class_docstr: Datasets for neural machine translation on French / English bilingual datasets.
  Available at http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz
  
  Arguments:
      time_steps (int) : Length of a sequence.
      path (str) : Path to text file.
      tokenizer (function) : Tokenizer function.
      onehot_input (boolean): One-hot representation of input
      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.
      split (str): "train" or "valid" split of the dataset
      dataset (str): 'un2000' for the United Nations dataset or 'eurparl7'
                     for the European Parliament datset.
      subset_pct (float): Percentage of the dataset to use (100 is the full dataset)
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docstr: Tokenizer and vocab are unused but provided to match superclass method signature
docstr_tok: ["Tokenizer","and","vocab","are","unused","but","provided","to","match","superclass","method","signature"]
code: def _get_data(self, path, tokenizer, vocab):
      """
      Tokenizer and vocab are unused but provided to match superclass method signature
      """
  
      def vocab_to_dicts(vocab):
          t2i = dict((t, i) for i, t in enumerate(vocab))
          i2t = dict((i, t) for i, t in enumerate(vocab))
          return t2i, i2t
  
      # get saved processed data
      logger.debug("Loading parsed data from %s", path)
      with h5py.File(path, "r") as f:
          self.s_vocab = f["s_vocab"][:].tolist()
          self.t_vocab = f["t_vocab"][:].tolist()
          self.s_token_to_index, self.s_index_to_token = vocab_to_dicts(
              self.s_vocab
          )
          self.t_token_to_index, self.t_index_to_token = vocab_to_dicts(
              self.t_vocab
          )
          X = f["X"][:]
          y = f["y"][:]
      self.nclass = len(self.t_vocab)
  
      # Trim subset and patial minibatch
      if self.subset_pct < 100:
          X = X[: int(X.shape[0] * self.subset_pct / 100.0), :]
          y = y[: int(y.shape[0] * self.subset_pct / 100.0), :]
          logger.debug("subset %d%% of data", self.subset_pct * 100)
  
      extra_sentences = X.shape[0] % self.be.bsz
      if extra_sentences:
          X = X[:-extra_sentences, :]
          y = y[:-extra_sentences, :]
          logger.debug("removing %d extra sentences", extra_sentences)
      self.nbatches = X.shape[0] // self.be.bsz
      self.ndata = self.nbatches * self.be.bsz  # no leftovers
  
      return X, y
  
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 TextNMT obj's underlying member method code with example usage: obj._get_data(path, tokenizer, vocab) and returns: X,  y, t2i,  i2t