🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/image_classification.py

@@ -38,7 +38,8 @@ class ImageClassificationPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/gif
             - image/png
             - image/webp

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/image_object_detection.py

@@ -38,7 +38,8 @@ class ImageObjectDetectionPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/gif
             - image/png
             - image/webp

google/cloud/aiplatform/v1/schema/predict/instance_v1/types/image_segmentation.py

@@ -37,7 +37,8 @@ class ImageSegmentationPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/png
     """
 

google/cloud/aiplatform/v1/schema/predict/prediction_v1/types/video_classification.py

@@ -44,7 +44,8 @@ class VideoClassificationPredictionResult(proto.Message):
         type_ (str):
             The type of the prediction. The requested
             types can be configured via parameters. This
-            will be one of - segment-classification
+            will be one of
+            - segment-classification
             - shot-classification
             - one-sec-interval-classification
         time_segment_start (google.protobuf.duration_pb2.Duration):

google/cloud/aiplatform/v1beta1/schema/predict/instance_v1beta1/types/image_classification.py

@@ -38,7 +38,8 @@ class ImageClassificationPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/gif
             - image/png
             - image/webp

google/cloud/aiplatform/v1beta1/schema/predict/instance_v1beta1/types/image_object_detection.py

@@ -38,7 +38,8 @@ class ImageObjectDetectionPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/gif
             - image/png
             - image/webp

google/cloud/aiplatform/v1beta1/schema/predict/instance_v1beta1/types/image_segmentation.py

@@ -37,7 +37,8 @@ class ImageSegmentationPredictionInstance(proto.Message):
         mime_type (str):
             The MIME type of the content of the image.
             Only the images in below listed MIME types are
-            supported. - image/jpeg
+            supported.
+            - image/jpeg
             - image/png
     """
 

google/cloud/aiplatform/v1beta1/schema/predict/prediction_v1beta1/types/video_classification.py

@@ -44,7 +44,8 @@ class VideoClassificationPredictionResult(proto.Message):
         type_ (str):
             The type of the prediction. The requested
             types can be configured via parameters. This
-            will be one of - segment-classification
+            will be one of
+            - segment-classification
             - shot-classification
             - one-sec-interval-classification
         time_segment_start (google.protobuf.duration_pb2.Duration):

google/cloud/aiplatform_v1/services/featurestore_service/async_client.py

@@ -2355,12 +2355,13 @@ async def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         .. code-block:: python

google/cloud/aiplatform_v1/services/featurestore_service/client.py

@@ -2623,12 +2623,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         .. code-block:: python

google/cloud/aiplatform_v1/services/featurestore_service/transports/grpc.py

@@ -728,12 +728,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         Returns:

google/cloud/aiplatform_v1/services/featurestore_service/transports/grpc_asyncio.py

@@ -746,12 +746,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         Returns:

google/cloud/aiplatform_v1/types/explanation_metadata.py

@@ -117,7 +117,8 @@ class InputMetadata(proto.Message):
                 input tensor. Defaults to IDENTITY.
             modality (str):
                 Modality of the feature. Valid values are:
-                numeric, image. Defaults to numeric.
+                numeric, image. Defaults to
+                numeric.
             feature_value_domain (google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.FeatureValueDomain):
                 The domain details of the input feature
                 value. Like min/max, original mean or standard
@@ -381,10 +382,10 @@ class ColorMap(proto.Enum):
                         Positive: green. Negative: pink.
                     VIRIDIS (2):
                         Viridis color map: A perceptually uniform
-                        color mapping which is easier to see by those
-                        with colorblindness and progresses from yellow
-                        to green to blue. Positive: yellow. Negative:
-                        blue.
+                        color mapping which is
+                        easier to see by those with colorblindness and
+                        progresses from yellow to green to blue.
+                        Positive: yellow. Negative: blue.
                     RED (3):
                         Positive: red. Negative: red.
                     GREEN (4):

