In deep learning frameworks, saving and loading the individual weight vectors of an entire model can be extremely tedious because models often contain hundreds of parameter groups. To address this, frameworks offer built-in functionalities to serialize and deserialize the entire set of model parameters collectively. This is typically achieved by storing the parameters as a file via a parameter dictionary, which allows practitioners to save and restore a network's complete parameter state in a single operation.