code: def check_device_map(
      model: nn.Module,
      device_map: Dict[str, Union[int, str, torch.device]],
  ):
      """
      Checks a device map covers everything in a given model.
  
      Args:
          model (`torch.nn.Module`): The model to check the device map against.
          device_map (`Dict[str, Union[int, str, torch.device]]`): The device map to check.
      """
      all_model_tensors = [
          name for name, _ in model.state_dict().items()
      ]
      for module_name in device_map.keys():
          if module_name == "":
              all_model_tensors.clear()
              break
          else:
              all_model_tensors = [
                  name
                  for name in all_model_tensors
                  if not name == module_name
                  and not name.startswith(module_name + ".")
              ]
      if len(all_model_tensors) > 0:
          non_covered_params = ", ".join(all_model_tensors)
          raise ValueError(
              f"The device_map provided does not give any device for the following parameters: {non_covered_params}"
          )
  
imports: import contextlib
  import gc
  import inspect
  import json
  import logging
  import os
  import re
  import shutil
  import tempfile
  from collections import OrderedDict, defaultdict
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ..state import AcceleratorState
  from .constants import SAFE_WEIGHTS_NAME, WEIGHTS_NAME
  from .dataclasses import AutocastKwargs, CustomDtype, DistributedType
  from .imports import is_mps_available, is_npu_available, is_xpu_available
  from .offload import load_offloaded_weight, offload_weight, save_offload_index
  from .tqdm import is_tqdm_available, tqdm
  from safetensors import safe_open
  from safetensors.torch import load_file
label_desc: Use image node_id 23 for calling a global function with example usage: check_device_map(model, device_map) without return types