class_docstr: Randomly apply the Sharpness transformation
  with the random value selected from an interval.
class_docstr_tok: ["Randomly","apply","the","Sharpness","transformation","with","the","random","value","selected","from","an","interval","."]
docstr: Args:
      min_v (float): minimum value
      max_v (float): maximum value
      root_transform (transform object): transform that will be applied.
                                         must accept a value as input.
      vtype (string): value type - "float"
docstr_tok: ["Args",":","min_v","(","float",")",":","minimum","value","max_v","(","float",")",":","maximum","value","root_transform","(","transform","object",")",":","transform","that","will","be","applied",".","must","accept","a","value","as","input",".","vtype","(","string",")",":","value","type","-","``","float","''"]
code: def __init__(
      self,
      min_v=0.1,
      max_v=1.9,
      root_transform=Sharpness,
      vtype="float",
  ):
      """
      Args:
          min_v (float): minimum value
          max_v (float): maximum value
          root_transform (transform object): transform that will be applied.
                                             must accept a value as input.
          vtype (string): value type - "float"
      """
      super(RandomSharpnessTransform, self).__init__(
          min_v, max_v, root_transform, vtype
      )
  
imports: import numpy
  import PIL
label_desc: Use image node_id 1 to create a new RandomSharpnessTransform object from inherited base classes: RandomValueApplier with example: obj = RandomSharpnessTransform(min_v, max_v, root_transform, vtype)