class_docstr: Return a fixed label space partition
  
  This clusterer takes a predefined fixed ``clustering`` of the label space and returns it in fit_predict as the label
  space division. This is useful for employing expert knowledge about label space division or partitions in ensemble
  classifiers such as: :class:`~skmultilearn.ensemble.LabelSpacePartitioningClassifier` or
  :class:`~skmultilearn.ensemble.MajorityVotingClassifier`.
  
  Parameters
  ----------
  clusters : array of arrays of int
      provided partition of the label space in the for of numpy array of
      numpy arrays of indexes for each partition, ex. ``[[0,1],[2,3]]``
  
  
  An example use of the fixed clusterer with a label partitioning classifier to train randomforests for a set of
  subproblems defined upon expert knowledge:
  
  .. code :: python
  
      from skmultilearn.ensemble import LabelSpacePartitioningClassifier
      from skmultilearn.cluster import FixedLabelSpaceClusterer
      from skmultilearn.problem_transform import LabelPowerset
      from sklearn.ensemble import RandomForestClassifier
  
      classifier = LabelSpacePartitioningClassifier(
          classifier = LabelPowerset(
              classifier=RandomForestClassifier(n_estimators=100),
              require_dense = [False, True]
          ),
          require_dense = [True, True],
          clusterer = FixedLabelSpaceClusterer(clustering=[[1,2,3], [0,4]])
      )
  
      # train
      classifier.fit(X_train, y_train)
  
      # predict
      predictions = classifier.predict(X_test)
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docstr: Returns the provided label space division
  
  Parameters
  ----------
  X : None
      currently unused, left for scikit compatibility
  y : scipy.sparse
      label space of shape :code:`(n_samples, n_labels)`
  
  Returns
  -------
  arrray of arrays of label indexes (numpy.ndarray)
      label space division, each sublist represents labels that are in that community
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code: def fit_predict(self, X, y):
      """Returns the provided label space division
  
      Parameters
      ----------
      X : None
          currently unused, left for scikit compatibility
      y : scipy.sparse
          label space of shape :code:`(n_samples, n_labels)`
  
      Returns
      -------
      arrray of arrays of label indexes (numpy.ndarray)
          label space division, each sublist represents labels that are in that community
      """
  
      return self.clusters
  
imports: from __future__ import absolute_import
  from .base import LabelSpaceClustererBase
label_desc: Use image node_id 2 for calling the FixedLabelSpaceClusterer obj's underlying member method code with example usage: obj.fit_predict(X, y) and returns: self