code: def warm_up():
      register_model()
  
      if is_workflow(execution_params["url"]):
          execution_params[
              "inference_model_url"
          ] = "wfpredict/benchmark"
  
      click.secho("\n\nExecuting warm-up ...", fg="green")
  
      ab_cmd = (
          f"ab -c {execution_params['concurrency']} -s 300 -n {execution_params['requests']/10} -k -p "
          f"{execution_params['tmp_dir']}/benchmark/input -T  {execution_params['content_type']} "
          f"{execution_params['inference_url']}/{execution_params['inference_model_url']} > "
          f"{execution_params['result_file']}"
      )
      execute(ab_cmd, wait=True)
  
      warm_up_lines = sum(
          1 for _ in open(execution_params["metric_log"])
      )
  
      return warm_up_lines
  
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 4 for calling a global function with example usage: warm_up() and returns: warm_up_lines