Summary: Cursor adds advanced PyTorch experiment framework

Why It Matters: This change shows Cursor is making its AI coding tools better for complex machine learning work. It helps users do more advanced AI tasks right inside Cursor.

What Changed:

• The code example for train_model.py was greatly expanded.
• The new code adds features like mixed precision training, validation splitting, and configuration management for PyTorch experiments.
• It includes new imports like yaml, Path, json, random_split, transforms, and tqdm.
• The get_dataloaders function was rewritten to support data augmentation and train/validation/test splits.
• The MLP class was updated to use a config for hidden size and dropout.
• The train_model function was updated to load configurations, use AMP (Automatic Mixed Precision), apply gradient clipping, save checkpoints, and record training history.
• New text was added describing the enhanced experiment framework, including details on training and experimentation features.