docstr: Applies a transform by sampling a random value between [min_v, max_v]
  
  Args:
      min_v (float or int): minimum value
      max_v (float or int): maximum value
      root_transform (transform object): transform that will be applied.
                                         must accept a value as input.
      vtype (string): value type - either "float" or "int"
      closed_interval (bool): sample from [min_v, max_v] (when True)
                              or [min_v, max_v) when False
docstr_tok: ["Applies","a","transform","by","sampling","a","random","value","between","[","min_v",",","max_v","]","Args",":","min_v","(","float","or","int",")",":","minimum","value","max_v","(","float","or","int",")",":","maximum","value","root_transform","(","transform","object",")",":","transform","that","will","be","applied",".","must","accept","a","value","as","input",".","vtype","(","string",")",":","value","type","-","either","``","float","''","or","``","int","''","closed_interval","(","bool",")",":","sample","from","[","min_v",",","max_v","]","(","when","True",")","or","[","min_v",",","max_v",")","when","False"]
code: def __init__(
      self,
      min_v,
      max_v,
      root_transform,
      vtype="float",
      closed_interval=False,
  ):
      """
      Applies a transform by sampling a random value between [min_v, max_v]
  
      Args:
          min_v (float or int): minimum value
          max_v (float or int): maximum value
          root_transform (transform object): transform that will be applied.
                                             must accept a value as input.
          vtype (string): value type - either "float" or "int"
          closed_interval (bool): sample from [min_v, max_v] (when True)
                                  or [min_v, max_v) when False
      """
      self.min_v = min_v
      self.max_v = max_v
      self.root_transform = root_transform
      self.vtype = vtype
      self.closed_interval = closed_interval
      if self.closed_interval:
          assert self.vtype == "int"
  
imports: import numpy
  import PIL
label_desc: Use image node_id 1 to create a new RandomValueApplier object from inherited base classes: TransformObject with example: obj = RandomValueApplier(min_v, max_v, root_transform, vtype, closed_interval)