doc: remove D212 from ignore to comply with PEP257 standards

.pydocstylerc

@@ -1,4 +1,4 @@
 [pydocstyle]
 inherit = false
-ignore = D104,D107,D202,D203,D212,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417
+ignore = D104,D107,D202,D203,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417
 match = (?!record_pb2).*\.py

src/sagemaker/amazon/factorization_machines.py

@@ -290,8 +290,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for FactorizationMachinesPredictor class.
+        """Initialization for FactorizationMachinesPredictor class.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -321,8 +320,7 @@ class FactorizationMachinesModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for FactorizationMachinesModel class.
+        """Initialization for FactorizationMachinesModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/ipinsights.py

@@ -193,9 +193,9 @@ def __init__(
         serializer=CSVSerializer(),
         deserializer=JSONDeserializer(),
     ):
-        """
-        Args:
+        """Creates object to be used to get dot product of entity nad IP address.
 
+        Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                 requests are sent.
             sagemaker_session (sagemaker.session.Session): A SageMaker Session
@@ -223,9 +223,9 @@ class IPInsightsModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Args:
+        """Creates object to get insights on S3 model data.
 
+        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

src/sagemaker/amazon/kmeans.py

@@ -217,8 +217,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for KMeansPredictor class.
+        """Initialization for KMeansPredictor class.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -248,8 +247,7 @@ class KMeansModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for KMeansModel class.
+        """Initialization for KMeansModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/knn.py

@@ -209,8 +209,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Function to initialize KNNPredictor.
+        """Function to initialize KNNPredictor.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -240,8 +239,7 @@ class KNNModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Function to initialize KNNModel.
+        """Function to initialize KNNModel.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/lda.py

@@ -53,8 +53,7 @@ def __init__(
         tol=None,
         **kwargs
     ):
-        """
-        Latent Dirichlet Allocation (LDA) is :class:`Estimator` used for unsupervised learning.
+        """Latent Dirichlet Allocation (LDA) is :class:`Estimator` used for unsupervised learning.
 
         Amazon SageMaker Latent Dirichlet Allocation is an unsupervised
         learning algorithm that attempts to describe a set of observations as a
@@ -192,9 +191,9 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Args:
+        """Creates "LDAPredictor" object to be used for transforming input vectors.
 
+        Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                 requests are sent.
             sagemaker_session (sagemaker.session.Session): A SageMaker Session
@@ -222,8 +221,7 @@ class LDAModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for LDAModel class.
+        """Initialization for LDAModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/linear_learner.py

@@ -452,8 +452,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for LinearLearnerPredictor.
+        """Initialization for LinearLearnerPredictor.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -483,8 +482,7 @@ class LinearLearnerModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for LinearLearnerModel.
+        """Initialization for LinearLearnerModel.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/ntm.py

@@ -220,8 +220,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for NTMPredictor class.
+        """Initialization for NTMPredictor class.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -251,8 +250,7 @@ class NTMModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for NTMModel class.
+        """Initialization for NTMModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/object2vec.py

@@ -345,8 +345,7 @@ class Object2VecModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for Object2VecModel class.
+        """Initialization for Object2VecModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/pca.py

@@ -208,8 +208,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for PCAPredictor.
+        """Initialization for PCAPredictor.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -239,8 +238,7 @@ class PCAModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for PCAModel.
+        """Initialization for PCAModel.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/amazon/randomcutforest.py

@@ -180,8 +180,7 @@ def __init__(
         serializer=RecordSerializer(),
         deserializer=RecordDeserializer(),
     ):
-        """
-        Initialization for RandomCutForestPredictor class.
+        """Initialization for RandomCutForestPredictor class.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -211,8 +210,7 @@ class RandomCutForestModel(Model):
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
-        """
-        Initialization for RandomCutForestModel class.
+        """Initialization for RandomCutForestModel class.
 
         Args:
             model_data (str): The S3 location of a SageMaker model data

src/sagemaker/clarify.py

@@ -262,8 +262,7 @@ def __init__(
         use_logit=False,
         save_local_shap_values=True,
     ):
-        """
-        Initializes config for SHAP.
+        """Initializes config for SHAP.
 
         Args:
             baseline (str or list): A list of rows (at least one) or S3 object URI to be used as

src/sagemaker/debugger/framework_profile.py

@@ -38,8 +38,7 @@
 
 
 class FrameworkProfile:
-    """
-    Sets up the profiling configuration for framework metrics.
+    """Sets up the profiling configuration for framework metrics.
 
     Validates user inputs and fills in default values if no input is provided.
     There are three main profiling options to choose from:

src/sagemaker/estimator.py

@@ -1570,8 +1570,7 @@ def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config):
         return update_args
 
     def wait(self, logs="All"):
-        """
-        Placeholder docstring.
+        """Placeholder docstring.
 
