class_docstr: Base class for multi-objective samplers.
  
  The abstract methods of this class are the same as ones defined by
  :class:`~optuna.samplers.BaseSampler` except for taking
  multi-objective versions of study and trial instances as the arguments.
docstr: Sample parameters in a given search space.
  
  This method is called once at the beginning of each trial, i.e., right before the
  evaluation of the objective function. This method is suitable for sampling algorithms
  that use the relationship between parameters.
  
  Args:
      study:
          Target study object.
      trial:
          Target trial object.
      search_space:
          The search space returned by
          :func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space`.
  
  Returns:
      A dictionary containing the parameter names and the values.
code: def sample_relative(
      self,
      study: "multi_objective.study.MultiObjectiveStudy",
      trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
      search_space: Dict[str, BaseDistribution],
  ) -> Dict[str, Any]:
      """Sample parameters in a given search space.
  
      This method is called once at the beginning of each trial, i.e., right before the
      evaluation of the objective function. This method is suitable for sampling algorithms
      that use the relationship between parameters.
  
      Args:
          study:
              Target study object.
          trial:
              Target trial object.
          search_space:
              The search space returned by
              :func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space`.
  
      Returns:
          A dictionary containing the parameter names and the values.
  
      """
  
      raise NotImplementedError
  
file_path: optuna/optuna/multi_objective/samplers/_base.py
imports: import abc
  from typing import Any
  from typing import Dict
  from optuna import multi_objective
  from optuna._deprecated import deprecated_class
  from optuna.distributions import BaseDistribution
label_desc: Use image node_id 2 for calling the BaseMultiObjectiveSampler obj's underlying member method code with example usage: obj.sample_relative(study, trial, search_space) without return types