code: def __init__(
      self,
      parametrization: IntOrParameter,
      budget: tp.Optional[int] = None,
      num_workers: int = 1,
      sampler: str = "Halton",
      scrambled: bool = False,
      middle_point: bool = False,
      opposition_mode: tp.Optional[str] = None,
      cauchy: bool = False,
      autorescale: tp.Union[bool, str] = False,
      scale: float = 1.0,
      rescaled: bool = False,
      recommendation_rule: str = "pessimistic",
  ) -> None:
      super().__init__(
          parametrization, budget=budget, num_workers=num_workers
      )
      self._sampler_instance: tp.Optional[sequences.Sampler] = None
      self._rescaler: tp.Optional[sequences.Rescaler] = None
      self._opposable_data: tp.Optional[np.ndarray] = None
      self._sampler = sampler
      self.opposition_mode = opposition_mode
      self.middle_point = middle_point
      self.scrambled = scrambled
      self.cauchy = cauchy
      self.autorescale = autorescale
      self.scale = scale
      self.rescaled = rescaled
      self.recommendation_rule = recommendation_rule
      # rescale to the bounds if both are provided
      self._no_hypervolume = True
      self._normalizer = p.helpers.Normalizer(self.parametrization)
  
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 _SamplingSearch object from inherited base classes: OneShotOptimizer with example: obj = _SamplingSearch(parametrization, budget, num_workers, sampler, scrambled, middle_point, opposition_mode, cauchy, autorescale, scale, rescaled, recommendation_rule)