class_docstr: Apply Contrast transformation to image. The bboxes, masks and
  segmentations are not modified.
  
  Args:
      level (int | float): Should be in range [0,_MAX_LEVEL].
      prob (float): The probability for performing Contrast transformation.
class_docstr_tok: ["Apply","Contrast","transformation","to","image",".","The","bboxes",",","masks","and","segmentations","are","not","modified",".","Args",":","level","(","int","|","float",")",":","Should","be","in","range","[","0",",","_MAX_LEVEL","]",".","prob","(","float",")",":","The","probability","for","performing","Contrast","transformation","."]
code: def __init__(self, level, prob=0.5):
      assert isinstance(
          level, (int, float)
      ), "The level must be type int or float."
      assert (
          0 <= level <= _MAX_LEVEL
      ), "The level should be in range [0,_MAX_LEVEL]."
      assert (
          0 <= prob <= 1.0
      ), "The probability should be in range [0,1]."
      self.level = level
      self.prob = prob
      self.factor = enhance_level_to_value(level)
  
imports: import copy
  import cv2
  import mmcv
  import numpy
  from ..builder import PIPELINES
  from .compose import Compose
label_desc: Use image node_id 1 to create a new ContrastTransform object from inherited base classes: object with example: obj = ContrastTransform(level, prob)