Missing folder error when using TensorBoardLogger with S3 uri

[celpas]

Bug description

I'm receiving an error (similar to #18044) when using an S3 uri with TensorBoardLogger

File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
    call._call_and_handle_interrupt(
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
    return function(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 963, in _run
    _log_hyperparams(self)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loggers/utilities.py", line 95, in _log_hyperparams
    logger.save()
  File "/opt/conda/lib/python3.10/site-packages/lightning_utilities/core/rank_zero.py", line 32, in wrapped_fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loggers/tensorboard.py", line 224, in save
    save_hparams_to_yaml(hparams_file, self.hparams)
  File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/saving.py", line 296, in save_hparams_to_yaml
    raise RuntimeError(f"Missing folder: {os.path.dirname(config_yaml)}.")

What version are you seeing the problem on?

v2.0

How to reproduce the bug

No response

Error messages and logs

# Error messages and logs here please

Environment

Current environment

#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):

More info

No response