code: def test_torch_quantizer_modules_detection(self):
      # test if modules can be detected
      model = TorchModel()
      config_list = [
          {
              "quant_types": ["weight"],
              "quant_bits": 8,
              "op_types": ["Conv2d", "Linear"],
          },
          {
              "quant_types": ["output"],
              "quant_bits": 8,
              "quant_start_step": 0,
              "op_types": ["ReLU"],
          },
      ]
  
      model.relu = torch.nn.ReLU()
      quantizer = QAT_Quantizer(model, config_list)
      quantizer.compress()
      modules_to_compress = quantizer.get_modules_to_compress()
      modules_to_compress_name = [
          t[0].name for t in modules_to_compress
      ]
      assert "conv1" in modules_to_compress_name
      assert "conv2" in modules_to_compress_name
      assert "fc1" in modules_to_compress_name
      assert "fc2" in modules_to_compress_name
      assert "relu" in modules_to_compress_name
      assert len(modules_to_compress_name) == 5
  
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 1 for calling the CompressorTestCase obj's underlying member method code with example usage: obj.test_torch_quantizer_modules_detection() without return types