code: def _get_nash(
      optimizer: tp.Any,
  ) -> tp.List[tp.Tuple[tp.Tuple[float, ...], int]]:
      """Returns an empirical distribution. limited using a threshold
      equal to max_num_trials^(1/4).
      """
      if not optimizer.archive:
          return [(optimizer.current_bests["pessimistic"].x, 1)]
      max_num_trial = max(p.count for p in optimizer.archive.values())
      sum_num_trial = sum(p.count for p in optimizer.archive.values())
      threshold = np.power(max_num_trial, 0.5)
      if threshold <= np.power(sum_num_trial, 0.25):
          return [(optimizer.provide_recommendation(), 1)]
      # make deterministic at the price of sort complexity
      return sorted(
          ((np.frombuffer(k), p.count) for k, p in optimizer.archive.bytesdict.items() if p.count >= threshold),  # type: ignore
          key=operator.itemgetter(1),
      )
  
imports: import math
  import operator
  import numpy
  from nevergrad.parametrization import parameter
  from nevergrad.parametrization.utils import float_penalty
  import nevergrad.common.typing
  from nevergrad.common.tools import OrderedSet
label_desc: Use image node_id 1 for calling a global function with example usage: _get_nash(optimizer) and returns: sorted, list, list