code: def test_predict(self):
      # Default, output_type = class
      y1 = self.model.predict(self.dtest)
      self.assertEqual(len(y1), self.dtest.num_rows())
      self.assertEqual(y1.dtype, self.type)
  
      # Default, output_type = class
      y1 = self.model.predict(self.dtest, output_type="class")
      self.assertEqual(len(y1), self.dtest.num_rows())
      self.assertEqual(y1.dtype, self.type)
  
      # output_type = probability vector
      y1 = self.model.predict(
          self.dtest, output_type="probability_vector"
      )
      self.assertEqual(len(y1), self.dtest.num_rows())
      self.assertEqual(y1.dtype, array)
      self.assertTrue(all(y1.apply(lambda x: abs(sum(x) - 1.0)) < 1e-5))
  
      k = self.model.num_classes
      if k == 2:
          class_one = sorted(self.model.classes)[1]
          y_class = self.model.predict(self.dtest, "class") == class_one
  
          y1 = self.model.predict(self.dtest, "margin")
          self.assertEqual(len(y1), self.dtest.num_rows())
          self.assertTrue(all(y_class == (y1 > 0.0)))
  
          y1 = self.model.predict(self.dtest, "probability")
          self.assertEqual(len(y1), self.dtest.num_rows())
          self.assertTrue(all(y_class == (y1 > 0.5)))
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import turicreate
  import uuid
  import random
  import copy
  from turicreate.toolkits._main import ToolkitError
  import turicreate.toolkits.evaluation
  import shutil
  from array import array
  import os
label_desc: Use image node_id 8 for calling the BoostedTreesClassifierTest obj's underlying member method code with example usage: obj.test_predict() without return types