code: def unpack_data(data):
      """Unpack data returned by the net's iterator into a 2-tuple.
  
      If the wrong number of items is returned, raise a helpful error
      message.
  
      """
      # Note: This function cannot detect it when a user only returns 1
      # item that is exactly of length 2 (e.g. because the batch size is
      # 2). In that case, the item will be erroneously split into X and
      # y.
      try:
          X, y = data
          return X, y
      except ValueError:
          # if a 1-tuple/list or something else like a torch tensor
          if not isinstance(data, (tuple, list)) or len(data) < 2:
              raise ValueError(ERROR_MSG_1_ITEM)
          raise ValueError(
              ERROR_MSG_MORE_THAN_2_ITEMS.format(len(data))
          )
  
imports: import warnings
  from collections.abc import Mapping
  from functools import partial
  from numbers import Number
  import numpy
  from scipy import sparse
  from sklearn.model_selection import ShuffleSplit
  from sklearn.model_selection import StratifiedKFold
  from sklearn.model_selection import StratifiedShuffleSplit
  from sklearn.model_selection import check_cv
  import torch
  import torch.utils.data
  from skorch.utils import flatten
  from skorch.utils import is_pandas_ndframe
  from skorch.utils import check_indexing
  from skorch.utils import multi_indexing
  from skorch.utils import to_numpy
label_desc: Use image node_id 5 for calling a global function with example usage: unpack_data(data) and returns: X,  y