From cbe2c96de224a14e007561df31cc61c59d926d6b Mon Sep 17 00:00:00 2001
From: Max Hniebergall <max.hniebergall@elastic.co>
Date: Tue, 3 Oct 2023 10:37:35 -0400
Subject: [PATCH] make generate

---
 output/schema/schema.json  | 14 +++++++++++++-
 output/typescript/types.ts |  1 +
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/output/schema/schema.json b/output/schema/schema.json
index 08181442bc..53404d277c 100644
--- a/output/schema/schema.json
+++ b/output/schema/schema.json
@@ -153689,6 +153689,18 @@
               }
             }
           },
+          {
+            "description": "The platform architecture (if applicable) of the trained mode. If the model\nonly works on one platform, because it is heavily optimized for a particular\nprocessor architecture and OS combination, then this field specifies which.\nThe format of the string must match the platform identifiers used by Elasticsearch,\nso one of, `linux-x86_64`, `linux-aarch64`, `darwin-x86_64`, `darwin-aarch64`,\nor `windows-x86_64`. For portable models (those that work independent of processor\narchitecture or OS features), leave this field unset.",
+            "name": "platform_architecture",
+            "required": false,
+            "type": {
+              "kind": "instance_of",
+              "type": {
+                "name": "string",
+                "namespace": "_builtins"
+              }
+            }
+          },
           {
             "description": "An array of tags to organize the model.",
             "name": "tags",
@@ -153753,7 +153765,7 @@
           }
         }
       ],
-      "specLocation": "ml/put_trained_model/MlPutTrainedModelRequest.ts#L28-L96"
+      "specLocation": "ml/put_trained_model/MlPutTrainedModelRequest.ts#L28-L106"
     },
     {
       "body": {
diff --git a/output/typescript/types.ts b/output/typescript/types.ts
index 65a485072d..92f674c936 100644
--- a/output/typescript/types.ts
+++ b/output/typescript/types.ts
@@ -14236,6 +14236,7 @@ export interface MlPutTrainedModelRequest extends RequestBase {
     metadata?: any
     model_type?: MlTrainedModelType
     model_size_bytes?: long
+    platform_architecture?: string
     tags?: string[]
   }
 }