class_docstr: The PharMeBINet dataset from [koenigs2022]_.
  
  ---
  name: PharMeBINet
  citation:
      github: ckoenigs/PharMeBINet
      author: Königs
      year: 2022
      link: https://www.nature.com/articles/s41597-022-01510-3
  single: true
  statistics:
      entities: 2869407
      relations: 208
      triples: 15883653
      training: 12702210
      testing: 1587776
      validation: 1587777
docstr: Initialize the PharMeBINet dataset from [koenigs2022]_.
  
  :param random_state: An optional random state to make the training/testing/validation split reproducible.
  :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.TarFileSingleDataset`.
code: def __init__(
      self,
      random_state: TorchRandomHint = 0,
      **kwargs,
  ):
      """Initialize the PharMeBINet dataset from [koenigs2022]_.
  
      :param random_state: An optional random state to make the training/testing/validation split reproducible.
      :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.TarFileSingleDataset`.
      """
      super().__init__(
          url=RAW_URL,
          relative_path="edges.tsv",
          random_state=random_state,
          read_csv_kwargs=dict(
              usecols=["start_id", "type", "end_id"],
              sep="\t",
              dtype={"start_id": str, "end_id": str},
          ),
          **kwargs,
      )
  
file_path: pykeen/src/pykeen/datasets/pharmebinet.py
imports: import click
  from docdata import parse_docdata
  from more_click import verbose_option
  from .base import TarFileSingleDataset
  from ..typing import TorchRandomHint
label_desc: Use image node_id 1 to create a new PharMeBINet object from inherited base classes: TarFileSingleDataset with example: obj = PharMeBINet(random_state)