class_docstr: BitcoinOTC dataset for fraud detection
  
  This is who-trusts-whom network of people who trade using Bitcoin on
  a platform called Bitcoin OTC. Since Bitcoin users are anonymous,
  there is a need to maintain a record of users' reputation to prevent
  transactions with fraudulent and risky users.
  
  Offical website: `<https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html>`_
  
  Bitcoin OTC dataset statistics:
  
  - Nodes: 5,881
  - Edges: 35,592
  - Range of edge weight: -10 to +10
  - Percentage of positive edges: 89%
  
  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
  ----------
  graphs : list
      A list of DGLGraph objects
  is_temporal : bool
      Indicate whether the graphs are temporal graphs
  
  Raises
  ------
  UserWarning
      If the raw data is changed in the remote server by the author.
  
  Examples
  --------
  >>> dataset = BitcoinOTCDataset()
  >>> len(dataset)
  136
  >>> for g in dataset:
  ....    # get edge feature
  ....    edge_weights = g.edata['h']
  ....    # your code here
  >>>
docstr: Are the graphs temporal graphs
  
  Returns
  -------
  bool
docstr_tok: ["Are","the","graphs","temporal","graphs","Returns","--","--","--","-","bool"]
code: def is_temporal(self):
      r"""Are the graphs temporal graphs
  
      Returns
      -------
      bool
      """
      return True
  
imports: import datetime
  import gzip
  import os
  import shutil
  import numpy
  from ..None import backend
  from ..convert import graph
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import check_sha1, download, load_graphs, makedirs, save_graphs
label_desc: Use image node_id 11 for calling the BitcoinOTCDataset obj's underlying member method code with example usage: obj.is_temporal() and returns: True