class_docstr: Provides random suggestions.
  
  Parameters
  ----------
  stupid: bool
      Provides a random recommendation instead of the best point so far (for baseline)
  middle_point: bool
      enforces that the first suggested point (ask) is zero.
  opposition_mode: str or None
      symmetrizes exploration wrt the center: (e.g. https://ieeexplore.ieee.org/document/4424748)
           - full symmetry if "opposite"
           - random * symmetric if "quasi"
  sampler: str
      - parametrization: uses the default sample() method of the parametrization, which samples uniformly
        between bounds and a Gaussian otherwise
      - gaussian: uses a Gaussian distribution
      - cauchy: uses a Cauchy distribution
      use a Cauchy distribution instead of Gaussian distribution
  scale: float or "random"
      scalar for multiplying the suggested point values, or string:
       - "random": uses a randomized pattern for the scale.
       - "auto": scales in function of dimension and budget (version 1: sigma = (1+log(budget)) / (4log(dimension)) )
       - "autotune": scales in function of dimension and budget (version 2: sigma = sqrt(log(budget) / dimension) )
  recommendation_rule: str
      "average_of_best" or "pessimistic" or "average_of_exp_best"; "pessimistic" is
      the default and implies selecting the pessimistic best.
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code: def __init__(
      self,
      *,
      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:
      assert sampler in ["gaussian", "cauchy", "parametrization"]
      super().__init__(_RandomSearch, locals())
  
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 RandomSearchMaker object from inherited base classes: base with example: obj = RandomSearchMaker()