code: def create_rai_insights_object_multiclass_classification(
      with_model=True,
  ):
      # 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 = [
          name.replace(" (cm)", "") for name in iris.feature_names
      ]
      X_train = pd.DataFrame(X_train, columns=feature_names)
      X_test = pd.DataFrame(X_test, columns=feature_names)
  
      knn = sklearn.neighbors.KNeighborsClassifier()
      knn.fit(X_train, y_train)
  
      if not with_model:
          all_data = pd.concat([X_test, X_train])
          model_predict_output = knn.predict(all_data)
          model_predict_proba_output = knn.predict_proba(all_data)
          knn_wrapper = PredictionsModelWrapperClassification(
              all_data, model_predict_output, model_predict_proba_output
          )
          verify_predict_outputs(knn, knn_wrapper, all_data)
          verify_predict_proba_outputs(knn, knn_wrapper, all_data)
          knn = knn_wrapper
  
      X_train["target"] = y_train
      X_test["target"] = y_test
  
      ri = RAIInsights(knn, X_train, X_test, "target", "classification")
      ri.explainer.add()
      if with_model:
          ri.counterfactual.add(10, desired_class=2)
      ri.error_analysis.add()
      ri.compute()
      return ri
  
imports: import numpy
  import pandas
  import pytest
  import shap
  import sklearn
  from ml_wrappers.model.predictions_wrapper import PredictionsModelWrapperClassification, PredictionsModelWrapperRegression
  from sklearn.datasets import fetch_california_housing, load_iris
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.model_selection import train_test_split
  from responsibleai import RAIInsights
label_desc: Use image node_id 5 for calling a global function with example usage: create_rai_insights_object_multiclass_classification(with_model) and returns: ri