docstr: Performs Gamma Correction on the input image with some
  randomly selected gamma value between min_val and max_val. 
  Also known as Power Law Transform. This function transforms 
  the input image pixelwise according to the equation 
  Out = In**gamma after scaling each pixel to the range 0 to 1.
  
  Arguments
  ---------
  min_val : float
      min range
  max_val : float
      max range
  
  NOTE:
  for values:
      <1 : image will tend to be lighter
      =1 : image will stay the same
      >1 : image will tend to be darker
code: def __init__(self, min_val, max_val):
      """
      Performs Gamma Correction on the input image with some
      randomly selected gamma value between min_val and max_val.
      Also known as Power Law Transform. This function transforms
      the input image pixelwise according to the equation
      Out = In**gamma after scaling each pixel to the range 0 to 1.
  
      Arguments
      ---------
      min_val : float
          min range
      max_val : float
          max range
  
      NOTE:
      for values:
          <1 : image will tend to be lighter
          =1 : image will stay the same
          >1 : image will tend to be darker
      """
      self.values = (min_val, max_val)
  
imports: import random
  import torch
  from torchvision.transforms.functional import to_tensor
  import numpy
  from ..utils import th_random_choice
label_desc: Use image node_id 1 to create a new RandomGamma object  with example: obj = RandomGamma(min_val, max_val)