code: def parse_decoder_outputs(
      self, mel_outputs, gate_outputs, alignments
  ):
      # (T_out, B) -> (B, T_out)
      alignments = torch.stack(alignments).transpose(0, 1)
      # (T_out, B) -> (B, T_out)
      # Add a -1 to prevent squeezing the batch dimension in case
      # batch is 1
      gate_outputs = (
          torch.stack(gate_outputs).squeeze(-1).transpose(0, 1)
      )
      gate_outputs = gate_outputs.contiguous()
      # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
      mel_outputs = (
          torch.stack(mel_outputs).transpose(0, 1).contiguous()
      )
      # decouple frames per step
      mel_outputs = mel_outputs.view(
          mel_outputs.size(0), -1, self.n_mel_channels
      )
      # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
      mel_outputs = mel_outputs.transpose(1, 2)
  
      return mel_outputs, gate_outputs, alignments
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import Attention, ConvNorm, LinearNorm, Prenet
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types.elements import EmbeddedTextType, LengthsType, LogitsType, MelSpectrogramType, SequenceToSequenceAlignmentType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 8 for calling the Decoder obj's underlying member method code with example usage: obj.parse_decoder_outputs(mel_outputs, gate_outputs, alignments) and returns: mel_outputs,  gate_outputs,  alignments