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: Associate a trial with a Nevergrad suggestion.
  
  Returns
  -------
  True if the trial was not seen before, false otherwise.
docstr_tok: ["Associate","a","trial","with","a","Nevergrad","suggestion",".","Returns","--","--","--","-","True","if","the","trial","was","not","seen","before",",","false","otherwise","."]
code: def _associate_trial(self, trial: Trial, suggestion: Parameter):
      """Associate a trial with a Nevergrad suggestion.
  
      Returns
      -------
      True if the trial was not seen before, false otherwise.
  
      """
      trial_id = self.get_id(trial)
      seen = trial_id in self._trial_mapping
      if seen:
          orig_trial, existing = self._trial_mapping[trial_id]
          if orig_trial.status == "completed":
              self.algo.tell(suggestion, orig_trial.objective.value)
          else:
              existing.append(suggestion)
  
      else:
          self._trial_mapping[trial_id] = (trial, [suggestion])
  
      return not seen
  
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 5 for calling the NevergradOptimizer obj's underlying member method code with example usage: obj._associate_trial(trial, suggestion) and returns: unknown