code: def test_merge_ebms_multiclass():
      data = iris_classification()
      X = data["full"]["X"]
      y = data["full"]["y"]
      X_te = data["test"]["X"]
      y_te = data["test"]["y"]
  
      random_state = 1
      X_train, _, y_train, _ = train_test_split(
          X, y, test_size=0.20, random_state=random_state
      )
      ebm1 = ExplainableBoostingClassifier(
          random_state=random_state,
          interactions=0,
      )
      ebm1.fit(X_train, y_train)
  
      random_state += 10
      X_train, _, y_train, _ = train_test_split(
          X, y, test_size=0.40, random_state=random_state
      )
      ebm2 = ExplainableBoostingClassifier(
          random_state=random_state,
          interactions=0,
      )
      ebm2.fit(X_train, y_train)
  
      random_state += 10
      X_train, _, y_train, _ = train_test_split(
          X, y, test_size=0.60, random_state=random_state
      )
      ebm3 = ExplainableBoostingClassifier(
          random_state=random_state,
          interactions=0,
      )
      ebm3.fit(X_train, y_train)
  
      merged_ebm1 = merge_ebms([ebm1, ebm2, ebm3])
      valid_ebm(merged_ebm1)
      global_exp = merged_ebm1.explain_global()
      local_exp = merged_ebm1.explain_local(X_te, y_te)
      smoke_test_explanations(global_exp, local_exp, 6000)
  
      random_state += 10
      X_train, _, y_train, _ = train_test_split(
          X, y, test_size=0.10, random_state=random_state
      )
      ebm4 = ExplainableBoostingClassifier(
          random_state=random_state, interactions=0
      )
      ebm4.fit(X_train, y_train)
  
      merged_ebm2 = merge_ebms([merged_ebm1, ebm4])
      valid_ebm(merged_ebm2)
      global_exp = merged_ebm2.explain_global()
      local_exp = merged_ebm2.explain_local(X_te, y_te)
      smoke_test_explanations(global_exp, local_exp, 6000)
  
      random_state += 10
      X_train, _, y_train, _ = train_test_split(
          X, y, test_size=0.50, random_state=random_state
      )
      ebm5 = ExplainableBoostingClassifier(
          random_state=random_state, interactions=0
      )
      ebm5.fit(X_train, y_train)
  
      merged_ebm3 = merge_ebms([ebm5, merged_ebm2])
      valid_ebm(merged_ebm3)
      global_exp = merged_ebm3.explain_global()
      local_exp = merged_ebm3.explain_local(X_te, y_te)
      smoke_test_explanations(global_exp, local_exp, 6000)
  
file_path: interpret/python/interpret-core/tests/glassbox/ebm/test_merge_ebms.py
imports: from interpret.utils import make_synthetic
  from ...tutils import iris_classification, smoke_test_explanations
  from sklearn.model_selection import train_test_split
  from interpret.glassbox import ExplainableBoostingClassifier, merge_ebms
  import numpy
  import warnings
label_desc: Use image node_id 3 for calling a global function with example usage: test_merge_ebms_multiclass() without return types