code: def __init__(
      self,
      in_feats,
      n_hidden,
      n_classes,
      n_layers,
      activation,
      dropout,
      aggregator_type,
  ):
      super(GraphSAGE, self).__init__()
      self.layers = nn.ModuleList()
      self.dropout = nn.Dropout(dropout)
      self.activation = activation
  
      # input layer
      self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type))
      # hidden layers
      for i in range(n_layers - 1):
          self.layers.append(
              SAGEConv(n_hidden, n_hidden, aggregator_type)
          )
      # output layer
      self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type))
  
file_path: dgl/benchmarks/benchmarks/model_acc/bench_sage.py
imports: import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import SAGEConv
  from ..None import utils
label_desc: Use image node_id 1 to create a new GraphSAGE object from inherited base classes: nn with example: obj = GraphSAGE(in_feats, n_hidden, n_classes, n_layers, activation, dropout, aggregator_type)