code: def test_bf16_binary_broadcast_elemwise_funcs():
      dshape_0 = rand_shape_nd(4)
      dshape_1 = tuple()
      for i in range(4):
          if randint(0, 1):
              dshape_1 += (dshape_0[i],)
          else:
              dshape_1 += (1,)
  
      functions = [
          mx.sym.broadcast_add,
          mx.sym.broadcast_sub,
          mx.sym.broadcast_mul,
          mx.sym.broadcast_div,
      ]
  
      a_sym_fp32 = mx.sym.Variable(name="data")
      b_sym_fp32 = mx.sym.Variable(name="data_1")
  
      a_sym_bf16 = mx.sym.Variable(name="data", dtype="bfloat16")
      b_sym_bf16 = mx.sym.Variable(name="data_1", dtype="bfloat16")
  
      for func in functions:
          sym_fp32 = func(a_sym_fp32, b_sym_fp32)
          sym_bf16 = func(a_sym_bf16, b_sym_bf16)
          dshapes = {"data": dshape_0, "data_1": dshape_1}
          check_operator_accuracy(
              sym_fp32,
              sym_bf16,
              dshapes,
              num_input_data=2,
              bf16_use_fp32_params=False,
          )
  
imports: import os
  import sys
  import mxnet
  import numpy
  from random import randint
  import itertools
  from mxnet.test_utils import assert_almost_equal_with_err, rand_shape_nd
  import pytest
label_desc: Use image node_id 8 for calling a global function with example usage: test_bf16_binary_broadcast_elemwise_funcs() without return types