class_docstr: An implementation of the integrated Gated Graph Convolution Long Short
  Term Memory Layer. For details see this paper: `"Predictive Temporal Embedding
  of Dynamic Graphs." <https://ieeexplore.ieee.org/document/9073186>`_
  
  Args:
      conv_out_channels (int): Number of output channels for the GGCN.
      conv_num_layers (int): Number of Gated Graph Convolutions.
      conv_aggr (str): Aggregation scheme to use
          (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`).
      lstm_out_channels (int): Number of LSTM channels.
      lstm_num_layers (int): Number of neurons in LSTM.
code: def _create_layers(self):
      self.conv_layer = GatedGraphConv(
          out_channels=self.conv_out_channels,
          num_layers=self.conv_num_layers,
          aggr=self.conv_aggr,
          bias=True,
      )
  
      self.recurrent_layer = LSTM(
          input_size=self.conv_out_channels,
          hidden_size=self.lstm_out_channels,
          num_layers=self.lstm_num_layers,
      )
  
imports: import torch
  from torch.nn import LSTM
  from torch_geometric.nn import GatedGraphConv
label_desc: Use image node_id 2 for calling the DyGrEncoder obj's underlying member method code with example usage: obj._create_layers() without return types