code: def on_train_end(self):
      # this might fail if run in an environment with too many ranks, as the total
      # length of the dataloader will be distrbuted among them and then each rank might not do 3 steps
      assert self.called["training_step"] == 3
      assert self.called["on_train_batch_start"] == 3
      assert self.called["on_train_batch_end"] == 3
  
      self.opt_step_patch.stop()
      assert self.opt_step_mock.call_count == 2
  
imports: import collections
  import os
  from copy import deepcopy
  from unittest import mock
  from unittest.mock import MagicMock, call, patch
  import lightning.fabric
  import pytest
  import torch
  from lightning.fabric.utilities.imports import _IS_WINDOWS
  from lightning.pytorch import Trainer
  from lightning.pytorch.accelerators import CPUAccelerator, XLAAccelerator
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.plugins import Precision, XLACheckpointIO, XLAPrecision
  from lightning.pytorch.strategies import DDPStrategy, XLAStrategy
  from lightning.pytorch.utilities import find_shared_parameters
  from torch import nn
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.trainer.connectors.test_accelerator_connector import DeviceMock
  from tests_pytorch.trainer.optimization.test_manual_optimization import assert_emtpy_grad
label_desc: Use image node_id 7 for calling the ManualOptimizationModel obj's underlying member method code with example usage: obj.on_train_end() without return types