code: def get(
      dsk: Mapping,
      keys: Sequence[Key] | Key,
      cache=None,
      num_workers=None,
      pool=None,
      **kwargs,
  ):
      """Threaded cached implementation of dask.get
  
      Parameters
      ----------
  
      dsk: dict
          A dask dictionary specifying a workflow
      keys: key or list of keys
          Keys corresponding to desired data
      num_workers: integer of thread count
          The number of threads to use in the ThreadPool that will actually execute tasks
      cache: dict-like (optional)
          Temporary storage of results
  
      Examples
      --------
      >>> inc = lambda x: x + 1
      >>> add = lambda x, y: x + y
      >>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')}
      >>> get(dsk, 'w')
      4
      >>> get(dsk, ['w', 'y'])
      (4, 2)
      """
      global default_pool
      pool = pool or config.get("pool", None)
      num_workers = num_workers or config.get("num_workers", None)
      thread = current_thread()
  
      with pools_lock:
          if pool is None:
              if num_workers is None and thread is main_thread:
                  if default_pool is None:
                      default_pool = ThreadPoolExecutor(CPU_COUNT)
                      atexit.register(default_pool.shutdown)
                  pool = default_pool
              elif thread in pools and num_workers in pools[thread]:
                  pool = pools[thread][num_workers]
              else:
                  pool = ThreadPoolExecutor(num_workers)
                  atexit.register(pool.shutdown)
                  pools[thread][num_workers] = pool
          elif isinstance(pool, multiprocessing.pool.Pool):
              pool = MultiprocessingPoolExecutor(pool)
  
      results = get_async(
          pool.submit,
          pool._max_workers,
          dsk,
          keys,
          cache=cache,
          get_id=_thread_get_id,
          pack_exception=pack_exception,
          **kwargs,
      )
  
      # Cleanup pools associated to dead threads
      with pools_lock:
          active_threads = set(threading.enumerate())
          if thread is not main_thread:
              for t in list(pools):
                  if t not in active_threads:
                      for p in pools.pop(t).values():
                          p.shutdown()
  
      return results
  
file_path: dask/dask/threaded.py
imports: from __future__ import annotations
  import atexit
  import multiprocessing.pool
  import sys
  import threading
  from collections import defaultdict
  from collections.abc import Mapping, Sequence
  from concurrent.futures import Executor, ThreadPoolExecutor
  from threading import Lock, current_thread
  from dask import config
  from dask.local import MultiprocessingPoolExecutor, get_async
  from dask.system import CPU_COUNT
  from dask.typing import Key
label_desc: Use image node_id 3 for calling a global function with example usage: get(dsk, keys, cache, num_workers, pool) and returns: results