class_docstr: Sharpen given tensor image or a batch of tensor images randomly.
  
  .. image:: _static/img/RandomSharpness.png
  
  Args:
      p: probability of applying the transformation.
      sharpness: factor of sharpness strength. Must be above 0.
      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.sharpness`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> input = torch.rand(1, 1, 5, 5)
      >>> sharpness = RandomSharpness(1., p=1.)
      >>> sharpness(input)
      tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
                [0.6341, 0.4810, 0.7367, 0.4177, 0.6323],
                [0.3489, 0.4428, 0.1562, 0.2443, 0.2939],
                [0.5185, 0.6462, 0.7050, 0.2288, 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 = RandomSharpness(1., 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:
      factor = params["sharpness"]
      return sharpness(input, factor)
  
file_path: kornia/kornia/augmentation/_2d/intensity/sharpness.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 sharpness
label_desc: Use image node_id 2 for calling the RandomSharpness obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: sharpness