class_docstr: A SerialIterator having its shuffling property activated during training
docstr: Init the Iterator
  
  :param torch.nn.Tensor dataset: The dataset to take batches from
  :param int batch_size: The batch size
  :param bool repeat: Whether to repeat data (allow multiple epochs)
  :param bool shuffle: Whether to shuffle the batches
docstr_tok: ["Init","the","Iterator",":","param","torch.nn.Tensor","dataset",":","The","dataset","to","take","batches","from",":","param","int","batch_size",":","The","batch","size",":","param","bool","repeat",":","Whether","to","repeat","data","(","allow","multiple","epochs",")",":","param","bool","shuffle",":","Whether","to","shuffle","the","batches"]
code: def __init__(self, dataset, batch_size, repeat=True, shuffle=True):
      """Init the Iterator
  
      :param torch.nn.Tensor dataset: The dataset to take batches from
      :param int batch_size: The batch size
      :param bool repeat: Whether to repeat data (allow multiple epochs)
      :param bool shuffle: Whether to shuffle the batches
      """
      super(ToggleableShufflingSerialIterator, self).__init__(
          dataset, batch_size, repeat, shuffle
      )
  
file_path: espnet/espnet/utils/training/iterators.py
imports: import chainer
  import numpy
  from chainer.iterators import MultiprocessIterator, SerialIterator, ShuffleOrderSampler
  from chainer.training.extension import Extension
label_desc: Use image node_id 1 to create a new ToggleableShufflingSerialIterator object from inherited base classes: SerialIterator with example: obj = ToggleableShufflingSerialIterator(dataset, batch_size, repeat, shuffle)