code: def test_parallel_evaluation(self):
      dataset = self.spark.createDataFrame(
          [
              (Vectors.dense([0.0]), 0.0),
              (Vectors.dense([0.4]), 1.0),
              (Vectors.dense([0.5]), 0.0),
              (Vectors.dense([0.6]), 1.0),
              (Vectors.dense([1.0]), 1.0),
          ]
          * 10,
          ["features", "label"],
      )
      lr = LogisticRegression()
      grid = ParamGridBuilder().addGrid(lr.maxIter, [5, 6]).build()
      evaluator = BinaryClassificationEvaluator()
      tvs = TrainValidationSplit(
          estimator=lr, estimatorParamMaps=grid, evaluator=evaluator
      )
      tvs.setParallelism(1)
      tvsSerialModel = tvs.fit(dataset)
      tvs.setParallelism(2)
      tvsParallelModel = tvs.fit(dataset)
      self.assertEqual(
          tvsSerialModel.validationMetrics,
          tvsParallelModel.validationMetrics,
      )
  
imports: import tempfile
  import unittest
  import numpy
  from pyspark.ml import Estimator, Model
  from pyspark.ml.classification import LogisticRegression
  from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
  from pyspark.ml.linalg import Vectors
  from pyspark.ml.param import Param, Params
  from pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder, TrainValidationSplit, TrainValidationSplitModel
  from pyspark.sql.functions import rand
  from pyspark.testing.mlutils import SparkSessionTestCase
label_desc: Use image node_id 3 for calling the TrainValidationSplitTests obj's underlying member method code with example usage: obj.test_parallel_evaluation() without return types