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This commit implements comprehensive PyTorch compatibility improvements:
**Core Changes:**
- Add __call__ methods to all neural network components in modules 11-18
- Enable PyTorch-standard calling syntax: model(input) vs model.forward(input)
- Maintain backward compatibility - forward() methods still work
**Modules Updated:**
- Module 11 (Embeddings): Embedding, PositionalEncoding, EmbeddingLayer
- Module 12 (Attention): MultiHeadAttention
- Module 13 (Transformers): LayerNorm, MLP, TransformerBlock, GPT
- Module 17 (Quantization): QuantizedLinear
- Module 18 (Compression): Linear, Sequential classes
**Milestone Updates:**
- Replace all .forward() calls with direct () calls in milestone examples
- Update transformer milestones (vaswani_shakespeare, tinystories_gpt, tinytalks_gpt)
- Update CNN and MLP milestone examples
- Update MILESTONE_TEMPLATE.py for consistency
**Educational Benefits:**
- Students now write identical syntax to production PyTorch code
- Seamless transition from TinyTorch to PyTorch development
- Industry-standard calling conventions from day one
**Implementation Pattern:**
```python
def __call__(self, *args, **kwargs):
"""Allows the component to be called like a function."""
return self.forward(*args, **kwargs)
```
All changes maintain full backward compatibility while enabling PyTorch-style usage.(https://claude.ai/code)