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))]
      else:
          order = [np.arange(len(self))]
  
      return order[0]
  
file_path: fairseq/examples/data2vec/data/mae_finetuning_image_dataset.py
imports: import logging
  import numpy
  import os
  import torch
  from torchvision import datasets, transforms
  from timm.data import create_transform
  from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  import PIL
  from fairseq.data import FairseqDataset
  from .mae_image_dataset import caching_loader
label_desc: Use image node_id 7 for calling the MaeFinetuningImageDataset obj's underlying member method code with example usage: obj.ordered_indices() and returns: order