code: def test_pwgan_generator():
      model = ParallelWaveganGenerator(
          in_channels=1,
          out_channels=1,
          kernel_size=3,
          num_res_blocks=30,
          stacks=3,
          res_channels=64,
          gate_channels=128,
          skip_channels=64,
          aux_channels=80,
          dropout=0.0,
          bias=True,
          use_weight_norm=True,
          upsample_factors=[4, 4, 4, 4],
      )
      dummy_c = torch.rand((2, 80, 5))
      output = model(dummy_c)
      assert np.all(output.shape == (2, 1, 5 * 256)), output.shape
      model.remove_weight_norm()
      output = model.inference(dummy_c)
      assert np.all(output.shape == (2, 1, (5 + 4) * 256))
  
imports: import numpy
  import torch
  from TTS.vocoder.models.parallel_wavegan_generator import ParallelWaveganGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: test_pwgan_generator() without return types