         Args:
             logs ([str]): A list of strings specifying which logs to print. Acceptable

src/sagemaker/pytorch/estimator.py

@@ -51,8 +51,7 @@ def __init__(
         distribution=None,
         **kwargs
     ):
-        """
-        This ``Estimator`` executes an PyTorch script in a managed PyTorch execution environment.
+        """This ``Estimator`` executes a PyTorch script in a managed PyTorch execution environment.
 
         The managed PyTorch environment is an Amazon-built Docker container that executes functions
         defined in the supplied ``entry_point`` Python script within a SageMaker Training Job.

src/sagemaker/pytorch/model.py

@@ -272,8 +272,7 @@ def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
         )
 
     def _is_mms_version(self):
-        """
-        Determines if the framework corresponds to an and using MMS.
+        """Determines if the framework corresponds to an and using MMS.
 
         Whether the framework version corresponds to an inference image using
         the Multi-Model Server (https://github.com/awslabs/multi-model-server).

src/sagemaker/session.py

@@ -2029,14 +2029,13 @@ def create_tuning_job(
         self.sagemaker_client.create_hyper_parameter_tuning_job(**tune_request)
 
     def describe_tuning_job(self, job_name):
-        """
-        Calls the DescribeHyperParameterTuningJob API for the given job name, returns the response.
+        """Calls DescribeHyperParameterTuningJob API for the given job name, returns the response.
 
-            Args:
-                job_name (str): The name of the hyperparameter tuning job to describe.
+        Args:
+            job_name (str): The name of the hyperparameter tuning job to describe.
 
-            Returns:
-                dict: A dictionary response with the hyperparameter tuning job description.
+        Returns:
+            dict: A dictionary response with the hyperparameter tuning job description.
         """
         return self.sagemaker_client.describe_hyper_parameter_tuning_job(
             HyperParameterTuningJobName=job_name
@@ -2053,8 +2052,7 @@ def _map_tuning_config(
         objective_metric_name=None,
         parameter_ranges=None,
     ):
-        """
-        Construct tuning job configuration dictionary.
+        """Construct tuning job configuration dictionary.
 
         Args:
             strategy (str): Strategy to be used for hyperparameter estimations.
@@ -2097,8 +2095,7 @@ def _map_tuning_config(
 
     @classmethod
     def _map_tuning_objective(cls, objective_type, objective_metric_name):
-        """
-        Construct a dictionary of tuning objective from the arguments.
+        """Construct a dictionary of tuning objective from the arguments.
 
         Args:
             objective_type (str): The type of the objective metric for evaluating training jobs.
@@ -2148,8 +2145,7 @@ def _map_training_config(
         checkpoint_s3_uri=None,
         checkpoint_local_path=None,
     ):
-        """
-        Construct a dictionary of training job configuration from the arguments.
+        """Construct a dictionary of training job configuration from the arguments.
 
         Args:
             static_hyperparameters (dict): Hyperparameters for model training. These

src/sagemaker/sklearn/estimator.py

@@ -45,8 +45,7 @@ def __init__(
         image_uri=None,
         **kwargs
     ):
-        """
-        Creates a SKLearn Estimator for Scikit-learn environment.
+        """Creates a SKLearn Estimator for Scikit-learn environment.
 
         It will execute an Scikit-learn script within a SageMaker Training Job. The managed
         Scikit-learn environment is an Amazon-built Docker container that executes functions

src/sagemaker/tensorflow/estimator.py

@@ -265,8 +265,7 @@ def create_model(
         dependencies=None,
         **kwargs
     ):
-        """
-        Create a ``TensorFlowModel`` object that can be used for creating SageMaker model entities.
+        """Creates ``TensorFlowModel`` object to be used for creating SageMaker model entities.
 
         This can be done by deploying it to a SageMaker endpoint,
         or starting SageMaker Batch Transform jobs.

src/sagemaker/utils.py

@@ -570,8 +570,7 @@ def _botocore_resolver():
 
 
 def _aws_partition(region):
-    """
-    Given a region name (ex: "cn-north-1"), return the corresponding aws partition ("aws-cn").
+    """Given a region name (ex: "cn-north-1"), return the corresponding aws partition ("aws-cn").
 
     Args:
         region (str): The region name for which to return the corresponding partition.

src/sagemaker/workflow/airflow.py

@@ -388,8 +388,7 @@ def _extract_training_config_from_estimator(tuner, inputs, include_cls_metadata,
 def _extract_training_config_list_from_estimator_dict(
     tuner, inputs, include_cls_metadata, mini_batch_size
 ):
-    """
-    Extracts a list of training job configs from a Hyperparameter Tuner.
+    """Extracts a list of training job configs from a Hyperparameter Tuner.
 
     It uses the ``estimator_dict`` field.
     """
@@ -511,8 +510,7 @@ def update_estimator_from_task(estimator, task_id, task_type):
 
 
 def prepare_framework_container_def(model, instance_type, s3_operations):
-    """
-    This prepares the framework model container information and specifies related S3 operations.
+    """This prepares the framework model container information and specifies related S3 operations.
 
     Prepare the framework model container information. Specify related S3
     operations for Airflow to perform. (Upload `source_dir` )