class_docstr: This class defines methods for loading and iterating over text datasets.
class_docstr_tok: ["This","class","defines","methods","for","loading","and","iterating","over","text","datasets","."]
docstr: Construct a text dataset object.
  
  Arguments:
      time_steps (int) : Length of a sequence.
      path (str) : Path to text file.
      vocab (python.set) : A set of unique tokens.
      tokenizer (function) : Tokenizer function.
      onehot_input (boolean): One-hot representation of input
      reverse_target (boolean): for sequence to sequence models, set to
                                True to reverse target sequence. Also
                                disables shifting target by one.
      get_prev_target (boolean): for sequence to sequence models, set to
                                 True for training data to provide correct
                                 target from previous time step as decoder
                                 input. If condition, shape will be a tuple
                                 of shapes, corresponding to encoder and
                                 decoder inputs.
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code: def __init__(
      self,
      time_steps,
      path,
      vocab=None,
      tokenizer=None,
      onehot_input=True,
      reverse_target=False,
      get_prev_target=False,
  ):
      """
      Construct a text dataset object.
  
      Arguments:
          time_steps (int) : Length of a sequence.
          path (str) : Path to text file.
          vocab (python.set) : A set of unique tokens.
          tokenizer (function) : Tokenizer function.
          onehot_input (boolean): One-hot representation of input
          reverse_target (boolean): for sequence to sequence models, set to
                                    True to reverse target sequence. Also
                                    disables shifting target by one.
          get_prev_target (boolean): for sequence to sequence models, set to
                                     True for training data to provide correct
                                     target from previous time step as decoder
                                     input. If condition, shape will be a tuple
                                     of shapes, corresponding to encoder and
                                     decoder inputs.
      """
      super(Text, self).__init__(name=None)
  
      self.seq_length = time_steps
      self.onehot_input = onehot_input
      self.batch_index = 0
      self.reverse_target = reverse_target
      self.get_prev_target = get_prev_target
  
      X, y = self._get_data(path, tokenizer, vocab)
  
      # reshape to preserve sentence continuity across batches
      self.X = X.reshape(self.be.bsz, self.nbatches, time_steps)
      self.y = y.reshape(self.be.bsz, self.nbatches, time_steps)
  
      # stuff below this comment needs to be cleaned up and commented
      self.nout = self.nclass
      if self.onehot_input:
          self.shape = (self.nout, time_steps)
          self.dev_X = self.be.iobuf((self.nout, time_steps))
          if self.get_prev_target:
              self.dev_Z = self.be.iobuf((self.nout, time_steps))
      else:
          self.shape = (time_steps, 1)
          self.dev_X = self.be.iobuf(time_steps, dtype=np.int32)
          if self.get_prev_target:
              self.dev_Z = self.be.iobuf(time_steps, dtype=np.int32)
      self.decoder_shape = self.shape
  
      self.dev_y = self.be.iobuf((self.nout, time_steps))
      self.dev_lbl = self.be.iobuf(time_steps, dtype=np.int32)
      self.dev_lblflat = self.dev_lbl.reshape((1, -1))
  
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 Text object from inherited base classes: NervanaDataIterator with example: obj = Text(time_steps, path, vocab, tokenizer, onehot_input, reverse_target, get_prev_target)