class_docstr: Experiment writer for CSVLogger.
  
  Currently, supports to log hyperparameters and metrics in YAML and CSV
  format, respectively.
  
  This logger supports logging to remote filesystems via ``fsspec``. Make sure you have it installed.
  
  Args:
      log_dir: Directory for the experiment logs
class_docstr_tok: ["Experiment","writer","for","CSVLogger",".","Currently",",","supports","to","log","hyperparameters","and","metrics","in","YAML","and","CSV","format",",","respectively",".","This","logger","supports","logging","to","remote","filesystems","via","``","fsspec","``",".","Make","sure","you","have","it","installed",".","Args",":","log_dir",":","Directory","for","the","experiment","logs"]
docstr: Save recorded hparams and metrics into files.
docstr_tok: ["Save","recorded","hparams","and","metrics","into","files","."]
code: def save(self) -> None:
      """Save recorded hparams and metrics into files."""
      hparams_file = os.path.join(self.log_dir, self.NAME_HPARAMS_FILE)
      save_hparams_to_yaml(hparams_file, self.hparams)
      return super().save()
  
imports: import logging
  import os
  from argparse import Namespace
  from typing import Any, Dict, Optional, Union
  from typing_extensions import override
  from lightning.fabric.loggers.csv_logs import CSVLogger
  from lightning.fabric.loggers.csv_logs import _ExperimentWriter
  from lightning.fabric.loggers.logger import rank_zero_experiment
  from lightning.fabric.utilities.logger import _convert_params
  from lightning.fabric.utilities.types import _PATH
  from lightning.pytorch.core.saving import save_hparams_to_yaml
  from lightning.pytorch.loggers.logger import Logger
  from lightning.pytorch.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 3 for calling the ExperimentWriter obj's underlying member method code with example usage: obj.save() and returns: super