Static Inference
Static inference in programming languages, particularly in the context of machine learning, refers to the process of determining the types, shapes, and other properties of variables and expressions at compile-time, rather than at runtime.
How Static Inference Works
In machine learning frameworks, static inference can be used to optimize computational graphs before execution. For example, by knowing the shapes and types of tensors in advance, a compiler can optimize memory allocation and operation scheduling, leading to faster model training and inference.
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