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 _from_proto(self, pb):
      if pb.WhichOneof("optional_enabled") is not None:
          self.enabled = pb.enabled
      if pb.WhichOneof("optional_cpu_budget") is not None:
          self.cpu_budget = pb.cpu_budget
      if pb.WhichOneof("optional_ram_budget") is not None:
          self.ram_budget = pb.ram_budget
      if pb.WhichOneof("optional_autotune_algorithm") is not None:
          self.autotune_algorithm = AutotuneAlgorithm._from_proto(  # pylint: disable=protected-access
              pb.autotune_algorithm
          )
  
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 2 for calling the AutotuneOptions obj's underlying member method code with example usage: obj._from_proto(pb) without return types