code: def test_wavernn():
      model = WaveRNN(
          rnn_dims=512,
          fc_dims=512,
          mode=10,
          mulaw=False,
          pad=2,
          use_aux_net=True,
          use_upsample_net=True,
          upsample_factors=[4, 8, 8],
          feat_dims=80,
          compute_dims=128,
          res_out_dims=128,
          num_res_blocks=10,
          hop_length=256,
          sample_rate=22050,
      )
      dummy_x = torch.rand((2, 1280))
      dummy_m = torch.rand((2, 80, 9))
      y_size = random.randrange(20, 60)
      dummy_y = torch.rand((80, y_size))
      output = model(dummy_x, dummy_m)
      assert np.all(output.shape == (2, 1280, 4 * 256)), output.shape
      output = model.inference(dummy_y, True, 5500, 550)
      assert np.all(output.shape == (256 * (y_size - 1),))
  
imports: import numpy
  import torch
  import random
  from TTS.vocoder.models.wavernn import WaveRNN
label_desc: Use image node_id 1 for calling a global function with example usage: test_wavernn() without return types