docstr: Return an ordered list of indices. Batches will be constructed based
  on this order.
docstr_tok: ["Return","an","ordered","list","of","indices",".","Batches","will","be","constructed","based","on","this","order","."]
code: def ordered_indices(self):
      """Return an ordered list of indices. Batches will be constructed based
      on this order."""
  
      if self.shuffle:
          order = [np.random.permutation(len(self))]
          order.append(
              np.minimum(
                  np.array(self.sizes),
                  self.max_sample_size,
              )
          )
          return np.lexsort(order)[::-1]
      else:
          return np.arange(len(self))
  
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 11 for calling the RawAudioDataset obj's underlying member method code with example usage: obj.ordered_indices() and returns: np, np