code: def test_mask_lengths(self):
      (
          input_dummy,
          input_lengths,
          mel_spec,
          mel_lengths,
      ) = self._get_embedded_input()
      neural_hmm = self._get_neural_hmm()
      log_prob, log_alpha_scaled, transition_matrix, means = neural_hmm(
          input_dummy,
          input_lengths,
          mel_spec.transpose(1, 2),
          mel_lengths,
      )
      log_c = torch.randn(
          mel_spec.shape[0], mel_spec.shape[1], device=device
      )
      (
          log_c,
          log_alpha_scaled,
      ) = neural_hmm._mask_lengths(  # pylint: disable=protected-access
          mel_lengths, log_c, log_alpha_scaled
      )
      assertions = []
      for i in range(mel_spec.shape[0]):
          assertions.append(log_c[i, mel_lengths[i] :].sum() == 0.0)
      self.assertTrue(all(assertions), "Incorrect masking")
      assertions = []
      for i in range(mel_spec.shape[0]):
          assertions.append(
              log_alpha_scaled[
                  i, mel_lengths[i] :, : input_lengths[i]
              ].sum()
              == 0.0
          )
      self.assertTrue(all(assertions), "Incorrect masking")
  
imports: import os
  import random
  import unittest
  from copy import deepcopy
  import torch
  from tests import get_tests_output_path
  from TTS.tts.configs.overflow_config import OverflowConfig
  from TTS.tts.layers.overflow.common_layers import Encoder, Outputnet, OverflowUtils
  from TTS.tts.layers.overflow.decoder import Decoder
  from TTS.tts.layers.overflow.neural_hmm import EmissionModel, NeuralHMM, TransitionModel
  from TTS.tts.models.overflow import Overflow
  from TTS.tts.utils.helpers import sequence_mask
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 6 for calling the TestNeuralHMM obj's underlying member method code with example usage: obj.test_mask_lengths() without return types