code: def _log_time_fold(time_fold: Tuple[pd.Series, pd.Series]) -> LogType:
      train_time, test_time = time_fold
      return {
          "train_start": train_time.min(),
          "train_end": train_time.max(),
          "train_size": train_time.shape[0],
          "test_start": test_time.min(),
          "test_end": test_time.max(),
          "test_size": test_time.shape[0],
      }
  
imports: import operator
  from datetime import datetime, timedelta
  from itertools import chain, repeat, starmap
  from typing import Callable, Iterable, List, Tuple, Union
  import numpy
  import pandas
  from sklearn.model_selection import GroupKFold, KFold, StratifiedKFold
  from sklearn.utils import check_random_state
  from toolz.curried import curry, partial, pipe, assoc, accumulate, map, filter
  from fklearn.common_docstrings import splitter_return_docstring
  from fklearn.types import DateType, LogType, SplitterReturnType
label_desc: Use image node_id 1 for calling a global function with example usage: _log_time_fold(time_fold) and returns: dict