class_docstr: An implementation of the Long Short Term Memory Relational
  Graph Convolution Layer. For details see this paper: `"Predicting Path
  Failure In Time-Evolving Graphs." <https://arxiv.org/abs/1905.03994>`_
  
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      num_relations (int): Number of relations.
      num_bases (int): Number of bases.
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code: def _calculate_input_gate(self, X, edge_index, edge_type, H, C):
      I = self.conv_x_i(X, edge_index, edge_type)
      I = I + self.conv_h_i(H, edge_index, edge_type)
      I = torch.sigmoid(I)
      return I
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/recurrent/lrgcn.py
imports: import torch
  from torch.nn import Parameter
  from torch_geometric.nn import RGCNConv
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 9 for calling the LRGCN obj's underlying member method code with example usage: obj._calculate_input_gate(X, edge_index, edge_type, H, C) and returns: I