code: def data_scaling(Y):
      """Scaling of the data to have percent of baseline change columnwise
  
      Parameters
      ----------
      Y: array of shape(n_time_points, n_voxels)
         the input data
  
      Returns
      -------
      Y: array of shape (n_time_points, n_voxels),
         the data after mean-scaling, de-meaning and multiplication by 100
      mean : array of shape (n_voxels,)
          the data mean
      """
      mean = Y.mean(0)
      Y = 100 * (Y / mean - 1)
      return Y, mean
  
imports: from warnings import warn
  import numpy
  import scipy.stats
  from nibabel import Nifti1Image, load
  from nipy.algorithms.statistics.models.regression import ARModel, OLSModel
  from nipy.algorithms.statistics.utils import multiple_mahalanobis, z_score
  from nipy.io.nibcompat import get_affine, get_header
  from nipy.labs.mask import compute_mask_sessions
label_desc: Use image node_id 1 for calling a global function with example usage: data_scaling(Y) and returns: Y,  mean