code: def normalize_data(X, y):
      y_mean = np.mean(y)
      y = y - y_mean
      x_mean = np.mean(X, axis=0)
      x_scale = np.sqrt(np.var(X, axis=0) * X.shape[0])
      x_scale[x_scale == 0] = 1
      X = (X - x_mean) / x_scale
      return X, y, x_mean, x_scale, y_mean
  
file_path: cuml/python/cuml/tests/test_lars.py
imports: import sklearn
  from sklearn.linear_model import Lars
  from sklearn.datasets import fetch_california_housing
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.experimental.linear_model import Lars
  import sys
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
  from test_linear_model import make_regression_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: normalize_data(X, y) and returns: X,  y,  x_mean,  x_scale,  y_mean