code: def test_hierarchical_cnn_encoders(use_residual, hybridize) -> None:
      num_ts = 2
      ts_len = 10
      num_static_feat = 2
      num_dynamic_feat = 5
  
      test_data = nd.arange(num_ts * ts_len).reshape(
          shape=(num_ts, ts_len, 1)
      )
      test_static_feat = nd.random.randn(num_ts, num_static_feat)
      test_dynamic_feat = nd.random.randn(
          num_ts, ts_len, num_dynamic_feat
      )
  
      chl_dim = [30, 30, 30]
      ks_seq = [3] * len(chl_dim)
      dial_seq = [1, 3, 9]
  
      cnn = HierarchicalCausalConv1DEncoder(
          dial_seq,
          ks_seq,
          chl_dim,
          use_residual,
          use_dynamic_feat=True,
          use_static_feat=True,
      )
      cnn.collect_params().initialize()
  
      if hybridize:
          cnn.hybridize()
  
      true_shape = (
          (num_ts, ts_len, 31) if use_residual else (num_ts, ts_len, 30)
      )
  
      assert (
          cnn(test_data, test_static_feat, test_dynamic_feat)[1].shape
          == true_shape
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_hierarchical_cnn_encoders(use_residual, hybridize) without return types