code: def is_data_independent_always_opt_gaussian(
      num_teachers, num_classes, sigma, orders
  ):
      """Tests whether data-ind bound is always optimal for GNMax.
  
      Args:
        num_teachers: Number of teachers.
        num_classes: Number of classes.
        sigma: Standard deviation of the Gaussian noise.
        orders: An array_like list of Renyi orders.
  
      Returns:
        Boolean array of length |orders| (a scalar if orders is a scalar). True if
        the data-independent bound is always the same as the data-dependent bound.
  
      """
      unanimous = np.array([num_teachers] + [0] * (num_classes - 1))
      logq = compute_logq_gaussian(unanimous, sigma)
  
      rdp_dep = rdp_gaussian(logq, sigma, orders)
      rdp_ind = rdp_data_independent_gaussian(sigma, orders)
      return np.isclose(rdp_dep, rdp_ind)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl import app
  import numpy
  import scipy.stats
label_desc: Use image node_id 7 for calling a global function with example usage: is_data_independent_always_opt_gaussian(num_teachers, num_classes, sigma, orders) and returns: np