Add comprehensive multi-channel Conv2D support to Module 06 (Spatial)

MAJOR FEATURE: Multi-channel convolutions for real CNN architectures

Key additions:
- MultiChannelConv2D class with in_channels/out_channels support
- Handles RGB images (3 channels) and arbitrary channel counts
- He initialization for stable training
- Optional bias parameters
- Batch processing support

Testing & Validation:
- Comprehensive unit tests for single/multi-channel
- Integration tests for complete CNN pipelines
- Memory profiling and parameter scaling analysis
- QA approved: All mandatory tests passing

CIFAR-10 CNN Example:
- Updated train_cnn.py to use MultiChannelConv2D
- Architecture: Conv(3→32) → Pool → Conv(32→64) → Pool → Dense
- Demonstrates why convolutions matter for vision
- Shows parameter reduction vs MLPs (18KB vs 12MB)

Systems Analysis:
- Parameter scaling: O(in_channels × out_channels × kernel²)
- Memory profiling shows efficient scaling
- Performance characteristics documented
- Production context with PyTorch comparisons

This enables proper CNN training on CIFAR-10 with ~60% accuracy target.

docs/module-audit.md

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+# TinyTorch Module Audit: Essential vs Extra Components
+
+## Overview
+This audit examines what components are NEEDED for each milestone vs EXTRA components that enhance the framework but aren't strictly necessary.
+
+---
+
+## Part I: MLPs (Target: XORNet)
+
+### Module 02: Tensor
+**ESSENTIAL for XORNet:**
+- Basic Tensor class with data storage
+- Addition, subtraction, multiplication
+- Matrix multiply (for layers)
+- Shape, reshape operations
+
+**EXTRA (but good for framework):**
+- Broadcasting ✓ (nice but XOR doesn't need)
+- Fancy indexing ✓ 
+- Statistical operations (mean, sum, std) ✓
+- Comparison operators ✓
+
+### Module 03: Activations  
+**ESSENTIAL for XORNet:**
+- ReLU ✓ (used in XORNet)
+- Sigmoid (could use for XOR output)
+
+**EXTRA (but good for framework):**
+- Tanh ✓ (alternative to ReLU)
+- Softmax ✓ (not needed for XOR, but needed for CIFAR-10)
+- ActivationProfiler ✓ (pedagogical tool)
+
+### Module 04: Layers
+**ESSENTIAL for XORNet:**
+- Dense layer ✓ (fully connected)
+- Weight initialization
+- Forward pass
+
+**EXTRA:**
+- Different initialization strategies (Xavier, He, etc.)
+- Bias option
+
+### Module 05: Networks
+**ESSENTIAL for XORNet:**
+- Sequential model ✓
+- Forward pass through layers
+
+**EXTRA:**
+- Model summary/printing
+- Parameter counting
+
+---
+
+## Part II: CNNs (Target: CIFAR-10)
+
+### Module 06: Spatial
+**ESSENTIAL for CNN CIFAR-10:**
+- Conv2D ✓ (the key innovation!)
+- MaxPool2D ✓ (for downsampling)
+
+**EXTRA (but pedagogically valuable):**
+- Different padding modes
+- Stride options
+- AvgPool2D (alternative pooling)
+- Multiple filter support
+
+### Module 07: DataLoader
+**ESSENTIAL for CIFAR-10:**
+- CIFAR10Dataset ✓
+- DataLoader with batching ✓
+- Shuffling ✓
+
+**EXTRA:**
+- Data augmentation (but helps accuracy!)
+- Other datasets (MNIST, etc.)
+- Prefetching/parallel loading
+
+### Module 08: Autograd
+**ESSENTIAL for CIFAR-10:**
+- Variable class ✓
+- Backward pass ✓
+- Gradient computation ✓
+
+**EXTRA:**
+- Computation graph visualization
+- Gradient checking
+- Higher-order derivatives
+
+### Module 09: Optimizers
+**ESSENTIAL for CIFAR-10:**
+- SGD (basic, could work)
+- Adam ✓ (used in CIFAR-10, converges faster)
+
+**EXTRA:**
+- Learning rate scheduling
+- Momentum variants
+- RMSprop, AdaGrad
+
+### Module 10: Training
+**ESSENTIAL for CIFAR-10:**
+- Training loop ✓
+- CrossEntropyLoss ✓
+- Basic evaluation ✓
+
+**EXTRA (but very useful):**
+- Checkpointing ✓
+- Early stopping ✓
+- Metrics tracking ✓
+- Validation splits ✓
+- MeanSquaredError (for XOR)
+
+---
+
+## Part III: Transformers (Target: TinyGPT)
+
+### Module 11: Embeddings
+**ESSENTIAL for TinyGPT:**
+- Token embedding layer
+- Positional encoding (sinusoidal or learned)
+
+**EXTRA:**
+- Multiple embedding types
+- Embedding dropout
+
+### Module 12: Attention
+**ESSENTIAL for TinyGPT:**
+- Multi-head attention ✓ (already implemented!)
+- Scaled dot-product attention ✓
+- Causal masking ✓
+
+**EXTRA:**
+- Different attention variants
+- Attention visualization
+
+### Module 13: Normalization
+**ESSENTIAL for TinyGPT:**
+- LayerNorm (critical for transformer stability)
+
+**EXTRA:**
+- BatchNorm (not used in transformers)
+- GroupNorm, InstanceNorm
+
+### Module 14: Transformers
+**ESSENTIAL for TinyGPT:**
+- TransformerBlock (attention + FFN + residual)
+- Positional encoding integration
+- Stack of blocks
+
+**EXTRA:**
+- Encoder-decoder architecture
+- Cross-attention
+
+### Module 15: Generation
+**ESSENTIAL for TinyGPT:**
+- Autoregressive generation
+- Temperature sampling
+- Greedy decoding
+
+**EXTRA:**
+- Beam search
+- Top-k, Top-p sampling
+- Repetition penalty
+
+---
+
+## Summary
+
+### Truly Minimal Path
+If we wanted ONLY what's needed for milestones:
+- **XORNet**: Just needs Dense, ReLU, basic Tensor ops
+- **CIFAR-10 MLP**: Add DataLoader, Adam, CrossEntropyLoss
+- **CIFAR-10 CNN**: Add Conv2D, MaxPool2D
+- **TinyGPT**: Add Embeddings, Attention, LayerNorm, Generation
+
+### What We Have (Good Extras)
+- **More activation choices**: Good for experimentation
+- **Better optimizers**: Adam converges faster than SGD
+- **Training utilities**: Checkpointing, metrics (very practical!)
+- **Profiling tools**: Help understand performance
+
+### Missing Essentials
+For Part III (TinyGPT) we still need to implement:
+1. **Module 11**: Embedding layer, positional encoding
+2. **Module 13**: LayerNorm 
+3. **Module 14**: TransformerBlock
+4. **Module 15**: Generation strategies
+
+### Verdict
+The current modules have a good balance of essential + useful extras. The extras are:
+- **Pedagogically valuable** (show alternatives)
+- **Practically useful** (checkpointing, better optimizers)
+- **Framework completeness** (makes TinyTorch feel real)
+
+The only "bloat" might be multiple activation functions, but even those are good for showing students the options and tradeoffs.