code: def add_gradient_noise(
      model, iteration, duration=100, eta=1.0, scale_factor=0.55
  ):
      """Adds noise from a standard normal distribution to the gradients.
  
      The standard deviation (`sigma`) is controlled by the three hyper-parameters below.
      `sigma` goes to zero (no noise) with more iterations.
  
      Args:
          model (torch.nn.model): Model.
          iteration (int): Number of iterations.
          duration (int) {100, 1000}:
              Number of durations to control the interval of the `sigma` change.
          eta (float) {0.01, 0.3, 1.0}: The magnitude of `sigma`.
          scale_factor (float) {0.55}: The scale of `sigma`.
      """
      interval = (iteration // duration) + 1
      sigma = eta / interval**scale_factor
      for param in model.parameters():
          if param.grad is not None:
              _shape = param.grad.size()
              noise = sigma * torch.randn(_shape).to(param.device)
              param.grad += noise
  
imports: import argparse
  import copy
  import json
  import logging
  import os
  import shutil
  import tempfile
  import numpy
  import torch
label_desc: Use image node_id 9 for calling a global function with example usage: add_gradient_noise(model, iteration, duration, eta, scale_factor) without return types