def extract_feature(self, path):
        """
        Extracts features from the given audio file path.

        Parameters:
        path (str): The file path of the audio file.

        Returns:
        inputs (numpy.ndarray): Reshaped audio data for further processing.
        utt_id (str): The unique identifier of the audio file, usually the filename.
        length (int): The length of the original audio data.
        """
        # Extract the utterance ID from the file path (usually the filename).
        utt_id = os.path.basename(path)
        use_norm = False
        
        #We suggest to use norm for 'FRCRN_SE_16K' and 'MossFormer2_SS_16K' models
        if self.args.network in ['FRCRN_SE_16K','MossFormer2_SS_16K'] :
            use_norm = True
            
        # Read and normalize the audio data, converting it to float32 for processing.
        audios_norm, scalars, audio_info = audioread(path, self.sampling_rate, use_norm)
        if audios_norm is None: return None, None, None, None, None

        if self.args.network in ['MossFormer2_SR_48K']:
            audio_info['sample_rate'] = self.sampling_rate
            
        for i in range(len(audios_norm)):
            audios_norm[i] = audios_norm[i].astype(np.float32)
            # Reshape the data to ensure it's in the format [1, data_length].
            audios_norm[i] = np.reshape(audios_norm[i], [1, audios_norm[i].shape[0]])
        
        # Return the reshaped audio data, utterance ID, and the length of the original data.
        return audios_norm, utt_id, audios_norm[0].shape[1], scalars, audio_info

        Extracts features from the given audio file path.

        Parameters:
        path (str): The file path of the audio file.

        Returns:
        inputs (numpy.ndarray): Reshaped audio data for further processing.
        utt_id (str): The unique identifier of the audio file, usually the filename.
        length (int): The length of the original audio data.
        
train/speech_super_resolution/dataloader/dataloader.py