code: def load_connectomes(as_vectors=False):
      """Load data from brain connectomes.
  
      Load the correlation data from the kaggle MSLP 2014 Schizophrenia
      Challenge. The original data came as flattened vectors, but if `raw=True`
      is passed, the correlation values are reshaped as symmetric matrices with
      ones on the diagonal.
  
      Parameters
      ----------
      as_vectors : bool
          Whether to return raw data as vectors or as symmetric matrices.
          Optional, default: False
  
      Returns
      -------
      mat : array-like, shape=[86, {[28, 28], 378}
          Connectomes.
      patient_id : array-like, shape=[86,]
          Patient unique identifiers
      target : array-like, shape=[86,]
          Labels, whether patients belong to the diseased class (1) or control
          (0).
      """
      with open(CONNECTOMES_PATH) as csvfile:
          data_list = list(csv.reader(csvfile))
      patient_id = gs.array([int(row[0]) for row in data_list[1:]])
      data = gs.array(
          [[float(value) for value in row[1:]] for row in data_list[1:]]
      )
  
      with open(CONNECTOMES_LABELS_PATH) as csvfile:
          labels = list(csv.reader(csvfile))
      target = gs.array([int(row[1]) for row in labels[1:]])
      if as_vectors:
          return data, patient_id, target
      mat = SkewSymmetricMatrices(28).matrix_representation(data)
      mat = gs.eye(28) - gs.transpose(gs.tril(mat), (0, 2, 1))
      mat = 1.0 / 2.0 * (mat + gs.transpose(mat, (0, 2, 1)))
  
      return mat, patient_id, target
  
imports: import csv
  import json
  import os
  import zipfile
  import numpy
  import pandas
  import geomstats.backend
  from geomstats.datasets._base import RemoteFileMetadata, _fetch_remote
  from geomstats.datasets.prepare_graph_data import Graph
  from geomstats.geometry.hypersphere import Hypersphere
  from geomstats.geometry.skew_symmetric_matrices import SkewSymmetricMatrices
  from geomstats.geometry.special_orthogonal import SpecialOrthogonal
label_desc: Use image node_id 5 for calling a global function with example usage: load_connectomes(as_vectors) and returns: mat,  patient_id,  target, data,  patient_id,  target