class_docstr: Benchmarks for autotuning performance knobs.
class_docstr_tok: ["Benchmarks","for","autotuning","performance","knobs","."]
code: def _run_benchmark(
      self,
      dataset,
      autotune,
      benchmark_iters,
      benchmark_label,
      benchmark_id,
  ):
      options = options_lib.Options()
      options.experimental_optimization.apply_default_optimizations = (
          False
      )
      options.autotune.enabled = autotune
      dataset = dataset.with_options(options)
  
      autotune_string = "_autotune_parallelism_only"
      wall_time = self.run_and_report_benchmark(
          dataset=dataset,
          num_elements=benchmark_iters,
          warmup=True,
          iters=1,
          extras={
              "model_name": "autotune.benchmark.%s.%d"
              % (benchmark_label, benchmark_id),
              "parameters": "%s" % autotune,
          },
          name=benchmark_label + (autotune_string if autotune else ""),
      )
      return wall_time
  
file_path: tensorflow/tensorflow/python/data/experimental/benchmarks/autotune_benchmark.py
imports: import numpy
  from tensorflow.python.data.benchmarks import benchmark_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 1 for calling the AutotuneBenchmark obj's underlying member method code with example usage: obj._run_benchmark(dataset, autotune, benchmark_iters, benchmark_label, benchmark_id) and returns: wall_time