class_docstr: The Global Database of Events, Language, and Tone (GDELT) dataset used
  in the, *e.g.*, `"Recurrent Event Network for Reasoning over Temporal
  Knowledge Graphs" <https://arxiv.org/abs/1904.05530>`_ paper, consisting of
  events collected from 1/1/2018 to 1/31/2018 (15 minutes time granularity).
  
  Args:
      root (str): Root directory where the dataset should be saved.
      split (str, optional): If :obj:`"train"`, loads the training dataset.
          If :obj:`"val"`, loads the validation dataset.
          If :obj:`"test"`, loads the test dataset. (default: :obj:`"train"`)
      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`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def num_nodes(self) -> int:
      return 7691
  
file_path: pytorch_geometric/torch_geometric/datasets/gdelt.py
imports: from typing import Callable, List, Optional
  import torch
  from torch import Tensor
  from torch_geometric.data import download_url
  from torch_geometric.datasets.icews import EventDataset
  from torch_geometric.io import read_txt_array
label_desc: Use image node_id 2 for calling the GDELT obj's underlying member method code with example usage: obj.num_nodes() and returns: int