code: def train_gnn(
      num_workers=2,
      use_gpu=False,
      epochs=3,
      global_batch_size=32,
      dataset="reddit",
  ):
      per_worker_batch_size = global_batch_size // num_workers
  
      trainer = TorchTrainer(
          train_loop_per_worker=train_loop_per_worker,
          train_loop_config={
              "num_epochs": epochs,
              "batch_size": per_worker_batch_size,
              "dataset_fn": gen_reddit_dataset
              if dataset == "reddit"
              else gen_fake_dataset(),
          },
          scaling_config=ScalingConfig(
              num_workers=num_workers, use_gpu=use_gpu
          ),
      )
      result = trainer.fit()
      print(result.metrics)
  
imports: import argparse
  import os
  import torch
  import torch.nn.functional
  from filelock import FileLock
  from torch_geometric.datasets import FakeDataset, Reddit
  from torch_geometric.loader import NeighborSampler
  from torch_geometric.nn import SAGEConv
  from torch_geometric.transforms import RandomNodeSplit
  from ray import train
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 4 for calling a global function with example usage: train_gnn(num_workers, use_gpu, epochs, global_batch_size, dataset) without return types