class_docstr: Posterize given tensor image or a batch of tensor images randomly.
  
  .. image:: _static/img/RandomPosterize.png
  
  Args:
      p: probability of applying the transformation.
      bits: Integer that ranged from (0, 8], in which 0 gives black image and 8 gives the original.
          If int x, bits will be generated from (x, 8) then convert to int.
          If tuple (x, y), bits will be generated from (x, y) then convert to int.
      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.posterize`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> input = torch.rand(1, 1, 5, 5)
      >>> posterize = RandomPosterize(3., p=1.)
      >>> posterize(input)
      tensor([[[[0.4863, 0.7529, 0.0784, 0.1255, 0.2980],
                [0.6275, 0.4863, 0.8941, 0.4549, 0.6275],
                [0.3451, 0.3922, 0.0157, 0.1569, 0.2824],
                [0.5176, 0.6902, 0.8000, 0.1569, 0.2667],
                [0.6745, 0.9098, 0.3922, 0.8627, 0.4078]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomPosterize(3., p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      bits: Union[float, Tuple[float, float], Tensor] = 3,
      same_on_batch: bool = False,
      p: float = 0.5,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, keepdim=keepdim
      )
      # TODO: the generator should receive the device
      self._param_generator = rg.PosterizeGenerator(bits)
  
file_path: kornia/kornia/augmentation/_2d/intensity/posterize.py
imports: from typing import Any, Dict, Optional, Tuple, Union
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.core import Tensor
  from kornia.enhance import posterize
label_desc: Use image node_id 1 to create a new RandomPosterize object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomPosterize(bits, same_on_batch, p, keepdim)