code: def create_iris_data():
      # Import Iris dataset
      iris = load_iris()
      # Split data into train and test
      X_train, X_test, y_train, y_test = train_test_split(
          iris.data, iris.target, test_size=0.2, random_state=0
      )
      feature_names = iris.feature_names
      classes = iris.target_names
      return X_train, X_test, y_train, y_test, feature_names, classes
  
imports: import numpy
  import pandas
  import shap
  import sklearn
  from interpret.ext.blackbox import MimicExplainer
  from interpret.ext.glassbox import LGBMExplainableModel
  from interpret_community.common.constants import ModelTask
  from sklearn.datasets import load_iris, make_classification
  from sklearn.model_selection import train_test_split
  from erroranalysis._internal.constants import Metrics, metric_to_display_name
  from erroranalysis._internal.surrogate_error_tree import DEFAULT_MAX_DEPTH, DEFAULT_MIN_CHILD_SAMPLES, DEFAULT_NUM_LEAVES
  from raiwidgets import ErrorAnalysisDashboard
  from raiwidgets.explanation_constants import WidgetRequestResponseConstants
label_desc: Use image node_id 2 for calling a global function with example usage: create_iris_data() and returns: X_train,  X_test,  y_train,  y_test,  feature_names,  classes