code: def test_cohort_filter_multiclass_classification_outcome(
      self, arg, correct_prediction
  ):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          feature_names,
          _,
      ) = create_iris_pandas()
      model = create_sklearn_svm_classifier(X_train, y_train)
      model_task = ModelTask.CLASSIFICATION
      categorical_features = []
  
      # the index 1, corresponds to incorrect prediction
      # the index 0 correspond to correct prediction
      filters = [
          {
              ARG: arg,
              COLUMN: CLASSIFICATION_OUTCOME,
              METHOD: CohortFilterMethods.METHOD_INCLUDES,
          }
      ]
      pred_y = model.predict(X_test)
      validation_data = create_validation_data(X_test, y_test, pred_y)
      if correct_prediction:
          validation_filter = (
              validation_data[PRED_Y] == validation_data[TRUE_Y]
          )
      else:
          validation_filter = (
              validation_data[PRED_Y] != validation_data[TRUE_Y]
          )
      validation_data = validation_data.loc[validation_filter]
      validation_data = validation_data.drop(columns=PRED_Y)
      model_task = ModelTask.CLASSIFICATION
      run_error_analyzer(
          validation_data,
          model,
          X_test,
          y_test,
          feature_names,
          categorical_features,
          model_task,
          filters=filters,
          is_empty_validation_data=(not correct_prediction),
      )
  
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 8 for calling the TestCohortFilter obj's underlying member method code with example usage: obj.test_cohort_filter_multiclass_classification_outcome(arg, correct_prediction) without return types