class_docstr: Constructs a Audio Spectrogram Transformer (AST) feature extractor.
  
  This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
  most of the main methods. Users should refer to this superclass for more information regarding those methods.
  
  This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy
  otherwise, pads/truncates them to a fixed length and normalizes them using a mean and standard deviation.
  
  Args:
      feature_size (`int`, *optional*, defaults to 1):
          The feature dimension of the extracted features.
      sampling_rate (`int`, *optional*, defaults to 16000):
          The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
      num_mel_bins (`int`, *optional*, defaults to 128):
          Number of Mel-frequency bins.
      max_length (`int`, *optional*, defaults to 1024):
          Maximum length to which to pad/truncate the extracted features.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the log-Mel features using `mean` and `std`.
      mean (`float`, *optional*, defaults to -4.2677393):
          The mean value used to normalize the log-Mel features. Uses the AudioSet mean by default.
      std (`float`, *optional*, defaults to 4.5689974):
          The standard deviation value used to normalize the log-Mel features. Uses the AudioSet standard deviation
          by default.
      return_attention_mask (`bool`, *optional*, defaults to `False`):
          Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`.
code: def normalize(self, input_values: np.ndarray) -> np.ndarray:
      return (input_values - (self.mean)) / (self.std * 2)
  
file_path: transformers/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py
imports: from typing import List, Optional, Union
  import numpy
  from ...audio_utils import mel_filter_bank, spectrogram, window_function
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import TensorType, is_speech_available, is_torch_available, logging
label_desc: Use image node_id 3 for calling the ASTFeatureExtractor obj's underlying member method code with example usage: obj.normalize(input_values) and returns: unknown