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 _can_produce(self):
      if self.is_done:
          return False
  
      algo = self.algo
      is_sequential = algo.no_parallelization
  
      if not is_sequential and hasattr(algo, "optim"):
          is_sequential = algo.optim.no_parallelization
  
      if is_sequential and algo.num_ask > (
          algo.num_tell - algo.num_tell_not_asked
      ):
          logger.debug(
              "Cannot produce new trials because %d > (%d - %d)",
              algo.num_ask,
              algo.num_tell,
              algo.num_tell_not_asked,
          )
          return False
  
      return True
  
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 7 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj._can_produce() and returns: True, False, False