Summary: Cursor adds advanced PyTorch experiment framework

Why It Matters: This change shows Cursor is improving its AI coding tools for complex machine learning tasks.

What Changed:

• The code example for PyTorch MNIST Experiments was greatly expanded.
• The new code includes mixed precision training, validation splitting, and configuration management.
• The code now uses tqdm, yaml, pathlib, and json libraries.
• The get_dataloaders function was updated to support data augmentation and validation splits.
• The MLP model now includes dropout layers and takes a config for hidden size.
• The train_model function was rewritten to use a config, support AMP, gradient clipping, and save checkpoints and history.
• A new link to "Status↗" was added under the "Resources" section in the footer.
• The text "Where are these menu label colors defined? * Grepped" changed to "Where are these menu label colors defined? * Grepping".