🧪 Add standardized module testing to all modules

Ensures consistent testing framework across all TinyTorch modules with:

✅ Added standardized testing sections to modules that were missing them:
- 01_setup: Added complete testing section + module summary
- 02_tensor: Added testing section + comprehensive module summary
- 15_mlops: Standardized existing testing section to match convention

✅ All modules now follow the consistent pattern:
1. ## 🧪 Module Testing (markdown explanation)
2. Locked nbgrader cell with standardized-testing ID
3. run_module_tests_auto call to discover and run all tests
4. ## 🎯 Module Summary (educational wrap-up)

✅ Benefits:
- Consistent testing experience across all 16 modules
- Automatic test discovery and execution before module completion
- Standardized educational flow: learn → implement → test → reflect
- Professional testing practices with locked testing framework

✅ Verification: All 16 modules now have both:
- '## 🧪 Module Testing' section ✓
- 'run_module_tests_auto' call ✓

This ensures students always verify their implementations work correctly
before moving to the next module, following TinyTorch's educational philosophy.

modules/source/02_tensor/tensor_dev.py

@@ -815,6 +815,92 @@ Congratulations! You've successfully implemented the core Tensor class for TinyT
 **Ready for the next challenge?** Let's add the mathematical functions that make neural networks powerful!
 """
 
+# %% [markdown]
+"""
+## 🧪 Module Testing
+
+Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.
+
+**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
+# =============================================================================
+# STANDARDIZED MODULE TESTING - DO NOT MODIFY
+# This cell is locked to ensure consistent testing across all TinyTorch modules
+# =============================================================================
+
+if __name__ == "__main__":
+    from tito.tools.testing import run_module_tests_auto
+    
+    # Automatically discover and run all tests in this module
+    success = run_module_tests_auto("Tensor")
+
+# %% [markdown]
+"""
+## 🎯 Module Summary: Tensor Foundation Mastery!
+
+Congratulations! You've successfully implemented the fundamental data structure that powers all machine learning:
+
+### ✅ What You've Built
+- **Tensor Class**: N-dimensional array wrapper with professional interfaces
+- **Core Operations**: Creation, property access, and arithmetic operations
+- **Shape Management**: Automatic shape tracking and validation
+- **Data Types**: Proper NumPy integration and type handling
+- **Foundation**: The building block for all subsequent TinyTorch modules
+
+### ✅ Key Learning Outcomes
+- **Understanding**: How tensors work as the foundation of machine learning
+- **Implementation**: Built tensor operations from scratch
+- **Professional patterns**: Clean APIs, proper error handling, comprehensive testing
+- **Real-world connection**: Understanding PyTorch/TensorFlow tensor foundations
+- **Systems thinking**: Building reliable, reusable components
+
+### ✅ Mathematical Foundations Mastered
+- **N-dimensional arrays**: Shape, size, and dimensionality concepts
+- **Element-wise operations**: Addition, subtraction, multiplication, division
+- **Broadcasting**: Understanding how operations work with different shapes
+- **Memory management**: Efficient data storage and access patterns
+
+### ✅ Professional Skills Developed
+- **API design**: Clean, intuitive interfaces for tensor operations
+- **Error handling**: Graceful handling of invalid operations and edge cases
+- **Testing methodology**: Comprehensive validation of tensor functionality
+- **Documentation**: Clear, educational documentation with examples
+
+### ✅ Ready for Advanced Applications
+Your tensor implementation now enables:
+- **Neural Networks**: Foundation for all layer implementations
+- **Automatic Differentiation**: Gradient computation through computational graphs
+- **Complex Models**: CNNs, RNNs, Transformers - all built on tensors
+- **Real Applications**: Training models on real datasets
+
+### 🔗 Connection to Real ML Systems
+Your implementation mirrors production systems:
+- **PyTorch**: `torch.Tensor` provides identical functionality
+- **TensorFlow**: `tf.Tensor` implements similar concepts
+- **NumPy**: `numpy.ndarray` serves as the foundation
+- **Industry Standard**: Every major ML framework uses these exact principles
+
+### 🎯 The Power of Tensors
+You've built the fundamental data structure of modern AI:
+- **Universality**: Tensors represent all data: images, text, audio, video
+- **Efficiency**: Vectorized operations enable fast computation
+- **Scalability**: Handles everything from single numbers to massive matrices
+- **Flexibility**: Foundation for any mathematical operation
+
+### 🚀 What's Next
+Your tensor implementation is the foundation for:
+- **Activations**: Nonlinear functions that enable complex learning
+- **Layers**: Linear transformations and neural network building blocks
+- **Networks**: Composing layers into powerful architectures
+- **Training**: Optimizing networks to solve real problems
+
+**Next Module**: Activation functions - adding the nonlinearity that makes neural networks powerful!
+
+You've built the foundation of modern AI. Now let's add the mathematical functions that enable machines to learn complex patterns!
+"""
+
 def test_unit_tensor_creation():
     """Comprehensive test of tensor creation with all data types and shapes."""
     print("🔬 Testing comprehensive tensor creation...")