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"]
code: def gen_iterators(self):
      self.load_data()
  
      self._data_dict = {}
      self.vocab = None
      for phase in ["train", "test", "valid"]:
          file_path = self.file_paths[phase]
          get_prev_target = (
              self.get_prev_target if phase is "train" else False
          )
          self._data_dict[phase] = Text(
              self.timesteps,
              file_path,
              tokenizer=self.tokenizer_func,
              onehot_input=self.onehot_input,
              vocab=self.vocab,
              reverse_target=self.reverse_target,
              get_prev_target=get_prev_target,
          )
          if self.vocab is None:
              self.vocab = self._data_dict["train"].vocab
      return self._data_dict
  
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 5 for calling the PTB obj's underlying member method code with example usage: obj.gen_iterators() and returns: self