class_docstr: QM7b dataset for graph property prediction (regression)
  
  This dataset consists of 7,211 molecules with 14 regression targets.
  Nodes means atoms and edges means bonds. Edge data 'h' means
  the entry of Coulomb matrix.
  
  Reference: `<http://quantum-machine.org/datasets/>`_
  
  Statistics:
  
  - Number of graphs: 7,211
  - Number of regression targets: 14
  - Average number of nodes: 15
  - Average number of edges: 245
  - Edge feature size: 1
  
  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_tasks : int
      Number of prediction tasks
  num_labels : int
      (DEPRECATED, use num_tasks instead) Number of prediction tasks
  
  Raises
  ------
  UserWarning
      If the raw data is changed in the remote server by the author.
  
  Examples
  --------
  >>> data = QM7bDataset()
  >>> data.num_tasks
  14
  >>>
  >>> # iterate over the dataset
  >>> for g, label in data:
  ...     edge_feat = g.edata['h']  # get edge feature
  ...     # your code here...
  ...
  >>>
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docstr: Number of prediction tasks.
docstr_tok: ["Number","of","prediction","tasks","."]
code: def num_classes(self):
      """Number of prediction tasks."""
      return 14
  
file_path: dgl/python/dgl/data/qm7b.py
imports: import os
  from scipy import io
  from ..None import backend
  from ..convert import graph
  from .dgl_dataset import DGLDataset
  from .utils import check_sha1, download, load_graphs, save_graphs
label_desc: Use image node_id 10 for calling the QM7bDataset obj's underlying member method code with example usage: obj.num_classes() and returns: int