code: def test_torch_naive_quantizer(self):
      model = TorchModel()
      configure_list = [
          {
              "quant_types": ["weight"],
              "quant_bits": {
                  "weight": 8,
              },
              "op_types": ["Conv2d", "Linear"],
          }
      ]
      NaiveQuantizer(model, configure_list).compress()
  
imports: from unittest import TestCase, main
  import numpy
  import torch
  import torch.nn.functional
  import schema
  from aup.compression.torch.pruning import *
  from aup.compression.torch.quantization import *
  import math
label_desc: Use image node_id 3 for calling the CompressorTestCase obj's underlying member method code with example usage: obj.test_torch_naive_quantizer() without return types