class_docstr: Normalize a ndarray image with mean and standard deviation.
docstr: Args:
      img (numpy.ndarray): Images to be cropped.
  Returns:
      numpy.ndarray: Cropped image.
docstr_tok: ["Args",":","img","(","numpy.ndarray",")",":","Images","to","be","cropped",".","Returns",":","numpy.ndarray",":","Cropped","image","."]
code: def __call__(self, img):
      """
      Args:
          img (numpy.ndarray): Images to be cropped.
      Returns:
          numpy.ndarray: Cropped image.
      """
      frames, h, w = img.shape
  
      hole_length = random.randint(
          self.min_hole_length, self.max_hole_length
      )
  
      start_h = random.randint(0, h - hole_length)
      start_w = random.randint(0, w - hole_length)
  
      img[
          :,
          start_h : start_h + hole_length,
          start_w : start_w + hole_length,
      ] = 0.0
      return img
  
imports: import random
  import cv2
  import numpy
  import torch
  from scipy import signal
label_desc: Use image node_id 2 for calling the CutoutHole obj's underlying member method code with example usage: obj.__call__(img) and returns: img