code: def test_set_estimator_drop():
      # VotingClassifier set_params should be able to set estimators as drop
      # Test predict
      clf1 = LogisticRegression(random_state=123)
      clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
      clf3 = GaussianNB()
      eclf1 = VotingClassifier(
          estimators=[("lr", clf1), ("rf", clf2), ("nb", clf3)],
          voting="hard",
          weights=[1, 0, 0.5],
      ).fit(X, y)
  
      eclf2 = VotingClassifier(
          estimators=[("lr", clf1), ("rf", clf2), ("nb", clf3)],
          voting="hard",
          weights=[1, 1, 0.5],
      )
      eclf2.set_params(rf="drop").fit(X, y)
  
      assert_array_equal(eclf1.predict(X), eclf2.predict(X))
  
      assert dict(eclf2.estimators)["rf"] == "drop"
      assert len(eclf2.estimators_) == 2
      assert all(
          isinstance(est, (LogisticRegression, GaussianNB))
          for est in eclf2.estimators_
      )
      assert eclf2.get_params()["rf"] == "drop"
  
      eclf1.set_params(voting="soft").fit(X, y)
      eclf2.set_params(voting="soft").fit(X, y)
  
      assert_array_equal(eclf1.predict(X), eclf2.predict(X))
      assert_array_almost_equal(
          eclf1.predict_proba(X), eclf2.predict_proba(X)
      )
      msg = "All estimators are dropped. At least one is required"
      with pytest.raises(ValueError, match=msg):
          eclf2.set_params(lr="drop", rf="drop", nb="drop").fit(X, y)
  
      # Test soft voting transform
      X1 = np.array([[1], [2]])
      y1 = np.array([1, 2])
      eclf1 = VotingClassifier(
          estimators=[("rf", clf2), ("nb", clf3)],
          voting="soft",
          weights=[0, 0.5],
          flatten_transform=False,
      ).fit(X1, y1)
  
      eclf2 = VotingClassifier(
          estimators=[("rf", clf2), ("nb", clf3)],
          voting="soft",
          weights=[1, 0.5],
          flatten_transform=False,
      )
      eclf2.set_params(rf="drop").fit(X1, y1)
      assert_array_almost_equal(
          eclf1.transform(X1),
          np.array(
              [[[0.7, 0.3], [0.3, 0.7]], [[1.0, 0.0], [0.0, 1.0]]]
          ),
      )
      assert_array_almost_equal(
          eclf2.transform(X1), np.array([[[1.0, 0.0], [0.0, 1.0]]])
      )
      eclf1.set_params(voting="hard")
      eclf2.set_params(voting="hard")
      assert_array_equal(
          eclf1.transform(X1), np.array([[0, 0], [1, 1]])
      )
      assert_array_equal(eclf2.transform(X1), np.array([[0], [1]]))
  
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 16 for calling a global function with example usage: test_set_estimator_drop() without return types