The proposed ATTA framework can be extended to handle more complex distribution shifts, such as multi-modal or non-stationary distributions, by incorporating advanced techniques for sample selection and adaptation. One approach could involve integrating clustering algorithms to identify different modes within the test distribution and adapt the model accordingly. By clustering the test data based on their similarities or differences, the model can dynamically adjust to the varying modes present in the distribution. Additionally, leveraging generative models like variational autoencoders or generative adversarial networks can help capture the underlying structure of multi-modal distributions and facilitate better adaptation. These generative models can generate synthetic samples that represent different modes of the distribution, enabling the model to adapt more effectively to diverse data patterns. Furthermore, techniques like transfer learning and meta-learning can be employed to transfer knowledge from previously encountered distributions and adapt quickly to new modes or non-stationary patterns in the test data.