code: def __init__(self, in_size, hidden_size=16):
      super().__init__()
      self.layers = nn.ModuleList()
      self.layers.append(SAGEConv(in_size, hidden_size, "mean"))
      self.layers.append(SAGEConv(hidden_size, hidden_size, "mean"))
      self.predictor = nn.Sequential(
          nn.Linear(hidden_size, hidden_size),
          nn.ReLU(),
          nn.Linear(hidden_size, 1),
      )
  
file_path: dgl/examples/sampling/graphbolt/quickstart/link_prediction.py
imports: import dgl.graphbolt
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import SAGEConv
  from torcheval.metrics import BinaryAUROC
label_desc: Use image node_id 1 to create a new GraphSAGE object from inherited base classes: nn with example: obj = GraphSAGE(in_size, hidden_size)