code: def make_args_list(
      n_trials,
      dataset_names,
      algorithms,
      n_hparams_from,
      n_hparams,
      steps,
      data_dir,
      task,
      holdout_fraction,
      single_test_envs,
      hparams,
  ):
      args_list = []
      for trial_seed in range(n_trials):
          for dataset in dataset_names:
              for algorithm in algorithms:
                  if single_test_envs:
                      all_test_envs = [
                          [i]
                          for i in range(
                              datasets.num_environments(dataset)
                          )
                      ]
                  else:
                      all_test_envs = all_test_env_combinations(
                          datasets.num_environments(dataset)
                      )
                  for test_envs in all_test_envs:
                      for hparams_seed in range(
                          n_hparams_from, n_hparams
                      ):
                          train_args = {}
                          train_args["dataset"] = dataset
                          train_args["algorithm"] = algorithm
                          train_args["test_envs"] = test_envs
                          train_args[
                              "holdout_fraction"
                          ] = holdout_fraction
                          train_args["hparams_seed"] = hparams_seed
                          train_args["data_dir"] = data_dir
                          train_args["task"] = task
                          train_args["trial_seed"] = trial_seed
                          train_args["seed"] = misc.seed_hash(
                              dataset,
                              algorithm,
                              test_envs,
                              hparams_seed,
                              trial_seed,
                          )
                          if steps is not None:
                              train_args["steps"] = steps
                          if hparams is not None:
                              train_args["hparams"] = hparams
  
                          args_list.append(train_args)
      return args_list
  
imports: import argparse
  import copy
  import hashlib
  import json
  import os
  import shlex
  import shutil
  import sys
  import numpy
  import tqdm
  from domainbed import algorithms
  from domainbed import command_launchers
  from domainbed import datasets
  from domainbed.lib import misc
label_desc: Use image node_id 2 for calling a global function with example usage: make_args_list(n_trials, dataset_names, algorithms, n_hparams_from, n_hparams, steps, data_dir, task, holdout_fraction, single_test_envs, hparams) and returns: args_list