class_docstr: The (reduced) version of the Global Database of Events, Language, and
  Tone (GDELT) dataset used in the `"Do We Really Need Complicated Model
  Architectures for Temporal Networks?" <http://arxiv.org/abs/2302.11636>`_
  paper, consisting of events collected from 2016 to 2020.
  
  Each node (actor) holds a 413-dimensional multi-hot feature vector that
  represents CAMEO codes attached to the corresponding actor to server.
  
  Each edge (event) holds a timestamp and a 186-dimensional multi-hot vector
  representing CAMEO codes attached to the corresponding event to server.
  
  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
      * - 8,831
        - 1,912,909
        - 413
        -
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code: def process(self) -> None:
      import pandas as pd
  
      x = torch.load(self.raw_paths[0])
      df = pd.read_csv(self.raw_paths[1])
      edge_attr = torch.load(self.raw_paths[2])
  
      row = torch.from_numpy(df["src"].values)
      col = torch.from_numpy(df["dst"].values)
      edge_index = torch.stack([row, col], dim=0)
      time = torch.from_numpy(df["time"].values).to(torch.long)
  
      data = Data(
          x=x, edge_index=edge_index, edge_attr=edge_attr, time=time
      )
      data = (
          data
          if self.pre_transform is None
          else self.pre_transform(data)
      )
  
      self.save([data], self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/gdelt_lite.py
imports: import os
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
label_desc: Use image node_id 5 for calling the GDELTLite obj's underlying member method code with example usage: obj.process() without return types