code: def multiclass_classification_integer_target(cls):
      binary_classification_integer_target(cls)
  
      def create_multiclass_label(row):
          if row["label"] == 0:
              return 0
          elif row["cap-surface"] == "y":
              return 1
          else:
              return 2
  
      cls.dtrain["label"] = cls.dtrain.apply(create_multiclass_label)
      cls.dtest["label"] = cls.dtest.apply(create_multiclass_label)
      cls.model = tc.decision_tree_classifier.create(
          cls.dtrain,
          target=cls.target,
          validation_set=cls.dtest,
          **cls.param,
      )
  
      num_examples_per_class = {
          c: (cls.dtrain[cls.target] == c).sum()
          for c in cls.model.classes
      }
      cls.get_ans["num_examples_per_class"] = (
          lambda x: x == num_examples_per_class
      )
      cls.get_ans["num_trees"] = lambda x: x == 3
      cls.get_ans["num_classes"] = lambda x: x == 3
      cls.get_ans["classes"] = lambda x: set(x) == set([0, 1, 2])
      cls.get_ans["class_weights"] = lambda x: x == {
          0: 1.0,
          1: 1.0,
          2: 1.0,
      }
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import turicreate
  import math
  import uuid
  import random
  import copy
  from turicreate.toolkits import evaluation
  from turicreate.toolkits._main import ToolkitError
  import shutil
  import numpy
  from array import array
  import os
label_desc: Use image node_id 4 for calling a global function with example usage: multiclass_classification_integer_target(cls) and returns: int, int, int