code: def test_lagged_subsequences():
      N = 8
      T = 96
      C = 2
      lags = [1, 2, 3, 24, 48]
      I = len(lags)
      sequence = mx.nd.random.normal(shape=(N, T, C))
      S = 48
  
      # (batch_size, sub_seq_len, target_dim, num_lags)
      lagged_subsequences = (
          DeepARTrainingNetwork.get_lagged_subsequences(
              F=mx.nd,
              sequence=sequence,
              sequence_length=sequence.shape[1],
              indices=lags,
              subsequences_length=S,
          )
      )
  
      assert (N, S, C, I) == lagged_subsequences.shape
  
      # checks that lags value behave as described as in the get_lagged_subsequences contract
      for i, j, k in itertools.product(range(N), range(S), range(I)):
          assert (
              (
                  lagged_subsequences[i, j, :, k]
                  == sequence[i, -lags[k] - S + j, :]
              )
              .asnumpy()
              .all()
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_lagged_subsequences() without return types