code: def test_speedy_speech():
      num_chars = 7
      B = 8
      T_en = 37
      T_de = 74
  
      x_dummy = torch.randint(0, 7, (B, T_en)).long().to(device)
      x_lengths = torch.randint(31, T_en, (B,)).long().to(device)
      x_lengths[-1] = T_en
  
      # set durations. max total duration should be equal to T_de
      durations = torch.randint(1, 4, (B, T_en))
      durations = durations * (T_de / durations.sum(1)).unsqueeze(1)
      durations = durations.to(torch.long).to(device)
      max_dur = durations.sum(1).max()
      durations[:, 0] += T_de - max_dur if T_de > max_dur else 0
  
      y_lengths = durations.sum(1)
  
      model = SpeedySpeech(
          num_chars, out_channels=80, hidden_channels=128
      )
      if use_cuda:
          model.cuda()
  
      # forward pass
      o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations)
  
      assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
      assert list(attn.shape) == [B, T_de, T_en]
      assert list(o_dr.shape) == [B, T_en]
  
      # with speaker embedding
      model = SpeedySpeech(
          num_chars,
          out_channels=80,
          hidden_channels=128,
          num_speakers=10,
          c_in_channels=256,
      ).to(device)
      model.forward(
          x_dummy,
          x_lengths,
          y_lengths,
          durations,
          g=torch.randint(0, 10, (B,)).to(device),
      )
  
      assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
      assert list(attn.shape) == [B, T_de, T_en]
      assert list(o_dr.shape) == [B, T_en]
  
      # with speaker external embedding
      model = SpeedySpeech(
          num_chars,
          out_channels=80,
          hidden_channels=128,
          num_speakers=10,
          external_c=True,
          c_in_channels=256,
      ).to(device)
      model.forward(
          x_dummy,
          x_lengths,
          y_lengths,
          durations,
          g=torch.rand((B, 256)).to(device),
      )
  
      assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
      assert list(attn.shape) == [B, T_de, T_en]
      assert list(o_dr.shape) == [B, T_en]
  
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 4 for calling a global function with example usage: test_speedy_speech() without return types