code: def test_cohort_filter_includes(self):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          numeric,
          categorical,
      ) = create_simple_titanic_data()
      feature_names = categorical + numeric
      clf = create_titanic_pipeline(X_train, y_train)
      categorical_features = categorical
      # the indexes 0, 2 correspond to S, C
      filters = [
          {
              ARG: [0, 2],
              COLUMN: EMBARKED,
              METHOD: CohortFilterMethods.METHOD_INCLUDES,
          }
      ]
      validation_data = create_validation_data(X_test, y_test)
      filter_embarked = X_test[EMBARKED].isin(["S", "C"])
      validation_data = validation_data.loc[filter_embarked]
      model_task = ModelTask.CLASSIFICATION
      run_error_analyzer(
          validation_data,
          clf,
          X_test,
          y_test,
          feature_names,
          categorical_features,
          model_task,
          filters=filters,
      )
  
imports: import numpy
  import pandas
  import pytest
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import ARG, COLUMN, METHOD, PRED_Y, ROW_INDEX, TRUE_Y, ImageColumns, ModelTask
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from rai_test_utils.datasets.tabular import create_diabetes_data, create_iris_data, create_simple_titanic_data
  from rai_test_utils.datasets.vision import load_fridge_object_detection_dataset
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_regressor, create_sklearn_svm_classifier, create_titanic_pipeline
  from rai_test_utils.models.torch import get_object_detection_fridge_model
  from raiutils.cohort import CohortFilterMethods
label_desc: Use image node_id 9 for calling the TestCohortFilter obj's underlying member method code with example usage: obj.test_cohort_filter_includes() without return types