code: def __init__(
      self,
      sample_rate,
      max_sample_size=None,
      min_sample_size=0,
      shuffle=True,
      pad=False,
      normalize=False,
      compute_mask=False,
      feature_encoder_spec: str = "None",
      mask_prob: float = 0.75,
      mask_prob_adjust: float = 0,
      mask_length: int = 1,
      inverse_mask: bool = False,
      require_same_masks: bool = True,
      clone_batch: int = 1,
      expand_adjacent: bool = False,
      mask_dropout: float = 0,
      non_overlapping: bool = False,
      corpus_key=None,
  ):
      super().__init__()
  
      self.sample_rate = sample_rate
      self.sizes = []
      self.max_sample_size = (
          max_sample_size
          if max_sample_size is not None
          else sys.maxsize
      )
      self.min_sample_size = min_sample_size
      self.pad = pad
      self.shuffle = shuffle
      self.normalize = normalize
  
      self.is_compute_mask = compute_mask
      self.feature_encoder_spec = eval(feature_encoder_spec)
      self._features_size_map = {}
      self.mask_prob = mask_prob
      self.mask_prob_adjust = mask_prob_adjust
      self.mask_length = mask_length
      self.inverse_mask = inverse_mask
      self.require_same_masks = require_same_masks
      self.clone_batch = clone_batch
      self.expand_adjacent = expand_adjacent
      self.mask_dropout = mask_dropout
      self.non_overlapping = non_overlapping
      self.corpus_key = corpus_key
  
imports: import logging
  import os
  import sys
  import time
  import io
  import numpy
  import torch
  import torch.nn.functional
  from ..None import FairseqDataset
  from ..data_utils import compute_block_mask_1d, get_buckets, get_bucketed_sizes
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip, is_sf_audio_data
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
label_desc: Use image node_id 1 to create a new RawAudioDataset object from inherited base classes: FairseqDataset with example: obj = RawAudioDataset(sample_rate, max_sample_size, min_sample_size, shuffle, pad, normalize, compute_mask, feature_encoder_spec, mask_prob, mask_prob_adjust, mask_length, inverse_mask, require_same_masks, clone_batch, expand_adjacent, mask_dropout, non_overlapping, corpus_key)