code: def initialize_decoder_states(self, memory, mask):
      B = memory.size(0)
      MAX_TIME = memory.size(1)
  
      self.attention_hidden = Variable(
          memory.data.new(B, self.attention_rnn_dim).zero_()
      )
      self.attention_cell = Variable(
          memory.data.new(B, self.attention_rnn_dim).zero_()
      )
  
      self.decoder_hidden = Variable(
          memory.data.new(B, self.decoder_rnn_dim).zero_()
      )
      self.decoder_cell = Variable(
          memory.data.new(B, self.decoder_rnn_dim).zero_()
      )
  
      self.attention_weights = Variable(
          memory.data.new(B, MAX_TIME).zero_()
      )
      self.attention_weights_cum = Variable(
          memory.data.new(B, MAX_TIME).zero_()
      )
      self.attention_context = Variable(
          memory.data.new(B, self.encoder_embedding_dim).zero_()
      )
  
      self.memory = memory
      self.processed_memory = self.attention_layer.memory_layer(memory)
      self.mask = mask
  
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 6 for calling the Decoder obj's underlying member method code with example usage: obj.initialize_decoder_states(memory, mask) without return types