code: def test_trainer_spike_detection_integration(
      tmp_path, global_rank_spike, num_devices, spike_value, finite_only
  ):
      cb = MyTrainerSpikeDetection(
          exclude_batches_path=tmp_path, finite_only=finite_only
      )
      # spike_value == None -> typical spike detection
      # finite_only -> typical spike detection and raise with NaN +/- inf
      # if inf -> inf >> other values -> typical spike detection
      cb.should_raise = (
          spike_value is None
          or finite_only
          or spike_value == float("inf")
      )
  
      trainer = Trainer(
          callbacks=[cb],
          accelerator="cpu",
          devices=num_devices,
          max_epochs=1,
          strategy="ddp_spawn",
      )
      trainer.fit(
          IdentityModule(global_rank_spike, spike_value),
          torch.utils.data.DataLoader([1 for _ in range(10)]),
      )
  
file_path: pytorch-lightning/tests/tests_pytorch/callbacks/test_spike.py
imports: import contextlib
  import sys
  import pytest
  import torch
  from lightning.fabric.utilities.spike import _TORCHMETRICS_GREATER_EQUAL_1_0_0, TrainingSpikeException
  from lightning.pytorch import LightningModule, Trainer
  from lightning.pytorch.callbacks.spike import SpikeDetection
label_desc: Use image node_id 1 for calling a global function with example usage: test_trainer_spike_detection_integration(tmp_path, global_rank_spike, num_devices, spike_value, finite_only) without return types