class_docstr: The Coauthor CS and Coauthor Physics networks from the
  `"Pitfalls of Graph Neural Network Evaluation"
  <https://arxiv.org/abs/1811.05868>`_ paper.
  Nodes represent authors that are connected by an edge if they co-authored a
  paper.
  Given paper keywords for each author's papers, the task is to map authors
  to their respective field of study.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"CS"`, :obj:`"Physics"`).
      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
      * - CS
        - 18,333
        - 163,788
        - 6,805
        - 15
      * - Physics
        - 34,493
        - 495,924
        - 8,415
        - 5
code: def processed_file_names(self) -> str:
      return "data.pt"
  
file_path: pytorch_geometric/torch_geometric/datasets/coauthor.py
imports: import os.path
  from typing import Callable, Optional
  from torch_geometric.data import InMemoryDataset, download_url
  from torch_geometric.io import read_npz
label_desc: Use image node_id 5 for calling the Coauthor obj's underlying member method code with example usage: obj.processed_file_names() and returns: str