code: def test_infer_schema(self):
      rdd = self.sc.parallelize(
          [LabeledPoint(1.0, self.dv1), LabeledPoint(0.0, self.sv1)]
      )
      df = rdd.toDF()
      schema = df.schema
      field = [f for f in schema.fields if f.name == "features"][0]
      self.assertEqual(field.dataType, self.udt)
      vectors = df.rdd.map(lambda p: p.features).collect()
      self.assertEqual(len(vectors), 2)
      for v in vectors:
          if isinstance(v, SparseVector):
              self.assertEqual(v, self.sv1)
          elif isinstance(v, DenseVector):
              self.assertEqual(v, self.dv1)
          else:
              raise TypeError(
                  "expecting a vector but got %r of type %r"
                  % (v, type(v))
              )
  
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 3 for calling the VectorUDTTests obj's underlying member method code with example usage: obj.test_infer_schema() without return types