code: def arg_parse():
      parser = argparse.ArgumentParser(
          description="Sampling arguments."
      )
  
      parser.add_argument(
          "--device", type=str, help="CPU / GPU device."
      )
      parser.add_argument(
          "--epochs", type=int, help="Number of epochs to train."
      )
      parser.add_argument(
          "--num_layers", type=int, help="Number of GNN layers."
      )
      parser.add_argument(
          "--hidden_size",
          type=int,
          help="GNN layer hidden dimension size.",
      )
      parser.add_argument(
          "--dropout", type=float, help="The dropout ratio."
      )
      parser.add_argument("--lr", type=float, help="Learning rate.")
  
      parser.set_defaults(
          device="cuda:0",
          epochs=150,
          hidden_size=64,
          num_layers=2,
          dropout=0.5,
          lr=0.005,
      )
      return parser.parse_args()
  
file_path: deepsnap/examples/sampling/cluster.py
imports: import copy
  import torch
  import random
  import argparse
  import numpy
  import networkx
  import torch.nn
  import torch.nn.functional
  import community
  from torch_geometric.nn import SAGEConv
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.graph import Graph
  from model import *
label_desc: Use image node_id 1 for calling a global function with example usage: arg_parse() and returns: parser