class_docstr: Benchmark the optimizers with the CUTEST DFO benchmark of Moré and Wild.
  The original benchmark suite is available at
  https://github.com/POptUS/BenDFO
class_docstr_tok: ["Benchmark","the","optimizers","with","the","CUTEST","DFO","benchmark","of","Mor\u00e9","and","Wild",".","The","original","benchmark","suite","is","available","at","https",":","\/\/github.com\/POptUS\/BenDFO"]
code: def setup(self, prob_number, method_name, ret_val):
      probs = np.loadtxt(
          os.path.join(os.path.dirname(__file__), "cutest", "dfo.txt")
      )
      params = probs[prob_number]
      nprob = int(params[0])
      n = int(params[1])
      m = int(params[2])
      s = params[3]
      factor = 10**s
  
      def func(x):
          return calfun(x, m, nprob)
  
      x0 = dfoxs(n, nprob, factor)
      b = getattr(self, "run_cutest")(
          func, x0, prob_number=prob_number, methods=[method_name]
      )
      r = b.average_results().get(method_name)
      if r is None:
          raise NotImplementedError()
      self.result = getattr(r, ret_val)
  
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 for calling the BenchDFO obj's underlying member method code with example usage: obj.setup(prob_number, method_name, ret_val) and returns: calfun