code: def test_pytorch_profiler_multiple_loggers(tmpdir):
      """Tests whether the PyTorch profiler is able to write its trace locally when the Trainer is configured with
      multiple loggers.
  
      See issue #8157.
  
      """
  
      def look_for_trace(trace_dir):
          """Determines if a directory contains a PyTorch trace."""
          return any(
              "trace.json" in filename
              for filename in os.listdir(trace_dir)
          )
  
      model = BoringModel()
      loggers = [TensorBoardLogger(save_dir=tmpdir), CSVLogger(tmpdir)]
      trainer = Trainer(
          default_root_dir=tmpdir,
          profiler="pytorch",
          logger=loggers,
          limit_train_batches=5,
          max_epochs=1,
      )
      assert len(trainer.loggers) == 2
      trainer.fit(model)
      assert look_for_trace(tmpdir / "lightning_logs" / "version_0")
  
imports: import logging
  import os
  import platform
  import time
  from copy import deepcopy
  from unittest.mock import patch
  import numpy
  import pytest
  import torch
  from lightning.pytorch import Callback, Trainer
  from lightning.pytorch.callbacks import EarlyStopping, StochasticWeightAveraging
  from lightning.pytorch.demos.boring_classes import BoringModel, ManualOptimBoringModel
  from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
  from lightning.pytorch.profilers import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
  from lightning.pytorch.profilers.pytorch import _KINETO_AVAILABLE, RegisterRecordFunction, warning_cache
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 27 for calling a global function with example usage: test_pytorch_profiler_multiple_loggers(tmpdir) and returns: any