code: def get_grid(args):
      max_update = 22000
  
      return [
          hyperparam("run_type", "train_val"),
          hyperparam(
              "config",
              "projects/visual_bert/configs/vqa2/defaults.yaml",
          ),
          # hyperparam("--fp16", save_dir_key=lambda val: "fp16"),
          hyperparam("training.num_workers", 5),
          hyperparam("dataset", "vqa2"),
          hyperparam(
              "model", "visual_bert", save_dir_key=lambda val: val
          ),
          # For nlvr2, we are able to fit batch of size 16 on single GPU with 16GB
          # memory. Same number is 32 for VQA2, so scale accordingly
          hyperparam(
              "training.batch_size",
              [512, 256],
              save_dir_key=lambda val: f"bs{val}",
          ),
          hyperparam(
              "training.seed", 1, save_dir_key=lambda val: f"s{val}"
          ),
          hyperparam("scheduler.type", ["warmup_cosine"]),
          hyperparam("scheduler.params.num_warmup_steps", 2000),
          hyperparam("scheduler.params.num_training_steps", max_update),
          hyperparam(
              "optimizer.type", "adam_w", save_dir_key=lambda val: val
          ),
          hyperparam(
              "optimizer.params.lr",
              [5e-5, 1e-5],
              save_dir_key=lambda val: f"lr{val}",
          ),
          hyperparam("optimizer.params.eps", 1e-8),
          hyperparam(
              "training.max_updates",
              max_update,
              save_dir_key=lambda val: f"mu{val}",
          ),
          hyperparam("training.log_format", "json"),
          hyperparam("training.pin_memory", True),
          hyperparam("training.log_interval", 1000),
          hyperparam("training.checkpoint_interval", 1000),
          hyperparam("training.evaluation_interval", 4000),
          hyperparam("training.find_unused_parameters", True),
          hyperparam(
              "model_config.visual_bert.freeze_base",
              [False],
              save_dir_key=lambda val: f"fb{val}",
          ),
      ]
  
imports: import lib
  from lib import hyperparam
label_desc: Use image node_id 1 for calling a global function with example usage: get_grid(args) and returns: list