class_docstr: Apply Equalize transformation to image. The bboxes, masks and
  segmentations are not modified.
  
  Args:
      prob (float): The probability for performing Equalize transformation.
class_docstr_tok: ["Apply","Equalize","transformation","to","image",".","The","bboxes",",","masks","and","segmentations","are","not","modified",".","Args",":","prob","(","float",")",":","The","probability","for","performing","Equalize","transformation","."]
docstr: Call function for Equalize transformation.
  
  Args:
      results (dict): Results dict from loading pipeline.
  
  Returns:
      dict: Results after the transformation.
docstr_tok: ["Call","function","for","Equalize","transformation",".","Args",":","results","(","dict",")",":","Results","dict","from","loading","pipeline",".","Returns",":","dict",":","Results","after","the","transformation","."]
code: def __call__(self, results):
      """Call function for Equalize transformation.
  
      Args:
          results (dict): Results dict from loading pipeline.
  
      Returns:
          dict: Results after the transformation.
      """
      if np.random.rand() > self.prob:
          return results
      self._imequalize(results)
      return results
  
imports: import copy
  import cv2
  import mmcv
  import numpy
  from ..builder import PIPELINES
  from .compose import Compose
label_desc: Use image node_id 3 for calling the EqualizeTransform obj's underlying member method code with example usage: obj.__call__(results) and returns: results, results