code: def test_elementwise_grad_multiple_args():
      def simple_fun(a, b):
          return a + np.sin(a) + np.cosh(b)
  
      A = 0.9
      B = npr.randn(10)
      argnum = 1
  
      wrapped = elementwise_grad(simple_fun, argnum)(A, B)
      explicit = np.array(
          [grad(simple_fun, argnum)(A, B[i]) for i in range(len(B))]
      )
      check_equivalent(wrapped, explicit)
  
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 8 for calling a global function with example usage: test_elementwise_grad_multiple_args() and returns: unknown