# Source code for kornia.augmentation._2d.intensity.gaussian_noise

```from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D

def _randn_like(input: Tensor, mean: float, std: float) -> Tensor:
x = torch.randn_like(input)  # Generating on GPU is fastest with `torch.randn_like(...)`
if std != 1.0:  # `if` is cheaper than multiplication
x *= std
if mean != 0.0:  # `if` is cheaper than addition
x += mean
return x

[docs]class RandomGaussianNoise(IntensityAugmentationBase2D):
r"""Add gaussian noise to a batch of multi-dimensional images.

.. image:: _static/img/RandomGaussianNoise.png

Args:
mean: The mean of the gaussian distribution.
std: The standard deviation of the gaussian distribution.
same_on_batch: apply the same transformation across the batch.
p: probability of applying the transformation.
keepdim: whether to keep the output shape the same as input (True) or broadcast it
to the batch form (False).
Examples:
>>> rng = torch.manual_seed(0)
>>> img = torch.ones(1, 1, 2, 2)
>>> RandomGaussianNoise(mean=0., std=1., p=1.)(img)
tensor([[[[ 2.5410,  0.7066],
[-1.1788,  1.5684]]]])

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomGaussianNoise(mean=0., std=1., p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""

def __init__(
self,
mean: float = 0.0,
std: float = 1.0,
same_on_batch: bool = False,
p: float = 0.5,
keepdim: bool = False,
return_transform: Optional[bool] = None,
) -> None:
super().__init__(
p=p, return_transform=return_transform, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
)
self.flags = dict(mean=mean, std=std)

def generate_parameters(self, shape: torch.Size) -> Dict[str, Tensor]:
return {}

def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
if "gaussian_noise" in params:
gaussian_noise = params["gaussian_noise"]
else:
gaussian_noise = _randn_like(input, mean=flags["mean"], std=flags["std"])
self._params["gaussian_noise"] = gaussian_noise
return input + gaussian_noise
```