Consider two different approaches for building a deep, multi-layered neural network.

Approach A: The network is constructed by stacking the exact same computational block (with a single, shared set of weights) multiple times.

Approach B: Each layer in the network has its own unique weights for most of its operations, but for one specific, computationally expensive part of the block (e.g., the Key and Value projection matrices in an attention mechanism), it reuses the outputs generated by the preceding layer.

Analyze these two approaches. Compare and contrast their likely effects on the final model's total parameter count, its ability to learn distinct features at different depths, and its memory usage during operation.