code: def classifier():
      X, y = get_iris()
      model = RandomForestClassifier()
      model.fit(X, y)
  
      explainer = shap.KernelExplainer(
          model.predict_proba, shap.kmeans(X, 100)
      )
      shap_values = explainer.shap_values(X)
  
      return ModelWithExplanation(
          model, X, shap_values, explainer.expected_value
      )
  
imports: import os
  from collections import namedtuple
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  import shap
  from sklearn.datasets import load_diabetes, load_iris
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import mlflow
  from mlflow import MlflowClient
label_desc: Use image node_id 5 for calling a global function with example usage: classifier() and returns: ModelWithExplanation