Normal

Normal distributed

## Arguments

- mean
(tensor or scalar double) Mean of the normal distribution. If this is a

`torch_tensor()`

then the output has the same dim as`mean`

and it represents the per-element mean. If it's a scalar value, it's reused for all elements.- std
(tensor or scalar double) The standard deviation of the normal distribution. If this is a

`torch_tensor()`

then the output has the same size as`std`

and it represents the per-element standard deviation. If it's a scalar value, it's reused for all elements.- size
(integers, optional) only used if both

`mean`

and`std`

are scalars.- generator
a random number generator created with

`torch_generator()`

. If`NULL`

a default generator is used.- ...
Tensor option parameters like

`dtype`

,`layout`

, and`device`

. Can only be used when`mean`

and`std`

are both scalar numerics.

## Note

When the shapes do not match, the shape of `mean`

is used as the shape for the returned output tensor

## normal(mean, std, *) -> Tensor

Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.

The `mean`

is a tensor with the mean of
each output element's normal distribution

The `std`

is a tensor with the standard deviation of
each output element's normal distribution

The shapes of `mean`

and `std`

don't need to match, but the
total number of elements in each tensor need to be the same.

## normal(mean=0.0, std) -> Tensor

Similar to the function above, but the means are shared among all drawn elements.

## normal(mean, std=1.0) -> Tensor

Similar to the function above, but the standard-deviations are shared among all drawn elements.

## normal(mean, std, size, *) -> Tensor

Similar to the function above, but the means and standard deviations are shared
among all drawn elements. The resulting tensor has size given by `size`

.

## Examples

```
if (torch_is_installed()) {
torch_normal(mean=0, std=torch_arange(1, 0, -0.1) + 1e-6)
torch_normal(mean=0.5, std=torch_arange(1., 6.))
torch_normal(mean=torch_arange(1., 6.))
torch_normal(2, 3, size=c(1, 4))
}
#> torch_tensor
#> 0.0455 2.4500 3.9270 5.6783
#> [ CPUFloatType{1,4} ]
```