fix: Support multiple instances in custom predict sample

samples/snippets/prediction_service/predict_custom_trained_model_sample.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 # [START aiplatform_predict_custom_trained_model_sample]
-from typing import Dict
+from typing import Dict, List, Union
 
 from google.cloud import aiplatform
 from google.protobuf import json_format
@@ -23,18 +23,24 @@
 def predict_custom_trained_model_sample(
     project: str,
     endpoint_id: str,
-    instance_dict: Dict,
+    instances: Union[Dict, List[Dict]],
     location: str = "us-central1",
     api_endpoint: str = "us-central1-aiplatform.googleapis.com",
 ):
+    """
+    `instances` can be either single instance of type dict or a list
+    of instances.
+    """
     # The AI Platform services require regional API endpoints.
     client_options = {"api_endpoint": api_endpoint}
     # Initialize client that will be used to create and send requests.
     # This client only needs to be created once, and can be reused for multiple requests.
     client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
     # The format of each instance should conform to the deployed model's prediction input schema.
-    instance = json_format.ParseDict(instance_dict, Value())
-    instances = [instance]
+    instances = instances if type(instances) == list else [instances]
+    instances = [
+        json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
+    ]
     parameters_dict = {}
     parameters = json_format.ParseDict(parameters_dict, Value())
     endpoint = client.endpoint_path(

samples/snippets/prediction_service/predict_custom_trained_model_sample_test.py

@@ -32,9 +32,20 @@ def test_ucaip_generated_predict_custom_trained_model_sample(capsys):
 
     instance_dict = {"image_bytes": {"b64": encoded_content}, "key": "0"}
 
+    # Single instance as a dict
     predict_custom_trained_model_sample.predict_custom_trained_model_sample(
-        instance_dict=instance_dict, project=PROJECT_ID, endpoint_id=ENDPOINT_ID
+        instances=instance_dict, project=PROJECT_ID, endpoint_id=ENDPOINT_ID
+    )
+
+    # Multiple instances in a list
+    predict_custom_trained_model_sample.predict_custom_trained_model_sample(
+        instances=[instance_dict, instance_dict],
+        project=PROJECT_ID,
+        endpoint_id=ENDPOINT_ID,
     )
 
     out, _ = capsys.readouterr()
     assert "1.0" in out
+
+    # Two sets of scores for multi-instance, one score for single instance
+    assert out.count("scores") == 3