code: def gd_priv(w_cur, lrp, hyper_dict):
      """Implementation of DP-GD.
  
      w = current point
      lr = logistic regression
      i = iteration number
      pb = auxillary information
  
      output is the next iterate
      """
      iters = hyper_dict["num_iteration"]
      inv_lr_gd = 0.25  # learning rate based on the smoothness
      sens = 1 / (lrp.num_samples * (inv_lr_gd))  # sensitivity
      rho = (
          hyper_dict["total"] / iters
      )  # divide total privacy budget up
      noise = np.random.normal(
          scale=sens / np.sqrt(2 * rho), size=lrp.dim
      )
      return w_cur - lrp.grad_wor(w_cur) / (inv_lr_gd) + noise
  
imports: import time
  from my_logistic_regression import MyLogisticRegression
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: gd_priv(w_cur, lrp, hyper_dict) and returns: unknown