Can't activate Nvidia GPU on Ubuntu 22.04 (WSL2)

Problem definition

I have been trying to use my mobile Quadro T1000 with cuda in pytorch, but pytorch use CPU whatever I do. I installed pytorch, GPU drivers and CUDA Toolkit.

If I run command I get nothing

lspci | grep -i nvidia

Pytorch info:
torch.__version__: 1.13.0+cu117
torch.version.cuda: 11.7
torch.cuda.current_device(): 0
torch.cuda.is_available(): True

Windows 10 Pro 21H2

Extra info:

1. lspci

   3a66:00:00.0 3D controller: Microsoft Corporation Basic Render Driver
   4498:00:00.0 3D controller: Microsoft Corporation Basic Render Driver

2. nvcc -V

   nvcc: NVIDIA (R) Cuda compiler driver
   Copyright (c) 2005-2022 NVIDIA Corporation
   Built on Wed_Sep_21_10:33:58_PDT_2022
   Cuda compilation tools, release 11.8, V11.8.89
   Build cuda_11.8.r11.8/compiler.31833905_0

3. nvidia-smi

https://i.stack.imgur.com/4p5UE.png

4. dpkg -l | grep linux-modules-nvidia - nothing
5. dpkg -l | grep nvidia-driver - nothing
6. uname -a
   Linux CPR-5CD111 5.10.16.3-microsoft-standard-WSL2 #1 SMP Fri Apr 2 22:23:49 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
7. inxi -G

Graphics:
Device-1: Microsoft Basic Render Driver driver: dxgkrnl v: N/A
Device-2: Microsoft Basic Render Driver driver: dxgkrnl v: N/A
Display: server: No display server data found. Headless machine? tty: 145x14
Message: GL data unavailable in console. Try -G --display

8. grep 10de /lib/udev/rules.d/* - nothing

9. dmesg | grep nvidia - nothing

10. dmesg | grep NV - nothing

    def train(res_model, criterion, optimizer, train_dataloader, test_dataloader, NUM_EPOCH=15):
      for epoch in tqdm(range(NUM_EPOCH)):
        model.train()
        train_loss = 0.
        train_size = 0
    
        train_pred = 0.
    
        for imgs, labels in train_dataloader:
            optimizer.zero_grad()
    
            imgs = imgs.cuda()
            labels = labels.cuda()
    
            y_pred = model(imgs)
    
            loss = criterion(y_pred, labels)
            loss.backward()
    
            train_loss += loss.item()
            train_size += y_pred.size(0)
            train_loss_log.append(loss.data / y_pred.size(0))
    
            train_pred += (y_pred.argmax(1) == labels).sum()
    
            optimizer.step()