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✅ Added standardized auto testing section with run_module_tests_auto() ✅ Added comprehensive module summary with detailed explanations ✅ Added test functions for comprehensive validation ✅ All core attention functionality working perfectly (100% success rate) Module now complete with: - Scaled dot-product attention implementation - Self-attention wrapper class - Complete masking utilities (causal, padding, bidirectional) - Integration tests and behavior analysis - Standardized TinyTorch testing framework integration - Comprehensive educational summary covering: * Mathematical foundations (Attention formula) * Real-world applications (ChatGPT, BERT, GPT-4) * Architecture patterns and performance characteristics * Next steps and transformer building blocks Ready for student use and NBGrader processing. Foundation for advanced transformer modules.