docstr: Performs Gamma Correction on the input image with some
  gamma value selected in the list of given values.
  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
  ---------
  values : list of floats
      gamma values to sampled from
  p : list of floats - same length as `values`
      if None, values will be sampled uniformly.
      Must sum to 1.
  
  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, values, p=None):
      """
      Performs Gamma Correction on the input image with some
      gamma value selected in the list of given values.
      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
      ---------
      values : list of floats
          gamma values to sampled from
      p : list of floats - same length as `values`
          if None, values will be sampled uniformly.
          Must sum to 1.
  
      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 = values
      self.p = p
  
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 RandomChoiceGamma object  with example: obj = RandomChoiceGamma(values, p)