class_docstr: Interleave a random configuration after a constant number of configurations found by
  Bayesian optimization.
  
  Parameters
  ----------
  modulus : float
      Every modulus-th configuration will be at random.
  seed : int
      Integer used to initialize random state. This class does not use the seed.
code: def __init__(self, modulus: float = 2.0, seed: int = 0):
      super().__init__(seed)
      assert modulus > 0
      if modulus <= 1.0:
          logger.warning(
              "Using SMAC with random configurations only. ROAR is the better choice for this."
          )
  
      self._modulus = modulus
  
file_path: SMAC3/smac/random_design/modulus_design.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from smac.random_design.abstract_random_design import AbstractRandomDesign
  from smac.utils.logging import get_logger
label_desc: Use image node_id 1 to create a new ModulusRandomDesign object from inherited base classes: AbstractRandomDesign with example: obj = ModulusRandomDesign(modulus, seed)