code: def test_encoder():
      input_dummy = torch.rand(8, 14, 37).to(device)
      input_lengths = torch.randint(31, 37, (8,)).long().to(device)
      input_lengths[-1] = 37
      input_mask = torch.unsqueeze(
          sequence_mask(input_lengths, input_dummy.size(2)), 1
      ).to(device)
  
      # residual bn conv encoder
      layer = Encoder(
          out_channels=11,
          in_hidden_channels=14,
          encoder_type="residual_conv_bn",
      ).to(device)
      output = layer(input_dummy, input_mask)
      assert list(output.shape) == [8, 11, 37]
  
      # transformer encoder
      layer = Encoder(
          out_channels=11,
          in_hidden_channels=14,
          encoder_type="transformer",
          encoder_params={
              "hidden_channels_ffn": 768,
              "num_heads": 2,
              "kernel_size": 3,
              "dropout_p": 0.1,
              "num_layers": 6,
              "rel_attn_window_size": 4,
              "input_length": None,
          },
      ).to(device)
      output = layer(input_dummy, input_mask)
      assert list(output.shape) == [8, 11, 37]
  
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 1 for calling a global function with example usage: test_encoder() without return types