class_docstr: Tolokers dataset from the 'A Critical Look at the Evaluation of GNNs under Heterophily:
  Are We Really Making Progress? <https://arxiv.org/abs/2302.11640>'__ paper.
  
  This dataset is based on data from the Toloka crowdsourcing platform. The nodes represent
  tolokers (workers). An edge connects two tolokers if they have worked on the same task. The goal
  is to predict which tolokers have been banned in one of the projects. Node features are based on
  the worker’s profile information and task performance statistics.
  
  Statistics:
  
  - Nodes: 11758
  - Edges: 1038000
  - Classes: 2
  - Node features: 10
  - 10 train/val/test splits
  
  Parameters
  ----------
  raw_dir : str, optional
      Raw file directory to store the processed data. Default: ~/.dgl/
  force_reload : bool, optional
      Whether to re-download the data source. Default: False
  verbose : bool, optional
      Whether to print 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. Default: None
  
  Attributes
  ----------
  num_classes : int
      Number of node classes
  
  
  Examples
  --------
  
  >>> from dgl.data import TolokersDataset
  >>> dataset = TolokersDataset()
  >>> g = dataset[0]
  >>> num_classes = dataset.num_classes
  
  >>> # get node features
  >>> feat = g.ndata["feat"]
  
  >>> # get the first data split
  >>> train_mask = g.ndata["train_mask"][:, 0]
  >>> val_mask = g.ndata["val_mask"][:, 0]
  >>> test_mask = g.ndata["test_mask"][:, 0]
  
  >>> # get labels
  >>> label = g.ndata['label']
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code: def __init__(
      self,
      raw_dir=None,
      force_reload=False,
      verbose=True,
      transform=None,
  ):
      super(TolokersDataset, self).__init__(
          name="tolokers",
          raw_dir=raw_dir,
          force_reload=force_reload,
          verbose=verbose,
          transform=transform,
      )
  
imports: import os
  import numpy
  from ..convert import graph
  from ..transforms.functional import to_bidirected
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import download
label_desc: Use image node_id 1 to create a new TolokersDataset object from inherited base classes: HeterophilousGraphDataset with example: obj = TolokersDataset(raw_dir, force_reload, verbose, transform)