code: def __init__(
      self, in_channels, hidden_channels, out_channels, num_layers=2
  ):
      super().__init__()
      self.num_layers = num_layers
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(SAGEConv(in_channels, hidden_channels))
      for _ in range(self.num_layers - 2):
          self.convs.append(SAGEConv(hidden_channels, hidden_channels))
      self.convs.append(SAGEConv(hidden_channels, out_channels))
  
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 1 to create a new SAGE object from inherited base classes: torch.nn with example: obj = SAGE(in_channels, hidden_channels, out_channels, num_layers)