Capturing and Deploying PyTorch Models with torch.export

Deploying machine learning models from training to inference environments presents challenges in reliably restoring the model's definition and state. The new `torch.export` API in PyTorch aims to solve this by capturing a static, serializable graph representation of the model. This process is demonstrated using a HuggingFace image-to-text transformer model, showing how to export and later load the model for inference. The article also contrasts this modern approach with legacy methods like weights-only capture and TorchScript, explaining their limitations.