docs: Updated docstrings with exception error classes (#894)

kweinmeister · sasha-gitg · web-flow · commit f9aecd22fe08 · 2021-12-10T12:31:20.000-06:00
Co-authored-by: sasha-gitg &lt;44654632+sasha-gitg@users.noreply.github.com&gt;
diff --git a/google/cloud/aiplatform/base.py b/google/cloud/aiplatform/base.py
@@ -504,7 +504,7 @@ def _get_and_validate_project_location(
             location(str): The location of the resource noun.
 
         Raises:
-            RuntimeError if location is different from resource location
+            RuntimeError: If location is different from resource location
         """
 
         fields = utils.extract_fields_from_resource_name(
@@ -604,7 +604,7 @@ def _assert_gca_resource_is_available(self) -> None:
         """Helper method to raise when property is not accessible.
 
         Raises:
-            RuntimeError if _gca_resource is has not been created.
+            RuntimeError: If _gca_resource is has not been created.
         """
         if self._gca_resource is None:
             raise RuntimeError(
@@ -1115,7 +1115,7 @@ def _wait_for_resource_creation(self) -> None:
         job.run(sync=False, ...)
         job._wait_for_resource_creation()
         Raises:
-            RuntimeError if the resource has not been scheduled to be created.
+            RuntimeError: If the resource has not been scheduled to be created.
         """
 
         # If the user calls this but didn't actually invoke an API to create
@@ -1141,7 +1141,7 @@ def _assert_gca_resource_is_available(self) -> None:
         resource creation has failed asynchronously.
 
         Raises:
-            RuntimeError when resource has not been created.
+            RuntimeError: When resource has not been created.
         """
         if not getattr(self._gca_resource, "name", None):
             raise RuntimeError(
diff --git a/google/cloud/aiplatform/datasets/_datasources.py b/google/cloud/aiplatform/datasets/_datasources.py
@@ -71,7 +71,7 @@ def __init__(
                     "bq://project.dataset.table_name"
 
         Raises:
-            ValueError if source configuration is not valid.
+            ValueError: If source configuration is not valid.
         """
 
         dataset_metadata = None
@@ -215,7 +215,7 @@ def create_datasource(
         datasource (Datasource)
 
     Raises:
-        ValueError when below scenarios happen
+        ValueError: When below scenarios happen:
         - import_schema_uri is identified for creating TabularDatasource
         - either import_schema_uri or gcs_source is missing for creating NonTabularDatasourceImportable
     """
diff --git a/google/cloud/aiplatform/datasets/dataset.py b/google/cloud/aiplatform/datasets/dataset.py
@@ -91,7 +91,7 @@ def _validate_metadata_schema_uri(self) -> None:
         """Validate the metadata_schema_uri of retrieved dataset resource.
 
         Raises:
-            ValueError if the dataset type of the retrieved dataset resource is
+            ValueError: If the dataset type of the retrieved dataset resource is
             not supported by the class.
         """
         if self._supported_metadata_schema_uris and (
diff --git a/google/cloud/aiplatform/explain/metadata/tf/v1/saved_model_metadata_builder.py b/google/cloud/aiplatform/explain/metadata/tf/v1/saved_model_metadata_builder.py
@@ -50,7 +50,7 @@ def __init__(
               signature_name) specifies multiple outputs.
 
         Raises:
-            ValueError if outputs_to_explain contains more than 1 element or
+            ValueError: If outputs_to_explain contains more than 1 element or
             signature contains multiple outputs.
         """
         if outputs_to_explain:
diff --git a/google/cloud/aiplatform/explain/metadata/tf/v2/saved_model_metadata_builder.py b/google/cloud/aiplatform/explain/metadata/tf/v2/saved_model_metadata_builder.py
@@ -49,8 +49,8 @@ def __init__(
               Any keyword arguments to be passed to tf.saved_model.save() function.
 
         Raises:
-            ValueError if outputs_to_explain contains more than 1 element.
-            ImportError if tf is not imported.
+            ValueError: If outputs_to_explain contains more than 1 element.
+            ImportError: If tf is not imported.
         """
         if outputs_to_explain and len(outputs_to_explain) > 1:
             raise ValueError(
@@ -91,7 +91,7 @@ def _infer_metadata_entries_from_model(
               Inferred input metadata and output metadata from the model.
 
         Raises:
-              ValueError if specified name is not found in signature outputs.
+              ValueError: If specified name is not found in signature outputs.
         """
 
         loaded_sig = self._loaded_model.signatures[signature_name]
diff --git a/google/cloud/aiplatform/jobs.py b/google/cloud/aiplatform/jobs.py
@@ -1049,7 +1049,7 @@ def __init__(
                 staging_bucket set in aiplatform.init.
 
         Raises:
-            RuntimeError is not staging bucket was set using aiplatfrom.init and a staging
+            RuntimeError: If staging bucket was not set using aiplatform.init and a staging
             bucket was not passed in.
         """
 
@@ -1241,7 +1241,7 @@ def from_local_script(
                 staging_bucket set in aiplatform.init.
 
