class_docstr: Pytorch class for ART speech recognizers. This class is used to define common methods for using inside pytorch
  imperceptible asr attack.
docstr: Compute loss function and decoded output.
  
  :param masked_adv_input: The perturbed inputs.
  :param original_output: Target values of shape (nb_samples). Each sample in `original_output` is a string and
                          it may possess different lengths. A possible example of `original_output` could be:
                          `original_output = np.array(['SIXTY ONE', 'HELLO'])`.
  :return: The loss and the decoded output.
code: def compute_loss_and_decoded_output(
      self,
      masked_adv_input: "torch.Tensor",
      original_output: np.ndarray,
      **kwargs,
  ) -> Tuple["torch.Tensor", np.ndarray]:
      """
      Compute loss function and decoded output.
  
      :param masked_adv_input: The perturbed inputs.
      :param original_output: Target values of shape (nb_samples). Each sample in `original_output` is a string and
                              it may possess different lengths. A possible example of `original_output` could be:
                              `original_output = np.array(['SIXTY ONE', 'HELLO'])`.
      :return: The loss and the decoded output.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/speech_recognition/speech_recognizer.py
imports: from abc import ABC, abstractmethod
  from typing import Tuple, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 1 for calling the PytorchSpeechRecognizerMixin obj's underlying member method code with example usage: obj.compute_loss_and_decoded_output(masked_adv_input, original_output) without return types