class_docstr: Adjust the sharpness of the image or video with a given probability.
  
  If the input is a :class:`torch.Tensor`,
  it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
  
  Args:
      sharpness_factor (float):  How much to adjust the sharpness. Can be
          any non-negative number. 0 gives a blurred image, 1 gives the
          original image while 2 increases the sharpness by a factor of 2.
      p (float): probability of the image being sharpened. Default value is 0.5
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code: def __init__(self, sharpness_factor: float, p: float = 0.5) -> None:
      super().__init__(p=p)
      self.sharpness_factor = sharpness_factor
  
imports: import collections.abc
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torchvision import transforms
  from torchvision.transforms.v2 import functional, Transform
  from ._transform import _RandomApplyTransform
  from ._utils import query_chw
label_desc: Use image node_id 1 to create a new RandomAdjustSharpness object from inherited base classes: _RandomApplyTransform with example: obj = RandomAdjustSharpness(sharpness_factor, p)