code: def sample_nash(
      optimizer: tp.Any,
  ) -> tp.Tuple[float, ...]:  # Somehow like fictitious play.
      nash = _get_nash(optimizer)
      if len(nash) == 1:
          return nash[0][0]
      prob = [float(n[1]) for n in nash]
      prob = [p_ / sum(prob) for p_ in prob]
      index: int = np.random.choice(np.arange(len(prob)), p=prob)
      return nash[index][0]
  
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 2 for calling a global function with example usage: sample_nash(optimizer) and returns: nash, nash