code: def normalize_data(data, data_l2_norm):
      """Normalizes data such that each samples has bounded L2 norm.
  
      Args:
        data: the dataset. Each row represents one samples.
        data_l2_norm: the target upper bound on the L2 norm.
      """
  
      for i in range(data.shape[0]):
          norm = np.linalg.norm(data[i])
          if norm > data_l2_norm:
              data[i] = data[i] / norm * data_l2_norm
  
file_path: privacy/tutorials/mnist_lr_tutorial.py
imports: import math
  from absl import app
  from absl import flags
  from absl import logging
  import dp_accounting
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.optimizers import dp_optimizer
label_desc: Use image node_id 2 for calling a global function with example usage: normalize_data(data, data_l2_norm) without return types