google/cloud/aiplatform_v1/types/index.py

@@ -111,11 +111,12 @@ class IndexUpdateMethod(proto.Enum):
                 Should not be used.
             BATCH_UPDATE (1):
                 BatchUpdate: user can call UpdateIndex with
-                files on Cloud Storage of datapoints to update.
+                files on Cloud Storage of
+                datapoints to update.
             STREAM_UPDATE (2):
                 StreamUpdate: user can call
-                UpsertDatapoints/DeleteDatapoints to update the
-                Index and the updates will be applied in
+                UpsertDatapoints/DeleteDatapoints to update
+                the Index and the updates will be applied in
                 corresponding DeployedIndexes in nearly
                 real-time.
         """

google/cloud/aiplatform_v1beta1/services/featurestore_service/async_client.py

@@ -2356,12 +2356,13 @@ async def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         .. code-block:: python

google/cloud/aiplatform_v1beta1/services/featurestore_service/client.py

@@ -2624,12 +2624,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         .. code-block:: python

google/cloud/aiplatform_v1beta1/services/featurestore_service/transports/grpc.py

@@ -728,12 +728,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         Returns:

google/cloud/aiplatform_v1beta1/services/featurestore_service/transports/grpc_asyncio.py

@@ -746,12 +746,13 @@ def import_feature_values(
         There are also scenarios where the caller can cause
         inconsistency.
          - Source data for import contains multiple distinct
-        Feature values for    the same entity ID and timestamp.
+          Feature values for    the same entity ID and
+          timestamp.
          - Source is modified during an import. This includes
-        adding, updating, or  removing source data and/or
-        metadata. Examples of updating metadata  include but are
-        not limited to changing storage location, storage class,
-        or retention policy.
+          adding, updating, or  removing source data and/or
+          metadata. Examples of updating metadata  include but
+          are not limited to changing storage location, storage
+          class,  or retention policy.
          - Online serving cluster is under-provisioned.
 
         Returns:

google/cloud/aiplatform_v1beta1/services/migration_service/client.py

@@ -208,23 +208,18 @@ def parse_annotated_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
-        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
+        return "projects/{project}/datasets/{dataset}".format(
             project=project,
-            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(
-            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
-            path,
-        )
+        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
         return m.groupdict() if m else {}
 
     @staticmethod
@@ -252,18 +247,23 @@ def parse_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
+        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/datasets/{dataset}".format(
+        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
             project=project,
+            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
+        m = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
+            path,
+        )
         return m.groupdict() if m else {}
 
     @staticmethod

google/cloud/aiplatform_v1beta1/types/explanation_metadata.py

@@ -117,7 +117,8 @@ class InputMetadata(proto.Message):
                 input tensor. Defaults to IDENTITY.
             modality (str):
                 Modality of the feature. Valid values are:
-                numeric, image. Defaults to numeric.
+                numeric, image. Defaults to
+                numeric.
             feature_value_domain (google.cloud.aiplatform_v1beta1.types.ExplanationMetadata.InputMetadata.FeatureValueDomain):
                 The domain details of the input feature
                 value. Like min/max, original mean or standard
@@ -381,10 +382,10 @@ class ColorMap(proto.Enum):
                         Positive: green. Negative: pink.
                     VIRIDIS (2):
                         Viridis color map: A perceptually uniform
-                        color mapping which is easier to see by those
-                        with colorblindness and progresses from yellow
-                        to green to blue. Positive: yellow. Negative:
-                        blue.
+                        color mapping which is
+                        easier to see by those with colorblindness and
+                        progresses from yellow to green to blue.
+                        Positive: yellow. Negative: blue.
                     RED (3):
                         Positive: red. Negative: red.
                     GREEN (4):

google/cloud/aiplatform_v1beta1/types/index.py

@@ -111,11 +111,12 @@ class IndexUpdateMethod(proto.Enum):
                 Should not be used.
             BATCH_UPDATE (1):
                 BatchUpdate: user can call UpdateIndex with
-                files on Cloud Storage of datapoints to update.
+                files on Cloud Storage of
+                datapoints to update.
             STREAM_UPDATE (2):
                 StreamUpdate: user can call
-                UpsertDatapoints/DeleteDatapoints to update the
-                Index and the updates will be applied in
+                UpsertDatapoints/DeleteDatapoints to update
+                the Index and the updates will be applied in
                 corresponding DeployedIndexes in nearly
                 real-time.
         """