code: def test_none_estimator_with_weights(X, y, voter):
      # check that an estimator can be set to 'drop' and passing some weight
      # regression test for
      # https://github.com/scikit-learn/scikit-learn/issues/13777
      voter = clone(voter)
      # Scaled to solve ConvergenceWarning throw by Logistic Regression
      X_scaled = StandardScaler().fit_transform(X)
      voter.fit(X_scaled, y, sample_weight=np.ones(y.shape))
      voter.set_params(lr="drop")
      voter.fit(X_scaled, y, sample_weight=np.ones(y.shape))
      y_pred = voter.predict(X_scaled)
      assert y_pred.shape == y.shape
  
imports: import re
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.datasets import make_multilabel_classification
  from sklearn.dummy import DummyRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier, VotingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 19 for calling a global function with example usage: test_none_estimator_with_weights(X, y, voter) without return types