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---
title: Examples
---
# Examples :tada:
## A framework-agnostic `norm` function
Write your function using EagerPy:
```python
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**.
```python
import torch
norm(torch.tensor([1., 2., 3.]))
# tensor(3.7417)
```
```python
import tensorflow as tf
norm(tf.constant([1., 2., 3.]))
# <tf.Tensor: shape=(), dtype=float32, numpy=3.7416575>
```
```python
import jax.numpy as np
norm(np.array([1., 2., 3.]))
# DeviceArray(3.7416575, dtype=float32)
```
```python
import numpy as np
norm(np.array([1., 2., 3.]))
# 3.7416573867739413
```
::: tip NOTE
EagerPy already comes with a [builtin implementation of `norm`](/api/norms.md#l2).
:::
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