code: def create_mock_attention_batch_data(
      number_of_nodes, edge_per_node, in_channels, periods, batch_size
  ):
      """
      Creating a mock stacked feature matrix in batches and edge index.
      """
      graph = nx.watts_strogatz_graph(
          number_of_nodes, edge_per_node, 0.5
      )
      edge_index = torch.LongTensor(
          np.array([edge for edge in graph.edges()]).T
      )
      X = torch.FloatTensor(
          np.random.uniform(
              -1, 1, (batch_size, number_of_nodes, in_channels, periods)
          )
      )
      return X, edge_index
  
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 3 for calling a global function with example usage: create_mock_attention_batch_data(number_of_nodes, edge_per_node, in_channels, periods, batch_size) and returns: X,  edge_index