code: def inference(self, mels, batched, target, overlap):
      self.eval()
      device = mels.device
      output = []
      start = time.time()
      rnn1 = self.get_gru_cell(self.rnn1)
      rnn2 = self.get_gru_cell(self.rnn2)
  
      with torch.no_grad():
          if isinstance(mels, np.ndarray):
              mels = torch.FloatTensor(mels).to(device)
  
          if mels.ndim == 2:
              mels = mels.unsqueeze(0)
          wave_len = (mels.size(-1) - 1) * self.hop_length
  
          mels = self.pad_tensor(
              mels.transpose(1, 2), pad=self.pad, side="both"
          )
          mels, aux = self.upsample(mels.transpose(1, 2))
  
          if batched:
              mels = self.fold_with_overlap(mels, target, overlap)
              if aux is not None:
                  aux = self.fold_with_overlap(aux, target, overlap)
  
          b_size, seq_len, _ = mels.size()
  
          h1 = torch.zeros(b_size, self.rnn_dims).to(device)
          h2 = torch.zeros(b_size, self.rnn_dims).to(device)
          x = torch.zeros(b_size, 1).to(device)
  
          if self.use_aux_net:
              d = self.aux_dims
              aux_split = [
                  aux[:, :, d * i : d * (i + 1)] for i in range(4)
              ]
  
          for i in range(seq_len):
              m_t = mels[:, i, :]
  
              if self.use_aux_net:
                  a1_t, a2_t, a3_t, a4_t = (
                      a[:, i, :] for a in aux_split
                  )
  
              x = (
                  torch.cat([x, m_t, a1_t], dim=1)
                  if self.use_aux_net
                  else torch.cat([x, m_t], dim=1)
              )
              x = self.I(x)
              h1 = rnn1(x, h1)
  
              x = x + h1
              inp = (
                  torch.cat([x, a2_t], dim=1) if self.use_aux_net else x
              )
              h2 = rnn2(inp, h2)
  
              x = x + h2
              x = torch.cat([x, a3_t], dim=1) if self.use_aux_net else x
              x = F.relu(self.fc1(x))
  
              x = torch.cat([x, a4_t], dim=1) if self.use_aux_net else x
              x = F.relu(self.fc2(x))
  
              logits = self.fc3(x)
  
              if self.mode == "mold":
                  sample = sample_from_discretized_mix_logistic(
                      logits.unsqueeze(0).transpose(1, 2)
                  )
                  output.append(sample.view(-1))
                  x = sample.transpose(0, 1).to(device)
              elif self.mode == "gauss":
                  sample = sample_from_gaussian(
                      logits.unsqueeze(0).transpose(1, 2)
                  )
                  output.append(sample.view(-1))
                  x = sample.transpose(0, 1).to(device)
              elif isinstance(self.mode, int):
                  posterior = F.softmax(logits, dim=1)
                  distrib = torch.distributions.Categorical(posterior)
  
                  sample = (
                      2
                      * distrib.sample().float()
                      / (self.n_classes - 1.0)
                      - 1.0
                  )
                  output.append(sample)
                  x = sample.unsqueeze(-1)
              else:
                  raise RuntimeError(
                      "Unknown model mode value - ", self.mode
                  )
  
              if i % 100 == 0:
                  self.gen_display(i, seq_len, b_size, start)
  
      output = torch.stack(output).transpose(0, 1)
      output = output.cpu().numpy()
      output = output.astype(np.float64)
  
      if batched:
          output = self.xfade_and_unfold(output, target, overlap)
      else:
          output = output[0]
  
      if self.mulaw and isinstance(self.mode, int):
          output = ap.mulaw_decode(output, self.mode)
  
      # Fade-out at the end to avoid signal cutting out suddenly
      fade_out = np.linspace(1, 0, 20 * self.hop_length)
      output = output[:wave_len]
  
      if wave_len > len(fade_out):
          output[-20 * self.hop_length :] *= fade_out
  
      self.train()
      return output
  
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 3 for calling the WaveRNN obj's underlying member method code with example usage: obj.inference(mels, batched, target, overlap) and returns: output