code: def test_wavenet_forward(model_dict):
      batch_size = 2
      batch_length = 128
      args = make_wavenet_args(**model_dict)
      if args["use_first_conv"]:
          y = torch.randn(batch_size, 1, batch_length)
      else:
          y = torch.randn(
              batch_size, args["residual_channels"], batch_length
          )
      c, g = None, None
      if args["aux_channels"] > 0:
          c = torch.randn(
              batch_size, args["aux_channels"], batch_length
          )
      if args["global_channels"] > 0:
          g = torch.randn(batch_size, args["global_channels"], 1)
      model = WaveNet(**args)
      out = model(y, c=c, g=g)
      if args["use_last_conv"]:
          out.size(1) == args["out_channels"]
      else:
          out.size(1) == args["skip_channels"]
  
imports: import pytest
  import torch
  from espnet2.gan_tts.wavenet import WaveNet
label_desc: Use image node_id 2 for calling a global function with example usage: test_wavenet_forward(model_dict) without return types