class_docstr: GraphSAGE is a general inductive framework that leverages node feature
  information (e.g., text attributes) to efficiently generate node embeddings
  for previously unseen data.
code: def __init__(
      self, input_size, hidden_size, aggr_type="sum", act=None
  ):
      super(SageConv, self).__init__()
      assert aggr_type in [
          "sum",
          "mean",
          "max",
          "min",
      ], "Only support 'sum', 'mean', 'max', 'min' built-in receive type."
  
      self.aggr_type = aggr_type
      self.self_linear = nn.Linear(input_size, hidden_size)
      self.neigh_linear = nn.Linear(input_size, hidden_size)
  
file_path: PGL/examples/gnn_depoly/models/graphsage.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from .base_conv import BaseConv
label_desc: Use image node_id 1 to create a new SageConv object from inherited base classes: BaseConv with example: obj = SageConv(input_size, hidden_size, aggr_type, act)