class_docstr: LSTM aggregator for graphsage
class_docstr_tok: ["LSTM","aggregator","for","graphsage"]
code: def __init__(self, in_feats, hidden_feats):
      super(LSTMAggregator, self).__init__()
      self.lstm = nn.LSTM(in_feats, hidden_feats, batch_first=True)
      self.hidden_dim = hidden_feats
      self.hidden = self.init_hidden()
  
      nn.init.xavier_uniform_(
          self.lstm.weight, gain=nn.init.calculate_gain("relu")
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new LSTMAggregator object from inherited base classes: Aggregator with example: obj = LSTMAggregator(in_feats, hidden_feats)