code: def create_iris_pandas(use_str_labels=False):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          feature_names,
          classes,
      ) = create_iris_data()
  
      X_train = pd.DataFrame(X_train, columns=feature_names)
      X_test = pd.DataFrame(X_test, columns=feature_names)
  
      if use_str_labels:
          y_train = np.array([classes[y] for y in y_train])
          y_test = np.array([classes[y] for y in y_test])
  
      return X_train, X_test, y_train, y_test, feature_names, classes
  
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 1 for calling a global function with example usage: create_iris_pandas(use_str_labels) and returns: X_train,  X_test,  y_train,  y_test,  feature_names,  classes