apply correct formatting

Author: ofek

src/sagemaker/algorithm.py

@@ -68,7 +68,7 @@ def __init__(
         encrypt_inter_container_traffic: Union[bool, PipelineVariable] = False,
         use_spot_instances: Union[bool, PipelineVariable] = False,
         max_wait: Optional[Union[int, PipelineVariable]] = None,
-        **kwargs  # pylint: disable=W0613
+        **kwargs,  # pylint: disable=W0613
     ):
         """Initialize an ``AlgorithmEstimator`` instance.
 
@@ -271,7 +271,7 @@ def create_model(
         serializer=IdentitySerializer(),
         deserializer=BytesDeserializer(),
         vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
-        **kwargs
+        **kwargs,
     ):
         """Create a model to deploy.
 
@@ -325,7 +325,7 @@ def predict_wrapper(endpoint, session):
             vpc_config=self.get_vpc_config(vpc_config_override),
             sagemaker_session=self.sagemaker_session,
             predictor_cls=predictor_cls,
-            **kwargs
+            **kwargs,
         )
 
     def transformer(

src/sagemaker/amazon/amazon_estimator.py

@@ -55,7 +55,7 @@ def __init__(
         instance_type: Optional[Union[str, PipelineVariable]] = None,
         data_location: Optional[str] = None,
         enable_network_isolation: Union[bool, PipelineVariable] = False,
-        **kwargs
+        **kwargs,
     ):
         """Initialize an AmazonAlgorithmEstimatorBase.
 
@@ -91,7 +91,7 @@ def __init__(
             instance_count,
             instance_type,
             enable_network_isolation=enable_network_isolation,
-            **kwargs
+            **kwargs,
         )
 
         data_location = data_location or (

src/sagemaker/amazon/factorization_machines.py

@@ -114,7 +114,7 @@ def __init__(
         factors_init_scale: Optional[float] = None,
         factors_init_sigma: Optional[float] = None,
         factors_init_value: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         """Factorization Machines is :class:`Estimator` for general-purpose supervised learning.
 
@@ -266,7 +266,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
 
@@ -332,7 +332,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for FactorizationMachinesModel class.
 
@@ -365,5 +365,5 @@ def __init__(
             role,
             predictor_cls=FactorizationMachinesPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/ipinsights.py

@@ -75,7 +75,7 @@ def __init__(
         random_negative_sampling_rate: Optional[int] = None,
         shuffled_negative_sampling_rate: Optional[int] = None,
         weight_decay: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         """This estimator is for IP Insights.
 
@@ -168,7 +168,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
@@ -235,7 +235,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Creates object to get insights on S3 model data.
 
@@ -268,5 +268,5 @@ def __init__(
             role,
             predictor_cls=IPInsightsPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/kmeans.py

@@ -75,7 +75,7 @@ def __init__(
         epochs: Optional[int] = None,
         center_factor: Optional[int] = None,
         eval_metrics: Optional[List[Union[str, PipelineVariable]]] = None,
-        **kwargs
+        **kwargs,
     ):
         """A k-means clustering class :class:`~sagemaker.amazon.AmazonAlgorithmEstimatorBase`.
 
@@ -184,7 +184,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(self, records, mini_batch_size=5000, job_name=None):
@@ -261,7 +261,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for KMeansModel class.
 
@@ -294,5 +294,5 @@ def __init__(
             role,
             predictor_cls=KMeansPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/knn.py

@@ -85,7 +85,7 @@ def __init__(
         index_metric: Optional[str] = None,
         faiss_index_ivf_nlists: Optional[str] = None,
         faiss_index_pq_m: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         """k-nearest neighbors (KNN) is :class:`Estimator` used for classification and regression.
 
@@ -181,7 +181,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
@@ -252,7 +252,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Function to initialize KNNModel.
 
@@ -285,5 +285,5 @@ def __init__(
             role,
             predictor_cls=KNNPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/lda.py

@@ -59,7 +59,7 @@ def __init__(
         max_restarts: Optional[int] = None,
         max_iterations: Optional[int] = None,
         tol: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         """Latent Dirichlet Allocation (LDA) is :class:`Estimator` used for unsupervised learning.
 
@@ -159,7 +159,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(  # pylint: disable=signature-differs
@@ -236,7 +236,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for LDAModel class.
 
@@ -269,5 +269,5 @@ def __init__(
             role,
             predictor_cls=LDAPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/linear_learner.py

@@ -190,7 +190,7 @@ def __init__(
         accuracy_top_k: Optional[int] = None,
         f_beta: Optional[float] = None,
         balance_multiclass_weights: Optional[bool] = None,
-        **kwargs
+        **kwargs,
     ):
         """An :class:`Estimator` for binary classification and regression.
 
@@ -420,7 +420,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
@@ -505,7 +505,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for LinearLearnerModel.
 
@@ -538,5 +538,5 @@ def __init__(
             role,
             predictor_cls=LinearLearnerPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/ntm.py

@@ -89,7 +89,7 @@ def __init__(
         clip_gradient: Optional[float] = None,
         weight_decay: Optional[float] = None,
         learning_rate: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         """Neural Topic Model (NTM) is :class:`Estimator` used for unsupervised learning.
 
@@ -194,7 +194,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(  # pylint: disable=signature-differs
@@ -269,7 +269,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for NTMModel class.
 
@@ -302,5 +302,5 @@ def __init__(
             role,
             predictor_cls=NTMPredictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )

src/sagemaker/amazon/object2vec.py

@@ -189,7 +189,7 @@ def __init__(
         enc1_layers: Optional[int] = None,
         enc0_freeze_pretrained_embedding: Optional[bool] = None,
         enc1_freeze_pretrained_embedding: Optional[bool] = None,
-        **kwargs
+        **kwargs,
     ):
         """Object2Vec is :class:`Estimator` used for anomaly detection.
 
@@ -338,7 +338,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
-            **kwargs
+            **kwargs,
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
@@ -363,7 +363,7 @@ def __init__(
         model_data: Union[str, PipelineVariable],
         role: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
-        **kwargs
+        **kwargs,
     ):
         """Initialization for Object2VecModel class.
 
@@ -396,5 +396,5 @@ def __init__(
             role,
             predictor_cls=Predictor,
             sagemaker_session=sagemaker_session,
-            **kwargs
+            **kwargs,
         )