class_docstr: Apply a random auto-contrast of a tensor image.
  
  Args:
      p: probability of applying the transformation.
      clip_output: if true clip output
      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)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.normalize_min_max`
code: def __init__(
      self,
      clip_output: bool = True,
      same_on_batch: bool = False,
      p: float = 1.0,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, keepdim=keepdim
      )
  
      self.clip_output = clip_output
  
file_path: kornia/kornia/augmentation/_2d/intensity/auto_contrast.py
imports: from typing import Any, Dict, Optional
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.core import Tensor
  from kornia.enhance import normalize_min_max
label_desc: Use image node_id 1 to create a new RandomAutoContrast object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomAutoContrast(clip_output, same_on_batch, p, keepdim)