code: def print_benchmark_results(report):
      r"""
      Prints benchmark results
      Args:
          report (dict): JSON formatted dictionary containing relevant data on the run of this application
      """
      print(
          "--------------------------------------------------------------"
      )
      print(
          "PyTorch distributed rpc benchmark reinforcement learning suite"
      )
      print(
          "--------------------------------------------------------------"
      )
      for key, val in report.items():
          if key != "benchmark_results":
              print(f"{key} : {val}")
  
      x_axis_name = report.get("x_axis_name")
      col_width = 7
      heading = ""
      if x_axis_name:
          x_axis_output_label = f"{x_axis_name} |"
          heading += append_spaces(x_axis_output_label, col_width)
      metric_headers = [
          "agent latency (seconds)",
          "agent throughput",
          "observer latency (seconds)",
          "observer throughput",
      ]
      percentile_subheaders = ["p50", "p75", "p90", "p95"]
      subheading = ""
      if x_axis_name:
          subheading += append_spaces(
              " " * (len(x_axis_output_label) - 1), col_width
          )
      for header in metric_headers:
          heading += append_spaces(
              header, col_width * len(percentile_subheaders)
          )
          for percentile in percentile_subheaders:
              subheading += append_spaces(percentile, col_width)
      print(heading)
      print(subheading)
  
      for benchmark_run in report["benchmark_results"]:
          run_results = ""
          if x_axis_name:
              run_results += append_spaces(
                  benchmark_run[x_axis_name],
                  max(col_width, len(x_axis_output_label)),
              )
          for metric_name in metric_headers:
              percentile_results = benchmark_run[metric_name]
              for percentile in percentile_subheaders:
                  run_results += append_spaces(
                      percentile_results[percentile], col_width
                  )
          print(run_results)
  
file_path: pytorch/benchmarks/distributed/rpc/rl/launcher.py
imports: import argparse
  import json
  import os
  import time
  import torch.distributed.rpc
  import torch.multiprocessing
  from coordinator import CoordinatorBase
label_desc: Use image node_id 5 for calling a global function with example usage: print_benchmark_results(report) without return types