code: def test_cohort_filter_target(self, target_type, use_str_labels):
      if target_type == "Predicted Y":
          pytest.skip(
              "Skipping this test due to a bug condition "
              "in Predicted Y cohort filtering"
          )
      (
          X_train,
          X_test,
          y_train,
          y_test,
          feature_names,
          classes,
      ) = create_iris_pandas(use_str_labels)
      filters = [
          {
              ARG: [2],
              COLUMN: target_type,
              METHOD: CohortFilterMethods.METHOD_INCLUDES,
          }
      ]
      validation_data = create_validation_data(X_test, y_test)
      if use_str_labels:
          validation_filter = y_test == classes[2]
      else:
          validation_filter = y_test == 2
      validation_data = validation_data.loc[validation_filter]
      model_task = ModelTask.CLASSIFICATION
      model = create_sklearn_svm_classifier(X_train, y_train)
      categorical_features = []
      run_rai_insights(
          validation_data,
          model,
          X_train,
          y_train,
          X_test,
          y_test,
          feature_names,
          categorical_features,
          model_task,
          filters=filters,
      )
  
imports: import numpy
  import pandas
  import pytest
  from tests.common_utils import create_iris_data
  from erroranalysis._internal.constants import ARG, COLUMN, METHOD, PRED_Y, ROW_INDEX, TRUE_Y
  from rai_test_utils.datasets.tabular import create_housing_data, create_simple_titanic_data
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_regressor, create_sklearn_svm_classifier, create_titanic_pipeline
  from raiutils.cohort import CohortFilterMethods
  from raiutils.models import ModelTask
  from responsibleai.rai_insights import RAIInsights
label_desc: Use image node_id 2 for calling the TestCohortFilterRAIInsights obj's underlying member method code with example usage: obj.test_cohort_filter_target(target_type, use_str_labels) without return types