diff --git a/daal4py/sklearn/cluster/_k_means_0_23.py b/daal4py/sklearn/cluster/_k_means_0_23.py
index d0cf4723cf..669a0fcaf5 100755
--- a/daal4py/sklearn/cluster/_k_means_0_23.py
+++ b/daal4py/sklearn/cluster/_k_means_0_23.py
@@ -240,26 +240,27 @@ def _fit(self, X, y=None, sample_weight=None):
         are assigned equal weight (default: None)
 
     """
-    if self.precompute_distances != 'deprecated':
-        if sklearn_check_version('0.24'):
-            warnings.warn("'precompute_distances' was deprecated in version "
-                          "0.23 and will be removed in 1.0 (renaming of 0.25). It has no "
-                          "effect", FutureWarning)
-        elif sklearn_check_version('0.23'):
-            warnings.warn("'precompute_distances' was deprecated in version "
-                          "0.23 and will be removed in 0.25. It has no "
-                          "effect", FutureWarning)
-
-    if self.n_jobs != 'deprecated':
-        if sklearn_check_version('0.24'):
-            warnings.warn("'n_jobs' was deprecated in version 0.23 and will be"
-                          " removed in 1.0 (renaming of 0.25).", FutureWarning)
-        elif sklearn_check_version('0.23'):
-            warnings.warn("'n_jobs' was deprecated in version 0.23 and will be"
-                          " removed in 0.25.", FutureWarning)
-        self._n_threads = self.n_jobs
-    else:
-        self._n_threads = None
+    if hasattr(self, 'precompute_distances'):
+        if self.precompute_distances != 'deprecated':
+            if sklearn_check_version('0.24'):
+                warnings.warn("'precompute_distances' was deprecated in version "
+                              "0.23 and will be removed in 1.0 (renaming of 0.25)."
+                              " It has no effect", FutureWarning)
+            elif sklearn_check_version('0.23'):
+                warnings.warn("'precompute_distances' was deprecated in version "
+                              "0.23 and will be removed in 0.25. It has no "
+                              "effect", FutureWarning)
+
+    self._n_threads = None
+    if hasattr(self, 'n_jobs'):
+        if self.n_jobs != 'deprecated':
+            if sklearn_check_version('0.24'):
+                warnings.warn("'n_jobs' was deprecated in version 0.23 and will be"
+                              " removed in 1.0 (renaming of 0.25).", FutureWarning)
+            elif sklearn_check_version('0.23'):
+                warnings.warn("'n_jobs' was deprecated in version 0.23 and will be"
+                              " removed in 0.25.", FutureWarning)
+            self._n_threads = self.n_jobs
     self._n_threads = _openmp_effective_n_threads(self._n_threads)
 
     if self.n_init <= 0:
@@ -366,17 +367,29 @@ def _predict(self, X, sample_weight=None):
 class KMeans(KMeans_original):
     __doc__ = KMeans_original.__doc__
 
-    @_deprecate_positional_args
-    def __init__(self, n_clusters=8, *, init='k-means++', n_init=10,
-                 max_iter=300, tol=1e-4, precompute_distances='deprecated',
-                 verbose=0, random_state=None, copy_x=True,
-                 n_jobs='deprecated', algorithm='auto'):
-
-        super(KMeans, self).__init__(
-            n_clusters=n_clusters, init=init, max_iter=max_iter,
-            tol=tol, precompute_distances=precompute_distances,
-            n_init=n_init, verbose=verbose, random_state=random_state,
-            copy_x=copy_x, n_jobs=n_jobs, algorithm=algorithm)
+    if sklearn_check_version('1.0'):
+        @_deprecate_positional_args
+        def __init__(self, n_clusters=8, *, init='k-means++', n_init=10,
+                     max_iter=300, tol=1e-4, verbose=0, random_state=None,
+                     copy_x=True, algorithm='auto'):
+
+            super(KMeans, self).__init__(
+                n_clusters=n_clusters, init=init, max_iter=max_iter,
+                tol=tol, n_init=n_init, verbose=verbose,
+                random_state=random_state, copy_x=copy_x,
+                algorithm=algorithm)
+    else:
+        @_deprecate_positional_args
+        def __init__(self, n_clusters=8, *, init='k-means++', n_init=10,
+                     max_iter=300, tol=1e-4, precompute_distances='deprecated',
+                     verbose=0, random_state=None, copy_x=True,
+                     n_jobs='deprecated', algorithm='auto'):
+
+            super(KMeans, self).__init__(
+                n_clusters=n_clusters, init=init, max_iter=max_iter,
+                tol=tol, precompute_distances=precompute_distances,
+                n_init=n_init, verbose=verbose, random_state=random_state,
+                copy_x=copy_x, n_jobs=n_jobs, algorithm=algorithm)
 
     def fit(self, X, y=None, sample_weight=None):
         return _fit(self, X, y=y, sample_weight=sample_weight)