code: def decode(self, decoder_input):
      cell_input = torch.cat(
          (decoder_input, self.attention_context), -1
      )
  
      self.attention_hidden, self.attention_cell = self.attention_rnn(
          cell_input, (self.attention_hidden, self.attention_cell)
      )
      self.attention_hidden = F.dropout(
          self.attention_hidden, self.p_attention_dropout, self.training
      )
  
      attention_weights_cat = torch.cat(
          (
              self.attention_weights.unsqueeze(1),
              self.attention_weights_cum.unsqueeze(1),
          ),
          dim=1,
      )
      (
          self.attention_context,
          self.attention_weights,
      ) = self.attention_layer(
          self.attention_hidden,
          self.memory,
          self.processed_memory,
          attention_weights_cat,
          self.mask,
      )
  
      self.attention_weights_cum += self.attention_weights
      decoder_input = torch.cat(
          (self.attention_hidden, self.attention_context), -1
      )
  
      self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
          decoder_input, (self.decoder_hidden, self.decoder_cell)
      )
      self.decoder_hidden = F.dropout(
          self.decoder_hidden, self.p_decoder_dropout, self.training
      )
  
      decoder_hidden_attention_context = torch.cat(
          (self.decoder_hidden, self.attention_context), dim=1
      )
      decoder_output = self.linear_projection(
          decoder_hidden_attention_context
      )
  
      gate_prediction = self.gate_layer(
          decoder_hidden_attention_context
      )
      return decoder_output, gate_prediction, self.attention_weights
  
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 9 for calling the Decoder obj's underlying member method code with example usage: obj.decode(decoder_input) and returns: decoder_output,  gate_prediction,  self