code: def __call__(self, img):
      h, w = img.size(1), img.size(2)
      mask = np.ones((h, w), np.float32)
      y = np.random.randint(h)
      x = np.random.randint(w)
  
      y1 = np.clip(y - self.length // 2, 0, h)
      y2 = np.clip(y + self.length // 2, 0, h)
      x1 = np.clip(x - self.length // 2, 0, w)
      x2 = np.clip(x + self.length // 2, 0, w)
  
      mask[y1:y2, x1:x2] = 0.0
      mask = torch.from_numpy(mask)
      mask = mask.expand_as(img)
      img *= mask
      return img
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 2 for calling the Cutout obj's underlying member method code with example usage: obj.__call__(img) and returns: img