code: def get_predefined_args() -> argparse.ArgumentParser:
      argparser = argparse.ArgumentParser(
          "GNN distributed (DDP) training benchmark"
      )
      add = argparser.add_argument
  
      add(
          "--dataset",
          choices=["ogbn-mag", "ogbn-products", "Reddit"],
          default="Reddit",
          type=str,
      )
      add(
          "--model",
          choices=[
              "edge_cnn",
              "gat",
              "gcn",
              "pna",
              "rgat",
              "rgcn",
              "sage",
          ],
          default="sage",
          type=str,
      )
      add(
          "--root",
          default="../../data",
          type=str,
          help="relative path to look for the datasets",
      )
      add("--batch-size", default=4096, type=int)
      add("--num-layers", default=3, type=int)
      add("--num-hidden-channels", default=128, type=int)
      add(
          "--num-heads",
          default=2,
          type=int,
          help="number of hidden attention heads, applies only for gat and rgat",
      )
      add(
          "--num-neighbors",
          default=[10],
          type=ast.literal_eval,
          help="number of neighbors to sample per layer",
      )
      add("--num-workers", default=0, type=int)
      add("--num-epochs", default=1, type=int)
      add("--evaluate", action="store_true")
  
      return argparser
  
file_path: pytorch_geometric/benchmark/multi_gpu/training/common.py
imports: import argparse
  import ast
  from time import perf_counter
  from typing import Any, Callable, Tuple, Union
  import torch
  import torch.distributed
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from benchmark.utils import get_model, get_split_masks, test
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import PNAConv
label_desc: Use image node_id 6 for calling a global function with example usage: get_predefined_args() and returns: argparser