class_docstr: Stanford Sentiment Treebank dataset.
  
  Each sample is the constituency tree of a sentence. The leaf nodes
  represent words. The word is a int value stored in the ``x`` feature field.
  The non-leaf node has a special value ``PAD_WORD`` in the ``x`` field.
  Each node also has a sentiment annotation: 5 classes (very negative,
  negative, neutral, positive and very positive). The sentiment label is a
  int value stored in the ``y`` feature field.
  Official site: `<http://nlp.stanford.edu/sentiment/index.html>`_
  
  Statistics:
  
  - Train examples: 8,544
  - Dev examples: 1,101
  - Test examples: 2,210
  - Number of classes for each node: 5
  
  Parameters
  ----------
  mode : str, optional
      Should be one of ['train', 'dev', 'test', 'tiny']
      Default: train
  glove_embed_file : str, optional
      The path to pretrained glove embedding file.
      Default: None
  vocab_file : str, optional
      Optional vocabulary file. If not given, the default vacabulary file is used.
      Default: None
  raw_dir : str
      Raw file directory to download/contains the input data directory.
      Default: ~/.dgl/
  force_reload : bool
      Whether to reload the dataset. Default: False
  verbose : bool
      Whether to print out progress information. Default: True.
  transform : callable, optional
      A transform that takes in a :class:`~dgl.DGLGraph` object and returns
      a transformed version. The :class:`~dgl.DGLGraph` object will be
      transformed before every access.
  
  Attributes
  ----------
  vocab : OrderedDict
      Vocabulary of the dataset
  num_classes : int
      Number of classes for each node
  pretrained_emb: Tensor
      Pretrained glove embedding with respect the vocabulary.
  vocab_size : int
      The size of the vocabulary
  
  Notes
  -----
  All the samples will be loaded and preprocessed in the memory first.
  
  Examples
  --------
  >>> # get dataset
  >>> train_data = SSTDataset()
  >>> dev_data = SSTDataset(mode='dev')
  >>> test_data = SSTDataset(mode='test')
  >>> tiny_data = SSTDataset(mode='tiny')
  >>>
  >>> len(train_data)
  8544
  >>> train_data.num_classes
  5
  >>> glove_embed = train_data.pretrained_emb
  >>> train_data.vocab_size
  19536
  >>> train_data[0]
  Graph(num_nodes=71, num_edges=70,
    ndata_schemes={'x': Scheme(shape=(), dtype=torch.int64), 'y': Scheme(shape=(), dtype=torch.int64), 'mask': Scheme(shape=(), dtype=torch.int64)}
    edata_schemes={})
  >>> for tree in train_data:
  ...     input_ids = tree.ndata['x']
  ...     labels = tree.ndata['y']
  ...     mask = tree.ndata['mask']
  ...     # your code here
docstr: Vocabulary
  
  Returns
  -------
  OrderedDict
docstr_tok: ["Vocabulary","Returns","--","--","--","-","OrderedDict"]
code: def vocab(self):
      r"""Vocabulary
  
      Returns
      -------
      OrderedDict
      """
      return self._vocab
  
imports: from __future__ import absolute_import
  import os
  from collections import OrderedDict
  import networkx
  import numpy
  from ..None import backend
  from ..convert import from_networkx
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import _get_dgl_url, deprecate_property, load_graphs, load_info, save_graphs, save_info
label_desc: Use image node_id 9 for calling the SSTDataset obj's underlying member method code with example usage: obj.vocab() and returns: self