code: def train_forward(self, *, memory, decoder_inputs, memory_lengths):
      decoder_input = self.get_go_frame(memory).unsqueeze(0)
      decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
      decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
      decoder_inputs = self.prenet(decoder_inputs)
  
      self.initialize_decoder_states(
          memory, mask=~get_mask_from_lengths(memory_lengths)
      )
  
      mel_outputs, gate_outputs, alignments = [], [], []
      while len(mel_outputs) < decoder_inputs.size(0) - 1:
          decoder_input = decoder_inputs[len(mel_outputs)]
          mel_output, gate_output, attention_weights = self.decode(
              decoder_input
          )
  
          mel_outputs += [mel_output.squeeze(1)]
          gate_outputs += [gate_output]
          alignments += [attention_weights]
  
      (
          mel_outputs,
          gate_outputs,
          alignments,
      ) = self.parse_decoder_outputs(
          mel_outputs, gate_outputs, alignments
      )
      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 10 for calling the Decoder obj's underlying member method code with example usage: obj.train_forward() and returns: mel_outputs,  gate_outputs,  alignments