code: def __init__(self, metadata: TrainingSetMetadataDict):
      super().__init__()
      self.audio_feature_dict = {
          key: value
          for key, value in metadata["preprocessing"].items()
          if key in AUDIO_FEATURE_KEYS and value is not None
      }
      self.feature_dim = metadata["feature_dim"]
      self.max_length = metadata["max_length"]
      self.padding_value = metadata["preprocessing"]["padding_value"]
      self.normalization_type = metadata["preprocessing"]["norm"]
  
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 1 to create a new _AudioPreprocessing object from inherited base classes: torch.nn with example: obj = _AudioPreprocessing(metadata)