code: def _evaluate_bootstrap(means, lb, ub):
      in_bounds = (means > lb) & (means < ub)
      trials = len(in_bounds)
      successes = np.count_nonzero(in_bounds)
      failures = trials - successes
      return _BootstrapResult(failures, trials)
  
imports: import collections
  import numpy
  import scipy.optimize
  import scipy.stats
label_desc: Use image node_id 4 for calling a global function with example usage: _evaluate_bootstrap(means, lb, ub) and returns: _BootstrapResult