code: def compute_epsilon(steps):
      """Computes epsilon value for given hyperparameters."""
      if FLAGS.noise_multiplier == 0.0:
          return float("inf")
      orders = [1 + x / 10.0 for x in range(1, 100)] + list(
          range(12, 64)
      )
      accountant = dp_accounting.rdp.RdpAccountant(orders)
  
      sampling_probability = FLAGS.batch_size / 60000
      event = dp_accounting.SelfComposedDpEvent(
          dp_accounting.PoissonSampledDpEvent(
              sampling_probability,
              dp_accounting.GaussianDpEvent(FLAGS.noise_multiplier),
          ),
          steps,
      )
  
      accountant.compose(event)
  
      # Delta is set to 1e-5 because MNIST has 60000 training points.
      return accountant.get_epsilon(target_delta=1e-5)
  
file_path: privacy/tutorials/mnist_dpsgd_tutorial_vectorized.py
imports: 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_vectorized
label_desc: Use image node_id 1 for calling a global function with example usage: compute_epsilon(steps) and returns: accountant, float