class_docstr: Multi-objective sampler using random sampling.
  
  This sampler is based on *independent sampling*.
  See also :class:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler`
  for more details of 'independent sampling'.
  
  Example:
  
      .. testcode::
  
          import optuna
          from optuna.multi_objective.samplers import RandomMultiObjectiveSampler
  
  
          def objective(trial):
              x = trial.suggest_float("x", -5, 5)
              y = trial.suggest_float("y", -5, 5)
              return x**2, y + 10
  
  
          study = optuna.multi_objective.create_study(
              ["minimize", "minimize"], sampler=RandomMultiObjectiveSampler()
          )
          study.optimize(objective, n_trials=10)
  
      Args:
          seed: Seed for random number generator.
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code: def sample_relative(
      self,
      study: "multi_objective.study.MultiObjectiveStudy",
      trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
      search_space: Dict[str, BaseDistribution],
  ) -> Dict[str, Any]:
      # TODO(ohta): Convert `study` and `trial` to single objective versions before passing.
      return self._sampler.sample_relative(study, trial, search_space)
  
file_path: optuna/optuna/multi_objective/samplers/_random.py
imports: from typing import Any
  from typing import Dict
  from typing import Optional
  import optuna
  from optuna import multi_objective
  from optuna._deprecated import deprecated_class
  from optuna.distributions import BaseDistribution
  from optuna.multi_objective.samplers import BaseMultiObjectiveSampler
label_desc: Use image node_id 4 for calling the RandomMultiObjectiveSampler obj's underlying member method code with example usage: obj.sample_relative(study, trial, search_space) and returns: self