code: def test_duration_predictor():
      input_dummy = torch.rand(8, 128, 27).to(device)
      input_lengths = torch.randint(20, 27, (8,)).long().to(device)
      input_lengths[-1] = 27
  
      x_mask = torch.unsqueeze(
          sequence_mask(input_lengths, input_dummy.size(2)), 1
      ).to(device)
  
      layer = DurationPredictor(hidden_channels=128).to(device)
  
      output = layer(input_dummy, x_mask)
      assert list(output.shape) == [8, 1, 27]
  
file_path: TTS/tests/test_speedy_speech_layers.py
imports: import torch
  from TTS.tts.layers.speedy_speech.encoder import Encoder
  from TTS.tts.layers.speedy_speech.decoder import Decoder
  from TTS.tts.layers.speedy_speech.duration_predictor import DurationPredictor
  from TTS.tts.utils.generic_utils import sequence_mask
  from TTS.tts.models.speedy_speech import SpeedySpeech
label_desc: Use image node_id 3 for calling a global function with example usage: test_duration_predictor() without return types