PyTorch is fundamentally designed around imperative programming and dynamic computation graphs. To capture the efficiency and deployment portability characteristic of symbolic programming, PyTorch provides TorchScript. TorchScript enables a hybrid programming approach where developers can construct and debug their models using pure imperative Python, and then seamlessly convert these programs into optimized symbolic graphs to be executed when product-level computing performance is required. Specifically, standard PyTorch models, such as those defined with nn.Sequential, can be compiled into these optimized graphs by passing them to the torch.jit.script function.