code: def training_step(self, batch, batch_idx: int):
      # initialize it all to weights one so that input = output but with gradients
      with torch.no_grad():
          self.layer.weight.data = torch.ones_like(
              self.layer.weight.data
          )
  
      curr_loss_val = 1 / (batch_idx + 1)
      if (
          self.trainer.global_rank == self.spike_global_rank
          and batch_idx == 4
      ):
          curr_loss_val = self.spike_value
  
      if curr_loss_val is None:
          curr_loss_val = batch_idx
  
      loss = self.layer(
          torch.tensor(
              curr_loss_val, device=self.device, dtype=self.dtype
          ).view(1, 1)
      )
      return loss
  
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 2 for calling the IdentityModule obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: loss