input: Write a python function to optimize a linear combination of a variable and the sine of the variable with given parameters using the BFGS method.
  assert np.allclose(optimize_function([2, 3, 4]), [-0.237])
  assert np.allclose(optimize_function([1, 0, 1]), [0.])
  assert np.allclose(optimize_function([1, 1, 1]), [-0.467])
output: import numpy as np
  from scipy.optimize import minimize
  
  def optimize_function(params):
      def objective(x):
          return params[0] * x[0] + params[1] * np.sin(x[0]) + params[2]
      
      result = minimize(objective, x0=[0], method='BFGS')
      return result.x