code: def gd_priv_optls(w_cur, lrp, hyper_dict):
      """Implementation of DP-GD with back-tracking line search !!!
  
      this method is not private. We only use it as a baseline.
  
      w = current point
      lr = logistic regression
      i = iteration number
      pb = auxillary information
  
      output is the next iterate
      """
      iters = hyper_dict["num_iteration"]
      rho_grad = (
          hyper_dict["total"] / iters
      )  # divide total privacy budget up
      grad_scale = (1 / lrp.num_samples) * np.sqrt(0.5 / rho_grad)
      grad_noise = np.random.normal(scale=grad_scale, size=lrp.dim)
      dir_srch = lrp.grad(w_cur) + grad_noise
      stepsize_opt = backtracking_ls(lrp, dir_srch, w_cur)
      return w_cur - stepsize_opt * dir_srch
  
imports: import time
  from my_logistic_regression import MyLogisticRegression
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: gd_priv_optls(w_cur, lrp, hyper_dict) and returns: unknown