code: def toy_data_standardized(toy_data, toy_test_data):
      x, y = toy_data
      m = np.mean(x, axis=0)
      sd = np.std(x, axis=0)
      return ((x - m) / sd, y), (toy_test_data - m) / sd
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.preprocessing import scale
  from sksurv.column import encode_categorical
  from sksurv.datasets import load_gbsg2
  from sksurv.exceptions import NoComparablePairException
  from sksurv.svm._minlip import create_difference_matrix
  from sksurv.svm.minlip import HingeLossSurvivalSVM, MinlipSurvivalAnalysis
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.util import Surv
label_desc: Use image node_id 7 for calling a global function with example usage: toy_data_standardized(toy_data, toy_test_data) and returns: unknown,  y,  unknown