code: def test_in_out(self):
      dummy_input = T.rand(4, 80, 20)  # B x D x T
      dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
      model = LSTMSpeakerEncoder(
          input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3
      )
      # computing d vectors
      output = model.forward(dummy_input)
      assert output.shape[0] == 4
      assert output.shape[1] == 256
      output = model.inference(dummy_input)
      assert output.shape[0] == 4
      assert output.shape[1] == 256
      # compute d vectors by passing LSTM hidden
      # output = model.forward(dummy_input, dummy_hidden)
      # assert output.shape[0] == 4
      # assert output.shape[1] == 20
      # assert output.shape[2] == 256
      # check normalization
      output_norm = T.nn.functional.normalize(output, dim=1, p=2)
      assert_diff = (output_norm - output).sum().item()
      assert output.type() == "torch.FloatTensor"
      assert (
          abs(assert_diff) < 1e-4
      ), f" [!] output_norm has wrong values - {assert_diff}"
      # compute d for a given batch
      dummy_input = T.rand(1, 80, 240)  # B x T x D
      output = model.compute_embedding(
          dummy_input, num_frames=160, num_eval=5
      )
      assert output.shape[0] == 1
      assert output.shape[1] == 256
      assert len(output.shape) == 2
  
imports: import unittest
  import torch
  from tests import get_tests_input_path
  from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
  from TTS.encoder.models.lstm import LSTMSpeakerEncoder
  from TTS.encoder.models.resnet import ResNetSpeakerEncoder
label_desc: Use image node_id 1 for calling the LSTMSpeakerEncoderTests obj's underlying member method code with example usage: obj.test_in_out() without return types