class_docstr: Benchmarks for autotuning performance knobs.
class_docstr_tok: ["Benchmarks","for","autotuning","performance","knobs","."]
code: def _benchmark_batch(self, autotune, benchmark_id):
      batch_size = 128
      k = 1024
      dataset = dataset_ops.Dataset.from_tensors(
          (np.random.rand(1, 4 * k), np.random.rand(4 * k, 1))
      ).repeat()
      dataset = dataset.map(math_ops.matmul)
      dataset = dataset.batch(
          batch_size=batch_size, num_parallel_calls=dataset_ops.AUTOTUNE
      )
      return self._run_benchmark(
          dataset=dataset,
          autotune=autotune,
          benchmark_iters=10000,
          benchmark_label="batch",
          benchmark_id=benchmark_id,
      )
  
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 3 for calling the AutotuneBenchmark obj's underlying member method code with example usage: obj._benchmark_batch(autotune, benchmark_id) and returns: self