code: def __init__(
      self,
      ds: Dataset,
      batched_indices: tp.List[tp.List[int]],
      n_workers: int,
      prefetch: int = 2,
      timeout: int = 10,
      worker_type: str = "thread",
  ):
      self.ds = ds
      self.batched_indices = batched_indices
      self.timeout = timeout
  
      if worker_type == "thread":
          pool_class = multiprocessing.pool.ThreadPool
      else:
          worker_type = (
              None if worker_type == "process" else worker_type
          )  # None means default
          pool_class = multiprocessing.get_context(worker_type).Pool
      self.worker_pool = pool_class(
          n_workers,
          initializer=WorkerContext.init,
          initargs=(ds, worker_type),
      )
      # extra thread to transfer data to the device
      self.data_transfer_worker = multiprocessing.pool.ThreadPool(
          processes=1
      )
  
      self.async_results_queue = []
      for batch_of_indices in batched_indices[:prefetch]:
          self.dispatch_tasks(batch_of_indices)
      self.batched_indices = batched_indices[prefetch:]
  
imports: import multiprocessing.pool
  import os
  import typing
  import jax
  import jax.numpy
  import numpy
  from .data_adapter import DataAdapter
  from .utils import is_none_or_empty
label_desc: Use image node_id 1 to create a new MultiProcessIterator object  with example: obj = MultiProcessIterator(ds, batched_indices, n_workers, prefetch, timeout, worker_type)