code: def forward(self, x, mels):
      bsize = x.size(0)
      h1 = torch.zeros(1, bsize, self.rnn_dims).to(x.device)
      h2 = torch.zeros(1, bsize, self.rnn_dims).to(x.device)
      mels, aux = self.upsample(mels)
  
      if self.use_aux_net:
          aux_idx = [self.aux_dims * i for i in range(5)]
          a1 = aux[:, :, aux_idx[0] : aux_idx[1]]
          a2 = aux[:, :, aux_idx[1] : aux_idx[2]]
          a3 = aux[:, :, aux_idx[2] : aux_idx[3]]
          a4 = aux[:, :, aux_idx[3] : aux_idx[4]]
  
      x = (
          torch.cat([x.unsqueeze(-1), mels, a1], dim=2)
          if self.use_aux_net
          else torch.cat([x.unsqueeze(-1), mels], dim=2)
      )
      x = self.I(x)
      res = x
      self.rnn1.flatten_parameters()
      x, _ = self.rnn1(x, h1)
  
      x = x + res
      res = x
      x = torch.cat([x, a2], dim=2) if self.use_aux_net else x
      self.rnn2.flatten_parameters()
      x, _ = self.rnn2(x, h2)
  
      x = x + res
      x = torch.cat([x, a3], dim=2) if self.use_aux_net else x
      x = F.relu(self.fc1(x))
  
      x = torch.cat([x, a4], dim=2) if self.use_aux_net else x
      x = F.relu(self.fc2(x))
      return self.fc3(x)
  
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 2 for calling the WaveRNN obj's underlying member method code with example usage: obj.forward(x, mels) and returns: self