class_docstr: The Airports dataset from the `"struc2vec: Learning Node
  Representations from Structural Identity"
  <https://arxiv.org/abs/1704.03165>`_ paper, where nodes denote airports
  and labels correspond to activity levels.
  Features are given by one-hot encoded node identifiers, as described in the
  `"GraLSP: Graph Neural Networks with Local Structural Patterns"
  ` <https://arxiv.org/abs/1911.07675>`_ paper.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"USA"`, :obj:`"Brazil"`,
          :obj:`"Europe"`).
      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`)
code: def __repr__(self) -> str:
      return f"{self.name.capitalize()}Airports()"
  
file_path: pytorch_geometric/torch_geometric/datasets/airports.py
imports: import os.path
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 8 for calling the Airports obj's underlying member method code with example usage: obj.__repr__() and returns: self, str