class_docstr: A subset of the DBLP computer science bibliography website, as
  collected in the `"MAGNN: Metapath Aggregated Graph Neural Network for
  Heterogeneous Graph Embedding" <https://arxiv.org/abs/2002.01680>`_ paper.
  DBLP is a heterogeneous graph containing four types of entities - authors
  (4,057 nodes), papers (14,328 nodes), terms (7,723 nodes), and conferences
  (20 nodes).
  The authors are divided into four research areas (database, data mining,
  artificial intelligence, information retrieval).
  Each author is described by a bag-of-words representation of their paper
  keywords.
  
  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.HeteroData` 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.HeteroData` 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: 20 10 10 10
      :header-rows: 1
  
      * - Node/Edge Type
        - #nodes/#edges
        - #features
        - #classes
      * - Author
        - 4,057
        - 334
        - 4
      * - Paper
        - 14,328
        - 4,231
        -
      * - Term
        - 7,723
        - 50
        -
      * - Conference
        - 20
        - 0
        -
      * - Author-Paper
        - 196,425
        -
        -
      * - Paper-Term
        - 85,810
        -
        -
      * - Conference-Paper
        - 14,328
        -
        -
code: def raw_file_names(self) -> List[str]:
      return [
          "adjM.npz",
          "features_0.npz",
          "features_1.npz",
          "features_2.npy",
          "labels.npy",
          "node_types.npy",
          "train_val_test_idx.npz",
      ]
  
file_path: pytorch_geometric/torch_geometric/datasets/dblp.py
imports: import os
  import os.path
  from itertools import product
  from typing import Callable, List, Optional
  import numpy
  import scipy.sparse
  import torch
  from torch_geometric.data import HeteroData, InMemoryDataset, download_url, extract_zip
label_desc: Use image node_id 2 for calling the DBLP obj's underlying member method code with example usage: obj.raw_file_names() and returns: list