code: def test_eq(self):
      v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
      v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
      v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
      v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
      v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
      v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
      dm1 = DenseMatrix(2, 2, [2, 0, 0, 0])
      sm1 = SparseMatrix(2, 2, [0, 2, 3], [0], [2])
      self.assertEqual(v1, v2)
      self.assertEqual(v1, v3)
      self.assertFalse(v2 == v4)
      self.assertFalse(v1 == v5)
      self.assertFalse(v1 == v6)
      # this is done as Dense and Sparse matrices can be semantically
      # equal while still implementing a different __eq__ method
      self.assertEqual(dm1, sm1)
      self.assertEqual(sm1, dm1)
  
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 6 for calling the VectorTests obj's underlying member method code with example usage: obj.test_eq() without return types