Implementing multiple layers of a Recurrent Neural Network (RNN) from scratch involves handling numerous logistical details, but modern deep learning frameworks abstract these away through high-level APIs. A deep architecture can be concisely defined by utilizing built-in recurrent functionalities and explicitly specifying a nontrivial number of hidden layers (e.g., setting a num_layers parameter) rather than relying on the default single-layer configuration. This approach generalizes single-layer implementations, allowing developers to easily instantiate deep models like multilayer GRUs. However, in minimalist frameworks like JAX's Flax, functionalities for stacking layers or integrating dropout are not provided out of the box, requiring developers to manually build upon built-in single-layer cells by programming the iterative layer logic and injecting dropout operations.