refactor: Adjust pipeline orchestration in `PipelineExecutor`

[robinholzi]

Motivation

The current state of pipeline_executor.py doesn't follow clear sequential or logical order. Also, the setup doesn't allow to automatically profile and log pipeline stage execution. Restructuring the orchestration of a pipeline in a PipelineExecutor here.

Changes

• Split all logical pipeline stages into separate PipelineExecutor member functions and merge unused PipelineStages (like RUN_TRAINING and TRAINING_DONE). This allows to have a 1:1 mapping between logical pipeline stages and member functions.
• Sort the pipeline stage functions sequentially according to their position in the pipeline
• Add @pipeline_stage(...) decorator which wraps every pipeline stage function. When calling the decorated function we record meta information for logging (post-experiment analysis) and tracking (for online feedback) purposes. This includes start & end timestamps as well as the current "dataset/sample time". This is especially useful in experiments to be able to sort/plot different measurements by the time of the processed samples.
• Move all state (that is not enclosed in controller objects like grpc) into a central ExecutionState dataclass.
• Adjusted tests accordingly, removing code duplication in tests by parameterizing tests with pytest.mark.parameterize(...)
• Some renaming and other minor adjustments

TODO

• Fix broken integration tests
• merge most recent changes on main

[robinholzi]

GitHub could not determine anymore that merge conflicts were already resolved. The multi-stage merge conflict resolution and auto rebase likely bifurcated the git history. Getting rid of the commit history via a squash merge in #437