Implement a Parallel Image Processor

You are building a batch image-processing utility. Implement a function that receives a list of jobs. Each job contains: - `input_path`: the source image file - `output_path`: where to write the processed image - `operations`: an ordered list of image transformations Support these operations: 1. `grayscale` — convert the image to grayscale. 2. `scale(f)` — multiply both width and height by a positive scaling factor `f`. 3. `resize(w, h)` — resize the image to exactly `w x h` pixels. For each job, load the image, apply the operations in order, and save the final result to `output_path`. Follow-up: 1. First, implement a correct solution for a small number of images. 2. Then improve the implementation for a very large batch. Assume image transformations are CPU-intensive and that jobs are independent. Use process-based parallelism to increase throughput, while still returning one result record per input job and handling per-file failures without stopping the entire batch. Explain any important design choices, such as worker-count selection, memory considerations, and how you would preserve correctness while improving performance.