old_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  r"""Benchmark base to run and report benchmark results."""
  
  from __future__ import absolute_import as _absolute_import
  from __future__ import division as _division
  from __future__ import print_function as _print_function
  
  from tensorflow.python.eager import test
  
  
  class MicroBenchmarksBase(test.Benchmark):
    """Run and report benchmark results."""
  
    def run_report(self, run_benchmark, func, num_iters, execution_mode=None):
      """Run and report benchmark results."""
      total_time = run_benchmark(func, num_iters, execution_mode)
      mean_us = total_time * 1e6 / num_iters
      extras = {
          "examples_per_sec": float("{0:.3f}".format(num_iters / total_time)),
          "us_per_example": float("{0:.3f}".format(total_time * 1e6 / num_iters))
      }
      benchmark_name = self._get_benchmark_name()
      self.report_benchmark(
          iters=num_iters, wall_time=mean_us, extras=extras, name=benchmark_name)
  
new_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  r"""Benchmark base to run and report benchmark results."""
  
  from __future__ import absolute_import as _absolute_import
  from __future__ import division as _division
  from __future__ import print_function as _print_function
  
  import os
  import uuid
  
  from tensorflow.python.eager import test
  from tensorflow.python.platform import flags
  from tensorflow.python.profiler import profiler_v2 as profiler
  
  flags.DEFINE_bool("xprof", False, "Run and report benchmarks with xprof on")
  flags.DEFINE_string("logdir", "/tmp/xprof/", "Directory to store xprof data")
  
  
  class MicroBenchmarksBase(test.Benchmark):
    """Run and report benchmark results.
  
    The first run is without any profilng.
    Second run is with xprof and python trace. Third run is with xprof without
    python trace. Note: xprof runs are with fewer iterations.
    """
  
    def run_with_xprof(self, enable_python_trace, run_benchmark, func,
                       num_iters_xprof, execution_mode, suid):
      if enable_python_trace:
        options = profiler.ProfilerOptions(python_tracer_level=1)
        logdir = os.path.join(flags.FLAGS.logdir, suid + "_with_python")
      else:
        options = profiler.ProfilerOptions(python_tracer_level=0)
        logdir = os.path.join(flags.FLAGS.logdir, suid)
      with profiler.Profile(logdir, options):
        total_time = run_benchmark(func, num_iters_xprof, execution_mode)
      us_per_example = float("{0:.3f}".format(total_time * 1e6 / num_iters_xprof))
      return logdir, us_per_example
  
    def run_report(self, run_benchmark, func, num_iters, execution_mode=None):
      """Run and report benchmark results."""
      total_time = run_benchmark(func, num_iters, execution_mode)
      mean_us = total_time * 1e6 / num_iters
      extras = {
          "examples_per_sec": float("{0:.3f}".format(num_iters / total_time)),
          "us_per_example": float("{0:.3f}".format(total_time * 1e6 / num_iters))
      }
  
      if flags.FLAGS.xprof:
        suid = str(uuid.uuid4())
        # Re-run with xprof and python trace.
        num_iters_xprof = min(100, num_iters)
        xprof_link, us_per_example = self.run_with_xprof(True, run_benchmark,
                                                         func, num_iters_xprof,
                                                         execution_mode, suid)
        extras["xprof link with python trace"] = xprof_link
        extras["us_per_example with xprof and python"] = us_per_example
  
        # Re-run with xprof but no python trace.
        xprof_link, us_per_example = self.run_with_xprof(False, run_benchmark,
                                                         func, num_iters_xprof,
                                                         execution_mode, suid)
        extras["xprof link"] = xprof_link
        extras["us_per_example with xprof"] = us_per_example
  
      benchmark_name = self._get_benchmark_name()
      self.report_benchmark(
          iters=num_iters, wall_time=mean_us, extras=extras, name=benchmark_name)
  
subject: add benchmarks_test option to capture profiles