change: enable inconsistent-return-statements Pylint check

.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))