class_docstr: Apply motion blur to the input image using a random-sized kernel.
  
  Args:
      blur_limit (int): maximum kernel size for blurring the input image.
          Should be in range [3, inf). Default: (3, 7).
      allow_shifted (bool): if set to true creates non shifted kernels only,
          otherwise creates randomly shifted kernels. Default: True.
      p (float): probability of applying the transform. Default: 0.5.
  
  Targets:
      image
  
  Image types:
      uint8, float32
class_docstr_tok: ["Apply","motion","blur","to","the","input","image","using","a","random-sized","kernel",".","Args",":","blur_limit","(","int",")",":","maximum","kernel","size","for","blurring","the","input","image",".","Should","be","in","range","[","3",",","inf",")",".","Default",":","(","3",",","7",")",".","allow_shifted","(","bool",")",":","if","set","to","true","creates","non","shifted","kernels","only",",","otherwise","creates","randomly","shifted","kernels",".","Default",":","True",".","p","(","float",")",":","probability","of","applying","the","transform",".","Default",":","0.5",".","Targets",":","image","Image","types",":","uint8",",","float32"]
code: def get_transform_init_args_names(self) -> Tuple[str, ...]:
      return super().get_transform_init_args_names() + (
          "allow_shifted",
      )
  
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 2 for calling the MotionBlur obj's underlying member method code with example usage: obj.get_transform_init_args_names() and returns: unknown