code: def buffer_iterator(
      src, buffer_size: int = 1, timeout: float = 0.01, repeats: int = 1
  ):
      """
      Create a ThreadBuffer object using the `src`, `buffer_size`, and `timeout` parameters given for the constructor
      arguments of the same names, and yield each generated object `repeats` number of times successively.
  
      Args:
          src: Source data iterable
          buffer_size: Number of items to buffer from the source
          timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items
          repeats: Number of repeat generations to perform which is asynchronous from the generation of the next value
  
      Returns:
          Generator yield (repeated) values from `src` asynchronously
      """
      buffer = ThreadBuffer(
          src=src, buffer_size=buffer_size, timeout=timeout
      )
  
      for batch in buffer:
          for _ in range(repeats):
              yield batch
  
imports: from __future__ import annotations
  from multiprocessing.context import SpawnContext
  from queue import Empty, Full, Queue
  from threading import Thread
  import torch
  from monai.data import DataLoader, Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: buffer_iterator(src, buffer_size, timeout, repeats) without return types