class_docstr: Benchmark the optimizers with smooth, unbounded, functions
class_docstr_tok: ["Benchmark","the","optimizers","with","smooth",",","unbounded",",","functions"]
code: def run_LJ(self, methods=None):
      s = funcs.LJ()
      # print "checking gradient",
      # scipy.optimize.check_grad(s.get_energy, s.get_gradient,
      #                           np.random.uniform(-2,2,3*4))
      natoms = 4
      b = _BenchOptimizers(
          "%d atom Lennard Jones potential" % (natoms),
          fun=s.fun,
          der=s.der,
          hess=None,
      )
      for i in range(10):
          b.bench_run(
              np.random.uniform(-2, 2, natoms * 3), methods=methods
          )
      return b
  
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 12 for calling the BenchSmoothUnbounded obj's underlying member method code with example usage: obj.run_LJ(methods) and returns: b