code: def __init__(
      self,
      experiment: str = "test",
      name: str = "name",
      version: str = "1",
  ):
      super().__init__()
      self._experiment = experiment
      self._name = name
      self._version = version
      self.hparams_logged = None
      self.metrics_logged = {}
      self.finalized = False
      self.after_save_checkpoint_called = False
  
imports: import pickle
  from argparse import Namespace
  from copy import deepcopy
  from typing import Any, Dict, Optional
  from unittest.mock import patch
  import numpy
  import pytest
  import torch
  from lightning.fabric.utilities.logger import _convert_params, _sanitize_params
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel
  from lightning.pytorch.loggers import Logger, TensorBoardLogger
  from lightning.pytorch.loggers.logger import DummyExperiment, DummyLogger
  from lightning.pytorch.loggers.utilities import _scan_checkpoints
  from lightning.pytorch.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 1 to create a new CustomLogger object from inherited base classes: Logger with example: obj = CustomLogger(experiment, name, version)