code: def parallel_process(
      array, function, n_jobs=16, use_kwargs=False, front_num=10
  ):
      """
      A parallel version of the map function with a progress bar.
  
      Args:
          array (array-like): An array to iterate over.
          function (function): A python function to apply to the elements of array
          n_jobs (int, default=16): The number of cores to use
          use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of
              keyword arguments to function
          front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job.
              Useful for catching bugs
      Returns:
          [function(array[0]), function(array[1]), ...]
      """
      # We run the first few iterations serially to catch bugs
      if front_num > 0:
          front = [
              function(**a) if use_kwargs else function(a)
              for a in array[:front_num]
          ]
      else:
          front = []
      # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging.
      if n_jobs == 1:
          return front + [
              function(**a) if use_kwargs else function(a)
              for a in tqdm(array[front_num:])
          ]
      # Assemble the workers
      with ThreadPoolExecutor(max_workers=n_jobs) as pool:
          # Pass the elements of array into function
          if use_kwargs:
              futures = [
                  pool.submit(function, **a) for a in array[front_num:]
              ]
          else:
              futures = [
                  pool.submit(function, a) for a in array[front_num:]
              ]
          kwargs = {
              "total": len(futures),
              "unit": "it",
              "unit_scale": True,
              "leave": True,
          }
          # #Print out the progress as tasks complete
          # for _ in tqdm(as_completed(futures), **kwargs):
          #     pass
      out = []
      # Get the results from the futures.
      for future in futures:  # tqdm(futures):
          try:
              out.append(future.result())
          except Exception as e:
              out.append(e)
      return front + out
  
imports: from concurrent.futures import ThreadPoolExecutor, as_completed
  import os
  import numpy
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: parallel_process(array, function, n_jobs, use_kwargs, front_num) and returns: unknown, unknown