code: def run(self):
      if self._option.quantize:
          self.use_quant_in_out()
      self.add_op_output_type()
      self.ensure_bias_vector()
      self.ensure_binary_input()
      self.common_check()
      if (
          ConverterUtil.get_arg(
              self._model, MaceKeyword.mace_framework_type_str
          ).i
          == FrameworkType.TENSORFLOW.value
      ):
          self.add_tensorflow_padding_value()
      # Calculate the number of apu constant tensors
      # Any tensors which will be apu constant tensors should be added
      # above this line
      const_data_num_arg = self._model.arg.add()
      const_data_num_arg.name = MaceKeyword.mace_const_data_num_arg_str
      const_data_num_arg.i = len(self._model.tensors)
      apu_data_type_arg = self._model.arg.add()
      apu_data_type_arg.name = MaceKeyword.mace_apu_data_type_arg_str
      if self._option.quantize_schema == "mace_apu_16bit_per_tensor":
          apu_data_type_arg.i = mace_pb2.DT_INT16
      elif self._option.quantize:
          apu_data_type_arg.i = mace_pb2.DT_UINT8
      else:
          apu_data_type_arg.i = mace_pb2.DT_FLOAT
      self.convert_ops()
      self.add_node_id()
      return self._model
  
imports: import copy
  import numpy
  from enum import Enum
  from operator import mul
  from py_proto import mace_pb2
  from transform import base_converter
  from transform.base_converter import ConverterUtil
  from transform.base_converter import EltwiseType
  from transform.base_converter import MaceKeyword
  from transform.base_converter import MaceOp
  from transform.base_converter import PaddingMode
  from transform.base_converter import PadType
  from transform.base_converter import PoolingType
  from transform.base_converter import ReduceType
  from transform.base_converter import DataFormat
  from transform.base_converter import FrameworkType
  from utils.util import mace_check
label_desc: Use image node_id 2 for calling the ApuConverter obj's underlying member method code with example usage: obj.run() and returns: self