class_docstr: Penn Treebank data set from http://arxiv.org/pdf/1409.2329v5.pdf
  
  Arguments:
      timesteps (int): number of timesteps to embed the data
      onehot_input (bool):
      tokenizer (str): name of the tokenizer function within this
                       class to use on the data
class_docstr_tok: ["Penn","Treebank","data","set","from","http",":","\/\/arxiv.org\/pdf\/1409.2329v5.pdf","Arguments",":","timesteps","(","int",")",":","number","of","timesteps","to","embed","the","data","onehot_input","(","bool",")",":","tokenizer","(","str",")",":","name","of","the","tokenizer","function","within","this","class","to","use","on","the","data"]
docstr: Tokenizer which breaks on newlines.
  
  Arguments:
      s (str): String to tokenize.
  
  Returns:
      str: String with "<eos>" in place of newlines.
docstr_tok: ["Tokenizer","which","breaks","on","newlines",".","Arguments",":","s","(","str",")",":","String","to","tokenize",".","Returns",":","str",":","String","with","``","<","eos",">","''","in","place","of","newlines","."]
code: def newline_tokenizer(s):
      """
      Tokenizer which breaks on newlines.
  
      Arguments:
          s (str): String to tokenize.
  
      Returns:
          str: String with "<eos>" in place of newlines.
  
      """
      # replace newlines with '<eos>' so that
      # the newlines count as words
      return s.replace("\n", "<eos>").split()
  
imports: import logging
  import numpy
  import os
  import h5py
  from neon.data.dataiterator import NervanaDataIterator, ArrayIterator
  from neon.data.datasets import Dataset
  from neon.data.text_preprocessing import pad_sentences, pad_data
label_desc: Use image node_id 2 for calling the PTB obj's underlying member method code with example usage: obj.newline_tokenizer(s) and returns: s