code: def _is_compatible(self, trial: FrozenTrial) -> bool:
      # Thanks to `intersection_search_space()` function, in sequential optimization,
      # the parameters of complete trials are always compatible with the search space.
      #
      # However, in distributed optimization, incompatible trials may complete on a worker
      # just after an intersection search space is calculated on another worker.
  
      for name, distribution in self._search_space.items():
          if name not in trial.params:
              return False
  
          distributions.check_distribution_compatibility(
              distribution, trial.distributions[name]
          )
          param_value = trial.params[name]
          param_internal_value = distribution.to_internal_repr(
              param_value
          )
          if not distribution._contains(param_internal_value):
              return False
  
      return True
  
imports: import math
  import random
  from typing import Any
  from typing import Container
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Sequence
  import numpy
  import optuna
  from optuna import distributions
  from optuna import logging
  from optuna._deprecated import deprecated_class
  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.samplers import BaseSampler
  from optuna.search_space import IntersectionSearchSpace
  from optuna.study import Study
  from optuna.study import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 4 for calling the _Optimizer obj's underlying member method code with example usage: obj._is_compatible(trial) and returns: True, False, False