code: def test_redisual_pwgan_disciminator():
      model = ResidualParallelWaveganDiscriminator(
          in_channels=1,
          out_channels=1,
          kernel_size=3,
          num_layers=30,
          stacks=3,
          res_channels=64,
          gate_channels=128,
          skip_channels=64,
          dropout=0.0,
          bias=True,
          nonlinear_activation="LeakyReLU",
          nonlinear_activation_params={"negative_slope": 0.2},
      )
      dummy_x = torch.rand((4, 1, 64 * 256))
      output = model(dummy_x)
      assert np.all(output.shape == (4, 1, 64 * 256))
      model.remove_weight_norm()
  
imports: import numpy
  import torch
  from TTS.vocoder.models.parallel_wavegan_discriminator import ParallelWaveganDiscriminator, ResidualParallelWaveganDiscriminator
label_desc: Use image node_id 2 for calling a global function with example usage: test_redisual_pwgan_disciminator() without return types