-jipSSKJr SSKrSSKJr SSKr\(aSSKJr "SS\R5r g)) annotationsN) TYPE_CHECKING)Studyc\rSrSrSrSSjr\\RS Sj55r \RS Sj5r Sr g) BaseCrossover aBase class for crossovers. A crossover operation is used by :class:`~optuna.samplers.NSGAIISampler` to create new parameter combination from parameters of ``n`` parent individuals. .. note:: Concrete implementations of this class are expected to only accept parameters from numerical distributions. At the moment, only crossover operation for categorical parameters (uniform crossover) is built-in into :class:`~optuna.samplers.NSGAIISampler`. c.URR$)N) __class____name__selfs b/home/james-whalen/.local/lib/python3.13/site-packages/optuna/samplers/nsgaii/_crossovers/_base.py__str__BaseCrossover.__str__s~~&&&c[e)z;Number of parent individuals required to perform crossover.NotImplementedErrorr s r n_parentsBaseCrossover.n_parentss "!rc[e)aPerform crossover of selected parent individuals. This method is called in :func:`~optuna.samplers.NSGAIISampler.sample_relative`. Args: parents_params: A ``numpy.ndarray`` with dimensions ``num_parents x num_parameters``. Represents a parameter space for each parent individual. This space is continuous for numerical parameters. rng: An instance of ``numpy.random.RandomState``. study: Target study object. search_space_bounds: A ``numpy.ndarray`` with dimensions ``len_search_space x 2`` representing numerical distribution bounds constructed from transformed search space. Returns: A 1-dimensional ``numpy.ndarray`` containing new parameter combination. r)r parents_paramsrngstudysearch_space_boundss r crossoverBaseCrossover.crossover#s :"!rN)returnstr)rint) r np.ndarrayrznp.random.RandomStaterrrr"rr") r __module__ __qualname____firstlineno____doc__rpropertyabcabstractmethodrr__static_attributes__rrrrr sx '""  """#" " ( "  ""rr) __future__rr(typingrnumpynp optuna.studyrABCrrrrr1s)" "3"CGG3"r