class_docstr: The citation network datasets :obj:`"Cora"`, :obj:`"CiteSeer"` and
  :obj:`"PubMed"` from the `"Revisiting Semi-Supervised Learning with Graph
  Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper.
  Nodes represent documents and edges represent citation links.
  Training, validation and test splits are given by binary masks.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"Cora"`, :obj:`"CiteSeer"`,
          :obj:`"PubMed"`).
      split (str, optional): The type of dataset split (:obj:`"public"`,
          :obj:`"full"`, :obj:`"geom-gcn"`, :obj:`"random"`).
          If set to :obj:`"public"`, the split will be the public fixed split
          from the `"Revisiting Semi-Supervised Learning with Graph
          Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper.
          If set to :obj:`"full"`, all nodes except those in the validation
          and test sets will be used for training (as in the
          `"FastGCN: Fast Learning with Graph Convolutional Networks via
          Importance Sampling" <https://arxiv.org/abs/1801.10247>`_ paper).
          If set to :obj:`"geom-gcn"`, the 10 public fixed splits from the
          `"Geom-GCN: Geometric Graph Convolutional Networks"
          <https://openreview.net/forum?id=S1e2agrFvS>`_ paper are given.
          If set to :obj:`"random"`, train, validation, and test sets will be
          randomly generated, according to :obj:`num_train_per_class`,
          :obj:`num_val` and :obj:`num_test`. (default: :obj:`"public"`)
      num_train_per_class (int, optional): The number of training samples
          per class in case of :obj:`"random"` split. (default: :obj:`20`)
      num_val (int, optional): The number of validation samples in case of
          :obj:`"random"` split. (default: :obj:`500`)
      num_test (int, optional): The number of test samples in case of
          :obj:`"random"` split. (default: :obj:`1000`)
      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
      * - Cora
        - 2,708
        - 10,556
        - 1,433
        - 7
      * - CiteSeer
        - 3,327
        - 9,104
        - 3,703
        - 6
      * - PubMed
        - 19,717
        - 88,648
        - 500
        - 3
code: def raw_file_names(self) -> List[str]:
      names = [
          "x",
          "tx",
          "allx",
          "y",
          "ty",
          "ally",
          "graph",
          "test.index",
      ]
      return [f"ind.{self.name.lower()}.{name}" for name in names]
  
file_path: pytorch_geometric/torch_geometric/datasets/planetoid.py
imports: import os.path
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import InMemoryDataset
  from torch_geometric.io import fs, read_planetoid_data
label_desc: Use image node_id 4 for calling the Planetoid obj's underlying member method code with example usage: obj.raw_file_names() and returns: unknown