class_docstr: Wraps the nevergrad library to expose its algorithm to orion
  
  Parameters
  ----------
  space: `orion.algo.space.Space`
      Optimisation space with priors for each dimension.
  model_name: str
      Nevergrad model to use as optimizer
  budget: int
      Maximal number of trial to generated
  num_workers: int
      Number of worker to use
  seed: None, int or sequence of int
      Seed for the random number generator used to sample new trials.
      Default: ``None``
docstr: Seed the state of the random number generator.
  
  Parameters
  ----------
  seed: int
      Integer seed for the random number generator.
docstr_tok: ["Seed","the","state","of","the","random","number","generator",".","Parameters","--","--","--","--","--","seed",":","int","Integer","seed","for","the","random","number","generator","."]
code: def seed_rng(self, seed):
      """Seed the state of the random number generator.
  
      Parameters
      ----------
      seed: int
          Integer seed for the random number generator.
  
      """
      self.algo.parametrization.random_state.seed(seed)
  
imports: from __future__ import annotations
  import logging
  import pickle
  from typing import Callable, Iterable, Sequence, SupportsInt
  from orion.algo.base import BaseAlgorithm
  from orion.algo.space import Categorical, Dimension, Fidelity, Integer, Real, Space
  from orion.core.utils.format_trials import dict_to_trial
  from orion.core.utils.module_import import ImportOptional
  from orion.core.worker.trial import Trial
label_desc: Use image node_id 2 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj.seed_rng(seed) without return types