sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py [603:638]:
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                 trainingInputS3DataPath=S3AutoCreatePath(),
                 trainingOutputS3DataPath=S3AutoCreatePath(),
                 trainingInstanceVolumeSizeInGB=1024,
                 trainingProjectedColumns=None,
                 trainingChannelName="train",
                 trainingContentType=None,
                 trainingS3DataDistribution="ShardedByS3Key",
                 trainingSparkDataFormat="sagemaker",
                 trainingSparkDataFormatOptions=None,
                 trainingInputMode="File",
                 trainingCompressionCodec=None,
                 trainingMaxRuntimeInSeconds=24*60*60,
                 trainingKmsKeyId=None,
                 modelEnvironmentVariables=None,
                 endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
                 sagemakerClient=SageMakerClients.create_sagemaker_client(),
                 region=None,
                 s3Client=SageMakerClients.create_s3_default_client(),
                 stsClient=SageMakerClients.create_sts_default_client(),
                 modelPrependInputRowsToTransformationRows=True,
                 deleteStagingDataAfterTraining=True,
                 namePolicyFactory=RandomNamePolicyFactory(),
                 uid=None,
                 javaObject=None):

        if trainingSparkDataFormatOptions is None:
            trainingSparkDataFormatOptions = {}

        if modelEnvironmentVariables is None:
            modelEnvironmentVariables = {}

        if uid is None:
            uid = Identifiable._randomUID()

        kwargs = locals().copy()
        del kwargs['self']
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sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py [882:917]:
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            trainingInputS3DataPath=S3AutoCreatePath(),
            trainingOutputS3DataPath=S3AutoCreatePath(),
            trainingInstanceVolumeSizeInGB=1024,
            trainingProjectedColumns=None,
            trainingChannelName="train",
            trainingContentType=None,
            trainingS3DataDistribution="ShardedByS3Key",
            trainingSparkDataFormat="sagemaker",
            trainingSparkDataFormatOptions=None,
            trainingInputMode="File",
            trainingCompressionCodec=None,
            trainingMaxRuntimeInSeconds=24*60*60,
            trainingKmsKeyId=None,
            modelEnvironmentVariables=None,
            endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
            sagemakerClient=SageMakerClients.create_sagemaker_client(),
            region=None,
            s3Client=SageMakerClients.create_s3_default_client(),
            stsClient=SageMakerClients.create_sts_default_client(),
            modelPrependInputRowsToTransformationRows=True,
            deleteStagingDataAfterTraining=True,
            namePolicyFactory=RandomNamePolicyFactory(),
            uid=None,
            javaObject=None):

        if trainingSparkDataFormatOptions is None:
            trainingSparkDataFormatOptions = {}

        if modelEnvironmentVariables is None:
            modelEnvironmentVariables = {}

        if uid is None:
            uid = Identifiable._randomUID()

        kwargs = locals().copy()
        del kwargs['self']
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