生成一段基于FGSM攻击算法，输入32*32像素的图像，输出也为32*32像素图像的代码

以下是一个基于FGSM攻击算法的Python代码，用于输入32×32像素的图像并输出相同大小的图像：

import torch
import torch.nn as nn
import torchvision

def fgsm_attack(image, epsilon, data_grad):
    sign_data_grad = data_grad.sign()
    perturbed_image = image + epsilon * sign_data_grad
    perturbed_image = torch.clamp(perturbed_image, 0, 1)
    return perturbed_image

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()
criterion = nn.CrossEntropyLoss()

# Load the pre-trained model
PATH = './cifar_net.pth'
net.load_state_dict(torch.load(PATH))

# Load the test image
image = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(image, batch_size=1, shuffle=True)

# Set the device to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Set the image to the first element of the test loader
data, target = next(iter(test_loader))
data = data.to(device)
target = target.to(device)

# Set the epsilon value for the FGSM attack
epsilon = 0.1

# Set the data gradient to zero
data_grad = torch.zeros(data.shape)

# Enable gradient calculation for the input data tensor
data.requires_grad = True

# Forward pass to obtain the predicted class
output = net(data)
loss = criterion(output, target)

# Backward pass to calculate the data gradient
loss.backward()
data_grad = data.grad.data

# Perturb the input image with the FGSM attack
perturbed_data = fgsm_attack(data, epsilon, data_grad)

# Forward pass with the perturbed image to obtain the predicted class
perturbed_output = net(perturbed_data)
perturbed_pred = perturbed_output.max(1, keepdim=True)[1]

# Print the original and perturbed images
print("Original Image")
torchvision.utils.save_image(data, 'original.png')
print("Perturbed Image")
torchvision.utils.save_image(perturbed_data, 'perturbed.png')

该代码使用了一个简单的卷积神经网络（CNN）来分类CIFAR-10图像，并使用FGSM攻击算法来修改输入图像。代码使用PyTorch框架编写，并在GPU上运行以提高性能。