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
  
file_path: dgl/examples/pytorch/graphsage/node_classification.py
imports: import argparse
  import dgl
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from dgl.data import AsNodePredDataset
  from dgl.dataloading import DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from ogb.nodeproppred import DglNodePropPredDataset
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)