code: def test_ebm_uniform_multiclass():
      data = iris_classification()
      X_train = data["train"]["X"]
      y_train = data["train"]["y"]
  
      X_test = data["test"]["X"]
      y_test = data["test"]["y"]
  
      feature_types = [None] * X_train.shape[1]
      feature_types[0] = "uniform"
  
      clf = ExplainableBoostingClassifier(
          interactions=0, feature_types=feature_types
      )
      clf.fit(X_train, y_train)
  
      assert accuracy_score(y_test, clf.predict(X_test)) > 0.9
  
      global_exp = clf.explain_global()
      local_exp = clf.explain_local(X_test, y_test)
  
      smoke_test_explanations(global_exp, local_exp, 6001)
  
imports: import math
  from sklearn.metrics import log_loss
  from interpret.utils import make_synthetic
  from ...tutils import synthetic_multiclass, synthetic_classification, iris_classification, smoke_test_explanations
  from ...tutils import synthetic_regression
  from interpret.glassbox import ExplainableBoostingRegressor, ExplainableBoostingClassifier
  from interpret.privacy import DPExplainableBoostingClassifier, DPExplainableBoostingRegressor
  from interpret.utils import inv_link
  from io import StringIO
  import json
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn.utils.estimator_checks import check_estimator
  import pytest
  import warnings
label_desc: Use image node_id 23 for calling a global function with example usage: test_ebm_uniform_multiclass() without return types