def _get_java_obj()

in sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py [0:0]


    def _get_java_obj(self, **kwargs):

        return self._new_java_obj(
            SageMakerEstimator._wrapped_class,
            kwargs['trainingImage'],
            kwargs['modelImage'],
            kwargs['sagemakerRole'],
            kwargs['trainingInstanceType'],
            kwargs['trainingInstanceCount'],
            kwargs['endpointInstanceType'],
            kwargs['endpointInitialInstanceCount'],
            kwargs['requestRowSerializer'],
            kwargs['responseRowDeserializer'],
            kwargs['trainingInputS3DataPath'],
            kwargs['trainingOutputS3DataPath'],
            kwargs['trainingInstanceVolumeSizeInGB'],
            Option(kwargs['trainingProjectedColumns']),
            kwargs['trainingChannelName'],
            Option(kwargs['trainingContentType']),
            kwargs['trainingS3DataDistribution'],
            kwargs['trainingSparkDataFormat'],
            kwargs['trainingSparkDataFormatOptions'],
            kwargs['trainingInputMode'],
            Option(kwargs['trainingCompressionCodec']),
            kwargs['trainingMaxRuntimeInSeconds'],
            Option(kwargs['trainingKmsKeyId']),
            kwargs['modelEnvironmentVariables'],
            kwargs['endpointCreationPolicy'],
            kwargs['sagemakerClient'],
            kwargs['s3Client'],
            kwargs['stsClient'],
            kwargs['modelPrependInputRowsToTransformationRows'],
            kwargs['deleteStagingDataAfterTraining'],
            kwargs['namePolicyFactory'],
            kwargs['uid'],
            kwargs['hyperParameters']
        )