input: Write a python function to calculate the squared error of a linear regression model.
  assert np.isclose(calculate_squared_error(np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]), np.array([1.0, 2.9, 3.2, 4.2, 5.3, 6.3])), 0.06, atol=0.01)
  assert np.isclose(calculate_squared_error(np.array([1.0, 2.0, 3.0]), np.array([2.0, 2.9, 3.2])), 1.03, atol=0.01)
  assert np.isclose(calculate_squared_error(np.array([2.0, 4.0, 6.0]), np.array([2.0, 4.1, 6.1])), 0.02, atol=0.01)
output: def calculate_squared_error(y_true, y_pred):
      return np.sum((y_true - y_pred) ** 2) / len(y_true)