我希望你写一个基于MINIST数据集的神经网络，使用pytorch，不需要给出神经网络的结构，并输出测试结果。从if name == 'main':开始。不要解释，给出代码

import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Load MNIST dataset
train_dataset = datasets.MNIST(root='path/to/dataset', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='path/to/dataset', train=False, transform=transforms.ToTensor())

# Create DataLoaders
train_loader = DataLoader(train_dataset, batch_size=100, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)

# Define neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # Define the layers of your neural network here
        
    def forward(self, x):
        # Define the forward pass of your neural network here
        return x

# Initialize the model and move it to the GPU
model = Net()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# Train the model
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        # Move the data to the GPU
        images, labels = images.to(device), labels.to(device)
        
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
# Test the model
model.eval()  # Evaluation mode
with torch.no_grad():  # No need to track gradients while testing
    correct = 0
    total = 0
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))