code: def __init__(
      self,
      in_channels: int,
      hidden_channels: int,
      out_channels: int,
      num_layers: int = 2,
  ):
      super().__init__()
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(SAGEConv(in_channels, hidden_channels))
      for _ in range(num_layers - 2):
          self.convs.append(SAGEConv(hidden_channels, hidden_channels))
      self.convs.append(SAGEConv(hidden_channels, out_channels))
  
file_path: pytorch_geometric/examples/multi_gpu/distributed_sampling.py
imports: import copy
  import os
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import SAGEConv
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)