code: def parse_output(self, outputs, output_lengths=None):
      if self.mask_padding and output_lengths is not None:
          mask = ~get_mask_from_lengths(output_lengths)
          mask = mask.expand(
              self.n_mel_channels, mask.size(0), mask.size(1)
          )
          mask = mask.permute(1, 0, 2)
  
          outputs[0].data.masked_fill_(mask, 0.0)
          outputs[1].data.masked_fill_(mask, 0.0)
          outputs[2].data.masked_fill_(
              mask[:, 0, :], 1e3
          )  # gate energies
  
      return outputs
  
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 3 for calling the Tacotron2 obj's underlying member method code with example usage: obj.parse_output(outputs, output_lengths) and returns: outputs