code: def test_multiclass_postprocess_smoke():
      n = 1000
      d = 2
      k = 3
      b = 10
  
      X_binned = np.random.randint(b, size=(d, n))
      feature_graphs = []
      for _ in range(d):
          feature_graphs.append(np.random.rand(b, k))
  
      def binned_predict_proba(X_binned, k=3):
          n = X_binned.shape[1]
          return 1 / k * np.ones((n, k))
  
      feature_types = ["numeric"] * d
      results = multiclass_postprocess_RESTORE_THIS(
          X_binned, feature_graphs, binned_predict_proba, feature_types
      )
  
      assert "intercepts" in results
      assert "feature_graphs" in results
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_multiclass_postprocess_smoke() and returns: unknown