code: def generate_csv_output():
      click.secho("*Generating CSV output...", fg="green")
      batched_requests = (
          execution_params["requests"] / execution_params["batch_size"]
      )
      line50 = int(batched_requests / 2)
      line90 = int(batched_requests * 9 / 10)
      line99 = int(batched_requests * 99 / 100)
  
      click.secho(
          f"Saving benchmark results to {execution_params['report_location']}"
      )
  
      artifacts = {}
      with open(execution_params["result_file"]) as f:
          data = f.readlines()
  
      artifacts["Benchmark"] = "AB"
      artifacts["Batch size"] = execution_params["batch_size"]
      artifacts["Batch delay"] = execution_params["batch_delay"]
      artifacts["Workers"] = execution_params["workers"]
      artifacts["Model"] = "[.mar]({})".format(execution_params["url"])
      artifacts["Concurrency"] = execution_params["concurrency"]
      artifacts["Input"] = "[input]({})".format(
          execution_params["input"]
      )
      artifacts["Requests"] = execution_params["requests"]
      artifacts["TS failed requests"] = extract_entity(
          data, "Failed requests:", -1
      )
      artifacts["TS throughput"] = extract_entity(
          data, "Requests per second:", -3
      )
      artifacts["TS latency P50"] = extract_entity(data, "50%", -1)
      artifacts["TS latency P90"] = extract_entity(data, "90%", -1)
      artifacts["TS latency P99"] = extract_entity(data, "99%", -1)
      artifacts["TS latency mean"] = extract_entity(
          data, "Time per request:.*mean\)", -3
      )
      if isinstance(artifacts["TS failed requests"], type(None)):
          artifacts["TS error rate"] = 0.0
      else:
          artifacts["TS error rate"] = (
              int(artifacts["TS failed requests"])
              / execution_params["requests"]
              * 100
          )
  
      with open(
          os.path.join(
              execution_params["tmp_dir"], "benchmark", "predict.txt"
          )
      ) as f:
          lines = f.readlines()
          lines.sort(key=float)
          artifacts["Model_p50"] = lines[line50].strip()
          artifacts["Model_p90"] = lines[line90].strip()
          artifacts["Model_p99"] = lines[line99].strip()
  
      with open(
          os.path.join(
              execution_params["tmp_dir"],
              "benchmark",
              "waiting_time.txt",
          )
      ) as f:
          lines = f.readlines()
          lines.sort(key=float)
          num_requests = len(lines)
          line50 = int(num_requests / 2)
          line90 = int(num_requests * 9 / 10)
          line99 = int(num_requests * 99 / 100)
          artifacts["Queue time p50"] = lines[line50].strip()
          artifacts["Queue time p90"] = lines[line90].strip()
          artifacts["Queue time p99"] = lines[line99].strip()
  
      for m in metrics:
          df = pd.read_csv(
              os.path.join(
                  *(execution_params["tmp_dir"], "benchmark", m)
              ),
              header=None,
              names=["data"],
          )
          if df.empty:
              artifacts[m.split(".txt")[0] + "_mean"] = 0.0
          else:
              artifacts[m.split(".txt")[0] + "_mean"] = (
                  df["data"].values.mean().round(2)
              )
  
      with open(
          os.path.join(
              execution_params["report_location"],
              "benchmark",
              "ab_report.csv",
          ),
          "w",
      ) as csv_file:
          csvwriter = csv.writer(csv_file)
          csvwriter.writerow(artifacts.keys())
          csvwriter.writerow(artifacts.values())
  
      return artifacts
  
imports: import csv
  import json
  import os
  import re
  import shutil
  import sys
  import tempfile
  import time
  from subprocess import PIPE, Popen
  from urllib.parse import urlparse
  import click
  import click_config_file
  import matplotlib.pyplot
  import numpy
  import pandas
  import requests
label_desc: Use image node_id 20 for calling a global function with example usage: generate_csv_output() and returns: artifacts