code: def infer(self, *, memory, memory_lengths):
      decoder_input = self.get_go_frame(memory)
  
      if memory.size(0) > 1:
          mask = ~get_mask_from_lengths(memory_lengths)
      else:
          mask = None
  
      self.initialize_decoder_states(memory, mask=mask)
  
      mel_lengths = torch.zeros([memory.size(0)], dtype=torch.int32).to(
          memory.device
      )
      not_finished = torch.ones([memory.size(0)], dtype=torch.int32).to(
          memory.device
      )
  
      mel_outputs, gate_outputs, alignments = [], [], []
      stepped = False
      while True:
          decoder_input = self.prenet(decoder_input, inference=True)
          mel_output, gate_output, alignment = self.decode(
              decoder_input
          )
  
          dec = (
              torch.le(
                  torch.sigmoid(gate_output.data), self.gate_threshold
              )
              .to(torch.int32)
              .squeeze(1)
          )
  
          not_finished = not_finished * dec
          mel_lengths += not_finished
  
          if (
              self.early_stopping
              and torch.sum(not_finished) == 0
              and stepped
          ):
              break
          stepped = True
  
          mel_outputs += [mel_output.squeeze(1)]
          gate_outputs += [gate_output]
          alignments += [alignment]
  
          if len(mel_outputs) == self.max_decoder_steps:
              logging.warning(
                  "Reached max decoder steps %d.",
                  self.max_decoder_steps,
              )
              break
  
          decoder_input = mel_output
  
      (
          mel_outputs,
          gate_outputs,
          alignments,
      ) = self.parse_decoder_outputs(
          mel_outputs, gate_outputs, alignments
      )
  
      return mel_outputs, gate_outputs, alignments, mel_lengths
  
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 11 for calling the Decoder obj's underlying member method code with example usage: obj.infer() and returns: mel_outputs,  gate_outputs,  alignments,  mel_lengths