         Raises:
-            RuntimeError is not staging bucket was set using aiplatfrom.init and a staging
+            RuntimeError: If staging bucket was not set using aiplatform.init and a staging
             bucket was not passed in.
         """
 
diff --git a/google/cloud/aiplatform/metadata/metadata.py b/google/cloud/aiplatform/metadata/metadata.py
@@ -157,8 +157,8 @@ def log_metrics(self, metrics: Dict[str, Union[float, int]]):
             metrics (Dict):
                 Required. Metrics key/value pairs. Only flot and int are supported format for value.
         Raises:
-            TypeError if value contains unsupported types.
-            ValueError if Experiment or Run is not set.
+            TypeError: If value contains unsupported types.
+            ValueError: If Experiment or Run is not set.
         """
 
         self._validate_experiment_and_run(method_name="log_metrics")
@@ -265,7 +265,7 @@ def _validate_metrics_value_type(metrics: Dict[str, Union[float, int]]):
             metrics (Dict):
                 Required. Metrics key/value pairs. Only flot and int are supported format for value.
         Raises:
-            TypeError if value contains unsupported types.
+            TypeError: If value contains unsupported types.
         """
 
         for key, value in metrics.items():
diff --git a/google/cloud/aiplatform/metadata/resource.py b/google/cloud/aiplatform/metadata/resource.py
@@ -451,7 +451,7 @@ def _extract_metadata_store_id(resource_name, resource_noun) -> str:
             metadata_store_id (str):
                 The metadata store id for the particular resource name.
         Raises:
-            ValueError if it does not exist.
+            ValueError: If it does not exist.
         """
         pattern = re.compile(
             r"^projects\/(?P<project>[\w-]+)\/locations\/(?P<location>[\w-]+)\/metadataStores\/(?P<store>[\w-]+)\/"
diff --git a/google/cloud/aiplatform/models.py b/google/cloud/aiplatform/models.py
@@ -786,7 +786,7 @@ def _deploy(
                 will be executed in concurrent Future and any downstream object will
                 be immediately returned and synced when the Future has completed.
         Raises:
-            ValueError if there is not current traffic split and traffic percentage
+            ValueError: If there is not current traffic split and traffic percentage
             is not 0 or 100.
         """
         _LOGGER.log_action_start_against_resource(
@@ -2366,9 +2366,9 @@ def export_model(
                 Details of the completed export with output destination paths to
                 the artifacts or container image.
         Raises:
-            ValueError if model does not support exporting.
+            ValueError: If model does not support exporting.
 
-            ValueError if invalid arguments or export formats are provided.
+            ValueError: If invalid arguments or export formats are provided.
         """
 
         # Model does not support exporting
diff --git a/google/cloud/aiplatform/training_jobs.py b/google/cloud/aiplatform/training_jobs.py
@@ -4060,7 +4060,7 @@ def run(
                 produce a Vertex AI Model.
 
         Raises:
-            RuntimeError if Training job has already been run or is waiting to run.
+            RuntimeError: If Training job has already been run or is waiting to run.
         """
 
         if model_display_name:
@@ -4269,7 +4269,7 @@ def _run_with_experiments(
                 produce a Vertex AI Model.
 
         Raises:
-            RuntimeError if Training job has already been run or is waiting to run.
+            RuntimeError: If Training job has already been run or is waiting to run.
         """
 
         if additional_experiments:
diff --git a/google/cloud/aiplatform/utils/featurestore_utils.py b/google/cloud/aiplatform/utils/featurestore_utils.py
@@ -47,7 +47,7 @@ def validate_and_get_entity_type_resource_ids(
         Tuple[str, str] - featurestore ID and entity_type ID
 
     Raises:
-        ValueError if the provided entity_type_name is not in form of a fully-qualified
+        ValueError: If the provided entity_type_name is not in form of a fully-qualified
         entityType resource name nor an entity_type ID with featurestore_id passed.
     """
     match = CompatFeaturestoreServiceClient.parse_entity_type_path(
@@ -91,7 +91,7 @@ def validate_and_get_feature_resource_ids(
         Tuple[str, str, str] - featurestore ID, entity_type ID, and feature ID
 
     Raises:
-        ValueError if the provided feature_name is not in form of a fully-qualified
+        ValueError: If the provided feature_name is not in form of a fully-qualified
         feature resource name nor a feature ID with featurestore_id and entity_type_id passed.
     """
 
diff --git a/google/cloud/aiplatform/utils/tensorboard_utils.py b/google/cloud/aiplatform/utils/tensorboard_utils.py
@@ -33,7 +33,7 @@ def _parse_experiment_name(experiment_name: str) -> Dict[str, str]:
       Components of the experiment name.
 
     Raises:
-      ValueError if the experiment_name is invalid.
+      ValueError: If the experiment_name is invalid.
     """
     matched = TensorboardServiceClient.parse_tensorboard_experiment_path(
         experiment_name
