input: Write a python function to perform linear regression using numpy and sklearn.
  assert np.allclose(train_linear_regression([[5, 6], [3, 4], [7, 9], [4, 5]], [7, 5, 11, 7]), ([1.5, 1.5], -4.0))
  assert np.allclose(train_linear_regression([[1, 2], [2, 3], [3, 4], [4, 5]], [2, 3, 4, 5]), ([1.0, 1.0], 0.0))
  assert np.allclose(train_linear_regression([[1, 1], [2, 2], [3, 3], [4, 4]], [1, 2, 3, 4]), ([0.5, 0.5], 0.0))
output: def train_linear_regression(X, y):
      import numpy as np
      from sklearn.linear_model import LinearRegression
  
      X = np.array(X)
      y = np.array(y)
  
      model = LinearRegression()
      model.fit(X, y)
  
      coefficients = model.coef_
      intercept = model.intercept_
  
      return (coefficients.tolist(), intercept)