code: def __init__(
      self,
      parametrization: IntOrParameter,
      budget: tp.Optional[int] = None,
      num_workers: int = 1,
      middle_point: bool = False,
      stupid: bool = False,
      opposition_mode: tp.Optional[str] = None,
      sampler: str = "parametrization",
      scale: tp.Union[float, str] = 1.0,
      recommendation_rule: str = "pessimistic",
  ) -> None:
      super().__init__(
          parametrization, budget=budget, num_workers=num_workers
      )
      assert opposition_mode is None or opposition_mode in [
          "quasi",
          "opposite",
          "special",
      ]
      assert isinstance(scale, (int, float)) or scale in [
          "auto",
          "random",
          "autotune",
      ]
      self.middle_point = middle_point
      self.opposition_mode = opposition_mode
      self.stupid = stupid
      self.recommendation_rule = recommendation_rule
      self.scale = scale
      self.sampler = sampler
      self._opposable_data: tp.Optional[np.ndarray] = None
      self._no_hypervolume = True
  
imports: import copy
  import numpy
  from scipy.spatial import ConvexHull
  import nevergrad.common.typing
  from nevergrad.parametrization import parameter
  from nevergrad.parametrization import transforms
  from .None import sequences
  from .None import base
  from .base import IntOrParameter
  from .None import utils
label_desc: Use image node_id 1 to create a new _RandomSearch object from inherited base classes: OneShotOptimizer with example: obj = _RandomSearch(parametrization, budget, num_workers, middle_point, stupid, opposition_mode, sampler, scale, recommendation_rule)