code: def testOptimizationMapParallelization(
      self, autotune, map_parallelization
  ):
      dataset = dataset_ops.Dataset.range(5)
      if (
          autotune is not False and map_parallelization is not False
      ):  # pylint: disable=g-bool-id-comparison
          dataset = dataset.apply(testing.assert_next(["ParallelMap"]))
      else:
          dataset = dataset.apply(testing.assert_next(["Map"]))
      dataset = dataset.map(lambda x: x + 1)
  
      options = options_lib.Options()
      if autotune is not None:
          options.autotune.enabled = autotune
      if map_parallelization is not None:
          options.experimental_optimization.map_parallelization = (
              map_parallelization
          )
      dataset = dataset.with_options(options)
  
      self.assertDatasetProduces(
          dataset, expected_output=list(range(1, 6))
      )
  
imports: import functools
  import time
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.experimental.ops import batching
  from tensorflow.python.data.experimental.ops import grouping
  from tensorflow.python.data.experimental.ops import scan_ops
  from tensorflow.python.data.experimental.ops import testing
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 6 for calling the OptimizationTest obj's underlying member method code with example usage: obj.testOptimizationMapParallelization(autotune, map_parallelization) without return types