code: def init_scale_zp(
      self, wrapper: ModuleWrapper, target_name: str, target: Tensor
  ):
      def mean_reduce_func(converted_target: Tensor) -> torch.Tensor:
          return converted_target.detach().mean(dim=-1)
  
      if self.is_init or not self.check_target(wrapper, target_name):
          return
      target_space = wrapper.quantization_target_spaces[target_name]
      # init_target = target.data.detach().abs().mean() * 2 / (target_space.qmax ** 0.5)
      init_scale_target = torch.tensor([0.01]).to(target.device)
      init_zp_target = torch.tensor(
          [(target_space.qmax - target_space.qmin) / 2]
      ).to(target.device)
      if not target_space._scaler:
          target_space.scale.data = init_scale_target  # type: ignore
          target_space.zero_point.data = init_zp_target  # type: ignore
      else:
          # scale
          new_target_scale = init_scale_target.expand(target.shape).to(
              target.device
          )
          new_target_scale = target_space._scaler.shrink(
              new_target_scale, mean_reduce_func, keepdim=True
          )
          target_space.scale.data = new_target_scale  # type: ignore
          # zp
          new_target_zp = init_zp_target.expand(target.shape).to(
              target.device
          )
          new_target_zp = target_space._scaler.shrink(
              new_target_zp, mean_reduce_func, keepdim=True
          )
          target_space.zero_point.data = new_target_zp
  
imports: from __future__ import annotations
  import logging
  from typing import List, Dict, overload
  import torch
  from torch import Tensor
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 7 for calling the LsqPlusQuantizer obj's underlying member method code with example usage: obj.init_scale_zp(wrapper, target_name, target) and returns: converted_target