Update CIFAR10 tutorial to use torchvision v2 transforms and torch.ac… (#3882)

…celerator

Fixes #3878 

## Description
Updated CIFAR10 tutorial APIs and device handling

- Replace deprecated ToTensor with v2 transforms pipeline
- Use transforms.v2 namespace consistently
- Replace CUDA-only device detection with torch.accelerator
- Switch model checkpoint extension from .pth to .pt
- Update GitHub links from master to main
<img width="1815" height="615" alt="image"
src="https://github.com/user-attachments/assets/1910355b-139a-48e0-935f-f3392c5ae140"
/>


## Checklist
<!--- Make sure to add `x` to all items in the following checklist: -->
- [ ] The issue that is being fixed is referred in the description (see
above "Fixes #ISSUE_NUMBER")
- [ ] Only one issue is addressed in this pull request
- [ ] Labels from the issue that this PR is fixing are added to this
pull request
- [ ] No unnecessary issues are included into this pull request.

Co-authored-by: sekyondaMeta <127536312+sekyondaMeta@users.noreply.github.com>

Author: saurabhkthakur

beginner_source/blitz/cifar10_tutorial.py

@@ -57,7 +57,7 @@
 """
 import torch
 import torchvision
-import torchvision.transforms as transforms
+from torchvision.transforms import v2
 
 ########################################################################
 # The output of torchvision datasets are PILImage images of range [0, 1].
@@ -69,9 +69,10 @@
 #     BrokenPipeError or RuntimeError related to multiprocessing, try setting
 #     the num_worker of torch.utils.data.DataLoader() to 0.
 
-transform = transforms.Compose(
-    [transforms.ToTensor(),
-     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+transform = v2.Compose([
+    v2.ToImage(),
+    v2.ToDtype(torch.float32, scale=True),
+    v2.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
 batch_size = 4
 
@@ -191,7 +192,7 @@ def forward(self, x):
 ########################################################################
 # Let's quickly save our trained model:
 
-PATH = './cifar_net.pth'
+PATH = './cifar_net.pt'
 torch.save(net.state_dict(), PATH)
 
 ########################################################################
@@ -302,7 +303,7 @@ def forward(self, x):
 # Let's first define our device as the first visible cuda device if we have
 # CUDA available:
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device(torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else 'cpu')
 
 # Assuming that we are on a CUDA machine, this should print a CUDA device:
 
@@ -355,9 +356,9 @@ def forward(self, x):
 # -  `Discuss PyTorch on the Forums`_
 # -  `Chat with other users on Slack`_
 #
-# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet
-# .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/master/dcgan
-# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/master/word_language_model
+# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/main/imagenet
+# .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/main/dcgan
+# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/main/word_language_model
 # .. _More examples: https://github.com/pytorch/examples
 # .. _More tutorials: https://github.com/pytorch/tutorials
 # .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/