class_docstr: The Hetionet dataset from [himmelstein2017]_.
  
  In its publication [himmelstein2017]_, it is demonstrated to be useful for link prediction in drug repositioning
  and made publicly available through its `GitHub repository <https://github.com/hetio/hetionet>`_ in several formats.
  The link prediction algorithm showcased does not rely on embeddings, which leaves room for interesting comparison.
  One such comparison was made during the master's thesis of Lingling Xu [xu2019]_.
  ---
  name: Hetionet
  citation:
      author: Himmelstein
      year: 2017
      link: https://doi.org/10.7554/eLife.26726
      github: hetio/hetionet
  single: true
  statistics:
      entities: 45158
      relations: 24
      triples: 2250197
      training: 1800157
      testing: 225020
      validation: 225020
docstr: Initialize the `Hetionet <https://github.com/hetio/hetionet>`_ dataset from [himmelstein2017]_.
  
  :param random_state: The random seed to use in splitting the dataset. Defaults to 0.
  :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.SingleTabbedDataset`.
code: def __init__(
      self,
      random_state: TorchRandomHint = 0,
      **kwargs,
  ):
      """Initialize the `Hetionet <https://github.com/hetio/hetionet>`_ dataset from [himmelstein2017]_.
  
      :param random_state: The random seed to use in splitting the dataset. Defaults to 0.
      :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.SingleTabbedDataset`.
      """
      super().__init__(
          url=URL,
          random_state=random_state,
          **kwargs,
      )
  
file_path: pykeen/src/pykeen/datasets/hetionet.py
imports: import click
  from docdata import parse_docdata
  from more_click import verbose_option
  from .base import SingleTabbedDataset
  from ..typing import TorchRandomHint
label_desc: Use image node_id 1 to create a new Hetionet object from inherited base classes: SingleTabbedDataset with example: obj = Hetionet(random_state)