code: def test_load_quantized_model__pth(pytorch_mobilenetv2_quantized):
      # some CI environments wont offer torch, so skip in case it is not present
      pytest.importorskip("torch")
      pytest.importorskip("torchvision")
  
      tvmc_model = tvmc.load(
          pytorch_mobilenetv2_quantized,
          shape_dict={"input": [1, 3, 224, 224]},
      )
      assert type(tvmc_model) is TVMCModel
      assert type(tvmc_model.mod) is IRModule
      assert type(tvmc_model.params) is dict
  
      # checking weights remain quantized and are not float32
      for p in tvmc_model.params.values():
          assert p.dtype in ["int8", "uint8", "int32"]
  
imports: import platform
  import pytest
  import builtins
  import importlib
  import tvm
  from unittest import mock
  from tvm.ir.module import IRModule
  from tvm.driver import tvmc
  from tvm.driver.tvmc import TVMCException, TVMCImportError
  from tvm.driver.tvmc.model import TVMCModel
label_desc: Use image node_id 26 for calling a global function with example usage: test_load_quantized_model__pth(pytorch_mobilenetv2_quantized) without return types