class_docstr: Blur the input image using a median filter with a random aperture linear size.
  
  Args:
      blur_limit (int): maximum aperture linear size for blurring the input image.
          Must be odd and in range [3, inf). Default: (3, 7).
      p (float): probability of applying the transform. Default: 0.5.
  
  Targets:
      image
  
  Image types:
      uint8, float32
class_docstr_tok: ["Blur","the","input","image","using","a","median","filter","with","a","random","aperture","linear","size",".","Args",":","blur_limit","(","int",")",":","maximum","aperture","linear","size","for","blurring","the","input","image",".","Must","be","odd","and","in","range","[","3",",","inf",")",".","Default",":","(","3",",","7",")",".","p","(","float",")",":","probability","of","applying","the","transform",".","Default",":","0.5",".","Targets",":","image","Image","types",":","uint8",",","float32"]
code: def __init__(
      self,
      blur_limit: ScaleIntType = 7,
      always_apply: bool = False,
      p: float = 0.5,
  ):
      super().__init__(blur_limit, always_apply, p)
  
      if self.blur_limit[0] % 2 != 1 or self.blur_limit[1] % 2 != 1:
          raise ValueError("MedianBlur supports only odd blur limits.")
  
imports: import random
  import warnings
  from typing import Any, Dict, List, Sequence, Tuple
  import cv2
  import numpy
  from albumentations import random_utils
  from albumentations.augmentations import functional
  from albumentations.augmentations.blur import functional
  from albumentations.core.transforms_interface import ImageOnlyTransform, ScaleFloatType, ScaleIntType, to_tuple
label_desc: Use image node_id 1 to create a new MedianBlur object from inherited base classes: Blur with example: obj = MedianBlur(blur_limit, always_apply, p)