class_docstr: LSTM aggregator for graphsage
class_docstr_tok: ["LSTM","aggregator","for","graphsage"]
docstr: aggregation function
docstr_tok: ["aggregation","function"]
code: def aggre(self, neighbours):
      """
      aggregation function
      """
      # N X F
      rand_order = torch.randperm(neighbours.size()[1])
      neighbours = neighbours[:, rand_order, :]
  
      (lstm_out, self.hidden) = self.lstm(
          neighbours.view(
              neighbours.size()[0], neighbours.size()[1], -1
          )
      )
      return lstm_out[:, -1, :]
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the LSTMAggregator obj's underlying member method code with example usage: obj.aggre(neighbours) and returns: lstm_out