code: def _warn_on_high_parallelism(requested_parallelism, num_read_tasks):
      available_cpu_slots = ray.available_resources().get("CPU", 1)
      if (
          requested_parallelism
          and num_read_tasks > available_cpu_slots * 4
          and num_read_tasks >= 5000
      ):
          logger.warn(
              f"{WARN_PREFIX} The requested parallelism of {requested_parallelism} "
              "is more than 4x the number of available CPU slots in the cluster of "
              f"{available_cpu_slots}. This can "
              "lead to slowdowns during the data reading phase due to excessive "
              "task creation. Reduce the parallelism to match with the available "
              "CPU slots in the cluster, or set parallelism to -1 for Ray Data "
              "to automatically determine the parallelism. "
              "You can ignore this message if the cluster is expected to autoscale."
          )
  
imports: import importlib
  import logging
  import os
  import pathlib
  import random
  import sys
  import threading
  import time
  import urllib.parse
  from collections import deque
  from types import ModuleType
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, TypeVar, Union
  import numpy
  import ray
  from ray._private.utils import _get_pyarrow_version
  from ray.data._internal.arrow_ops.transform_pyarrow import unify_schemas
  from ray.data.context import WARN_PREFIX, DataContext
label_desc: Use image node_id 7 for calling a global function with example usage: _warn_on_high_parallelism(requested_parallelism, num_read_tasks) without return types