class_docstr: 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)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      return equalize3d(input)
  
file_path: kornia/kornia/augmentation/_3d/intensity/equalize.py
imports: from typing import Any, Dict, Optional
  from torch import Tensor
  from kornia.augmentation._3d.intensity.base import IntensityAugmentationBase3D
  from kornia.enhance import equalize3d
label_desc: Use image node_id 3 for calling the RandomEqualize3D obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: equalize3d