class_docstr: Pubmed citation network dataset.
  
  Nodes mean scientific publications and edges
  mean citation relationships. Each node has a
  predefined feature with 500 dimensions. The
  dataset is designed for the node classification
  task. The task is to predict the category of
  certain publication.
  
  Statistics:
  
  - Nodes: 19717
  - Edges: 88651
  - Number of Classes: 3
  - Label Split:
  
      - Train: 60
      - Valid: 500
      - Test: 1000
  
  Parameters
  -----------
  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.
  reverse_edge : bool
      Whether to add reverse edges in graph. 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.
  reorder : bool
      Whether to reorder the graph using :func:`~dgl.reorder_graph`. Default: False.
  
  Attributes
  ----------
  num_classes: int
      Number of label classes
  
  Notes
  -----
  The node feature is row-normalized.
  
  Examples
  --------
  >>> dataset = PubmedGraphDataset()
  >>> g = dataset[0]
  >>> num_class = dataset.num_of_class
  >>>
  >>> # get node feature
  >>> feat = g.ndata['feat']
  >>>
  >>> # get data split
  >>> train_mask = g.ndata['train_mask']
  >>> val_mask = g.ndata['val_mask']
  >>> test_mask = g.ndata['test_mask']
  >>>
  >>> # get labels
  >>> label = g.ndata['label']
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docstr: Gets the graph object
  
  Parameters
  -----------
  idx: int
      Item index, PubmedGraphDataset has only one graph object
  
  Return
  ------
  :class:`dgl.DGLGraph`
  
      graph structure, node features and labels.
  
      - ``ndata['train_mask']``: mask for training node set
      - ``ndata['val_mask']``: mask for validation node set
      - ``ndata['test_mask']``: mask for test node set
      - ``ndata['feat']``: node feature
      - ``ndata['label']``: ground truth labels
code: def __getitem__(self, idx):
      r"""Gets the graph object
  
      Parameters
      -----------
      idx: int
          Item index, PubmedGraphDataset has only one graph object
  
      Return
      ------
      :class:`dgl.DGLGraph`
  
          graph structure, node features and labels.
  
          - ``ndata['train_mask']``: mask for training node set
          - ``ndata['val_mask']``: mask for validation node set
          - ``ndata['test_mask']``: mask for test node set
          - ``ndata['feat']``: node feature
          - ``ndata['label']``: ground truth labels
      """
      return super(PubmedGraphDataset, self).__getitem__(idx)
  
imports: from __future__ import absolute_import
  import os, sys
  import pickle
  import warnings
  import networkx
  import numpy
  import scipy.sparse
  from ..None import backend, batch, convert
  from ..convert import from_networkx, graph, to_networkx
  from ..transforms import reorder_graph
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import _get_dgl_url, deprecate_function, deprecate_property, generate_mask_tensor, load_graphs, load_info, makedirs, save_graphs, save_info
label_desc: Use image node_id 2 for calling the PubmedGraphDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: super