class_docstr: quantize weight to 8 bits
      
code: def quantize_weight(self, wrapper, **kwargs):
      weight = copy.deepcopy(wrapper.module.old_weight.data)
      new_scale = weight.abs().max() / 127
      scale = max(self.layer_scale.get(wrapper.name, 0), new_scale)
      self.layer_scale[wrapper.name] = scale
      orig_type = weight.type()  # TODO: user layer
      weight = (
          weight.div(scale).type(torch.int8).type(orig_type).mul(scale)
      )
      wrapper.module.weight = weight
      return weight
  
imports: import logging
  import copy
  import torch
  from schema import Schema, And, Or, Optional
  from ..utils.config_validation import CompressorSchema
  from ..compressor import Quantizer, QuantGrad, QuantType
label_desc: Use image node_id 3 for calling the NaiveQuantizer obj's underlying member method code with example usage: obj.quantize_weight(wrapper) and returns: weight