code: def download_only_test(
      test_script,
      test_hparam,
      test_flag,
      test_check,
      run_opts,
      run_tests_with_checks_only,
      output_folder,
  ):
      """Downloads only the needed data (useful for off-line tests).
  
      Arguments
      ---------
      test_script: dict
          A Dictionary containing recipe IDs as keys and test_scripts as values.
      test_hparam: dict
          A dictionary containing recipe IDs as keys and hparams as values.
      test_flag: dict
          A dictionary containing recipe IDs as keys and the test flags as values.
      test_check: dict
          A dictionary containing recipe IDs as keys and the checks as values.
      run_opts: str
          Running options to append to each test.
      run_tests_with_checks_only: str
              Running options to append to each test.
      run_tests_with_checks_only: bool
          If True skips all tests that do not have performance check criteria defined.
      output_folder: path
          The output folder where to store all the test outputs.
      """
  
      for i, recipe_id in enumerate(test_script.keys()):
          # If we are interested in performance checks only, skip
          check_str = test_check[recipe_id].strip()
          if run_tests_with_checks_only:
              if len(check_str) == 0:
                  continue
  
          print(
              "(%i/%i) Collecting pretrained models for %s..."
              % (i + 1, len(test_script.keys()), recipe_id)
          )
  
          output_fold = os.path.join(output_folder, recipe_id)
          os.makedirs(output_fold, exist_ok=True)
          stdout_file = os.path.join(output_fold, "stdout.txt")
          stderr_file = os.path.join(output_fold, "stderr.txt")
  
          cmd = (
              "python -c 'import sys;from hyperpyyaml import load_hyperpyyaml;import speechbrain;"
              "hparams_file, run_opts, overrides = speechbrain.parse_arguments(sys.argv[1:]);"
              "fin=open(hparams_file);hparams = load_hyperpyyaml(fin, overrides);fin.close();"
              # 'speechbrain.create_experiment_directory(experiment_directory=hparams["output_folder"],'
              # 'hyperparams_to_save=hparams_file,overrides=overrides,);'
          )
          with open(test_hparam[recipe_id]) as hparam_file:
              for line in hparam_file:
                  if "pretrainer" in line:
                      cmd += 'hparams["pretrainer"].collect_files();hparams["pretrainer"].load_collected(device="cpu");'
                  elif "from_pretrained" in line:
                      field = line.split(":")[0].strip()
                      cmd += f'hparams["{field}"]'
          cmd += (
              "' "
              + test_hparam[recipe_id]
              + " --output_folder="
              + output_fold
              + " "
              + test_flag[recipe_id]
              + " "
              + run_opts
          )
  
          # Prepare the test
          run_test_cmd(cmd, stdout_file, stderr_file)
  
file_path: speechbrain/tests/utils/recipe_tests.py
imports: import os
  import re
  import csv
  import sys
  import pydoc
  from time import time
  import subprocess
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.data_utils import download_file
label_desc: Use image node_id 9 for calling a global function with example usage: download_only_test(test_script, test_hparam, test_flag, test_check, run_opts, run_tests_with_checks_only, output_folder) without return types