code: def setup_generator(c):
      print(" > Generator Model: {}".format(c.generator_model))
      MyModel = importlib.import_module(
          "TTS.vocoder.models." + c.generator_model.lower()
      )
      MyModel = getattr(MyModel, to_camel(c.generator_model))
      if c.generator_model.lower() in "melgan_generator":
          model = MyModel(
              in_channels=c.audio["num_mels"],
              out_channels=1,
              proj_kernel=7,
              base_channels=512,
              upsample_factors=c.generator_model_params[
                  "upsample_factors"
              ],
              res_kernel=3,
              num_res_blocks=c.generator_model_params["num_res_blocks"],
          )
      if c.generator_model in "melgan_fb_generator":
          pass
      if c.generator_model.lower() in "multiband_melgan_generator":
          model = MyModel(
              in_channels=c.audio["num_mels"],
              out_channels=4,
              proj_kernel=7,
              base_channels=384,
              upsample_factors=c.generator_model_params[
                  "upsample_factors"
              ],
              res_kernel=3,
              num_res_blocks=c.generator_model_params["num_res_blocks"],
          )
      if c.generator_model.lower() in "fullband_melgan_generator":
          model = MyModel(
              in_channels=c.audio["num_mels"],
              out_channels=1,
              proj_kernel=7,
              base_channels=512,
              upsample_factors=c.generator_model_params[
                  "upsample_factors"
              ],
              res_kernel=3,
              num_res_blocks=c.generator_model_params["num_res_blocks"],
          )
      if c.generator_model.lower() in "parallel_wavegan_generator":
          model = MyModel(
              in_channels=1,
              out_channels=1,
              kernel_size=3,
              num_res_blocks=c.generator_model_params["num_res_blocks"],
              stacks=c.generator_model_params["stacks"],
              res_channels=64,
              gate_channels=128,
              skip_channels=64,
              aux_channels=c.audio["num_mels"],
              dropout=0.0,
              bias=True,
              use_weight_norm=True,
              upsample_factors=c.generator_model_params[
                  "upsample_factors"
              ],
          )
      if c.generator_model.lower() in "wavegrad":
          model = MyModel(
              in_channels=c["audio"]["num_mels"],
              out_channels=1,
              use_weight_norm=c["model_params"]["use_weight_norm"],
              x_conv_channels=c["model_params"]["x_conv_channels"],
              y_conv_channels=c["model_params"]["y_conv_channels"],
              dblock_out_channels=c["model_params"][
                  "dblock_out_channels"
              ],
              ublock_out_channels=c["model_params"][
                  "ublock_out_channels"
              ],
              upsample_factors=c["model_params"]["upsample_factors"],
              upsample_dilations=c["model_params"][
                  "upsample_dilations"
              ],
          )
      return model
  
file_path: TTS/TTS/vocoder/utils/generic_utils.py
imports: import re
  import torch
  import importlib
  import numpy
  from matplotlib import pyplot
  from TTS.tts.utils.visual import plot_spectrogram
label_desc: Use image node_id 5 for calling a global function with example usage: setup_generator(c) and returns: model