class_docstr: Sample parameters search space
  
  With given probability ``probability``, it will sample a new set of parameters
  from the search space totally independently of the ``parameters`` passed to ``__call__``.
  Otherwise, it will return the passed ``parameters``.
  
  Parameters
  ----------
  probability: float, optional
      Probability of sampling a new set of parameters. Default: 0.2
docstr: Execute resampling
  
  With given probability ``self.probability``, it will sample a new set of parameters from the
  search space totally independently of the ``parameters`` passed to ``__call__``.  Otherwise,
  it will return the passed ``parameters``.
  
  Parameters
  ----------
  rng: numpy.random.Generator
      A random number generator. It is not contained in ``BaseExplore`` because the explore
      class must be stateless.
  space: Space
      The search space optimized by the algorithm.
  params: dict
      Dictionary representing the parameters of the current trial under examination
      (`trial.params`).
  
  Returns
  -------
  ``dict``
      The new set of parameters for the trial to be branched.
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code: def __call__(self, rng, space, params):
      """Execute resampling
  
      With given probability ``self.probability``, it will sample a new set of parameters from the
      search space totally independently of the ``parameters`` passed to ``__call__``.  Otherwise,
      it will return the passed ``parameters``.
  
      Parameters
      ----------
      rng: numpy.random.Generator
          A random number generator. It is not contained in ``BaseExplore`` because the explore
          class must be stateless.
      space: Space
          The search space optimized by the algorithm.
      params: dict
          Dictionary representing the parameters of the current trial under examination
          (`trial.params`).
  
      Returns
      -------
      ``dict``
          The new set of parameters for the trial to be branched.
  
      """
  
      if rng.random() < self.probability:
          trial = space.sample(
              1, seed=tuple(rng.randint(0, 1000000, size=3))
          )[0]
          params = trial.params
  
      return params
  
imports: import numpy
  from orion.core.utils import GenericFactory
  from orion.core.utils.flatten import flatten, unflatten
label_desc: Use image node_id 2 for calling the ResampleExplore obj's underlying member method code with example usage: obj.__call__(rng, space, params) and returns: params