code: def barycenter_kneighbors_graph(
      X, n_neighbors, reg=1e-3, n_jobs=None
  ):
      """Computes the barycenter weighted graph of k-Neighbors for points in X
  
      Parameters
      ----------
      X : {array-like, NearestNeighbors}
          Sample data, shape = (n_samples, n_features), in the form of a
          numpy array or a NearestNeighbors object.
  
      n_neighbors : int
          Number of neighbors for each sample.
  
      reg : float, default=1e-3
          Amount of regularization when solving the least-squares
          problem. Only relevant if mode='barycenter'. If None, use the
          default.
  
      n_jobs : int or None, default=None
          The number of parallel jobs to run for neighbors search.
          ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
  
      Returns
      -------
      A : sparse matrix in CSR format, shape = [n_samples, n_samples]
          A[i, j] is assigned the weight of edge that connects i to j.
  
      See Also
      --------
      sklearn.neighbors.kneighbors_graph
      sklearn.neighbors.radius_neighbors_graph
      """
      knn = NearestNeighbors(
          n_neighbors=n_neighbors + 1, n_jobs=n_jobs
      ).fit(X)
      X = knn._fit_X
      n_samples = knn.n_samples_fit_
      ind = knn.kneighbors(X, return_distance=False)[:, 1:]
      data = barycenter_weights(X, X, ind, reg=reg)
      indptr = np.arange(0, n_samples * n_neighbors + 1, n_neighbors)
      return csr_matrix(
          (data.ravel(), ind.ravel(), indptr),
          shape=(n_samples, n_samples),
      )
  
imports: from numbers import Integral, Real
  import numpy
  from scipy.linalg import eigh, qr, solve, svd
  from scipy.sparse import csr_matrix, eye
  from scipy.sparse.linalg import eigsh
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context, _UnstableArchMixin
  from ..neighbors import NearestNeighbors
  from ..utils import check_array, check_random_state
  from ..utils._arpack import _init_arpack_v0
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import stable_cumsum
  from ..utils.validation import FLOAT_DTYPES, check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: barycenter_kneighbors_graph(X, n_neighbors, reg, n_jobs) and returns: csr_matrix