fix: Set prediction client when listing Endpoints

google/cloud/aiplatform/base.py

@@ -819,8 +819,9 @@ def _sync_object_with_future_result(
             if value:
                 setattr(self, attribute, value)
 
+    @classmethod
     def _construct_sdk_resource_from_gapic(
-        self,
+        cls,
         gapic_resource: proto.Message,
         project: Optional[str] = None,
         location: Optional[str] = None,
@@ -846,7 +847,7 @@ def _construct_sdk_resource_from_gapic(
             VertexAiResourceNoun:
                 An initialized SDK object that represents GAPIC type.
         """
-        sdk_resource = self._empty_constructor(
+        sdk_resource = cls._empty_constructor(
             project=project, location=location, credentials=credentials
         )
         sdk_resource._gca_resource = gapic_resource
@@ -894,14 +895,14 @@ def _list(
         Returns:
             List[VertexAiResourceNoun] - A list of SDK resource objects
         """
-        self = cls._empty_constructor(
+        resource = cls._empty_constructor(
             project=project, location=location, credentials=credentials
         )
 
         # Fetch credentials once and re-use for all `_empty_constructor()` calls
         creds = initializer.global_config.credentials
 
-        resource_list_method = getattr(self.api_client, self._list_method)
+        resource_list_method = getattr(resource.api_client, resource._list_method)
 
         list_request = {
             "parent": initializer.global_config.common_location_path(
@@ -916,7 +917,7 @@ def _list(
         resource_list = resource_list_method(request=list_request) or []
 
         return [
-            self._construct_sdk_resource_from_gapic(
+            cls._construct_sdk_resource_from_gapic(
                 gapic_resource, project=project, location=location, credentials=creds
             )
             for gapic_resource in resource_list

google/cloud/aiplatform/models.py

@@ -116,9 +116,9 @@ def __init__(
             resource_name=endpoint_name,
         )
         self._gca_resource = self._get_gca_resource(resource_name=endpoint_name)
+
         self._prediction_client = self._instantiate_prediction_client(
-            location=location or initializer.global_config.location,
-            credentials=credentials,
+            location=self.location, credentials=credentials,
         )
 
     @property
@@ -324,6 +324,46 @@ def _create(
             credentials=credentials,
         )
 
+    @classmethod
+    def _construct_sdk_resource_from_gapic(
+        cls,
+        gapic_resource: proto.Message,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+        credentials: Optional[auth_credentials.Credentials] = None,
+    ) -> "Endpoint":
+        """Given a GAPIC Endpoint object, return the SDK representation.
+
+        Args:
+            gapic_resource (proto.Message):
+                A GAPIC representation of a Endpoint resource, usually
+                retrieved by a get_* or in a list_* API call.
+            project (str):
+                Optional. Project to construct Endpoint object from. If not set,
+                project set in aiplatform.init will be used.
+            location (str):
+                Optional. Location to construct Endpoint object from. If not set,
+                location set in aiplatform.init will be used.
+            credentials (auth_credentials.Credentials):
+                Optional. Custom credentials to use to construct Endpoint.
+                Overrides credentials set in aiplatform.init.
+
+        Returns:
+            Endpoint:
+                An initialized Endpoint resource.
+        """
+        endpoint = cls._empty_constructor(
+            project=project, location=location, credentials=credentials
+        )
+
+        endpoint._gca_resource = gapic_resource
+
+        endpoint._prediction_client = cls._instantiate_prediction_client(
+            location=endpoint.location, credentials=credentials,
+        )
+
+        return endpoint
+
     @staticmethod
     def _allocate_traffic(
         traffic_split: Dict[str, int], traffic_percentage: int,