code: def __init__(
      self,
      dimension: int,
      budget: int,
      scrambling: bool = False,
      random_state: tp.Optional[RandomState] = None,
  ) -> None:
      if scrambling:
          raise ValueError("LHSSampler does not support scrambling")
      super().__init__(dimension, budget, random_state=random_state)
      self.permutations = np.zeros((dimension, budget), dtype=int)
      for k in range(dimension):
          self.permutations[k] = self.random_state.permutation(budget)
      self.seed = self.random_state.randint(2**32, dtype=np.uint32)
      self.randg = np.random.RandomState(self.seed)
  
imports: import numpy
  from numpy.random import RandomState
  import nevergrad.common.typing
  from nevergrad.common.decorators import Registry
label_desc: Use image node_id 1 to create a new LHSSampler object from inherited base classes: Sampler with example: obj = LHSSampler(dimension, budget, scrambling, random_state)