code: def standardizer(X, X_t=None, keep_scalar=False):
      """Conduct Z-normalization on data to turn input samples become zero-mean
      and unit variance.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The training samples
  
      X_t : numpy array of shape (n_samples_new, n_features), optional (default=None)
          The data to be converted
  
      keep_scalar : bool, optional (default=False)
          The flag to indicate whether to return the scalar
  
      Returns
      -------
      X_norm : numpy array of shape (n_samples, n_features)
          X after the Z-score normalization
  
      X_t_norm : numpy array of shape (n_samples, n_features)
          X_t after the Z-score normalization
  
      scalar : sklearn scalar object
          The scalar used in conversion
  
      """
      X = check_array(X)
      scaler = StandardScaler().fit(X)
  
      if X_t is None:
          if keep_scalar:
              return scaler.transform(X), scaler
          else:
              return scaler.transform(X)
      else:
          X_t = check_array(X_t)
          if X.shape[1] != X_t.shape[1]:
              raise ValueError(
                  "The number of input data feature should be consistent"
                  "X has {0} features and X_t has {1} features.".format(
                      X.shape[1], X_t.shape[1]
                  )
              )
          if keep_scalar:
              return scaler.transform(X), scaler.transform(X_t), scaler
          else:
              return scaler.transform(X), scaler.transform(X_t)
  
imports: from __future__ import division
  from __future__ import print_function
  import numbers
  import numpy
  import sklearn
  from numpy import percentile
  from sklearn.metrics import precision_score
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import check_array
  from sklearn.utils import check_consistent_length
  from sklearn.utils import check_random_state
  from sklearn.utils import column_or_1d
  from sklearn.utils.random import sample_without_replacement
label_desc: Use image node_id 3 for calling a global function with example usage: standardizer(X, X_t, keep_scalar) and returns: scaler,  scaler, scaler, scaler,  scaler,  scaler, scaler,  scaler