Skip currently failed linting issues

Author: aktech

.bazelrc

@@ -90,7 +90,7 @@ build --experimental_cc_shared_library
 # uses TensorFlow for all IO operations.
 build --define=use_tensorflow_io=1
 
-# TensorFlow Decision Forests need absl::anyInvocable support for new versions 
+# TensorFlow Decision Forests need absl::anyInvocable support for new versions
 # of Tensorflow.
 build --define use_absl_anyinvocable=1
 

WORKSPACE

@@ -130,4 +130,3 @@ load(
 )
 
 nccl_configure(name = "local_config_nccl")
-

ruff.toml

@@ -78,6 +78,7 @@ ignore = [
     "PT027",  # Use `pytest.raises` instead of unittest-style `assertRaisesRegex`
     "UP028",  # Replace `yield` over `for` loop with `yield from`
     "UP029",  # Unnecessary builtin import
+    "RET503", # Missing explicit `return` at the end of function able to return non-`None` value
 ]
 
 [lint.pyupgrade]
@@ -86,4 +87,4 @@ ignore = [
 keep-runtime-typing = true
 
 [lint.pydocstyle]
-convention = "google"
+convention = "google"

tensorflow_serving/apis/model_service_pb2.py

@@ -18,19 +18,25 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/model_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pb2
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+    get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+    model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
     name='tensorflow_serving/apis/model_service.proto',

tensorflow_serving/apis/model_service_pb2_grpc.py

@@ -19,14 +19,18 @@
 
 import grpc
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+  get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+  model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 
-class ModelServiceStub(object):
+class ModelServiceStub:
   """ModelService provides methods to query and update the state of the server,
   e.g. which models/versions are being served.
-  """
+  """  # noqa: D205
 
   def __init__(self, channel):
     """Constructor.
@@ -50,26 +54,26 @@ def __init__(self, channel):
     )
 
 
-class ModelServiceServicer(object):
+class ModelServiceServicer:
   """ModelService provides methods to query and update the state of the server,
   e.g. which models/versions are being served.
-  """
+  """  # noqa: D205
 
-  def GetModelStatus(self, request, context):
+  def GetModelStatus(self, request, context):  # noqa: ARG002
     """Gets status of model. If the ModelSpec in the request does not specify
     version, information about all versions of the model will be returned. If
     the ModelSpec in the request does specify a version, the status of only
     that version will be returned.
-    """
+    """  # noqa: D205
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def HandleReloadConfigRequest(self, request, context):
+  def HandleReloadConfigRequest(self, request, context):  # noqa: ARG002
     """Reloads the set of served models. The new config supersedes the old one,
     so if a model is omitted from the new config it will be unloaded and no
     longer served.
-    """
+    """  # noqa: D205
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')

tensorflow_serving/apis/prediction_service_pb2.py

@@ -22,23 +22,34 @@
 # source: tensorflow_serving/apis/prediction_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pb2
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
-
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
   name='tensorflow_serving/apis/prediction_service.proto',

tensorflow_serving/apis/prediction_service_pb2_grpc.py

@@ -18,18 +18,28 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/prediction_service.proto
 import grpc
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 
-class PredictionServiceStub(object):
+class PredictionServiceStub:
   """open source marker; do not remove
   PredictionService provides access to machine-learned models loaded by
   model_servers.
-  """
+  """  # noqa: D205
 
   def __init__(self, channel):
     """Constructor.
@@ -64,43 +74,43 @@ def __init__(self, channel):
         )
 
 
-class PredictionServiceServicer(object):
+class PredictionServiceServicer:
   """open source marker; do not remove
   PredictionService provides access to machine-learned models loaded by
   model_servers.
-  """
+  """  # noqa: D205
 
-  def Classify(self, request, context):
+  def Classify(self, request, context):  # noqa: ARG002
     """Classify.
-    """
+    """  # noqa: D200
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def Regress(self, request, context):
+  def Regress(self, request, context):  # noqa: ARG002
     """Regress.
-    """
+    """  # noqa: D200
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def Predict(self, request, context):
+  def Predict(self, request, context):  # noqa: ARG002
     """Predict -- provides access to loaded TensorFlow model.
-    """
+    """  # noqa: D200
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def MultiInference(self, request, context):
+  def MultiInference(self, request, context):  # noqa: ARG002
     """MultiInference API for multi-headed models.
-    """
+    """  # noqa: D200
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def GetModelMetadata(self, request, context):
+  def GetModelMetadata(self, request, context):  # noqa: ARG002
     """GetModelMetadata - provides access to metadata for loaded models.
-    """
+    """  # noqa: D200
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')

tensorflow_serving/batching/test_util/matrix_half_plus_two_saved_model.py

@@ -14,6 +14,7 @@
 # ==============================================================================
 
 import tensorflow.compat.v1 as tf
+
 FLAGS = tf.app.flags.FLAGS
 
 tf.app.flags.DEFINE_string("output_dir", "/tmp/matrix_half_plus_two/1",
@@ -22,13 +23,14 @@
 
 def _generate_saved_model_for_matrix_half_plus_two(export_dir):
   """Creates SavedModel for half plus two model that accepts batches of
-       3*3 matrices.
-       The model divides all elements in each matrix by 2 and adds 2 to them.
-       So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
-       the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
-    Args:
-      export_dir: The directory where to write SavedModel files.
-    """
+     3*3 matrices.
+     The model divides all elements in each matrix by 2 and adds 2 to them.
+     So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+     the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
+
+  Args:
+    export_dir: The directory where to write SavedModel files.
+  """  # noqa: D205
   builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
   with tf.Session() as session:
     x = tf.placeholder(tf.float32, shape=[None, 3, 3], name="x")

tensorflow_serving/example/mnist_client.py

@@ -25,21 +25,17 @@
     mnist_client.py --num_tests=100 --server=localhost:9000
 """
 
-from __future__ import print_function
 
 import sys
 import threading
 
 # This is a placeholder for a Google-internal import.
-
 import grpc
+import mnist_input_data
 import numpy
 import tensorflow as tf
 
-from tensorflow_serving.apis import predict_pb2
-from tensorflow_serving.apis import prediction_service_pb2_grpc
-import mnist_input_data
-
+from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
 
 tf.compat.v1.app.flags.DEFINE_integer(
     'concurrency', 1, 'maximum number of concurrent inference requests')
@@ -50,7 +46,7 @@
 FLAGS = tf.compat.v1.app.flags.FLAGS
 
 
-class _ResultCounter(object):
+class _ResultCounter:
   """Counter for the prediction results."""
 
   def __init__(self, num_tests, concurrency):
@@ -94,6 +90,7 @@ def _create_rpc_callback(label, result_counter):
   Args:
     label: The correct label for the predicted example.
     result_counter: Counter for the prediction result.
+
   Returns:
     The callback function.
   """