class_docstr: An implementation of the Message Passing Neural Network with Long Short Term Memory.
  For details see this paper: `"Transfer Graph Neural Networks for Pandemic Forecasting." <https://arxiv.org/abs/2009.08388>`_
  
  Args:
      in_channels (int): Number of input features.
      hidden_size (int): Dimension of hidden representations.
      num_nodes (int): Number of nodes in the network.
      window (int): Number of past samples included in the input.
      dropout (float): Dropout rate.
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code: def _create_parameters_and_layers(self):
      self._convolution_1 = GCNConv(self.in_channels, self.hidden_size)
      self._convolution_2 = GCNConv(self.hidden_size, self.hidden_size)
  
      self._batch_norm_1 = nn.BatchNorm1d(self.hidden_size)
      self._batch_norm_2 = nn.BatchNorm1d(self.hidden_size)
  
      self._recurrent_1 = nn.LSTM(
          2 * self.hidden_size, self.hidden_size, 1
      )
      self._recurrent_2 = nn.LSTM(self.hidden_size, self.hidden_size, 1)
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/recurrent/mpnn_lstm.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 2 for calling the MPNNLSTM obj's underlying member method code with example usage: obj._create_parameters_and_layers() without return types