code: def _fine_tune_layers(self):
      children = list(self.model.children())
      added_layer, cur_layer = 0, -1
      while (
          added_layer < self.additional_layers_to_train
          and -cur_layer < len(children)
      ):
          tunable = False
          for p in children[cur_layer].parameters():
              p.requires_grad = True
              tunable = True
          # only tune those layers contain parameters
          if tunable:
              added_layer += 1
          cur_layer -= 1
  
imports: import inspect
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from pytorch_lightning.utilities import _module_available
  from urllib.error import URLError
  import time
label_desc: Use image node_id 4 for calling the LitDeepVisionModel obj's underlying member method code with example usage: obj._fine_tune_layers() without return types