Clear side input caches of dofninvoker when finishing bundle. Add microbenchmarks for map_fn with side inputs.

[scwhittle]

Clearing the side-input in finish_bundle is a balance that allows refreshing the input in streaming while not affecting performance as much. However this requires the sdk state_cache to be enabled to avoid repeated lookups to the runner.
I was originally trying to optimize the code other ways but most of the overhead appears to be due to cython/Python interactions so the fast-path cache for repeated process invocations is important.

Improve the map_fn test by using common utilities.
Add support to benchmarks to add profiling.

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[gemini-code-assist]

Summary of Changes

Hello @scwhittle, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing the performance and observability of Apache Beam pipelines, particularly concerning side input handling and microbenchmarking. It introduces a refined caching strategy for side inputs within the DoFnInvoker to ensure data freshness in streaming while maintaining efficiency. Concurrently, the PR expands the microbenchmark suite with new tests for map_fn transformations involving side inputs and integrates profiling capabilities, providing better tools for performance measurement and optimization.

Highlights

• Side Input Cache Management: The PerWindowInvoker now explicitly clears cached side input arguments at the end of each bundle via a new invoke_finish_bundle method. This change allows side inputs to be refreshed in streaming pipelines without impacting performance, provided the SDK state cache is enabled.
• Refactored Caching Logic: The internal caching mechanism within PerWindowInvoker has been refactored, replacing boolean flags like recalculate_window_args and has_cached_window_args with dedicated should_cache_args and explicit cached_args_for_process/cached_kwargs_for_process variables for clearer state management.
• New Microbenchmarks for map_fn with Side Inputs: Added new microbenchmarks specifically designed to measure the performance of map_fn operations, including scenarios with global and fixed-windowed side inputs, and an uncached global side input scenario for comparison.
• RuntimeValueProvider Experiment Propagation: Ensured that RuntimeValueProvider correctly receives and propagates experiment flags from pipeline options in both DirectRunner and FnApiRunner contexts.
• Profiling Support in Benchmarks: The microbenchmark utility (apache_beam.tools.utils) now includes support for cProfile, allowing users to generate and dump profiling statistics during benchmark runs for detailed performance analysis.

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