class_docstr: 'Computer Science (CS)' part of the Coauthor dataset for node classification task.
  
  Coauthor CS and Coauthor Physics are co-authorship graphs based on the Microsoft Academic Graph
  from the KDD Cup 2016 challenge. Here, nodes are authors, that are connected by an edge if they
  co-authored a paper; node features represent paper keywords for each author’s papers, and class
  labels indicate most active fields of study for each author.
  
  Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
  
  Statistics:
  
  - Nodes: 18,333
  - Edges: 163,788 (note that the original dataset has 81,894 edges but DGL adds
    the reverse edges and remove the duplicates, hence with a different number)
  - Number of classes: 15
  - Node feature size: 6,805
  
  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.
  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
  ----------
  num_classes : int
      Number of classes for each node.
  
  Examples
  --------
  >>> data = CoauthorCSDataset()
  >>> g = data[0]
  >>> num_class = data.num_classes
  >>> feat = g.ndata['feat']  # get node feature
  >>> label = g.ndata['label']  # get node labels
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code: def __init__(
      self,
      raw_dir=None,
      force_reload=False,
      verbose=False,
      transform=None,
  ):
      super(CoauthorCSDataset, self).__init__(
          name="coauthor_cs",
          raw_dir=raw_dir,
          force_reload=force_reload,
          verbose=verbose,
          transform=transform,
      )
  
imports: import os
  import numpy
  import scipy.sparse
  from ..None import backend, transforms
  from ..convert import graph
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import _get_dgl_url, deprecate_class, deprecate_property, load_graphs, save_graphs
label_desc: Use image node_id 1 to create a new CoauthorCSDataset object from inherited base classes: GNNBenchmarkDataset with example: obj = CoauthorCSDataset(raw_dir, force_reload, verbose, transform)