code: def _print_neural_compute_device(
      cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True
  ):
      """
      Print a message making it clear to the user what compute resource is used in
      neural network training.
      """
      num_cuda_gpus = len(cuda_gpus)
      if num_cuda_gpus >= 1:
          gpu_names = ", ".join(gpu["name"] for gpu in cuda_gpus)
  
      if use_mps:
          from ._mps_utils import mps_device_name
  
          print(
              "Using GPU to create model ({})".format(mps_device_name())
          )
      elif num_cuda_gpus >= 1:
          plural = "s" if num_cuda_gpus >= 2 else ""
          print(
              "Using GPU{} to create model ({})".format(
                  plural, gpu_names
              )
          )
      else:
          import sys
  
          print("Using CPU to create model")
          if (
              sys.platform == "darwin"
              and _mac_ver() < (10, 14)
              and has_mps_impl
          ):
              print(
                  "NOTE: If available, an AMD GPU can be leveraged on macOS 10.14+ for faster model creation"
              )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from ..data_structures.sframe import SArray
  from ..data_structures.sframe import SFrame
  from ..data_structures.sgraph import SGraph
  from ..data_structures.sgraph import Vertex
  from ..data_structures.sgraph import Edge
  from .._cython.cy_sarray import UnitySArrayProxy
  from .._cython.cy_sframe import UnitySFrameProxy
  from .._cython.cy_graph import UnityGraphProxy
  from ..toolkits._main import ToolkitError
  import json
  import six
label_desc: Use image node_id 30 for calling a global function with example usage: _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req, has_mps_impl) without return types