code: def parse_cuda_device(cuda_device: Union[str, int, List[int]]) -> int:
      """
      Disambiguates single GPU and multiple GPU settings for cuda_device param.
      """
  
      message = """
      In allennlp 1.0, the Trainer cannot be passed multiple cuda devices.
      Instead, use the faster Distributed Data Parallel. For instance, if you previously had config like:
          {
            "trainer": {
              "cuda_device": [0, 1, 2, 3],
              "num_epochs": 20,
              ...
            }
          }
          simply change it to:
          {
            "distributed": {
              "cuda_devices": [0, 1, 2, 3],
            },
            "trainer": {
              "num_epochs": 20,
              ...
            }
          }
          """
  
      def from_list(strings):
          if len(strings) > 1:
              raise ConfigurationError(message)
          elif len(strings) == 1:
              return int(strings[0])
          else:
              return -1
  
      if isinstance(cuda_device, str):
          return from_list(re.split(r",\s*", cuda_device))
      elif isinstance(cuda_device, int):
          return cuda_device
      elif isinstance(cuda_device, list):
          return from_list(cuda_device)
      else:
          # TODO(brendanr): Determine why mypy can't tell that this matches the Union.
          return int(cuda_device)
  
file_path: allennlp/allennlp/common/checks.py
imports: import logging
  import re
  import subprocess
  from typing import Any, List, Tuple, Union
  import torch
  from torch import cuda
label_desc: Use image node_id 3 for calling a global function with example usage: parse_cuda_device(cuda_device) and returns: from_list, cuda_device, int, unknown, from_list, int