# tensorplay.functional.renorm

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.renorm.html

```python
tensorplay.functional.renorm(input, p, dim, maxnorm, *, out=None) → Tensor
```

Returns a tensor where each sub-tensor of input along dimension
dim is normalized such that the p-norm of the sub-tensor is lower
than the value maxnorm

> **Note**
>
> If the norm of a row is lower than maxnorm, the row is unchanged

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- p ([float](https://docs.python.org/3/builtins/functions.html#float)) – the power for the norm computation

- dim ([int](https://docs.python.org/3/builtins/functions.html#int)) – the dimension to slice over to get the sub-tensors

- maxnorm ([float](https://docs.python.org/3/builtins/functions.html#float)) – the maximum norm to keep each sub-tensor under

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> x = tensorplay.ones(3, 3)
>>> x[1].fill_(2)
tensor([ 2.,  2.,  2.])
>>> x[2].fill_(3)
tensor([ 3.,  3.,  3.])
>>> x
tensor([[ 1.,  1.,  1.],
        [ 2.,  2.,  2.],
        [ 3.,  3.,  3.]])
>>> tensorplay.renorm(x, 1, 0, 5)
tensor([[ 1.0000,  1.0000,  1.0000],
        [ 1.6667,  1.6667,  1.6667],
        [ 1.6667,  1.6667,  1.6667]])
```