code: def test_ask(
      search_space: Dict[str, BaseDistribution], x0: Dict[str, Any]
  ) -> None:
      trials = [
          _create_frozen_trial(
              params=x0, param_distributions=search_space, number=i
          )
          for i in range(3)
      ]
  
      # Create 0-th individual.
      optimizer = _Optimizer(
          search_space, x0, 0.2, None, {"popsize": 3, "seed": 1}
      )
      last_told = optimizer.tell(trials, StudyDirection.MINIMIZE)
      params0 = optimizer.ask(trials, last_told)
  
      # Ignore parameters with incompatible distributions and create new individual.
      optimizer = _Optimizer(
          search_space, x0, 0.2, None, {"popsize": 3, "seed": 1}
      )
      last_told = optimizer.tell(trials, StudyDirection.MINIMIZE)
      distributions = trials[0].distributions.copy()
      distributions["additional"] = FloatDistribution(0, 100)
      trials.append(
          _create_frozen_trial(
              params=x0,
              param_distributions=search_space,
              number=len(trials),
          )
      )
      params1 = optimizer.ask(trials, last_told)
  
      assert params0 != params1
      assert "additional" not in params1
  
      # Create first individual.
      optimizer = _Optimizer(
          search_space, x0, 0.2, None, {"popsize": 3, "seed": 1}
      )
      trials.append(
          _create_frozen_trial(
              params=x0,
              param_distributions=search_space,
              number=len(trials),
          )
      )
      last_told = optimizer.tell(trials, StudyDirection.MINIMIZE)
      params2 = optimizer.ask(trials, last_told)
  
      assert params0 != params2
  
      optimizer = _Optimizer(
          search_space, x0, 0.2, None, {"popsize": 3, "seed": 1}
      )
      last_told = optimizer.tell(trials, StudyDirection.MINIMIZE)
      # Other worker adds three trials.
      for _ in range(3):
          trials.append(
              _create_frozen_trial(
                  params=x0,
                  param_distributions=search_space,
                  number=len(trials),
              )
          )
      params3 = optimizer.ask(trials, last_told)
  
      assert params0 != params3
      assert params2 != params3
  
imports: import math
  from typing import Any
  from typing import Dict
  from unittest.mock import call
  from unittest.mock import patch
  import warnings
  import _pytest.capture
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.distributions import BaseDistribution
  from optuna.distributions import CategoricalDistribution
  from optuna.distributions import FloatDistribution
  from optuna.distributions import IntDistribution
  from optuna.integration.cma import _Optimizer
  from optuna.study._study_direction import StudyDirection
  from optuna.testing.distributions import UnsupportedDistribution
  from optuna.testing.trials import _create_frozen_trial
  from optuna.trial import Trial
  from optuna.trial import TrialState
label_desc: Use image node_id 8 for calling the TestOptimizer obj's underlying member method code with example usage: obj.test_ask(search_space, x0) without return types