code: def fit_one_cycle(
      self: Learner,
      n_epoch,
      lr_max=None,
      div=25.0,
      div_final=1e5,
      pct_start=0.25,
      wd=None,
      moms=None,
      cbs=None,
      reset_opt=False,
      start_epoch=0,
  ):
      "Fit `self.model` for `n_epoch` using the 1cycle policy."
      if self.opt is None:
          self.create_opt()
      self.opt.set_hyper("lr", self.lr if lr_max is None else lr_max)
      lr_max = np.array([h["lr"] for h in self.opt.hypers])
      scheds = {
          "lr": combined_cos(
              pct_start, lr_max / div, lr_max, lr_max / div_final
          ),
          "mom": combined_cos(
              pct_start, *(self.moms if moms is None else moms)
          ),
      }
      self.fit(
          n_epoch,
          cbs=ParamScheduler(scheds) + L(cbs),
          reset_opt=reset_opt,
          wd=wd,
          start_epoch=start_epoch,
      )
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 13 for calling a global function with example usage: fit_one_cycle(n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt, start_epoch) without return types