def __init__(self, model_data, role, entry_point, image=None, py_version='py3', framework_version=SKLEARN_VERSION,
                 predictor_cls=SKLearnPredictor, model_server_workers=None, **kwargs):
        """Initialize an SKLearnModel.

        Args:
            model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file.
            role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
                that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
                After the endpoint is created, the inference code might use the IAM role,
                if it needs to access an AWS resource.
            entry_point (str): Path (absolute or relative) to the Python source file which should be executed
                as the entry point to model hosting. This should be compatible with either Python 2.7 or Python 3.5.
            image (str): A Docker image URI (default: None). If not specified, a default image for Scikit-learn
                will be used.
            py_version (str): Python version you want to use for executing your model training code (default: 'py2').
            framework_version (str): Scikit-learn version you want to use for executing your model training code.
            predictor_cls (callable[str, sagemaker.session.Session]): A function to call to create a predictor
                with an endpoint name and SageMaker ``Session``. If specified, ``deploy()`` returns the result of
                invoking this function on the created endpoint name.
            model_server_workers (int): Optional. The number of worker processes used by the inference server.
                If None, server will use one worker per vCPU.
            **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer.
        """
        super(SKLearnModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls,
                                           **kwargs)
        self.py_version = py_version
        self.framework_version = framework_version
        self.model_server_workers = model_server_workers
def __init__ ( self , model_data , role , entry_point , image = None , py_version = 'py3' , framework_version = SKLEARN_VERSION , predictor_cls = SKLearnPredictor , model_server_workers = None , ** kwargs ) : super ( SKLearnModel , self ) . __init__ ( model_data , image , role , entry_point , predictor_cls = predictor_cls , ** kwargs ) self . py_version = py_version self . framework_version = framework_version self . model_server_workers = model_server_workers
Initialize an SKLearnModel.

        Args:
            model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file.
            role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
                that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
                After the endpoint is created, the inference code might use the IAM role,
                if it needs to access an AWS resource.
            entry_point (str): Path (absolute or relative) to the Python source file which should be executed
                as the entry point to model hosting. This should be compatible with either Python 2.7 or Python 3.5.
            image (str): A Docker image URI (default: None). If not specified, a default image for Scikit-learn
                will be used.
            py_version (str): Python version you want to use for executing your model training code (default: 'py2').
            framework_version (str): Scikit-learn version you want to use for executing your model training code.
            predictor_cls (callable[str, sagemaker.session.Session]): A function to call to create a predictor
                with an endpoint name and SageMaker ``Session``. If specified, ``deploy()`` returns the result of
                invoking this function on the created endpoint name.
            model_server_workers (int): Optional. The number of worker processes used by the inference server.
                If None, server will use one worker per vCPU.
            **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer.