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 __init__(self, game: str = "war") -> None:
      self.game = game
      self.game_object = _Game()
      dimension = (
          self.game_object.play_game(self.game) * 2
      )  # times 2 because we consider both players separately.
      super().__init__(self._simulate_game, p.Array(shape=(dimension,)))
      self.parametrization.function.deterministic = False
      self.parametrization.function.metrizable = game not in [
          "war",
          "batawaf",
      ]
  
imports: import functools
  import numpy
  from nevergrad.functions import ExperimentFunction
  from nevergrad.parametrization import parameter
label_desc: Use image node_id 1 to create a new Game object from inherited base classes: ExperimentFunction with example: obj = Game(game)