Add Torch: a PyTorch-like machine learning library for Source Academy

This PR introduces Torch, a TypeScript implementation of a PyTorch-compatible ML library designed for educational use within the Source Academy platform.

Docs (also work in progress):

• for JS/TS: https://veehz.github.io/torch/
• for Pyodide: https://veehz.github.io/torch-pyodide-docs/

What's included

Core library (src/)

• Tensor class with automatic differentiation, broadcasting, and zero-copy views via TensorStorage
• 50+ tensor operations (unary, binary, reductions, shape manipulation, linear algebra) implemented as TorchFunction subclasses with correct forward and backward passes
• nn.Module hierarchy: Linear, Conv1d/2d/3d, Sequential, and loss functions (MSELoss, BCELoss, CrossEntropyLoss)
• SGD (with momentum and Nesterov) and Adam (with AMSGrad) optimizers
• Seeded PRNG (Mulberry32) for reproducible tensor initialisation
• no_grad() context, export_() for ATen-compatible graph introspection
• Event bus exposing 8 event types (operation forward/backward, gradient accumulation) for external visualisation tools

Python bridge (examples/pyodide/bridge.py)

• Wraps the JavaScript library for use from Python running in Pyodide, including operator overloading and context-manager support for no_grad()

Testing

• scripts/generate_tests.py runs real PyTorch to produce numerical ground-truth fixtures
• Generated tests cover forward passes and gradients for all operation classes
• CI runs the full suite in both Node.js and a real browser via Playwright

Build targets

• Node.js: CommonJS + ESM with TypeScript declarations
• Browser: ESM module bundle
• CDN: minified UMD bundle