code: def _pretty_print_results(results: dict):
      """Placeholder docstring"""
      avg_latencies = []
      tensor_parallel_degrees = []
      dtypes = []
      p90s = []
      avg_tokens_per_seconds = []
      throughput_per_seconds = []
      standard_deviations = []
      ordered = collections.OrderedDict(sorted(results.items()))
  
      for key, value in ordered.items():
          avg_latencies.append(key)
          tensor_parallel_degrees.append(
              value[0]["option.tensor_parallel_degree"]
          )
          dtypes.append(value[0]["option.dtype"])
          p90s.append(value[1])
          avg_tokens_per_seconds.append(value[2])
          throughput_per_seconds.append(value[3])
          standard_deviations.append(value[4])
  
      df = pd.DataFrame(
          {
              "AverageLatency (Serial)": avg_latencies,
              "P90_Latency (Serial)": p90s,
              "AverageTokensPerSecond (Serial)": avg_tokens_per_seconds,
              "ThroughputPerSecond (Concurrent)": throughput_per_seconds,
              "StandardDeviationResponse (Concurrent)": standard_deviations,
              "TensorParallelDegree": tensor_parallel_degrees,
              "DType": dtypes,
          }
      )
      logger.info(
          "\n================================================================== Benchmark "
          "Results ==================================================================\n%s"
          "\n============================================================================"
          "===========================================================================\n",
          df.to_string(),
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/utils/tuning.py
imports: from __future__ import absolute_import
  import logging
  from time import perf_counter
  import collections
  from multiprocessing.pool import ThreadPool
  from math import ceil
  import pandas
  from numpy import percentile, std
  from sagemaker.serve.model_server.djl_serving.utils import _tokens_from_chars, _tokens_from_words
  from sagemaker.base_predictor import PredictorBase
label_desc: Use image node_id 1 for calling a global function with example usage: _pretty_print_results(results) without return types