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``
code: def _ask(self):
      suggestion = self.algo.ask()
      if suggestion.args:
          raise RuntimeError(
              "Nevergrad sampled a trial with args but this should never happen."
              " Please report this issue at"
              " https://github.com/Epistimio/orion.algo.nevergrad/issues"
          )
      new_trial = dict_to_trial(suggestion.kwargs, self.space)
  
      if self._associate_trial(new_trial, suggestion):
          self.register(new_trial)
          return new_trial
      else:
          logger.debug("Ignoring duplicated trial")
          return None
  
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 6 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj._ask() and returns: new_trial, None