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 load_state_dict(self, state_dict: Dict[str, Any]) -> None:
      self.last_val = state_dict.pop("last_val")
      self.mode = state_dict.pop("mode")
      self.warmup = state_dict.pop("warmup")
      self.atol = state_dict.pop("atol")
      self.rtol = state_dict.pop("rtol")
      self.bad_batches = state_dict.pop("bad_batches")
      self.exclude_batches_path = state_dict.pop("bad_batches_path")
      self.running.load_state_dict(state_dict.pop("running"))
      self.running_mean.base_metric.load_state_dict(
          state_dict.pop("mean")
      )
  
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 10 for calling the SpikeDetection obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types