code: def test_elemwise_add():
      def ref_add(a, b):
          return np.add(a, b)
  
      a_sym = mx.sym.Variable("a")
      b_sym = mx.sym.Variable("b")
      dshape = rand_shape_nd(4)
      a_shape = tuple(dshape)
      b_shape = tuple(dshape)
      z = mx.sym.elemwise_add(a_sym, b_sym)
      a = np.random.uniform(-1, 1, a_shape)
      b = np.random.uniform(-1, 1, b_shape)
      exe = z._simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
      out = exe.forward(is_train=False, a=a, b=b)
      ref_out = ref_add(a, b)
      out = out[0].asnumpy()
      assert_almost_equal(out, ref_out, rtol=1e-6, atol=1e-6)
  
      def check_elemwise_add_training(stype):
          data_shape = rand_shape_nd(4)
          for density in [1.0, 0.5, 0.0]:
              a_sym = mx.sym.Variable("a")
              b_sym = mx.sym.Variable("b")
              sym = mx.sym.elemwise_add(a_sym, b_sym)
              a = rand_ndarray(
                  shape=data_shape, stype=stype, density=density
              )
              b = rand_ndarray(
                  shape=data_shape, stype=stype, density=density
              )
              in_location = [a, b]
              check_numeric_gradient(
                  sym,
                  in_location,
                  numeric_eps=1e-3,
                  rtol=1e-3,
                  atol=5e-3,
              )
  
      stypes = ["row_sparse", "default"]
      for stype in stypes:
          check_elemwise_add_training(stype)
  
imports: import sys
  import os
  import numpy
  import mxnet
  import pytest
  from mxnet.test_utils import rand_ndarray, assert_almost_equal
  from mxnet import gluon, context, use_np
  from mxnet.gluon import nn
  from mxnet.test_utils import *
  import itertools
label_desc: Use image node_id 25 for calling a global function with example usage: test_elemwise_add() and returns: np