code: def test_dnntsp():
      model = DNNTSP(items_total=100, item_embedding_dim=16, n_heads=4)
  
      g = nx.watts_strogatz_graph(1000, 10, 0.4)
  
      edges = torch.LongTensor(
          np.array([[edge[0], edge[1]] for edge in g.edges()])
      ).T
  
      edge_weight = torch.FloatTensor(np.random.uniform(0, 1, (5000,)))
  
      node_features = torch.FloatTensor(
          np.random.uniform(0, 1, (1000, 16))
      )
  
      z = model(node_features, edges, edge_weight)
  
      assert z.shape == (10, 100, 16)
  
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 13 for calling a global function with example usage: test_dnntsp() without return types