class_docstr: The Elliptic Bitcoin dataset of Bitcoin transactions from the
  `"Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional
  Networks for Financial Forensics" <https://arxiv.org/abs/1908.02591>`_
  paper.
  
  :class:`EllipticBitcoinDataset` maps Bitcoin transactions to real entities
  belonging to licit categories (exchanges, wallet providers, miners,
  licit services, etc.) versus illicit ones (scams, malware, terrorist
  organizations, ransomware, Ponzi schemes, etc.)
  
  There exists 203,769 node transactions and 234,355 directed edge payments
  flows, with two percent of nodes (4,545) labelled as illicit, and
  twenty-one percent of nodes (42,019) labelled as licit.
  The remaining transactions are unknown.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10
      :header-rows: 1
  
      * - #nodes
        - #edges
        - #features
        - #classes
      * - 203,769
        - 234,355
        - 165
        - 2
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code: def _process_df(
      self, feat_df: Any, edge_df: Any, class_df: Any
  ) -> Tuple[Any, Any, Any]:
      return feat_df, edge_df, class_df
  
file_path: pytorch_geometric/torch_geometric/datasets/elliptic.py
imports: from typing import Any, Callable, List, Optional, Tuple
  import torch
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.io import fs
label_desc: Use image node_id 5 for calling the EllipticBitcoinDataset obj's underlying member method code with example usage: obj._process_df(feat_df, edge_df, class_df) and returns: feat_df,  edge_df,  class_df