code: def warn_if_parameter_server_with_multi_gpu(
      training_instance_type, distribution
  ):
      """Warn the user about training when it doesn't leverage all the GPU cores.
  
      Warn the user that training will not fully leverage all the GPU
      cores if parameter server is enabled and a multi-GPU instance is selected.
      Distributed training with the default parameter server setup doesn't
      support multi-GPU instances.
  
      Args:
          training_instance_type (str): A string representing the type of training instance selected.
          distribution (dict): A dictionary with information to enable distributed training.
              (Defaults to None if distributed training is not enabled.) For example:
  
              .. code:: python
  
                  {
                      "parameter_server": {
                          "enabled": True
                      }
                  }
  
  
      """
      if training_instance_type == "local" or distribution is None:
          return
      if is_pipeline_variable(training_instance_type):
          # The training_instance_type is not available in compile time.
          # Rather, it's given in Pipeline execution time
          return
  
      is_multi_gpu_instance = (
          training_instance_type == "local_gpu"
          or training_instance_type.split(".")[1].startswith("p")
      ) and training_instance_type not in SINGLE_GPU_INSTANCE_TYPES
  
      ps_enabled = "parameter_server" in distribution and distribution[
          "parameter_server"
      ].get("enabled", False)
  
      if is_multi_gpu_instance and ps_enabled:
          logger.warning(PARAMETER_SERVER_MULTI_GPU_WARNING)
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import re
  import time
  import shutil
  import tempfile
  from collections import namedtuple
  from typing import List, Optional, Union, Dict
  from packaging import version
  import sagemaker.image_uris
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.s3_utils import s3_path_join
  from sagemaker.session_settings import SessionSettings
  import sagemaker.utils
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.deprecations import renamed_warning, renamed_kwargs
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.deprecations import deprecation_warn_base
label_desc: Use image node_id 11 for calling a global function with example usage: warn_if_parameter_server_with_multi_gpu(training_instance_type, distribution) without return types