code: def test_tcn_adding():
      """
      Sanity check -
      Test function checks to make sure training loss drops to ~0 on small dummy dataset
      """
      n_features = 2
      hidden_sizes = [64] * 3
      kernel_size = 3
      dropout = 0.0
      seq_len = 10
      n_train = 5000
      n_val = 100
      batch_size = 32
      n_epochs = 10
      num_iterations = int(n_train * n_epochs * 1.0 / batch_size)
      lr = 0.002
      grad_clip_value = 10
      results_dir = "./"
  
      adding_dataset = Adding(
          seq_len=seq_len, n_train=n_train, n_test=n_val
      )
  
      model = TCNForAdding(
          seq_len,
          n_features,
          hidden_sizes,
          kernel_size=kernel_size,
          dropout=dropout,
      )
  
      model.build_train_graph(lr, max_gradient_norm=grad_clip_value)
  
      training_loss = model.run(
          adding_dataset,
          num_iterations=num_iterations,
          log_interval=1e6,
          result_dir=results_dir,
      )
  
      assert training_loss < 1e-3
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_tcn_adding() without return types