Std

## Arguments

- self
(Tensor) the input tensor.

- dim
(int or tuple of ints) the dimension or dimensions to reduce.

- unbiased
(bool) whether to use the unbiased estimation or not

- keepdim
(bool) whether the output tensor has

`dim`

retained or not.

## std(input, unbiased=TRUE) -> Tensor

Returns the standard-deviation of all elements in the `input`

tensor.

If `unbiased`

is `FALSE`

, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.

## std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor

Returns the standard-deviation of each row of the `input`

tensor in the
dimension `dim`

. If `dim`

is a list of dimensions,
reduce over all of them.

If `keepdim`

is `TRUE`

, the output tensor is of the same size
as `input`

except in the dimension(s) `dim`

where it is of size 1.
Otherwise, `dim`

is squeezed (see `torch_squeeze`

), resulting in the
output tensor having 1 (or `len(dim)`

) fewer dimension(s).

If `unbiased`

is `FALSE`

, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.

## Examples

```
if (torch_is_installed()) {
a = torch_randn(c(1, 3))
a
torch_std(a)
a = torch_randn(c(4, 4))
a
torch_std(a, dim=1)
}
#> torch_tensor
#> 1.1563
#> 0.3994
#> 0.5251
#> 1.2504
#> [ CPUFloatType{4} ]
```