class_docstr: CLUSTER dataset for semi-supervised clustering task.
  
  Each graph contains 6 SBM clusters with sizes randomly selected between
  [5, 35] and probabilities p = 0.55, q = 0.25. The graphs are of sizes 40
  -190 nodes. Each node can take an input feature value in {0, 1, 2, ..., 6}
  and values 1~6 correspond to classes 0~5 respectively, while value 0 means
  that the class of the node is unknown. There is only one labeled node that
  is randomly assigned to each community and most node features are set to 0.
  
  Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
  
  Statistics:
  
  - Train examples: 10,000
  - Valid examples: 1,000
  - Test examples: 1,000
  - Number of classes for each node: 6
  
  Parameters
  ----------
  mode : str
      Must be one of ('train', 'valid', 'test').
      Default: 'train'
  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: False
  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
  --------
  >>> from dgl.data import CLUSTERDataset
  >>>
  >>> trainset = CLUSTERDataset(mode='train')
  >>>
  >>> trainset.num_classes
  6
  >>> len(trainset)
  10000
  >>> trainset[0]
  Graph(num_nodes=117, num_edges=4104,
        ndata_schemes={'label': Scheme(shape=(), dtype=torch.int16),
                       'feat': Scheme(shape=(), dtype=torch.int64)}
        edata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32)})
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code: def load(self):
      graph_path = os.path.join(
          self.save_path, "CLUSTER_{}.bin".format(self.mode)
      )
      self._graphs, _ = load_graphs(graph_path)
  
file_path: dgl/python/dgl/data/cluster.py
imports: import os
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import _get_dgl_url, load_graphs
label_desc: Use image node_id 4 for calling the CLUSTERDataset obj's underlying member method code with example usage: obj.load() without return types