input: Write a function to perform linear regression and predict the value of y for a given dataset using the LinearRegression model from sklearn. If the input is empty, return an empty list.
  
  def linear_regression_predict(X, Y, x_predict):
      if not X or not Y:
          return []
      import numpy as np
      from sklearn.linear_model import LinearRegression
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[x_predict]])[0]
  
  assert linear_regression_predict([1,2,3,4,5], [3,5,7,9,13], 7) == 15.0
  assert linear_regression_predict([], [3,5,7,9,13], 7) == []
  assert linear_regression_predict([1,2,3], [3,5,7], 4) == 9.0
  assert linear_regression_predict([0,1,2,3], [-1,1,3,5], 4) == 7.0
  assert linear_regression_predict([-1,0,1], [-3,-5,-7], 2) == -9.0
output: 
  def linear_regression_predict(X, Y, x_predict):
      if not X or not Y:
          return []
      import numpy as np
      from sklearn.linear_model import LinearRegression
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[x_predict]])[0]
  