code: def test_in_out(self):  # pylint: disable=no-self-use
      # test input == target
      layer = L1LossMasked(seq_len_norm=False)
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.ones(4, 8, 128).float()
      dummy_length = (T.ones(4) * 8).long()
      output = layer(dummy_input, dummy_target, dummy_length)
      assert output.item() == 0.0
  
      # test input != target
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.zeros(4, 8, 128).float()
      dummy_length = (T.ones(4) * 8).long()
      output = layer(dummy_input, dummy_target, dummy_length)
      assert output.item() == 1.0, "1.0 vs {}".format(output.item())
  
      # test if padded values of input makes any difference
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.zeros(4, 8, 128).float()
      dummy_length = (T.arange(5, 9)).long()
      mask = (
          (sequence_mask(dummy_length).float() - 1.0) * 100.0
      ).unsqueeze(2)
      output = layer(dummy_input + mask, dummy_target, dummy_length)
      assert output.item() == 1.0, "1.0 vs {}".format(output.item())
  
      dummy_input = T.rand(4, 8, 128).float()
      dummy_target = dummy_input.detach()
      dummy_length = (T.arange(5, 9)).long()
      mask = (
          (sequence_mask(dummy_length).float() - 1.0) * 100.0
      ).unsqueeze(2)
      output = layer(dummy_input + mask, dummy_target, dummy_length)
      assert output.item() == 0, "0 vs {}".format(output.item())
  
      # seq_len_norm = True
      # test input == target
      layer = L1LossMasked(seq_len_norm=True)
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.ones(4, 8, 128).float()
      dummy_length = (T.ones(4) * 8).long()
      output = layer(dummy_input, dummy_target, dummy_length)
      assert output.item() == 0.0
  
      # test input != target
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.zeros(4, 8, 128).float()
      dummy_length = (T.ones(4) * 8).long()
      output = layer(dummy_input, dummy_target, dummy_length)
      assert output.item() == 1.0, "1.0 vs {}".format(output.item())
  
      # test if padded values of input makes any difference
      dummy_input = T.ones(4, 8, 128).float()
      dummy_target = T.zeros(4, 8, 128).float()
      dummy_length = (T.arange(5, 9)).long()
      mask = (
          (sequence_mask(dummy_length).float() - 1.0) * 100.0
      ).unsqueeze(2)
      output = layer(dummy_input + mask, dummy_target, dummy_length)
      assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(
          output.item()
      )
  
      dummy_input = T.rand(4, 8, 128).float()
      dummy_target = dummy_input.detach()
      dummy_length = (T.arange(5, 9)).long()
      mask = (
          (sequence_mask(dummy_length).float() - 1.0) * 100.0
      ).unsqueeze(2)
      output = layer(dummy_input + mask, dummy_target, dummy_length)
      assert output.item() == 0, "0 vs {}".format(output.item())
  
imports: import unittest
  import torch
  from TTS.tts.layers.tacotron import Prenet, CBHG, Decoder, Encoder
  from TTS.tts.layers.losses import L1LossMasked, SSIMLoss
  from TTS.tts.utils.generic_utils import sequence_mask
label_desc: Use image node_id 1 for calling the L1LossMaskedTests obj's underlying member method code with example usage: obj.test_in_out() without return types