text: import torch
  import timeit
  import warnings
  
  
  def find_optimal_workers(
      loader,
      start_workers=0,
      max_workers=12,
      worker_step=1,
      iterations_per_worker=20,
      num_averages=3,
      set_pin_memory=True,
      verbose=True,
  ):
      """
          Find the (approximately) best number of workers to use for a particular DataLoader.
  
          Parameters
          ----------
          loader: DataLoader Class
              DataLoader for the dataset you want to check on
          start_workers: Integer
              How many workers to start from
          max_workers: Integer
              How many workers to end at
          worker_step: Integer
              Step size of number of workers
          iterations_per_worker: Integer
              The number of iterations we run each loader for, the larger the better
              in order to get a better approximation, but the longer the time.
          num_averages: Integer
              How many times we do the same experiment for specific number of workers
          set_pin_memory: Boolean
              Change DataLoader pin memory to True by default
          verbose: Boolean
              Prints information of iterations/second for every worker
  
          Returns
          -------
          int
              Number of workers found to be best
          """
  
      best_num_workers, best_iterations = None, float("-inf")
  
      if verbose:
          if not loader.pin_memory:
              warnings.warn(
                  "Pin memory is not True, I'm setting it to True (send set_pin_memory=False is not desired)"
              )
  
      loader.pin_memory = set_pin_memory
  
      for num_workers in range(start_workers, max_workers, worker_step):
          loader.num_workers = num_workers
          average_iterations = 0
  
          for _ in range(num_averages):
              iterations = 0
              for i, data in enumerate(loader):
                  if i == 1:
                      start_time = timeit.default_timer()
  
                  if i > 1:
                      iterations += 1
  
                  if i == iterations_per_worker:
                      end_time = timeit.default_timer()
                      iterations_divided_by_time = iterations / (end_time - start_time)
                      average_iterations += iterations_divided_by_time
                      break
  
          if verbose:
              print(
                  f"Iterations/second when num_workers={num_workers} is: {average_iterations/num_averages}"
              )
  
          if average_iterations / num_averages > best_iterations:
              best_num_workers = num_workers
              best_iterations = average_iterations / num_averages
  
      return best_num_workers