class_docstr: KlusterFold cross validator. Create folds based on provided cluster method
  
  Parameters
  ----------
  cluster_method : Clusterer
      Clustering method to use for the fold validation.
class_docstr_tok: ["KlusterFold","cross","validator",".","Create","folds","based","on","provided","cluster","method","Parameters","--","--","--","--","--","cluster_method",":","Clusterer","Clustering","method","to","use","for","the","fold","validation","."]
docstr: Check if the `cluster_method` is fitted
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Array to use if the method is fitted
  
  Returns
  -------
  bool
      True if fitted, else False
docstr_tok: ["Check","if","the","`","cluster_method","`","is","fitted","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","Array","to","use","if","the","method","is","fitted","Returns","--","--","--","-","bool","True","if","fitted",",","else","False"]
code: def _method_is_fitted(self, X):
      """Check if the `cluster_method` is fitted
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Array to use if the method is fitted
  
      Returns
      -------
      bool
          True if fitted, else False
      """
  
      try:
          self.cluster_method.predict(X[0:1, :])
          return True
      except NotFittedError:
          return False
  
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 3 for calling the KlusterFoldValidation obj's underlying member method code with example usage: obj._method_is_fitted(X) and returns: True, False