class_docstr: Invert the tensor images values randomly.
  
  .. image:: _static/img/RandomInvert.png
  
  Args:
      max_val: The expected maximum value in the input tensor. The shape has to
        according to the input tensor shape, or at least has to work with broadcasting.
      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).
  .. note::
      This function internally uses :func:`kornia.enhance.invert`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> img = torch.rand(1, 1, 5, 5)
      >>> inv = RandomInvert()
      >>> inv(img)
      tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
                [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],
                [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],
                [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],
                [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomInvert(p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      max_val: Union[float, Tensor] = torch.tensor(1.0),
      same_on_batch: bool = False,
      p: float = 0.5,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
      )
      self.flags = {"max_val": max_val}
  
file_path: kornia/kornia/augmentation/_2d/intensity/invert.py
imports: from typing import Any, Dict, Optional, Union
  import torch
  from torch import Tensor
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.enhance import invert
label_desc: Use image node_id 1 to create a new RandomInvert object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomInvert(max_val, same_on_batch, p, keepdim)