class_docstr: Uniform random sampling pseudo-optimiser.
docstr: Initialise the :class:`RandomSearch` search space.
  
  Args:
      domain: :class:`Domain`. The domain of the objective function.
          It will be sampled uniformly using the :py:meth:`sample()`
          method of the :class:`Domain`.
      seed: (optional) :obj:`int`. The seed for the domain sampling.
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code: def __init__(self, domain: Domain, seed: int = None):
      """Initialise the :class:`RandomSearch` search space.
  
      Args:
          domain: :class:`Domain`. The domain of the objective function.
              It will be sampled uniformly using the :py:meth:`sample()`
              method of the :class:`Domain`.
          seed: (optional) :obj:`int`. The seed for the domain sampling.
      """
      if seed is not None:
          domain = Domain(domain.as_dict(), seed=seed)
      super(RandomSearch, self).__init__(domain)
  
imports: from typing import List
  from hypertunity.domain import Domain, Sample
  from hypertunity.optimisation.base import Optimiser
label_desc: Use image node_id 1 to create a new RandomSearch object from inherited base classes: Optimiser with example: obj = RandomSearch(domain, seed)