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.
        """
        # Transform data into the model input space
        batch_dict, batch_idx = self._preprocess_transform_model_input(
            x=masked_adv_input.to(self.device),
            y=original_output,
        )

        # Compute the loss
        self.model.train()
        loss, _, _ = self.criterion(self.model, batch_dict)

        # Compute transcription
        def get_symbols_to_strip_from_output(generator):
            if hasattr(generator, "symbols_to_strip_from_output"):
                return generator.symbols_to_strip_from_output

            return {generator.eos, generator.pad}

        # Put the model in the eval mode
        self.model.eval()

        # Get decoded output
        decoded_output = []
        hypos = self.task.inference_step(self.generator, self._models, batch_dict)

        for _, hypos_i in enumerate(hypos):
            # Process top predictions
            for _, hypo in enumerate(hypos_i[: self.esp_args.nbest]):
                hypo_str = self.dictionary.string(
                    hypo["tokens"].int().cpu(),
                    bpe_symbol=None,
                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(self.generator),
                )  # not removing bpe at this point
                detok_hypo_str = self.bpe.decode(hypo_str)
                decoded_output.append(detok_hypo_str)

        decoded_output_array = np.array(decoded_output)
        decoded_output_copy = decoded_output_array.copy()
        decoded_output_array[batch_idx] = decoded_output_copy  # revert decoded output to its original order

        return loss, decoded_output_array

        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.
        
art/estimators/speech_recognition/pytorch_espresso.py