code: def _select_highest_test(options_array, seed=None):
      """
      (Test version with seed argument for reproducibility) Selects the highest value among the provided ones. If the higher value is more than once and they're closer than 1e-8, a random choice is made.
      Parameters
      ----------
      options_array: ``numpy.ndarray``
          The array to examine
  
      Returns
      -------
      The highest value in the given list
      """
      options_with_idx = np.array(list(enumerate(options_array)))
      same_prob = options_with_idx[
          abs(options_with_idx[:, 1] - np.amax(options_with_idx[:, 1]))
          <= 1e-8
      ][:, 0]
      if len(same_prob) > 1:
          # If some of the most likely actions have nearly equal probability, sample from this subset of actions, instead of using argmax
          rng = np.random.default_rng(seed)
          return int(same_prob[rng.choice(len(same_prob))])
  
      return int(same_prob[0])
  
imports: import itertools
  import numpy
  from pymdp.maths import softmax, softmax_obj_arr, spm_dot, spm_wnorm, spm_MDP_G, spm_log_single, spm_log_obj_array
  from pymdp import utils
  import copy
label_desc: Use image node_id 16 for calling a global function with example usage: _select_highest_test(options_array, seed) and returns: int, int