class_docstr: Sliding window time series split.
  
  Create `n_splits` folds with an as equally possible size through a smart variant of a brute force search.
  Groups parameter in `.split()` method should be filled with the time groups (e.g. years)
  
  If `n_splits` is 3 ("*" = train, "x" = test):
  
  ```console
  * * * x x x - - - - -
  - - - * * * x x x - -
  - - - - - - * * * x x
  ```
  
  Parameters
  ----------
  n_splits : int
      Amount of (train, test) splits to generate.
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docstr: Get the amount of splits
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features), default=None
      Ignored, present for compatibility.
  y : array-like of shape (n_samples,), default=None
      Ignored, present for compatibility.
  groups : array-like of shape (n_samples,), default=None
      Ignored, present for compatibility.
  
  Returns
  -------
  int
      Amount of splits.
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code: def get_n_splits(self, X=None, y=None, groups=None):
      """Get the amount of splits
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features), default=None
          Ignored, present for compatibility.
      y : array-like of shape (n_samples,), default=None
          Ignored, present for compatibility.
      groups : array-like of shape (n_samples,), default=None
          Ignored, present for compatibility.
  
      Returns
      -------
      int
          Amount of splits.
      """
      return self.n_splits
  
imports: import numbers
  from datetime import timedelta
  from itertools import combinations
  from warnings import warn
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection._split import _BaseKFold, check_array
  from sklearn.utils.validation import indexable
  from sklego.base import Clusterer
  from sklego.common import sliding_window
label_desc: Use image node_id 4 for calling the GroupTimeSeriesSplit obj's underlying member method code with example usage: obj.get_n_splits(X, y, groups) and returns: self