class_docstr: The temporal graph datasets
  from the `"JODIE: Predicting Dynamic Embedding
  Trajectory in Temporal Interaction Networks"
  <https://cs.stanford.edu/~srijan/pubs/jodie-kdd2019.pdf>`_ paper.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"Reddit"`,
          :obj:`"Wikipedia"`, :obj:`"MOOC"`, and :obj:`"LastFM"`).
      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
  
      * - Name
        - #nodes
        - #edges
        - #features
        - #classes
      * - Reddit
        - 6,509
        - 25,470
        - 172
        - 1
      * - Wikipedia
        - 9,227
        - 157,474
        - 172
        - 2
      * - MOOC
        - 7,144
        - 411,749
        - 4
        - 2
      * - LastFM
        - 1,980
        - 1,293,103
        - 2
        - 1
code: def download(self) -> None:
      download_url(self.url.format(self.name), self.raw_dir)
  
file_path: pytorch_geometric/torch_geometric/datasets/jodie.py
imports: import os.path
  from typing import Callable, Optional
  import torch
  from torch_geometric.data import InMemoryDataset, TemporalData, download_url
label_desc: Use image node_id 6 for calling the JODIEDataset obj's underlying member method code with example usage: obj.download() without return types