code: def test_emission_model(self):
      model = self._get_emission_model()
      (
          input_dummy,
          input_lengths,
          mel_spec,
          mel_lengths,
      ) = self._get_embedded_input()
      x_t = torch.randn(
          input_dummy.shape[0], config_global.out_channels
      ).to(device)
      means = torch.randn(
          input_dummy.shape[0],
          input_dummy.shape[1],
          config_global.out_channels,
      ).to(device)
      std = (
          torch.rand_like(means).to(device).clamp_(1e-3)
      )  # std should be positive
      out = model(x_t, means, std, input_lengths)
      self.assertEqual(
          out.shape, (input_dummy.shape[0], input_dummy.shape[1])
      )
  
      # testing sampling
      for temp in [0, 0.334, 0.667]:
          out = model.sample(means, std, 0)
          self.assertEqual(out.shape, means.shape)
          if temp == 0:
              self.assertTrue(torch.isclose(out, means).all())
  
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 12 for calling the TestNeuralHMM obj's underlying member method code with example usage: obj.test_emission_model() without return types