code: def forward(self, inputs):
      (
          text_inputs,
          text_lengths,
          obs_labels,
          mels,
          max_len,
          output_lengths,
      ) = inputs
      text_lengths, output_lengths = (
          text_lengths.data,
          output_lengths.data,
      )
  
      embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
  
      encoder_outputs = self.encoder(embedded_inputs, text_lengths)
  
      obs = None
      if self.obs_embedding is not None:
          obs = self.obs_embedding(obs_labels)
  
      lat, lat_mu, lat_logvar = None, None, None
      if self.lat_encoder is not None:
          (lat, lat_mu, lat_logvar) = self.lat_encoder(
              mels, output_lengths
          )
  
      obs_and_lat = [x for x in [obs, lat] if x is not None]
      if bool(obs_and_lat):
          obs_and_lat = torch.cat(obs_and_lat, dim=-1)
      else:
          obs_and_lat = None
  
      mel_outputs, gate_outputs, alignments = self.decoder(
          encoder_outputs,
          obs_and_lat,
          mels,
          memory_lengths=text_lengths,
      )
  
      mel_outputs_postnet = self.postnet(mel_outputs)
      mel_outputs_postnet = mel_outputs + mel_outputs_postnet
  
      return self.parse_output(
          [
              mel_outputs,
              mel_outputs_postnet,
              gate_outputs,
              alignments,
              lat_mu,
              lat_logvar,
          ],
          output_lengths,
      )
  
imports: from math import sqrt
  import torch
  import torch.distributions
  from torch.autograd import Variable
  from torch import nn
  from torch.nn import functional
  from .layers import ConvNorm, LinearNorm, GlobalAvgPool
  from .utils import to_gpu, get_mask_from_lengths
label_desc: Use image node_id 4 for calling the Tacotron2 obj's underlying member method code with example usage: obj.forward(inputs) and returns: self