class_docstr: Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples
  
  Args:
      dataset (~torch.utils.data.Dataset): dataset to subsample
      size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive)
class_docstr_tok: ["Subsamples","a","given","dataset","by","a","specified","ratio",".","Subsampling","is","done","on","the","number","of","examples","Args",":","dataset","(","~torch.utils.data.Dataset",")",":","dataset","to","subsample","size_ratio","(","float",")",":","the","ratio","to","subsample","to",".","must","be","between","0","and","1","(","exclusive",")"]
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))]
      order.append(self.sizes)
      return np.lexsort(order)
  
file_path: fairseq/fairseq/data/subsample_dataset.py
imports: import contextlib
  import logging
  import numpy
  from fairseq.data.data_utils import numpy_seed
  from .None import BaseWrapperDataset
label_desc: Use image node_id 9 for calling the SubsampleDataset obj's underlying member method code with example usage: obj.ordered_indices() and returns: np