input: Write a function to perform linear regression on a given dataset and predict the value of y for a new x value. Use the sklearn linear regression model.
  assert predict_y([1,2,3,4,5], [3,5,7,9,13], 7)==15.0
  assert predict_y([1,3,5,7,9], [4,10,16,22,28], 11)==34.0
  assert predict_y([2,4,6,8,10], [6,12,18,24,30], 12)==36.0
  assert predict_y([1,3,4,5,7], [3,11,14,17,25], 10)==33.0
  assert predict_y([0,1,2,3,4], [2,4,6,8,10], 5)==12.0
output: def predict_y(x_values, y_values, new_x):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      model = LinearRegression()
      model.fit(np.array(x_values).reshape(-1, 1), y_values)
      return model.predict(np.array([new_x]).reshape(-1, 1))[0]