code: def _reduce_data_and_connectivity(
      X, labels, n_components, connectivity, threshold=1e-7
  ):
      """Perform feature grouping and reduce the connectivity matrix.
  
      During the reduction step one changes the value of each cluster
      by their mean.
      In addition, connected nodes are merged.
  
      Parameters
      ----------
      X : ndarray, shape = [n_samples, n_features]
          Training data.
  
      labels : ndarray
          Contains the label assignation for each voxel.
  
      n_components : int
          The number of clusters in the current iteration.
  
      connectivity : a sparse matrix in COOrdinate format.
          Sparse matrix representation of the weighted adjacency graph.
  
      threshold : float in the close interval [0, 1], default=1e-7
          The threshold is set to handle eccentricities.
  
      Returns
      -------
      reduced_connectivity : a sparse matrix in COOrdinate format.
  
      reduced_X : ndarray
          Data reduced with agglomerated signal for each cluster.
  
      """
      n_features = len(labels)
  
      incidence = coo_matrix(
          (np.ones(n_features), (labels, np.arange(n_features))),
          shape=(n_components, n_features),
          dtype=np.float32,
      ).tocsc()
  
      inv_sum_col = dia_matrix(
          (np.array(1.0 / incidence.sum(axis=1)).squeeze(), 0),
          shape=(n_components, n_components),
      )
  
      incidence = inv_sum_col * incidence
  
      reduced_X = (incidence * X.T).T
      reduced_connectivity = (incidence * connectivity) * incidence.T
  
      reduced_connectivity = reduced_connectivity - dia_matrix(
          (reduced_connectivity.diagonal(), 0),
          shape=(reduced_connectivity.shape),
      )
  
      i_idx, j_idx = reduced_connectivity.nonzero()
  
      weights_ = np.sum(
          (reduced_X[:, i_idx] - reduced_X[:, j_idx]) ** 2, axis=0
      )
      weights_ = np.maximum(threshold, weights_)
      reduced_connectivity.data = weights_
  
      return reduced_connectivity, reduced_X
  
imports: import warnings
  import numpy
  from joblib import Memory
  from nibabel import Nifti1Image
  from scipy.sparse import coo_matrix, csgraph, dia_matrix
  from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from nilearn._utils import fill_doc
  from nilearn.image import get_data
  from nilearn.masking import unmask_from_to_3d_array
label_desc: Use image node_id 6 for calling a global function with example usage: _reduce_data_and_connectivity(X, labels, n_components, connectivity, threshold) and returns: reduced_connectivity,  reduced_X