code: def profile_tf_runningmeanstd():
      import time
      from baselines.common import tf_util
  
      tf_util.get_session(
          config=tf.ConfigProto(
              inter_op_parallelism_threads=1,
              intra_op_parallelism_threads=1,
              allow_soft_placement=True,
          )
      )
  
      x = np.random.random((376,))
  
      n_trials = 10000
      rms = RunningMeanStd()
      tfrms = TfRunningMeanStd()
  
      tic1 = time.time()
      for _ in range(n_trials):
          rms.update(x)
  
      tic2 = time.time()
      for _ in range(n_trials):
          tfrms.update(x)
  
      tic3 = time.time()
  
      print(
          "rms update time ({} trials): {} s".format(
              n_trials, tic2 - tic1
          )
      )
      print(
          "tfrms update time ({} trials): {} s".format(
              n_trials, tic3 - tic2
          )
      )
  
      tic1 = time.time()
      for _ in range(n_trials):
          z1 = rms.mean
  
      tic2 = time.time()
      for _ in range(n_trials):
          z2 = tfrms.mean
  
      assert z1 == z2
  
      tic3 = time.time()
  
      print(
          "rms get mean time ({} trials): {} s".format(
              n_trials, tic2 - tic1
          )
      )
      print(
          "tfrms get mean time ({} trials): {} s".format(
              n_trials, tic3 - tic2
          )
      )
  
      """
      options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) #pylint: disable=E1101
      run_metadata = tf.RunMetadata()
      profile_opts = dict(options=options, run_metadata=run_metadata)
  
  
  
      from tensorflow.python.client import timeline
      fetched_timeline = timeline.Timeline(run_metadata.step_stats) #pylint: disable=E1101
      chrome_trace = fetched_timeline.generate_chrome_trace_format()
      outfile = '/tmp/timeline.json'
      with open(outfile, 'wt') as f:
          f.write(chrome_trace)
      print('Successfully saved profile to {}. Exiting.'.format(outfile))
      exit(0)
      """
  
file_path: baselines/baselines/common/running_mean_std.py
imports: import tensorflow
  import numpy
  from baselines.common.tf_util import get_session
label_desc: Use image node_id 4 for calling a global function with example usage: profile_tf_runningmeanstd() without return types