class_docstr: The Bitcoin-OTC dataset from the `"EvolveGCN: Evolving Graph
  Convolutional Networks for Dynamic Graphs"
  <https://arxiv.org/abs/1902.10191>`_ paper, consisting of 138
  who-trusts-whom networks of sequential time steps.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      edge_window_size (int, optional): The window size for the existence of
          an edge in the graph sequence since its initial creation.
          (default: :obj:`10`)
      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 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - 138
        - 6,005
        - ~2,573.2
        - 0
        - 0
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code: def num_nodes(self) -> int:
      assert isinstance(self._data, Data)
      assert self._data.edge_index is not None
      return int(self._data.edge_index.max()) + 1
  
file_path: pytorch_geometric/torch_geometric/datasets/bitcoin_otc.py
imports: import datetime
  import os
  from typing import Callable, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_gz
label_desc: Use image node_id 4 for calling the BitcoinOTC obj's underlying member method code with example usage: obj.num_nodes() and returns: unknown