class_docstr: Get candidate solutions via `DifferentialEvolutionSolvers` from scipy.
  
  According to scipy 1.9.2 documentation:
  
  'Finds the global minimum of a multivariate function.
  Differential Evolution is stochastic in nature (does not use gradient methods) to find the minimum,
  and can search large areas of candidate space, but often requires larger numbers of function
  evaluations than conventional gradient-based techniques.
  The algorithm is due to Storn and Price [1].'
  
  [1] Storn, R and Price, K, Differential Evolution - a Simple and Efficient Heuristic for Global
   Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11, 341 - 359.
code: def _maximize(
      self,
      previous_configs: list[Configuration],
      n_points: int,
  ) -> list[tuple[float, Configuration]]:
      configs: list[tuple[float, Configuration]] = []
  
      def func(x: np.ndarray) -> np.ndarray:
          assert self._acquisition_function is not None
          return -self._acquisition_function(
              [Configuration(self._configspace, vector=x)]
          )
  
      ds = DifferentialEvolutionSolver(
          func,
          bounds=[[0, 1] for _ in range(len(self._configspace))],
          args=(),
          strategy="best1bin",
          maxiter=1000,
          popsize=50,
          tol=0.01,
          mutation=(0.5, 1),
          recombination=0.7,
          seed=self._rng.randint(1000),
          polish=True,
          callback=None,
          disp=False,
          init="latinhypercube",
          atol=0,
      )
  
      _ = ds.solve()
      for pop, val in zip(ds.population, ds.population_energies):
          rc = Configuration(self._configspace, vector=pop)
          rc.origin = (
              "Acquisition Function Maximizer: Differential Evolution"
          )
          configs.append((-val, rc))
  
      configs.sort(key=lambda t: t[0])
      configs.reverse()
  
      return configs
  
imports: from __future__ import annotations
  import numpy
  from ConfigSpace import Configuration
  from scipy.optimize._differentialevolution import DifferentialEvolutionSolver
  from smac.acquisition.maximizer import AbstractAcquisitionMaximizer
label_desc: Use image node_id 1 for calling the DifferentialEvolution obj's underlying member method code with example usage: obj._maximize(previous_configs, n_points) and returns: configs, unknown