code: def function_add_quantized(
      data_shape,
      add_op,
      quantize_mode,
      relu,
      out_type,
      broadcast,
      calib_mode,
  ):
      class SumExample(nn.HybridBlock):
          def __init__(self, add_op, **kwargs):
              super(SumExample, self).__init__(**kwargs)
              self.elemwise_add = add_op == "ele_add"
              self.relu = relu == "relu"
  
          def forward(self, data1a, data2):
              fc_out = data1a
              if self.relu:
                  fc_out = mx.npx.relu(fc_out)
              if self.elemwise_add:
                  sum1 = mx.nd.elemwise_add(
                      data2.as_nd_ndarray(), fc_out.as_nd_ndarray()
                  ).as_np_ndarray()
              else:
                  sum1 = data2 + fc_out
              return sum1
  
      attrs = {add_op: {}}
      net = SumExample(add_op)
      if broadcast:
          broadcasted_shape = (1,) + data_shape[1:-1] + (1,)
          data_shapes = [broadcasted_shape, data_shape]
      else:
          data_shapes = [data_shape, data_shape]
  
      # check_calibration could be enabled if check_qsym_calibrated will be reimplemented
      # to find operator names instead of node names
      check_quantize(
          net,
          data_shapes,
          out_type,
          name="contrib_quantized_" + add_op,
          quantize_mode=quantize_mode,
          attrs_dict=attrs,
          calib_mode=calib_mode,
          check_calibration=(calib_mode != "none") and False,
          check_fusion=False,
      )
  
imports: import mxnet
  import pytest
  from subgraph_common import check_fusion, check_neg_fusion, check_neg_fusion_quantized, check_quantize
  from subgraph_common import CustomNormalInit, DATA_SHAPE, TailNegBlock
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal_with_err
label_desc: Use image node_id 15 for calling a global function with example usage: function_add_quantized(data_shape, add_op, quantize_mode, relu, out_type, broadcast, calib_mode) and returns: sum1