code: def create_mock_sequence(
      sequence_length,
      number_of_nodes,
      edge_per_node,
      in_channels,
      number_of_classes,
  ):
      """
      Creating mock sequence data
  
      Note that this is a static graph discrete signal type sequence
      The target is the "next" item in the sequence
      """
      input_sequence = torch.zeros(
          sequence_length, number_of_nodes, in_channels
      )
  
      X, edge_index = create_mock_data(
          number_of_nodes=number_of_nodes,
          edge_per_node=edge_per_node,
          in_channels=in_channels,
      )
      edge_weight = create_mock_edge_weight(edge_index)
      targets = create_mock_target(number_of_nodes, number_of_classes)
  
      for t in range(sequence_length):
          input_sequence[t] = X + t
  
      return input_sequence, targets, edge_index, edge_weight
  
imports: import torch
  import math
  import numpy
  import networkx
  from torch_geometric.data import Data
  from torch_geometric.utils import barabasi_albert_graph
  from torch_geometric.transforms import LaplacianLambdaMax
  from torch_geometric_temporal.nn.attention import TemporalConv, STConv, ASTGCN, MSTGCN, MTGNN, ChebConvAttention, AAGCN, GraphAAGCN, DNNTSP
  from torch_geometric_temporal.nn.attention import GMAN, SpatioTemporalAttention, SpatioTemporalEmbedding
label_desc: Use image node_id 4 for calling a global function with example usage: create_mock_sequence(sequence_length, number_of_nodes, edge_per_node, in_channels, number_of_classes) and returns: input_sequence,  targets,  edge_index,  edge_weight