code: def _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True):
      
      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:
          from . import _mxnet_utils
          plural = 's' if num_cuda_gpus >= 2 else ''
          print('Using GPU{} to create model ({})'.format(plural, gpu_names))
          if cuda_mem_req is not None:
              _mxnet_utils._warn_if_less_than_cuda_free_memory(cuda_mem_req, max_devices=num_cuda_gpus)
      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')
docs: Print a message making it clear to the user what compute resource is used in
      neural network training.