class_docstr: Sampler using random sampling.
  
  This sampler is based on *independent sampling*.
  See also :class:`~optuna.samplers.BaseSampler` for more details of 'independent sampling'.
  
  Example:
  
      .. testcode::
  
          import optuna
          from optuna.samplers import RandomSampler
  
  
          def objective(trial):
              x = trial.suggest_float("x", -5, 5)
              return x**2
  
  
          study = optuna.create_study(sampler=RandomSampler())
          study.optimize(objective, n_trials=10)
  
  Args:
      seed: Seed for random number generator.
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code: def sample_relative(
      self,
      study: Study,
      trial: FrozenTrial,
      search_space: Dict[str, BaseDistribution],
  ) -> Dict[str, Any]:
      return {}
  
file_path: optuna/optuna/samplers/_random.py
imports: from typing import Any
  from typing import Dict
  from typing import Optional
  from optuna import distributions
  from optuna._transform import _SearchSpaceTransform
  from optuna.distributions import BaseDistribution
  from optuna.samplers import BaseSampler
  from optuna.samplers._lazy_random_state import LazyRandomState
  from optuna.study import Study
  from optuna.trial import FrozenTrial
label_desc: Use image node_id 4 for calling the RandomSampler obj's underlying member method code with example usage: obj.sample_relative(study, trial, search_space) and returns: dict