class_docstr: Randomly change the brightness, contrast and saturation of an image.
  
  Args:
      brightness (float): How much to jitter brightness. brightness_factor
          is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
      contrast (float): How much to jitter contrast. contrast_factor
          is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
      saturation (float): How much to jitter saturation. saturation_factor
          is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
      hue(float): How much to jitter hue. hue_factor is chosen uniformly from
          [-hue, hue]. Should be >=0 and <= 0.5.
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docstr: Args:
      img (PIL Image): Input image.
  
  Returns:
      PIL Image: Color jittered image.
docstr_tok: ["Args",":","img","(","PIL","Image",")",":","Input","image",".","Returns",":","PIL","Image",":","Color","jittered","image","."]
code: def __call__(self, img):
      """
      Args:
          img (PIL Image): Input image.
  
      Returns:
          PIL Image: Color jittered image.
      """
      transform = self.get_params(
          self.brightness, self.contrast, self.saturation, self.hue
      )
      return transform(img)
  
imports: import random
  import os
  import math
  import mmcv
  import torch
  import numpy
  import torchvision.transforms
  from torchvision import datasets
  from torch.utils.data import Dataset
  from megatron import print_rank_0
  from megatron import get_args
  from PIL import Image, ImageOps, ImageEnhance
  import torchvision.transforms
label_desc: Use image node_id 3 for calling the ColorJitter obj's underlying member method code with example usage: obj.__call__(img) and returns: transform