code: def _internal_ask(self) -> tp.ArrayLike:
      # pylint: disable=not-callable
      mode = self.opposition_mode
      if self._opposable_data is not None and mode is not None:
          data = self._opposable_data
          data *= -(
              self._rng.uniform(0.0, 1.0)
              if mode == "quasi"
              else (
                  np.exp(-self._rng.uniform(0.0, 5.0))
                  if mode == "special"
                  else 1.0
              )
          )
          self._opposable_data = None
          return data
      if self.middle_point and not self._num_ask:
          self._opposable_data = np.zeros(self.dimension)
          return self._opposable_data
      scale = self.scale
      if isinstance(scale, str) and scale == "auto":
          # Some variants use a rescaling depending on the budget and the dimension (1st version).
          assert self.budget is not None
          scale = (1 + np.log(self.budget)) / (
              4 * np.log(self.dimension)
          )
      if isinstance(scale, str) and scale == "autotune":
          assert self.budget is not None
          scale = np.sqrt(np.log(self.budget) / self.dimension)
      if isinstance(scale, str) and scale == "random":
          scale = np.exp(self._rng.normal(0.0, 1.0) - 2.0) / np.sqrt(
              self.dimension
          )
      # sample the new point
      if self.sampler == "gaussian":
          point = self._rng.normal(0, 1, self.dimension)
      elif self.sampler == "cauchy":
          point = self._rng.standard_cauchy(self.dimension)
      elif self.sampler == "parametrization":
          point = self.parametrization.sample().get_standardized_data(
              reference=self.parametrization
          )
      else:
          raise ValueError("Unkwnown sampler {self.sampler}")
      self._opposable_data = scale * point  # type: ignore
      return self._opposable_data
  
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 2 for calling the _RandomSearch obj's underlying member method code with example usage: obj._internal_ask() and returns: self, data, self