code: def test_gconv_lstm_layer():
      """
      Testing the GConvLSTM Layer.
      """
      number_of_nodes = 100
      edge_per_node = 10
      in_channels = 64
      out_channels = 16
      K = 2
  
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      X, edge_index = create_mock_data(
          number_of_nodes, edge_per_node, in_channels
      )
      X = X.to(device)
      edge_index = edge_index.to(device)
      edge_weight = create_mock_edge_weight(edge_index).to(device)
  
      layer = GConvLSTM(
          in_channels=in_channels, out_channels=out_channels, K=K
      ).to(device)
  
      H, C = layer(X, edge_index)
  
      assert H.shape == (number_of_nodes, out_channels)
      assert C.shape == (number_of_nodes, out_channels)
  
      H, C = layer(X, edge_index, edge_weight)
  
      assert H.shape == (number_of_nodes, out_channels)
      assert C.shape == (number_of_nodes, out_channels)
  
      H, C = layer(X, edge_index, edge_weight, H, C)
  
      assert H.shape == (number_of_nodes, out_channels)
      assert C.shape == (number_of_nodes, out_channels)
  
imports: import torch
  import numpy
  import networkx
  from torch_geometric_temporal.nn.recurrent import GConvLSTM, GConvGRU, DCRNN, AGCRN
  from torch_geometric_temporal.nn.recurrent import GCLSTM, LRGCN, DyGrEncoder
  from torch_geometric_temporal.nn.recurrent import EvolveGCNH, EvolveGCNO, TGCN, A3TGCN, A3TGCN2, MPNNLSTM
label_desc: Use image node_id 7 for calling a global function with example usage: test_gconv_lstm_layer() without return types