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✅ STANDARDIZED TESTING ARCHITECTURE: - All inline tests now use consistent 'Unit Test: [Component]' naming - Progressive testing: small portions tested as students implement - Consistent print statements with �� Unit Test: format ✅ PROGRESSIVE TESTING STRUCTURE: - Tensor Module: Unit Test: Creation → Properties → Arithmetic → Comprehensive - Activations Module: Unit Test: ReLU → Sigmoid → Tanh → Softmax → Comprehensive - Layers Module: Unit Test: Matrix Multiplication → Dense Layer → Comprehensive - Networks Module: Unit Test: Sequential → MLP Creation → Comprehensive - CNN Module: Unit Test: Convolution → Conv2D → Flatten → Comprehensive - DataLoader Module: Unit Test: Dataset → DataLoader → Pipeline → Comprehensive - Autograd Module: Unit Test: Variables → Operations → Chain Rule → Comprehensive ✅ EDUCATIONAL CONSISTENCY: - Each unit test focuses on one specific component in isolation - Immediate feedback after each implementation step - Clear explanations of what each test validates - Consistent error messages and success indicators ✅ TESTING GRANULARITY VERIFIED: - Unit tests test small, specific functionality - Comprehensive tests cover edge cases and integration - All tests follow NBGrader-compliant cell structure - Proper separation between educational and assessment testing Total: 25+ individual unit tests across 7 modules with consistent naming and structure