code: def test_quantized_fc_bias_overflow(
      data_min, data_max, weight_min, weight_max
  ):
      data_shape = (1, 32)
      data = mx.symbol.Variable(
          "data", shape=data_shape, dtype="float32"
      )
      weight = mx.symbol.Variable("weight", dtype="float32")
      bias = mx.symbol.Variable("bias", dtype="float32")
      sym = mx.symbol.FullyConnected(
          data=data, weight=weight, bias=bias, name="fc", num_hidden=64
      )
      data_nd = mx.random.uniform(
          data_min, data_max, shape=data_shape, ctx=mx.cpu()
      )
      weight_nd = mx.random.uniform(
          weight_min, weight_max, shape=[64, 32], ctx=mx.cpu()
      )
      bias_nd = mx.random.uniform(-1, +1, shape=[64], ctx=mx.cpu())
      arg_params = {"weight": weight_nd, "bias": bias_nd}
  
      ex = sym._bind(mx.cpu(), arg_params, args_grad=None)
      ex.forward(data=data_nd)
      ex.outputs[0].wait_to_read()
      sym_sg = sym.optimize_for(
          QUANTIZE_SG_PASS_NAME, dedup_subgraph=True, skip_infer=True
      )
  
      calib_data = mx.gluon.data.DataLoader(data_nd, batch_size=1)
      qsym, qarg_params, qaux_params = quantization.quantize_model(
          sym=sym_sg,
          arg_params=arg_params,
          aux_params={},
          device=mx.cpu(),
          excluded_sym_names=None,
          excluded_op_names=None,
          quantized_dtype="int8",
          calib_mode="naive",
          calib_data=calib_data,
          num_calib_batches=1,
          quantize_mode="full",
      )
      qarg_params["data"] = data_nd
      qsym = qsym.optimize_for(
          QUANTIZE_SG_PASS_NAME, dedup_subgraph=True, skip_infer=True
      )
      qex = qsym._bind(mx.cpu(), qarg_params, args_grad=None)
      qex.forward()
      qex.outputs[0].wait_to_read()
      assert_almost_equal_with_err(
          ex.outputs[0].asnumpy(),
          qex.outputs[0].asnumpy(),
          rtol=1e-2,
          atol=1e-2,
          etol=0.01,
      )
  
imports: import copy
  import mxnet
  import pytest
  from subgraph_common import check_fusion, check_neg_fusion, check_quantize
  from subgraph_common import CustomNormalInit, DATA_SHAPE, RELU6, TailNegBlock
  from subgraph_common import DATA_SHAPE, SG_PASS_NAME, QUANTIZE_SG_PASS_NAME
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal, assert_almost_equal_with_err
label_desc: Use image node_id 26 for calling a global function with example usage: test_quantized_fc_bias_overflow(data_min, data_max, weight_min, weight_max) without return types