class_docstr: Training with exponentially growing learning rate
class_docstr_tok: ["Training","with","exponentially","growing","learning","rate"]
code: def after_fit(self):
      self.learn.opt.zero_grad()  # Needed before detaching the optimizer for future fits
      tmp_f = self.path / self.model_dir / self.tmp_p / "_tmp.pth"
      if tmp_f.exists():
          self.learn.load(f"{self.tmp_p}/_tmp", with_opt=True)
          self.tmp_d.cleanup()
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 6 for calling the LRFinder obj's underlying member method code with example usage: obj.after_fit() without return types