code: def padded_everygram_pipeline(order, text):
      """Default preprocessing for a sequence of sentences.
  
      Creates two iterators:
  
      - sentences padded and turned into sequences of `nltk.util.everygrams`
      - sentences padded as above and chained together for a flat stream of words
  
      :param order: Largest ngram length produced by `everygrams`.
      :param text: Text to iterate over. Expected to be an iterable of sentences.
      :type text: Iterable[Iterable[str]]
      :return: iterator over text as ngrams, iterator over text as vocabulary data
      """
      padding_fn = partial(pad_both_ends, n=order)
      return (
          (
              everygrams(list(padding_fn(sent)), max_len=order)
              for sent in text
          ),
          flatten(map(padding_fn, text)),
      )
  
imports: from functools import partial
  from itertools import chain
  from nltk.util import everygrams, pad_sequence
label_desc: Use image node_id 2 for calling a global function with example usage: padded_everygram_pipeline(order, text) and returns: unknown,  flatten