code: def __init__(self, in_size, hid_size, out_size):
      super().__init__()
      self.layers = nn.ModuleList()
      # three-layer GraphSAGE-mean
      self.layers.append(dglnn.SAGEConv(in_size, hid_size, "mean"))
      self.layers.append(dglnn.SAGEConv(hid_size, hid_size, "mean"))
      self.layers.append(dglnn.SAGEConv(hid_size, out_size, "mean"))
      self.dropout = nn.Dropout(0.5)
      self.hid_size = hid_size
      self.out_size = out_size
  
imports: import warnings
  import time
  import cugraph_dgl
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import dgl
  import dgl.nn
  from dgl.data import AsNodePredDataset
  from dgl.dataloading import DataLoader, NeighborSampler, MultiLayerFullNeighborSampler
  from ogb.nodeproppred import DglNodePropPredDataset
  import tqdm
  import argparse
label_desc: Use image node_id 1 to create a new SAGE object from inherited base classes: nn with example: obj = SAGE(in_size, hid_size, out_size)