code: def barycenter_kneighbors_graph(X, n_neighbors, reg=1e-3, n_jobs=1):
      """Computes the barycenter weighted graph of k-Neighbors for points in X
  
      Parameters
      ----------
      X : {array-like, sparse matrix, BallTree, KDTree, NearestNeighbors}
          Sample data, shape = (n_samples, n_features), in the form of a
          numpy array, sparse array, precomputed tree, or NearestNeighbors
          object.
  
      n_neighbors : int
          Number of neighbors for each sample.
  
      reg : float, optional
          Amount of regularization when solving the least-squares
          problem. Only relevant if mode='barycenter'. If None, use the
          default.
  
      n_jobs : int, optional (default = 1)
          The number of parallel jobs to run for neighbors search.
          If ``-1``, then the number of jobs is set to the number of CPU cores.
  
      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 + 1, n_jobs=n_jobs).fit(X)
      X = knn._fit_X
      n_samples = X.shape[0]
      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: import numpy
  from scipy.linalg import eigh, svd, qr, solve
  from scipy.sparse import eye, csr_matrix
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import check_random_state, check_array
  from ..utils.arpack import eigsh
  from ..utils.validation import check_is_fitted
  from ..utils.validation import FLOAT_DTYPES
  from ..neighbors import NearestNeighbors
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