def _preprocess_transform_model_input(
        self,
        x: "torch.Tensor",
        y: np.ndarray,
    ) -> tuple[dict, list]:
        """
        Apply preprocessing and then transform the user input space into the model input space. This function is used
        by the ASR attack to attack into the PyTorchDeepSpeech estimator whose defences are called with the
        `_apply_preprocessing` function.

        :param x: Samples of shape (nb_samples, seq_length).
        :param y: Target values of shape (nb_samples). Each sample in `y` is a string, and it may possess different
                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
        :param real_lengths: Real lengths of original sequences.
        :return: A tuple of inputs and targets in the model space with the original index
                 `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
                 - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
                 - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
                 - input_percentages: percentages of real inputs in inputs.
                 - target_sizes: list of real seq_lengths.
                 - batch_idx: original index of inputs.
        """
        import torch

        # Apply preprocessing
        x_batch = []
        for i, _ in enumerate(x):
            preprocessed_x_i, _ = self._apply_preprocessing(x=x[i], y=None, no_grad=False)
            x_batch.append(preprocessed_x_i)

        x = torch.stack(x_batch)

        # Transform the input space
        batch_dict, batch_idx = self._transform_model_input(
            x=x,
            y=y,
            compute_gradient=False,
        )

        return batch_dict, batch_idx

        Apply preprocessing and then transform the user input space into the model input space. This function is used
        by the ASR attack to attack into the PyTorchDeepSpeech estimator whose defences are called with the
        `_apply_preprocessing` function.

        :param x: Samples of shape (nb_samples, seq_length).
        :param y: Target values of shape (nb_samples). Each sample in `y` is a string, and it may possess different
                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
        :param real_lengths: Real lengths of original sequences.
        :return: A tuple of inputs and targets in the model space with the original index
                 `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
                 - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
                 - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
                 - input_percentages: percentages of real inputs in inputs.
                 - target_sizes: list of real seq_lengths.
                 - batch_idx: original index of inputs.
        
art/estimators/speech_recognition/pytorch_espresso.py