input: Write a python function to create and fit a voting classifier with SVC, LogisticRegression, and KNeighborsClassifier, and then return its accuracy.
  assert fit_voting_classifier(x_train, y_train, x_test, y_test) > 0.7
  assert fit_voting_classifier(x_train2, y_train2, x_test2, y_test2) < 0.9
output: def fit_voting_classifier(x_train, y_train, x_test, y_test):
      from sklearn.ensemble import VotingClassifier
      from sklearn.svm import SVC
      from sklearn.linear_model import LogisticRegression
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import accuracy_score
      
      # Initialize the classifiers
      svc = SVC(probability=True)
      lr = LogisticRegression(max_iter=1000)
      knn = KNeighborsClassifier()
      
      # Create the voting classifier
      voting_clf = VotingClassifier(estimators=[
          ('svc', svc),
          ('lr', lr),
          ('knn', knn)
      ], voting='soft')
      
      # Fit the model
      voting_clf.fit(x_train, y_train)
      
      # Predict and calculate accuracy
      y_pred = voting_clf.predict(x_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy