code: def input_types(self):
      input_dict = {
          "memory": NeuralType(("B", "T", "D"), EmbeddedTextType()),
          "memory_lengths": NeuralType(("B"), LengthsType()),
      }
      if self.training:
          input_dict["decoder_inputs"] = NeuralType(
              ("B", "D", "T"), MelSpectrogramType()
          )
      return input_dict
  
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 2 for calling the Decoder obj's underlying member method code with example usage: obj.input_types() and returns: input_dict