code: def __init__(self, in_size, hid_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, hid_size, "mean"))
      self.hid_size = hid_size
      self.predictor = nn.Sequential(
          nn.Linear(hid_size, hid_size),
          nn.ReLU(),
          nn.Linear(hid_size, hid_size),
          nn.ReLU(),
          nn.Linear(hid_size, 1),
      )
  
file_path: dgl/examples/pytorch/graphsage/link_pred.py
imports: import argparse
  import dgl
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, negative_sampler, NeighborSampler
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
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)