code: def create_mock_data(number_of_nodes, edge_per_node, in_channels):
      """
      Creating a mock feature matrix 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, (number_of_nodes, in_channels))
      )
      return X, edge_index
  
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 1 for calling a global function with example usage: create_mock_data(number_of_nodes, edge_per_node, in_channels) and returns: X,  edge_index