class_docstr: Parameters
  ----------
  nint intaum_stocks: number of stocks to be managed
  depth: number of layers in the neural networks
  width: number of neurons per hidden layer
class_docstr_tok: ["Parameters","--","--","--","--","--","nint","intaum_stocks",":","number","of","stocks","to","be","managed","depth",":","number","of","layers","in","the","neural","networks","width",":","number","of","neurons","per","hidden","layer"]
code: def _simulate_game(self, x: np.ndarray) -> float:
      # FIXME: an adaptive opponent, e.g. bandit, would be better.
      # We play a game as player 1.
      p1 = x[: (self.dimension // 2)]
      p2 = np.random.normal(size=self.dimension // 2)
      r = self.game_object.play_game(self.game, p1, p2)
      result = 0.0 if r == 1 else 0.5 if r == 0 else 1.0
      # We play a game as player 2.
      p1 = np.random.normal(size=self.dimension // 2)
      p2 = x[(self.dimension // 2) :]
      r = self.game_object.play_game(self.game, p1, p2)
      return (result + (0.0 if r == 2 else 0.5 if r == 0 else 1.0)) / 2
  
imports: import functools
  import numpy
  from nevergrad.functions import ExperimentFunction
  from nevergrad.parametrization import parameter
label_desc: Use image node_id 2 for calling the Game obj's underlying member method code with example usage: obj._simulate_game(x) and returns: unknown