# Source code for kornia.augmentation._3d.intensity.equalize

```from typing import Any, Dict, Optional

from torch import Tensor

from kornia.augmentation._3d.base import AugmentationBase3D
from kornia.enhance import equalize3d

[docs]class RandomEqualize3D(AugmentationBase3D):
r"""Apply random equalization to 3D volumes (5D tensor).

Args:
p: probability of the image being equalized.
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, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)`
- Output: :math:`(B, C, D, H, W)`

Note:
Input tensor must be float and normalized into [0, 1] for the best differentiability support.
Additionally, this function accepts another transformation tensor (:math:`(B, 4, 4)`), then the
applied transformation will be merged int to the input transformation tensor and returned.

Examples:
>>> import torch
>>> rng = torch.manual_seed(0)
>>> input = torch.rand(1, 1, 3, 3, 3)
>>> aug = RandomEqualize3D(p=1.0)
>>> aug(input)
tensor([[[[[0.4963, 0.7682, 0.0885],
[0.1320, 0.3074, 0.6341],
[0.4901, 0.8964, 0.4556]],
<BLANKLINE>
[[0.6323, 0.3489, 0.4017],
[0.0223, 0.1689, 0.2939],
[0.5185, 0.6977, 0.8000]],
<BLANKLINE>
[[0.1610, 0.2823, 0.6816],
[0.9152, 0.3971, 0.8742],
[0.4194, 0.5529, 0.9527]]]]])

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.rand(1, 3, 32, 32, 32)
>>> aug = RandomEqualize3D(p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""

def __init__(
self,
p: float = 0.5,
same_on_batch: bool = False,
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)

def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor:
return self.identity_matrix(input)

def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
return equalize3d(input)
```