code: def gen_fake_dataset():
      """Returns a function to be called on each worker that returns a Fake Dataset."""
  
      # For fake dataset, since the dataset is randomized, we create it once on the
      # driver, and then send the same dataset to all the training workers.
      # Use 10% of nodes for validation and 10% for testing.
      fake_dataset = FakeDataset(
          transform=RandomNodeSplit(num_val=0.1, num_test=0.1)
      )
  
      def gen_dataset():
          return fake_dataset
  
      return gen_dataset
  
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 2 for calling a global function with example usage: gen_fake_dataset() and returns: gen_dataset, fake_dataset