code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      if not torch.jit.isinstance(v, List[Tuple[torch.Tensor, int]]):
          raise ValueError(f"Unsupported input: {v}")
  
      processed_audio_matrix = []
      for audio, sampling_rate_in_hz in v:
          processed_audio = AudioFeatureMixin._transform_to_feature(
              audio,
              sampling_rate_in_hz,
              self.audio_feature_dict,
              self.feature_dim,
              self.max_length,
              self.padding_value,
              self.normalization_type,
          )
          processed_audio_matrix.append(processed_audio)
      return torch.stack(processed_audio_matrix)
  
imports: import logging
  import os
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torchaudio
  from packaging import version
  from ludwig.constants import AUDIO, AUDIO_FEATURE_KEYS, COLUMN, NAME, PREPROCESSING, PROC_COLUMN, SRC, TYPE
  from ludwig.features.base_feature import BaseFeatureMixin
  from ludwig.features.sequence_feature import SequenceInputFeature
  from ludwig.schema.features.audio_feature import AudioInputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.audio_utils import calculate_mean, calculate_var, get_default_audio, get_fbank, get_group_delay, get_length_in_samp, get_max_length_stft_based, get_non_symmetric_length, get_phase_stft_magnitude, get_stft_magnitude, is_torch_audio_tuple, read_audio_from_bytes_obj, read_audio_from_path
  from ludwig.utils.data_utils import get_abs_path
  from ludwig.utils.fs_utils import has_remote_protocol
  from ludwig.utils.misc_utils import set_default_value
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _AudioPreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: torch