code: def slide(
      lrs: list,
      losses: list,
      num_it: int,
      lr_diff: int = 15,
      thresh: float = 0.005,
      adjust_value: float = 1.0,
  ):
      "Suggests a learning rate following an interval slide rule and returns its index"
      losses = to_np(losses)
      loss_grad = np.gradient(losses)
  
      r_idx = -1
      l_idx = r_idx - lr_diff
      local_min_lr = lrs[l_idx]
      while (l_idx >= -len(losses)) and (
          abs(loss_grad[r_idx] - loss_grad[l_idx]) > thresh
      ):
          local_min_lr = lrs[l_idx]
          r_idx -= 1
          l_idx -= 1
  
      suggestion = float(local_min_lr) * adjust_value
      idx = np.interp(np.log10(suggestion), np.log10(lrs), losses)
      return suggestion, (suggestion, idx)
  
imports: from __future__ import annotations
  from ..basics import *
  from .tracker import SaveModelCallback
label_desc: Use image node_id 19 for calling a global function with example usage: slide(lrs, losses, num_it, lr_diff, thresh, adjust_value) and returns: suggestion,  suggestion,  idx