class_docstr: Blur the input image using a random-sized kernel.
  
  Args:
      blur_limit (int, (int, int)): maximum kernel size for blurring the input image.
          Should be 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","random-sized","kernel",".","Args",":","blur_limit","(","int",",","(","int",",","int",")",")",":","maximum","kernel","size","for","blurring","the","input","image",".","Should","be","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 get_params(self) -> Dict[str, Any]:
      return {
          "ksize": int(
              random.choice(
                  list(
                      range(
                          self.blur_limit[0], self.blur_limit[1] + 1, 2
                      )
                  )
              )
          )
      }
  
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 3 for calling the Blur obj's underlying member method code with example usage: obj.get_params() and returns: dict