class_docstr: Mixin for receiving updates whenever the epoch increments.
class_docstr_tok: ["Mixin","for","receiving","updates","whenever","the","epoch","increments","."]
docstr: Whether we can reuse the :class:`fairseq.data.EpochBatchIterator` for
  this dataset across epochs.
  
  This needs to return ``False`` if the sample sizes can change across
  epochs, in which case we may need to regenerate batches at each epoch.
  If your dataset relies in ``set_epoch`` then you should consider setting
  this to ``False``.
docstr_tok: ["Whether","we","can","reuse","the",":","class",":","`","fairseq.data.EpochBatchIterator","`","for","this","dataset","across","epochs",".","This","needs","to","return","``","False","``","if","the","sample","sizes","can","change","across","epochs",",","in","which","case","we","may","need","to","regenerate","batches","at","each","epoch",".","If","your","dataset","relies","in","``","set_epoch","``","then","you","should","consider","setting","this","to","``","False","``","."]
code: def can_reuse_epoch_itr_across_epochs(self):
      """
      Whether we can reuse the :class:`fairseq.data.EpochBatchIterator` for
      this dataset across epochs.
  
      This needs to return ``False`` if the sample sizes can change across
      epochs, in which case we may need to regenerate batches at each epoch.
      If your dataset relies in ``set_epoch`` then you should consider setting
      this to ``False``.
      """
      return True
  
imports: import logging
  import numpy
  import torch.utils.data
  from fairseq.data import data_utils
label_desc: Use image node_id 1 for calling the EpochListening obj's underlying member method code with example usage: obj.can_reuse_epoch_itr_across_epochs() and returns: True