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
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code: def __init__(
      self,
      blur_limit: ScaleIntType = 7,
      allow_shifted: bool = True,
      always_apply: bool = False,
      p: float = 0.5,
  ):
      super().__init__(
          blur_limit=blur_limit, always_apply=always_apply, p=p
      )
      self.allow_shifted = allow_shifted
  
      if (
          not allow_shifted
          and self.blur_limit[0] % 2 != 1
          or self.blur_limit[1] % 2 != 1
      ):
          raise ValueError(
              f"Blur limit must be odd when centered=True. Got: {self.blur_limit}"
          )
  
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 MotionBlur object from inherited base classes: Blur with example: obj = MotionBlur(blur_limit, allow_shifted, always_apply, p)