code: def parse_benchmark_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          default="facebook/opt-125m",
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--plugin",
          type=str,
          default="gemini",
          help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=32,
          help="Batch size (per dp group) for the training dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=20,
          help="Total number of training steps to perform.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--mem_cap",
          type=int,
          default=0,
          help="Limit on the usage of space for each GPU (in GB).",
      )
      args = parser.parse_args()
  
      return args
  
label_desc: Use image node_id 2 for calling a global function with example usage: parse_benchmark_args() and returns: args