code: def test_ml_mllib_vector_conversion(self):
      # to ml
      # dense
      mllibDV = Vectors.dense([1, 2, 3])
      mlDV1 = newlinalg.Vectors.dense([1, 2, 3])
      mlDV2 = mllibDV.asML()
      self.assertEqual(mlDV2, mlDV1)
      # sparse
      mllibSV = Vectors.sparse(4, {1: 1.0, 3: 5.5})
      mlSV1 = newlinalg.Vectors.sparse(4, {1: 1.0, 3: 5.5})
      mlSV2 = mllibSV.asML()
      self.assertEqual(mlSV2, mlSV1)
      # from ml
      # dense
      mllibDV1 = Vectors.dense([1, 2, 3])
      mlDV = newlinalg.Vectors.dense([1, 2, 3])
      mllibDV2 = Vectors.fromML(mlDV)
      self.assertEqual(mllibDV1, mllibDV2)
      # sparse
      mllibSV1 = Vectors.sparse(4, {1: 1.0, 3: 5.5})
      mlSV = newlinalg.Vectors.sparse(4, {1: 1.0, 3: 5.5})
      mllibSV2 = Vectors.fromML(mlSV)
      self.assertEqual(mllibSV1, mllibSV2)
  
imports: import array
  import unittest
  from numpy import array, array_equal, zeros, arange, tile, ones, inf
  import pyspark.ml.linalg
  from pyspark.serializers import CPickleSerializer
  from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector, DenseMatrix, SparseMatrix, Vectors, Matrices, MatrixUDT
  from pyspark.mllib.linalg.distributed import RowMatrix, IndexedRowMatrix, IndexedRow
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.sql import Row
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.testing.utils import have_scipy
label_desc: Use image node_id 18 for calling the VectorTests obj's underlying member method code with example usage: obj.test_ml_mllib_vector_conversion() without return types