class_docstr: Benchmarks for the Minimizer class
code: def setup(self):
      self.x = np.linspace(1, 10, 250)
      np.random.seed(0)
      self.y = (
          3.0 * np.exp(-self.x / 2)
          - 5.0 * np.exp(-(self.x - 0.1) / 10.0)
          + 0.1 * np.random.randn(len(self.x))
      )
  
      self.p = Parameters()
      self.p.add_many(
          ("a1", 4.0, True, 0.0, 10.0),
          ("a2", 4.0, True, -10.0, 10.0),
          ("t1", 3.0, True, 0.01, 10.0),
          ("t2", 3.0, True, 0.01, 20.0),
      )
  
      self.p_emcee = deepcopy(self.p)
      self.p_emcee.add("noise", 0.2, True, 0.001, 1.0)
  
      self.mini_de = Minimizer(
          Minimizer_Residual,
          self.p,
          fcn_args=(self.x, self.y),
          kws={"seed": 1, "polish": False, "maxiter": 100},
      )
  
      self.mini_emcee = Minimizer(
          Minimizer_lnprob, self.p_emcee, fcn_args=(self.x, self.y)
      )
  
file_path: lmfit-py/asv_benchmarking/benchmarks/benchmarks.py
imports: from copy import deepcopy
  import numpy
  from lmfit import Minimizer, Parameters, conf_interval, minimize
label_desc: Use image node_id 1 for calling the MinimizerClassSuite obj's underlying member method code with example usage: obj.setup() without return types