class_docstr: a framework for benchmarking the optimizer
  
  Parameters
  ----------
  function_name : string
  fun : callable
  der : callable
      function that returns the derivative (jacobian, gradient) of fun
  hess : callable
      function that returns the hessian of fun
  minimizer_kwargs : kwargs
      additional keywords passed to the minimizer.  e.g. tol, maxiter
class_docstr_tok: ["a","framework","for","benchmarking","the","optimizer","Parameters","--","--","--","--","--","function_name",":","string","fun",":","callable","der",":","callable","function","that","returns","the","derivative","(","jacobian",",","gradient",")","of","fun","hess",":","callable","function","that","returns","the","hessian","of","fun","minimizer_kwargs",":","kwargs","additional","keywords","passed","to","the","minimizer",".","e.g",".","tol",",","maxiter"]
code: def __init__(
      self, function_name, fun, der=None, hess=None, **minimizer_kwargs
  ):
      self.function_name = function_name
      self.fun = fun
      self.der = der
      self.hess = hess
      self.minimizer_kwargs = minimizer_kwargs
      if "tol" not in minimizer_kwargs:
          minimizer_kwargs["tol"] = 1e-4
  
      self.results = []
  
imports: import os
  import time
  import inspect
  import json
  import traceback
  from collections import defaultdict
  import numpy
  from .None import test_functions
  from .None import go_benchmark_functions
  from .common import Benchmark, is_xslow, safe_import
  from .lsq_problems import extract_lsq_problems
label_desc: Use image node_id 1 to create a new _BenchOptimizers object from inherited base classes: Benchmark with example: obj = _BenchOptimizers(function_name, fun, der, hess)