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 apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      out = normalize_min_max(input)
  
      if self.clip_output:
          return out.clamp(0.0, 1.0)
  
      return out
  
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 2 for calling the RandomAutoContrast obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: out, out