写一个将fruit360 图像加载到 DataLoader,并探索数据集，打印一个示例图像及其类和标签

import torch
from torchvision import transforms, datasets

# 定义数据变换
data_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 加载数据集
fruit_dataset = datasets.ImageFolder(root='fruit360', transform=data_transform)

# 创建 DataLoader
batch_size = 32
fruit_dataloader = torch.utils.data.DataLoader(fruit_dataset, batch_size=batch_size, shuffle=True)

# 探索数据集
print('数据集大小：', len(fruit_dataset))
print('类别数：', len(fruit_dataset.classes))
print('类别名称：', fruit_dataset.classes)

# 打印一个示例图像及其类和标签
dataiter = iter(fruit_dataloader)
images, labels = dataiter.next()

print('图像大小：', images.size())
print('标签大小：', labels.size())
print('标签：', labels)

import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
img = img / 2 + 0.5 # 将图像还原为原始范围
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))

# 显示一个 batch 的图像
imshow(torchvision.utils.make_grid(images))

# 显示标签
print(' '.join('%5s' % fruit_dataset.classes[labels[j]] for j in range(batch_size)))