human
Give me a the Python code you requested given the following:
I am working on the file "FAI02_old/Lesson9/neural-style-GPU.ipynb".
The first task is:
Create Model
Can you write Python code for it?
assistant
#Here is the Python code you requested.

def download_convert_vgg16_model():
    model_url = 'http://cs.stanford.edu/people/jcjohns/fast-neural-style/models/vgg16.t7'
    file = get_file(model_url, cache_subdir='models')
    vgglua = load_lua(file).parameters()
    vgg = models.VGGFeature()
    for (src, dst) in zip(vgglua[0], vgg.parameters()): dst[:] = src[:]
    torch.save(vgg.state_dict(), path + 'vgg16_feature.pth')

url = 'https://s3-us-west-2.amazonaws.com/jcjohns-models/'
fname = 'vgg16-00b39a1b.pth'
file = get_file(fname, url+fname, cache_subdir='models')

vgg = models.vgg.vgg16()
vgg.load_state_dict(torch.load(file))
optimizer = optim.Adam(vgg.parameters())

vgg.cuda();

arr_lr = bcolz.open(path + 'trn_resized_72.bc')[:]
arr_hr = bcolz.open(path + 'trn_resized_288.bc')[:]

arr = bcolz.open(dpath + 'trn_resized.bc')[:]

x = Variable(arr[0])
y = model(x)

url = 'http://www.files.fast.ai/models/'
fname = 'imagenet_class_index.json'
fpath = get_file(fname, url + fname, cache_subdir='models')

class ResidualBlock(nn.Module):
    def __init__(self, num):
        super(ResideualBlock, self).__init__()
        self.c1 = nn.Conv2d(num, num, kernel_size=3, stride=1, padding=1)
        self.c2 = nn.Conv2d(num, num, kernel_size=3, stride=1, padding=1)
        self.b1 = nn.BatchNorm2d(num)
        self.b2 = nn.BatchNorm2d(num)
    
    def forward(self, x):
        h = F.relu(self.b1(self.c1(x)))
        h = self.b2(self.c2(h))
        return h + x

class FastStyleNet(nn.Module):
    def __init__(self):
        super(FastStyleNet, self).__init__()
        self.cs = [nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4),
                   nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1),
                   nn.Conv2d(64, 128, kernel_size=4, stride=2, padding1)]
        self.b1s = [nn.BatchNorm2d(i) for i in [32, 64, 128]]
        self.rs = [ResidualBlock(128) for i in range(5)]
        self.ds = [nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
                   nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)]
       ...