code: def deactivate_requires_grad(model: nn.Module):
      """Deactivates the requires_grad flag for all parameters of a model.
  
      This has the same effect as permanently executing the model within a `torch.no_grad()`
      context. Use this method to disable gradient computation and therefore
      training for a model.
  
      Examples:
          >>> backbone = resnet18()
          >>> deactivate_requires_grad(backbone)
      """
      for param in model.parameters():
          param.requires_grad = False
  
imports: import math
  import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from numpy.typing import NDArray
  from torch.nn import Module
  from torch.nn.modules.batchnorm import _BatchNorm
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 6 for calling a global function with example usage: deactivate_requires_grad(model) without return types