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
  
imports: import sys
  import torch
  import torch.nn
  import numpy
  import torch.nn.functional
  import time
  from TTS.utils.audio import AudioProcessor
  from TTS.vocoder.utils.distribution import sample_from_gaussian, sample_from_discretized_mix_logistic
label_desc: Use image node_id 9 for calling the WaveRNN obj's underlying member method code with example usage: obj.load_checkpoint(config, checkpoint_path, eval) without return types