How to use the imblearn.utils._docstring._n_jobs_docstring function in imblearn

import numpy as np

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, _safe_indexing

from ..base import BaseCleaningSampler
from ._tomek_links import TomekLinks
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class OneSidedSelection(BaseCleaningSampler):
    """Class to perform under-sampling based on one-sided selection method.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or object, default=None
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from

from scipy.stats import mode

from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ._edited_nearest_neighbours import EditedNearestNeighbours
from ...utils import check_neighbors_object
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring

SEL_KIND = ("all", "mode")


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class NeighbourhoodCleaningRule(BaseCleaningSampler):
    """Undersample based on the neighbourhood cleaning rule.

    This class uses ENN and a k-NN to remove noisy samples from the datasets.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    n_neighbors : int or object, default=3
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to

#          Fernando Nogueira
#          Christos Aridas
# License: MIT

import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class TomekLinks(BaseCleaningSampler):
    """Under-sampling by removing Tomek's links.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {n_jobs}

    Attributes
    ----------
    sample_indices_ : ndarray of shape (n_new_samples)
        Indices of the samples selected.

from sklearn.base import clone
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier

from ..pipeline import Pipeline
from ..under_sampling import RandomUnderSampler
from ..under_sampling.base import BaseUnderSampler
from ..utils import Substitution, check_target_type
from ..utils._docstring import _n_jobs_docstring
from ..utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class BalancedBaggingClassifier(BaggingClassifier):
    """A Bagging classifier with additional balancing.

    This implementation of Bagging is similar to the scikit-learn
    implementation. It includes an additional step to balance the training set
    at fit time using a ``RandomUnderSampler``.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    base_estimator : object, default=None
        The base estimator to fit on random subsets of the dataset.
        If None, then the base estimator is a decision tree.

forest,
        X_resampled,
        y_resampled,
        sample_weight,
        tree_idx,
        n_trees,
        verbose=verbose,
        class_weight=class_weight,
        n_samples_bootstrap=n_samples_bootstrap,
    )
    return sampler, tree


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class BalancedRandomForestClassifier(RandomForestClassifier):
    """A balanced random forest classifier.

    A balanced random forest randomly under-samples each boostrap sample to
    balance it.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    n_estimators : int, default=100
        The number of trees in the forest.

    criterion : str, default="gini"

# tie breaking argmax
            is_max = np.isclose(col_maxs, col_maxs.max(axis=1, keepdims=True))
            max_idxs = rng.permutation(np.argwhere(is_max))
            xs, idx_sels = np.unique(max_idxs[:, 0], return_index=True)
            col_sels = max_idxs[idx_sels, 1]

            ys = start_idx + col_sels
            X_new[:, start_idx:end_idx] = 0
            X_new[xs, ys] = 1

        return X_new


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class KMeansSMOTE(BaseSMOTE):
    """Apply a KMeans clustering before to over-sample using SMOTE.

    This is an implementation of the algorithm described in [1]_.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    k_neighbors : int or object, default=2

from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier

from ..under_sampling import RandomUnderSampler
from ..under_sampling.base import BaseUnderSampler
from ..utils import Substitution, check_target_type
from ..utils._docstring import _n_jobs_docstring
from ..utils._docstring import _random_state_docstring
from ..pipeline import Pipeline

MAX_INT = np.iinfo(np.int32).max


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class EasyEnsembleClassifier(BaggingClassifier):
    """Bag of balanced boosted learners also known as EasyEnsemble.

    This algorithm is known as EasyEnsemble [1]_. The classifier is an
    ensemble of AdaBoost learners trained on different balanced boostrap
    samples. The balancing is achieved by random under-sampling.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    n_estimators : int, default=10
        Number of AdaBoost learners in the ensemble.

from sklearn.base import clone
from sklearn.cluster import KMeans
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import _safe_indexing

from ..base import BaseUnderSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring

VOTING_KIND = ("auto", "hard", "soft")


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class ClusterCentroids(BaseUnderSampler):
    """Undersample by generating centroids based on clustering methods.

    Method that under samples the majority class by replacing a
    cluster of majority samples by the cluster centroid of a KMeans
    algorithm.  This algorithm keeps N majority samples by fitting the
    KMeans algorithm with N cluster to the majority class and using
    the coordinates of the N cluster centroids as the new majority
    samples.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------

from scipy.sparse import issparse

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class CondensedNearestNeighbour(BaseCleaningSampler):
    """Undersample based on the condensed nearest neighbour method.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or object, default=\
            KNeighborsClassifier(n_neighbors=1)
        If ``int``, size of the neighbourhood to consider to compute the

from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble._base import _set_random_states
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_predict
from sklearn.utils import check_random_state
from sklearn.utils import _safe_indexing

from ..base import BaseUnderSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class InstanceHardnessThreshold(BaseUnderSampler):
    """Undersample based on the instance hardness threshold.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    estimator : object, default=None
        Classifier to be used to estimate instance hardness of the samples.  By
        default a :class:`sklearn.ensemble.RandomForestClassifier` will be used.
        If ``str``, the choices using a string are the following: ``'knn'``,
        ``'decision-tree'``, ``'random-forest'``, ``'adaboost'``,
        ``'gradient-boosting'`` and ``'linear-svm'``.  If object, an estimator
        inherited from :class:`sklearn.base.ClassifierMixin` and having an