class_docstr: LR finder object. This object stores the results of lr_find().
  
  Args:
      mode: either `linear` or `exponential`, how to increase lr after each step
  
      lr_min: lr to start search from
  
      lr_max: lr to stop search
  
      num_training: number of steps to take between lr_min and lr_max
  
  Example::
      # Run lr finder
      lr_finder = trainer.lr_find(model)
  
      # Results stored in
      lr_finder.results
  
      # Plot using
      lr_finder.plot()
  
      # Get suggestion
      lr = lr_finder.suggestion()
docstr: This will propose a suggestion for an initial learning rate based on the point with the steepest negative
  gradient.
  
  Args:
      skip_begin: how many samples to skip in the beginning; helps to avoid too naive estimates
      skip_end: how many samples to skip in the end; helps to avoid too optimistic estimates
  
  Returns:
      The suggested initial learning rate to use, or `None` if a suggestion is not possible due to too few
      loss samples.
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code: def suggestion(
      self, skip_begin: int = 10, skip_end: int = 1
  ) -> Optional[float]:
      """This will propose a suggestion for an initial learning rate based on the point with the steepest negative
      gradient.
  
      Args:
          skip_begin: how many samples to skip in the beginning; helps to avoid too naive estimates
          skip_end: how many samples to skip in the end; helps to avoid too optimistic estimates
  
      Returns:
          The suggested initial learning rate to use, or `None` if a suggestion is not possible due to too few
          loss samples.
  
      """
      losses = torch.tensor(self.results["loss"][skip_begin:-skip_end])
      losses = losses[torch.isfinite(losses)]
  
      if len(losses) < 2:
          # computing np.gradient requires at least 2 points
          log.error(
              "Failed to compute suggestion for learning rate because there are not enough points. Increase the loop"
              " iteration limits or the size of your dataset/dataloader."
          )
          self._optimal_idx = None
          return None
  
      # TODO: When computing the argmin here, and some losses are non-finite, the expected indices could be
      #   incorrectly shifted by an offset
      gradients = torch.gradient(losses)[0]  # Unpack the tuple
      min_grad = torch.argmin(gradients).item()
  
      self._optimal_idx = min_grad + skip_begin
      return self.results["lr"][self._optimal_idx]
  
imports: import importlib
  import logging
  import os
  import uuid
  from copy import deepcopy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch.callbacks import Callback
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.parsing import lightning_hasattr, lightning_setattr
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.types import STEP_OUTPUT, LRScheduler, LRSchedulerConfig
label_desc: Use image node_id 4 for calling the _LRFinder obj's underlying member method code with example usage: obj.suggestion(skip_begin, skip_end) and returns: self, None