input: Write a python function to create and train a KNeighborsClassifier model, and predict the output for a given input.
  assert np.array_equal(knn_predict([[2]]), [[3]])
  assert np.array_equal(knn_predict([[1]]), [[2]])
  assert np.array_equal(knn_predict([[6]]), [[7]])
output: def knn_predict(X_new):
      from sklearn.neighbors import KNeighborsClassifier
      import numpy as np
      
      # Create sample data
      X_train = np.array([[1], [2], [3], [4], [5]])
      y_train = np.array([2, 3, 4, 5, 7])
      
      # Initialize the model
      knn = KNeighborsClassifier(n_neighbors=1)
      
      # Train the model
      knn.fit(X_train, y_train)
      
      # Make predictions
      return knn.predict(X_new).reshape(-1, 1)