code: def minimum(lrs: list, losses: list, num_it: int):
      "Suggests a learning rate one-tenth the minumum before divergance and returns its index"
      lr_min = lrs[losses.argmin()].item()
      loss_idx = losses[
          min(range(len(lrs)), key=lambda i: abs(lrs[i] - lr_min))
      ]
      return lr_min / 10, (lr_min, loss_idx)
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 20 for calling a global function with example usage: minimum(lrs, losses, num_it) and returns: unknown,  lr_min,  loss_idx