class_docstr: The relational link prediction datasets from the
  `"Modeling Relational Data with Graph Convolutional Networks"
  <https://arxiv.org/abs/1703.06103>`_ paper.
  Training and test splits are given by sets of triplets.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"FB15k-237"`).
      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
      :header-rows: 1
  
      * - #nodes
        - #edges
        - #features
        - #classes
      * - 14,541
        - 544,230
        - 0
        - 0
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code: def raw_dir(self) -> str:
      return osp.join(self.root, self.name, "raw")
  
file_path: pytorch_geometric/torch_geometric/datasets/rel_link_pred_dataset.py
imports: import os.path
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url
label_desc: Use image node_id 3 for calling the RelLinkPredDataset obj's underlying member method code with example usage: obj.raw_dir() and returns: osp