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This skill acts as an elite deep learning engineer to transform working PyTorch code into exemplary, production-grade implementations. It identifies architectural smells such as DRY violations and deep nesting while injecting modern PyTorch 2.x optimizations like torch.compile, Automatic Mixed Precision (AMP), and memory-efficient attention. By enforcing the Single Responsibility Principle and modular nn.Module design, it ensures your models are not only faster to train but also significantly easier to maintain and scale.
