Repository: Lightning-AI/pytorch-lightning
File: pytorch_lightning/loggers/neptune.py (Python)

Code before:
          Pytorch Lightning with Neptune.

    Args:
        api_key: Required in online mode.
            Neptune API token, found on https://neptune.ai.
            Read how to get your
            `API key <https://docs.neptune.ai/python-api/tutorials/get-started.html#copy-api-token>`_.
            It is recommended to keep it in the `NEPTUNE_API_TOKEN`
            environment variable and then you can leave ``api_key=None``.
        project_name: Required in online mode. Qualified name of a project in a form of
            "namespace/project_name" for example "tom/minst-classification".
            If ``None``, the value of `NEPTUNE_PROJECT` environment variable will be taken.
            You need to create the project in https://neptune.ai first.
        offline_mode: Optional default ``False``. If ``True`` no logs will be sent
            to Neptune. Usually used for debug purposes.
        close_after_fit: Optional default ``True``. If ``False`` the experiment
            will not be closed after training and additional metrics,
            images or artifacts can be logged. Also, remember to close the experiment explicitly
            by running ``neptune_logger.experiment.stop()``.
        experiment_name: Optional. Editable name of the experiment.
            Name is displayed in the experiment’s Details (Metadata section) and
            in experiments view as a column.
        experiment_id: Optional. Default is ''None''. ID of the existing experiment.
            If specified, connect to experiment with experiment_id in project_name.
            Input arguments "experiment_name", "params", "properties" and "tags" will be overriden based
            on fetched experiment data.
        \**kwargs: Additional arguments like `params`, `tags`, `properties`, etc. used by
            :func:`neptune.Session.create_experiment` can be passed as keyword arguments in this logger.
    """

    def __init__(
        self,
        api_key: Optional[str] = None,
        project_name: Optional[str] = None,
        close_after_fit: Optional[bool] = True,
        offline_mode: bool = False,
        experiment_name: Optional[str] = None,
        experiment_id: Optional[str] = None,
        **kwargs
    ):
        if neptune is None:
            raise ImportError('You want to use `neptune` logger which is not installed yet,'
                              ' install it with `pip install neptune-client`.')
        super().__init__()
        self.api_key = api_key
        self.project_name = project_name
        self.offline_mode = offline_mode
        self.close_after_fit = close_after_fit
        self.experiment_name = experiment_name
        self._kwargs = kwargs
        self.experiment_id = experiment_id

Review comment (suggestion):
```suggestion
        experiment_id: Optional. Default is ``None``. The ID of the existing experiment.
            If specified, connect to experiment with experiment_id in project_name.
            Input arguments "experiment_name", "params", "properties" and "tags" will be overriden based
            on fetched experiment data.
```

Code after:
          pytorch-lightning-integration/e/PYTOR-66/charts>`_ showing the UI of Neptune.
        - `Tutorial <https://docs.neptune.ai/integrations/pytorch_lightning.html>`_ on how to use
          Pytorch Lightning with Neptune.

    Args:
        api_key: Required in online mode.
            Neptune API token, found on https://neptune.ai.
            Read how to get your
            `API key <https://docs.neptune.ai/python-api/tutorials/get-started.html#copy-api-token>`_.
            It is recommended to keep it in the `NEPTUNE_API_TOKEN`
            environment variable and then you can leave ``api_key=None``.
        project_name: Required in online mode. Qualified name of a project in a form of
            "namespace/project_name" for example "tom/minst-classification".
            If ``None``, the value of `NEPTUNE_PROJECT` environment variable will be taken.
            You need to create the project in https://neptune.ai first.
        offline_mode: Optional default ``False``. If ``True`` no logs will be sent
            to Neptune. Usually used for debug purposes.
        close_after_fit: Optional default ``True``. If ``False`` the experiment
            will not be closed after training and additional metrics,
            images or artifacts can be logged. Also, remember to close the experiment explicitly
            by running ``neptune_logger.experiment.stop()``.
        experiment_name: Optional. Editable name of the experiment.
            Name is displayed in the experiment’s Details (Metadata section) and
            in experiments view as a column.
        experiment_id: Optional. Default is ``None``. The ID of the existing experiment.
            If specified, connect to experiment with experiment_id in project_name.
            Input arguments "experiment_name", "params", "properties" and "tags" will be overriden based
            on fetched experiment data.
        \**kwargs: Additional arguments like `params`, `tags`, `properties`, etc. used by
            :func:`neptune.Session.create_experiment` can be passed as keyword arguments in this logger.
    """

    def __init__(
        self,
        api_key: Optional[str] = None,
        project_name: Optional[str] = None,
        close_after_fit: Optional[bool] = True,
        offline_mode: bool = False,
        experiment_name: Optional[str] = None,
        experiment_id: Optional[str] = None,
        **kwargs
    ):
        if neptune is None:
            raise ImportError('You want to use `neptune` logger which is not installed yet,'
                              ' install it with `pip install neptune-client`.')
        super().__init__()
        self.api_key = api_key
        self.project_name = project_name
        self.offline_mode = offline_mode
        self.close_after_fit = close_after_fit
        self.experiment_name = experiment_name