code: def steep(lrs: list, losses: list, num_it: int) -> (float, tuple):
      "Suggests a learning rate when the slope is the steepest and returns its index"
      grads = (losses[1:] - losses[:-1]) / (
          lrs[1:].log() - lrs[:-1].log()
      )
      lr_steep = lrs[grads.argmin()].item()
      loss_idx = losses[
          min(range(len(lrs)), key=lambda i: abs(lrs[i] - lr_steep))
      ]
      return lr_steep, (lr_steep, loss_idx)
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 21 for calling a global function with example usage: steep(lrs, losses, num_it) and returns: lr_steep,  lr_steep,  loss_idx