Hypernetworks extend traditional neural networks to effectively handle hierarchical data, acknowledging that real-world data often consists of multiple distinct datasets rather than a single flat mapping. This method allows for the modeling of variations in observations, such as those seen in clinical trials across different hospitals, where hidden parameters influence outcomes.