class_docstr: A class to calculate the intersection search space of a :class:`~optuna.study.Study`.
  
  Intersection search space contains the intersection of parameter distributions that have been
  suggested in the completed trials of the study so far.
  If there are multiple parameters that have the same name but different distributions,
  neither is included in the resulting search space
  (i.e., the parameters with dynamic value ranges are excluded).
  
  Note that an instance of this class is supposed to be used for only one study.
  If different studies are passed to
  :func:`~optuna.search_space.IntersectionSearchSpace.calculate`,
  a :obj:`ValueError` is raised.
  
  Args:
      include_pruned:
          Whether pruned trials should be included in the search space.
docstr: Returns the intersection search space of the :class:`~optuna.study.Study`.
  
  Args:
      study:
          A study with completed trials. The same study must be passed for one instance
          of this class through its lifetime.
  
  Returns:
      A dictionary containing the parameter names and parameter's distributions sorted by
      parameter names.
code: def calculate(self, study: Study) -> Dict[str, BaseDistribution]:
      """Returns the intersection search space of the :class:`~optuna.study.Study`.
  
      Args:
          study:
              A study with completed trials. The same study must be passed for one instance
              of this class through its lifetime.
  
      Returns:
          A dictionary containing the parameter names and parameter's distributions sorted by
          parameter names.
      """
  
      if self._study_id is None:
          self._study_id = study._study_id
      else:
          # Note that the check below is meaningless when `InMemoryStorage` is used
          # because `InMemoryStorage.create_new_study` always returns the same study ID.
          if self._study_id != study._study_id:
              raise ValueError(
                  "`IntersectionSearchSpace` cannot handle multiple studies."
              )
  
      self._search_space, self._cached_trial_number = _calculate(
          study.get_trials(deepcopy=False),
          self._include_pruned,
          self._search_space,
          self._cached_trial_number,
      )
      search_space = self._search_space or {}
      search_space = dict(
          sorted(search_space.items(), key=lambda x: x[0])
      )
      return copy.deepcopy(search_space)
  
file_path: optuna/optuna/search_space/intersection.py
imports: from __future__ import annotations
  import copy
  from typing import Dict
  from typing import Tuple
  import optuna
  from optuna.distributions import BaseDistribution
  from optuna.study import Study
label_desc: Use image node_id 2 for calling the IntersectionSearchSpace obj's underlying member method code with example usage: obj.calculate(study) and returns: copy