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 _is_spike(self, loss: torch.Tensor) -> bool:
      # we might call compute more often than update which is fine as long as the
      # metric has at least one internal value.
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          running_val = self.running_mean.compute()
      curr_diff = loss - self.last_val
  
      if self.finite_only and not torch.isfinite(loss):
          return True
  
      if self._is_better(curr_diff):
          return False
  
      return self._check_atol(loss, running_val) and self._check_rtol(
          loss, running_val
      )
  
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 3 for calling the SpikeDetection obj's underlying member method code with example usage: obj._is_spike(loss) and returns: unknown, True, False