class_docstr: Adapter for the HEBO algorithm from https://github.com/huawei-noah/HEBO
  
  Parameters
  ----------
  :param space: Optimisation space with priors for each dimension.
  :param seed: Base seed for the random number generators. Defaults to `None`, in which case the
  randomness is not seeded.
  :param parameters: Parameters for the HEBO algorithm.
docstr: Seed the random number generators.
docstr_tok: ["Seed","the","random","number","generators","."]
code: def seed_rng(self, seed: int | None) -> None:
      """Seed the random number generators."""
      logger.debug("Using a base seed of %s.", seed)
      self.random_state = RandomState.seed(seed)
  
imports: from __future__ import annotations
  import copy
  import typing
  import warnings
  from dataclasses import dataclass
  from logging import getLogger
  from typing import Any, ClassVar, Literal
  import numpy
  import pandas
  from typing_extensions import TypedDict
  from orion.algo.base import BaseAlgorithm
  from orion.algo.space import Dimension, Fidelity, Space
  from orion.core.utils.format_trials import dict_to_trial
  from orion.core.utils.module_import import ImportOptional
  from orion.core.utils.random_state import RandomState, control_randomness
  from orion.core.worker.transformer import TransformedDimension
  from orion.core.worker.trial import Trial
label_desc: Use image node_id 2 for calling the HEBO obj's underlying member method code with example usage: obj.seed_rng(seed) without return types