def load_runner(tag: t.Union[(str, Tag)], *, predict_fn_name: str='__call__', device_id: str='CPU:0', name: t.Optional[str]=None, partial_kwargs: t.Optional[t.Dict[(str, t.Any)]]=None) -> '_TensorflowRunner':
    '\n    Runner represents a unit of serving logic that can be scaled horizontally to\n    maximize throughput. `bentoml.tensorflow.load_runner` implements a Runner class that\n    wrap around a Tensorflow model, which optimize it for the BentoML runtime.\n\n    Args:\n        tag (:code:`Union[str, Tag]`):\n            Tag of a saved model in BentoML local modelstore.\n        predict_fn_name (:code:`str`, default to :code:`__call__`):\n            Inference function to be used.\n        partial_kwargs (:code:`Dict[str, Any]`, `optional`, default to :code:`None`):\n            Dictionary of partial kwargs that can be shared across different model.\n        device_id (:code:`str`, `optional`, default to the first CPU):\n            Optional devices to put the given model on. Refers to `Logical Devices <https://www.tensorflow.org/api_docs/python/tf/config/list_logical_devices>`_ from TF documentation.\n\n    Returns:\n        :obj:`~bentoml._internal.runner.Runner`: Runner instances for :mod:`bentoml.tensorflow` model\n\n    Examples:\n\n    .. code-block:: python\n\n        import bentoml\n\n        # load a runner from a given flag\n        runner = bentoml.tensorflow.load_runner(tag)\n\n        # load a runner on GPU:0\n        runner = bentoml.tensorflow.load_runner(tag, resource_quota=dict(gpus=0), device_id="GPU:0")\n\n    '
    return _TensorflowRunner(tag=tag, predict_fn_name=predict_fn_name, device_id=device_id, partial_kwargs=partial_kwargs, name=name)
Runner represents a unit of serving logic that can be scaled horizontally to
maximize throughput. `bentoml.tensorflow.load_runner` implements a Runner class that
wrap around a Tensorflow model, which optimize it for the BentoML runtime.

Args:
    tag (:code:`Union[str, Tag]`):
        Tag of a saved model in BentoML local modelstore.
    predict_fn_name (:code:`str`, default to :code:`__call__`):
        Inference function to be used.
    partial_kwargs (:code:`Dict[str, Any]`, `optional`, default to :code:`None`):
        Dictionary of partial kwargs that can be shared across different model.
    device_id (:code:`str`, `optional`, default to the first CPU):
        Optional devices to put the given model on. Refers to `Logical Devices <https://www.tensorflow.org/api_docs/python/tf/config/list_logical_devices>`_ from TF documentation.

Returns:
    :obj:`~bentoml._internal.runner.Runner`: Runner instances for :mod:`bentoml.tensorflow` model

Examples:

.. code-block:: python

    import bentoml

    # load a runner from a given flag
    runner = bentoml.tensorflow.load_runner(tag)

    # load a runner on GPU:0
    runner = bentoml.tensorflow.load_runner(tag, resource_quota=dict(gpus=0), device_id="GPU:0")
def load_runner(tag: t.Union[(str, Tag)], *, predict_fn_name: str='__call__', device_id: str='CPU:0', name: t.Optional[str]=None, partial_kwargs: t.Optional[t.Dict[(str, t.Any)]]=None) -> '_TensorflowRunner':
    '\n    Runner represents a unit of serving logic that can be scaled horizontally to\n    maximize throughput. `bentoml.tensorflow.load_runner` implements a Runner class that\n    wrap around a Tensorflow model, which optimize it for the BentoML runtime.\n\n    Args:\n        tag (:code:`Union[str, Tag]`):\n            Tag of a saved model in BentoML local modelstore.\n        predict_fn_name (:code:`str`, default to :code:`__call__`):\n            Inference function to be used.\n        partial_kwargs (:code:`Dict[str, Any]`, `optional`, default to :code:`None`):\n            Dictionary of partial kwargs that can be shared across different model.\n        device_id (:code:`str`, `optional`, default to the first CPU):\n            Optional devices to put the given model on. Refers to `Logical Devices <https://www.tensorflow.org/api_docs/python/tf/config/list_logical_devices>`_ from TF documentation.\n\n    Returns:\n        :obj:`~bentoml._internal.runner.Runner`: Runner instances for :mod:`bentoml.tensorflow` model\n\n    Examples:\n\n    .. code-block:: python\n\n        import bentoml\n\n        # load a runner from a given flag\n        runner = bentoml.tensorflow.load_runner(tag)\n\n        # load a runner on GPU:0\n        runner = bentoml.tensorflow.load_runner(tag, resource_quota=dict(gpus=0), device_id="GPU:0")\n\n    '
    return _TensorflowRunner(tag=tag, predict_fn_name=predict_fn_name, device_id=device_id, partial_kwargs=partial_kwargs, name=name)