code: def make_generator_args(**kwargs):
      defaults = dict(
          in_channels=1,
          out_channels=1,
          kernel_size=3,
          layers=6,
          stacks=3,
          residual_channels=8,
          gate_channels=16,
          skip_channels=8,
          aux_channels=10,
          aux_context_window=0,
          use_weight_norm=True,
          upsample_conditional_features=True,
          upsample_net="ConvInUpsampleNetwork",
          upsample_params={"upsample_scales": [4, 4]},
      )
      defaults.update(kwargs)
      return defaults
  
file_path: espnet/test/espnet2/gan_tts/parallel_wavegan/test_parallel_wavegan.py
imports: import numpy
  import pytest
  import torch
  from espnet2.gan_tts.hifigan.loss import DiscriminatorAdversarialLoss, GeneratorAdversarialLoss
  from espnet2.gan_tts.parallel_wavegan import ParallelWaveGANDiscriminator, ParallelWaveGANGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: make_generator_args() and returns: defaults