code: def __init__(self, in_size, hidden_size, out_size):
      super().__init__()
      self.layers = nn.ModuleList()
      # Three-layer GraphSAGE-mean.
      self.layers.append(dglnn.SAGEConv(in_size, hidden_size, "mean"))
      self.layers.append(
          dglnn.SAGEConv(hidden_size, hidden_size, "mean")
      )
      self.layers.append(dglnn.SAGEConv(hidden_size, out_size, "mean"))
      self.dropout = nn.Dropout(0.5)
      self.hidden_size = hidden_size
      self.out_size = out_size
      # Set the dtype for the layers manually.
      self.set_layer_dtype(torch.float32)
  
imports: import os
  import time
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from torch.distributed.algorithms.join import Join
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 to create a new SAGE object from inherited base classes: nn with example: obj = SAGE(in_size, hidden_size, out_size)