In deep learning frameworks, parameters can be shared or 'tied' across multiple layers within a sequential model. This is typically achieved by explicitly allocating a layer—such as a fully connected layer—and referencing that exact same object multiple times when constructing the network architecture. Because the tied layers are represented by the identical underlying parameter tensor in memory, they are not merely equal in value; any modification applied to the parameters of one layer will immediately alter the parameters of all other layers tied to it.