code: def _center_scale_xy(X, Y, scale=True):
      """Center X, Y and scale if the scale parameter==True
  
      Returns
      -------
          X, Y, x_mean, y_mean, x_std, y_std
      """
      # center
      x_mean = X.mean(axis=0)
      X -= x_mean
      y_mean = Y.mean(axis=0)
      Y -= y_mean
      # scale
      if scale:
          x_std = X.std(axis=0, ddof=1)
          x_std[x_std == 0.0] = 1.0
          X /= x_std
          y_std = Y.std(axis=0, ddof=1)
          y_std[y_std == 0.0] = 1.0
          Y /= y_std
      else:
          x_std = np.ones(X.shape[1])
          y_std = np.ones(Y.shape[1])
      return X, Y, x_mean, y_mean, x_std, y_std
  
imports: from distutils.version import LooseVersion
  from sklearn.utils.extmath import svd_flip
  from ..base import BaseEstimator, RegressorMixin, TransformerMixin
  from ..utils import check_array, check_consistent_length
  from ..externals import six
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import linalg
  from ..utils import arpack
  from ..utils.validation import check_is_fitted, FLOAT_DTYPES
  import scipy
label_desc: Use image node_id 3 for calling a global function with example usage: _center_scale_xy(X, Y, scale) and returns: X,  Y,  x_mean,  y_mean,  x_std,  y_std