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: Reset the state of the algorithm based on the given state_dict
  
  Parameters
  ----------
  state_dict: dict
      Dictionary representing state of an algorithm
docstr_tok: ["Reset","the","state","of","the","algorithm","based","on","the","given","state_dict","Parameters","--","--","--","--","--","state_dict",":","dict","Dictionary","representing","state","of","an","algorithm"]
code: def set_state(self, state_dict):
      """Reset the state of the algorithm based on the given state_dict
  
      Parameters
      ----------
      state_dict: dict
          Dictionary representing state of an algorithm
  
      """
      super().set_state(state_dict)
      self.algo = pickle.loads(state_dict["algo"])
      self._is_done = state_dict["_is_done"]
      self._fresh = state_dict["_fresh"]
      self._trial_mapping = state_dict["_trial_mapping"]
  
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 4 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj.set_state(state_dict) without return types