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

```from typing import Any, Dict, List, Optional, Tuple, Union

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.core import Tensor
from kornia.enhance import solarize

[docs]class RandomSolarize(IntensityAugmentationBase2D):
r"""Solarize given tensor image or a batch of tensor images randomly.

.. image:: _static/img/RandomSolarize.png

Args:
p: probability of applying the transformation.
thresholds:
If float x, threshold will be generated from (0.5 - x, 0.5 + x).
If tuple (x, y), threshold will be generated from (x, y).
If float x, addition will be generated from (-x, x).
If tuple (x, y), addition will be generated from (x, y).
same_on_batch: apply the same transformation across the batch.
keepdim: whether to keep the output shape the same as input (True) or broadcast it
to the batch form (False).

Shape:
- Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
- Output: :math:`(B, C, H, W)`

.. note::
This function internally uses :func:`kornia.enhance.solarize`.

Examples:
>>> rng = torch.manual_seed(0)
>>> input = torch.rand(1, 1, 5, 5)
>>> solarize = RandomSolarize(0.1, 0.1, p=1.)
>>> solarize(input)
tensor([[[[0.4132, 0.1412, 0.1790, 0.2226, 0.3980],
[0.2754, 0.4194, 0.0130, 0.4538, 0.2771],
[0.4394, 0.4923, 0.1129, 0.2594, 0.3844],
[0.3909, 0.2118, 0.1094, 0.2516, 0.3728],
[0.2278, 0.0000, 0.4876, 0.0353, 0.5100]]]])

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomSolarize(0.1, 0.1, p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""

def __init__(
self,
thresholds: Union[Tensor, float, Tuple[float, float], List[float]] = 0.1,
additions: Union[Tensor, float, Tuple[float, float], List[float]] = 0.1,
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, keepdim=keepdim)
self._param_generator = rg.PlainUniformGenerator(