Improve the handling of randomness in documentation notebooks

Currently, some documentation notebooks (e.g. [shapley basic spotify](https://github.com/aai-institute/pyDVL/blob/develop/notebooks/shapley_basic_spotify.ipynb)) do not pass seeds or random number generators when calculating shapley values.

We should go over all documentation notebooks and make sure that best practices for controlling randomness are fulfilled and users can see all places where randomness needs to be handled.

检查 `notebooks/` 目录中的所有 notebook，从 `shapley_basic_spotify.ipynb` 开始。识别计算 Shapley 值或其他概率算法的函数调用。确保每个调用都使用 seed 或随机数生成器参数，以保证结果可复现。通过重新运行这些 notebook 来验证更改，确认输出是确定性的。

由索引模型根据 Issue 内容生成。