def METHOD_NAME(
    name: Tag | str,
    model: pl.LightningModule,
    *,
    signatures: ModelSignaturesType | None = None,
    labels: t.Dict[str, str] | None = None,
    custom_objects: t.Dict[str, t.Any] | None = None,
    external_modules: t.List[ModuleType] | None = None,
    metadata: t.Dict[str, t.Any] | None = None,
) -> bentoml.Model:
    """
    Save a model instance to BentoML modelstore.
    Args:
        name (:code:`str`):
            Name for given model instance. This should pass Python identifier check.
        model (`pl.LightningModule`):
            Instance of model to be saved
        labels (:code:`Dict[str, str]`, `optional`, default to :code:`None`):
            user-defined labels for managing models, e.g. team=nlp, stage=dev
        custom_objects (:code:`Dict[str, Any]]`, `optional`, default to :code:`None`):
            user-defined additional python objects to be saved alongside the model,
            e.g. a tokenizer instance, preprocessor function, model configuration json
        external_modules (:code:`List[ModuleType]`, `optional`, default to :code:`None`):
            user-defined additional python modules to be saved alongside the model or custom objects,
            e.g. a tokenizer module, preprocessor module, model configuration module
        metadata (:code:`Dict[str, Any]`, `optional`, default to :code:`None`):
            Custom metadata for given model.
        model_store (:mod:`~bentoml._internal.models.store.ModelStore`, default to :mod:`BentoMLContainer.model_store`):
            BentoML modelstore, provided by DI Container.
    Returns:
        :obj:`~bentoml.Tag`: A :obj:`tag` with a format `name:version` where `name` is the user-defined model's name, and a generated `version` by BentoML.
    Examples:
    .. code-block:: python
        import bentoml
        import torch
        import pytorch_lightning as pl
        class LitClassifier(pl.LightningModule):
            def __init__(self, hidden_dim: int = 128, learning_rate: float = 0.0001):
                super().__init__()
                self.save_hyperparameters()
                self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
                self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
            def forward(self, x):
                x = x.view(x.size(0), -1)
                x = torch.relu(self.l1(x))
                x = torch.relu(self.l2(x))
                return x
            def training_step(self, batch, batch_idx):
                x, y = batch
                y_hat = self(x)
                loss = F.cross_entropy(y_hat, y)
                return loss
            def validation_step(self, batch, batch_idx):
                x, y = batch
                y_hat = self(x)
                loss = F.cross_entropy(y_hat, y)
                self.log("valid_loss", loss)
            def test_step(self, batch, batch_idx):
                x, y = batch
                y_hat = self(x)
                loss = F.cross_entropy(y_hat, y)
                self.log("test_loss", loss)
            def configure_optimizers(self):
                return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
        tag = bentoml.pytorch_lightning.save_model("lit_classifier", LitClassifier())
    """
    if not isinstance(model, pl.LightningModule):
        raise TypeError(
            f"'model' must be an instance of 'pl.LightningModule', got {type(model)} instead."
        )
    script_module = model.to_torchscript()
    assert not isinstance(
        script_module, dict
    ), "Saving a dict of pytorch_lightning Module into one BentoModel is not supported"
    return script_save_model(
        name,
        script_module,
        signatures=signatures,
        labels=labels,
        custom_objects=custom_objects,
        external_modules=external_modules,
        metadata=metadata,
        _framework_name="pytorch_lightning",
        _module_name=MODULE_NAME,
    )