code: def test_predict(self):
      y1 = self.model.predict(self.dtest)
      self.assertEqual(len(y1), self.dtest.num_rows())
      self.assertEqual(y1.dtype, self.type)
  
      y1 = self.model.predict(
          self.dtest, output_type="probability_vector"
      )
      self.assertEqual(len(y1), self.dtest.num_rows())
      self.assertEqual(y1.dtype, array)
  
      k = self.model.num_classes
      if k == 2:
          y1 = self.model.predict(self.dtest, "margin")
          y2 = self.model.predict(list(self.dtest), "margin")
          self.assertEqual(len(y1), self.dtest.num_rows())
          self.assertEqual(len(y2), self.dtest.num_rows())
          self.assertEqual(list(y1), list(y2))
  
          y1 = self.model.predict(self.dtest, "probability")
          y2 = self.model.predict(list(self.dtest), "probability")
          self.assertEqual(len(y1), self.dtest.num_rows())
          self.assertEqual(len(y2), self.dtest.num_rows())
          self.assertEqual(list(y1), list(y2))
  
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 8 for calling the DecisionTreeClassifierTest obj's underlying member method code with example usage: obj.test_predict() without return types