuals(
          query_instances=x_test.iloc[0:1],
          total_CFs=10,
          desired_range=desired_range,
      )
  
      assert cf_explanation is not None
  
      exp.serialize_explainer("explainer.pkl")
      new_exp = ExplainerBase.deserialize_explainer("explainer.pkl")
  
      cf_explanation = new_exp.generate_counterfactuals(
          query_instances=x_test.iloc[0:1],
          total_CFs=10,
          desired_range=desired_range,
      )
  
      assert cf_explanation is not None
  
imports: import re
  import numpy
  import pandas
  import pytest
  from rai_test_utils.datasets.tabular import create_housing_data
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.ensemble import RandomForestRegressor
  import dice_ml
  from dice_ml.diverse_counterfactuals import CounterfactualExamples
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
  from dice_ml.utils import helpers
  from ..conftest import _load_custom_testing_binary_model
label_desc: Use image node_id 2 for calling the TestExplainerBaseRegression obj's underlying member method code with example usage: obj.test_numeric_categories(desired_range, method) without return types