input: Write a function to perform linear regression for predicting the numbers in a given dataset. The function should take two lists X and Y as input, where X contains the independent variables and Y contains the dependent variables. The function should return a function that can be used to predict the output for any given input value. Use the sklearn.linear_model library for this task.
  
  assert perform_linear_regression([1,2,3,4,5], [3,5,7,9,13])(7)==11.4
  assert perform_linear_regression([1,3,5,7,9], [3,6,9,12,15])(6)==9.0
  assert perform_linear_regression([1,2,3,4,5], [3,4,5,6,10])(5)==7.4
  assert perform_linear_regression([1,2,3,4,5], [3,5,7,9,11])(8)==12.2
  assert perform_linear_regression([1,2,3,4,5], [3,4,5,6,7])(0)==2.2
output: def perform_linear_regression(X, Y):
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(np.array(X).reshape(-1, 1), Y)
      return lambda x: model.predict(np.array([x]).reshape(-1, 1))[0]