code: def fine_tune(
      self: Learner,
      epochs,
      base_lr=2e-3,
      freeze_epochs=1,
      lr_mult=100,
      pct_start=0.3,
      div=5.0,
      **kwargs,
  ):
      "Fine tune with `Learner.freeze` for `freeze_epochs`, then with `Learner.unfreeze` for `epochs`, using discriminative LR."
      self.freeze()
      self.fit_one_cycle(
          freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs
      )
      base_lr /= 2
      self.unfreeze()
      self.fit_one_cycle(
          epochs,
          slice(base_lr / lr_mult, base_lr),
          pct_start=pct_start,
          div=div,
          **kwargs,
      )
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 17 for calling a global function with example usage: fine_tune(epochs, base_lr, freeze_epochs, lr_mult, pct_start, div) without return types