+  __attribute__ ((__alloc_size__ params))
+
 #include <iostream>
 
 using namespace std;
diff --git a/sysdeps/aarch64/dl-machine.h b/sysdeps/aarch64/dl-machine.h
index 185402f..bbdeae0 100644
--- a/sysdeps/aarch64/dl-machine.h
+++ b/sysdeps/aarch64/dl-machine.h
@@ -49,23 +49,11 @@ elf_machine_load_address (void)
   /* To figure out the load address we use the definition that for any symbol:

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

    * **Intuitif** : Excellent support des éditeurs. <abbr title="aussi appelée auto-complétion, autocomplétion, IntelliSense...">Complétion</abbr> partout. Moins de temps passé à déboguer.
    * **Facile** : Fait pour être facile à utiliser et apprendre. Moins de temps passé à lire de la documentation.
    * **Concis** : Minimise la duplication de code. Plusieurs fonctionnalités à chaque déclaration de paramètre.

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

		}
	}

	return host, port, nil
}

// isLocalHost - checks if the given parameter
// correspond to one of the local IP of the
// current machine
func isLocalHost(host string, port string, localPort string) (bool, error) {
	hostIPs, err := getHostIP(host)
	if err != nil {
		return false, err
	}

	nonInterIPV4s := mustGetLocalIP4().Intersection(hostIPs)

Articles:
  English:
  - author: Kurtis Pykes - NVIDIA
    link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
    title: Building a Machine Learning Microservice with FastAPI
  - author: Ravgeet Dhillon - Twilio
    link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
    title: Booking Appointments with Twilio, Notion, and FastAPI
  - author: Abhinav Tripathi - Microsoft Blogs

[ ] Extensions and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[ ] Developer Infrastructure

**Affected features (please put an X in all that apply)**

[ ] Multi Cluster
[ ] Virtual Machine
[ ] Multi Control Plane

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

// extern int notoc_func(void);
// int TestPPC64Stubs(void) {
//	return notoc_func();
// }
import "C"
import "testing"

func testPPC64CallStubs(t *testing.T) {
	// Verify the trampolines run on the testing machine. If they
	// do not, or are missing, a crash is expected.
	if C.TestPPC64Stubs() != 0 {
		t.Skipf("This test requires binutils 2.35 or newer.")
	}

- [ ] Security
- [ ] Test and Release
- [ ] User Experience
- [ ] Developer Infrastructure

**Please check any characteristics that apply to this pull request.**