code: def _prepare_markdown(
      results, runtime_features=None, profiler="native"
  ):
      results_markdown = []
      if runtime_features and "runtime_features" in runtime_features:
          results_markdown.append("# Runtime Features")
          idx = 0
          for key, value in runtime_features[
              "runtime_features"
          ].items():
              results_markdown.append(f"{idx}. {key} : {value}")
  
      results_markdown.append("# Benchmark Results")
      if profiler == "native":
          results_markdown.append(
              "| Operator | Inputs | Max Mem Usage (Storage) (Bytes) | Avg Forward Time (ms)"
              " | Avg. Backward Time (ms) |"
          )
          results_markdown.append(
              "| :---: | :---: | :---: | :---: | :---: |"
          )
      elif profiler == "python":
          results_markdown.append(
              "| Operator | Avg Time (ms) | P50 Time (ms) | P90 Time (ms) | P99 Time (ms) | Inputs |"
          )
          results_markdown.append(
              "| :---: | :---: | :---: | :---: | :---: | :---: |"
          )
  
      for op, op_bench_results in sorted(
          results.items(), key=itemgetter(0)
      ):
          for op_bench_result in op_bench_results:
              results_markdown.append(
                  _prepare_op_benchmark_result(
                      op, op_bench_result, profiler
                  )
              )
  
      return os.linesep.join(results_markdown)
  
file_path: mxnet/benchmark/opperf/utils/common_utils.py
imports: import os
  import json
  from operator import itemgetter
  import logging
label_desc: Use image node_id 5 for calling a global function with example usage: _prepare_markdown(results, runtime_features, profiler) and returns: os