class_docstr: Apply Color 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 Color transformation.
class_docstr_tok: ["Apply","Color","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","Color","transformation","."]
docstr: Apply Color transformation to image.
docstr_tok: ["Apply","Color","transformation","to","image","."]
code: def _adjust_color_img(self, results, factor=1.0):
      """Apply Color transformation to image."""
      for key in results.get("img_fields", ["img"]):
          # NOTE defaultly the image should be BGR format
          img = results[key]
          results[key] = mmcv.adjust_color(img, factor).astype(
              img.dtype
          )
  
imports: import copy
  import cv2
  import mmcv
  import numpy
  from ..builder import PIPELINES
  from .compose import Compose
label_desc: Use image node_id 2 for calling the ColorTransform obj's underlying member method code with example usage: obj._adjust_color_img(results, factor) without return types