doc: modified docstrings to comply with PEP257 standards

Author: ahsan-z-khan

src/sagemaker/algorithm.py

@@ -423,6 +423,7 @@ def fit(self, inputs=None, wait=True, logs=True, job_name=None):
         super(AlgorithmEstimator, self).fit(inputs, wait, logs, job_name)
 
     def _validate_input_channels(self, channels):
+        """Placeholder docstring"""
         train_spec = self.algorithm_spec["TrainingSpecification"]
         algorithm_name = self.algorithm_spec["AlgorithmName"]
         training_channels = {c["Name"]: c for c in train_spec["TrainingChannels"]}
@@ -440,6 +441,7 @@ def _validate_input_channels(self, channels):
                 raise ValueError("Required input channel: %s Was not provided." % (name))
 
     def _validate_and_cast_hyperparameter(self, name, v):
+        """Placeholder docstring"""
         algorithm_name = self.algorithm_spec["AlgorithmName"]
 
         if name not in self.hyperparameter_definitions:
@@ -533,6 +535,7 @@ def _hyperparameter_range_and_class(self, parameter_type, hyperparameter):
         return parameter_class, parameter_range
 
     def _algorithm_training_input_modes(self, training_channels):
+        """Placeholder docstring"""
         current_input_modes = {"File", "Pipe"}
         for channel in training_channels:
             supported_input_modes = set(channel["SupportedInputModes"])

src/sagemaker/multidatamodel.py

@@ -27,7 +27,7 @@
 
 
 class MultiDataModel(Model):
-    """A SageMaker ``MultiDataModel`` that can be used to deploy multiple models to the same ``Endpoint``.
+    """SageMaker ``MultiDataModel`` can be used to deploy multiple models to the same ``Endpoint``.
 
     And also deploy additional models to an existing SageMaker multi-model ``Endpoint``
     """
@@ -44,7 +44,7 @@ def __init__(
     ):
         """Initialize a ``MultiDataModel``.
 
-        In addition to these arguments, it supports all arguments supported by ``Model`` constructor.
+        Addition to these arguments, it supports all arguments supported by ``Model`` constructor.
 
         Args:
             name (str): The model name.

src/sagemaker/parameter.py

@@ -57,7 +57,7 @@ def cast_to_type(cls, value):
     def as_tuning_range(self, name):
         """Represent the parameter range as a dictionary.
 
-        Dictionary is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.
+        It is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.
 
         Args:
             name (str): The name of the hyperparameter.
@@ -110,7 +110,7 @@ def __init__(self, values):  # pylint: disable=super-init-not-called
     def as_tuning_range(self, name):
         """Represent the parameter range as a dictionary.
 
-        Dictionary is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.
+        It is suitable for a request to create an Amazon SageMaker hyperparameter tuning job.
 
         Args:
             name (str): The name of the hyperparameter.

src/sagemaker/predictor.py

@@ -126,11 +126,13 @@ def predict(self, data, initial_args=None, target_model=None, target_variant=Non
         return self._handle_response(response)
 
     def _handle_response(self, response):
+        "Placeholder docstring"
         response_body = response["Body"]
         content_type = response.get("ContentType", "application/octet-stream")
         return self.deserializer.deserialize(response_body, content_type)
 
     def _create_request_args(self, data, initial_args=None, target_model=None, target_variant=None):
+        "Placeholder docstring"
         args = dict(initial_args) if initial_args else {}
 
         if "EndpointName" not in args:

src/sagemaker/transformer.py

@@ -301,7 +301,9 @@ def attach(cls, transform_job_name, sagemaker_session=None):
 
     @classmethod
     def _prepare_init_params_from_job_description(cls, job_details):
-        """Convert the transform job description to init params that can be handled by the class constructor.
+        """Convert the transform job description to init params.
+
+        It can be handled by the class constructor.
 
         Args:
             job_details (dict): the returned job details from a