code: def __init__(self, in_feats, n_hidden, n_classes):
      super().__init__()
      self.layers = nn.ModuleList()
      self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
      self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
      self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
      self.dropout = nn.Dropout(0.5)
  
file_path: dgl/examples/pytorch/cluster_gcn/cluster_gcn.py
imports: import time
  import dgl
  import dgl.nn
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  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_feats, n_hidden, n_classes)