class_docstr: Represents options for autotuning dataset performance.
  
  ```python
  options = tf.data.Options()
  options.autotune.enabled = False
  dataset = dataset.with_options(options)
  ```
class_docstr_tok: ["Represents","options","for","autotuning","dataset","performance",".","``","`","python","options","=","tf.data.Options","(",")","options.autotune.enabled","=","False","dataset","=","dataset.with_options","(","options",")","``","`"]
code: def _to_proto(self):
      pb = dataset_options_pb2.AutotuneOptions()
      if self.enabled is not None:
          pb.enabled = self.enabled
      if self.cpu_budget is not None:
          pb.cpu_budget = self.cpu_budget
      if self.ram_budget is not None:
          pb.ram_budget = self.ram_budget
      if self.autotune_algorithm is not None:
          pb.autotune_algorithm = AutotuneAlgorithm._to_proto(  # pylint: disable=protected-access
              self.autotune_algorithm
          )
      return pb
  
imports: import enum
  import platform
  from absl import logging
  from tensorflow.core.framework import dataset_options_pb2
  from tensorflow.core.framework import model_pb2
  from tensorflow.python.data.ops import test_mode
  from tensorflow.python.data.util import options
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the AutotuneOptions obj's underlying member method code with example usage: obj._to_proto() and returns: pb