code: def init(
      self, in_feats, n_hidden, n_classes, n_layers, activation, dropout
  ):
      self.n_layers = n_layers
      self.n_hidden = n_hidden
      self.n_classes = n_classes
      self.layers = nn.ModuleList()
      if n_layers > 1:
          self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
          for i in range(1, n_layers - 1):
              self.layers.append(
                  dglnn.SAGEConv(n_hidden, n_hidden, "mean")
              )
          self.layers.append(
              dglnn.SAGEConv(n_hidden, n_classes, "mean")
          )
      else:
          self.layers.append(
              dglnn.SAGEConv(in_feats, n_classes, "mean")
          )
      self.dropout = nn.Dropout(dropout)
      self.activation = activation
  
file_path: dgl/examples/pytorch/labor/model.py
imports: import dgl
  import dgl.nn
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.functional
  import torch.nn
  import tqdm
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.init(in_feats, n_hidden, n_classes, n_layers, activation, dropout) without return types