change: enable inconsistent-return-statements Pylint check (#930)

Note that this commit also raises ValueErrors in situations that would
previously have returned None.

Per PEP8: Be consistent in return statements. Either all return
statements in a function should return an expression, or none of them
should. If any return statement returns an expression, any return
statements where no value is returned should explicitly state this as
return None, and an explicit return statement should be present at the
end of the function (if reachable).

Author: knakad

.pylintrc

@@ -91,7 +91,6 @@ disable=
     useless-object-inheritance, # TODO: Remove unnecessary imports
     cyclic-import, # TODO: Resolve cyclic imports
     no-self-use, # TODO: Convert methods to functions where appropriate
-    inconsistent-return-statements, # TODO: Make returns consistent
     consider-merging-isinstance, # TODO: Merge isinstance where appropriate
     consider-using-in, # TODO: Consider merging comparisons with "in"
     too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
         input_mode=None,
     ):
         if not channel_uri:
-            return
+            return None
         if not channel_name:
             raise ValueError(
                 "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
         sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
             S3 if required.
 
-    Returns
+    Returns:
         :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+    Raises:
+        ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
     """
     parsed_uri = urlparse(data_source)
     if parsed_uri.scheme == "file":
         return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
     if parsed_uri.scheme == "s3":
         return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+    raise ValueError(
+        "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+    )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
         if self.predictor_cls:
             return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+        return None
 
     def transformer(
         self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
         )
         if self.predictor_cls:
             return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+        return None
 
     def _create_sagemaker_pipeline_model(self, instance_type):
         """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
                 return np.load(BytesIO(stream.read()))
         finally:
             stream.close()
+        raise ValueError(
+            "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+                content_type
+            )
+        )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
                     MXNet was used as RL backend;
                 * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
                     TensorFlow was used as RL backend.
-
+        Raises:
+            ValueError: If image_name was not specified and framework enum is not valid.
         """
         base_args = dict(
             model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
             return MXNetModel(
                 framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
             )
+        raise ValueError(
+            "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+        )
 
     def train_image(self):
         """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
         Returns:
             list: metric definitions
+
+        Raises:
+            ValueError: If toolkit enum is not valid.
         """
         if toolkit is RLToolkit.COACH:
             return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
                 {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
                 {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
             ]
+        raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))