input: Write a python function to create a KNeighborsClassifier, fit it to the data, and predict labels for a given test set.
  assert knn_classifier(X=[[1, 2], [2, 3], [3, 4]], y=[0, 1, 0], X_test=[[1, 2], [2, 3]]) == [0, 1]
  assert knn_classifier(X=[[1, 1], [1, 2], [2, 2]], y=[0, 1, 0], X_test=[[1, 1.5], [2, 2]]) == [0, 0]
output: def knn_classifier(X, y, X_test):
      from sklearn.neighbors import KNeighborsClassifier
      
      # Create the KNeighborsClassifier
      knn = KNeighborsClassifier(n_neighbors=3)
      
      # Fit the classifier to the data
      knn.fit(X, y)
      
      # Predict the labels for the test set
      predictions = knn.predict(X_test)
      
      return predictions.tolist()