# Examples 🎉

# A framework-agnostic norm function

Write your function using EagerPy:

import eagerpy as ep

def norm(x):
    x = ep.astensor(x)
    result = x.square().sum().sqrt()
    return result.raw

You can now use the norm function with native tensors and arrays from PyTorch, TensorFlow, JAX and NumPy with virtually no overhead compared to native code. Of course, it also works with GPU tensors.

import torch
norm(torch.tensor([1., 2., 3.]))
# tensor(3.7417)
import tensorflow as tf
norm(tf.constant([1., 2., 3.]))
# <tf.Tensor: shape=(), dtype=float32, numpy=3.7416575>
import jax.numpy as np
norm(np.array([1., 2., 3.]))
# DeviceArray(3.7416575, dtype=float32)
import numpy as np
norm(np.array([1., 2., 3.]))
# 3.7416573867739413


EagerPy already comes with a builtin implementation of norm.