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from sagemaker .amazon .amazon_estimator import AmazonAlgorithmEstimatorBase , registry
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from sagemaker .amazon .common import numpy_to_record_serializer , record_deserializer
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from sagemaker .amazon .hyperparameter import Hyperparameter as hp # noqa
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- from sagemaker .amazon .validation import isin , gt , lt , isbool
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+ from sagemaker .amazon .validation import isin , gt , lt
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from sagemaker .predictor import RealTimePredictor
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from sagemaker .model import Model
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from sagemaker .session import Session
@@ -35,7 +35,7 @@ class LinearLearner(AmazonAlgorithmEstimatorBase):
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epochs = hp ('epochs' , gt (0 ), "An integer greater-than 0" , int )
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predictor_type = hp ('predictor_type' , isin ('binary_classifier' , 'regressor' ),
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'One of "binary_classifier" or "regressor"' , str )
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- use_bias = hp ('use_bias' , isbool , "Either True or False" , bool )
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+ use_bias = hp ('use_bias' , () , "Either True or False" , bool )
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num_models = hp ('num_models' , gt (0 ), "An integer greater-than 0" , int )
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num_calibration_samples = hp ('num_calibration_samples' , gt (0 ), "An integer greater-than 0" , int )
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init_method = hp ('init_method' , isin ('uniform' , 'normal' ), 'One of "uniform" or "normal"' , str )
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