code: def quantized_linear_relu(
      g: jit_utils.GraphContext,
      q_input,
      q_weight,
      bias,
      op_scale,
      op_zero_point,
  ):
      input, input_scale, _, _ = symbolic_helper.dequantize_helper(
          g, q_input
      )
      weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(
          g, q_weight
      )
      q_bias = symbolic_helper.requantize_bias_helper(
          g, bias, input_scale, weight_scale, axis
      )
      bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)
  
      output = opset9.linear(g, input, weight, bias)
      output = opset9.relu(g, output)
  
      return symbolic_helper.quantize_helper(
          g, output, op_scale, op_zero_point
      )
  
imports: import functools
  import torch
  import torch._C._onnx
  from torch.onnx import _constants, _type_utils, errors, symbolic_helper, symbolic_opset11, symbolic_opset9, utils
  from torch.onnx._internal import _beartype, jit_utils, registration
label_desc: Use image node_id 23 for calling a global function with example usage: quantized_linear_relu(g, q_input, q_weight, bias, op_scale, op_zero_point) and returns: symbolic_helper