class_docstr: Wrapper over Scipy optimizer implementations, in standard ask and tell format.
  This is actually an import from scipy-optimize, including Sequential Quadratic Programming,
  
  Parameters
  ----------
  method: str
      Name of the method to use among:
  
      - Nelder-Mead
      - COBYLA
      - SQP (or SLSQP): very powerful e.g. in continuous noisy optimization. It is based on
        approximating the objective function by quadratic models.
      - Powell
      - NLOPT* (https://nlopt.readthedocs.io/en/latest/; by default, uses Sbplx, based on Subplex);
          can be NLOPT,
              NLOPT_LN_SBPLX,
              NLOPT_LN_PRAXIS,
              NLOPT_GN_DIRECT,
              NLOPT_GN_DIRECT_L,
              NLOPT_GN_CRS2_LM,
              NLOPT_GN_AGS,
              NLOPT_GN_ISRES,
              NLOPT_GN_ESCH,
              NLOPT_LN_COBYLA,
              NLOPT_LN_BOBYQA,
              NLOPT_LN_NEWUOA_BOUND,
              NLOPT_LN_NELDERMEAD.
  random_restart: bool
      whether to restart at a random point if the optimizer converged but the budget is not entirely
      spent yet (otherwise, restarts from best point)
  
  Note
  ----
  These optimizers do not support asking several candidates in a row
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code: def __init__(
      self, *, method: str = "Nelder-Mead", random_restart: bool = False
  ) -> None:
      super().__init__(_NonObjectMinimizeBase, locals())
  
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 NonObjectOptimizer object from inherited base classes: base with example: obj = NonObjectOptimizer()