code: def test_imperceptible_asr_pytorch(
      art_warning, expected_values, use_amp, device_type
  ):
      import torch
  
      from art.estimators.speech_recognition.pytorch_deep_speech import (
          PyTorchDeepSpeech,
      )
      from art.attacks.evasion.imperceptible_asr.imperceptible_asr_pytorch import (
          ImperceptibleASRPyTorch,
      )
      from art.preprocessing.audio import LFilterPyTorch
  
      try:
          # Skip test if gpu is not available and use_amp is true
          if use_amp and not torch.cuda.is_available():
              return
  
          # Load data for testing
          expected_data = expected_values()
  
          x1 = expected_data["x1"]
          x2 = expected_data["x2"]
          x3 = expected_data["x3"]
  
          # Create signal data
          x = np.array(
              [
                  np.array(x1 * 200, dtype=ART_NUMPY_DTYPE),
                  np.array(x2 * 200, dtype=ART_NUMPY_DTYPE),
                  np.array(x3 * 200, dtype=ART_NUMPY_DTYPE),
              ]
          )
  
          # Create labels
          y = np.array(["S", "I", "GD"])
  
          # Create DeepSpeech estimator with preprocessing
          numerator_coef = np.array(
              [0.0000001, 0.0000002, -0.0000001, -0.0000002],
              dtype=ART_NUMPY_DTYPE,
          )
          denominator_coef = np.array(
              [1.0, 0.0, 0.0, 0.0], dtype=ART_NUMPY_DTYPE
          )
          audio_filter = LFilterPyTorch(
              numerator_coef=numerator_coef,
              denominator_coef=denominator_coef,
              device_type=device_type,
          )
  
          speech_recognizer = PyTorchDeepSpeech(
              pretrained_model="librispeech",
              device_type=device_type,
              use_amp=use_amp,
              preprocessing_defences=audio_filter,
          )
  
          # Create attack
          asr_attack = ImperceptibleASRPyTorch(
              estimator=speech_recognizer,
              eps=0.001,
              max_iter_1=5,
              max_iter_2=5,
              learning_rate_1=0.00001,
              learning_rate_2=0.001,
              optimizer_1=torch.optim.Adam,
              optimizer_2=torch.optim.Adam,
              global_max_length=3200,
              initial_rescale=1.0,
              decrease_factor_eps=0.8,
              num_iter_decrease_eps=5,
              alpha=0.01,
              increase_factor_alpha=1.2,
              num_iter_increase_alpha=5,
              decrease_factor_alpha=0.8,
              num_iter_decrease_alpha=5,
              win_length=2048,
              hop_length=512,
              n_fft=2048,
              batch_size=2,
              use_amp=use_amp,
              opt_level="O1",
          )
  
          # Test transcription output
          transcriptions_preprocessing = speech_recognizer.predict(
              x, batch_size=2, transcription_output=True
          )
  
          expected_transcriptions = np.array(["", "", ""])
  
          assert (
              expected_transcriptions == transcriptions_preprocessing
          ).all()
  
          # Generate attack
          x_adv_preprocessing = asr_attack.generate(x, y)
  
          # Test shape
          assert x_adv_preprocessing[0].shape == x[0].shape
          assert x_adv_preprocessing[1].shape == x[1].shape
          assert x_adv_preprocessing[2].shape == x[2].shape
  
          # Test content
          assert not (x_adv_preprocessing[0] == x[0]).all()
          assert not (x_adv_preprocessing[1] == x[1]).all()
          assert not (x_adv_preprocessing[2] == x[2]).all()
  
          assert np.sum(x_adv_preprocessing[0]) != np.inf
          assert np.sum(x_adv_preprocessing[1]) != np.inf
          assert np.sum(x_adv_preprocessing[2]) != np.inf
  
          assert np.sum(x_adv_preprocessing[0]) != 0
          assert np.sum(x_adv_preprocessing[1]) != 0
          assert np.sum(x_adv_preprocessing[2]) != 0
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  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_imperceptible_asr_pytorch(art_warning, expected_values, use_amp, device_type) without return types