Implement Tensor slicing with progressive disclosure and fix embedding gradient flow

WHAT: Added Tensor.__getitem__ (slicing) following progressive disclosure principles

MODULE 01 (Tensor):
- Added __getitem__ method for basic slicing operations
- Clean implementation with NO gradient mentions (progressive disclosure)
- Supports all NumPy-style indexing: x[0], x[:3], x[1:4], x[:, 1]
- Ensures scalar results are wrapped in arrays

MODULE 05 (Autograd):
- Added SliceBackward function for gradient computation
- Implements proper gradient scatter: zeros everywhere except sliced positions
- Added monkey-patching in enable_autograd() for __getitem__
- Follows same pattern as existing operations (add, mul, matmul)

MODULE 11 (Embeddings):
- Updated PositionalEncoding to use Tensor slicing instead of .data
- Fixed multiple .data accesses that broke computation graphs
- Removed Tensor() wrapping that created gradient-disconnected leafs
- Uses proper Tensor operations to preserve gradient flow

TESTING:
- All 6 component tests PASS (Embedding, Attention, FFN, Residual, Forward, Training)
- 19/19 parameters get gradients (was 18/19 before)
- Loss dropping better: 1.54→1.08 (vs 1.62→1.24 before)
- Model still not learning (0% accuracy) - needs fresh session to test monkey-patching

WHY THIS MATTERS:
- Tensor slicing is FUNDAMENTAL - needed by transformers for position embeddings
- Progressive disclosure maintains educational integrity
- Follows existing TinyTorch architecture patterns
- Enables position embeddings to potentially learn (pending verification)

DOCUMENTS CREATED:
- milestones/05_2017_transformer/TENSOR_SLICING_IMPLEMENTATION.md
- milestones/05_2017_transformer/STATUS.md
- milestones/05_2017_transformer/FIXES_SUMMARY.md
- milestones/05_2017_transformer/DEBUG_REVERSAL.md
- tests/milestones/test_reversal_debug.py (component tests)

ARCHITECTURAL PRINCIPLE:
Progressive disclosure is not just nice-to-have, it's CRITICAL for educational systems.
Don't expose Module 05 concepts (gradients) in Module 01 (basic operations).
Monkey-patch when features are needed, not before.

tinytorch/models/transformer.py

@@ -1,5 +1,19 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/13_transformers/transformers_dev.ipynb.
-
+# ╔═══════════════════════════════════════════════════════════════════════════════╗
+# ║                        🚨 CRITICAL WARNING 🚨                                ║
+# ║                     AUTOGENERATED! DO NOT EDIT!                              ║
+# ║                                                                               ║
+# ║  This file is AUTOMATICALLY GENERATED from source modules.                   ║
+# ║  ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported!            ║
+# ║                                                                               ║
+# ║  ✅ TO EDIT: modules/XX_transformer/transformer.py                  ║
+# ║  ✅ TO EXPORT: Run 'tito module complete <module_name>'                      ║
+# ║                                                                               ║
+# ║  🛡️ STUDENT PROTECTION: This file contains optimized implementations.        ║
+# ║     Editing it directly may break module functionality and training.         ║
+# ║                                                                               ║
+# ║  🎓 LEARNING TIP: Work in modules/ - that's where real development    ║
+# ║     happens! The tinytorch/ directory is just the compiled output.           ║
+# ╚═══════════════════════════════════════════════════════════════════════════════╝
 # %% auto 0
 __all__ = ['LayerNorm', 'MLP', 'TransformerBlock', 'GPT']