code: def _check_groups_dict_format(groups_dict):
      # The original groups_dict must be convertible to native Python dict
      groups_dict = dict(groups_dict)
  
      # Check if the format of the groups is correct
      for map_key in groups_dict:
          if not isinstance(map_key, str):
              raise ValueError(
                  "Every group must be identified by a string."
                  'Wrong key was: "' + str(map_key) + '"'
              )
  
      if "test" in groups_dict:
          warnings.warn(
              'A transformation group named "test" has been found. Beware '
              "that by default AvalancheDataset supports test transformations"
              ' through the "eval" group. Consider using that one!'
          )
  
imports: from collections import OrderedDict, defaultdict, deque
  from typing import TYPE_CHECKING, Any, Callable, Generic, Iterator, List, Iterable, Mapping, Optional, Sequence, TypeVar, Union, Dict, SupportsInt
  import warnings
  import torch
  from torch import Tensor
  from torch.utils.data import Subset, ConcatDataset
  from .data import AvalancheDataset
  from .data_attribute import DataAttribute
  from .dataset_definitions import ISupportedClassificationDataset
  from .dataset_utils import SubSequence, find_list_from_index
  from .flat_data import ConstantSequence
  from .transform_groups import TransformGroupDef, TransformGroups, XTransform, YTransform
label_desc: Use image node_id 14 for calling a global function with example usage: _check_groups_dict_format(groups_dict) without return types