This example demonstrates a multiprocessing pipeline where eight producer processes concurrently process one hundred documents from a shared task queue, while the main process consumes and prints the results. The code uses Python's `multiprocessing` module, specifically the `Process`, `Queue`, and `join()` APIs to coordinate work and synchronization. Each producer pulls documents, simulates variable processing time with `time.sleep` and `random.uniform`, and pushes results to a separate result queue. The consumer function tracks completion until all documents are processed, illustrating a classic producer-consumer pattern with sentinel values to terminate workers.