```from __future__ import absolute_import

from torchvision.transforms import *

from PIL import Image
import random
import math
import numpy as np
import torch

class RandomErasing(object):
'''
Class that performs Random Erasing in Random Erasing Data Augmentation by Zhong et al.
-------------------------------------------------------------------------------------
probability: The probability that the operation will be performed.
sl: min erasing area
sh: max erasing area
r1: min aspect ratio
mean: erasing value
-------------------------------------------------------------------------------------
'''
def __init__(self, probability = 0.5, sl = 0.02, sh = 0.4, r1 = 0.3, mean=[0.4914, 0.4822, 0.4465]):
self.probability = probability
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1

def __call__(self, img):

if random.uniform(0, 1) > self.probability:
return img

for attempt in range(100):
area = img.size()[1] * img.size()[2]

target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1/self.r1)

h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))

if w < img.size()[2] and h < img.size()[1]:
x1 = random.randint(0, img.size()[1] - h)
y1 = random.randint(0, img.size()[2] - w)
if img.size()[0] == 3:
img[0, x1:x1+h, y1:y1+w] = self.mean[0]
img[1, x1:x1+h, y1:y1+w] = self.mean[1]
img[2, x1:x1+h, y1:y1+w] = self.mean[2]
else:
img[0, x1:x1+h, y1:y1+w] = self.mean[0]
return img

return img

```