code: def train_arguments():
      """Commandline arguments for running DreamBooth training script."""
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--model_dir",
          type=str,
          default=None,
          required=True,
          help="Path to a pretrained huggingface Stable Diffusion model.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          required=True,
          help="Directory where trained models or LoRA weights are saved.",
      )
      parser.add_argument(
          "--use_lora",
          default=False,
          action="store_true",
          help="Use LoRA.",
      )
      parser.add_argument(
          "--instance_images_dir",
          type=str,
          default=None,
          required=True,
          help=(
              "Directory where a few images of the instance to be fine tuned "
              "into the model are saved."
          ),
      )
      parser.add_argument(
          "--instance_prompt",
          type=str,
          default=None,
          required=True,
          help=("Prompt for creating the instance images."),
      )
      parser.add_argument(
          "--class_images_dir",
          type=str,
          default=None,
          required=True,
          help=(
              "Directory where images of similar objects for preserving "
              "model priors are saved."
          ),
      )
      parser.add_argument(
          "--class_prompt",
          type=str,
          default=None,
          required=True,
          help=("Prompt for creating the class images."),
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=1,
          help="Train batch size.",
      )
      parser.add_argument(
          "--lr", type=float, default=5e-6, help="Train learning rate."
      )
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=4,
          help="Number of epochs to train.",
      )
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=800,
          help="Maximum number of fine-tuning update steps to take.",
      )
      parser.add_argument(
          "--prior_loss_weight",
          type=float,
          default=1.0,
          help="The weight for prior preservation loss.",
      )
      parser.add_argument(
          "--max_grad_norm",
          type=float,
          default=1.0,
          help="Maximum gradient norm.",
      )
      parser.add_argument(
          "--num_workers",
          type=int,
          default=2,
          help="Number of workers.",
      )
  
      return parser
  
label_desc: Use image node_id 1 for calling a global function with example usage: train_arguments() and returns: parser