code: def __init__(
      self,
      rnn_dims,
      fc_dims,
      mode,
      mulaw,
      pad,
      use_aux_net,
      use_upsample_net,
      upsample_factors,
      feat_dims,
      compute_dims,
      res_out_dims,
      num_res_blocks,
      hop_length,
      sample_rate,
  ):
      super().__init__()
      self.mode = mode
      self.mulaw = mulaw
      self.pad = pad
      self.use_upsample_net = use_upsample_net
      self.use_aux_net = use_aux_net
      if isinstance(self.mode, int):
          self.n_classes = 2**self.mode
      elif self.mode == "mold":
          self.n_classes = 3 * 10
      elif self.mode == "gauss":
          self.n_classes = 2
      else:
          raise RuntimeError("Unknown model mode value - ", self.mode)
  
      self.rnn_dims = rnn_dims
      self.aux_dims = res_out_dims // 4
      self.hop_length = hop_length
      self.sample_rate = sample_rate
  
      if self.use_upsample_net:
          assert (
              np.cumproduct(upsample_factors)[-1] == self.hop_length
          ), " [!] upsample scales needs to be equal to hop_length"
          self.upsample = UpsampleNetwork(
              feat_dims,
              upsample_factors,
              compute_dims,
              num_res_blocks,
              res_out_dims,
              pad,
              use_aux_net,
          )
      else:
          self.upsample = Upsample(
              hop_length,
              pad,
              num_res_blocks,
              feat_dims,
              compute_dims,
              res_out_dims,
              use_aux_net,
          )
      if self.use_aux_net:
          self.I = nn.Linear(feat_dims + self.aux_dims + 1, rnn_dims)
          self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
          self.rnn2 = nn.GRU(
              rnn_dims + self.aux_dims, rnn_dims, batch_first=True
          )
          self.fc1 = nn.Linear(rnn_dims + self.aux_dims, fc_dims)
          self.fc2 = nn.Linear(fc_dims + self.aux_dims, fc_dims)
          self.fc3 = nn.Linear(fc_dims, self.n_classes)
      else:
          self.I = nn.Linear(feat_dims + 1, rnn_dims)
          self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
          self.rnn2 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
          self.fc1 = nn.Linear(rnn_dims, fc_dims)
          self.fc2 = nn.Linear(fc_dims, fc_dims)
          self.fc3 = nn.Linear(fc_dims, self.n_classes)
  
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 1 to create a new WaveRNN object from inherited base classes: nn with example: obj = WaveRNN(rnn_dims, fc_dims, mode, mulaw, pad, use_aux_net, use_upsample_net, upsample_factors, feat_dims, compute_dims, res_out_dims, num_res_blocks, hop_length, sample_rate)