code: def test_squared_distance(self):
      def squared_distance(a, b):
          if isinstance(a, Vector):
              return a.squared_distance(b)
          else:
              return b.squared_distance(a)
  
      sv = SparseVector(4, {1: 1, 3: 2})
      dv = DenseVector(array([1.0, 2.0, 3.0, 4.0]))
      lst = DenseVector([4, 3, 2, 1])
      lst1 = [4, 3, 2, 1]
      arr = pyarray.array("d", [0, 2, 1, 3])
      narr = array([0, 2, 1, 3])
      self.assertEqual(15.0, squared_distance(sv, dv))
      self.assertEqual(25.0, squared_distance(sv, lst))
      self.assertEqual(20.0, squared_distance(dv, lst))
      self.assertEqual(15.0, squared_distance(dv, sv))
      self.assertEqual(25.0, squared_distance(lst, sv))
      self.assertEqual(20.0, squared_distance(lst, dv))
      self.assertEqual(0.0, squared_distance(sv, sv))
      self.assertEqual(0.0, squared_distance(dv, dv))
      self.assertEqual(0.0, squared_distance(lst, lst))
      self.assertEqual(25.0, squared_distance(sv, lst1))
      self.assertEqual(3.0, squared_distance(sv, arr))
      self.assertEqual(3.0, squared_distance(sv, narr))
  
imports: import unittest
  import array
  from numpy import arange, array, array_equal, inf, ones, tile, zeros
  from pyspark.serializers import CPickleSerializer
  from pyspark.ml.linalg import DenseMatrix, DenseVector, MatrixUDT, SparseMatrix, SparseVector, Vector, VectorUDT, Vectors
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.sql import Row
  from pyspark.sql.functions import unwrap_udt
label_desc: Use image node_id 4 for calling the VectorTests obj's underlying member method code with example usage: obj.test_squared_distance() and returns: a, b