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: Return a state dict that can be used to reset the state of the algorithm.
docstr_tok: ["Return","a","state","dict","that","can","be","used","to","reset","the","state","of","the","algorithm","."]
code: def state_dict(self):
      """Return a state dict that can be used to reset the state of the algorithm."""
      state_dict = super().state_dict
      state_dict["algo"] = pickle.dumps(self.algo)  # type: ignore
      state_dict["_is_done"] = self._is_done
      state_dict["_fresh"] = self._fresh
      state_dict["_trial_mapping"] = {
          trial_id: list(suggestions)
          for trial_id, suggestions in self._trial_mapping.items()
      }
      return state_dict
  
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 3 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj.state_dict() and returns: state_dict