class_docstr: Represents the type of autotuning algorithm to use.
  
  DEFAULT: The default behavior is implementation specific and may change over
  time.
  
  HILL_CLIMB: In each optimization step, this algorithm chooses the optimial
  parameter and increases its value by 1.
  
  GRADIENT_DESCENT: In each optimization step, this algorithm updates the
  parameter values in the optimal direction.
  
  MAX_PARALLELISM: Similar to HILL_CLIMB but uses a relaxed stopping condition,
  allowing the optimization to oversubscribe the CPU.
  
  STAGE_BASED: In each optimization step, this algorithm chooses the worst
  bottleneck parameter and increases its value by 1.
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code: def _to_proto(cls, obj):
      if obj == cls.DEFAULT:
          return model_pb2.AutotuneAlgorithm.DEFAULT
      if obj == cls.HILL_CLIMB:
          return model_pb2.AutotuneAlgorithm.HILL_CLIMB
      if obj == cls.GRADIENT_DESCENT:
          return model_pb2.AutotuneAlgorithm.GRADIENT_DESCENT
      if obj == cls.MAX_PARALLELISM:
          return model_pb2.AutotuneAlgorithm.MAX_PARALLELISM
      if obj == cls.STAGE_BASED:
          return model_pb2.AutotuneAlgorithm.STAGE_BASED
      raise ValueError(
          f"Invalid `obj.` Supported values include `DEFAULT`, `HILL_CLIMB` "
          f"`GRADIENT_DESCENT`, and `STAGE_BASED`. Got {obj.name}."
      )
  
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 AutotuneAlgorithm obj's underlying member method code with example usage: obj._to_proto(cls, obj) and returns: model_pb2, model_pb2, model_pb2, model_pb2, model_pb2