class_docstr: Test creation of the Kmeans model with various parameter and dataset
  configurations.
docstr: Make sure randomly initialized cluster centers work correctly.
code: def test_random_initial_centers(self):
      """
      Make sure randomly initialized cluster centers work correctly.
      """
      ## Check the basics
      m = tc.kmeans.create(
          self.sf, num_clusters=self.K, max_iterations=0, verbose=False
      )
  
      self.assertEqual(m.cluster_id.num_rows(), self.n)
      self.assertItemsEqual(
          m.cluster_id.column_names(),
          ["row_id", "cluster_id", "distance"],
      )
      self.assertItemsEqual(
          m.cluster_id["cluster_id"].unique(), range(10)
      )
  
      self.assertItemsEqual(m.cluster_info["cluster_id"], range(10))
      self.assertTrue((m.cluster_info["size"] > 0).all())
      self.assertEqual(m.cluster_info.num_rows(), self.K)
      self.assertItemsEqual(
          m.cluster_info.column_names(),
          self.sf.column_names()
          + ["cluster_id", "size", "sum_squared_distance"],
      )
  
      self.assertEqual(m.training_iterations, 0)
      self.assertGreaterEqual(m.training_time, 0.0)
      self.assertEqual(m.num_clusters, self.K)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import copy
  import numpy
  from numpy.testing import assert_allclose
  import turicreate
  from .None import util
  from turicreate.util import _assert_sframe_equal
  from turicreate.toolkits._main import ToolkitError
  import sys
label_desc: Use image node_id 10 for calling the KmeansCreateTest obj's underlying member method code with example usage: obj.test_random_initial_centers() without return types