class_docstr: PWGAN generator as in https://arxiv.org/pdf/1910.11480.pdf.
  It is similar to WaveNet with no causal convolution.
      It is conditioned on an aux feature (spectrogram) to generate
  an output waveform from an input noise.
class_docstr_tok: ["PWGAN","generator","as","in","https",":","\/\/arxiv.org\/pdf\/1910.11480.pdf",".","It","is","similar","to","WaveNet","with","no","causal","convolution",".","It","is","conditioned","on","an","aux","feature","(","spectrogram",")","to","generate","an","output","waveform","from","an","input","noise","."]
code: def load_checkpoint(
      self, config, checkpoint_path, eval=False
  ):  # pylint: disable=unused-argument, redefined-builtin
      state = torch.load(
          checkpoint_path, map_location=torch.device("cpu")
      )
      self.load_state_dict(state["model"])
      if eval:
          self.eval()
          assert not self.training
          if self.use_weight_norm:
              self.remove_weight_norm()
  
file_path: TTS/TTS/vocoder/models/parallel_wavegan_generator.py
imports: import math
  import numpy
  import torch
  from TTS.vocoder.layers.parallel_wavegan import ResidualBlock
  from TTS.vocoder.layers.upsample import ConvUpsample
label_desc: Use image node_id 8 for calling the ParallelWaveganGenerator obj's underlying member method code with example usage: obj.load_checkpoint(config, checkpoint_path, eval) without return types