code: def __init__(self, in_size, hid_size, out_size):
      super().__init__()
      self.layers = nn.ModuleList()
      # two-layer GraphSAGE-mean
      self.layers.append(dglnn.SAGEConv(in_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 argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  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, hid_size, out_size)