code: def test_multigrad_onearg():
      fun = lambda x, y: np.sum(x + np.sin(y))
      packed_fun = lambda xy: np.sum(xy[0] + np.sin(xy[1]))
      A, B = npr.randn(3), npr.randn(3)
      check_equivalent(
          grad(fun, argnum=[0])(A, B), (grad(packed_fun)((A, B))[0],)
      )
  
imports: from __future__ import absolute_import
  from builtins import range
  import warnings
  from functools import partial
  import autograd.numpy
  import autograd.numpy.random
  from autograd.test_util import check_grads, check_equivalent
  from autograd.tracer import primitive, isbox
  from autograd import grad, elementwise_grad, jacobian, value_and_grad, hessian_tensor_product, hessian, make_hvp, tensor_jacobian_product, checkpoint, make_jvp, make_ggnvp, grad_and_aux
label_desc: Use image node_id 6 for calling a global function with example usage: test_multigrad_onearg() without return types