code: def test_pytorch_espresso(art_warning, expected_values, device_type):
      import torch
  
      from art.estimators.speech_recognition.pytorch_espresso import (
          PyTorchEspresso,
      )
  
      try:
          # Initialize a speech recognizer
          speech_recognizer = PyTorchEspresso(
              model="librispeech_transformer",
              espresso_config_filepath=None,
              device_type=device_type,
          )
  
          # Load data for testing
          expected_data = expected_values()
  
          x1 = expected_data["x1"]
          x2 = expected_data["x2"]
          x3 = expected_data["x3"]
          # expected_sizes = expected_data["expected_sizes"]
          expected_transcriptions1 = expected_data[
              "expected_transcriptions1"
          ]
          expected_transcriptions2 = expected_data[
              "expected_transcriptions2"
          ]
          # expected_probs = expected_data["expected_probs"]
          expected_gradients1 = expected_data["expected_gradients1"]
          expected_gradients2 = expected_data["expected_gradients2"]
          expected_gradients3 = expected_data["expected_gradients3"]
  
          # Create signal data
          x = np.array(
              [
                  np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
                  np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
                  np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
              ]
          )
  
          # Create labels
          y = np.array(["SIX", "HI", "GOOD"])
  
          # Test probability outputs
          # probs, sizes = speech_recognizer.predict(x, batch_size=2,)
          #
          # np.testing.assert_array_almost_equal(probs[1][1], expected_probs, decimal=3)
          # np.testing.assert_array_almost_equal(sizes, expected_sizes)
  
          # Test transcription outputs
          _ = speech_recognizer.predict(x[[0]], batch_size=2)
  
          # Test transcription outputs
          transcriptions = speech_recognizer.predict(x, batch_size=2)
  
          assert (expected_transcriptions1 == transcriptions).all()
  
          # Test transcription outputs, corner case
          transcriptions = speech_recognizer.predict(
              np.array([x[0]]), batch_size=2
          )
  
          assert (expected_transcriptions2 == transcriptions).all()
  
          # Now test loss gradients
          # Compute gradients
          grads = speech_recognizer.loss_gradient(x, y)
  
          assert grads[0].shape == (1300,)
          assert grads[1].shape == (1500,)
          assert grads[2].shape == (1400,)
  
          np.testing.assert_array_almost_equal(
              grads[0][:20], expected_gradients1, decimal=-2
          )
          np.testing.assert_array_almost_equal(
              grads[1][:20], expected_gradients2, decimal=-2
          )
          np.testing.assert_array_almost_equal(
              grads[2][:20], expected_gradients3, decimal=-2
          )
  
          # Train the estimator
          with pytest.raises(NotImplementedError):
              speech_recognizer.fit(x=x, y=y, batch_size=2, nb_epochs=5)
  
          # Compute local shape
          local_batch_size = len(x)
          real_lengths = np.array([x_.shape[0] for x_ in x])
          local_max_length = np.max(real_lengths)
  
          # Reformat input
          input_mask = np.zeros(
              [local_batch_size, local_max_length], dtype=np.float64
          )
          original_input = np.zeros(
              [local_batch_size, local_max_length], dtype=np.float64
          )
  
          for local_batch_size_idx in range(local_batch_size):
              input_mask[
                  local_batch_size_idx, : len(x[local_batch_size_idx])
              ] = 1
              original_input[
                  local_batch_size_idx, : len(x[local_batch_size_idx])
              ] = x[local_batch_size_idx]
  
          # compute_loss_and_decoded_output
          (
              loss,
              decoded_output,
          ) = speech_recognizer.compute_loss_and_decoded_output(
              masked_adv_input=torch.tensor(original_input),
              original_output=y,
          )
  
          assert loss.detach().numpy() == pytest.approx(
              46.3156, abs=20.0
          )
          assert all(decoded_output == ["EH", "EH", "EH"])
  
      except ARTTestException as e:
          art_warning(e)
  
imports: import logging
  import numpy
  import pytest
  from art.config import ART_NUMPY_DTYPE
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: test_pytorch_espresso(art_warning, expected_values, device_type) without return types