code: def request_resources(
      num_cpus: Optional[int] = None,
      bundles: Optional[List[dict]] = None,
  ) -> None:
      """Command the autoscaler to scale to accommodate the specified requests.
  
      The cluster will immediately attempt to scale to accommodate the requested
      resources, bypassing normal upscaling speed constraints. This takes into
      account existing resource usage.
  
      For example, suppose you call ``request_resources(num_cpus=100)`` and
      there are 45 currently running tasks, each requiring 1 CPU. Then, enough
      nodes will be added so up to 100 tasks can run concurrently. It does
      **not** add enough nodes so that 145 tasks can run.
  
      This call is only a hint to the autoscaler. The actual resulting cluster
      size may be slightly larger or smaller than expected depending on the
      internal bin packing algorithm and max worker count restrictions.
  
      Args:
          num_cpus: Scale the cluster to ensure this number of CPUs are
              available. This request is persistent until another call to
              request_resources() is made to override.
          bundles (List[ResourceDict]): Scale the cluster to ensure this set of
              resource shapes can fit. This request is persistent until another
              call to request_resources() is made to override.
  
      Examples:
          >>> from ray.autoscaler.sdk import request_resources
          >>> # Request 1000 CPUs.
          >>> request_resources(num_cpus=1000) # doctest: +SKIP
          >>> # Request 64 CPUs and also fit a 1-GPU/4-CPU task.
          >>> request_resources( # doctest: +SKIP
          ...     num_cpus=64, bundles=[{"GPU": 1, "CPU": 4}])
          >>> # Same as requesting num_cpus=3.
          >>> request_resources( # doctest: +SKIP
          ...     bundles=[{"CPU": 1}, {"CPU": 1}, {"CPU": 1}])
      """
      if num_cpus is not None and not isinstance(num_cpus, int):
          raise TypeError("num_cpus should be of type int.")
      if bundles is not None:
          if isinstance(bundles, List):
              for bundle in bundles:
                  if isinstance(bundle, Dict):
                      for key in bundle.keys():
                          if not (
                              isinstance(key, str)
                              and isinstance(bundle[key], int)
                          ):
                              raise TypeError(
                                  "each bundle key should be str and value as int."
                              )
                  else:
                      raise TypeError("each bundle should be a Dict.")
          else:
              raise TypeError("bundles should be of type List")
  
      return commands.request_resources(num_cpus, bundles)
  
imports: import json
  import os
  import tempfile
  from contextlib import contextmanager
  from typing import Any, Callable, Dict, Iterator, List, Optional, Union
  from ray.autoscaler._private import commands
  from ray.autoscaler._private.cli_logger import cli_logger
  from ray.autoscaler._private.event_system import CreateClusterEvent
  from ray.autoscaler._private.event_system import global_event_system
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 7 for calling a global function with example usage: request_resources(num_cpus, bundles) and returns: commands