    def create(
        cls,
        name: Tag | str,
        *,
        module: str,
        api_version: str,
        signatures: ModelSignaturesType,
        labels: dict[str, str] | None = None,
        options: ModelOptions | None = None,
        custom_objects: dict[str, t.Any] | None = None,
        metadata: dict[str, t.Any] | None = None,
        context: ModelContext,
    ) -> Model:
        """Create a new Model instance in temporary filesystem used for serializing
        model artifacts and save to model store

        Args:
            name: model name in target model store, model version will be automatically
                generated
            module: import path of module used for saving/loading this model, e.g.
                "bentoml.tensorflow"
            labels:  user-defined labels for managing models, e.g. team=nlp, stage=dev
            options: default options for loading this model, defined by runner
                implementation, e.g. xgboost booster_params
            custom_objects: user-defined additional python objects to be saved
                alongside the model, e.g. a tokenizer instance, preprocessor function,
                model configuration json
            metadata: user-defined metadata for storing model training context
                information or model evaluation metrics, e.g. dataset version,
                training parameters, model scores
            context: Environment context managed by BentoML for loading model,
                e.g. {"framework:" "tensorflow", "framework_version": _tf_version}

        Returns:
            object: Model instance created in temporary filesystem
        """
        tag = Tag.from_taglike(name)
        if tag.version is None:
            tag = tag.make_new_version()
        labels = {} if labels is None else labels
        metadata = {} if metadata is None else metadata
        options = ModelOptions() if options is None else options

        model_fs = fs.open_fs(f"temp://bentoml_model_{tag.name}")

        return cls(
            tag,
            model_fs,
            ModelInfo(
                tag=tag,
                module=module,
                api_version=api_version,
                signatures=signatures,
                labels=labels,
                options=options,
                metadata=metadata,
                context=context,
            ),
            custom_objects=custom_objects,
            _internal=True,
        )
Create a new Model instance in temporary filesystem used for serializing
model artifacts and save to model store

Args:
name: model name in target model store, model version will be automatically
generated
module: import path of module used for saving/loading this model, e.g.
"bentoml.tensorflow"
labels:  user-defined labels for managing models, e.g. team=nlp, stage=dev
options: default options for loading this model, defined by runner
implementation, e.g. xgboost booster_params
custom_objects: user-defined additional python objects to be saved
alongside the model, e.g. a tokenizer instance, preprocessor function,
model configuration json
metadata: user-defined metadata for storing model training context
information or model evaluation metrics, e.g. dataset version,
training parameters, model scores
context: Environment context managed by BentoML for loading model,
e.g. {"framework:" "tensorflow", "framework_version": _tf_version}

Returns:
object: Model instance created in temporary filesystem
src/bentoml/_internal/models/model.py