code: def __init__(
      self,
      parametrization: IntOrParameter,
      budget: tp.Optional[int] = None,
      num_workers: int = 1,
      *,
      method: str = "Nelder-Mead",
      random_restart: bool = False,
  ) -> None:
      super().__init__(
          parametrization, budget=budget, num_workers=num_workers
      )
      self.multirun = 1  # work in progress
      self._normalizer: tp.Any = None
      self.initial_guess: tp.Optional[tp.ArrayLike] = None
      # configuration
      # assert method in ["Nelder-Mead", "COBYLA", "SLSQP", "Powell", "BOBYQA", "AX"], f"Unknown method '{method}'"
      # assert method in ["SMAC3", "SMAC", "Nelder-Mead", "COBYLA", "SLSQP", "Powell"], f"Unknown method '{method}'"
      self.method = method
      self.random_restart = random_restart
      # self._normalizer = p.helpers.Normalizer(self.parametrization)
      assert (
          method
          in [
              "CmaFmin2",
              "gomea",
              "gomeablock",
              "gomeatree",
              "SMAC3",
              "BFGS",
              "RBFGS",
              "LBFGSB",
              "L-BFGS-B",
              "SMAC",
              "AX",
              "Lamcts",
              "Nelder-Mead",
              "COBYLA",
              "BOBYQA",
              "SLSQP",
              "pysot",
              "negpysot",
              "Powell",
          ]
          or "NLOPT" in method
          or "BFGS" in method
      ), f"Unknown method '{method}'"
      if (
          method == "CmaFmin2"
          or "NLOPT" in method
          or "AX" in method
          or "BOBYQA" in method
          or "pysot" in method
          or "SMAC" in method
      ):
          normalizer = p.helpers.Normalizer(self.parametrization)
          #            if normalizer.fully_bounded or method == "AX" or "pysot" == method or "SMAC" in method:
          #                self._normalizer = normalizer
          self._normalizer = normalizer
  
imports: import functools
  import math
  import warnings
  import weakref
  import numpy
  from scipy import optimize
  import nevergrad.common.typing
  from nevergrad.parametrization import parameter
  from nevergrad.common import errors
  from .None import base
  from .base import IntOrParameter
  from .None import recaster
label_desc: Use image node_id 1 to create a new _NonObjectMinimizeBase object from inherited base classes: recaster with example: obj = _NonObjectMinimizeBase(parametrization, budget, num_workers)