code: def __init__(self, in_channels, out_channels):
      super(SAGEConv, self).__init__(aggr="add")
  
      self.in_channels = in_channels
      self.out_channels = out_channels
  
      self.lin = nn.Linear(in_channels, out_channels)
      self.lin_update = nn.Linear(
          out_channels + in_channels, out_channels
      )
  
imports: from functools import reduce
  import random
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  import torch_geometric.utils
  import utils
label_desc: Use image node_id 1 to create a new SAGEConv object from inherited base classes: pyg_nn with example: obj = SAGEConv(in_channels, out_channels)