code: def _check_f_arguments(caller_name, **kwargs):
      """Check file name arguments and return them
  
      This is used for checking if arguments to, e.g., ``save_params``
      are correct.
  
      Parameters
      ----------
      caller_name : str
        Name of caller, is only required for the error message.
  
      kwargs : dict
        Keyword arguments that are intended to be checked.
  
      Returns
      -------
      kwargs_module : dict
        Keyword arguments for saving/loading modules.
  
      kwargs_other : dict
        Keyword arguments for saving/loading everything else.
  
      Raises
      ------
      TypeError
        There are two possibilities for arguments to be
        incorrect. First, if they're not called 'f_*'. Second, if both
        'f_params' and 'f_module' are passed, since those designate the
        same thing.
  
      """
      if kwargs.get("f_params") and kwargs.get("f_module"):
          raise TypeError(
              "{} called with both f_params and f_module, please choose one".format(
                  caller_name
              )
          )
  
      kwargs_module = {}
      kwargs_other = {}
      keys_other = {"f_history", "f_pickle"}
      for key, val in kwargs.items():
          if not key.startswith("f_"):
              raise TypeError(
                  "{name} got an unexpected argument '{key}', did you mean 'f_{key}'?".format(
                      name=caller_name, key=key
                  )
              )
  
          if val is None:
              continue
          if key in keys_other:
              kwargs_other[key] = val
          else:
              # strip 'f_' prefix and attach '_', and normalize 'params' to 'module'
              # e.g. 'f_optimizer' becomes 'optimizer_', 'f_params' becomes 'module_'
              key = "module_" if key == "f_params" else key[2:] + "_"
              kwargs_module[key] = val
      return kwargs_module, kwargs_other
  
imports: from collections.abc import Mapping, Sequence
  from contextlib import contextmanager
  from enum import Enum
  from functools import partial
  import io
  from itertools import tee
  import pathlib
  import warnings
  import numpy
  from scipy import sparse
  import sklearn
  from sklearn.exceptions import NotFittedError
  from sklearn.utils import _safe_indexing
  from sklearn.utils.validation import check_is_fitted
  import torch
  from torch.nn import BCELoss
  from torch.nn import BCEWithLogitsLoss
  from torch.nn import CrossEntropyLoss
  from torch.nn.utils.rnn import PackedSequence
  from torch.utils.data.dataset import Subset
  from skorch.exceptions import DeviceWarning
  from skorch.exceptions import NotInitializedError
label_desc: Use image node_id 36 for calling a global function with example usage: _check_f_arguments(caller_name) and returns: kwargs_module,  kwargs_other