code: def test_fried_scale():
      x_scale_test = np.zeros([100, 2])
      x_scale_test[0:5, 0] = -100
      x_scale_test[5:10, 0] = 100
      x_scale_test[10:55, 0] = 1
      x_scale_test[
          5:55, 1
      ] = 1  # winsorised version of first column at trim=0.1: note, will not be scaled because it is already an indicator function, as per FP004
      fs = FriedScale()  # trim_quantile=0.1)
      fs.train(x_scale_test)
      """
      np.testing.assert_array_equal(fs.scale(x_scale_test),
                                    np.hstack([x_scale_test[:, 1].reshape([-1, 1]) * 0.4 / np.std(x_scale_test[:, 1]),
                                               x_scale_test[:, 1].reshape([-1, 1])]))
                                               
  """
  
file_path: imodels/tests/rulefit_test.py
imports: import numpy
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils._testing import ignore_warnings
  from imodels.rule_set.rule_fit import RuleFitRegressor
  from imodels.util.transforms import FriedScale
label_desc: Use image node_id 1 for calling a global function with example usage: test_fried_scale() without return types