code: def test_bf16_conv():
      data_sym_fp32 = mx.sym.Variable(name="data")
      data_sym_bf16 = mx.sym.Variable(name="data", dtype="bfloat16")
  
      conv_params = {
          "kernel": (3, 3),
          "num_filter": 128,
          "pad": (1, 1),
          "stride": (1, 1),
          "no_bias": True,
          "name": "conv",
      }
      conv_fp32 = mx.sym.Convolution(data_sym_fp32, **conv_params)
      conv_bf16 = mx.sym.Convolution(data_sym_bf16, **conv_params)
      check_operator_accuracy(
          sym_fp32=conv_fp32,
          sym_bf16=conv_bf16,
          data_shape=(3, 32, 28, 28),
          bf16_use_fp32_params=False,
      )
      check_operator_accuracy(
          sym_fp32=conv_fp32,
          sym_bf16=conv_bf16,
          data_shape=(128, 56, 14, 14),
          bf16_use_fp32_params=False,
      )
  
      conv_params = {
          "kernel": (1, 1),
          "num_filter": 32,
          "pad": (0, 0),
          "stride": (1, 1),
          "no_bias": False,
          "name": "conv",
      }
      conv_fp32 = mx.sym.Convolution(data_sym_fp32, **conv_params)
      conv_bf16 = mx.sym.Convolution(data_sym_bf16, **conv_params)
      check_operator_accuracy(
          sym_fp32=conv_fp32,
          sym_bf16=conv_bf16,
          data_shape=(3, 32, 28, 28),
          bf16_use_fp32_params=False,
      )
      check_operator_accuracy(
          sym_fp32=conv_fp32,
          sym_bf16=conv_bf16,
          data_shape=(128, 56, 14, 14),
          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 3 for calling a global function with example usage: test_bf16_conv() without return types