Based on the issue description, I need to provide a patch for scikit-learn's VotingClassifier/VotingRegressor. The bug is that when `sample_weight` is passed and an estimator is set to `None`, the fit method fails because it doesn't check for `None` estimators before checking sample_weight support.

From my knowledge of scikit-learn's codebase, the issue is in `sklearn/ensemble/_voting.py`. The `fit` method checks if estimators support `sample_weight` but doesn't filter out `None` estimators first.

Here's the patch:

--- a/sklearn/ensemble/_voting.py
+++ b/sklearn/ensemble/_voting.py
@@ -78,7 +78,7 @@ class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble):
 
         if sample_weight is not None:
             for name, step in self.estimators:
-                if not has_fit_parameter(step, 'sample_weight'):
+                if step is not None and not has_fit_parameter(step, 'sample_weight'):
                     raise ValueError('Underlying estimator \'%s\' does not'
                                      ' support sample weights.' % name)