class_docstr: Spike Detection Callback.
  
  Terminates training with a ``TrainingSpikeException`` when a loss-spike was detected and
  saves the batches to skip when resuming to a file.
  
  We skip the current and the previous batch since it is unclear whether the previous batch
  altered the weights in a way that it causes the spike or just the current batch is corrupted somehow.
  
  Args:
      mode: Whether to minimize or maximize the tracked metric
      window: A running mean of metrics with ``window`` size. Serves as reference value for spikes.
      warmup: After how many batches spike-tracking should start
      atol: An absolute tolerance.  Every diff between the running mean and the current value,
          that's not an improvement and above ``atol`` will be considered a spike
      rtol: A relative tolerance. Every diff between the running mean and the current value,
          that's higher than ``rtol * running_mean`` is considered a spike
      exclude_batches_path: Where to save the file that contains the batches to exclude.
          Will default to current directory.
      finite_only: If set to ``False``, consider non-finite values like NaN, inf and -inf a spike as well.
code: def _check_rtol(
      self,
      val_a: Union[float, torch.Tensor],
      val_b: Union[float, torch.Tensor],
  ) -> bool:
      return (self.rtol is None) or bool(
          abs(val_a - val_b) >= abs(self.rtol * val_b)
      )
  
imports: import json
  import operator
  import os
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union
  import torch
  from lightning_utilities.core.imports import compare_version
  from lightning.fabric.utilities.types import _PATH
label_desc: Use image node_id 6 for calling the SpikeDetection obj's underlying member method code with example usage: obj._check_rtol(val_a, val_b) and returns: unknown