code: def test_num_channels(self):
      auto_tcn = AutoTCN(
          input_feature_num=input_feature_dim,
          output_target_num=output_feature_dim,
          past_seq_len=past_seq_len,
          future_seq_len=future_seq_len,
          optimizer="Adam",
          loss=torch.nn.MSELoss(),
          metric="mse",
          hidden_units=4,
          levels=hp.randint(1, 3),
          num_channels=[8] * 2,
          kernel_size=hp.choice([2, 3]),
          lr=hp.choice([0.001, 0.003, 0.01]),
          dropout=hp.uniform(0.1, 0.2),
          logs_dir="/tmp/auto_tcn",
          cpus_per_trial=2,
          name="auto_tcn",
      )
      auto_tcn.fit(
          data=train_dataloader_creator,
          epochs=1,
          batch_size=hp.choice([32, 64]),
          validation_data=valid_dataloader_creator,
          n_sampling=1,
      )
      assert auto_tcn.get_best_model()
      best_config = auto_tcn.get_best_config()
      assert best_config["num_channels"] == [8] * 2
  
imports: from torch.utils.data import Dataset, DataLoader
  import torch
  import numpy
  from unittest import TestCase
  import pytest
  import tempfile
  from zoo.chronos.autots.model.auto_tcn import AutoTCN
  from zoo.orca.automl import hp
label_desc: Use image node_id 6 for calling the TestAutoTCN obj's underlying member method code with example usage: obj.test_num_channels() without return types