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0e4c1a2c61
Final stage of TinyTorch API simplification: - Exported updated tensor module with Parameter function - Exported updated layers module with Linear class and Module base class - Fixed nn module to use unified Module class from core.layers - Complete modern API now working with automatic parameter registration ✅ All 7 stages completed successfully: 1. Unified Tensor with requires_grad support 2. Module base class for automatic parameter registration 3. Dense renamed to Linear for PyTorch compatibility 4. Spatial helpers (flatten, max_pool2d) and Conv2d rename 5. Package organization with nn and optim modules 6. Modern API examples showing 50-70% code reduction 7. Complete export with working PyTorch-compatible interface 🎉 Students can now write PyTorch-like code while still implementing all core algorithms (Conv2d, Linear, ReLU, Adam, autograd) The API achieves the goal: clean professional interfaces that enhance learning by reducing cognitive load on framework mechanics.