code: def test_cohort_filter_index(self):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          feature_names,
          _,
      ) = create_iris_pandas()
      # filter on index, which can be done from the RAI dashboard
      filters = [
          {
              ARG: [40],
              COLUMN: ROW_INDEX,
              METHOD: CohortFilterMethods.METHOD_LESS_AND_EQUAL,
          }
      ]
      validation_data = create_validation_data(X_test, y_test)
      validation_data = validation_data.loc[
          validation_data[ROW_INDEX] <= 40
      ]
      model_task = ModelTask.CLASSIFICATION
      model = create_sklearn_svm_classifier(X_train, y_train)
      categorical_features = []
      run_error_analyzer(
          validation_data,
          model,
          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 12 for calling the TestCohortFilter obj's underlying member method code with example usage: obj.test_cohort_filter_index() without return types