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 __init__(
      self,
      timesteps,
      path=".",
      onehot_input=True,
      tokenizer=None,
      reverse_target=False,
      get_prev_target=False,
  ):
      url = "https://raw.githubusercontent.com/wojzaremba/lstm/master/data"
      self.filemap = {"train": 5101618, "test": 449945, "valid": 399782}
      keys = list(self.filemap.keys())
      filenames = [self.gen_filename(phase) for phase in keys]
      sizes = [self.filemap[phase] for phase in keys]
      super(PTB, self).__init__(filenames, url, sizes, path=path)
      self.timesteps = timesteps
      self.onehot_input = onehot_input
      self.tokenizer = tokenizer
      if tokenizer is not None:
          assert hasattr(self, self.tokenizer)
          self.tokenizer_func = getattr(self, self.tokenizer)
      else:
          self.tokenizer_func = None
  
      self.reverse_target = reverse_target
      self.get_prev_target = get_prev_target
  
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 1 to create a new PTB object from inherited base classes: Dataset with example: obj = PTB(timesteps, path, onehot_input, tokenizer, reverse_target, get_prev_target)