code: def test_auto_parameters_tying_tpus(tmpdir):
      model = WeightSharingModule()
      shared_params = find_shared_parameters(model)
  
      assert shared_params[0] == ["layer_1.weight", "layer_3.weight"]
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=3,
          accelerator="tpu",
          devices="auto",
          max_epochs=1,
      )
      trainer.fit(model)
  
      assert torch.equal(model.layer_1.weight, model.layer_3.weight)
  
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 9 for calling a global function with example usage: test_auto_parameters_tying_tpus(tmpdir) without return types